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IEEE Networking Letters 2025年第1期7卷 31-35页

作者： Li, Yantao Ling, shijun Huang, Hongyu Rabevohitra, Feno H. Chongqing University College of Computer Science Chongqing400044 China Studio A Department of Research and Development Antananarivo101 Madagascar

Contrastive unsupervised learning has made significant progress, but there is still potential for improvement by capturing finer details in input data. In this letter, we present PGMoCo, a Progressive Growth-based Momentum Contrast framework for unsupervised representative learning in classification tasks. PGMoCo begins by learning the overall distribution of samples at a coarse scale and progressively refines the representation by incorporating increasingly finer details. PGMoCo consists of data augmentation, progressive growth, an alternative multilayer perceptron (MLP) head, and a loss function. First, PGMoCo applies transformation-based data augmentation to the input samples. Then, it progressively learns features at multiple scales, uses an alternative MLP head to project latent representations into a contrastive loss space, and finally employs a specialized loss function to classify the samples. We evaluate PGMoCo on three datasets: CIFAR-10 and PolyU Palmprint (image classification) and H-MOG (person identification). PGMoCo achieves classification accuracies of 86.76% on CIFAR-10, 95.94% on PolyU Palmprint, and 80.10% on H-MOG, outperforming existing state-of-the-art methods. © 2019 IEEE.

关键词： Contrastive learning

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Transfer learning in Motor Imagery Brain computer Interface: A Review

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Journal of shanghai Jiaotong university(science) 2024年第1期29卷 37-59页

作者：李明爱许冬芹 Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insuficient training *** recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the *** paper surveys the development of transfer learning and reviews the transfer learning approaches in *** addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer ***,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,*** the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.

关键词： transfer learning brain-computer interface(BCI) electroencephalogram machine learning

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Cuckoo search-Optimized Deep CNN for Enhanced Cyber security in IoT Networks

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computers, Materials & Continua 2024年第12期81卷 4109-4124页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057MaharashtraIndia Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Science Faculty of ScienceNorthern Border UniversityArar91431Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)*** phishing attack detection solutions have been developed for IoT;however,many of these are either not optimally efficient or lack the lightweight characteristics needed for practical *** paper proposes and optimizes a lightweight deep-learning model for phishing attack *** model employs a two-fold optimization approach:first,it utilizes the analysis of the variance(ANOVA)F-test to select the optimal features for phishing detection,and second,it applies the Cuckoo search algorithm to tune the hyperparameters(learning rate and dropout rate)of the deep learning ***,our model is trained in only five epochs,making it more lightweight than other deep learning(DL)and machine learning(ML)*** proposed model achieved a phishing detection accuracy of 91%,with a precision of 92%for the’normal’class and 91%for the‘attack’***,the model’s recall and F1-score are 91%for both *** also compared our approach with traditional DL/ML models and past literature,demonstrating that our model is more *** study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.

关键词： Deep learning phishing Cuckoo search cable news network(CNN) IoT ANOVA F-test

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Design and Application of WEB based Blended Teaching system using Optimized ResNet50

Design and Application of WEB based Blended Teaching System ...

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2024 International Conference on Distributed systems, computer Networks and Cybersecurity, ICDsCNC 2024

作者： Liu, Lina Changchun Humanities and Sciences College Department of Public Computer Education and Research Changchun China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375442

The Blended teaching system is a learning method that mixes the online classes with traditional offline classroom methods. Web-based blended teaching system influences web technologies for create a blended learning environment. The web-based blended teaching system is effective in training centers, higher studies institutions and it helps to study the students in their own place and reduces the need for infrastructure and materials. The blended teaching system is affecting the staffs in institution because of rules and policies of institutions. The paper proposed an optimized ResNet50 with effective hyperparameter optimizations of Random Opposition learning strategy and Greedy selection based Flamingo search Algorithm namely ResNet50-RGFsA. The proposed ResNet50-RGFsA used to predict the student's academic performance via web-based blending teaching system. ResNet50 utilized to determine the academic performance of student and RGFsA is used to identify optimum hyperparameters. The ResNet50-RGFsA is used for effective prediction of academic performance in a WEB based blended learning. The experimental results show that comparison between the Root Mean squared Error (RMsE), Mean squared Error (MsE), Mean Absolute Error (MAE) and R2 of 0.196, 0.178, 0.145 and 0.165 for ResNet50-RGFsA compared with existing model like FsA. © 2024 IEEE.

关键词： Mean square error

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Echo state Network based on Improved Knowledge Distillation for Edge Intelligence

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Chinese Journal of Electronics 2024年第1期33卷 101-111页

作者： Jian ZHOU Yuwen JIANG Lijie XU Lu ZHAO Fu XIAO College of Computer Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks

Echo state network(EsN) as a novel artificial neural network has drawn much attention from time series prediction in edge intelligence. EsN is slightly insufficient in long-term memory, thereby impacting the prediction performance. It suffers from a higher computational overhead when deploying on edge devices. We firstly introduce the knowledge distillation into the reservoir structure optimization, and then propose the echo state network based on improved knowledge distillation(EsN-IKD) for edge intelligence to improve the prediction performance and reduce the computational overhead. The model of EsN-IKD is constructed with the classic EsN as a student network, the long and short-term memory network as a teacher network, and the EsN with double loop reservoir structure as an assistant network. The student network learns the long-term memory capability of the teacher network with the help of the assistant network. The training algorithm of EsN-IKD is proposed to correct the learning direction through the assistant network and eliminate the redundant knowledge through the iterative pruning. It can solve the problems of error learning and redundant learning in the traditional knowledge distillation process. Extensive experimental simulation shows that EsN-IKD has a good time series prediction performance in both long-term and short-term memory, and achieves a lower computational overhead.

关键词： Echo state network Reservoir structure optimization Knowledge distillation Edge intelligence Time series prediction

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Adaptive AI-based Personalized learning for Accelerated Vocabulary and syntax Mastery in Young English Learners

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International Journal of Advanced computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： students

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Predicting Lactobacillus delbrueckii ***-streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method

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science China(Life sciences) 2025年第2期68卷 558-574页

作者： shujuan Yang Mei Bai Weichi Liu Weicheng Li Zhi Zhong Lai-Yu Kwok Gaifang Dong Zhihong sun Key Laboratory of Dairy Biotechnology and Engineering Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China Key Laboratory of Dairy Products Processing Ministry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Collaborative Innovative Center for Lactic Acid Bacteria and Fermented Dairy Products Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China College of Computer and Information Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot 010018China

Lactobacillus delbrueckii ***(***)and streptococcus thermophilus(***)are commonly used starters in milk *** experiments revealed that *** interactions(Lb st I)substantially impact dairy product quality and *** biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations,an artificial intelligence-based method for screening interactive starter combinations is ***,in the current research on artificial intelligence based interaction prediction in the field of bioinformatics,most successful models adopt supervised learning methods,and there is a lack of research on interaction prediction with only a small number of labeled ***,this study aimed to develop a semi-supervised learning framework for predicting Lb st I using genomic data from 362 isolates(181per species).The framework consisted of a two-part model:a co-clustering prediction model(based on the Kyoto Encyclopedia of Genes and Genomes(KEGG)dataset)and a Laplacian regularized least squares prediction model(based on K-mer analysis and gene composition of all isolates datasets).To enhance accuracy,we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate Lb st I prediction results,which were verified through milk fermentation *** through milk fermentation experiments confirmed a high precision rate of 85%(17/20;validated with 20 randomly selected combinations of expected interacting isolates).Our data suggest that the biosynthetic pathways of cysteine,riboflavin,teichoic acid,and exopolysaccharides,as well as the ATP-binding cassette transport systems,contribute to the mutualistic relationship between these starter bacteria during milk ***,this finding requires further experimental *** presented model and data are valuable resources for academics an

关键词： Lactobacillus delbrueckii subsp.bulgaricus and streptococcus thermophilus interaction prediction semi-supervised learning dairy starter artificial intelligence milk fermentation

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From Global to Hybrid: A Review of supervised Deep learning for 2D Image Feature Representation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第06期6卷 1540-1560页

作者： Dong, Xinyu Wang, Qi Deng, Hongyu Yang, Zhenguo Ruan, Weijian Liu, Wu Lei, Liang Wu, Xue Tian, Youliang Guizhou University State Key Laboratory of Public Big Data College of computer science Guiyang550025 China Guangdong University of Technology Guangzhou510006 China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518000 China AI Research of *** Beijing100101 China

computer vision is the science that aims to enable computers to emulate human visual perception, and it encompasses various techniques and methods for extracting and interpreting information from two-dimensional images. supervised deep 2D image feature representation is a fundamental problem in computer vision that applies deep learning techniques to extract and process information from a given 2D image under supervised settings. The goal is to obtain a feature vector that can be utilized for various downstream computer vision applications. The quality of supervised deep 2D image feature representation algorithms directly affects the performance of downstream applications. However, most of the existing vision research only explores supervised deep 2D image feature representation for specific subtasks. Therefore, a comprehensive discussion on this topic is needed. In this paper, we propose a taxonomy of supervised deep 2D image feature representation methods based on four categories: global representation, region representation, hash representation, and hybrid representation, and we introduce their typical approaches. Furthermore, we perform a comparative analysis of the representative methods on three fundamental tasks: image classification, object detection, and semantic segmentation, as well as other common tasks. We also discuss the limitations of supervised deep 2D image feature representation and investigate future directions in image representation to facilitate the advancement of computer vision through image representation. © 2020 IEEE.

关键词： self-supervised learning

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Exploring Deep learning Methods for computer Vision Applications across Multiple sectors:Challenges and Future Trends

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computer Modeling in Engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran shankar Miroslav Mahdal Janakiraman senthilMurugan Jasgurpreet singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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research on a quantification model of online learning cognitive load based on eye-tracking technology

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Multimedia Tools and Applications 2024年 1-15页

作者： Xue, Yaofeng Zhu, Fangqing Li, Jiaxuan Department of Educational Information Technology and Shanghai Engineering Research Center of Digital Educational Equipment East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China

Online learning is characterized by a high degree of complexity and a wealth of information when compared to traditional classroom learning. This can have an adverse influence on the learning outcomes of online learners. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. © The Author(s), under exclusive licence to springer science+Business Media, LLC, part of springer Nature 2024.

关键词： Eye movements

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