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4th International Conference on computer, Communication, Control and information technology, C3IT 2024

作者： Majilya, Mouli Maji, Giridhar Ghosh, Partha Sen, Soumya A.K.Choudhury School of Information Technology University of Calcutta Kolkata India Asansol Polytechnic Department of Electrical Engineering Asansol India Academy of Technology Department of Computer Science and Engineering Adisaptagram India

ISBN: (纸本)9798350389128

In the fiercely competitive landscape of modern business, making effective decisions is imperative for generating substantial revenue. With organizations employing diverse strategies to stay ahead, leveraging data for informed decision-making is crucial. Understanding individual preferences and purchasing behaviors can be challenging, but machine learning techniques offer a solution by organizing and analyzing vast datasets. Customer segmentation, particularly through K-means clustering, is instrumental in identifying target groups with similar attributes and interests. The present study focuses on customer clustering method-ologies to give businesses a competitive edge. By collecting, analyzing, and visualizing customer data, we aim to develop a data science model that utilizes K-means clustering and the RFM model (Recency, Frequency, Monetary) for existing customers. Utilizing the UK's e-commerce dataset, which delves into customer purchasing behaviors, we aim to group customers into clusters such as high-value customers, mid-range, and those at risk of churn based on RFM scores. © 2024 IEEE.

关键词： Sales

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Pre-Training and Fine-Tuning for Efficient Routing in Opportunistic Networks

Pre-Training and Fine-Tuning for Efficient Routing in Opport...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hao, Jia Wu, Xiaorui Seah, Winston K. G. Zhang, Feng Xu, Gang College of Computer Science Engineering Research Center of Ecological Big Data Inner Mongolia University Ministry of Education Hohhot China School of Engineering & Computer Science Victoria University of Wellington Wellington New Zealand School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (纸本)9781665410205

In opportunistic networks, it is a challenge to find the best relay node instead of blindly selecting from among available nodes to forward messages and effectively transmit them to their destinations. By comparing the similarity between nodes, the next hop node that is most similar to the destination node is found in time. Existing node similarity-based opportunistic networks routing algorithms only calculate similarity between nodes according to nodes' properties that can be directly obtained from the network, such as the historical meeting record between nodes, etc. However, this superficial similarity calculation is obviously inadequate to describe the inherent dynamic nature of opportunistic networks at both spatial and temporal levels, and ignores the salient movement characteristics of nodes, resulting in poor routing performance. Therefore, this paper proposes an opportunistic network routing strategy based on pre-training and fine-tuning (PTFT) model. Firstly, an autoencoder is added to the graph neural network model to encode node movement behavior. Then, a pre-training graph neural network model in large scale opportunistic network scenarios is applied to learn potential features of nodes through fine-tuning. Finally, we calculate the similarity between nodes based on the node potential feature, thereby assisting the nodes to achieve an efficient routing decision. The simulation results show that our PTFT-based algorithm not only has superior performance compared to traditional routing algorithms, but also faster learning speed than other machine learning-based routing algorithms. © 2024 IEEE.

关键词： Routing algorithms

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LiDAR Image-Based Earth Carbon Emission Analysis and Its Impact on Public Health: A Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 455-464页

作者： Meghana, S. Padmaja, Dhyaram Lakshmi Gundu, Krishna Sriharsha Kudari, Rajeev Somasekar, J. Chandran, Likha Department of Computer Science Engineering Neil Gogte Institute of Technology Hyderabad India Information Technology Anurag University Hyderabad India Enterprise Technology Arizona State University Tempe AZ United States Department of CSE Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh Guntur 522502 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bengaluru 562112 India Department of Chemistry VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

Afforestation is a practical way to absorb carbon dioxide in contemporary context of carbon neutrality. In the assessment of forest emission reduction projects, stock can be utilized as a crucial indicator of biomass and carbon storage estimation in addition to serving as an economic value index for forest wood resources. We suggest a framework and technique for data-driven machine learning that uses satellite remote sensing and aerial LiDAR to forecast plantation stock. This research proposes a novel method in LiDAR image-based carbon emission analysis with their health impact using a machine learning method. Here, the input is collected as LiDAR image-based healthcare data for processing and smoothening. Then, this image has been segmented and classified using convolutional contour graph cut equalization with VGG-net transfer encoder Gaussian neural networks. The experimental analysis for LiDAR image dataset has been carried out for analysis of carbon emission, and healthcare data analysis has been carried out in terms of training accuracy, average precision, recall, F-1 score, and AUC. The proposed technique attained a training accuracy of 98%, an average precision of 93%, a recall of 96%, an F-1 score of 92%, and an AUC of 94%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Carbon emission analysis Health impact LiDAR image Machine learning model Transfer encoder

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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Mutual Coupling Reduction in MIMO Antenna for 5G Application by Self-Decoupled Method 2

Mutual Coupling Reduction in MIMO Antenna for 5G Application...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rashid, Aamir Shah, Syed Shahid Hussain, Irshad Jalal, Muhammad Ramalakshmi, R. Ramachandran, V. University of Engineering and Technology Faculty of Electrical and Computer Engineering Peshawar Pakistan College of Computer and Information Engineering King Saud University Department of Computer Engineering Riyadh Saudi Arabia Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798350347982

This paper shows a simple, compact and small 8×8 MIMO antenna. The ability of MIMO antennas is to broadcast signals in a various patterns and polarizations is extremely advantageous for contemporary communications systems. Excellent diversity gain (DG > 9.99dB), a low envelop correlation coefficient (ECC 0.002), and extremely low mutual coupling (S2l≥ 27) dB are all characteristics of the constructed MIMO antenna. S-boundaries, energy pattern characteristics, maximum gain, diversity gain, and envelop coefficient are used to evaluate the antenna performance. The mutual Coupling is the most basic fundamental parameter for the MIMO antenna, was likewise analyzed and it was observed that its value is under -27dB. self-decoupled MIMO antennas have a loop type ground antenna which reduces the requirement for extra decoupling structures or a postponed tuning method Antennas for ground radiation and loop antenna are magnetic-current and electric-current components, respectively. The suggested MIMO antennas feature a self-decoupled despite the antenna parts being near together, reduced profile and easy fabrication advantages the suggested, a revolutionary approach called MIMO antenna system is expected to offer excellent isolation and minimal mutual coupling, making it suitable for 5G applications. © 2023 IEEE.

关键词： MIMO systems

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Empathetic Dialogue Generation with Contrastive Learning and Multi-Source information Fusion 14

Empathetic Dialogue Generation with Contrastive Learning and...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Miao, Yuqing Lu, Chenggang Liu, Tonglai Zhang, Wanzhen Wen, Yimin Zhou, Ming Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Zhongkai University of Agriculture and Engineering College of Information Science and Technology Guangzhou China Guilin Hivision Technology Company Guilin China

ISBN: (纸本)9798350371024

An empathic dialogue generation model based on comparative learning and multi-source data fusion is proposed to address problems of the under-extraction of contextual emotional expressions in discourse and limited empathic response diversity in current empathic dialogue generation tasks. The model introduces cognitive, affective, and dialogue intention information to guide dialogue generation. In the emotion classification stage, contrastive learning is used to capture the emotion information of the contextual discourse and to improve the contextual discourse representation. In the empathic response generation phase, the multi-attention mechanism fuses multiple sources of cognitive, emotional, and dialogue intention information, capturing more in-depth semantic and emotional information in the dialogue. Experimental results on the public dialogue dataset ED show that the proposed model can fully exploit cognitive, affective, and dialogue intent information to improve classification accuracy and generate more empathetic dialogue responses. © 2023 IEEE.

关键词： Data fusion

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-CommunicationsChofuTokyo 182-8585Japan Information Technology Center Nagoya UniversityNagoya 464-8601Japan National Institute of Informatics(NII) Tokyo 101-8430Japan Hohhot Minzu College Hohhot 010051China Toyota Motor Corporation Tokyo 112-8701Japan Department of Computer Science and Engineering Shanghai Jiaotong UniversityShanghai 200240China

Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central *** requirement outpaces the capacity of traditional communication *** tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise *** solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge *** system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient ***,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain *** results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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AcquainLay: An Efficient Blockchain Block Relay Method Based on Acquaintance Immunization

AcquainLay: An Efficient Blockchain Block Relay Method Based...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Wang, Hui Yang, Zhenyu Li, Ming Hu, Yanlan Hu, Donghui School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Physics Hefei University of Technology Hefei China

ISBN: (纸本)9798350316308

The propagation of information in blockchain plays a key role in its scalability improvement, and it is difficult to maintain the original decentralized characteristics and security in the existing methods of accelerating block propagation. In this paper, we combine blockchain and complex network epidemic immunization strategies and then propose an approach (Acquain-Lay) based on acquaintance immunization to find suitable nodes to disseminate information without the need for global topolog-ical information. The evaluation results show that AcquainLay improves transaction throughput by 43.53 % at best and reduces the orphan block rate significantly. In addition, AcquainLay is easy to deploy because it does not change the key data structures and network topology of the blockchain. © 2023 IEEE.

关键词： Complex networks

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