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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wang, Ruiqian Wu, Wenhao Li, Hengxiao Wu, Jiarui Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder's dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis. © 2024 IEEE.

关键词： Dimensionality reduction

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MG-CR: Factor Memory Network and Graph Neural Network Based Personalized Course Recommendation 28th

MG-CR: Factor Memory Network and Graph Neural Network Based...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Yun Yu, Minghe Sun, Jintong Zhang, Tiancheng Yu, Ge School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China

ISBN: (纸本)9783031306716

Course recommendations in universities select the most suitable courses for students according to their interests and academic requirements. However, existing works often focus on modeling course selection history, without paying sufficient attention to rich features contained in students’ personal information and courses’ teaching attributes, and various relationships between them. In order to realize the personalization and dynamics of course recommendation, we consider students and courses as two types of nodes to construct a heterogeneous information network (HIN), and propose a factor Memory network and Graph neural network based personalized Course Recommendation (MG-CR) on top of HIN. MG-CR captures multiple features of students and courses simultaneously in the input module, thereby maximizing the retention of personalized information. The feature transfer module optimizes course features by considering various types of relationships between courses in the HIN, and together with the input module, it ensures high recommendation accuracy in the cold-start scenario. The updating module explicitly stores the student’s interest level for each factor in the state matrix, which is then used to dynamically recommend courses for students in the predicting module. Experimental results show that MG-CR provides accurate, dynamic and personalized recommendations, and has superior performance in the cold-start scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Combining Strategy and Roles to Formally Analyze Multi-Agent Reinforcement Learning 3

Combining Strategy and Roles to Formally Analyze Multi-Agent...

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3rd International Conference on Electronic Information engineering and computer science, EIECS 2023

作者： Wang, Xiaoyan Zhang, Yujuan Li, Bing Jilin University College of Computer Science and Technology Changchun China Jilin University Software Engineering Changchun China

ISBN: (纸本)9798350316674

A novel semantics for MARL called neural concurrent game structure (NCGS) is introduced, which extends CGS with neural network and roles where the agents are implemented via feed-forward ReLU neural networks. To formally verify concrete NCGS systems and reduce the complexity, multi-role strategy logic(mrSL) was put forward. mrSL is suitable for the properties of the NCGS, regardless of which agent is responsible for the specific task. Parameterized model checking(PMC) is used to solve the NCGS verification problem against mrSL. Finally an algorithm for MILP-based verification process is introduced, and report the experimental results. © 2023 IEEE.

关键词： CGS MARL MILP mrSL PMC

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

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Time-enhanced Dynamic Graph Network for Next POI Recommendation

Time-enhanced Dynamic Graph Network for Next POI Recommendat...

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2024 International Symposium on Internet of Things and Smart Cities, ISITSC 2024

作者： Liu, Zhao Liu, Wei Chen, Junyang School of Artificial Intelligence Sun Yat-sen University Zhuhai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798331505516

Predicting the next Point-of-Interest (POI) is crucial for location-based services. In this paper, we propose the Time-enhanced Sequence Prediction Model (TSPM) to improve the accuracy of next POI recommendations by incorporating temporal information and dynamic graph *** approach utilizes a Time-enhanced Sequence-based Dynamic Graph (TSDG) that captures both temporal transitions of POIs and sequential dependencies in user behavior. By embedding temporal information directly into the graph structure, TSPM effectively models user movements. We further enhance POI embeddings using knowledge graph techniques and Eigenmap to preserve the topological properties of the *** proposed model integrates these enriched embeddings into a Time-aware Recurrent Neural Network (TiRNN) to capture the influence of past check-ins across different time intervals. Experiments on real-world datasets demonstrate that TSPM significantly outperforms existing methods in prediction accuracy. © 2024 IEEE.

关键词： Graph embeddings

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Predicting Pre-Owned Bike Values with Machine Learning Techniques in Bangladesh 2

Predicting Pre-Owned Bike Values with Machine Learning Techn...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Ferdous, Kazi Hassanul Arman, Md.Shohel Ahmed, Kazi Rifat Tasnim, Nusrat Mahmud, Imran Revathi, P. Daffodil International University Diu Data Science Lab Department of Software Engineering Dhaka Bangladesh Holy Cross College Department of Computer Science Tamilnadu Tiruchirapalli India

ISBN: (纸本)9798350373110

As the importance of sustainable practices in the automobile sector grows, it's critical to anticipate motorcycle prices and offerings. With so many variables to consider when buying a secondhand motorcycle-condition, mileage, brand reputation, model characteristics, etc.-accurate pricing prediction is essential for both customers looking for good values and sellers hoping to maximize profits. This study investigates the effectiveness of four machine learning techniques Decision Tree models, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Stochastic Gradient Descent (SGD) Regressions-in predicting motorcycle prices using a dataset obtained from motorcycle merchants. It's noteworthy that these methods are used with a hybrid architecture to improve prediction accuracy. Different approaches are compared and contrasted to find the best fit for the given dataset, and the difficulties and roadblocks that come with them are also discussed. After extensive testing, the decision tree model's effectiveness is shown with an amazing 97% accuracy rate, providing accurate price estimates that are essential for encouraging sustainable habits in the ever-changing Internet of Things environment. With an impressive accuracy rate, this methodology provides precise pricing estimations, which are critical for supporting sustainable practices in the automotive industry. © 2024 IEEE.

关键词： Motorcycles

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Improving multi-class opinion classification in social networks by combining Fuzzy SVM and CNN 28th

Improving multi-class opinion classification in social netwo...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Ghezaiel Hammouda, Nadia Toujani, Radhia College of Computer Science and Software Engineering University of Hail Hail Saudi Arabia ESTMOD Department Higher Institute of management University of Tunis Tunisia

Machine learning techniques, particularly fuzzy Support Vector Machines (fuzzy SVM), have demonstrated effectiveness in sentiment classification within social networks. However, the evolving complexity of social networks content and the necessity to handle diverse opinions prompt ongoing advancements in sentiment analysis methodologies. In this study, we propose an innovative approach by integrating a Convolutional Neural Network (CNN) model with fuzzy SVM. Our key contribution lies in the fusion of these two methodologies, capitalizing on the strengths of fuzzy SVM for opinion mining and CNN for extracting intricate features. Additionally, we extend this method to enable multi-class opinion classification, allowing for a finer understanding of nuances in expressed sentiments. Through evaluation on benchmark datasets, we highlight a notable improvement in performance compared to baseline methods. The results underscore the enhanced generalization capabilities and increased accuracy in opinion classification offered by our hybrid model, rendering it a promising asset for decision-making applications based on multi-opinion analysis. © 2024 The Authors.

关键词： Convolutional neural networks

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

Crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease Inception v3 CNN crop diseases

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Artificial Intelligence For Realtime Face Recognition Attendance Using college Classrooms and Buses

Artificial Intelligence For Realtime Face Recognition Attend...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Ahila, A. Hosanna Princye, P. Poonguzhali, A. Kavivendhan Deepa, M. Arthy, A. Saravanakumar, R. Sri Sairam College of Engineering Department of Electronics and Communication Engineering Anekal Bangalore India Francis Xavier Engineering College Department of Computer Science and Business Systems Tamil Nadu Tirunelveli India Iconix Software Solution Software Analyst Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350378092

Teachers take attendance by having pupils sign in or check-in classes and transportation. Student absences often result from individual mistakes. This article examines a technology that records data from classroom photographs of every student's face. This research uses an Adaptive Boost Classifier, Random Forest (RF), and Deep Convolutional Neural Networks (DCNNs). The model performs well on the DCNN model with 88 and 92% accuracy and on the ResNet50 pre-trained model with 97.21% accuracy. After detecting each student's face, they recorded their present status in an Excel document. It kept the best system implementation approach based on performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

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