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Choi KP, Kam EHH, Tong XT, Wong WK. Appropriate noise addition to metaheuristic algorithms can enhance their performance. Sci Rep 2023; 13:5291. [PMID: 37002274 PMCID: PMC10066303 DOI: 10.1038/s41598-023-29618-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 02/07/2023] [Indexed: 04/04/2023] Open
Abstract
Nature-inspired swarm-based algorithms are increasingly applied to tackle high-dimensional and complex optimization problems across disciplines. They are general purpose optimization algorithms, easy to implement and assumption-free. Some common drawbacks of these algorithms are their premature convergence and the solution found may not be a global optimum. We propose a general, simple and effective strategy, called heterogeneous Perturbation-Projection (HPP), to enhance an algorithm's exploration capability so that our sufficient convergence conditions are guaranteed to hold and the algorithm converges almost surely to a global optimum. In summary, HPP applies stochastic perturbation on half of the swarm agents and then project all agents onto the set of feasible solutions. We illustrate this approach using three widely used nature-inspired swarm-based optimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT) and Ant Colony Optimization for continuous domains (ACO). Extensive numerical experiments show that the three algorithms with the HPP strategy outperform the original versions with 60-80% the times with significant margins.
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Affiliation(s)
- Kwok Pui Choi
- Department of Statistics and Data Science, National University of Singapore, Singapore, 117546, Singapore
| | - Enzio Hai Hong Kam
- Department of Computer Science, National University of Singapore, Singapore, 117417, Singapore
| | - Xin T Tong
- Department of Mathematics, National University of Singapore, Singapore, 119076, Singapore
| | - Weng Kee Wong
- Department of Biostatistics, University of California at Los Angeles, Los Angeles, 90095, USA.
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Zhang Y, Dong Z, Li S, Cattani C. Deep learning methods for biomedical information analysis. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2023; 14:5293-5296. [PMCID: PMC10088667 DOI: 10.1007/s12652-023-04617-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Affiliation(s)
- Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH UK
| | - Zhengchao Dong
- Molecular Imaging and Neuropathology Division, Columbia University and New York State Psychiatric Institute, New York, NY 10032 USA
| | - Shuai Li
- Department of Electronics and Electrical Engineering, Swansea University, Swansea, SA2 8PP UK
| | - Carlo Cattani
- Engineering School (DEIM), University of Tuscia, Viterbo, Lazio 01100 Italy
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Mahmood T, Choi J, Ryoung Park K. Artificial Intelligence-based Classification of Pollen Grains Using Attention-guided Pollen Features Aggregation Network. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Alyasseri ZAA, Alomari OA, Al-Betar MA, Makhadmeh SN, Doush IA, Awadallah MA, Abasi AK, Elnagar A. Recent advances of bat-inspired algorithm, its versions and applications. Neural Comput Appl 2022; 34:16387-16422. [PMID: 35971379 PMCID: PMC9366842 DOI: 10.1007/s00521-022-07662-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 07/18/2022] [Indexed: 11/25/2022]
Abstract
Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.
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Affiliation(s)
- Zaid Abdi Alkareem Alyasseri
- ECE Department, Faculty of Engineering, University of Kufa, P.O. Box 21, Najaf, Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq
- Information Research and Development Center (ITRDC), University of Kufa, Najaf, Iraq
| | | | - Mohammed Azmi Al-Betar
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
- Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - Iyad Abu Doush
- Department of Computing, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait
- Computer Science Department, Yarmouk University, Irbid, Jordan
| | - Mohammed A. Awadallah
- Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
| | - Ammar Kamal Abasi
- Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates
| | - Ashraf Elnagar
- Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates
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Liu S, Wang X, Zhao L, Zhao J, Xin Q, Wang SH. Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1710-1721. [PMID: 32833640 DOI: 10.1109/tcbb.2020.3018137] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computing. Although emotion recognition based on electroencephalogram (EEG) has attracted more and more attention at home and abroad, subject-independent emotion recognition still faces enormous challenges. We proposed a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges. Combining the advantages of empirical mode decomposition (EMD) and differential entropy (DE), we proposed a dynamic differential entropy (DDE) algorithm to extract the features of EEG signals. After that, the extracted DDE features were classified by convolutional neural networks (CNN). Finally, the proposed algorithm is verified on SJTU Emotion EEG Dataset (SEED). In addition, we discuss the brain area closely related to emotion and design the best profile of electrode placements to reduce the calculation and complexity. Experimental results show that the accuracy of this algorithm is 3.53 percent higher than that of the state-of-the-art emotion recognition methods. What's more, we studied the key electrodes for EEG emotion recognition, which is of guiding significance for the development of wearable EEG devices.
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Momeni S, Fazlollahi A, Yates P, Rowe C, Gao Y, Liew AWC, Salvado O. Synthetic microbleeds generation for classifier training without ground truth. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106127. [PMID: 34051412 DOI: 10.1016/j.cmpb.2021.106127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Cerebral microbleeds (CMB) are important biomarkers of cerebrovascular diseases and cognitive dysfunctions. Susceptibility weighted imaging (SWI) is a common MRI sequence where CMB appear as small hypointense blobs. The prevalence of CMB in the population and in each scan is low, resulting in tedious and time-consuming visual assessment. Automated detection methods would be of value but are challenged by the CMB low prevalence, the presence of mimics such as blood vessels, and the difficulty to obtain sufficient ground truth for training and testing. In this paper, synthetic CMB (sCMB) generation using an analytical model is proposed for training and testing machine learning methods. The main aim is creating perfect synthetic ground truth as similar as reals, in high number, with a high diversity of shape, volume, intensity, and location to improve training of supervised methods. METHOD sCMB were modelled with a random Gaussian shape and added to healthy brain locations. We compared training on our synthetic data to standard augmentation techniques. We performed a validation experiment using sCMB and report result for whole brain detection using a 10-fold cross validation design with an ensemble of 10 neural networks. RESULTS Performance was close to state of the art (~9 false positives per scan), when random forest was trained on synthetic only and tested on real lesion. Other experiments showed that top detection performance could be achieved when training on synthetic CMB only. Our dataset is made available, including a version with 37,000 synthetic lesions, that could be used for benchmarking and training. CONCLUSION Our proposed synthetic microbleeds model is a powerful data augmentation approach for CMB classification with and should be considered for training automated lesion detection system from MRI SWI.
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Affiliation(s)
- Saba Momeni
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia; School of Engineering and Built Environment, Griffith University, Brisbane, Australia.
| | - Amir Fazlollahi
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia
| | - Paul Yates
- Department of Aged Care, Austin Health, Heidelberg, Victoria, Australia
| | - Christopher Rowe
- Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, Australia
| | - Yongsheng Gao
- School of Engineering and Built Environment, Griffith University, Brisbane, Australia
| | - Alan Wee-Chung Liew
- School of Information & Communication Technology, Griffith University, Gold Coast, Australia
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Abstract
Much work has recently identified the need to combine deep learning with extreme learning in order to strike a performance balance with accuracy, especially in the domain of multimedia applications. When considering this new paradigm—namely, the convolutional extreme learning machine (CELM)—we present a systematic review that investigates alternative deep learning architectures that use the extreme learning machine (ELM) for faster training to solve problems that are based on image analysis. We detail each of the architectures that are found in the literature along with their application scenarios, benchmark datasets, main results, and advantages, and then present the open challenges for CELM. We followed a well-structured methodology and established relevant research questions that guided our findings. Based on 81 primary studies, we found that object recognition is the most common problem that is solved by CELM, and CCN with predefined kernels is the most common CELM architecture proposed in the literature. The results from experiments show that CELM models present good precision, convergence, and computational performance, and they are able to decrease the total processing time that is required by the learning process. The results presented in this systematic review are expected to contribute to the research area of CELM, providing a good starting point for dealing with some of the current problems in the analysis of computer vision based on images.
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