1
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Liu P, Azeem M, Sarfraz M, Swaray S, Almohsen B. A parametric similarity measure for neutrosophic set and its applications in energy production. Heliyon 2024; 10:e38272. [PMID: 39435059 PMCID: PMC11493198 DOI: 10.1016/j.heliyon.2024.e38272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 09/18/2024] [Accepted: 09/20/2024] [Indexed: 10/23/2024] Open
Abstract
As a useful tool for managing ambiguous and inconsistent data, the Single Value Neutrosophic Set (SVNSs) is an extension of both Fuzzy Sets (FSs) and Intuitionistic Fuzzy Sets (IFSs). In the field of information theory, metrics like similarity, entropy, and distance are important. Although a number of entropy measures for SVNSs have been put forth and used in real-world situations, both academic research and real-world applications have pointed out certain drawbacks. Additionally, the Similarity Measures (SMs) is a useful instrument for determining how similar any two fuzzy values are to one another. The distance between the values allows the current SMs to evaluate the similarity. However, due to a few characteristics and intricate value operations, there are irrational and nonsensical cases. To deal with these preposterous cases, this paper proposed a parametric similarity measure in view of three parametersm 1 , m 2 , m 3 in which decision makers can obtain the appropriate SMs by changing parameters with different decision styles. Furthermore, we analyze some existing SMs from a mathematical perspective and demonstrate the success of the proposed SMs using mathematical models. Ultimately, we apply the suggested SMs to resolve the Multi-Attribute Decision-Making (MADM) problems. We learn from the correlation and analysis that the suggested SM outperforms certain other SMs that are based on the SVNSs.
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Affiliation(s)
- Peide Liu
- School of Business Administration, Shandong Women's University, Shandong, Jinan, 250300, China
- School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, Shandong, 250014, China
| | - Muhammad Azeem
- Department of Mathematics, Riphah International University, Lahore, 54000, Pakistan
| | | | - Senesie Swaray
- Tree Crops Unit, Sierra Leone Agricultural Research Institute, Freetown, Sierra Leone
| | - Bandar Almohsen
- Department of Mathematics, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
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2
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Gholizadeh H, Fazlollahtabar H, Fathollahi-Fard AM, Dulebenets MA. RETRACTED ARTICLE: Preventive maintenance for the flexible flowshop scheduling under uncertainty: a waste-to-energy system. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:33163. [PMID: 34519989 DOI: 10.1007/s11356-021-16234-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 08/25/2021] [Indexed: 06/13/2023]
Affiliation(s)
- Hadi Gholizadeh
- Department of Mechanical Engineering, Université Laval, Québec, Canada
| | - Hamed Fazlollahtabar
- Department of Industrial Engineering, School of Engineering, Damghan University, Damghan, Iran
| | - Amir M Fathollahi-Fard
- Department of Electrical Engineering, École de Technologie Supérieure, University of Québec, 1100, Notre-Dame St. W, Montréal, Canada.
| | - Maxim A Dulebenets
- Department of Civil & Environmental Engineering, Florida A&M University-Florida State University (FAMU-FSU) College of Engineering, 2525 Pottsdamer Street, Building A, Suite A124, Tallahassee, FL, 32310-6046, USA
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3
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Alzoubi S, Jawarneh M, Bsoul Q, Keshta I, Soni M, Khan MA. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology. Open Life Sci 2023; 18:20220764. [PMID: 38027230 PMCID: PMC10668111 DOI: 10.1515/biol-2022-0764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 10/02/2023] [Accepted: 10/05/2023] [Indexed: 12/01/2023] Open
Abstract
In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.
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Affiliation(s)
- Sharaf Alzoubi
- Information Technology Department, Amman Arab University, Amman, Jordan
| | - Malik Jawarneh
- Department of Computer Science and MIS, Oman College of Management and Technology, Muscat, Oman
| | - Qusay Bsoul
- Faculty of Information Technology, Applied Science Private University, Amman, Jordan
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Mukesh Soni
- Department of CSE, University Centre for Research & Development Chandigarh University, Mohali, Punjab, 140413, India
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4
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Tian Y, Zhang K. Bipolar neutrosophic WINGS for green technology innovation. Sci Rep 2023; 13:19159. [PMID: 37932404 PMCID: PMC10628252 DOI: 10.1038/s41598-023-46699-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 11/03/2023] [Indexed: 11/08/2023] Open
Abstract
Green technology innovation is a crucial assurance of achieving sustainable economic and environmental development, so improving the capability of green technology innovation is an urgent problem. In order to provide a more objective and accurate tool for identifying the most important impact factor of green technology innovation, this study innovatively proposes a new method by combining the bipolar neutrosophic sets with Weighted Influence Nonlinear Gauge System (WINGS) method. Furthermore, this paper intends to provide recommendations in improving green technology innovation capability. We invite five experts to evaluate fifteen factors influencing green technology innovation using the bipolar neutrosophic linguistic variables. Then, the proposed bipolar neutrosophic set WINGS (Bipolar NS-WINGS) method is applied to measure the influence of each impact factor of green technology innovation. Finally, we divide all the factors into cause group and effect group. Moreover, the network relation map is constructed to visualize the interrelationships between all impact factors. The Bipolar NS-WINGS suggests that Science and Technology Innovation Environment (Ω7) is the most important factor of green technology innovation. The result also indicates that R&D Investment (Ω8) is the most influential factor in which it has impacted many other factors. It is obvious that the integrated method not only enriches the research in the field of decision theory, which has not combined the bipolar-NS and WINGS method for analyzing relationships of factors, but also contributes to the improvement of green technology innovation capabilities.
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Affiliation(s)
- Yuan Tian
- School of Economics and Management, Shandong Agricultural University, Taian, China
| | - Kecheng Zhang
- School of Business Administration, Shandong Women's University, Jinan, China.
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5
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Pigliautile M, König T, Mayer CC, Colombo M, Guazzarini AG, Müllner-Rieder M, Águila O, Christophorou C, Constantinides A, Curia R, Stillo M, Arambarri J, Schüler C, Stögmann E, Mecocci P. Usability testing of the first prototype of the Memento system: a technological device to promote an independent living in people with dementia. Disabil Rehabil Assist Technol 2023; 18:1411-1420. [PMID: 35061557 DOI: 10.1080/17483107.2021.2017029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 11/29/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE Assistive technologies have the potential for supporting people with memory complaints in their daily life. User-centered interaction design research helps developers to create systems that are suitable for users. The aim of this work is to describe the methodology and the results of the usability test for the first Memento prototype involving users. MATERIALS AND METHODS In each country, 5 subjects with different levels of cognitive reserve and technical proficiency were enrolled in Italy, Austria and Spain, respectively (15 subjects; 6 M; 9 F, age 72.8 ± 10.8 years, MMSE score 25.6 ± 1.6). Observation methods, performance metrics and the System Usability Scale were used to collect data. RESULTS The results are presented in terms of design, technical problems, target-group-related challenges and usability perception from the participant perspective. Suggestions for improvement were pointed out by the users. Considering the usability scores interpretation, the first prototype was classified as "OK" and "Good" by users. CONCLUSIONS The results of the Lab Trials provide important information on usability and the users' needs in order to improve the Memento prototype and to create a final system to be evaluated during the Field Trials phase of the project.Implication for rehabilitationThe MEMENTO project mission is to improve the quality of life of people in the early and middle stages of dementia, by supporting the management of daily activities that are usually affected by the loss of memory and cognition. The Lab Trial phase is essential to have feedback on the usability of the Memento prototype to allow a better understanding of users' needs and expectations.
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Affiliation(s)
- Martina Pigliautile
- Department of Medicine and Surgery, Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Theresa König
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Christopher C Mayer
- Center for Health & Bioresources, Biomedical Systems; AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | - Matteo Colombo
- Department of Medicine and Surgery, Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Anna Giulia Guazzarini
- Department of Medicine and Surgery, Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Markus Müllner-Rieder
- Center for Health & Bioresources, Biomedical Systems; AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | - Oscar Águila
- Bidaideak - Sociedad Vasca de Personas con Diversidad Funcional, Vienna, Austria
| | | | | | - Rosario Curia
- Integris S.p.A., Innovation Lab, Rende and Pisa, Italy
| | - Maria Stillo
- Integris S.p.A., Innovation Lab, Rende and Pisa, Italy
| | | | | | | | - Patrizia Mecocci
- Department of Medicine and Surgery, Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
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Zaki M, Rowles LS, Adjeroh DA, Orner KD. A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste. ACS ES&T ENGINEERING 2023; 3:1424-1467. [PMID: 37854077 PMCID: PMC10580293 DOI: 10.1021/acsestengg.3c00043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/20/2023]
Abstract
Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.
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Affiliation(s)
- Mohammed
T. Zaki
- Wadsworth
Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
| | - Lewis S. Rowles
- Department
of Civil Engineering and Construction, Georgia
Southern University, Statesboro, Georgia 30458, United States
| | - Donald A. Adjeroh
- Lane
Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
| | - Kevin D. Orner
- Wadsworth
Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
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Hammink JHWC, Moor JAN, Mohammadi MM. Influencing health behaviour using smart building interventions for people with dementia and mild cognitive impairment: expert interviews and a systematic literature review. Disabil Rehabil Assist Technol 2023; 18:1175-1191. [PMID: 34731590 DOI: 10.1080/17483107.2021.1994032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 10/11/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE Behaviour can have an influence on (coping with) chronic conditions such as dementia. Assistive technology can stimulate the daily behaviour of people with dementia, but the mechanisms through which this happens are unclear. Therefore, this paper focuses on potential behaviour change mechanisms, that can be employed in smart building interventions for people with dementia or MCI. METHODS This research uses expert interviews with medical experts (n = 9) and a systematic literature review of smart building interventions stimulating health behaviour (n = 12). RESULTS Results show how facilitation, incentive motivation (i.e., feedback), observational learning and self-efficacy are most promising according to medical experts; if they are appropriately personalised towards needs, preferences as well as abilities. The literature review shows how most of the examined research uses facilitation and incentive motivation to stimulate behaviour. Although positive results are reported in all studies, methodological quality could be improved. CONCLUSION For the design of smart building interventions for people with MCI or dementia, facilitation and incentive motivation seem to be promising behaviour change mechanisms. Outcome expectation, observational learning and self-efficacy could reinforcing the aforementioned mechanisms. Future research should focus on how different (environmental, digital) cues can be personalized and can adapt over time, as dementia progresses.IMPLICATIONS FOR REHABILITATIONAssistive technology for people with dementia can have an effect on (health) behaviour, which may in turn influence coping strategies or quality of life.Behaviour change mechanisms can inform the design of assistive technology such as smart building interventions.Facilitation, Incentive Motivation, Observational Learning and Self-efficacy seem promising behaviour change mechanisms for people with dementia or MCI.In any intervention for people with dementia, personalized and adaptable cues are of vital importance.
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Affiliation(s)
- J H W Coosje Hammink
- Research Group Architecture in Health, HAN University of Applied Sciences, Arnhem, The Netherlands
| | - J A Nienke Moor
- Research Group Architecture in Health, HAN University of Applied Sciences, Arnhem, The Netherlands
| | - M Masi Mohammadi
- Research Group Architecture in Health, HAN University of Applied Sciences, Arnhem, The Netherlands
- Smart Architectural Technologies, Eindhoven University of Technology, Eindhoven, The Netherlands
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8
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Lu S, Weng H, Dai M, Zhang B, Xu Z, Gu H, Liu Y, Li Y, Peng K. Dynamic 3D phase-shifting profilometry based on a corner optical flow algorithm. APPLIED OPTICS 2023; 62:6447-6455. [PMID: 37706838 DOI: 10.1364/ao.494119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/06/2023] [Indexed: 09/15/2023]
Abstract
Real-time 3D reconstruction has been applied in many fields, calling for many ongoing efforts to improve the speed and accuracy of the used algorithms. Phase shifting profilometry based on the Lucas-Kanade optical flow method is a fast and highly precise method to construct and display the three-dimensional shape of objects. However, in this method, a dense optical flow calculation is required for the modulation image corresponding to the acquired deformed fringe pattern, which consumes a lot of time and affects the real-time performance of 3D reconstruction and display. Therefore, this paper proposes a dynamic 3D phase shifting profilometry based on a corner optical flow algorithm to mitigate this issue. Therein, the Harris corner algorithm is utilized to locate the feature points of the measured object, so that the optical flow needs to calculate for only the feature points which, greatly reduces the amount of calculation time. Both our experiments and simulations show that our method improves the efficiency of pixel matching by four times and 3D reconstruction by two times.
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Wang R, Wang H, Shi L, Han C, He Q, Che Y, Luo L. A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network. Front Aging Neurosci 2023; 15:1160534. [PMID: 37455939 PMCID: PMC10339813 DOI: 10.3389/fnagi.2023.1160534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023] Open
Abstract
Background Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients. Objective This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition. Methods First, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective. Results Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy. Conclusion These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.
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Affiliation(s)
- Ruofan Wang
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Haodong Wang
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Lianshuan Shi
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Chunxiao Han
- Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Qiguang He
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Yanqiu Che
- Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Li Luo
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
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Jeon I, Kim T. Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network. Front Comput Neurosci 2023; 17:1092185. [PMID: 37449083 PMCID: PMC10336230 DOI: 10.3389/fncom.2023.1092185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.
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Affiliation(s)
| | - Taegon Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
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11
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Hidayatullah AF, Apong RA, Lai DT, Qazi A. Corpus creation and language identification for code-mixed Indonesian-Javanese-English Tweets. PeerJ Comput Sci 2023; 9:e1312. [PMID: 37409088 PMCID: PMC10319257 DOI: 10.7717/peerj-cs.1312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 03/06/2023] [Indexed: 07/07/2023]
Abstract
With the massive use of social media today, mixing between languages in social media text is prevalent. In linguistics, the phenomenon of mixing languages is known as code-mixing. The prevalence of code-mixing exposes various concerns and challenges in natural language processing (NLP), including language identification (LID) tasks. This study presents a word-level language identification model for code-mixed Indonesian, Javanese, and English tweets. First, we introduce a code-mixed corpus for Indonesian-Javanese-English language identification (IJELID). To ensure reliable dataset annotation, we provide full details of the data collection and annotation standards construction procedures. Some challenges encountered during corpus creation are also discussed in this paper. Then, we investigate several strategies for developing code-mixed language identification models, such as fine-tuning BERT, BLSTM-based, and CRF. Our results show that fine-tuned IndoBERTweet models can identify languages better than the other techniques. This is the result of BERT's ability to understand each word's context from the given text sequence. Finally, we show that sub-word language representation in BERT models can provide a reliable model for identifying languages in code-mixed texts.
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Affiliation(s)
- Ahmad Fathan Hidayatullah
- School of Digital Science, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei
- Department of Informatics, Universitas Islam Indonesia, Sleman, Yogyakarta, Indonesia
| | - Rosyzie Anna Apong
- School of Digital Science, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei
| | - Daphne T.C. Lai
- School of Digital Science, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei
| | - Atika Qazi
- Centre for Lifelong Learning, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei
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Das O, Bagci Das D, Birant D. Machine learning for fault analysis in rotating machinery: A comprehensive review. Heliyon 2023; 9:e17584. [PMID: 37408928 PMCID: PMC10319205 DOI: 10.1016/j.heliyon.2023.e17584] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 04/09/2023] [Accepted: 06/21/2023] [Indexed: 07/07/2023] Open
Abstract
As the concept of Industry 4.0 is introduced, artificial intelligence-based fault analysis is attracted the corresponding community to develop effective intelligent fault diagnosis and prognosis (IFDP) models for rotating machinery. Hence, various challenges arise regarding model assessment, suitability for real-world applications, fault-specific model development, compound fault existence, domain adaptability, data source, data acquisition, data fusion, algorithm selection, and optimization. It is essential to resolve those challenges for each component of the rotating machinery since each issue of each part has a unique impact on the vital indicators of a machine. Based on these major obstacles, this study proposes a comprehensive review regarding IFDP procedures of rotating machinery by minding all the challenges given above for the first time. In this study, the developed IFDP approaches are reviewed regarding the pursued fault analysis strategies, considered data sources, data types, data fusion techniques, machine learning techniques within the frame of the fault type, and compound faults that occurred in components such as bearings, gear, rotor, stator, shaft, and other parts. The challenges and future directions are presented from the perspective of recent literature and the necessities concerning the IFDP of rotating machinery.
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Affiliation(s)
- Oguzhan Das
- National Defence University, Air NCO Higher Vocational School, Department of Aeronautics Sciences, Izmir, Turkey
| | - Duygu Bagci Das
- Ege University, Ege Vocational School, Department of Computer Programming, Izmir, Turkey
| | - Derya Birant
- Dokuz Eylül University, Department of Computer Engineering, Izmir, Turkey
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Gull S, Parah SA. Advances in medical image watermarking: a state of the art review. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-41. [PMID: 37362709 PMCID: PMC10161187 DOI: 10.1007/s11042-023-15396-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/21/2023] [Accepted: 04/18/2023] [Indexed: 06/28/2023]
Abstract
Watermarking has been considered to be a potent and persuasive gizmo for its application in healthcare setups that work online, especially in the current COVID-19 scenario. The security and protection of medical image data from various manipulations that take place over the internet is a topic of concern that needs to be addressed. A detailed review of security and privacy protection using watermarking has been presented in this paper. Watermarking of medical images helps in the protection of image content, authentication of Electronic Patient Record (EPR), and integrity verification. At first, we discuss the various prerequisites of medical image watermarking systems, followed by the classification of Medical Image Watermarking Techniques (MIWT) that include state-of-the-art. We have classified MIWT's into four broader classes for providing better understanding of medical image watermarking. The existing schemes have been presented along with their cons so that the reader may be able to grasp the shortcomings of the technique in order to develop novel techniques proving the inevitability of the presented review. Further, various evaluation parameters along with potential challenges pertaining to medical image watermarking systems have been discussed to provide a deep insight into this research area.
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Affiliation(s)
- Solihah Gull
- Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, 190006 India
| | - Shabir A. Parah
- Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, 190006 India
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Mujirishvili T, Maidhof C, Florez-Revuelta F, Ziefle M, Richart-Martinez M, Cabrero-García J. Acceptance and Privacy Perceptions Toward Video-based Active and Assisted Living Technologies: Scoping Review. J Med Internet Res 2023; 25:e45297. [PMID: 37126390 PMCID: PMC10186188 DOI: 10.2196/45297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/14/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND The aging society posits new socioeconomic challenges to which a potential solution is active and assisted living (AAL) technologies. Visual-based sensing systems are technologically among the most advantageous forms of AAL technologies in providing health and social care; however, they come at the risk of violating rights to privacy. With the immersion of video-based technologies, privacy-preserving smart solutions are being developed; however, the user acceptance research about these developments is not yet being systematized. OBJECTIVE With this scoping review, we aimed to gain an overview of existing studies examining the viewpoints of older adults and/or their caregivers on technology acceptance and privacy perceptions, specifically toward video-based AAL technology. METHODS A total of 22 studies were identified with a primary focus on user acceptance and privacy attitudes during a literature search of major databases. Methodological quality assessment and thematic analysis of the selected studies were executed and principal findings are summarized. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines were followed at every step of this scoping review. RESULTS Acceptance attitudes toward video-based AAL technologies are rather conditional, and are summarized into five main themes seen from the two end-user perspectives: caregiver and care receiver. With privacy being a major barrier to video-based AAL technologies, security and medical safety were identified as the major benefits across the studies. CONCLUSIONS This review reveals a very low methodological quality of the empirical studies assessing user acceptance of video-based AAL technologies. We propose that more specific and more end user- and real life-targeting research is needed to assess the acceptance of proposed solutions.
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Affiliation(s)
| | - Caterina Maidhof
- Communication Science, Human-Computer Interaction Center, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen, Germany
| | | | - Martina Ziefle
- Communication Science, Human-Computer Interaction Center, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen, Germany
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15
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Optimized hadoop map reduce system for strong analytics of cloud big product data on amazon web service. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2023.103271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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16
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Li Z, Ramos A, Li Z, Osborn ML, Zaid W, Li X, Li Y, Xu J. Nearly-lossless-to-lossy medical image compression by the optimized JPEGXT and JPEG algorithms through the anatomical regions of interest. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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17
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Tiwari D, Nagpal B, Bhati BS, Mishra A, Kumar M. A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques. Artif Intell Rev 2023; 56:1-55. [PMID: 37362894 PMCID: PMC10091348 DOI: 10.1007/s10462-023-10472-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2023] [Indexed: 06/28/2023]
Abstract
Sentiment Analysis (SA) of text reviews is an emerging concern in Natural Language Processing (NLP). It is a broadly active method for analyzing and extracting opinions from text using individual or ensemble learning techniques. This field has unquestionable potential in the digital world and social media platforms. Therefore, we present a systematic survey that organizes and describes the current scenario of the SA and provides a structured overview of proposed approaches from traditional to advance. This work also discusses the SA-related challenges, feature engineering techniques, benchmark datasets, popular publication platforms, and best algorithms to advance the automatic SA. Furthermore, a comparative study has been conducted to assess the performance of bagging and boosting-based ensemble techniques for social network SA. Bagging and Boosting are two major approaches of ensemble learning that contain various ensemble algorithms to classify sentiment polarity. Recent studies recommend that ensemble learning techniques have the potential of applicability for sentiment classification. This analytical study examines the bagging and boosting-based ensemble techniques on four benchmark datasets to provide extensive knowledge regarding ensemble techniques for SA. The efficiency and accuracy of these techniques have been measured in terms of TPR, FPR, Weighted F-Score, Weighted Precision, Weighted Recall, Accuracy, ROC-AUC curve, and Run-Time. Moreover, comparative results reveal that bagging-based ensemble techniques outperformed boosting-based techniques for text classification. This extensive review aims to present benchmark information regarding social network SA that will be helpful for future research in this field.
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Affiliation(s)
- Dimple Tiwari
- Ambedkar Institute of Advanced Communication Technologies and Research (GGSIPU), Delhi, India
| | - Bharti Nagpal
- NSUT East Campus (Formerly Ambedkar Institute of Advanced Communication Technologies and Research), Delhi, India
| | | | - Ashutosh Mishra
- School of Integrated Technology, Yonsei University, Seoul, South Korea
- Department of Electronics & Communication Engineering, Graphic Era Deemed to be University, Dehradun, India
| | - Manoj Kumar
- Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, UAE
- MEU Research Unit, Faculty of Information Technology, Middle East University, Amman 11831, Jordan
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18
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Wang C, Zhang J, Yang Q. Research and improvement of C-means clustering algorithm based on Image segmentation application. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-222912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
The traditional fuzzy C-means clustering technology only considers one performance Angle of image segmentation process when processing data, resulting in low accuracy of image segmentation. In this paper, the traditional FCM algorithm is analyzed, and the low clustering accuracy, noise interference and lack of flexibility and other problems are fully considered from the relationship between parameter components, non-local spatial information elements and noise sensitivity. Firstly, a distance calculation method based on robust statistics theory is proposed, which can deal with abnormal noise stably. Secondly, based on the extreme learning machine theory, the non-local spatial information coefficient is introduced to improve the identification ability of the influence factors. This method not only guarantees the anti-noise performance of the algorithm, but also preserves the image data, improving the iteration efficiency and segmentation accuracy of the algorithm. The test results show that the accuracy of the improved C-means clustering algorithm for image segmentation is 95.5%, which is compared with the traditional C-means clustering technique and other optimization algorithms.
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Affiliation(s)
- Chunying Wang
- School of Water Resources and Environment, China University of Geosciences, Beijing, China
| | - Jiahui Zhang
- Big Data Application Center, Hebei Sailhero Environmental Protection High-Tech Co., Ltd., China
| | - Qi Yang
- School of Water Resources and Environment, China University of Geosciences, Beijing, China
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19
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Sule O, Viriri S. Contrast Enhancement of RGB Retinal Fundus Images for Improved Segmentation of Blood Vessels Using Convolutional Neural Networks. J Digit Imaging 2023; 36:414-432. [PMID: 36456839 PMCID: PMC10039198 DOI: 10.1007/s10278-022-00738-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 09/27/2021] [Accepted: 11/03/2021] [Indexed: 12/03/2022] Open
Abstract
Retinal fundus images are non-invasively acquired and faced with low contrast, noise, and uneven illumination. The low-contrast problem makes objects in the retinal fundus image indistinguishable and the segmentation of blood vessels very challenging. Retinal blood vessels are significant because of their diagnostic importance in ophthalmologic diseases. This paper proposes improved retinal fundus images for optimal segmentation of blood vessels using convolutional neural networks (CNNs). This study explores some robust contrast enhancement tools on the RGB and the green channel of the retinal fundus images. The improved images undergo quality evaluation using mean square error (MSE), peak signal to noise ratio (PSNR), Similar Structure Index Matrix (SSIM), histogram, correlation, and intersection distance measures for histogram comparison before segmentation in the CNN-based model. The simulation results analysis reveals that the improved RGB quality outperforms the improved green channel. This revelation implies that the choice of RGB to the green channel for contrast enhancement is adequate and effectively improves the quality of the fundus images. This improved contrast will, in turn, boost the predictive accuracy of the CNN-based model during the segmentation process. The evaluation of the proposed method on the DRIVE dataset achieves an accuracy of 94.47, sensitivity of 70.92, specificity of 98.20, and AUC (ROC) of 97.56.
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Affiliation(s)
- Olubunmi Sule
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa
| | - Serestina Viriri
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
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20
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Rehman AU, Gulistan M, Ali M, Al-Shamiri MM, Abdulla S. Development of neutrosophic cubic hesitant fuzzy exponential aggregation operators with application in environmental protection problems. Sci Rep 2023; 13:5262. [PMID: 37002236 PMCID: PMC10066305 DOI: 10.1038/s41598-022-22399-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 10/13/2022] [Indexed: 04/03/2023] Open
Abstract
The population growth and urbanization has caused an exponential increase in waste material. The proper disposal of waste is a challenging problem nowadays. The proper disposal site selection with typical sets and operators may not yield fruitful results. To handle such problems, the exponential aggregation operators based on neutrosophic cubic hesitant fuzzy sets are proposed. For appropriate decisions in a decision-making problem, it is important to have a handy environment and aggregation operators. Many multi attribute decision making methods often ignore the uncertainty and hence yields the results which are not reliable. The neutrosophic cubic hesitant fuzzy set can efficiently handle the complex information in a decision-making problem, as it combines the advantages of neutrosophic cubic set and hesitant fuzzy set. In this paper first we establish exponential operational laws in neutrosophic cubic hesitant fuzzy sets, in which the exponents are neutrosophic cubic hesitant fuzzy numbers and bases are positive real numbers. In order to use neutrosophic cubic hesitant fuzzy sets in decision making, we are developing exponential aggregation operators and investigate their properties in the current study. In many multi expert decision-making methods there are different decision matrices but same weighting vector for attributes. The results of a multi expert decision-making problem becomes more reliable if every decision expert has its own decision matrix along with his own weighting vector for attributes. In this study, we are developing multi expert decision-making method that uses different weights for an attribute corresponding to different experts. At the end we present two applications of exponential aggregation operators in environmental protection multi attribute decision making problems.
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Affiliation(s)
- Ateeq Ur Rehman
- Department of Mathematics and Statistics, Hazara University Mansehra, Khyber Pakhtunkhwa, Pakistan
| | - Muhammad Gulistan
- Department of Mathematics and Statistics, Hazara University Mansehra, Khyber Pakhtunkhwa, Pakistan.
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
| | - Mumtaz Ali
- UniSQ College, University of Southern Queensland, Darling Heights, QLD, 4300, Australia.
| | - Mohammed M Al-Shamiri
- Department of Mathematics, Faculty of Science and Arts, Muhayl Asser, King Khalid University, Abha, Kingdom of Saudi Arabia
- Department of Mathematics and Computer, Faculty of Science, Ibb University, Ibb, Yemen
| | - Shahab Abdulla
- UniSQ College, University of Southern Queensland, Darling Heights, QLD, 4300, Australia
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21
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Gezici K, Şengül S. Estimation and analysis of missing temperature data in high altitude and snow-dominated regions using various machine learning methods. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:517. [PMID: 36976414 DOI: 10.1007/s10661-023-11143-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 06/18/2023]
Abstract
Considering the importance of limited natural resources, accurately recording and evaluating temperature data is critical. The daily average temperature values obtained for the years 2019-2021 of eight highly correlated meteorological stations, characterized by mountainous and cold climate features in the northeast of Turkey, were analyzed by an artificial neural network (ANN), support vector regression (SVR), and regression tree (RT) methods. Output values produced by different machine learning methods compared with different statistical evaluation criteria and the Taylor diagram. ANN6, ANN12, medium gaussian SVR, and linear SVR were chosen as the most suitable methods, especially due to their success in estimating data at high (> 15 ℃) and low (< 0 ℃) temperatures. All the methodologies and network architectures used produced successful results (NSE-R2 > 0.90). Some deviations have been observed in the estimation results due to the decrease in the amount of heat emitted from the ground due to fresh snow, especially in the -1 ~ 5 ℃ range, where snowfall begins, in the mountainous areas characterized by heavy snowfall. In models with low neuron numbers (ANN1,2,3) in ANN architecture, the increase in the number of layers does not affect the results. However, the increase in the number of layers in models with high neuron counts positively affects the accuracy of the estimation.
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Affiliation(s)
- Kadir Gezici
- Department of Civil Engineering, Faculty of Engineering, Atatürk University, Erzurum, 25100, Turkey
| | - Selim Şengül
- Department of Civil Engineering, Faculty of Engineering, Atatürk University, Erzurum, 25100, Turkey.
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22
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Dhingra D, Dua M. A novel Sine–Tangent–Sine chaotic map and dynamic S-box-based video encryption scheme. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2187513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2023]
Affiliation(s)
- Deepti Dhingra
- Department of Computer Engineering, National Institute of Technology, Kurukshetra, India
| | - Mohit Dua
- Department of Computer Engineering, National Institute of Technology, Kurukshetra, India
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23
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Jin M. Computer Network Information Security and Protection Strategy Based on Big Data Environment. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2023. [DOI: 10.4018/ijitsa.319722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
Abstract
This paper proposed a study on computer network information security and protection strategy based on a big data environment. The research purpose was to use big data technology to network information security protection technology and seek more efficient network information protection technology. The algorithm proposed in this article was a time-series network detection algorithm based on big data, which could improve the early warning rate of abnormal network information, reduce the early warning time, and improve the detection accuracy and interception rate of virus information. The results of this study could effectively show that big data technology had excellent performance in computer network information security protection, which also led to an advanced reform path for future network information security protection technology.
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Affiliation(s)
- Min Jin
- State Grid Chongqing Electric Power Company Information Communication Branch, China
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24
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Tyagi N, Bhushan B. Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions. WIRELESS PERSONAL COMMUNICATIONS 2023; 130:857-908. [PMID: 37168438 PMCID: PMC10019426 DOI: 10.1007/s11277-023-10312-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/25/2023] [Indexed: 05/13/2023]
Abstract
Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.
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Affiliation(s)
- Nemika Tyagi
- Department of Computer Science and Engineering School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201310 India
| | - Bharat Bhushan
- Department of Computer Science and Engineering School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201310 India
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25
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Kolarik M, Sarnovsky M, Paralic J, Babic F. Explainability of deep learning models in medical video analysis: a survey. PeerJ Comput Sci 2023; 9:e1253. [PMID: 37346619 PMCID: PMC10280416 DOI: 10.7717/peerj-cs.1253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 01/20/2023] [Indexed: 06/23/2023]
Abstract
Deep learning methods have proven to be effective for multiple diagnostic tasks in medicine and have been performing significantly better in comparison to other traditional machine learning methods. However, the black-box nature of deep neural networks has restricted their use in real-world applications, especially in healthcare. Therefore, explainability of the machine learning models, which focuses on providing of the comprehensible explanations of model outputs, may affect the possibility of adoption of such models in clinical use. There are various studies reviewing approaches to explainability in multiple domains. This article provides a review of the current approaches and applications of explainable deep learning for a specific area of medical data analysis-medical video processing tasks. The article introduces the field of explainable AI and summarizes the most important requirements for explainability in medical applications. Subsequently, we provide an overview of existing methods, evaluation metrics and focus more on those that can be applied to analytical tasks involving the processing of video data in the medical domain. Finally we identify some of the open research issues in the analysed area.
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Affiliation(s)
- Michal Kolarik
- Department of Cybernetics and Artificial Intelligence, Technical University in Kosice, Kosice, Slovakia
| | - Martin Sarnovsky
- Department of Cybernetics and Artificial Intelligence, Technical University in Kosice, Kosice, Slovakia
| | - Jan Paralic
- Department of Cybernetics and Artificial Intelligence, Technical University in Kosice, Kosice, Slovakia
| | - Frantisek Babic
- Department of Cybernetics and Artificial Intelligence, Technical University in Kosice, Kosice, Slovakia
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26
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Liu P, Geng X. Evaluation model of green supplier selection for coal enterprises with similarity measures of double-valued neutrosophic sets based on cosine function. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-224123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
Abstract
Coal is a vital basic energy source for any economy in the world, and our country is no exception. Our coal resources are abundant, with high production and demand, not comparable to oil and natural gas. The coal supply chain plays an equally important role in economic production, but unfortunately, the current coal supply chain is not focused on greening while creating profits. Unfortunately, the current coal supply chain does not focus on green production and energy conservation and emission reduction while creating profits, which has caused irreversible harm and loss to resources and environment. This has caused irreversible damage and loss to resources and the environment. The green supplier selection for coal enterprises is affirmed as multiple attribute decision making (MADM). In such paper, motivated by the idea of cosine similarity measure (CSM), the CSMs are extended to DVNSs and four CSMs are created under DVNSs. Then, two weighted CSMs are built for MADM under DVNSs. Finally, a numerical example for Green supplier selection for coal enterprises is affirmed and some comparative algorithms are produced to affirm the built method.
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Affiliation(s)
- Peng Liu
- North China University of Science and Technology, Tangshan, Hebei, China
- University of Perpetual Help System Dalta, Alabang-Zapote Road, Pamplona 3, Las Piñas City, Las Piñas Campus, Republic of the Philippines
| | - Xiaonan Geng
- North China University of Science and Technology, Tangshan, Hebei, China
- University of Perpetual Help System Dalta, Alabang-Zapote Road, Pamplona 3, Las Piñas City, Las Piñas Campus, Republic of the Philippines
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27
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Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems. Sci Rep 2023; 13:4098. [PMID: 36907914 PMCID: PMC10008842 DOI: 10.1038/s41598-023-31081-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/06/2023] [Indexed: 03/14/2023] Open
Abstract
Due to its low dependency on the control parameters and straightforward operations, the Artificial Electric Field Algorithm (AEFA) has drawn much interest; yet, it still has slow convergence and low solution precision. In this research, a hybrid Artificial Electric Field Employing Cuckoo Search Algorithm with Refraction Learning (AEFA-CSR) is suggested as a better version of the AEFA to address the aforementioned issues. The Cuckoo Search (CS) method is added to the algorithm to boost convergence and diversity which may improve global exploration. Refraction learning (RL) is utilized to enhance the lead agent which can help it to advance toward the global optimum and improve local exploitation potential with each iteration. Tests are run on 20 benchmark functions to gauge the proposed algorithm's efficiency. In order to compare it with the other well-studied metaheuristic algorithms, Wilcoxon rank-sum tests and Friedman tests with 5% significance level are used. In order to evaluate the algorithm's efficiency and usability, some significant tests are carried out. As a result, the overall effectiveness of the algorithm with different dimensions and populations varied between 61.53 and 90.0% by overcoming all the compared algorithms. Regarding the promising results, a set of engineering problems are investigated for a further validation of our methodology. The results proved that AEFA-CSR is a solid optimizer with its satisfactory performance.
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28
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Shibata Y, Victorino JN, Natsuyama T, Okamoto N, Yoshimura R, Shibata T. Estimation of subjective quality of life in schizophrenic patients using speech features. FRONTIERS IN REHABILITATION SCIENCES 2023; 4:1121034. [PMID: 36968213 PMCID: PMC10036834 DOI: 10.3389/fresc.2023.1121034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 02/13/2023] [Indexed: 03/12/2023]
Abstract
IntroductionPatients with schizophrenia experience the most prolonged hospital stay in Japan. Also, the high re-hospitalization rate affects their quality of life (QoL). Despite being an effective predictor of treatment, QoL has not been widely utilized due to time constraints and lack of interest. As such, this study aimed to estimate the schizophrenic patients' subjective quality of life using speech features. Specifically, this study uses speech from patients with schizophrenia to estimate the subscale scores, which measure the subjective QoL of the patients. The objectives were to (1) estimate the subscale scores from different patients or cross-sectional measurements, and 2) estimate the subscale scores from the same patient in different periods or longitudinal measurements.MethodsA conversational agent was built to record the responses of 18 schizophrenic patients on the Japanese Schizophrenia Quality of Life Scale (JSQLS) with three subscales: “Psychosocial,” “Motivation and Energy,” and “Symptoms and Side-effects.” These three subscales were used as objective variables. On the other hand, the speech features during measurement (Chromagram, Mel spectrogram, Mel-Frequency Cepstrum Coefficient) were used as explanatory variables. For the first objective, a trained model estimated the subscale scores for the 18 subjects using the Nested Cross-validation (CV) method. For the second objective, six of the 18 subjects were measured twice. Then, another trained model estimated the subscale scores for the second time using the 18 subjects' data as training data. Ten different machine learning algorithms were used in this study, and the errors of the learned models were compared.Results and DiscussionThe results showed that the mean RMSE of the cross-sectional measurement was 13.433, with k-Nearest Neighbors as the best model. Meanwhile, the mean RMSE of the longitudinal measurement was 13.301, using Random Forest as the best. RMSE of less than 10 suggests that the estimated subscale scores using speech features were close to the actual JSQLS subscale scores. Ten out of 18 subjects were estimated with an RMSE of less than 10 for cross-sectional measurement. Meanwhile, five out of six had the same observation for longitudinal measurement. Future studies using a larger number of subjects and the development of more personalized models based on longitudinal measurements are needed to apply the results to telemedicine for continuous monitoring of QoL.
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Affiliation(s)
- Yuko Shibata
- Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
- Correspondence: Yuko Shibata
| | - John Noel Victorino
- Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
| | - Tomoya Natsuyama
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Naomichi Okamoto
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Tomohiro Shibata
- Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
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García-Méndez S, de Arriba-Pérez F, Barros-Vila A, González-Castaño FJ, Costa-Montenegro E. Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04452-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
AbstractFinancial news items are unstructured sources of information that can be mined to extract knowledge for market screening applications. They are typically written by market experts who describe stock market events within the context of social, economic and political change. Manual extraction of relevant information from the continuous stream of finance-related news is cumbersome and beyond the skills of many investors, who, at most, can follow a few sources and authors. Accordingly, we focus on the analysis of financial news to identify relevant text and, within that text, forecasts and predictions. We propose a novel Natural Language Processing (nlp) system to assist investors in the detection of relevant financial events in unstructured textual sources by considering both relevance and temporality at the discursive level. Firstly, we segment the text to group together closely related text. Secondly, we apply co-reference resolution to discover internal dependencies within segments. Finally, we perform relevant topic modelling with Latent Dirichlet Allocation (lda) to separate relevant from less relevant text and then analyse the relevant text using a Machine Learning-oriented temporal approach to identify predictions and speculative statements. Our solution outperformed a rule-based baseline system. We created an experimental data set composed of 2,158 financial news items that were manually labelled by nlp researchers to evaluate our solution. Inter-agreement Alpha-reliability and accuracy values, and rouge-l results endorse its potential as a valuable tool for busy investors. The rouge-l values for the identification of relevant text and predictions/forecasts were 0.662 and 0.982, respectively. To our knowledge, this is the first work to jointly consider relevance and temporality at the discursive level. It contributes to the transfer of human associative discourse capabilities to expert systems through the combination of multi-paragraph topic segmentation and co-reference resolution to separate author expression patterns, topic modelling with lda to detect relevant text, and discursive temporality analysis to identify forecasts and predictions within this text. Our solution may have compelling applications in the financial field, including the possibility of extracting relevant statements on investment strategies to analyse authors’ reputations.
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Murugan S, Sivakumar PK, Kavitha C, Harichandran A, Lai WC. An Electro-Oculogram (EOG) Sensor's Ability to Detect Driver Hypovigilance Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:2944. [PMID: 36991654 PMCID: PMC10058593 DOI: 10.3390/s23062944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 06/19/2023]
Abstract
Driving safely is crucial to avoid death, injuries, or financial losses that can be sustained in an accident. Thus, a driver's physical state should be monitored to prevent accidents, rather than vehicle-based or behavioral measurements, and provide reliable information in this regard. Electrocardiography (ECG), electroencephalography (EEG), electrooculography (EOG), and surface electromyography (sEMG) signals are used to monitor a driver's physical state during a drive. The purpose of this study was to detect driver hypovigilance (drowsiness, fatigue, as well as visual and cognitive inattention) using signals collected from 10 drivers while they were driving. EOG signals from the driver were preprocessed to remove noise, and 17 features were extracted. ANOVA (analysis of variance) was used to select statistically significant features that were then loaded into a machine learning algorithm. We then reduced the features by using principal component analysis (PCA) and trained three classifiers: support vector machine (SVM), k-nearest neighbor (KNN), and ensemble. A maximum accuracy of 98.7% was obtained for the classification of normal and cognitive classes under the category of two-class detection. Upon considering hypovigilance states as five-class, a maximum accuracy of 90.9% was achieved. In this case, the number of detection classes increased, resulting in a reduction in the accuracy of detecting more driver states. However, with the possibility of incorrect identification and the presence of issues, the ensemble classifier's performance produced an enhanced accuracy when compared to others.
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Affiliation(s)
- Suganiya Murugan
- Department of Computing Technologies, SRM Institute of Science and Technology—KTR, Chennai 603203, India
| | - Pradeep Kumar Sivakumar
- Department of Electrical and Electronics Engineering, Vels Institute of Science Technology and Advanced Studies, Chennai 600117, India
| | - C. Kavitha
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India
| | - Anandhi Harichandran
- Department of Biomedical Engineering, Agni College of Technology, Chennai 600130, India
| | - Wen-Cheng Lai
- Bachelor Program in Industrial Projects, National Yunlin University of Science and Technology, Douliu 640301, Taiwan
- Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu 640301, Taiwan
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Cullen AC, Rubinstein BIP, Kandeepan S, Flower B, Leong PHW. Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10449-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
AbstractThe advent of the Internet of Things and 5G has further accelerated the growth in devices attempting to gain access to the wireless spectrum. A consequence of this has been the commensurate growth in spectrum conflict and congestion across the wireless spectrum, which has begun to impose a significant impost upon innovation in both the public and private sectors. One potential avenue for resolving these issues, and improving the efficiency of spectrum utilisation can be found in devices making intelligent decisions about their access to spectrum through Dynamic Spectrum Allocation. Changing to a system of Dynamic Spectrum Allocation would require the development of complex and sophisticated inference frameworks, that would be able to be deployed at a scale able to support significant numbers of devices. The development and deployment of these systems cannot exist in isolation, but rather would require the development of tools that can simulate, measure, and predict Spectral Occupancy. To support the development such tools, this work reviews not just the available prediction frameworks for networked systems with sparse sensing over large scale geospatial environments, but also holistically considers the myriad of technological approaches required to support Dynamic Spectrum Allocation.
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Ahuja B, Doriya R, Salunke S, Hashmi MF, Gupta A. Advanced 5D logistic and DNA encoding for medical images. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2178097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Bharti Ahuja
- Department of Information Technology, National Institute of Technology Raipur, Chhattisgarh, India
| | - Rajesh Doriya
- Department of Information Technology, National Institute of Technology Raipur, Chhattisgarh, India
| | - Sharad Salunke
- Department of Electronics and Communication Engineering, Amity University Madhya Pradesh, Gwalior, India
| | - Md. Farukh Hashmi
- Department of Electronics and Communication Engineering, NIT Warangal, Warangal, India
| | - Aditya Gupta
- Department of Information and Communication Technology, University of Agder, Grimstad, Norway
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Chen H, Zhang Y, Wang L. A study on the quality evaluation index system of smart home care for older adults in the community --based on Delphi and AHP. BMC Public Health 2023; 23:411. [PMID: 36859259 PMCID: PMC9975439 DOI: 10.1186/s12889-023-15262-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 02/13/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND In the context of the "silver wave" and "technology wave", smart home care for older adults in the community provide new ways for China and other countries to support ageing in place. Yet, only very few studies have focused on developing a quality index system of smart care. This study attempted to draw on the SERVQUAL model to establish a quality evaluation index system for smart senior care for older adults in the community. METHODS On the basis of the service quality model, this paper has integrated qualitative and quantitative analyses using the Delphi and Analytic Hierarchy Process (AHP) methods to construct the index system of smart home care in the community and obtain the weights. These were based on literature research and field interviews in Guangzhou and Shenzhen pilot districts. RESULTS A quality evaluation indexes system of smart home care for older adults in the community was developed, with 5 primary indices and 33 secondary indices. The weights of the 5 stair indices from high to low were smart emergency assistance 0.332, smart meal assistance 0.272, smart medical assistance 0.229, smart cleaning assistance 0.110 and smart amusement assistance 0.057. CONCLUSION The results from the weight allocation revealed smart emergency assistance, smart meal assistance, and smart medical care assistance were the most important and crucial aspects of community-based smart home care. The study also suggested that "timeliness", "reliability", and "ease of use" should be given more attention. It is recommended to use this index system as a regulatory benchmark to guide the government bodies, senior care enterprises and communities to take measures to enhance the quality.
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Affiliation(s)
- Huaxiao Chen
- Institute of Health Management, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China
| | - Yuwei Zhang
- Institute of Health Management, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China
| | - Li Wang
- Institute of Health Management, Southern Medical University, No.1023-1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, China.
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34
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Land use policies and their effects on Brazilian farming production. J Nat Conserv 2023. [DOI: 10.1016/j.jnc.2023.126373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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Read E, Woolsey C, Donelle L, Weeks L, Chinho N. Passive Remote Monitoring and Aging in Place: A Scoping Review. Can J Aging 2023; 42:20-32. [PMID: 35912590 DOI: 10.1017/s0714980822000198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Passive remote monitoring is a relatively new technology that may support older adults to age in place. However, current knowledge about the effectiveness of this technology in extending older adults' independence is lacking. Therefore, we conducted a scoping review of studies examining passive remote monitoring to systematically synthesize evidence about the technology's effectiveness as an intervention. Our initial search of Embase, CINAHL, PubMed, and Scopus databases identified 486 unique articles. Of these, 14 articles met our inclusion criteria. Results show that passive remote monitoring technologies are being used in innovative and diverse ways to support older adults aging in place and their caregivers. More high-quality research on this topic is needed.
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Affiliation(s)
- Emily Read
- Faculty of Nursing, University of New Brunswick, Moncton, NB, Canada
| | - Cora Woolsey
- Faculty of Nursing, University of New Brunswick, Moncton, NB, Canada
| | - Lorie Donelle
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - Lori Weeks
- School of Nursing, Dalhousie University, Halifax, NS, Canada
| | - Norma Chinho
- Faculty of Nursing, University of New Brunswick, Moncton, NB, Canada
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Ayyildiz E. Interval valued intuitionistic fuzzy analytic hierarchy process-based green supply chain resilience evaluation methodology in post COVID-19 era. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:42476-42494. [PMID: 34669128 PMCID: PMC8526357 DOI: 10.1007/s11356-021-16972-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/06/2021] [Indexed: 04/13/2023]
Abstract
Supply chain organizations should calmly and cautiously take the most accurate and sustainable decisions quickly and put them into practice. It is obvious that traditional time series-based demand and supply planning approaches are insufficient to meet current business needs due to factors such as sharp changes in market and commercial dynamics, pandemics, and natural disasters on the management of green supply chains, especially these days. In the near future, there will be a need for more resilient supply chains with a flexible business models that are not affected by sudden changes and that can make sustainable decisions dynamically. Additionally, all stakeholders must act with a green supply chain approach to conduct production and service activities in a way that causes the least damage to nature. Companies must build more resilient supply chains by considering environmental sensitivities to compete in the market and ensure their continuity. In this context, the green supply chains should be evaluated according to their resilience. For this purpose, Supply Chain Operations Reference (SCOR) model is extended with novel performance attributes to evaluate resilience of green supply chains in this study. The SCOR-embedded novel green supply chain resilience evaluation model is structured as a three-level performance attribute hierarchical structure. Then, the model is handled as a multi-criteria decision-making problem to determine importance of the performance attributes. Best Worst Method integrated Interval Valued Intuitionistic Fuzzy Analytic Hierarchy Process is used to determine the importance of performance attributes. Most important performance attributes are determined in each level of hierarchy. According to results, organizational factors play a key role to build more resilient supply chains. Especially, integrated systems are required for supply chain resilience.
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Affiliation(s)
- Ertugrul Ayyildiz
- Department of Industrial Engineering, Karadeniz Technical University, Merkez Campus, 61080, Trabzon, Turkey.
- Department of Industrial Engineering, Yildiz Technical University, Yildiz Campus, 34349, İstanbul, Turkey.
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37
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Lash MT, Sajeesh S, Araz OM. Predicting mobility using limited data during early stages of a pandemic. JOURNAL OF BUSINESS RESEARCH 2023; 157:113413. [PMID: 36628355 PMCID: PMC9815965 DOI: 10.1016/j.jbusres.2022.113413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 06/17/2023]
Abstract
The COVID-19 pandemic has changed consumer behavior substantially. In this study, we explore the drivers of consumer mobility in several metropolitan areas in the United States under the perceived risks of COVID-19. We capture multiple dimensions of perceived risk using local and national cases and death counts of COVID-19, along with real-time Google Trends data for personal protective equipment (PPE). While Google Trends data are popular inputs in many studies, the risk of multicollinearity escalates with the addition of more relevant terms. Therefore, multicollinearity-alleviating methods are needed to appropriately leverage information provided by Google Trends data. We develop and utilize a novel optimization scheme to induce linear models containing strictly significant covariates and minimal multicollinearity. We find that there are a variety of unique factors that drive mobility in different geographic locations, as well as several factors that are common to all locations.
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Affiliation(s)
- Michael T Lash
- School of Business, University of Kansas, Lawrence, KS 66045, United States
| | - S Sajeesh
- College of Business, University of Nebraska - Lincoln, Lincoln, NE 68588, United States
| | - Ozgur M Araz
- College of Business, University of Nebraska - Lincoln, Lincoln, NE 68588, United States
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38
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Hamed Alnaish ZA, Algamal ZY. Improving binary crow search algorithm for feature selection. JOURNAL OF INTELLIGENT SYSTEMS 2023. [DOI: 10.1515/jisys-2022-0228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023] Open
Abstract
Abstract
The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was employed in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%), G-mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time.
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Affiliation(s)
| | - Zakariya Yahya Algamal
- Department of Statistics and Informatics, University of Mosul , 41001 Mosul , Iraq
- College of Engineering, University of Warith Al-Anbiyaa , 56001 Karbala , Iraq
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39
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Fan X. Artificial Intelligence Technology-Based Semantic Sentiment Analysis on Network Public Opinion Texts. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2023. [DOI: 10.4018/ijitsa.318447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Considering that the current social network text analysis works poorly in accurate and effective sentiment prediction and management, a deep learning (D-L)-based text sentiment analysis method is proposed for the big data environment. First, the autoregressive language model mode XLNet is used to capture bidirectional text information and a sentiment analysis model XLNet-Multi-Attention-BiGRU. Then, considering the context information of social network texts, the defect of traditional GRU units only reading texts in order is overcome by introducing a BiGRU model to extract features in both directions. Finally, a multi-headed attention layer is added between the BiGRU and CRF layers to better capture the key information in the sentence by integrating multiple single-head attention. The results show that the precision, recall, and F1 value of the method proposed in this paper are the largest, with the highest reaching 92.64%, 92.32%, and 91.25%, respectively, which are 12.40%, 10.17%, and 9.63% higher than the maximum values of the other three methods, respectively.
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Affiliation(s)
- Xingliang Fan
- Chongqing Vocational College of Applied Technology, China
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40
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Cerón JC, Sunny MSH, Brahmi B, Mendez LM, Fareh R, Ahmed HU, Rahman MH. A Novel Multi-Modal Teleoperation of a Humanoid Assistive Robot with Real-Time Motion Mimic. MICROMACHINES 2023; 14:461. [PMID: 36838161 PMCID: PMC9961134 DOI: 10.3390/mi14020461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/10/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
This research shows the development of a teleoperation system with an assistive robot (NAO) through a Kinect V2 sensor, a set of Meta Quest virtual reality glasses, and Nintendo Switch controllers (Joycons), with the use of the Robot Operating System (ROS) framework to implement the communication between devices. In this paper, two interchangeable operating models are proposed. An exclusive controller is used to control the robot's movement to perform assignments that require long-distance travel. Another teleoperation protocol uses the skeleton joints information readings by the Kinect sensor, the orientation of the Meta Quest, and the button press and thumbstick movements of the Joycons to control the arm joints and head of the assistive robot, and its movement in a limited area. They give image feedback to the operator in the VR glasses in a first-person perspective and retrieve the user's voice to be spoken by the assistive robot. Results are promising and can be used for educational and therapeutic purposes.
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Affiliation(s)
- Julio C. Cerón
- Mechatronics Engineering, Universidad Nacional de Colombia, Cra 45, Bogatá 111321, Colombia
| | | | - Brahim Brahmi
- Electrical Engineering, College Ahuntsic, Montreal, QC 9155, Canada
| | - Luis M. Mendez
- Mechatronics Engineering, Universidad Nacional de Colombia, Cra 45, Bogatá 111321, Colombia
| | - Raouf Fareh
- Electrical Engineering, University of Sharjah, University City, Sharjah 27272, United Arab Emirates
| | - Helal Uddin Ahmed
- Biorobotics Laboratory, Mechanical Engineering, University of Wisconsin Milwaukee, Milwaukee, WI 53212, USA
| | - Mohammad H. Rahman
- Computer Science, University of Wisconsin Milwaukee, Milwaukee, WI 53212, USA
- Biorobotics Laboratory, Mechanical Engineering, University of Wisconsin Milwaukee, Milwaukee, WI 53212, USA
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41
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Abdullah SM, Jaber MM. Deep learning for content-based image retrieval in FHE algorithms. JOURNAL OF INTELLIGENT SYSTEMS 2023. [DOI: 10.1515/jisys-2022-0222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
Abstract
Abstract
Content-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim of this article is to address the issues of accuracy utilizing deep learning techniques such as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon–Kim–Kim–Song (CKKS). The system has been proposed, namely RCNN_CKKS, which includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.87% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.
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Affiliation(s)
| | - Mustafa Musa Jaber
- Department of Computer Science, Dijlah University College , Baghdad , Iraq
- Department of Medical Instruments Engineering Techniques, Al-Farahidi University , Baghdad 10021 , Iraq
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Kotenko I, Fedorchenko E, Novikova E, Jha A. Cyber Attacker Profiling for Risk Analysis Based on Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:2028. [PMID: 36850628 PMCID: PMC9958722 DOI: 10.3390/s23042028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/28/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
The notion of the attacker profile is often used in risk analysis tasks such as cyber attack forecasting, security incident investigations and security decision support. The attacker profile is a set of attributes characterising an attacker and their behaviour. This paper analyzes the research in the area of attacker modelling and presents the analysis results as a classification of attacker models, attributes and risk analysis techniques that are used to construct the attacker models. The authors introduce a formal two-level attacker model that consists of high-level attributes calculated using low-level attributes that are in turn calculated on the basis of the raw security data. To specify the low-level attributes, the authors performed a series of experiments with datasets of attacks. Firstly, the requirements of the datasets for the experiments were specified in order to select the appropriate datasets, and, afterwards, the applicability of the attributes formed on the basis of such nominal parameters as bash commands and event logs to calculate high-level attributes was evaluated. The results allow us to conclude that attack team profiles can be differentiated using nominal parameters such as bash history logs. At the same time, accurate attacker profiling requires the extension of the low-level attributes list.
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Schneider C, Bousbiat H. Coaching Robots for Older Seniors: Do They Get What They Expect? Insights from an Austrian Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2965. [PMID: 36833659 PMCID: PMC9963592 DOI: 10.3390/ijerph20042965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
To support the increasing number of older people, new (assistive) technologies are constantly being developed. For these technologies to be used successfully, future users need to be trained. Due to demographic change, this will become difficult in the future, as the resources for training will no longer be available. In this respect, coaching robots could have great potential to support younger seniors in particular. However, there is little evidence in the literature about the perceptions and potential impact of this technology on the well-being of older people. This paper provides insights into the use of a robot coach (robo-coach) to train younger seniors in the use of a new technology. The study was carried out in Austria in autumn 2020, involving 34 participants equally distributed among employees in their last three years of service and retirees in their first three years of retirement (23 female; 11 male). The aim was to assess participants' expectations and perceptions by examining the perceived ease of use and user experience of the robot in providing assistance during a learning session. The findings reveal a positive impression of the participants and promising results for using the robot as a coaching assistant in daily tasks.
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Affiliation(s)
- Cornelia Schneider
- Institute of Computer Science, University of Applied Sciences Wiener Neustadt, 2700 Wiener Neustadt, Austria
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44
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Glenn A, LaCasse P, Cox B. Emotion classification of Indonesian Tweets using Bidirectional LSTM. Neural Comput Appl 2023. [DOI: 10.1007/s00521-022-08186-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
AbstractEmotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. This research found a statistical significance in the performance of a single-layer bidirectional long short-term memory model over a two-layer stacked bidirectional long short-term memory model. This research also found that a single-layer bidirectional long short-term memory recurrent neural network met the performance of a state-of-the-art logistic regression model with supplemental closed-source features from a study by Saputri et al. [8] when classifying the emotion of Indonesian tweets.
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45
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Chang W, Wang X, Yang J, Qin T. An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries. SENSORS (BASEL, SWITZERLAND) 2023; 23:1811. [PMID: 36850409 PMCID: PMC9961688 DOI: 10.3390/s23041811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0.921, which is 2.68% higher than that of the CatBoost (AUC = 0.897) and is significantly higher than Logistic Regression (AUC = 0.855), Support Vector Machine (AUC = 0.864), and Random Forest (AUC = 0.875). Furthermore, the ecological suitability of blueberries in Majiang County is mapped according to the classification results of different models. When comparing the actual blueberry cultivation situation in Majiang County, the classification results of the SSA-CatBoost model proposed in this paper matches best with the real blueberry cultivation situation in Majiang County, which is of a high reference value for the selection of blueberry cultivation sites.
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Affiliation(s)
- Wenfeng Chang
- Department of Electrical Engineering, Guizhou University, Guiyang 550025, China
| | - Xiao Wang
- Department of Electrical Engineering, Guizhou University, Guiyang 550025, China
| | - Jing Yang
- Department of Electrical Engineering, Guizhou University, Guiyang 550025, China
| | - Tao Qin
- Department of Electrical Engineering, Guizhou University, Guiyang 550025, China
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Zheng Y, Xu Z, Xiao A. Deep learning in economics: a systematic and critical review. Artif Intell Rev 2023; 56:1-43. [PMID: 36777109 PMCID: PMC9898707 DOI: 10.1007/s10462-022-10272-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
From the perspective of historical review, the methodology of economics develops from qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of the superiority in learning inherent law and representative level, deep learning models assist in realizing intelligent decision-making in economics. After presenting some statistical results of relevant researches, this paper systematically investigates deep learning in economics, including a survey of frequently-used deep learning models in economics, several applications of deep learning models used in economics. Then, some critical reviews of deep learning in economics are provided, including models and applications, why and how to implement deep learning in economics, research gap and future challenges, respectively. It is obvious that several deep learning models and their variants have been widely applied in different subfields of economics, e.g., financial economics, macroeconomics and monetary economics, agricultural and natural resource economics, industrial organization, urban, rural, regional, real estate and transportation economics, health, education and welfare, business administration and microeconomics, etc. We are very confident that decision-making in economics will be more intelligent with the development of deep learning, because the research of deep learning in economics has become a hot and important topic recently.
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Affiliation(s)
- Yuanhang Zheng
- College of Computer Science, Sichuan University, 610064 Chengdu, PR China
| | - Zeshui Xu
- Business School, Sichuan University, 610064 Chengdu, PR China
| | - Anran Xiao
- Business School, Sichuan University, 610064 Chengdu, PR China
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Balasubramanian K, Ramya K, Gayathri Devi K. Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals. Cogn Neurodyn 2023; 17:133-151. [PMID: 36704627 PMCID: PMC9871147 DOI: 10.1007/s11571-022-09817-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/23/2022] [Accepted: 04/27/2022] [Indexed: 02/01/2023] Open
Abstract
Schizophrenia is a chronic mental disorder that impairs a person's thinking capacity, feelings and emotions, behavioural traits, etc., Emotional distortions, delusions, hallucinations, and incoherent speech are all some of the symptoms of schizophrenia, and cause disruption of routine activities. Computer-assisted diagnosis of schizophrenia is significantly needed to give its patients a higher quality of life. Hence, an improved adaptive neuro-fuzzy inference system based on the Hybrid Grey Wolf-Bat Algorithm for accurate prediction of schizophrenia from multi-channel EEG signals is presented in this study. The EEG signals are pre-processed using a Butterworth band pass filter and wICA initially, from which statistical, time-domain, frequency-domain, and spectral features are extracted. Discriminating features are selected using the ReliefF algorithm and are then forwarded to ANFIS for classification into either schizophrenic or normal. ANFIS is optimized by the Hybrid Grey Wolf-Bat Algorithm (HWBO) for better efficiency. The method is experimented on two separate EEG datasets-1 and 2, demonstrating an accuracy of 99.54% and 99.35%, respectively, with appreciable F1-score and MCC. Further experiments reveal the efficiency of the Hybrid Wolf-Bat algorithm in optimizing the ANFIS parameters when compared with traditional ANFIS model and other proven algorithms like genetic algorithm-ANFIS, particle optimization-ANFIS, crow search optimization algorithm-ANFIS and ant colony optimization algorithm-ANFIS, showing high R2 value and low RSME value. To provide a bias free classification, tenfold cross validation is performed which produced an accuracy of 97.8% and 98.5% on the two datasets respectively. Experimental outcomes demonstrate the superiority of the Hybrid Grey Wolf-Bat Algorithm over the similar techniques in predicting schizophrenia.
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Affiliation(s)
| | - K. Ramya
- PA College of Engineering and Technology, Pollachi, India
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Kenger ÖN, Özceylan E. Fuzzy min–max neural networks: a bibliometric and social network analysis. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08267-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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Wegener EK, Bergschöld JM, Whitmore C, Winters M, Kayser L. Involving Older People With Frailty or Impairment in the Design Process of Digital Health Technologies to Enable Aging in Place: Scoping Review. JMIR Hum Factors 2023; 10:e37785. [PMID: 36705959 PMCID: PMC9919541 DOI: 10.2196/37785] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 07/08/2022] [Accepted: 11/20/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND With an increase in life expectancy globally, the focus on digital health technologies that can enhance physical and mental health among older people with frailty and impairment has increased. Similarly, research interest in how digital health technology can promote well-being and self-management of health in older age has increased, including an increased focus on methods for designing digital health technologies that meet the various medical, psychological, and social needs of older population. Despite the increased focus, there remains a necessity to further understand the needs of this population group to ensure uptake and to avoid introduction of additional challenges when introducing technologies, for example, because of poor technological design. The scope is limited to digital health technologies meant to enable older people with frailty and impairment to age in place. OBJECTIVE In this study, we aimed to explore how older people with frailty and impairment are involved in various parts of the design processes of digital health technologies and identify gaps or neglected steps in a user-involving design process. This included a focus on recruitment strategies, contributions, and methods used to address the perspectives, needs, and desires of older people with frailty and impairment in the development of digital health technologies. METHODS A scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting from February 2021 to April 2021. Literature searches were conducted in PubMed, Scopus, Embase, and IEEE using a search string covering the concepts of health technology, older people, frailty and impairment, user-centered design, and self-management. RESULTS In total, 1891 studies were imported for screening from the initial search. A total of 22 studies were included in this review after full-text screening and manual search. Invitation through partners was the most reported recruitment strategy to involve older people with frailty and impairment in the design process of digital health technologies. Furthermore, they were commonly involved in the final evaluation of the development process. Three main gaps identified were the use of outreach approaches to recruit older people with frailty and impairment in the design process of digital health technologies, description of the value of involvement and outcome of the contribution of participants, and knowledge regarding involvement in all parts of the design process. CONCLUSIONS Although there is literature on methods for involving older people with frailty and impairment in the design of digital health technology, there is little methodological dialogue on the nuances of how different methods for involvement relate to and shape the outcome of the development process.
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Affiliation(s)
| | | | - Carly Whitmore
- School of Nursing, McMaster University, Hamilton, ON, Canada
| | | | - Lars Kayser
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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Bahaj SA. A hybrid intelligent model for early validation of infectious diseases: An explorative study of machine learning approaches. Microsc Res Tech 2023; 86:507-515. [PMID: 36704844 DOI: 10.1002/jemt.24290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 12/17/2022] [Accepted: 01/05/2023] [Indexed: 01/28/2023]
Abstract
Literature reports several infectious diseases news validation approaches, but none is economically effective for collecting and classifying information on different infectious diseases. This work presents a hybrid machine-learning model that could predict the validity of the infectious disease's news spread on the media. The proposed hybrid machine learning (ML) model uses the Dynamic Classifier Selection (DCS) process to validate news. Several machine learning models, such as K-Neighbors-Neighbor (KNN), AdaBoost (AB), Decision Tree (DT), Random Forest (RF), SVC, Gaussian Naïve Base (GNB), and Logistic Regression (LR) are tested in the simulation process on benchmark dataset. The simulation employs three DCS process methods: overall Local Accuracy (OLA), Meta Dynamic ensemble selection (META-DES), and Bagging. From seven ML classifiers, the AdaBoost with Bagging DCS method got a 97.45% high accuracy rate for training samples and a 97.56% high accuracy rate for testing samples. The second high accuracy was obtained at 96.12% for training and 96.45% for testing samples from AdaBoost with the Meta-DES method. Overall, the AdaBoost with Bagging model obtained higher accuracy, AUC, sensitivity, and specificity rate with minimum FPR and FNR for validation.
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Affiliation(s)
- Saeed Ali Bahaj
- MIS Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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