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Bhadra S, Kumar CJ. Enhancing the efficacy of depression detection system using optimal feature selection from EHR. Comput Methods Biomech Biomed Engin 2024; 27:222-236. [PMID: 36820618 DOI: 10.1080/10255842.2023.2181660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 02/13/2023] [Indexed: 02/24/2023]
Abstract
Diagnosing depression at an early stage is crucial and majorly depends on the clinician's skill. The present work aims to develop an automated tool for assisting the diagnostic procedure of depression using multiple machine-learning techniques. The dataset of sample size 4184 used in this study contains biometric and demographic information of individuals with or without depression, accessed from the University of Nice Sophia-Antipolis. The Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) are used for classifying the depressed from the control group. To enhance the computational efficiency, various feature selection algorithms like Recursive Feature Elimination (RFE), Mutual Information (MI) and three bio-inspired techniques, viz. Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Firefly Algorithms (FA) have been incorporated. To enhance the feature selection process further, majority voting is carried out in all possible combinations of three, four and five feature selection techniques. These feature selection techniques bring down the feature set size significantly to a mean of 33 from the actual size of 61 which is a reduction of 45.90%. The classification accuracy of the enhanced model varies between 84.18% and 88.46%, which is a significant improvement in performance as compared to the pre-existing models (83.76-85.89%). The proposed predictive models outperform the pre-existing classification models without feature selection and thereby enhancing both the performance and efficiency of the diagnostic process.
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Affiliation(s)
- Sweta Bhadra
- Department of Computer Science and Information Technology, Cotton University, Guwahati, India
| | - Chandan Jyoti Kumar
- Department of Computer Science and Information Technology, Cotton University, Guwahati, India
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2
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Singh M, Goyat R, Panwar R. Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment. TQM 2023. [DOI: 10.1108/tqm-07-2022-0231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
PurposeAt the present time, Industry 4.0 has proven its effectiveness and significance in automation and data exchange within industries across different sectors worldwide. In the current literature, there is still a lack of research on adopting Industry 4.0 in the manufacturing setting in developing economies. The main purpose of the present study is to explore the fundamental pillars and framework for ease of adoption of Industry 4.0 in manufacturing environments, along with highlighting the benefits and challenges.Design/methodology/approachIn this study, a systematic literature review has been conducted through protocol, search, appraisal, synthesis, analysis, report (PSALSAR) model. In the literature, the articles are included within time span of 2008–2022, consisting keywords like Industry 4.0, blockchain, machine learning, artificial intelligence, Internet of Things, 3D printing, big data analytics, etc. Based on available literature, conceptual implementation framework of Industry 4.0 is proposed.FindingsThis study explored the key ingredients that play an essential role to bridge the gap and construct a strong relationship among physical and cyber world. The results reveals that the emerging technologies such as IoT, blockchain, artificial intelligence, augmented reality, 3D printing, big-data analytics, cloud-computing join hands to accomplish success in Industry 4.0 by reducing human interference for effective and efficient systems. In addition, the study also explored the possible benefits of emerging technologies with challenges faced by manufacturing setting during adaptation of Industry 4.0.Originality/valueAs per the authors' best knowledge, no research articles are found in literature which explore various emerging technologies in Industry 4.0 with its implementation framework in the manufacturing setting in developing economies. The main focus of the present study is to discover the literature review in defined area and find the research gap among current scenario and future trend for execution of Industry 4.0 in manufacturing environment.
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3
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Nath G, Coursey A, Ekong J, Rastegari E, Sengupta S, Dag AZ, Delen D. Determining the temporal factors of survival associated with brain and nervous system cancer patients: A hybrid machine learning methodology. International Journal of Healthcare Management 2023. [DOI: 10.1080/20479700.2023.2196101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Affiliation(s)
- Gopal Nath
- Department of Mathematics and Statistics, Murray State University, Murray, KY, USA
| | - Austin Coursey
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Joseph Ekong
- Department of Industrial Engineering, Western New England University, Springfield, MA, USA
| | - Elham Rastegari
- Department of Business, Intelligence and Analytics, Creighton University, Omaha, NE, USA
| | - Saptarshi Sengupta
- Department of Computer Science, San José State University, San José, CA, USA
| | - Asli Z. Dag
- Heider College of Business, Creighton University, Omaha, NE, USA
| | - Dursun Delen
- Spears School of Business, Oklahoma State University, Stillwater, OK, USA
- Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey
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4
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Srinivas S, Young AJ. Machine Learning and Artificial Intelligence in Surgical Research. Surg Clin North Am 2023; 103:299-316. [PMID: 36948720 DOI: 10.1016/j.suc.2022.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Machine learning, a subtype of artificial intelligence, is an emerging field of surgical research dedicated to predictive modeling. From its inception, machine learning has been of interest in medical and surgical research. Built on traditional research metrics for optimal success, avenues of research include diagnostics, prognosis, operative timing, and surgical education, in a variety of surgical subspecialties. Machine learning represents an exciting and developing future in the world of surgical research that will not only allow for more personalized and comprehensive medical care.
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Affiliation(s)
- Shruthi Srinivas
- Department of Surgery, The Ohio State University, 370 West 9th Avenue, Columbus, OH 43210, USA
| | - Andrew J Young
- Division of Trauma, Critical Care, and Burn, The Ohio State University, 181 Taylor Avenue, Suite 1102K, Columbus, OH 43203, USA.
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5
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Shankar KCP, Shyry SP. A novel hybrid encryption method using S-box and Henon maps for multidimensional 3D medical images. Soft comput 2023. [DOI: 10.1007/s00500-023-08006-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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6
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Affiliation(s)
- Pietro Cipresso
- Department of Psychology, University of Turin, Turin, Italy
- Istituto Auxologico Italiano, IRCCS, Milan, Italy
- *Correspondence: Pietro Cipresso
| | | | - Alice Chirico
- Department of Psychology, Research Center in Communication Psychology, Universitá Cattolica del Sacro Cuore, Milan, Italy
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Singh R, Gehlot A, Kuchhal P, Choudhury S, Akram SV, Priyadarshi N, Khan B. Internet of Things Enabled Intelligent Automation for Smart Home with the Integration of PSO Algorithm and PID Controller. Journal of Electrical and Computer Engineering 2023. [DOI: 10.1155/2023/9611321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Currently, due to the widespread population growth, there is a widespread concern about an electricity shortage. As a result, smart devices have evolved and gained significant attention to reduce power consumption in home appliances due to electricity shortages. However, it lacks a universal remote control that can control home appliances based on environmental conditions. To overcome these challenges, this study proposed a hardware-based remote-control system that operates both in autonomous and semiautonomous modes to control home appliances based on environmental conditions. In the autonomous mode, the receiver section regulates the parameters under ambient conditions by varying the appliance’s applied voltage levels via a dimmer. The parameters in semiautonomous are monitored by the user via various levels of remote control. A 2.4 GHz RF modem is used to establish wireless personal network (WPAN) communication between the remote and the receiver. In addition, a Wi-Fi modem is built into the receiver to enable internet-based mobile applications to operate appliances. During the MATLAB analysis, a proportional integral derivative (PID) controller with a particle swarm optimization (PSO) method was found as a superior approach to control the home appliance with adequate environmental conditions. It is concluded from the MATLAB study that the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to the conventional appliances.
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8
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Tan CW, Pickard CJ, Witt WC. Automatic differentiation for orbital-free density functional theory. J Chem Phys 2023; 158:124801. [PMID: 37003740 DOI: 10.1063/5.0138429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
Differentiable programming has facilitated numerous methodological advances in scientific computing. Physics engines supporting automatic differentiation have simpler code, accelerating the development process and reducing the maintenance burden. Furthermore, fully differentiable simulation tools enable direct evaluation of challenging derivatives-including those directly related to properties measurable by experiment-that are conventionally computed with finite difference methods. Here, we investigate automatic differentiation in the context of orbital-free density functional theory (OFDFT) simulations of materials, introducing PROFESS-AD. Its automatic evaluation of properties derived from first derivatives, including functional potentials, forces, and stresses, facilitates the development and testing of new density functionals, while its direct evaluation of properties requiring higher-order derivatives, such as bulk moduli, elastic constants, and force constants, offers more concise implementations than conventional finite difference methods. For these reasons, PROFESS-AD serves as an excellent prototyping tool and provides new opportunities for OFDFT.
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Affiliation(s)
- Chuin Wei Tan
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom
| | - Chris J Pickard
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom
| | - William C Witt
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom
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9
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Glory Precious J, Keren Evangeline I, Kirubha SPA. Brain tumour segmentation and survival prognostication using 3D radiomics features and machine learning algorithms. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2023. [DOI: 10.1080/21681163.2023.2189487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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10
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Alagappan G, Png CE. Group refractive index via auto-differentiation and neural networks. Sci Rep 2023; 13:4450. [PMID: 36932110 PMCID: PMC10023661 DOI: 10.1038/s41598-023-29952-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/10/2023] [Indexed: 03/19/2023] Open
Abstract
In this article, using principles of automatic differentiation, we demonstrate a generic deep learning representation of group refractive index for photonic channel waveguides. It enables evaluation of group refractive indices in a split of second, without any traditional numerical calculations. Traditionally, the group refractive index is calculated by a repetition of the optical mode calculations via a parametric wavelength sweep of finite difference (or element) calculations. To the direct contrary, in this work, we show that the group refractive index can be quasi-instantaneously obtained from the auto-gradients of the neural networks that models the effective refractive index. We embed the wavelength dependence of the effective index in the deep learning model by applying the scaling property of the Maxwell's equations and this eliminates the problems caused by the curse of dimensionality. This work portrays a very clear illustration on how physics-based derived optical quantities can be calculated instantly from the underlying deep learning models of the parent quantities using automatic differentiation.
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Affiliation(s)
- G Alagappan
- Fusionopolis, Institute of High-Performance Computing, Agency for Science, Technology, and Research (A-STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Singapore.
| | - C E Png
- Fusionopolis, Institute of High-Performance Computing, Agency for Science, Technology, and Research (A-STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Singapore
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11
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Al-Janabi S, Al-Barmani Z. Intelligent multi-level analytics of soft computing approach to predict water quality index (IM12CP-WQI). Soft comput 2023. [DOI: 10.1007/s00500-023-07953-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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12
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Riatti P, Thiel A. The role of the body in electronic sport: a scoping review. Ger J Exerc Sport Res 2023. [DOI: 10.1007/s12662-023-00880-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
Abstract
AbstractThe popularity of competitive computer and videogaming, also known as electronic sport (esport), has been rising rapidly during the past decades. Because of many parallels with traditional sports, like competitiveness, skill requirements, degree of professionalization, or the way it is portrayed in the media, esport has been adopted as part of the sport canon in many countries. Still, critics argue that playing computer games lacks the physicality commonly seen in traditional sports. A significant part of the competition is mediated through digital platforms and the spotlight shifts from the players’ appearance and actions to their digital avatars. This paper takes on this issue by exploring existing evidence about the role of the body in esport via a scoping review approach. According to the findings of 47 studies, the body’s role in esport is akin to that in traditional sport, including specific motoric requirements or biometric responses. Beyond that, the body can be seen as a link between the digital and physical worlds. Players embody digital avatars in the form of esport-specific movements, transfer of norms and ideals, and identification with the in-game characters. Future research can use this review as a basis for scientific approaches to individual phenomena regarding corporeality in esport and inter-corporeality.
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Guo Z, Li J, Ramesh R. Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter? Information Systems Research 2023. [DOI: 10.1287/isre.2023.1208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Energy costs represent a significant share of the total cost of ownership in high-performance computing systems. Using a unique data set collected by massive sensor networks in a petascale national supercomputing center, we first present an explanatory model to identify key factors affecting energy consumption in supercomputing. Our analytic results show that workload distribution among the nodes has significant effects and could effectively be leveraged to improve energy efficiency. We then establish the high model performance using in-sample and out-of-sample analyses and develop prescriptive models for energy-optimal runtime workload management. We present four dynamic resource management methodologies (packing, load balancing, threshold-based switching, and energy optimization), model their application at two levels (within-rack and cross-rack resource allocation), and explore runtime resource redistribution policies for jobs under the computational steering and comparatively evaluate strategies that use computational steering with those that do not. Our experimental results lead to a threshold strategy that yields near-optimal energy efficiency under all workload conditions. We further calibrate the energy-optimal resource allocations over the full range of workloads and present a bi-criteria evaluation to consider energy consumption and job performance tradeoffs. We conclude with implementation guidelines and policy insights into energy-efficient computing resource management in large supercomputing centers.
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Affiliation(s)
- Zhiling Guo
- School of Computing and Information Systems, Singapore Management University, Singapore 178902, Singapore
| | - Jin Li
- School of Management, Xi’an Jiaotong University, Xi’an 710049, China
| | - Ram Ramesh
- Department of Management Science and Systems, State University of New York, Buffalo, New York 14260
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14
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Murugaiyan S, Uyyala SR. Aspect-Based Sentiment Analysis of Customer Speech Data Using Deep Convolutional Neural Network and BiLSTM. Cognit Comput 2023. [DOI: 10.1007/s12559-023-10127-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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15
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Liu W, Zhang L, Bao L, Shen G, Feng J. Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure. J Proteome Res 2023; 22:758-767. [PMID: 36710647 DOI: 10.1021/acs.jproteome.2c00488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, f1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.
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Affiliation(s)
- Wuping Liu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
| | - Lirong Zhang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
| | - Lijun Bao
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
| | - Guiping Shen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
| | - Jianghua Feng
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China
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16
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Kumar C, Singh S. LFSS-KF: lightweight fast real-time security standards with key fusion for surveillance videos. The Imaging Science Journal 2023. [DOI: 10.1080/13682199.2023.2171550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Chandan Kumar
- CSE, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India
| | - Shailendra Singh
- CSE, National Institute of Technical Teachers’ Training and Research, Bhopal, Madhya Pradesh, India
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17
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Woldan P, Duda P, Cader A, Laktionov I. A New Approach to Image-Based Recommender Systems with the Application of Heatmaps Maps. Journal of Artificial Intelligence and Soft Computing Research 2023; 13:63-72. [DOI: 10.2478/jaiscr-2023-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023] Open
Abstract
Abstract
One of the fundamental issues of modern society is access to interesting and useful content. As the amount of available content increases, this task becomes more and more challenging. Our needs are not always formulated in words; sometimes we have to use complex data types like images. In this paper, we consider the three approaches to creating recommender systems based on image data. The proposed systems are evaluated on a real-world dataset. Two case studies are presented. The first one presents the case of an item with many similar objects in a database, and the second one with only a few similar items.
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Singh S, Ru J. Goals of sustainable infrastructure, industry, and innovation: a review and future agenda for research. Environ Sci Pollut Res Int 2023; 30:28446-28458. [PMID: 36670221 PMCID: PMC9859666 DOI: 10.1007/s11356-023-25281-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 01/08/2023] [Indexed: 04/16/2023]
Abstract
Sustainable Development Goal 9 targets (SDG 9 targets) are mainly tracked through the indicators of penetration of internet and mobile broadband subscription, logistic performance index, quality and ranking of the universities, investment in research and development initiatives, industrial reforms and emission control, and connectivity to rural areas. The attainment of many of these targets and tracking of indicators is confronted by challenges of poor awareness, funding issues, distorted policies, and implementation failures. This systematic review on achievements, challenges, and future scope in attaining SDG 9 consolidates the literature from the Web of Science, related to SDG 9 and indicators, since 2017; develops bibliometric patterns; conducts thematic analysis by focusing the leading indicators of SDG 9; and develops agenda for future research. The major limitations of this study include focusing on selected indicators and limited literature availability. This review recommends policymakers, researchers, and administrators to focus on promising themes such as tackling the digital divide and ensuring digital justice and digital equality; clean fuel and technology adoption; enhancing internet and mobile broadband subscription with reduced negative impacts, logistic sector reforms; industrial policy reforms and technology integration; improving the quality and sustainability of universities; and increasing funding and support for research and development initiatives and improving the rural connectivity.
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Affiliation(s)
- Sanjeet Singh
- University Centre for Research & Development & Department of Management Studies, Chandigarh University, Gharuan, Mohali, Punjab, India, 140413.
| | - Jayaram Ru
- University Centre for Research & Development & Department of Management Studies, Chandigarh University, Gharuan, Mohali, Punjab, India, 140413
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Yue Y, Cao L, Lu D, Hu Z, Xu M, Wang S, Li B, Ding H. Review and empirical analysis of sparrow search algorithm. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10435-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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Barrio-Conde M, Zanella MA, Aguiar-Perez JM, Ruiz-Gonzalez R, Gomez-Gil J. A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties. Sensors (Basel) 2023; 23:2471. [PMID: 36904675 PMCID: PMC10007379 DOI: 10.3390/s23052471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.
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Affiliation(s)
- Mikel Barrio-Conde
- Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, ETSI Telecomunicación, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Marco Antonio Zanella
- Agricultural Engineering Department, Federal University of Lavras, P.O. Box 3037, Lavras 37200-000, Brazil
| | - Javier Manuel Aguiar-Perez
- Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, ETSI Telecomunicación, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Ruben Ruiz-Gonzalez
- Department of Electromechanical Engineering, Escuela Politécnica Superior, University of Burgos, Avda. Cantabria s/n, 09006 Burgos, Spain
| | - Jaime Gomez-Gil
- Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, ETSI Telecomunicación, Paseo de Belén 15, 47011 Valladolid, Spain
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Almutairi SM, Manimurugan S, Aborokbah MM, Narmatha C, Ganesan S, Karthikeyan P. An Efficient USE-Net Deep Learning Model for Cancer Detection. INT J INTELL SYST 2023; 2023:1-14. [DOI: 10.1155/2023/8509433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Breast cancer (BrCa) is the most common disease in women worldwide. Classifying the BrCa image is extremely important for finding BrCa at an earlier stage and monitoring BrCa during treatment. The computer-aided detection methods have been used to interpret BrCa and improve the detection of BrCa during the screening and treatment stages. However, if a new BrCa image is generated for the treatment, it will not classify correctly. The main objective of this research is to classify the BrCa images for newly generated images. The model performs preprocessing, segmentation, feature extraction, and classification. In preprocessing, a hybrid median filtering (HMF) is used to eliminate the noise in the images. The contrast of the images is enhanced using quadrant dynamic histogram equalization (QDHE). Then, ROI segmentation is performed using the USE-Net deep learning model. The CaffeNet model is used for feature extraction on the segmented images, and finally, classification is made using the improved random forest (IRF) with extreme gradient boosting (XGB). The model obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specificity, 98.96% precision, and 98.70% f1-score for ultrasound images. The model gives 98.31% accuracy, 99.29% sensitivity, 90.20% specificity, 98.82% precision, and 99.05% f1-score for mammogram images.
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Stupar I, Huljenic D. Model-based cloud service deployment optimisation method for minimisation of application service operational cost. J Cloud Comp 2023. [DOI: 10.1186/s13677-023-00389-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
AbstractMany currently existing cloud cost optimisation solutions are aimed at cloud infrastructure providers, and they often deal only with specific types of application services. Unlike infrastructure providers, the providers of cloud applications are often left without a suitable cost optimisation solution, especially concerning the wide range of different application types. This paper presents an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is the development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, intending to minimise the operational cost of the cloud service while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, knowledge is gained about the effects of the cloud application context parameters on the service cost and quality of service, which is then used to determine the optimal service deployment option. The service models are validated using cloud applications deployed in laboratory conditions, and the optimisation method is validated using the simulations based on the proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service and use this information for reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain valuable insights regarding service deployment decision without acquiring extensive datasets for the analysis.
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24
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Duan C, Liu Y. Collaborative possibilistic fuzzy clustering based on information bottleneck. IFS 2023. [DOI: 10.3233/jifs-223854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
In fuzzy clustering algorithms, the possibilistic fuzzy clustering algorithm has been widely used in many fields. However, the traditional Euclidean distance cannot measure the similarity between samples well in high-dimensional data. Moreover, if there is an overlap between clusters or a strong correlation between features, clustering accuracy will be easily affected. To overcome the above problems, a collaborative possibilistic fuzzy clustering algorithm based on information bottleneck is proposed in this paper. This algorithm retains the advantages of the original algorithm, on the one hand, using mutual information loss as the similarity measure instead of Euclidean distance, which is conducive to reducing subjective errors caused by arbitrary choices of similarity measures and improving the clustering accuracy; on the other hand, the collaborative idea is introduced into the possibilistic fuzzy clustering based on information bottleneck, which can form an accurate and complete representation of the data organization structure based on make full use of the correlation between different feature subsets for collaborative clustering. To examine the clustering performance of this algorithm, five algorithms were selected for comparison experiments on several datasets. Experimental results show that the proposed algorithm outperforms the comparison algorithms in terms of clustering accuracy and collaborative validity.
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Affiliation(s)
- Chen Duan
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China
| | - Yongli Liu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China
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25
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Wang W, Lin W, Gao F, Chang S. Intelligent decision methodology for business English teaching quality evaluation based on GHM and PG operators with 2-tuple linguistic neutrosophic numbers. IFS 2023. [DOI: 10.3233/jifs-223850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Business English teaching quality evaluation Business English is a new type of composite specialty, which is a discipline innovation made by China’s higher education to adapt to the new market demand and international standards since the reform and opening up. Over the past 20 years, it has cultivated a number of compound talents for the cause of China’s reform and opening up. However, the backwardness of business English theoretical research has greatly restricted the development of business English. At present, Business English has been officially approved as a new major for undergraduate enrollment by the Ministry of Education of the People’s Republic of China. Its subject nature, specialty structure, training objectives, and specialty compound characteristics need to be qualitatively studied theoretically. The business English teaching quality evaluation is viewed as the multiple attribute decision making (MADM) issue. In this paper, we connect the geometric Heronian mean (GHM) operator and power geometric (PG) with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose the generalized 2-tuple linguistic neutrosophic power geometric HM (G2TLNPGHM) operator. Then, the G2TLNGHM operator is applied to deal with the MADM problems under 2TLNNs. Finally, an example for business English teaching quality evaluation is used to show the proposed methods. Some comparative analysis and parameter influence analysis are fully given. The results show that the built algorithms method is useful for business English teaching quality evaluation.
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Affiliation(s)
- Wenpu Wang
- Chengdu Technological University, Chengdu, China
| | - Wei Lin
- Chengdu Technological University, Chengdu, China
| | | | - Shuli Chang
- Chengdu Technological University, Chengdu, China
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26
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Peng J, Feng Y, Zhang Q, Liu X. Multi-objective integrated optimization study of prefabricated building projects introducing sustainable levels. Sci Rep 2023; 13:2821. [PMID: 36807357 PMCID: PMC9938254 DOI: 10.1038/s41598-023-29881-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 02/11/2023] [Indexed: 02/19/2023] Open
Abstract
As construction becomes greener, people have higher and higher requirements for engineering project management, which makes it necessary to deeply study the comprehensive optimization of schedule, cost and sustainability level. Adhering to the concept of low carbon and green, the article takes carbon emission factor into the total cost of building construction and improves the traditional cost objective of engineering projects; then quantitatively analyzes the economic, environmental and social impacts of assembled buildings from the perspective of sustainability, and introduces the sustainability objective into the traditional duration-cost problem study, taking the duration of each job in the double code arrow diagram as the independent variable to construct the duration -cost-sustainability level multi-objective optimization model. In order to solve the type effectively, a series of Pareto optimal solutions are obtained using the NSGA-II algorithm, and the efficacy coefficient method is used for program decision making. The results show that the Pareto solution set can provide effective support for the project manager's decision making, and the NSGA-II algorithm is effective in solving the multi-objective optimization model.
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Affiliation(s)
- Junlong Peng
- grid.440669.90000 0001 0703 2206School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, 410114 China
| | - Yue Feng
- School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
| | - Qi Zhang
- grid.440669.90000 0001 0703 2206School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, 410114 China
| | - Xiangjun Liu
- grid.440669.90000 0001 0703 2206School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, 410114 China
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Bhowate VG, Reddy TH. Spark-based deep classifier framework for imbalanced data classification. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2023. [DOI: 10.1080/21681163.2023.2177821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Affiliation(s)
- Vikas Gajananrao Bhowate
- Information technology, Information Technology St. Vincent Pallotti College of Engineering & Technology Gavsi Manapur, Nagpur, India
| | - T. Hanumantha Reddy
- Computer science & Engineering, Computer Science & Engineering Rao Bahadur Y Mahabaleswarappa College of Engineering (RYMEC), Ballari, India
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Méndez M, Merayo MG, Núñez M. Machine learning algorithms to forecast air quality: a survey. Artif Intell Rev 2023; 56:1-36. [PMID: 36820441 PMCID: PMC9933038 DOI: 10.1007/s10462-023-10424-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2023] [Indexed: 02/18/2023]
Abstract
Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011-2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.
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Affiliation(s)
- Manuel Méndez
- Design and Testing of Reliable Systems Research Group, Universidad Complutense de Madrid, C/ Profesor José García Santesmases, 9, 28040 Madrid, Madrid Spain
| | - Mercedes G. Merayo
- Design and Testing of Reliable Systems Research Group, Universidad Complutense de Madrid, C/ Profesor José García Santesmases, 9, 28040 Madrid, Madrid Spain
| | - Manuel Núñez
- Design and Testing of Reliable Systems Research Group, Universidad Complutense de Madrid, C/ Profesor José García Santesmases, 9, 28040 Madrid, Madrid Spain
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Hirosawa T, Harada Y, Yokose M, Sakamoto T, Kawamura R, Shimizu T. Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study. Int J Environ Res Public Health 2023; 20:3378. [PMID: 36834073 PMCID: PMC9967747 DOI: 10.3390/ijerph20043378] [Citation(s) in RCA: 69] [Impact Index Per Article: 69.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/09/2023] [Accepted: 02/13/2023] [Indexed: 06/01/2023]
Abstract
The diagnostic accuracy of differential diagnoses generated by artificial intelligence (AI) chatbots, including the generative pretrained transformer 3 (GPT-3) chatbot (ChatGPT-3) is unknown. This study evaluated the accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical vignettes with common chief complaints. General internal medicine physicians created clinical cases, correct diagnoses, and five differential diagnoses for ten common chief complaints. The rate of correct diagnosis by ChatGPT-3 within the ten differential-diagnosis lists was 28/30 (93.3%). The rate of correct diagnosis by physicians was still superior to that by ChatGPT-3 within the five differential-diagnosis lists (98.3% vs. 83.3%, p = 0.03). The rate of correct diagnosis by physicians was also superior to that by ChatGPT-3 in the top diagnosis (53.3% vs. 93.3%, p < 0.001). The rate of consistent differential diagnoses among physicians within the ten differential-diagnosis lists generated by ChatGPT-3 was 62/88 (70.5%). In summary, this study demonstrates the high diagnostic accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical cases with common chief complaints. This suggests that AI chatbots such as ChatGPT-3 can generate a well-differentiated diagnosis list for common chief complaints. However, the order of these lists can be improved in the future.
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Affiliation(s)
- Takanobu Hirosawa
- Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi 321-0293, Japan
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Ajibade S, Simon B, Gulyas M, Balint C. Sustainable intensification of agriculture as a tool to promote food security: A bibliometric analysis. Front Sustain Food Syst 2023. [DOI: 10.3389/fsufs.2023.1101528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
Abstract
Sustainable intensification (SI) of agriculture is required to satisfy the growing populations' nutritional needs, and therefore food security while limiting negative environmental impacts. The study aims to investigate the global scientific output of sustainable intensification research from 2010 to 20 August 2021. The data was retrieved from the Web of Science (WoS) Core Collection and was analyzed using a bibliometric method and VOS viewer to determine the most productive countries and organizations by collaboration analysis, including the keywords to analyze the research hotspots and trends, and the most cited publications in the field. From the 1,610 studies published in the theme of sustainable agriculture by 6,346 authors belonging to 1,981 organizations and 115 countries, the study found an increased number of publications and citations in 2020, with 293 publications and 10,275 citations. The United States ranked highest in countries collaborating with the most publications in the field. The occurrence of keywords like “food security”, “climate change”, “agriculture”, “ecosystem services”, “conservation agriculture”, “Sub-Sahara Africa”, “Africa”, “biodiversity”, and “maize” in both author and all keywords (author and index) reveal the significance of sustainable intensification in Africa, as a solution to food insecurity under climate change conditions. The availability of funding agencies from big economies explains the growing interest by developing countries in the SI of agriculture research due to the growing population, food insecurity, and access to limited land for farming.
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Barik K, Misra S, Ray AK, Bokolo A. LSTM-DGWO-Based Sentiment Analysis Framework for Analyzing Online Customer Reviews. Comput Intell Neurosci 2023; 2023:6348831. [PMID: 36820054 DOI: 10.1155/2023/6348831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 02/13/2023]
Abstract
Sentiment analysis furnishes consumer concerns regarding products, enabling product enhancement development. Existing sentiment analysis using machine learning techniques is computationally intensive and less reliable. Deep learning in sentiment analysis approaches such as long short term memory has adequately evolved, and the selection of optimal hyperparameters is a significant issue. This study combines the LSTM with differential grey wolf optimization (LSTM-DGWO) deep learning model. The app review dataset is processed using the bidirectional encoder representations from transformers (BERT) framework for efficient word embeddings. Then, review features are extracted by the genetic algorithm (GA), and the optimal review feature set is extracted using the firefly algorithm (FA). Finally, the LSTM-DGWO model categorizes app reviews, and the DGWO algorithm optimizes the hyperparameters of the LSTM model. The proposed model outperformed conventional methods with a greater accuracy of 98.89%. The findings demonstrate that sentiment analysis can be practically applied to understand the customer's perception of enhancing products from a business perspective.
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Nong X, Bai W, Liu G. Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features. PLoS One 2023; 18:e0280346. [PMID: 36763685 PMCID: PMC9917240 DOI: 10.1371/journal.pone.0280346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 12/23/2022] [Indexed: 02/12/2023] Open
Abstract
Compared with other point clouds, the airborne LiDAR point cloud has its own characteristics. The deep learning network PointNet++ ignores the inherent properties of airborne LiDAR point, and the classification precision is low. Therefore, we propose a framework based on the PointNet++ network. In this work, we proposed an interpolation method that uses adaptive elevation weight to make full use of the objects in the airborne LiDAR point, which exhibits discrepancies in elevation distributions. The class-balanced loss function is used for the uneven density distribution of point cloud data. Moreover, the relationship between a point and its neighbours is captured, densely connecting point pairs in multiscale regions and adding centroid features to learn contextual information. Experiments are conducted on the Vaihingen 3D semantic labelling benchmark dataset and GML(B) benchmark dataset. The experiments show that the proposed method, which has additional contextual information and makes full use of the airborne LiDAR point cloud properties to support classification, achieves high accuracy and can be widely used in airborne LiDAR point classification.
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Affiliation(s)
- Xingzhong Nong
- Guangzhou Metro Design & Research Institute Co., Ltd., Guangdong, Guangzhou, China
| | - Wenfeng Bai
- Guangzhou Metro Design & Research Institute Co., Ltd., Guangdong, Guangzhou, China
- * E-mail:
| | - Guanlan Liu
- School of Geodesy and Geomatics, Wuhan University, Wuhan, China
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Xu H, Ren J, Lin J, Mao S, Xu Z, Chen Z, Zhao J, Wu Y, Xu N, Wang P. The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review. Food Measure 2023. [DOI: 10.1007/s11694-023-01822-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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34
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Huo T, Xie Y, Fang Y, Wang Z, Liu P, Duan Y, Zhang J, Wang H, Xue M, Liu S, Ye Z. Deep learning-based algorithm improves radiologists' performance in lung cancer bone metastases detection on computed tomography. Front Oncol 2023; 13:1125637. [PMID: 36845701 PMCID: PMC9946454 DOI: 10.3389/fonc.2023.1125637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 01/13/2023] [Indexed: 02/10/2023] Open
Abstract
Purpose To develop and assess a deep convolutional neural network (DCNN) model for the automatic detection of bone metastases from lung cancer on computed tomography (CT). Methods In this retrospective study, CT scans acquired from a single institution from June 2012 to May 2022 were included. In total, 126 patients were assigned to a training cohort (n = 76), a validation cohort (n = 12), and a testing cohort (n = 38). We trained and developed a DCNN model based on positive scans with bone metastases and negative scans without bone metastases to detect and segment the bone metastases of lung cancer on CT. We evaluated the clinical efficacy of the DCNN model in an observer study with five board-certified radiologists and three junior radiologists. The receiver operator characteristic curve was used to assess the sensitivity and false positives of the detection performance; the intersection-over-union and dice coefficient were used to evaluate the segmentation performance of predicted lung cancer bone metastases. Results The DCNN model achieved a detection sensitivity of 0.894, with 5.24 average false positives per case, and a segmentation dice coefficient of 0.856 in the testing cohort. Through the radiologists-DCNN model collaboration, the detection accuracy of the three junior radiologists improved from 0.617 to 0.879 and the sensitivity from 0.680 to 0.902. Furthermore, the mean interpretation time per case of the junior radiologists was reduced by 228 s (p = 0.045). Conclusions The proposed DCNN model for automatic lung cancer bone metastases detection can improve diagnostic efficiency and reduce the diagnosis time and workload of junior radiologists.
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Affiliation(s)
- Tongtong Huo
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,Research Institute of Imaging, National Key Laboratory of Multi-Spectral Information Processing Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Fang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziyi Wang
- Research Institute of Imaging, National Key Laboratory of Multi-Spectral Information Processing Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Pengran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuyu Duan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiayao Zhang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Honglin Wang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mingdi Xue
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Songxiang Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Songxiang Liu, ; Zhewei Ye,
| | - Zhewei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Songxiang Liu, ; Zhewei Ye,
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Ma X, Yu R, Gao C, Wei Z, Xia Y, Wang X, Liu H. Research on named entity recognition method of marine natural products based on attention mechanism. Front Chem 2023; 11:958002. [PMID: 36846857 PMCID: PMC9944735 DOI: 10.3389/fchem.2023.958002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 01/24/2023] [Indexed: 02/11/2023] Open
Abstract
Marine natural product (MNP) entity property information is the basis of marine drug development, and this entity property information can be obtained from the original literature. However, the traditional methods require several manual annotations, the accuracy of the model is low and slow, and the problem of inconsistent lexical contexts cannot be solved well. In order to solve the aforementioned problems, this study proposes a named entity recognition method based on the attention mechanism, inflated convolutional neural network (IDCNN), and conditional random field (CRF), combining the attention mechanism that can use the lexicality of words to make attention-weighted mentions of the extracted features, the ability of the inflated convolutional neural network to parallelize operations and long- and short-term memory, and the excellent learning ability. A named entity recognition algorithm model is developed for the automatic recognition of entity information in the MNP domain literature. Experiments demonstrate that the proposed model can properly identify entity information from the unstructured chapter-level literature and outperform the control model in several metrics. In addition, we construct an unstructured text dataset related to MNPs from an open-source dataset, which can be used for the research and development of resource scarcity scenarios.
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Affiliation(s)
- Xiaodong Ma
- College of Computer Science and Technology, Ocean University of China, Qingdao, China
| | - Rilei Yu
- College of Computer Science and Technology, Ocean University of China, Qingdao, China
| | - Chunxiao Gao
- College of Computer Science and Technology, Ocean University of China, Qingdao, China
| | - Zhiqiang Wei
- College of Computer Science and Technology, Ocean University of China, Qingdao, China,Pilot National Laboratory for Marine Science and Technology, Qingdao, China
| | - Yimin Xia
- College of Computer Science and Technology, Ocean University of China, Qingdao, China
| | - Xiaowei Wang
- College of Computer Science and Technology, Ocean University of China, Qingdao, China
| | - Hao Liu
- College of Computer Science and Technology, Ocean University of China, Qingdao, China,Pilot National Laboratory for Marine Science and Technology, Qingdao, China,*Correspondence: Hao Liu,
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Dweekat OY, Lam SS. Optimized design of hybrid genetic algorithm with multilayer perceptron to predict patients with diabetes. Soft comput 2023. [DOI: 10.1007/s00500-023-07876-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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37
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Liu C. GRA method for probabilistic simplified neutrosophic MADM and application to talent training quality evaluation of segmented education. IFS 2023. [DOI: 10.3233/jifs-224494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The “3 + 2” segmented training between higher vocational colleges and applied undergraduate courses has opened up the rising channel of vocational education from junior college level to undergraduate level, and promoted the organic connection between higher vocational colleges and Universities of Applied Sciences. It is one of the important ways to establish a modern vocational education system. Exploring the monitoring mechanism of talent training quality is an important measure to ensure the achievement of the segmented training goal, and it is a necessary condition to successfully train high-quality skilled applied talents. The talent training quality evaluation of segmented education is viewed as multiple attribute decision-making (MADM) issue. In this paper, an extended probabilistic simplified neutrosophic number GRA (PSNN-GRA) method is established for talent training quality evaluation of segmented education. The PSNN-GRA method integrated with CRITIC method in probabilistic simplified neutrosophic sets (PSNSs) circumstance is applied to rank the optional alternatives and a numerical example for talent training quality evaluation of segmented education is used to proof the newly proposed method’s practicability along with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute.
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Affiliation(s)
- Chang Liu
- Jilin Business and Technology College, Changchun, Jilin, China
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38
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Zheng L, Shi J, Yang Y. A two-stage surrogate-assisted meta-heuristic algorithm for high-dimensional expensive problems. Soft comput 2023. [DOI: 10.1007/s00500-023-07855-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Ismail WN, Alsalamah HA, Hassan MM, Mohamed E. AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design. Heliyon 2023; 9:e13636. [PMID: 36852018 PMCID: PMC9958436 DOI: 10.1016/j.heliyon.2023.e13636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 12/04/2022] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of time- series data when used for Human Activity Recognition (HAR). The manual design of such neural architectures is an error-prone and time-consuming process. The search for optimal CNN architectures is considered a revolution in the design of neural networks. By means of Neural Architecture Search (NAS), network architectures can be designed and optimized automatically. Thus, the optimal CNN architecture representation can be found automatically because of its ability to overcome the limitations of human experience and thinking modes. Evolution algorithms, which are derived from evolutionary mechanisms such as natural selection and genetics, have been widely employed to develop and optimize NAS because they can handle a blackbox optimization process for designing appropriate solution representations and search paradigms without explicit mathematical formulations or gradient information. The Genetic optimization algorithm (GA) is widely used to find optimal or near-optimal solutions for difficult problems. Considering these characteristics, an efficient human activity recognition architecture (AUTO-HAR) is presented in this study. Using the evolutionary GA to select the optimal CNN architecture, the current study proposes a novel encoding schema structure and a novel search space with a much broader range of operations to effectively search for the best architectures for HAR tasks. In addition, the proposed search space provides a reasonable degree of depth because it does not limit the maximum length of the devised task architecture. To test the effectiveness of the proposed framework for HAR tasks, three datasets were utilized: UCI-HAR, Opportunity, and DAPHNET. Based on the results of this study, it has been found that the proposed method can efficiently recognize human activity with an average accuracy of 98.5% (∓1.1), 98.3%, and 99.14% (∓0.8) for UCI-HAR, Opportunity, and DAPHNET, respectively.
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Affiliation(s)
- Walaa N Ismail
- Department of Management Information Systems, College of Business Administration, Al Yamamah University, 11512, Riyadh, Saudi Arabia.,Faculty of Computers and Information, Minia University, 61519, Minia, Egypt
| | - Hessah A Alsalamah
- Information Systems Department, College of Computer and Information Sciences, King Saud University, 4545, Riyadh, Saudi Arabia.,Computer Engineering Department, College of Engineering and Architecturen, Al Yamamah University, 11512, Riyadh, Saudi Arabia
| | - Mohammad Mehedi Hassan
- Information Systems Department, College of Computer and Information Sciences, King Saud University, 4545, Riyadh, Saudi Arabia
| | - Ebtesam Mohamed
- Faculty of Computers and Information, Minia University, 61519, Minia, Egypt
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40
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Yazdinejad A, Dehghantanha A, Parizi RM, Epiphaniou G. An optimized fuzzy deep learning model for data classification based on NSGA-II. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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41
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Anish TP, Joe Prathap PM. An efficient and low complex model for optimal RBM features with weighted score-based ensemble multi-disease prediction. Comput Methods Biomech Biomed Engin 2023; 26:350-372. [PMID: 36218238 DOI: 10.1080/10255842.2022.2129969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Multi-disease prediction is regarded as the capacity to simultaneously identify various diseases that are expected to be affected an individual at a certain period. These multiple diseases are seemed to be at various progression levels and need to be detected in the patient at the time of clinical visits. Diverse studies in the literature have included the predictive models for particular diseases yet, it is unable to notice humans with multiple diseases since humans are mostly suffered not only from a single disease but also from multiple diseases. Hence, this article aims to implement a novel multi-disease prediction model using an ensemble learning approach with deep features. The required data for the multi-disease prediction is collected from the standard datasets. Then, the collected data are given into the "Deep Belief Network (DBN)" approach, where the features are obtained from the RBM layers. These RBM features are tuned with the help of Deviation-based Hybrid Grasshopper Barnacles Mating Optimization (D-HGBMO) for improving the prediction performance. The optimized RBM features are considered in the ensemble learning model named Ensemble, in which the multi-disease prediction is performed with "Deep Neural Network (DNN), Extreme Learning Machine (ELM), and Long Short Term Memory." The predicted score from three classifiers is used in the optimized weighted score and thresholding-based final prediction using the same D-HGBMO for determining the accurate multi-disease prediction results. The experimental results show the effective performance of the proposed model by comparing it with the existing classifiers with the help of different quantitative measures.
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Affiliation(s)
- T P Anish
- Assistant Professor, Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, Puduvoyal, India
| | - P M Joe Prathap
- Professor, Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, India
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42
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Hu W, Shao Y, Liu Y. A novel MADM-based efficient methodology with 2-tuple linguistic neutrosophic numbers and applications to physical education teaching quality evaluation. IFS 2023. [DOI: 10.3233/jifs-224539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
With the successful promotion of the new round of basic education curriculum reform, China’s physical education (PE) teaching ideology and PE teaching mode have undergone profound changes, and these changes urgently require schools to establish a PE teaching quality (PETQ) evaluation system that is compatible with them, and urgently resolve the contradiction between theory and practice. The evaluation of teaching quality is not only a value judgment of teachers’ teaching ability and teaching effect, but also a value judgment of students’ learning ability and learning achievement changes. Therefore, it is an important issue of higher education research to construct a university PE teaching quality evaluation system and actively promote the healthy development of university PE teaching evaluation. The PETQ evaluation is viewed as the multi-attribute decision-making (MADM). In order to take the full use of power average (PA) operator and Heronian mean (HM) operator, in this article, we combine the generalized Heronian mean (GHM) operator and PA with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose the generalized 2-tuple linguistic neutrosophic power weighted HM (G2TLNPWHM) operator. The G2TLNPWHM could relieve the influence of the awkward data through power weights and it could also consider the relationships between the attributes, and it can give more accurate ranking order then the existing methods. The new MADM method is built on G2TLNPWHM operators. Finally, an example for PETQ evaluation in is used to show the proposed methods.
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Affiliation(s)
- Wujin Hu
- School of P.E., East China University of Technology, Nanchang, Jiangxi, China
| | - Yi Shao
- Shanghai Customs College, Shanghai, Shanghai, China
| | - Yefei Liu
- School of Physical Education, Yulin University, Yulin, Shaanxi, China
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43
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Kumar V, Altahan BR, Rasheed T, Singh P, Soni D, Alsaab HO, Ahmadi F. Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules. Comput Intell Neurosci 2023; 2023:9739264. [PMID: 36756162 DOI: 10.1155/2023/9739264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/26/2022] [Accepted: 08/08/2022] [Indexed: 01/31/2023]
Abstract
Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning, and metaheuristics to identify cancer in its early stages. At this point, a new convolutional neural network is constructed. The predator technique has the potential to increase network architecture and accuracy. Deep learning identified lung cancer spinal metastases in as energy consumption increased CT readings for lung cancer bone metastases decreased. Qualified physicians, on the other hand, discovered 71.14 and 74.60 percent of targets with energies of 140 and 60 keV, respectively, whereas the proposed model gives 76.51 and 81.58 percent, respectively. Expert physicians' detection rate was 74.60 percent lower than deep learning's detection rate of 81.58 percent. The proposed method has the highest accuracy, sensitivity, and specificity (93.4, 98.4, and 97.1 percent, respectively), as well as the lowest error rate (1.6 percent). Finally, in lung segmentation, the proposed model outperforms the CNN model. High-intensity energy-spectral CT images are more difficult to segment than low-intensity energy-spectral CT images.
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Han Y, Yan R, Gou C. An integrated multiple attribute decision making methodology for quality evaluation of innovation and entrepreneurship education with interval-valued intuitionistic fuzzy information. IFS 2023. [DOI: 10.3233/jifs-221701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Today’s higher vocational colleges have already put innovation and entrepreneurship education at the top of vocational education, and integrated it into the entire education and teaching work, in order to continuously improve the innovation and entrepreneurship ability of students in higher vocational colleges and improve their job competition. strength, and improve the quality of education in higher vocational colleges. The quality evaluation of innovation and entrepreneurship education in vocational colleges is a classical multiple attribute decision making (MADM) problems. In this paper, we introduced some calculating laws on interval-valued intuitionistic fuzzy sets (IVIFSs), Hamacher sum and Hamacher product and further propose the induced interval-valued intuitionistic fuzzy Hamacher power ordered weighted geometric (I-IVIFHPOWG) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IVIFHPOWG operator to deal with the MADM problems under IVIFSs. Finally, an example for quality evaluation of innovation and entrepreneurship education in vocational colleges is used to test this new approach.
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Affiliation(s)
| | - Rong Yan
- Chongqing City Vocational College, Chongqing, China
| | - Chang Gou
- Chengdu Vocational & Technical College of Industry, Chengdu, Sichuan, China
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45
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Faiza, Khalil K. Airline flight delays using artificial intelligence in COVID-19 with perspective analytics. IFS 2023. [DOI: 10.3233/jifs-222827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
This study envisages assessing the effects of the COVID-19 on the on-time performance of US-airlines industry in the disrupted situations. The deep learning techniques used are neural network regression, decision forest regression, boosted decision tree regression and multi class logistic regression. The best technique is identified. In the perspective data analytics, it is suggested what the airlines should do for the on-time performance in the disrupted situation. The performances of all the methods are satisfactory. The coefficient of determination for the neural network regression is 0.86 and for decision forest regression is 0.85, respectively. The coefficient of determination for the boosted decision tree is 0.870984. Thus boosted decision tree regression is better. Multi class logistic regression gives an overall accuracy and precision of 98.4%. Recalling/remembering performance is 99%. Thus multi class logistic regression is the best model for prediction of flight delays in the COVID-19. The confusion matrix for the multi class logistic regression shows that 87.2% flights actually not delayed are predicted not delayed. The flights actually not delayed but wrongly predicted delayed are12.7%. The strength of relation with departure delay, carrier delay, late aircraft delay, weather delay and NAS delay, are 94%, 53%, 35%, 21%, and 14%, respectively. There is a weak negative relation (almost unrelated) with the air time and arrival delay. Security delay and arrival delay are also almost unrelated with strength of 1% relationship. Based on these diagnostic analytics, it is recommended as perspective to take due care reducing departure delay, carrier delay, Late aircraft delay, weather delay and Nas delay, respectively, considerably with effect of 94%, 53%, 35%, 21%, and 14% in disrupted situations. The proposed models have MAE of 2% for Neural Network Regression, Decision Forest Regression, Boosted Decision Tree Regression, respectively, and, RMSE approximately, 11%, 12%, 11%, respectively.
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Affiliation(s)
- Faiza
- School of Transportation and Logistics, Malaysia University of Science and Technology, Kota Damansara, Petaling Jaya, Selangor, Malaysia
| | - K. Khalil
- School of Transportation and Logistics, Malaysia University of Science and Technology, Kota Damansara, Petaling Jaya, Selangor, Malaysia
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46
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Nguyen-trang T, Nguyen-thoi T, Vo-van T. Globally automatic fuzzy clustering for probability density functions and its application for image data. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04470-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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47
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Soleymani F, Paquet E, Viktor HL, Michalowski W, Spinello D. ProtInteract: A deep learning framework for predicting protein-protein interactions. Comput Struct Biotechnol J 2023; 21:1324-1348. [PMID: 36817951 PMCID: PMC9929211 DOI: 10.1016/j.csbj.2023.01.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.
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Affiliation(s)
- Farzan Soleymani
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada,Corresponding author.
| | - Herna Lydia Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON K1N 6N5, Canada
| | | | - Davide Spinello
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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48
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Kurman S, Kisan S. An in-depth and contrasting survey of meta-heuristic approaches with classical feature selection techniques specific to cervical cancer. Knowl Inf Syst 2023. [DOI: 10.1007/s10115-022-01825-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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49
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B S, B S, B PR. Theoretical analysis and comparative study of top 10 optimization algorithms with DMS algorithm. IDT 2023. [DOI: 10.3233/idt-220114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The significance of big data are prone to complication in solving optimization issues. In several scenarios, one requires adapting several contradictory goals and satisfies various criterions. This made the research on multi-objective optimization more vital and has become main topic. This paper presents theoretical analysis and comparative study of top ten optimization algorithms with respect to DMS. The performance analysis and study of optimization algorithms in big data streaming are explicated. Here, the top ten algorithms of optimization based on recency and popularity are considered. In addition, the performance analysis based on Efficiency, Reliability, Quality of solution, and superiority of DMS algorithm over other top 10 algorithms are examined. From analysis, the DMS provides better efficiency as it endeavours less computational effort to generate better solution, due to acquisition of both DA and MS algorithm’s benefits and DMS takes less time to process a task. Moreover, the DMS needs less number of iterations in the process of optimization and helps to stop optimization process in local optimum. In addition, the DMS has better reliability as it poses the potential to handle specific level of performance. In addition, the DMS utilizes heuristic information for attaining high reliability. Moreover, the DMS produced high computation accuracy, which reveals its solution quality. From the analysis, it is noted that DMS attained improved outcomes in terms of efficiency, reliability and solution quality in contrast to other top 10 optimization algorithms.
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Affiliation(s)
- Srivani B
- College of Engineering, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India
| | - Sandhya B
- VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India
| | - Padmaja Rani B
- College of Engineering, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India
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50
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Zhang J, Song L, Chan K, Miller Z, Huang KL. Predictive Models and Features of Patient Mortality across Dementia Types. Res Sq 2023:rs.3.rs-2350961. [PMID: 36711767 PMCID: PMC9882612 DOI: 10.21203/rs.3.rs-2350961/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to identify patients at risk of near-term mortality. Here, we developed machine learning models predicting survival using a dataset of 45,275 unique participants and 163,782 visit records from the U.S. National Alzheimer's Coordinating Center (NACC). Our models achieved an AUC-ROC of over 0.82 utilizing nine parsimonious features for all one-, three-, five-, and ten-year thresholds. The trained models mainly consisted of dementia-related predictors such as specific neuropsychological tests and were minimally affected by other age-related causes of death, e.g., stroke and cardiovascular conditions. Notably, stratified analyses revealed shared and distinct predictors of mortality across eight dementia types. Unsupervised clustering of mortality predictors grouped vascular dementia with depression and Lewy body dementia with frontotemporal lobar dementia. This study demonstrates the feasibility of flagging dementia patients at risk of mortality for personalized clinical management.
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Affiliation(s)
| | - Luo Song
- School of Medicine, The University of Queensland
| | - Kwun Chan
- National Alzheimer's Coordinating Center, University of Washington
| | - Zachary Miller
- National Alzheimer's Coordinating Center, University of Washington
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