1
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Kumar V, Ali A, Mittal P, Aqeel I, Shuaib M, Alam S, Aalsalem MY. E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers. PLoS One 2024; 19:e0303313. [PMID: 38857300 PMCID: PMC11164395 DOI: 10.1371/journal.pone.0303313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/23/2024] [Indexed: 06/12/2024] Open
Abstract
Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.
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Affiliation(s)
- Vaneet Kumar
- Department of Computer Science and Engineering, Chandigarh University, Gharuan, India
| | - Aleem Ali
- Department of Computer Science and Engineering, Chandigarh University, Gharuan, India
| | - Payal Mittal
- University Institute of Pharma Sciences, Chandigarh University, Gharuan, India
| | - Ibrahim Aqeel
- College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
| | - Mohammed Shuaib
- College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
| | - Shadab Alam
- College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
| | - Mohammed Y. Aalsalem
- College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia
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2
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Tang W, Cao L, Chen Y, Chen B, Yue Y. Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm. Biomimetics (Basel) 2024; 9:298. [PMID: 38786508 PMCID: PMC11118741 DOI: 10.3390/biomimetics9050298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.
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Affiliation(s)
- Wenjie Tang
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Li Cao
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Yaodan Chen
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Binhe Chen
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Yinggao Yue
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
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3
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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4
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Zhang Y, Liu Z, Bi Y. Node deployment optimization of underwater wireless sensor networks using intelligent optimization algorithm and robot collaboration. Sci Rep 2023; 13:15920. [PMID: 37741883 PMCID: PMC10517991 DOI: 10.1038/s41598-023-43272-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 09/21/2023] [Indexed: 09/25/2023] Open
Abstract
This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research.
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Affiliation(s)
- Yangmei Zhang
- School of Electronic Engineering, Xi'an Aeronautical Institute, Xi'an, 710077, China.
| | - Zhouzhou Liu
- School of Computer Science, Xi'an Aeronautical Institute, Xi'an, 710077, China
| | - Yang Bi
- Department of Science and Technology, Xi'an Aeronautical Institute, Xi'an, 710077, China
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5
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Seilsepour A, Ravanmehr R, Nassiri R. Topic sentiment analysis based on deep neural network using document embedding technique. THE JOURNAL OF SUPERCOMPUTING 2023; 79:1-39. [PMID: 37359345 PMCID: PMC10241384 DOI: 10.1007/s11227-023-05423-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 06/28/2023]
Abstract
Sentiment Analysis (SA) is a domain- or topic-dependent task since polarity terms convey different sentiments in various domains. Hence, machine learning models trained on a specific domain cannot be employed in other domains, and existing domain-independent lexicons cannot correctly recognize the polarity of domain-specific polarity terms. Conventional approaches of Topic Sentiment Analysis perform Topic Modeling (TM) and SA sequentially, utilizing the previously trained models on irrelevant datasets for classifying sentiments that cannot provide acceptable accuracy. However, some researchers perform TM and SA simultaneously using topic-sentiment joint models, which require a list of seeds and their sentiments from widely used domain-independent lexicons. As a result, these methods cannot find the polarity of domain-specific terms correctly. This paper proposes a novel supervised hybrid TSA approach, called Embedding Topic Sentiment Analysis using Deep Neural Networks (ETSANet), that extracts the semantic relationships between the hidden topics and the training dataset using Semantically Topic-Related Documents Finder (STRDF). STRDF discovers those training documents in the same context as the topic based on the semantic relationships between the Semantic Topic Vector, a newly introduced concept that encompasses the semantic aspects of a topic, and the training dataset. Then, a hybrid CNN-GRU model is trained by these semantically topic-related documents. Moreover, a hybrid metaheuristic method utilizing Grey Wolf Optimization and Whale Optimization Algorithm is employed to fine-tune the hyperparameters of the CNN-GRU network. The evaluation results demonstrate that ETSANet increases the accuracy of the state-of-the-art methods by 1.92%.
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Affiliation(s)
- Azam Seilsepour
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Reza Ravanmehr
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Ramin Nassiri
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
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6
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Nadimi-Shahraki MH, Zamani H, Asghari Varzaneh Z, Mirjalili S. A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-47. [PMID: 37359740 PMCID: PMC10220350 DOI: 10.1007/s11831-023-09928-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.
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Affiliation(s)
- Mohammad H. Nadimi-Shahraki
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Hoda Zamani
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, 4006 Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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7
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Zhang M, Wu Q, Chen H, Heidari AA, Cai Z, Li J, Md Abdelrahim E, Mansour RF. Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction. Biomed Signal Process Control 2023; 83:104638. [PMID: 36741073 PMCID: PMC9889265 DOI: 10.1016/j.bspc.2023.104638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/01/2022] [Accepted: 01/25/2023] [Indexed: 02/04/2023]
Abstract
Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.
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Affiliation(s)
- Meilin Zhang
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Qianxi Wu
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Ali Asghar Heidari
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Zhennao Cai
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Jiaren Li
- Wenzhou People's Hospital, Wenzhou, Zhejiang 325099, China
| | - Elsaid Md Abdelrahim
- Faculty of Science, Northern Border University, Arar, Saudi Arabia.,Faculty of Science, Tanta University, Tanta, Egypt
| | - Romany F Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
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8
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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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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9
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Karlupia N, Abrol P. Wrapper-based optimized feature selection using nature-inspired algorithms. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08383-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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10
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Application of improved hybrid whale optimization algorithm to optimization problems. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08370-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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11
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An Optimization-Linked Intelligent Security Algorithm for Smart Healthcare Organizations. Healthcare (Basel) 2023; 11:healthcare11040580. [PMID: 36833114 PMCID: PMC9956199 DOI: 10.3390/healthcare11040580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/24/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
IoT-enabled healthcare apps are providing significant value to society by offering cost-effective patient monitoring solutions in IoT-enabled buildings. However, with a large number of users and sensitive personal information readily available in today's fast-paced, internet, and cloud-based environment, the security of these healthcare systems must be a top priority. The idea of safely storing a patient's health data in an electronic format raises issues regarding patient data privacy and security. Furthermore, with traditional classifiers, processing large amounts of data is a difficult challenge. Several computational intelligence approaches are useful for effectively categorizing massive quantities of data for this goal. For many of these reasons, a novel healthcare monitoring system that tracks disease processes and forecasts diseases based on the available data obtained from patients in distant communities is proposed in this study. The proposed framework consists of three major stages, namely data collection, secured storage, and disease detection. The data are collected using IoT sensor devices. After that, the homomorphic encryption (HE) model is used for secured data storage. Finally, the disease detection framework is designed with the help of Centered Convolutional Restricted Boltzmann Machines-based whale optimization (CCRBM-WO) algorithm. The experiment is conducted on a Python-based cloud tool. The proposed system outperforms current e-healthcare solutions, according to the findings of the experiments. The accuracy, precision, F1-measure, and recall of our suggested technique are 96.87%, 97.45%, 97.78%, and 98.57%, respectively, according to the proposed method.
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12
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Zitar RA, Abualigah L, Barbaresco F, Seghrouchni AE. Modified arithmetic optimization algorithm for drones measurements and tracks assignment problem. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08242-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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13
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Alizadehsani R, Roshanzamir M, Izadi NH, Gravina R, Kabir HMD, Nahavandi D, Alinejad-Rokny H, Khosravi A, Acharya UR, Nahavandi S, Fortino G. Swarm Intelligence in Internet of Medical Things: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031466. [PMID: 36772503 PMCID: PMC9920579 DOI: 10.3390/s23031466] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 05/13/2023]
Abstract
Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
- Correspondence:
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Vali asr Blvd, Fasa 74617-81189, Iran
| | - Navid Hoseini Izadi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Daneshgah e Sanati Hwy, Isfahan 84156-83111, Iran
| | - Raffaele Gravina
- Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Cosenza, Italy
| | - H. M. Dipu Kabir
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
| | - Darius Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia
- UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
- Health Data Analytics Program, AI-Enabled Processes (AIP) Research Centre, Macquarie University, Sydney, NSW 2109, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
- Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134, USA
| | - Giancarlo Fortino
- Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Cosenza, Italy
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14
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Shami TM, Mirjalili S, Al-Eryani Y, Daoudi K, Izadi S, Abualigah L. Velocity pausing particle swarm optimization: a novel variant for global optimization. Neural Comput Appl 2023. [DOI: 10.1007/s00521-022-08179-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
AbstractParticle swarm optimization (PSO) is one of the most well-regard metaheuristics with remarkable performance when solving diverse optimization problems. However, PSO faces two main problems that degrade its performance: slow convergence and local optima entrapment. In addition, the performance of this algorithm substantially degrades on high-dimensional problems. In the classical PSO, particles can move in each iteration with either slower or faster speed. This work proposes a novel idea called velocity pausing where particles in the proposed velocity pausing PSO (VPPSO) variant are supported by a third movement option that allows them to move with the same velocity as they did in the previous iteration. As a result, VPPSO has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, VPPSO modifies the first term of the PSO velocity equation. In addition, the population of VPPSO is divided into two swarms to maintain diversity. The performance of VPPSO is validated on forty three benchmark functions and four real-world engineering problems. According to the Wilcoxon rank-sum and Friedman tests, VPPSO can significantly outperform seven prominent algorithms on most of the tested functions on both low- and high-dimensional cases. Due to its superior performance in solving complex high-dimensional problems, VPPSO can be applied to solve diverse real-world optimization problems. Moreover, the velocity pausing concept can be easily integrated with new or existing metaheuristic algorithms to enhance their performances. The Matlab code of VPPSO is available at: https://uk.mathworks.com/matlabcentral/fileexchange/119633-vppso.
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15
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Artificial Neural Network Assisted P &O-Based MPPT Controller for a Partially Shaded Grid-Connected Solar PV Panel. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-07566-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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16
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Kaur S, Kumar Y, Koul A, Kumar Kamboj S. A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:1863-1895. [PMID: 36465712 PMCID: PMC9702927 DOI: 10.1007/s11831-022-09853-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
There is a need for some techniques to solve various problems in today's computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study's primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.
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Affiliation(s)
- Sukhpreet Kaur
- Department of Computer Science and Engineering, CGC Landran, Mohali, India
| | - Yogesh Kumar
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
| | - Apeksha Koul
- Department of Computer Science and Engineering, Punjabi University, Patiala, India
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Tefek MF. Rao algorithms based on elite local search method. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07932-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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18
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S. Sánchez AG, Perez-Pinal FJ, Espinosa-Calderón A. Optimization and Its Implementation Impact of Two-Modes Controller Fractional Approximation for Buck Converters. MICROMACHINES 2022; 13:1600. [PMID: 36295953 PMCID: PMC9609563 DOI: 10.3390/mi13101600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
Additional degrees of freedom in a fractional-order control strategy for power electronic converters are well received despite the lack of reliable tuning methods. Despite artificial/swarm intelligence techniques have been used to adjust controller parameters to improve more than one characteristic/property at the same time, smart tuning not always leads to realizable structures or reachable parameter values. Thus, adjustment boundaries to ensure controller viability are needed. In this manuscript the fractional-order approach is described in terms of El-Khazali biquadratic module, which produces the lowest order approximation, instead of using a definition. A two-modes controller structure is synthesize depending on uncontrolled plant needs and parameters are adjusted through particle swarm and genetic optimization algorithms for comparison. Two error-based minimization criteria are used to consider output performance into the process. Two restrictions complement the optimization scheme, one seeks to ensure desired robustness while the other prevents from synthesizing a high-gain controller. Optimization results showed similarity between minima obtained and significant difference between parameters of those controller optimized without the proposed constraints was determined. Numerical and experimental results are provide to validate proposed approach effectiveness. Effective regulation, good tracking characteristic and robustness in the presence of load variations are the main results.
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Affiliation(s)
| | | | - Alejandro Espinosa-Calderón
- Regional Center for Optimization and Device Development (CRODE), Tecnológico Nacional de México, Celaya 38020, Mexico
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A novel whale optimization algorithm of path planning strategy for mobile robots. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04030-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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20
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Guo Z, Zhang L, Chen Q, Han M, Liu W. Monophenolase assay using excitation-emission matrix fluorescence and ELMAN neural network assisted by whale optimization algorithm. Anal Biochem 2022; 655:114838. [PMID: 35961401 DOI: 10.1016/j.ab.2022.114838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 11/01/2022]
Abstract
Tyrosinase plays a vital role for melanogenesis and inherently involves both monophenolase activity and diphenolase activity. Monophenolase catalyzes hydroxylation of tyrosine to l-DOPA (L-3,4-dihydroxyphenylalanine). Real-time monophenolase assay method is of outstanding interest for both scientific research and industrial application. A combined strategy of three-dimensional excitation-emission matrix (EEM) fluorescence spectra and artificial neural network was developed to determine monophenolase activity. A quantitation system for tyrosine in presence of l-DOPA was designed based on ELMAN neural network. Principal component analysis (PCA) was conducted to reduce the dimensionality of fluorescence spectra. Four principal components was used as input variables. Whale optimization algorithm (WOA) was implemented to optimize the initial weights and threshold network. Real-time concentration of tyrosine in monophenolase reaction was monitored to calculate the initial velocity for tyrosine consumption. The exclusive monophenolase activity without interference from diphenolase reaction was determined. Limit of detection (LOD) for monophenolase assay is 0.0113 U mL-1. Using the proposed method, enzyme kinetics for monophenolase was investigate. Km was calculated as 14.16 μM. Inhibitor for monophenolase was screened by using model molecule kojic acid with IC50 of 3.49 μM. The assay method exhibited a promising prospect to characterize the kinetics and inhibitor of monophenolase.
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Affiliation(s)
- Zhenyu Guo
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China
| | - Ling Zhang
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China
| | - Qinfei Chen
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China
| | - Mengqi Han
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China
| | - Wenbin Liu
- Department of Pharmaceutical and Biological Engineering, School of Chemical Engineering, Sichuan University, Chengdu, 610065, China.
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Karunakar Reddy V, Kumar AV R. Multi-channel neuro signal classification using Adam-based coyote optimization enabled deep belief network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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22
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An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00827-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractWhale Optimization Algorithm (WOA), as a newly proposed swarm-based algorithm, has gradually become a popular approach for optimization problems in various engineering fields. However, WOA suffers from the poor balance of exploration and exploitation, and premature convergence. In this paper, a new enhanced WOA (EWOA), which adopts an improved dynamic opposite learning (IDOL) and an adaptive encircling prey stage, is proposed to overcome the problems. IDOL plays an important role in the initialization part and the algorithm iterative process of EWOA. By evaluating the optimal solution in the current population, IDOL can adaptively switch exploitation/exploration modes constructed by the DOL strategy and a modified search strategy, respectively. On the other hand, for the encircling prey stage of EWOA in the latter part of the iteration, an adaptive inertia weight strategy is introduced into this stage to adaptively adjust the prey’s position to avoid falling into local optima. Numerical experiments, with unimodal, multimodal, hybrid and composition benchmarks, and three typical engineering problems are utilized to evaluate the performance of EWOA. The proposed EWOA also evaluates against canonical WOA, three sub-variants of EWOA, three other common algorithms, three advanced algorithms and four advanced variants of WOA. Results indicate that according to Wilcoxon rank sum test and Friedman test, EWOA has balanced exploration and exploitation ability in coping with global optimization, and it has obvious advantages when compared with other state-of-the-art algorithms.
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Feng LY, Kamari NAM, Ibrahim AA, Halim SA, Zulkifley MA, Ahmad H. Solving Economic and Emission Dispatch Problem Using Search And Rescue Optimization Technique. 2022 IEEE 13TH CONTROL AND SYSTEM GRADUATE RESEARCH COLLOQUIUM (ICSGRC) 2022. [DOI: 10.1109/icsgrc55096.2022.9845165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Lim Yuan Feng
- Built Environment Universiti Kebangsaan Malaysia,Electronic and Systems Engineering, Faculty of Engineering,Department of Electrical,Selangor,Malaysia
| | - Nor Azwan Mohamed Kamari
- Built Environment Universiti Kebangsaan Malaysia,Electronic and Systems Engineering, Faculty of Engineering,Department of Electrical,Selangor,Malaysia
| | - Ahmad Asrul Ibrahim
- Built Environment Universiti Kebangsaan Malaysia,Electronic and Systems Engineering, Faculty of Engineering,Department of Electrical,Selangor,Malaysia
| | - Syahirah Abd Halim
- Built Environment Universiti Kebangsaan Malaysia,Electronic and Systems Engineering, Faculty of Engineering,Department of Electrical,Selangor,Malaysia
| | - Mohd Asyraf Zulkifley
- Built Environment Universiti Kebangsaan Malaysia,Electronic and Systems Engineering, Faculty of Engineering,Department of Electrical,Selangor,Malaysia
| | - Halimah Ahmad
- Universiti Kuala Lumpur-Malaysia France Institute (UniKL-MFI),Japanese Collaboration Program,Selangor,Malaysia
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Agarwal M, Agarwal S, Saba L, Chabert GL, Gupta S, Carriero A, Pasche A, Danna P, Mehmedovic A, Faa G, Shrivastava S, Jain K, Jain H, Jujaray T, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Sobel DW, Miner M, Balestrieri A, Sfikakis PP, Tsoulfas G, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Yadav RR, Nagy F, Kincses ZT, Ruzsa Z, Naidu S, Viskovic K, Kalra MK, Suri JS. Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0. Comput Biol Med 2022; 146:105571. [PMID: 35751196 PMCID: PMC9123805 DOI: 10.1016/j.compbiomed.2022.105571] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/05/2022] [Accepted: 04/26/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization. METHOD ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted. RESULTS Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions. CONCLUSIONS Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.
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Affiliation(s)
- Mohit Agarwal
- Department of Computer Science Engineering, Bennett University, India
| | - Sushant Agarwal
- Department of Computer Science Engineering, PSIT, Kanpur, India; Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Suneet Gupta
- Department of Computer Science Engineering, Bennett University, India
| | - Alessandro Carriero
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Alessio Pasche
- Depart of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | - Pietro Danna
- Depart of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | | | - Gavino Faa
- Department of Pathology - AOU of Cagliari, Italy
| | - Saurabh Shrivastava
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Kanishka Jain
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Harsh Jain
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Tanay Jujaray
- Dept of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, CA, USA
| | | | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | | | - Amer M Johri
- Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - David W Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, Thessaloniki, Greece
| | | | | | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Canada
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and Univ. of Nicosia Medical School, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | | | - Mostafa Fatemi
- Dept. of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN, USA
| | - Azra Alizad
- Dept. of Radiology, Mayo Clinic College of Medicine and Science, MN, USA
| | | | | | - Frence Nagy
- Department of Radiology, University of Szeged, 6725, Hungary
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Budapest, Hungary
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN, USA
| | | | - Manudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jasjit S Suri
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India; Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA.
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COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans. Diagnostics (Basel) 2022; 12:diagnostics12061482. [PMID: 35741292 PMCID: PMC9221733 DOI: 10.3390/diagnostics12061482] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 02/07/2023] Open
Abstract
Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.
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Parameter identification and sensitivity analysis of lithium-ion battery via whale optimization algorithm. Electrochim Acta 2022. [DOI: 10.1016/j.electacta.2021.139574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Optimization of the Curved Metal Damper to Improve Structural Energy Dissipation Capacity. BUILDINGS 2022. [DOI: 10.3390/buildings12010067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Structural curved metal dampers are implemented in various applications to mitigate the damages at a specific area efficiently. A stable and saturated hysteretic behavior for the in-plane direction is dependent on the shape of a curved-shaped damper. However, it has been experimentally shown that the hysteretic behavior in the conventional curved-shaped damper is unstable, mainly as a result of bi-directional deformations. Therefore, it is necessary to conduct shape optimization for curved dampers to enhance their hysteretic behavior and energy dissipation capability. In this study, the finite element (FE) model built in ABAQUS, is utilized to obtain optimal shape for the curved-shaped damper. The effectiveness of the model is checked by comparisons of the FE model and experimental results. The parameters for the optimization include the curved length and shape of the damper, and the improved approach is conducted by investigating the curved sections. In addition, the design parameters are represented by B-spline curves (to ensure enhanced system performance), regression analysis is implemented to derive optimization formulations considering energy dissipation, constitutive material model, and cumulative plastic strain. Results determine that the energy dissipation capacity of the curved steel damper could be improved by 32% using shape optimization techniques compared to the conventional dampers. Ultimately, the study proposes simple optimal shapes for further implementations in practical designs.
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Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques. INFORMATICS 2022. [DOI: 10.3390/informatics9010004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.
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Brodzicki A, Piekarski M, Jaworek-Korjakowska J. The Whale Optimization Algorithm Approach for Deep Neural Networks. SENSORS 2021; 21:s21238003. [PMID: 34884004 PMCID: PMC8659805 DOI: 10.3390/s21238003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 11/26/2021] [Accepted: 11/28/2021] [Indexed: 11/22/2022]
Abstract
One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many challenges including local minima, saddle points, and vanishing gradients. In this paper, we introduce the Whale Optimisation Algorithm (WOA) based on the swarm foraging behavior of humpback whales to optimise neural network hyperparameters. We wish to stress that to the best of our knowledge this is the first attempt that uses Whale Optimisation Algorithm for the optimisation task of hyperparameters. After a detailed description of the WOA algorithm we formulate and explain the application in deep learning, present the implementation, and compare the proposed algorithm with other well-known algorithms including widely used Grid and Random Search methods. Additionally, we have implemented a third dimension feature analysis to the original WOA algorithm to utilize 3D search space (3D-WOA). Simulations show that the proposed algorithm can be successfully used for hyperparameters optimization, achieving accuracy of 89.85% and 80.60% for Fashion MNIST and Reuters datasets, respectively.
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Aliyar Vellameeran F, Brindha T. A new variant of deep belief network assisted with optimal feature selection for heart disease diagnosis using IoT wearable medical devices. Comput Methods Biomech Biomed Engin 2021; 25:387-411. [PMID: 34311642 DOI: 10.1080/10255842.2021.1955360] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
In this paper, the information related to heart disease using IoT wearable devices is collected from any benchmark site, which is publicly available. With the collected data, feature extraction process is performed initially, in which heart rate, zero crossing rate, and higher order statistical features like standard deviation, median, skewness, kurtosis, variance, mean, peak amplitude, and entropy are extracted. For acquiring most significant features, the optimal feature selection process is implemented. As a novel contribution, the feature selection process is done by the hybrid optimization algorithm called PS-GWO by integrating GWO and PSO. Next, the extracted features are subjected to a famous deep learning algorithm named modified DBN, in which the activation function and number of hidden neurons is optimized using the same developed hybrid algorithm to improve the heart diagnosis accuracy. From the analysis, for the test case 1, the accuracy of the developed PS-GWO-DBN is 60%, 52.5%, 35% and 35% increased than NN, KNN, SVM, and DBN. For test case 2, the accuracy of the proposed PS-GWO-DBN is 26%, 24%, 21.6% and 17% increased than NN, KNN, SVM, and DBN, respectively. The accuracy of the designed PS-GWO-DBN is 26% advanced than NN, 24% advanced than KNN, 21.6% advanced than SVM and 17% advanced than DBN for test case 3. Thus, the proposed heart disease prediction model using PS-GWO-DBN performs better than other classifiers.
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Affiliation(s)
| | - Thomas Brindha
- Department of Information Technology, Noorul Islam Centre for Higher Education, Thuckalay, Kanyakumari, Tamil Nadu, India
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Nguyen DT, Doan DTV, Tran NNC, Tran DH. A novel multiple objective whale optimization for time-cost-quality tradeoff in non-unit repetitive projects. INTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT 2021. [DOI: 10.1080/15623599.2021.1938939] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Dang-Trinh Nguyen
- Faculty of Civil Engineering, Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, Vietnam
| | - Dinh-Thien-Vuong Doan
- Department of Civil Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam
| | - Nguyen-Ngoc Cuong Tran
- Faculty of Civil Engineering, Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, Vietnam
| | - Duc-Hoc Tran
- Faculty of Civil Engineering, Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, Vietnam
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Vukelic D, Simunovic K, Kanovic Z, Saric T, Tadic B, Simunovic G. Multi-objective optimization of steel AISI 1040 dry turning using genetic algorithm. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05877-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Ouhame S, Hadi Y, Ullah A. An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05770-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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