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Multiobjective Many-Tasking Evolutionary Optimization using Diversified Gaussian-Based Knowledge Transfer

IEEE Transactions on Evolutionary Computation 2024年

作者： Lin, Qiuzhen Wang, Qianhui Chen, Baihao Ye, Yulong Ma, Lijia Tan, Kay Chen Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence Hong Kong Hong Kong

Multiobjective multitasking evolutionary algorithms have shown promising performance for tackling a set of multiobjective optimization tasks simultaneously, as the optimization experience gained within one task can be transferred to accelerate the solving of others. However, most studies only select similar transfer tasks based on their designed metrics, which become less efficient when tackling a large number of optimization tasks, as their transferred knowledge may be insufficiently diversified. To alleviate this issue, this article proposes a Multiobjective Many-Tasking Evolutionary Algorithm using Diversified Gaussian-based knowledge Transfer, named MMaTEA-DGT. In this algorithm, a diversified transfer selection strategy is presented to choose a number of similar and complementary source tasks for knowledge transfer. Then, based on the above diversified source tasks, a Gaussian-based transfer strategy is designed to transfer their various optimization knowledge. In this way, MMaTEA-DGT is more effective in transferring optimization knowledge to speed up the solving of many tasks. Experimental studies on both the benchmark suites and a real-world dynamic vaccine prioritization problem have indicated the superiority of MMaTEA-DGT over some recently proposed multiobjective many-tasking evolutionary algorithms. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Gender Recognition using EEG during Mobile Game Play

Gender Recognition using EEG during Mobile Game Play

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2021 International Conference on Information Technology, ICIT 2021

作者： Fatima, Bushra Raheel, Aasim Arsalan, Aamir Majid, Muhammad Ehatisham-Ul-Haq, Muhammad Anwar, Syed Muhammad University of Engineering and Technology Department of Computer Engineering Taxila47050 Pakistan Sino-Pak Center for Artificial Intelligence PAF-IAST Haripur22620 Pakistan University of Engineering and Technology Department of Software Engineering Taxila47050 Pakistan

ISBN: (纸本)9781665428705

In recent years, due to an exponential increase in mobile game users, automatic recognition of personal traits i.e., gender and user identity is gaining significance. Personal trait recognition also plays a vital role in information security. Traditional approaches for personal trait recognition, including vision and voice-based schemes, are subjected to privacy constraints. To address these challenges, physiological signals such as electroencephalogram (EEG) and electrocardiogram have been utilized in information security for bio-metric and personal attribute identification. Herein, we propose a new technique for EEG-based gender recognition during mobile game play. Towards this, EEG data of 38 users are recorded using the Muse headband while playing a mobile game named Traffic Racer. Different time and frequency-domain features are extracted from the acquired EEG data. Three classification algorithms, i.e., K-nearest neighbor (KNN), random forest, and the Naive bayes, are used to train and validate the proposed model. In particular, KNN achieved an accuracy of up to 97.36% in gender recognition, which shows the efficacy of the proposed approach when compared with other techniques. © 2021 IEEE.

关键词： Electroencephalography

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Muhammad Yusuf Kardawi Alexander Agung Santoso Gunawan Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.

关键词： Adaptation models Scene classification Accuracy Electronic learning Data science Feature extraction Convolutional neural networks Data mining Remote sensing Information systems

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Group Recommendation Method Based on Graph Neural Network

Group Recommendation Method Based on Graph Neural Network

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Mengye Lu Peng Zhu Chun Miao Youjian Wang Jiacun Wang School of Economics and Management Nanjing University of Science and Technology Nanjing China Department of Computer Science and Software Engineering Monmouth University New Jersey USA

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

Group activities are becoming more and more common on the Internet in the big data environment. Which makes many scholars focus on how to recommend items or activities to a group. However, conventional recommendation systems grapple with issues like data sparsity and the cold start problem, which limits the relevance and accuracy of recommendation. Therefore, this paper conducts an in-depth exploration of group recommendation methods utilizing graph neural networks and introduces a novel recommendation framework aimed at enhancing the efficacy of group recommendations. Firstly, this paper discusses how to construct an effective graph structure to encode the social interaction and individual characteristics of users. Then, an improved graph neural network architecture is proposed. Finally, extensive experimental evaluation is carried out on actual datasets, and the findings demonstrate that our framework markedly enhances the accuracy of recommendations.

关键词： Accuracy Big Data Graph neural networks Social intelligence Internet Recommender systems

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Backpropagation-Free 4D Continuous Ant-Based Neural Topology Search

arXiv

引用

arXiv 2023年

作者： ElSaid, AbdElRahman Ricanek, Karl Lyu, Zeming Ororbia, Alexander Desell, Travis Department of Computer Science University of North Carolina Wilmington United States Software Engineering Department Rochester Institute of Technology United States

Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (ACO). CANTS utilizes a continuous search space to indirectly-encode a neural architecture search space. Synthetic ant agents explore CANTS’ continuous search space based on the density and distribution of pheromones, strongly inspired by how ants move in the real world. This continuous search space allows CANTS to automate the design of artificial neural networks (ANNs) of any size, removing a key limitation inherent to many current NAS algorithms that must operate within structures of a size that is predetermined by the user. This work expands CANTS by adding a fourth dimension to its search space representing potential neural synaptic weights. Adding this extra dimension allows CANTS agents to optimize both the architecture as well as the weights of an ANN without applying backpropagation (BP), which leads to a significant reduction in the time consumed in the optimization process: at least an average of 96% less time consumption with very competitive optimization performance, if not better. The experiments of this study - using real-world data - demonstrate that the BP-Free CANTS algorithm exhibits highly competitive performance compared to both CANTS and ANTS while requiring significantly less operation time. © 2023, CC BY-NC-ND.

关键词： Topology

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks

arXiv

引用

arXiv 2023年

作者： Dong, Fan Abbasi, Ali Leung, Henry Wang, Xin Zhou, Jiayu Drew, Steve Department of Electrical and Software Engineering University of Calgary CalgaryAB Canada Department of Geomatics Engineering University of Calgary CalgaryAB Canada Department of Computer Science and Engineering Michigan State University East LansingMI United States

Federated learning offers a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs), utilizing large-scale private edge data from drones, balloons, and satellites. Existing research has extensively studied the optimization of the learning process, computing efficiency, and communication overhead. An important yet often overlooked aspect is that participants contribute predictive knowledge with varying diversity of knowledge, affecting the quality of the learned federated models. In this paper, we propose a novel approach to address this issue by introducing a Weighted Averaging and Client Selection (WeiAvgCS) framework that emphasizes updates from high-diversity clients and diminishes the influence of those from low-diversity clients. Direct sharing of the data distribution may be prohibitive due to the additional private information that is sent from the clients. As such, we introduce an estimation for the diversity using a projectionbased method. Extensive experiments have been performed to show WeiAvgCS’s effectiveness. WeiAvgCS could converge 46% faster on FashionMNIST and 38% faster on CIFAR10 than its benchmarks on average in our experiments. © 2023, CC BY.

关键词： Data privacy

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An AI-Based Chicken Disease Management System 1st

An AI-Based Chicken Disease Management System

引用

1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Quach, Luyl-Da Quoc, Nghi Pham Thi, Nhien Huynh Le Thanh, Nhan Thanh, Xuan Truong Thi Tran, Duc Chung Hassan, Mohd Fadzil Software Engineering Department FPT University Cantho city Viet Nam Computing Fundamental Department FPT University Hanoi Viet Nam Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia

ISBN: (纸本)9789811621826

Food stability has always received worldwide attention, especially in the development of the poultry industry. However, the poultry’s diseases have caused the loss of the poultry population and direct income of the owners. In this work, a system comprising of a website and a mobile application has been developed to support disease identification in chickens;the disease spread locations can be managed and traced with maps. In addition, the system allows farmers to make contact and get support from experts through chat, voice calls, and video calls. It also has some e-commerce functions for improving sales of poultry’s owners. The system uses the improved ResNet-50 model, with an accuracy of about 93.56%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Malware Attacks Detection in IoT Using Recurrent Neural Network(RNN)

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Intelligent Automation & Soft Computing 2024年第2期39卷 135-155页

作者： Abeer Abdullah Alsadhan Abdullah A.Al-Atawi Hanen karamti Abid Jameel Islam Zada Tan N.Nguyen Computer Science Department Imam Abdulrahman Bin Faisal UniversityDamam32232Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk47512Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences and Information Technology Hazara UniversityMansehraPakistan Faculty of Computing Department of Software EngineeringInternational Islamic UniversityIslamabadPakistan Department of Architectural Engineering Sejong UniversitySeoul05006Korea

IoT(Internet of Things)devices are being used more and more in a variety of businesses and for a variety of tasks,such as environmental data collection in both civilian and military *** are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing *** this study,we investigate the possibility of detecting IoT malware using recurrent neural networks(RNNs).RNNis used in the proposed method to investigate the execution operation codes of ARM-based Internet of Things apps(OpCodes).To train our algorithms,we employ a dataset of IoT applications that includes 281 malicious and 270 benign pieces of *** trained model is then put to the test using 100 brand-new IoT malware samples across three separate LSTM *** exposure was not previously conducted on these *** newly crafted malware samples with 2-layer neurons had the highest accuracy(98.18%)in the 10-fold cross validation experiment.A comparison of the LSTMtechnique to other machine learning classifiers shows that it yields the best results.

关键词： Malware malicious code code obfuscation IoT machine learning deep learning

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Mental Workload Classification from fNIRS Signals by Leveraging Machine Learning

Mental Workload Classification from fNIRS Signals by Leverag...

引用

2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023

作者： Hasan, M. Mahmud, M. Poudel, S. Donthula, K. Poudel, K. University of Memphis Department of Electrical and Computer Engineering TN United States Senior Software Engineer Optum Inc MurfreesboroTN United States Middle Tennessee State University Computational and Data Science MurfreesboroTN United States Middle Tennessee State University Computer Science MurfreesboroTN United States

ISBN: (纸本)9798350341256

Mental workload (MWL) identification is vital to know human cognitive functioning, performance, and well-being. In this work, we develop models for identifying low vs. high MWL using different genres of machine learning classifiers. We used non-invasive functional near-infrared spectroscopy (fNIRS) signals while participants classified the low vs. high levels of MWL tasks. Our analysis shows the low vs. high MWL can be identified best from the whole brain data. The k-nearest neighbors classifier showed the best performance with an accuracy of 98.8%, an area under the curve (AUC) of 98.8%, F1 score, precision, and recall of 98.0% from the whole brain data without overlapping signals. A separate hemisphere analysis using left hemisphere (LH) and right hemisphere (RH) activity showed that the LH activity has better classification ability than the RH activity. We also examined the classification with the top six features that could identify the low vs. high MWL with an accuracy of 97.4%, (AUC) 97.4%, F1 score, precision, and recall of 97.0%. These findings would be useful for developing more intuitive and user-friendly interfaces in the human-computer interface. © 2023 IEEE.

关键词： Infrared devices

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