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Wang C, Wang Z, Kou Z. Adaptive Bi-Operator Evolution for Multitasking Optimization Problems. Biomimetics (Basel) 2024; 9:604. [PMID: 39451810 PMCID: PMC11505233 DOI: 10.3390/biomimetics9100604] [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: 07/26/2024] [Revised: 10/05/2024] [Accepted: 10/05/2024] [Indexed: 10/26/2024] Open
Abstract
The field of evolutionary multitasking optimization (EMTO) has been a highly anticipated research topic in recent years. EMTO aims to utilize evolutionary algorithms to concurrently solve complex problems involving multiple tasks. Despite considerable advancements in this field, numerous evolutionary multitasking algorithms continue to use a single evolutionary search operator (ESO) throughout the evolution process. This strategy struggles to completely adapt to different tasks, consequently hindering the algorithm's performance. To overcome this challenge, this paper proposes multitasking evolutionary algorithms via an adaptive bi-operator strategy (BOMTEA). BOMTEA adopts a bi-operator strategy and adaptively controls the selection probability of each ESO according to its performance, which can determine the most suitable ESO for various tasks. In an experiment, BOMTEA showed outstanding results on two well-known multitasking benchmark tests, CEC17 and CEC22, and significantly outperformed other comparative algorithms.
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Affiliation(s)
- Changlong Wang
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
| | - Zijia Wang
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
| | - Zheng Kou
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
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2
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Granato G, Baldassarre G. Bridging flexible goal-directed cognition and consciousness: The Goal-Aligning Representation Internal Manipulation theory. Neural Netw 2024; 176:106292. [PMID: 38657422 DOI: 10.1016/j.neunet.2024.106292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/27/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
Goal-directed manipulation of internal representations is a key element of human flexible behaviour, while consciousness is commonly associated with higher-order cognition and human flexibility. Current perspectives have only partially linked these processes, thus preventing a clear understanding of how they jointly generate flexible cognition and behaviour. Moreover, these limitations prevent an effective exploitation of this knowledge for technological scopes. We propose a new theoretical perspective that extends our 'three-component theory of flexible cognition' toward higher-order cognition and consciousness, based on the systematic integration of key concepts from Cognitive Neuroscience and AI/Robotics. The theory proposes that the function of conscious processes is to support the alignment of representations with multi-level goals. This higher alignment leads to more flexible and effective behaviours. We analyse here our previous model of goal-directed flexible cognition (validated with more than 20 human populations) as a starting GARIM-inspired model. By bridging the main theories of consciousness and goal-directed behaviour, the theory has relevant implications for scientific and technological fields. In particular, it contributes to developing new experimental tasks and interpreting clinical evidence. Finally, it indicates directions for improving machine learning and robotics systems and for informing real-world applications (e.g., in digital-twin healthcare and roboethics).
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Affiliation(s)
- Giovanni Granato
- Laboratory of Embodied Natural and Artificial Intelligence, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy.
| | - Gianluca Baldassarre
- Laboratory of Embodied Natural and Artificial Intelligence, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy.
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3
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Wang X, Li S, Pun CM, Guo Y, Xu F, Gao H, Lu H. A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:912-923. [PMID: 37027659 DOI: 10.1109/tcbb.2023.3246961] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on Artificial Bee Colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension Artificial Bee Colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.
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Ferri P, Lomonaco V, Passaro LC, Félix-De Castro A, Sánchez-Cuesta P, Sáez C, García-Gómez JM. Deep continual learning for medical call incidents text classification under the presence of dataset shifts. Comput Biol Med 2024; 175:108548. [PMID: 38718666 DOI: 10.1016/j.compbiomed.2024.108548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 04/11/2024] [Accepted: 04/28/2024] [Indexed: 05/15/2024]
Abstract
The aim of this work is to develop and evaluate a deep classifier that can effectively prioritize Emergency Medical Call Incidents (EMCI) according to their life-threatening level under the presence of dataset shifts. We utilized a dataset consisting of 1982746 independent EMCI instances obtained from the Health Services Department of the Region of Valencia (Spain), with a time span from 2009 to 2019 (excluding 2013). The dataset includes free text dispatcher observations recorded during the call, as well as a binary variable indicating whether the event was life-threatening. To evaluate the presence of dataset shifts, we examined prior probability shifts, covariate shifts, and concept shifts. Subsequently, we designed and implemented four deep Continual Learning (CL) strategies-cumulative learning, continual fine-tuning, experience replay, and synaptic intelligence-alongside three deep CL baselines-joint training, static approach, and single fine-tuning-based on DistilBERT models. Our results demonstrated evidence of prior probability shifts, covariate shifts, and concept shifts in the data. Applying CL techniques had a statistically significant (α=0.05) positive impact on both backward and forward knowledge transfer, as measured by the F1-score, compared to non-continual approaches. We can argue that the utilization of CL techniques in the context of EMCI is effective in adapting deep learning classifiers to changes in data distributions, thereby maintaining the stability of model performance over time. To our knowledge, this study represents the first exploration of a CL approach using real EMCI data.
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Affiliation(s)
- Pablo Ferri
- Biomedical Data Science Laboratory (BDSLab), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València (UPV), Valencia, Spain.
| | - Vincenzo Lomonaco
- Department of Computer Science, University of Pisa (Unipi), Pisa, Italy.
| | - Lucia C Passaro
- Department of Computer Science, University of Pisa (Unipi), Pisa, Italy.
| | - Antonio Félix-De Castro
- Conselleria de Sanitat Universal i Salut Pública, Generalitat Valenciana (GVA), Valencia, Spain.
| | | | - Carlos Sáez
- Biomedical Data Science Laboratory (BDSLab), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València (UPV), Valencia, Spain.
| | - Juan M García-Gómez
- Biomedical Data Science Laboratory (BDSLab), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València (UPV), Valencia, Spain.
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5
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Ma W, Bi X, Jiang H, Zhang S, Wei Z. CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions. IEEE J Biomed Health Inform 2024; 28:3167-3177. [PMID: 38466584 DOI: 10.1109/jbhi.2024.3375621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the exploration of data-driven deep learning technologies as a viable, efficient, and accurate alternative. Nonetheless, most current deep learning-based methods regarded a pair of proteins to be predicted for possible interaction as two separate entities when extracting PPI features, thus neglecting the knowledge sharing among the collaborative protein and the target protein. Aiming at the above issue, a collaborative learning framework CollaPPI was proposed in this study, where two kinds of collaboration, i.e., protein-level collaboration and task-level collaboration, were incorporated to achieve not only the knowledge-sharing between a pair of proteins, but also the complementation of such shared knowledge between biological domains closely related to PPI (i.e., protein function, and subcellular location). Evaluation results demonstrated that CollaPPI obtained superior performance compared to state-of-the-art methods on two PPI benchmarks. Besides, evaluation results of CollaPPI on the additional PPI type prediction task further proved its excellent generalization ability.
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Yu G, Deng Z, Bao Z, Zhang Y, He B. Gesture Classification in Electromyography Signals for Real-Time Prosthetic Hand Control Using a Convolutional Neural Network-Enhanced Channel Attention Model. Bioengineering (Basel) 2023; 10:1324. [PMID: 38002448 PMCID: PMC10669079 DOI: 10.3390/bioengineering10111324] [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: 10/24/2023] [Revised: 11/11/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
Accurate and real-time gesture recognition is required for the autonomous operation of prosthetic hand devices. This study employs a convolutional neural network-enhanced channel attention (CNN-ECA) model to provide a unique approach for surface electromyography (sEMG) gesture recognition. The introduction of the ECA module improves the model's capacity to extract features and focus on critical information in the sEMG data, thus simultaneously equipping the sEMG-controlled prosthetic hand systems with the characteristics of accurate gesture detection and real-time control. Furthermore, we suggest a preprocessing strategy for extracting envelope signals that incorporates Butterworth low-pass filtering and the fast Hilbert transform (FHT), which can successfully reduce noise interference and capture essential physiological information. Finally, the majority voting window technique is adopted to enhance the prediction results, further improving the accuracy and stability of the model. Overall, our multi-layered convolutional neural network model, in conjunction with envelope signal extraction and attention mechanisms, offers a promising and innovative approach for real-time control systems in prosthetic hands, allowing for precise fine motor actions.
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Affiliation(s)
- Guangjie Yu
- College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; (G.Y.); (Z.D.); (Z.B.)
| | - Ziting Deng
- College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; (G.Y.); (Z.D.); (Z.B.)
| | - Zhenchen Bao
- College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; (G.Y.); (Z.D.); (Z.B.)
| | - Yue Zhang
- College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; (G.Y.); (Z.D.); (Z.B.)
- Fujian Engineering Research Center of Joint Intelligent Medical Engineering, Fuzhou 350108, China
| | - Bingwei He
- College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; (G.Y.); (Z.D.); (Z.B.)
- Fujian Engineering Research Center of Joint Intelligent Medical Engineering, Fuzhou 350108, China
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Alzubaidi L, Fadhel MA, Albahri AS, Salhi A, Gupta A, Gu Y. Domain Adaptation and Feature Fusion for the Detection of Abnormalities in X-Ray Forearm Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38082960 DOI: 10.1109/embc40787.2023.10340309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The main challenge in adopting deep learning models is limited data for training, which can lead to poor generalization and a high risk of overfitting, particularly when detecting forearm abnormalities in X-ray images. Transfer learning from ImageNet is commonly used to address these issues. However, this technique is ineffective for grayscale medical imaging because of a mismatch between the learned features. To mitigate this issue, we propose a domain adaptation deep TL approach that involves training six pre-trained ImageNet models on a large number of X-ray images from various body parts, then fine-tuning the models on a target dataset of forearm X-ray images. Furthermore, the feature fusion technique combines the extracted features with deep neural models to train machine learning classifiers. Gradient-based class activation heat map (Grad CAM) was used to verify the accuracy of our results. This method allows us to see which parts of an image the model uses to make its classification decisions. The statically results and Grad CAM have shown that the proposed TL approach is able to alleviate the domain mismatch problem and is more accurate in their decision-making compared to models that were trained using the ImageNet TL technique, achieving an accuracy of 90.7%, an F1-score of 90.6%, and a Cohen's kappa of 81.3%. These results indicate that the proposed approach effectively improved the performance of the employed models individually and with the fusion technique. It helped to reduce the domain mismatch between the source of TL and the target task.
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8
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Zhao H, Tang L, Li JR, Liu J. Strengthening evolution-based differential evolution with prediction strategy for multimodal optimization and its application in multi-robot task allocation. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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9
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Li JY, Du KJ, Zhan ZH, Wang H, Zhang J. Distributed Differential Evolution With Adaptive Resource Allocation. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:2791-2804. [PMID: 35286273 DOI: 10.1109/tcyb.2022.3153964] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Distributed differential evolution (DDE) is an efficient paradigm that adopts multiple populations for cooperatively solving complex optimization problems. However, how to allocate fitness evaluation (FE) budget resources among the distributed multiple populations can greatly influence the optimization ability of DDE. Therefore, this article proposes a novel three-layer DDE framework with adaptive resource allocation (DDE-ARA), including the algorithm layer for evolving various differential evolution (DE) populations, the dispatch layer for dispatching the individuals in the DE populations to different distributed machines, and the machine layer for accommodating distributed computers. In the DDE-ARA framework, three novel methods are further proposed. First, a general performance indicator (GPI) method is proposed to measure the performance of different DEs. Second, based on the GPI, a FE allocation (FEA) method is proposed to adaptively allocate the FE budget resources from poorly performing DEs to well-performing DEs for better search efficiency. This way, the GPI and FEA methods achieve the ARA in the algorithm layer. Third, a load balance strategy is proposed in the dispatch layer to balance the FE burden of different computers in the machine layer for improving load balance and algorithm speedup. Moreover, theoretical analyses are provided to show why the proposed DDE-ARA framework can be effective and to discuss the lower bound of its optimization error. Extensive experiments are conducted on all the 30 functions of CEC 2014 competitions at 10, 30, 50, and 100 dimensions, and some state-of-the-art DDE algorithms are adopted for comparisons. The results show the great effectiveness and efficiency of the proposed framework and the three novel methods.
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Wang W, Liu HL, Tan KC. A Surrogate-Assisted Differential Evolution Algorithm for High-Dimensional Expensive Optimization Problems. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:2685-2697. [PMID: 35687633 DOI: 10.1109/tcyb.2022.3175533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The radial basis function (RBF) model and the Kriging model have been widely used in the surrogate-assisted evolutionary algorithms (SAEAs). Based on their characteristics, a global and local surrogate-assisted differential evolution algorithm (GL-SADE) for high-dimensional expensive problems is proposed in this article, in which a global RBF model is trained with all samples to estimate a global trend, and then its optima is used to significantly accelerate the convergence process. A local Kriging model prefers to select points with good predicted fitness and great uncertainty, which can effectively prevent the search from getting trapped into local optima. When the local Kriging model finds the best solution so far, a reward search strategy is executed to further exploit the local Kriging model. The experiments on a set of benchmark functions with dimensions varying from 30 to 200 are conducted to evaluate the performance of the proposed algorithm. The experimental results of the proposed algorithm are compared to four state-of-the-art algorithms to show its effectiveness and efficiency in solving high-dimensional expensive problems. Besides, GL-SADE is applied to an airfoil optimization problem to show its effectiveness.
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11
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Ugli DBR, Kim J, Mohammed AFY, Lee J. Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:2869. [PMID: 36905075 PMCID: PMC10007679 DOI: 10.3390/s23052869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 06/18/2023]
Abstract
Nowadays, deep learning (DL)-based video surveillance services are widely used in smart cities because of their ability to accurately identify and track objects, such as vehicles and pedestrians, in real time. This allows a more efficient traffic management and improved public safety. However, DL-based video surveillance services that require object movement and motion tracking (e.g., for detecting abnormal object behaviors) can consume a substantial amount of computing and memory capacity, such as (i) GPU computing resources for model inference and (ii) GPU memory resources for model loading. This paper presents a novel cognitive video surveillance management with long short-term memory (LSTM) model, denoted as the CogVSM framework. We consider DL-based video surveillance services in a hierarchical edge computing system. The proposed CogVSM forecasts object appearance patterns and smooths out the forecast results needed for an adaptive model release. Here, we aim to reduce standby GPU memory by model release while avoiding unnecessary model reloads for a sudden object appearance. CogVSM hinges on an LSTM-based deep learning architecture explicitly designed for future object appearance pattern prediction by training previous time-series patterns to achieve these objectives. By referring to the result of the LSTM-based prediction, the proposed framework controls the threshold time value in a dynamic manner by using an exponential weighted moving average (EWMA) technique. Comparative evaluations on both simulated and real-world measurement data on the commercial edge devices prove that the LSTM-based model in the CogVSM can achieve a high predictive accuracy, i.e., a root-mean-square error metric of 0.795. In addition, the suggested framework utilizes up to 32.1% less GPU memory than the baseline and 8.9% less than previous work.
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Affiliation(s)
| | - Jingyeom Kim
- Advanced Research Team, NHN Cloud Corp., Seongnam-si 13487, Republic of Korea
| | | | - Joohyung Lee
- Department of Computing, Gachon University, Seongnam-si 13120, Republic of Korea
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12
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Yu H, Shi J, Qian J, Wang S, Li S. Single dendritic neural classification with an effective spherical search-based whale learning algorithm. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:7594-7632. [PMID: 37161164 DOI: 10.3934/mbe.2023328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
McCulloch-Pitts neuron-based neural networks have been the mainstream deep learning methods, achieving breakthrough in various real-world applications. However, McCulloch-Pitts neuron is also under longtime criticism of being overly simplistic. To alleviate this issue, the dendritic neuron model (DNM), which employs non-linear information processing capabilities of dendrites, has been widely used for prediction and classification tasks. In this study, we innovatively propose a hybrid approach to co-evolve DNM in contrast to back propagation (BP) techniques, which are sensitive to initial circumstances and readily fall into local minima. The whale optimization algorithm is improved by spherical search learning to perform co-evolution through dynamic hybridizing. Eleven classification datasets were selected from the well-known UCI Machine Learning Repository. Its efficiency in our model was verified by statistical analysis of convergence speed and Wilcoxon sign-rank tests, with receiver operating characteristic curves and the calculation of area under the curve. In terms of classification accuracy, the proposed co-evolution method beats 10 existing cutting-edge non-BP methods and BP, suggesting that well-learned DNMs are computationally significantly more potent than conventional McCulloch-Pitts types and can be employed as the building blocks for the next-generation deep learning methods.
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Affiliation(s)
- Hang Yu
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
| | - Jiarui Shi
- Department of Engineering, Wesoft Company Ltd., Kawasaki-shi 210-0024, Japan
| | - Jin Qian
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
| | - Shi Wang
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
| | - Sheng Li
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
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13
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Wang GG, Cheng H, Zhang Y, Yu H. ENSO Analysis and Prediction Using Deep Learning: A Review. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.11.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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14
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A survey of designing convolutional neural network using evolutionary algorithms. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10303-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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15
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Wang Y, Li J, Chen C, Zhang J, Zhan Z. Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2022. [DOI: 10.1049/cit2.12106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Ye‐Qun Wang
- School of Computer Science and Engineering South China University of Technology Guangzhou China
| | - Jian‐Yu Li
- School of Computer Science and Engineering South China University of Technology Guangzhou China
| | - Chun‐Hua Chen
- School of Software Engineering South China University of Technology Guangzhou China
| | - Jun Zhang
- Hanyang University Ansan South Korea
| | - Zhi‐Hui Zhan
- School of Computer Science and Engineering South China University of Technology Guangzhou China
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Abstract
Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.
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