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MVCSR: Multi-view Contrastive Sequential Recommendation 3rd

MVCSR: Multi-view Contrastive Sequential Recommendation

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Lee, Kai Wu, Xuan Zhao, Junfeng Yu, Chenyun Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China

ISBN: (纸本)9789819624676

Contrastive learning technology has shown significant potential in alleviating the data sparsity problem in sequential recommendation. Nevertheless, existing methods mainly derive self-supervised signals from a single perspective or rely on abundant domain-specific auxiliary information to extract high-quality user representations, which either fail to make the most of supervised information or lack of flexibility to extend to more scenarios. In this paper, we propose a novel Multi-View Contrastive Sequential Recommendation (MVCSR) method for sequential recommendation scenarios with few auxiliary information. MVCSR utilizes the set-level, sequence-level and preference-level user information to derive valuable supervised signals and provides three effective contrastive learning strategies to improve the item representations, sequence embeddings and user preferences, eventually boosting the recommendation performance. Experimental results reported from four Amazon datasets have demonstrated the superiority of MVCSR compared with all the state-of-the-art sequential recommendation baselines. Moreover, we validate the effectiveness of each contrastive learning strategy and show our advantage at dealing with the recommendation for users with short sequences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Construction of Fusion Visualization Platform for Meteorological Station and Monitoring Application Scenarios 3rd

Construction of Fusion Visualization Platform for Meteorolog...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： He, Tingting Jiang, Yuantong Hao, Panlin Zheng, Faling He, Yuanrong School of Cultural Industries and Tourism Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China Technology Co. Ltd. Xiamen China

ISBN: (纸本)9789819624676

With the continuous development of real-world 3D, the visualization of multi-source real 3D data fusion is significant for digital twins. Based on SketchUp, this study aims to utilize advanced laser point cloud scanning technology to conduct component-level realistic 3D modeling and to construct a high-precision movable meteorological station-based realistic 3D model. Meanwhile, by utilizing the WebGL architecture of a real-time 3D model visualization platform, the meteorological station model is integrated into the large model of the monitoring scene, achieving functions such as browsing, attribute querying, rotating, scaling, dismantling, explosion, and more flexible interaction and spatial analysis of the movable meteorological station 3D model. The real-time 3D model of this study can not only provide intuitive and comprehensive spatial information support for the design, deployment, and operation of meteorological stations, but also provide a visual foundation for environmental monitoring, disaster response, and other fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data fusion

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719457

The proceedings contain 35 papers. The special focus in this conference is on Computational Electronics for Wireless Communications. The topics include: Design of a Four-Port MIMO Antenna for C-, X-, and Ku-Band Applications;Exploration of Diverse Techniques for Design and Optimization of QCA-Based Multiplexer Circuits;melanoma Detection Via Deep Convolutional Neural Network;pattern Diversity mm-Wave Rectangular Ring Shaped MIMO Antenna for 5G Communication and Internet of Things Applications;Flexible Two/Eight-Port MIMO Antenna Configuration for LTE B41/WLAN/Wi-Fi 6E and C-Band Applications;Improvement in Machining Ability of Tungsten Carbide Tool Insert Through Microwave Hybrid Heating During Turning of EN 24 Steel;optimizing Airfare Pricing: A data-Driven Approach for Affordable Travel Planning;advancements in Emotion recognition: Systematic Review and Research Roadmap;epoc-Based Electroencephalography Signals Analysis of Different Stress Levels;Two-Element Tilted Rectangular MIMO UWB Antenna for Underwater Communications;deep learning-Based Maize Crop Disease Detection and Remedial Recommendation System;design of Splash Plate Dielectric Hat Feed for Ka-Band Tracking Radar Applications;soft Computing Paradigms for Load Balancing in Cloud Computing;strategies for Effective Network Congestion Control: Insights from Parameter-Based Analysis;BICC: Optimizing Sensor Network Performance Via an Efficient Bioinspired Iterative Approach with Congestion Control;Leakage Power Reduction and Stability Analysis of 5 nm Node GAA CNTFET SRAMs;bandwidth Enhancement and Improvement of Directivity of a Composite Conical Structure in the Presence of a Circular Loop Antenna;development of Compact Dual Polarized Corrugated Feed Horn Antenna with Low Cross-Polarization for Doppler Weather Radar Applications in C-Band;A Review on Recent Breakthroughs and Accomplishments in the Development of FeFET.

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719426

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A Sample-Quality-Based Imitation Approach for Offline Reinforcement learning 3rd

A Sample-Quality-Based Imitation Approach for Offline Reinfo...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Li, You Liu, Juntao Zeng, Cheng Tang, Hainan Gong, Hui Wuhan Digital Engineering Institute Wuhan430205 China Hubei University Wuhan430062 China

ISBN: (纸本)9789819624676

The objective of offline reinforcement learning is to derive an optimal policy using a static dataset. Due to the estimation errors of out-of-distribution (OOD) action value, most offline reinforcement learning algorithms constrain the policy within the actions in the dataset. TD3 + BC is one of these methods to alleviate OOD problem. In TD3 + BC, the agent employs behavior cloning to align its policy closely with that found in the dataset. Although TD3 + BC performances well in some environments, it is too conservative. TD3 + BC simply imitates the existing policy in the dataset. It disregards the quality of the state-action pairs within the dataset. This leads to a failure to learn good policies. Therefore, in this paper, a sample quality based imitation approach for offline reinforcement learning (SQIRL) is proposed. In the proposed method, the quality of each training sample in the dataset is evaluated, and then the high-quality samples are imitated. Therefore, the proposed method not only alleviates the OOD problem as original TD3 + BC, but also learns a better policy. This algorithm is compared with the existing methods, such as imitation learning methods, policy regularization methods, uncertainty penalty methods, and policy constraints methods. It is found that this algorithm outperforms the comparison methods in the MuJoCo environment. It is important to highlight that this approach is especially effective in scenarios where the overall data quality is low. For example, the average normalized rewards of the proposed algorithm are 200%, 181% higher than those of TD3 + BC in the Random dataset of halfcheetah and hopper environment. Meanwhile, in the Full-Replay dataset of halfcheetah, hopper and walker2d environments, compared with the TD3 + BC, the average normalized rewards obtained by the proposed method increase by 81.4%,157%, and 21.8%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An Asynchronous Robust Federated learning Approach for data Value in IoT 3rd

An Asynchronous Robust Federated Learning Approach for Data ...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Liang, Hua Jin, Min Yan, Hua Han, Shihai Li, Wei Yang, Zhigang Wang, Zhuotong State Grid Chongqing Electric Power Company Electric Power Research Institute Chongqing401123 China Digitization Department State Grid Chongqing Electric Power Company Chongqing400015 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China

ISBN: (纸本)9789819624676

The heterogeneity of IoT devices, resource bottlenecks, and security risks present challenges to the robustness and privacy of federated learning. The difference in the value of IoT data further weakens the robustness of the model. Additionally, attack methods such as Byzantine attacks and privacy inference attacks escalate the risks and vulnerabilities of federated learning models. To address these issues, we propose an asynchronous robust federated learning method based on data value. Firstly, an asynchronous grouping robust aggregation (AGRA) algorithm is designed and combined with a cosine anomaly filter to eliminate abnormal gradients. Simultaneously, an inter-group delay aggregation strategy oriented towards data value is proposed. Secondly, an efficient and robust aggregation protocol supporting asynchronous grouping is constructed to significantly reduce communication overhead while safeguarding data privacy. Finally, through data grading and secure storage adjustment using differential privacy, high-value data can be maximized, thereby enhancing the efficiency and performance of federated learning while protecting privacy. Results show that the algorithm can significantly improve accuracy by 0.9% to 65.8% in the presence of stragglers, while also reducing communication overhead by 4.2% to 5.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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machine learning Based Citrus Fruit Disease Prediction Using Support Vector machine Algorithm 3rd

Machine Learning Based Citrus Fruit Disease Prediction Using...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Mohanapriya, D. Kabilesh, S.K. Swaranambigai, R. Saritha, B. Deepikapriya, S.V.K. Jai Shriram Engineering College Tamil Nadu Tiruppur638660 India KGISL Institute of Technology Tamil Nadu Coimbatore641035 India Jansons Institute of Technology Tamilnadu Coimbatore641659 India Erode Sengunthar Engineering College Tamilnadu Erode638057 India

ISBN: (纸本)9789819730896

The recognition of fruit diseases is rapidly becoming a significant area in computer vision. Besides reducing fruit production, plant diseases also have a profound impact on the country's economy. Therefore, predicting plant illnesses and implementing remedial actions are of utmost importance. Plant illness directly affects the crop’s quality and overall productivity. To achieve swift and accurate disease prediction, an automated method is used to identify and diagnose affected leaves within a short span of time. Through the appropriate use of diagnosis and classification techniques, potential losses can be prevented. The features used for identifying plant diseases include color, strength, area of attack, and dimension. The current research primarily focuses on guava leaf disease prediction using K-means clustering techniques. In this proposed study, we aim to identify orange fruit and leaf diseases. To achieve this, K-means clustering is applied to segment the orange fruit images into distinct clusters. We leverage the gray-level co-occurrence matrix (GLCM) approach with the clustered data to extract the global features as feature vectors, and the orientation gradient approach is utilized to uncover real features. Implementing machine learning algorithms has significantly enhanced the accuracy of the system, achieving a remarkable 96% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Efficient acne classification based on Resnet18 3

Efficient acne classification based on Resnet18

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Cheng, Shi Sun, Mingze Huang, Zhongyi Wang, Yushan Fu, Jiaxuan School of Engineering University of Manchester ManchesterM13 9PL United Kingdom School of Computing Science University of Glasgow GlasgowG12 8RZ United Kingdom School of Data Science and Big Data Nanjing Institute of Technology Nanjing210000 China The Affiliated High School Renmin University of China Beijing100083 China International Department Nanjing Jinling High School Nanjing21000 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

In this study, we investigated the mechanical performance of lightweight composite slabs, specifically focusing on the application of an integrated face recognition and acne classification pipeline. We propose a comprehensive approach that commences with facial detection, proceeds to the acne region localization, and concludes with acne classification utilizing ResNet18 architecture. The aim is to develop a highly accurate and efficient system for acne classification, particularly beneficial for remote patients or those seeking a convenient self-assessment tool. Our dataset, though limited in size and diversity, is iteratively refined and augmented to mitigate data scarcity issues. Through experimental evaluations, we demonstrate that the combination of ResNet18 and Support Vector machine (SVM) achieves an accuracy rate of 96%, significantly outperforming traditional manual feature extraction methods. Additionally, visualization techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are employed to gain insights into the data distribution and the effectiveness of automatically extracted features. This study highlights the potential of deep learning for acne detection and underscores the importance of dataset refinement for enhancing model performance. © 2025 SPIE.

关键词： Support vector machines

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Supervised machine learning Method for Bone Cancer Detection and Classification 3rd

Supervised Machine Learning Method for Bone Cancer Detection...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Lingappa, Ediga Sriramya, P. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai600056 India Malla Reddy College of Engineering TS Dhulapally Secunderabad500100 India Department of Artificial Intelligence and Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai600056 India

ISBN: (纸本)9789819783540

Bone cancer kills most people worldwide. X-rays, MRIs, and CT scans aid bone cancer diagnosis. Manual processes take too much time and knowledge. Thus, a machine learning system must distinguish malignant bone from healthy bone. Cancer-damaged bones feel different. Cancer and healthy bone scans were similar. Classifying them is hard. Despite continuous innovation in the area of cancer research, it still remains one of the most fatal diseases in the world. It is paramount to harness all resources to bring breakthroughs in cancer treatment. Detection at the initial stage would be a significant stride in prolonging the life of the patient and decreasing the mortality rate. Hence, it is imperative to develop techniques that are innovative, efficient, and with lesser undesirable effects. This paper involves the study of all elements of bone cancer and features to evaluate/estimate its type. ACM is one of the most well-known techniques for segmenting a bone image into individual parts and the random forest method employs a clustering strategy with a maximum of five nodes. Insight into the organ's volumes and contours from an image is seen as highly challenging. It's a collection of operations including filtering out background noise, dividing up the data, mining for features, and making a final pick. It detaches the pixels from the edges of the object. They are treated further to bring about the intended effect. The achieved results are satisfactory, and this method is among the best ones for segmenting greyscale images currently available. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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Realization of Network Security Knowledge Base with Knowledge Graph 3rd

Realization of Network Security Knowledge Base with Knowledg...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Ouyang, Zepeng Li, Xiangyang Wang, Bing Ji, Zhenyan School of Software Engineering Beijing Jiaotong University No. 3 Shangyuan Village Beijing China Cyberspace Security Department China Railway Information Technology Group Co. Ltd. No. 10 Fuxing Avenue Beijing China

ISBN: (纸本)9789819616978

In the era of digital transformation, the sheer volume of network security data poses significant challenges in terms of organization and retrieval. Traditional search engines fail to provide contextual answers, necessitating the development of more advanced systems. This paper presents the design and implementation of a network security knowledge base enhanced with a knowledge graph. The proposed system aims to improve the efficiency of information management and retrieval for network security professionals. We discuss the multi-layer architecture of the knowledge base, which includes the UI, service, data access, and data storage layers. The knowledge graph is constructed through a four-layer architecture comprising the data, extraction, knowledge, and application layers. Using the Scrapy framework, we efficiently crawl and extract data from various online sources. This data is processed with the Jena tool and stored in Neo4j as structured triples, enabling complex queries and advanced analytic. Our system supports semantic queries, knowledge association discovery, and intelligent question answering, improving user experience and decision-making in network security management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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