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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Rithesh Manikandan, S. Prasuna, Gnaneethi Lakshmi Sivanesh, G. Divya Lakshmi, K. School of Computer Scienceand Engineering Vellore of Institute of Technology Vellore India

ISBN: (纸本)9798350385649

Indian Sign Language (ISL) is a crucial language used by the deaf and mute community. The paper presents a systematic approach that utilizes Indian Sign Language video as input and converts it into text. By leveraging support vector machines, convolutional neural networks, recurrent neural networks, Media Pipe, and deep learning methods, the proposed system effectively addresses the challenges faced by the deaf and mute community in accessing educational resources and engaging in daily activities. The approach takes a step-by-step, methodical approach to improve the efficiency and accuracy of hand gesture recognition, enhancing accessibility and inclusion for the deaf and mute community. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Deepfake Video Detection Performance with a Hybrid CNN Deep Learning Model

Enhancing Deepfake Video Detection Performance with a Hybrid...

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

作者： Sarkar, Tiyas Singh, Abha Kakkar, Rajan Lovely Professional University School of Computer Scienceand Engineering Punjab Phagwara India

ISBN: (纸本)9798350378092

This These days, a lot of phoney photos and movies are produced using modern AI (Artificial Intelligence) technology and software, which occasionally leaves traces of manipulation. Videos may be used in numerous unethical ways to intimidate, quarrel, or incite fear in others. Making ensuring that these techniques aren't applied to produce phoney videos is crucial. Deep Fake is an AI-based method for creating synthetic photographs of people. They are made by merging and overlaying preexisting videos with the original videos. This research develops a system that extracts frame-level characteristics using Convolutional Neural Network (CNN) hybrid made out of Xception and InceptionResnet v2. We do an experimental investigation with Kaggle's DFDC deep fake detecting challenge. By utilizing this dataset's for testing and training, these deep learning-based techniques are tuned to improve accuracy while cutting down on training time. Our results showed a 0.985 accuracy, 0.96 recall, 0.98 f1-score, and 0.968 support. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT based Smart wheelchair For Disabled Person

IoT based Smart wheelchair For Disabled Person

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

作者： Kumar, Lalit Kushwaha, Ajay Shriram Sherawat, Kartikeya Siddique, Zishan IILM University School of Computer Scienceand Engineering Department of Computer Application Uttar Pradesh Greater Noida India Sharda University Sharda School of Engineringand Technology Department of Computer Scienceand Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350378092

Smart wheelchairs are revolutionizing mobility for disabled individuals, offering enhanced independence and improved quality of life. These advanced devices incorporate cutting-edge technologies like obstacle avoidance systems, voice control, and self-navigation capabilities, transforming the traditional wheelchair into an intelligent mobility solution. Obstacle avoidance systems utilize sensors to detect and avoid obstacles, ensuring safe navigation in complex environments. Voice control enables users to operate the wheelchair hands-free, providing greater convenience and control. Self-navigation capabilities allow the wheelchair to autonomously navigate to pre-programmed destinations, reducing user burden and enhancing independence. Smart wheelchairs also offer connectivity features, enabling remote monitoring and control by caregivers. This provides an added layer of safety and support for users, ensuring timely assistance when needed. The integration of these advanced technologies into smart wheelchairs is transforming mobility for disabled individuals, empowering them with greater independence, safety, and control over their movement. As smart wheelchair technology continues to evolve, the possibilities for enhanced mobility and improved quality of life for disabled individuals are limitless. © 2024 IEEE.

关键词： Disabled persons

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Automated AI System for Online Phishing Detection and Mitigation

Automated AI System for Online Phishing Detection and Mitiga...

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

作者： Raj, Daksh Kumar, Rajiv Joshi, Shivani Amrita Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India Galgotias University Department of Computer Scienceand Engineering U.P. Greater Noida India Sharda University Center for Cyber Security and Cryptology Computer Scienceand Engineering Sharda School of Engineeringand Technology Greater Noida India

ISBN: (纸本)9798350378092

This paper presents the critical challenge of phishing attacks by using advanced machine learning (ML) approaches to efficiently identify and mitigate malicious websites. Phishing websites betray users by impersonating legitimate entities to extract sensitive information. This paper emphases on developing and implementing a robust ML model capable of differentiating between legitimate and phishing websites through a comprehensive analysis of URL features, website content, and associated metadata. The proposed approach employs a diverse dataset that includes both phishing and legitimate websites, with features extracted using both traditional approaches and innovative methods. Various ML approaches are explored, such as Decision Trees, Support Vector Machines, Random Forest (RF), and Neural Networks. These approaches are evaluated for accuracy, recall, precision, and F1-score to determine the most effective model. Feature selection approaches are employed to discover the most significant indicators of phishing activity, improving the model's performance and interpretability. The RF Classifier demonstrated superior performance with an accuracy of 96.6%. © 2024 IEEE.

关键词： Phishing

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Advances in Medical Image Processing: Enhancing Diagnostics and Treatment Planning

Advances in Medical Image Processing: Enhancing Diagnostics ...

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

作者： Rakhra, Manik Angra, Prikshat Kumar Singh, Pritpal Sharma, Anuj Cheema, Gagandeep Singh Sharma, Bhanu Lovely Professional University School of Computer Scienceand Engineering Phagwara India Lovely Professional University School of Computer Application Phagwara India Lovely Professional University Mittal School of Business Phagwara India

ISBN: (纸本)9798350378092

Recent advancements in medical image processing have significantly enhanced the capabilities of diagnostic imaging and treatment planning. This paper provides a comprehensive overview of the latest methodologies and technologies in image processing that contribute to more precise diagnoses and improved treatment strategies. We explore several key areas including deep learning algorithms for image segmentation, enhancement techniques for improving image clarity and detail, and innovative approaches for 3D visualization. Furthermore, we discuss the integration of artificial intelligence with traditional imaging techniques to accelerate decision-making processes and personalize patient care. The impact of these advancements is evaluated through case studies and clinical feedback, which demonstrate improved outcomes in patient diagnosis and treatment planning. Our findings suggest that the integration of advanced image processing techniques can transform medical imaging into a more dynamic tool for healthcare professionals, ultimately leading to better patient outcomes. © 2024 IEEE.

关键词： Medical image processing

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Gesture Guide: Empowering Deaf and Mute Individuals with ISL Recognition and Guidance System using Hand signs

Gesture Guide: Empowering Deaf and Mute Individuals with ISL...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： S Rithesh Manikandan Gnaneethi Lakshmi Prasuna G Sivanesh K Divya Lakshmi School of Computer Scienceand Engineering Vellore of Institute of Technology Vellore India

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Detection of Malware Utilizing Neural Networks and Machine Learning Methods 15

Detection of Malware Utilizing Neural Networks and Machine L...

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

作者： Krishna, Velpula Nithin Kumar, Senthil T. Kumar, Gireesh T. Tibrewal, Anjali Srinivasan, Kartik Vajipayajula, Sulakshan Department of Computer Scienceand Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India IBM Security. IBM India Pvt Ltd India Architect CTO Office IBM Security Bangalore India

ISBN: (纸本)9798350370249

In today's digitally interconnected landscape, the omnipresence of malware presents a significant threat to electronic devices, including computers, laptops, and mobile phones. Safeguarding against malware is essential to prevent unauthorized harm and exploitation of these devices. This study investigates the efficacy of various methodologies utilizing the NSL-Knowledge Discovery in Databases dataset from the University of New Brunswick to forecast malware attacks. Techniques such as K-Nearest Neighbors (KNN), Decision Trees, XGBoost, and Artificial Neural Network (ANN) are applied and evaluated for their predictive performance. Additionally, an Apriori model is deployed to uncover frequent itemsets within the dataset, exploiting inherent associations and correlations. A comprehensive examination of these approaches is presented, offering insights and conclusions regarding their effectiveness in combatting malware threats. ©2024 IEEE.

关键词： Adversarial machine learning

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Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

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Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1184-1193页

作者： Yunxuan Dong Shaodan Ma Hongcai Zhang Guanghua Yang IEEE State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao S.A.R.999078China Institute of Physical Internet and the School of Intelligent Systems Scienceand Engineering Jinan UniversityZhuhai 519070China

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

关键词： Wind power prediction wind generation time series analysis logistic function based classification

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BEARING INTELLIGENT FAULT DIAGNOSIS METHOD BASED ON MCNN-BILSTM-SVM 13

BEARING INTELLIGENT FAULT DIAGNOSIS METHOD BASED ON MCNN-BIL...

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13th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2023

作者： Chen, Ronghua Gu, Yiliu Li, Jing Qiu, Guangqi Gu, Yingkui School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology Ganzhou China College of Computer Scienceand Electronic Engineering Hunan University Changsha China School of Automotive Jiangxi College of Applied Technology Ganzhou China

ISBN: (纸本)9781839539848

The conventional convolutional neural network (CNN) adopts the softmax function as a classifier, which has problems of overflow and underflow. To address this issue, CNN and bi-directional long short-term memory (BiLSTM) are combined with multi-scales (MCNN-BiLSTM) as the feature extractor to improve feature extraction capability, and then support vector machine (SVM) is employed as the classifier to improve classification performance. Thus, an intelligent fault diagnosis method combined with MCNN-BiLSTM and SVM (MCNN-BiLSTM-SVM) is proposed. The wavelet threshold denoising algorithm is adopted for signal preprocessing. Transfer learning is used in MCNN-BiLSTM-SVM for different conditions. According to the bearing fault diagnosis experiments, the proposed MCNN-BiLSTM-SVM fault diagnosis method has higher diagnostic accuracy, stronger anti-noise performance and better stability under different conditions than other diagnostic methods. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Fault Diagnosis Method of Planetary Gearbox Based on Transfer Learning and EfficientNet 14

Fault Diagnosis Method of Planetary Gearbox Based on Transfe...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Li, Yin Gu, Yiliu Shi, Changwu Li, Ting School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology Ganzhou China College of Computer Scienceand Electronic Engineering Hunan University Changsha China School of Automotive Jiangxi College of Applied Technology Ganzhou China

ISBN: (纸本)9798350301359

In the field of gear fault diagnosis, the traditional convolutional neural networks model has a single precision improvement method and has limitations. This paper proposes a planetary gearbox fault diagnosis model based on Gramian Angular Fields (GAF) image coding technology and TL-EfficientNet network model. The collected one-dimensional vibration signal is preprocessed and converted into GAF diagram. The EfficientNet is pre-trained on the ImageNet dataset, the trained feature weights are migrated to the EfficientNet network, and the samples are trained to complete the fault identification of the planetary gearbox. The experimental results show that the proposed method can effectively improve the fault diagnosis accuracy of planetary gearboxes, reaching 99.49%, and has faster convergence speed and better stability than a single method. At the same time, the TL+EfficientNet-B2 model has strong generalization and can be applied to other fault diagnosis with similar feature distribution data. © 2023 IEEE.

关键词： convolutional neural network fault diagnosis Gramian Angular Fields planetary gear box transfer learning

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