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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Alex David, S. Begum, Almas Hemalatha, D. Senthil, K. Monikaa, R. Ruth Naveena, N. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology & Science Department of Mathematics Chennai India

ISBN: (纸本)9798350353778

Based on experimental results the predictive accuracy of Random Forest, Decision Tree, and XGBoost was much higher than the Linear Regression algorithm in EV Charging Management using machine learning models. Random Forest handled the charging station dynamics well, and the complex data relationships were managed easily with XGBoost. On the other hand, Linear Regression performed better because results displayed high errors and its model fit numbers were not significant. In contrast, the Decision Tree has glaring issues with overfitting and generalization. The results highlight the importance of advanced data science methods for efficient EV infrastructure management by yielding exact forecasts about demand and energy consumption behaviors. Future endeavors will continue to build upon these models, increasing the scale of analysis and providing state-of-the-art solutions towards developing an approach for both scalable development as well as a push toward more sustainable transportation with improved operational efficiencies and reliability within EV networks. © 2024 IEEE.

关键词： Adversarial machine learning

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From Tweets to Insights: BERT-Enhanced Models for Cyberbullying Detection

From Tweets to Insights: BERT-Enhanced Models for Cyberbully...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Sen, Madhura Masih, Jolly Rajasekaran, Rajkumar School of Computer Science and Engineering Vellore Institute of Technology Vellore India Bml Munjal University Gurgaon India

ISBN: (纸本)9798350372229

The rapid rise of social media usage, particularly during the COVID-19 pandemic, has amplified the prevalence of cyberbullying, necessitating effective detection and prevention measures. This research explores the application of sentiment analysis (SA) and deep learning models, specifically utilizing Bidirectional Encoder Representations from Transformers (BERT) in combination with Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP), to automatically detect cyberbullying in Twitter posts. The study utilizes a Hate Speech and Offensive Language dataset, employing data preprocessing techniques and exploratory data analysis to address imbalances and understand the dataset's intricacies. The proposed architecture demonstrates superior performance, achieving an accuracy range of 87.2% to 92.3%, outperforming existing methods such as Naive Bayes, Support Vector Machine (SVM), Deep Neural Network (DNN), and CNN. The research contributes to the ongoing efforts to develop robust cyberbullying detection systems, emphasizing the need for proactive measures in the face of escalating online harassment incidents, especially in the COVID-19 era. © 2024 IEEE.

关键词： COVID-19 Cyberbullying Deep Learning Natural Language Processing Sentiment Analysis

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Prediction of Heart Disease Using ML Algorithms 1

Prediction of Heart Disease Using ML Algorithms

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Panda, Ashok Kumar Pati, Chinmayee Pradhan, Sonali Pradhan, Amrutanshu Rath, Naba Kumar Silicon University Department Of Computer Science & Engineering Bhubaneswar India School of Computer Sciences Odisha University of Technology and Research Bhubaneswar India Bhubaneswar India

ISBN: (纸本)9798331518578

There are a variety of approaches utilised in the processing of healthcare data. Data mining is utilised quite frequently. The majority of deaths are caused by cardiovascular disorders. It is a risk factor for heart disease. The proportions of risk factors for heart disease can be computed using this technique. Data sources can be categorised based on healthcare factors. Using a data mining classification approach, this system does analysis on such parameters. The datasets are analysed using the Decision Tree Algorithm and the Naive Bayes Algorithm, both of which are written in Python. The Decision Tree Algorithm has the highest precision for heart disease. © 2025 IEEE.

关键词： classification techniques Data mining Heart disease Machine Learning methods

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Motorcycle driving violation detection based on improved YOLOv8 and DeepSORT

Motorcycle driving violation detection based on improved YOL...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Zhang, Long Jia, Wei The School of Computer Science and Information Engineering Hefei University of Technology 193 Tunxi Road Hefei230011 China

ISBN: (纸本)9781510681514

Aiming at the problem that it is not easy to detect motorcycle driving violations on the road, a motorcycle violation detection method with improved YOLOv8 and DeepSORT is proposed. The method uses the YOLOv8 algorithm as a target detection model, introduces a global attention mechanism (GAM Attention) in the backbone network to refine the learning of key semantic information and improve the feature extraction capability of the network;adopts the WIOU (Wise-IoU) loss function to accelerate the convergence speed and improve the regression accuracy;and uses DeepSORT as a target tracking model. ShuffleNetv2, a lightweight network, is introduced as the appearance feature extraction network to reduce the parameters of the model and maintain good accuracy;re-training on the motorcycle re-identification dataset improves the identity switching problem of the motorcycle target in the occlusion scenario. Finally, the tracked motorcycle targets are detected for driving violations. The experimental results show that compared with YOLOv8, the motorcycle and helmet detection accuracies are improved, reaching 98.2% and 98.6%, respectively;the size of the DeepSORT tracking model is reduced by 82%, which enhances the model's portability;and the algorithm has strong robustness in different traffic scenarios. Finally, the motorcycle target's driver and passenger did not wear a safety helmet, adding a parasol, and overloading three driving violations were detected, and the recognition rate reached 98.8%, 100%, and 98.5%, respectively, indicating that the method has some practical value. © 2024 SPIE.

关键词： Motorcycles

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Transaction Data Management Optimization Based on Multi-Partitioning in Blockchain Systems 21

Transaction Data Management Optimization Based on Multi-Part...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Jingyu Hou, Xingxing Teng, Yilong Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. First, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods. © 2023 IEEE.

关键词： Information management

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A survey on event connections and their discovery for cloud service

A survey on event connections and their discovery for cloud ...

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2023 International Conference on Electronic Information engineering and Data Processing, EIEDP 2023

作者： Zhang, Zhen Xia, Chunhe Li, Shuyu Liu, Zhiying Huang, Mingzhi Huang, Chengling Li, Xiaojian School of Computer Science & Engineering Guangxi Normal University Guilin China Beijing Key Laboratory of Network Technology School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781510666160

Connection is the effect between events. It is an important basis for judging of violations and source-trace of failure in cloud service accountability. This paper presents a survey on the event connections including the features, method of discovery and correction. Most of recent research on event connections in cloud services relying on manually labeling and correcting to event connections, and a few of them have achieved automatically labeling of event connections. Finally, we give out a taxonomy of the recent research, some problems in the method of discovery and correction, and an approach in the paper. © 2023 SPIE.

关键词： Distributed database systems

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Profit Maximization of Delay-Sensitive, Differential Accuracy Inference Services in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6209-6224页

作者： Zhang, Yuncan Liang, Weifa Xu, Zichuan Jia, Xiaohua Yang, Yuanyuan City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Dalian University of Technology School of Software Dalian China Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY United States

The integration of Artificial Itelligence (AI) and edge computing has sparked significant interest in edge inference services. In this paper, we consider delay-sensitive, differential accuracy inference services in a Mobile Edge Computing (MEC) network while meeting user stringent delay and accuracy requirements. We formulate two novel profit maximization problems under static and dynamic settings of service request arrivals, with the aim of maximizing the accumulative profit of admitted requests. We assign differential accuracy service requests to the corresponding resolution instances of their requested service models, assuming that each resolution instance can serve up to L≥ 1 the same type of service requests. Since the profit maximization problem is NP-hard, we first formulate an Integer Linear Program (ILP) solution if the problem size is small or medium;otherwise, we devise a constant randomized algorithm with high probability. Then, we consider dynamic service request admissions without the knowledge of future request arrivals for a given finite time horizon, for which we develop a simple yet effective prediction mechanism to accurately predict the number of different resolution instances of each model needed, and pre-deploy the predicted number of resolution instances into cloudlets to reduce instantiating delays. We then devise an online algorithm with a provable competitive ratio for the dynamic profit maximization problem by leveraging the primal-dual dynamic updating technique. Finally, we evaluate the performance of the proposed algorithms by simulations. The simulation results demonstrate that the proposed algorithms are promising. © 2002-2012 IEEE.

关键词： Mobile edge computing

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A Conditional GAN and Dual-Channel Hybrid Deep Feature Framework for Robust Sensor Fault Detection in WSNs

A Conditional GAN and Dual-Channel Hybrid Deep Feature Frame...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Khan, Rehan Saeed, Umer Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9798331510756

Sensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection. © 2025 IEEE.

关键词： Gallium nitride

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Webcam-Based OCR System with Text-to-Speech Conversion 5

Webcam-Based OCR System with Text-to-Speech Conversion

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Poornima, J. Sambasivudu, Madduru Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Applications Mangalore India Brindavan College of Engineering Department of Information Science and Engineering Bengaluru India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331530013

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields. © 2024 IEEE.

关键词： Optical character recognition

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An Extensive Examination of Methods for Detecting and Classifying Malignant and Benign Skin Cancers Using Deep Learning Techniques 3

An Extensive Examination of Methods for Detecting and Classi...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pasumarthy, Rambabu Gondi, Lakshmeeswari Gitam School Of Technology Gitam Deemed To Be University Department Of Computer Science And Engineering Andhra Pradesh Vishakhapatnam India

ISBN: (纸本)9798350318609

Skin cancer is a pressing health concern responsible for numerous fatalities in today's world, often diagnosed in its advanced stages, making treatment planning and patient survival challenging. Medical image interpretation, such as MRI as well as CT scans, is crucial for skin cancer diagnosis, but the manual analysis is costly, time-consuming, and can be influenced by biases. DL (Deep learning), a cutting-edge technology, is gaining attention as it allows for the automatic evaluation and interpretation of medical images, reducing the need for human intervention. Deep learning approaches have demonstrated remarkable outcomes in various domains by automatically extracting intrinsic image features, excluding the need for manual feature extraction. This review delves into the application of DL approaches in the diagnosis & detection of cancer, with a particular focus on skin cancer. Detecting melanoma from samples of dermoscopic skin is a formidable task, but employing a machine vision tool, like a deep learning approach, can address some of these challenges. The study introduces an automated melanoma classifier on the basis of DCNN (Deep Convolutional Neural Network) to accurately differentiate malignant from benign melanoma. It also outlines the various deep learning models and steps involved in utilizing these models for skin cancer detection. Recent advancements in the skin cancer detection Deep Learning (DL) methods are explored and summarized, shedding light on the critical challenges that have been faced in accurately detecting the cancer of skin in its early stages. For this research, dermoscopic images from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC 2017, and ISIC 2020), containing diverse cancer samples, were obtained and evaluated based on metrics like precision, accuracy, specificity, recall, and F1-score. © 2024 IEEE.

关键词： Convolutional neural networks

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