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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Zheng, Guangyuan Wen, Miaowen Tao, Yiwei Li, Qiang Ding, Zhiguo South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Jinan University College of Information Science and Technology Guangzhou510632 China

ISBN: (纸本)9798350363760

In this paper, we focus on semantic-aware computation offloading for executing the deep neural network (DNN) inference tasks in a mobile edge computing (MEC) system. To cope with the challenges of insufficient wireless resources during task offloading, we propose a compress-then-offload mode, in which each user first compresses its raw large-size task data into a small-size semantic feature and then uploads it to the edge server for remote execution. Specifically, we establish the relationship between the compression ratio and computation ratio for different DNN tasks. To achieve energy-efficient offloading, we formulate an optimization problem to minimize the energy consumption of all users by jointly optimizing the compression ratio, computation allocation, uploading time, and DNN layer selection. We propose a low-complexity iterative algorithm to solve the original non-convex problem. Simulation results demonstrate that our proposed semantic-aware computation offloading scheme can substantially reduce users' energy consumption compared to the conventional offloading, especially with scarce wireless resources. © 2024 IEEE.

关键词： Computation offloading

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Machine Learning with Explainability for Suicide Ideation Detection from Social Media Data 10

Machine Learning with Explainability for Suicide Ideation De...

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10th IEEE International Conference on Behavioural and Social Computing, BESC 2023

作者： Islam, Md Rafiqul Kowsar Hossain Sakib, Md. Prome, Shanjita Akter Wang, Xianzhi Ulhaq, Anwaar Sanin, Cesar Asirvatham, David Australian Institute of Higher Education Business Information Systems Sydney Australia Taylors University School of Computer Science Malaysia University of Technology School of Computer Science Sydney Australia Central Queensland University College of ICT Australia

ISBN: (纸本)9798350395884

Suicide is one of the major causes of death globally. Analysis of social media posts and in-depth insights show that some people have suicide ideas. In order to save more lives, it is crucial to comprehend the behavior of suicidal attempters. However, identifying and explaining suicidal thoughts poses a significant challenge in psychiatry. Additionally, analysing suicidal behavior is a complex procedure involving several variables based on the individual's preferences and the data type. Although traditional methods have been utilized to identify clinical factors for suicide ideation detection (SID), these models often lack interpretability and understanding. Therefore, the primary aim of this research is to apply several deep learning (DL) and machine learning (ML) techniques such as BERT, LSTM, BiLSTM, RF, SVM, GaussianNB, LR, and KNeighbors blending with interpretable models such as LIME and SHAP to provide valuable insights into the importance of different features and make models more transparent in the SID process. The experiments were conducted on a publicly available dataset comprising 24,101 posts, categorized as either suicidal or non-suicidal. The implemented method brings about significant enhancements in performance in comparison. A comparison of all performance measures reveals that the LSTM model is particularly good at processing and classifying textual data, with higher accuracy, precision, recall, and AUC scores than the other models tested. © 2023 IEEE.

关键词： Blending

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Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 884-895页

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Edinger, Janick Smith, Roger O. Gani, Md Osman The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States Miami University Department of Computer Science & Software Engineering OxfordOH45056 United States Bits Pilani K.K. Birla Goa Campus Department of Computer Science & Information Systems Appcair Zuarinagar403726 India University of Hamburg Department of Informatics & Distributed Operating Systems Hamburg20146 Germany University of Wisconsin-Milwaukee Department of Rehabilitation Sciences & Technology MilwaukeeWI53706 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE.

关键词： Accelerometers

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VLDFNet: Views-Graph and Latent Feature Disentangled Fusion Network for Multimodal Industrial Anomaly Detection

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Xia, Chenxing Liu, Chaofan Zhou, Yicong Li, Kuan-Ching Anhui University of Science and Technology School of Computer Science and Engineering Huainan232001 China University of Macau Department of Computer and Information Science China Providence University Department of Computer Science and Information Engineering Taichung43301 Taiwan

Accurate detection and precise localization of anomalies during precision component manufacturing are essential to maintaining high product quality. Multimodal industrial anomaly detection (MIAD) harnesses data from diverse sensors to effectively identify and pinpoint defects in industrial products. Recent MIAD approaches have made significant progress but often ignore point cloud data global contextual semantics and modality-specific information, resulting in an incomplete representation of point cloud and inadequate multimodal fusion. To confront these issues head-on, we propose a robust feature representation and comprehensive multimodal feature fusion network [views-graph and latent feature disentangled fusion network (VLDFNet)] for anomaly detection in industrial high-precision components. VLDFNet mainly consists of a point cloud views-graph representation model and a multimodal disentangled feature latent space fusion module. Specifically, the point cloud views-graph representation model explores spatial locations and semantic relationships between views using multilevel graph fusion. The multimodal disentangled feature latent space fusion module disentangles multimodal features into shared and specific representations to mitigate the omission of modality-specific information. VLDFNet introduces a cross-modal shared feature interaction (CSFI) strategy to extract coherent semantic information by aligning and integrating cross-modal features. Comprehensive experimental results on multiple datasets demonstrate that our method significantly outperforms existing approaches in detection accuracy. © 1963-2012 IEEE.

关键词： Unsupervised learning

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Recommendation of Music Based on Facial Recognition and Emotion Analysis 3rd

Recommendation of Music Based on Facial Recognition and Emot...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Chintha, Sai Ganesh Bharathi, V.C. Mayaluri, Anusha Bylapudi, Lahari Iqbal, M. Mohammed School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai603110 India

ISBN: (纸本)9789819743582

Emotions pose a challenge when it comes to precise definition and interpretation. As a remedy, music emerges as a suggested art form for its potential in aiding mood regulation. Our proposed approach exhibits the potential to enhance system accuracy. This is achieved through a reduction in computation time for result generation, leading to improved overall efficiency and lowered operational costs of the devised system. To evaluate the effectiveness of our approach, we employed the FER2013 dataset, utilizing an integrated camera to capture facial expressions. The process involves extracting features from facial photographs to discern various emotions, encompassing happiness, anger, sadness, surprise, and neutrality. Leveraging a Convolutional Neural Network (CNN), we implemented a mechanism for detecting facial expressions, attaining an impressive detection accuracy rate of approximately 95.14 percent. The implementation employs a range of Python packages including WebRTC, Keras, and MediaPipe to facilitate the capture and analysis of users’ facial emotional cues. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Analyzing the Impact of Congestion Control on ATM Network Performance 15

Analyzing the Impact of Congestion Control on ATM Network Pe...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Narmadha, T. Singh, Akhilendra Pratap Awasthi, Aishwary School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

This technical summary covers a study that examined the effect of congestion manipulation on the ATM community's overall performance. They take a look at using a simulated environment to measure the effect of a mixture of strategies for handling congestion on ATM networks. The look intended to assess the potential of ATM networks to successfully manage site visitors in a good way to reap the most appropriate network overall performance. The researchers used an incorporated service virtual network (ISDN) to emulate an asynchronous switch mode (ATM) community topology. Then, they implemented the techniques of Random Early Detection (pink), Weighted honest Queuing (WFQ), and Adaptive modern-day monitoring (ACT) to manage congestion. Metrics along with packet losing charge, throughput put off, and jitter were then measured to assess the performance of the exclusive techniques. Consequences from the simulation indicate that the aggregate of congestion control techniques can be used to enhance community overall performance in terms of lowering packet-dropping fees, enhancing throughput, and decreasing put-off and jitter. In addition, it turned into mentioned that the aggregate of techniques has a greater effect on community performance than any unmarried person method. Basic, they take a look at found that the implementation of congestion control strategies is an effective approach to managing visitors on ATM networks and improving community performance. Moreover, it means that the right combination of techniques gives greater benefits to end customers. © 2024 IEEE.

关键词： ATM Networks Consequences Implementation Performance Techniques

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Decision-based framework to facilitate EDGE computing in smart health care

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Multimedia Tools and Applications 2024年第38期83卷 85849-85869页

作者： Singh, Simranjit Sajwan, Mohit Kukreja, Sonal Department of Information Technology Dr B R Ambedkar National Institute of Technology Jalandhar Punjab India Department of Information Technology Netaji Subhas University of Technology New Delhi India School of Computer Science Engineering and Technology Bennett University U.P. Greater Noida India

In the past few years, with the increase in population and health concerns, there has been a need for efficient health monitoring solutions that can help patients monitor their health consistently to be aware of any health risks at the initial stage. The advancement in sensing and smart technologies helps monitor human behaviors to predict health risks. In this work, a dynamic decision-based activity prediction system is proposed using Random Forest, SVM, Decision Trees, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) on an edge device. We train the models using features from the MHealth dataset, such as acceleration, rate of turn, and magnetic field, to predict activities such as standing, climbing, running, and jogging, collected from various sensors. Our framework dynamically selects between machine learning (ML) and deep learning (DL) algorithms based on real-time data size and edge device capabilities, ensuring optimal performance and resource utilization. The results for the proposed models are compared and analyzed. The experimental results indicate that among all machine learning methods, Random Forest achieves the highest overall accuracy at 98%, while in deep learning algorithms, both LSTM and GRU reach a maximum accuracy of 98%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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Group Based Detection of Cryptocurrency Laundering Using Multi-Persona Analysis

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IEEE Transactions on information Forensics and Security 2025年 20卷 5992-6004页

作者： Li, Guang Mi, Yangtian Zhou, Jieying Zheng, Xianghan Wu, Weigang Sun Yat-sen University School of Computer Science and Engineering Guangzhou 510006 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350108 China

Money laundering using cryptocurrency poses significant threats to the blockchain ecosystem. Due to the decentralized and anonymous nature of cryptocurrencies, detecting such laundering activities is difficult. Although substantial research has been conducted, almost all existing methods detect cryptocurrency laundering from an individual perspective, ignoring the fact that money laundering is typically a group behavior. Group information should be very helpful in laundering behavior analysis, but such laundering groups are hard to be recognized due to anonymity and diversity of purposes of cryptocurrency transactions. To address this challenge, we design a multi-persona grouping algorithm that can effectively group accounts into persona subgraphs. Then, we extract two subgraph features: cycle basis number and cycle overlapping ratio, and build an unsupervised model to evaluate laundering scores of each subgraph. Extensive experiments on both synthetic and real-world datasets demonstrate that, compared with existing methods, our proposed method can improve detection accuracy by 17.4 percentage points on average. To the best of our knowledge, this is the first work on group-based detection of cryptocurrency laundering. © 2005-2012 IEEE.

关键词： blockchain cryptocurrency graph mining machine learning Money laundering detection

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Mixture of Gradient: A Unified Enhancing Approach for Deep Learning-Based Wireless Network Optimization

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IEEE Internet of Things Journal 2025年第13期12卷 25487-25499页

作者： Cheng, Nan Ma, Longfei Dai, Yanpeng Wang, Xiucheng Li, Qihao Quan, Wei Liang, Hui Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Xidian University State Key Laboratory of ISN and School of Telecommunications Engineering Xi’an710071 China Dalian Maritime University School of Information Science and Technology Dalian116026 China Jilin University College of Communication Engineering Jilin Changchun130021 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Peng Cheng Laboratory Shenzhen518000 China University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada

Deep learning plays increasingly important role in future wireless network management and optimization. Existing training methods such as label-based supervised learning and label-free learning have inherent limitations. The performance of supervised learning is limited by labels, while label-free training methods require extensive exploration. To address these limitations, this paper proposes a novel mixture of gradients (MoG) method, which integrates gradients from different sources within the training process in order to improve the convergence performance of neural networks (NNs). Particularly, MoG is a modular, plug-and-play solution requiring no structural modifications to existing NNs. Its implementation necessitates only minor modifications to the loss function, where the label-based supervised loss is combined with a label-free loss through weighted summation. The label-free loss can be either unsupervised loss or reinforcement learning loss. This flexibility allows seamless integration into nearly all NN-based methods, making it applicable to a wide range of wireless optimization problems with minimal implementation cost. Extensive simulations across multiple classic wireless scenarios demonstrate that MoG can significantly enhance the performance of NN decision-making, leading to higher transmission rates. © 2014 IEEE.

关键词： Self-supervised learning

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Enhanced Brain Tumor Detection Using Transfer Learning with Fine-Tuned Efficientnet-B3 Blocks and Graph-Based Spatial Attention Capsule Networks 21

Enhanced Brain Tumor Detection Using Transfer Learning with ...

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21st International computer Conference on Wavelet Active Media Technology and information Processing, ICCWAMTIP 2024

作者： Aggrey, Esther Stacy E.B Zhen, Qin Kodjiku, Seth Larweh Fiasem, Delali Linda Sey, Collins Ejiyi, Chukwuebuka Joseph Ntia, Nsikakabasi Williamson School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (纸本)9798331519254

Brain tumors are critical medical conditions that require timely and accurate diagnosis to improve survival outcomes. This research proposes a cutting-edge framework for brain tumor classification, combining fine-tuned EfficientNet-B3 blocks with graph-based spatial attention capsule networks. By addressing challenges such as limited data availability, overfitting, and the complex nature of tumor structures, the framework employs partial transfer learning, advanced feature extraction, and attention mechanisms to effectively model spatial relationships in MRI images. Evaluated on a dataset of 3,064 contrast-enhanced brain MRI images, the model demonstrated exceptional performance, achieving 99.85% classification accuracy and notable improvements in precision, recall, and F1 scores for glioma, meningioma, and pituitary tumours. The use of advanced data augmentation techniques significantly improved the model's ability to generalize, while its computational efficiency makes it well-suited for clinical diagnostic applications. © 2024 IEEE.

关键词： Dynamic contrast enhanced MRI

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