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Application of Deep learning-Based Malware Detection in Video Conferencing Systems 24

Application of Deep Learning-Based Malware Detection in Vide...

引用

3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Wang, Weitao Lei, Lei Shao, Lisong Peng, Wang Chang, Jiale Beijing KeDong Electric Power Control System Co. Ltd Beijing China China Electric Power Research Institute Beijing China Nari Group Corporation Beijing China State Grid Electric Power Research Institute Beijing China

ISBN: (纸本)9798400718120

This paper explores the application of deep learning-based malware detection models in video conferencing systems. By constructing a large-scale dataset of malware and training deep neural network models such as CNN, RNN, and LSTM, high-precision detection of malware is achieved, outperforming traditional machine learning models. Integrating this deep learning model into the video conferencing system, a comprehensive malware defense system is built, effectively blocking a large number of attacks through real-time monitoring and intelligent analysis, significantly improving the security of video conferencing. The research results demonstrate that deep learning technology has broad prospects for application in this field, providing strong support for building a more secure video conferencing environment. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Analysis of optimization Techniques for Dynamic Neural Networks 3

Analysis of optimization Techniques for Dynamic Neural Netwo...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mallegowda, M. Nair, Saanvi Khirwal, Sahil Animesh, Manik Mishra, Vasuman Kanavalli, Anita Ms Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Dept. of Cse Bengaluru India

ISBN: (纸本)9798331515911

Models based on dynamic neural networks such as DeBERTa and Vision Transformers ViT have disrupted the two fields. However, there is one complication - their performance requires so much computation which limits its realistic applications. The aim of this study is to propose advanced optimization techniques that can increase the efficiency of these models without compromising their performance. We adopt approaches such as DistilBERT, DeBERTa and Vision Transformer models. Then we carry out detailed empirical analysis of the consequences of these techniques on the model accuracy, time performance, throughput as well as resources consumption. The findings show enhancement models, where some methods improved the throughput by 29% and reduced the GPU usage by 61% at the same or higher accuracy. This reveals the capacity of dynamic neural networks in resource-constrained settings and enhances the overall vision of the use of such models in practice. © 2025 IEEE.

关键词： Neural network models

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Predicting Sector Indices Using Machine learning: A Comprehensive Analysis Incorporating Macroeconomic Indicators 3

Predicting Sector Indices Using Machine Learning: A Comprehe...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Murugan, Vasundra Jangale, Sumit Gangawane, Prasenjeet Gupta, Nidhi Sonawane, Prasanna Jadhav, Anil Symbiosis Centre for Information Technology Pune India

ISBN: (纸本)9798331515911

The influence of macroeconomic indicators on stock market movement continues to attract attention, particularly in emerging markets, especially countries such as India. The objective of this study is to predict NIFTY 50 sectoral indices namely Automobile, Energy, Banking, IT, FMCG, Metal, and Realty using macroeconomic indicators such as exports, imports, exchange rates, GDP, interest discount rates, and inflation rates. Traditional regression models like Lasso, Linear Regression, Elastic Net, and advanced machine learning models like XGBoost, Decision Tree, and Random Forest Regressor, Support Vector Regressor have been used for the prediction. The experiments show that the Random Forest Regressor came out with the best performance, providing a lower Mean Squared Error (MSE) and the highest R-squared values in almost all the sectors, except the Realty sector. It proves that advanced machine learning techniques go beyond ordinary regression methods in capturing nonlinear relationships. This study highlights the importance of macroeconomic factors as drivers of sector-specific stock performances in emerging markets and the potential of using machine learning models to understand them. © 2025 IEEE.

关键词： Inflation

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Deep learning Model with optimization Strategies for DDoS Attack Detection in Cloud Computing 3

Deep Learning Model with Optimization Strategies for DDoS At...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Radhika, P. Kamalakkannan, S. Department of Computer Science Vel’s Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

Cloud Computing (CC) remains as a chief research area for analysts because of its numerous applications and advantages. The dispersed nature of CC and its dependence on internet service poses several attacks and challenges. Mainly, the attacks like DDoS attempts to disrupt the online operations. Insider attacks cannot be detected using conventional detection techniques like firewalls. This study suggests a DDoS detection method for reducing the DDoS activity. The proposed work contributes under big data perspective by handling certain procedure for detection process. Data Generation phase is the initial step, and according to the work, DDoS dataset is considered under big data perspective. As the work is considered under the big data perspective, Map reduce (MR) framework is used, Mapper handles the data and process the feature extraction, which includes the extraction of raw features, Packet feature extractor, Improved Correlation and statistical Feature. Reducer provides the combined feature set. From the extracted feature set, appropriate features are selected via Weightage based Improved hybrid model combining the models like Long short term memory(LSTM) and Deep Maxout(DMO) networks is used. The weights of LSTM and DMO are optimally chosen using White Shark-Remora optimization (WSROA) algorithm. Recursive Feature Elimination (RFE) process is used to precisely select the features for DDoS attack detection. The findings shows that the proposed hybrid bio-inspired algorithm outperforms with an accuracy of 94% compared to LSTM, Convolutional Neural Networks(CNN) and Deep Belief Networks(DBN). © 2025 IEEE.

关键词： Convolutional neural networks

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An intelligent Machine learning Approach for Prediction of Road Accident Severity 3

An Intelligent Machine Learning Approach for Prediction of R...

引用

3rd IEEE International conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Rahul, Namana Sri Balaji, Pandi Jyothi, Sankati Danaveni, Pavithra School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India VIT-AP University Software Development Cell Andhra Pradesh Amaravati522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety. © 2025 IEEE.

关键词： Highway accidents

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Oppositional artificial rabbit optimization for the optimal tunning of single input power system stabilizer 3

Oppositional artificial rabbit optimization for the optimal ...

引用

3rd International conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chakraborty, Arnab Mukherjee, Devchayan Paul, Sourav Sultana, Sneha Dutta, Susanta Roy, Provas Kumar Dr. B. C. Roy Engineering College Electrical Engineering Durgapur India Kalyani Government Engineering College Electrical Engineering Kalyani India

ISBN: (纸本)9798331523893

The methodology of Oppositional Artificial Rabbit optimization (OARO) has been effectively applied in this study to a single input power system stabilizer for the most effective tuning to lessen volatility at low frequencies. A single machine infinite bus (SMIB) system has been used to test the suggested algorithm's efficacy using the Heffron-Phillips framework. To assert the relevance of OARO in an adaptable situation, the implementation of the suggested method is tested for a wide loading environment. The novelity and superiority of the present work has been validated by comparing the result with other work. © 2025 IEEE.

关键词： Artificial rabbit optimization Low Frequency Oscillation Oppositional based learning Power System Stabilizer Single Machine Infinite Bus System

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HL-Net: Zero-shot image denoising algorithm based on hybrid learning 24

HL-Net: Zero-shot image denoising algorithm based on hybrid ...

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3rd International conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Wang, Yi Luo, Xiaohui School of Computer and Software Engineering Xihua University Sichuan ChengDu China

ISBN: (纸本)9798400707308

Deep neural networks trained on large datasets have achieved good results in image denoising. However, networks trained on specific datasets often have poor generalization, which is not conducive to practical applications. In addition, most denoisers often ignore the detail differences between image texture areas and smooth areas during the denoising process, resulting in the loss of details in texture areas. In this paper, we propose a zero-shot denoising network (HL-Net) with a Texture Noise Attention module (TNA) and a hybrid learning architecture. TNA allows the network to better extract noise information from texture areas without destroying their details, while the hybrid learning architecture uses two denoising methods for image smooth areas and texture areas: content learning LC (learning non-noise information in noisy images) and noise learning LN (learning noise information in noisy images), which can better preserve the image details in complex texture areas while extracting content information from smooth areas of noisy images. Additionally, the loss function proposed for the zero-sample hybrid learning network uses residual loss, consistency loss, and mean loss, enabling the network to better perform content learning and noise learning. Experiments show that our method outperforms most zero-sample denoising methods on various types of synthetic noise datasets and real-world noise datasets. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Research on a Bridge Health Monitoring System Based on Machine learning 24

Research on a Bridge Health Monitoring System Based on Machi...

引用

3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Zhang, DaYong Changjiang Survey Planning Design and Research Limited Company Chengdu China

ISBN: (纸本)9798400718120

This study designs and implements a bridge health monitoring system based on machine learning. The system adopts a four-layer architecture, including data acquisition, processing, analysis and decision-making, and user interaction layers. Core functions encompass data preprocessing, feature extraction, model training, health assessment, and early warning. It integrates algorithms such as Support Vector Machines, Random Forests, and Deep Neural Networks to enhance the accuracy of damage identification and status prediction. The development utilizes modern tools to improve efficiency. Test results show that the system is stable, responsive, and has strong processing capabilities. This system provides an intelligent solution for bridge health monitoring, improving the automation level and management efficiency of monitoring, and offers strong support for bridge safety management. © 2024 Copyright held by the owner/author(s).

关键词： Damage detection

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intelligent Traffic Management System using Multi-Agent Reinforcement learning 3

Intelligent Traffic Management System using Multi-Agent Rein...

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shaheen, Dalaali Paulraj, Getzi Jeba Leelipushpam Jebadurai, Immanuel Johnraja Kirubakaran, Stewart Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Cse Coimbatore India

ISBN: (纸本)9798331509675

This paper presents a decentralized multi-agent system for intelligent traffic management in urban environments, where each agent represents a traffic light controller at an intersection. The proposed system leverages a combination of Distributed W-learning and Deep Q-Networks (DQN) to optimize traffic flow. Distributed W-learning enables agents to prioritize decisions based on multiple performance policies, while DQN enhances their ability to handle complex state-action mappings through neural network approximations. By utilizing locally available traffic data, each agent adapts dynamically to diverse traffic conditions. The integration of these machine learning techniques ensures a fully decentralized and self-organizing approach, minimizing congestion, improving traffic efficiency, and addressing multiple objectives simultaneously. Simulation experiments are conducted using SUMO (Simulation of Urban MObility) to evaluate the system's performance in realistic traffic scenarios. This study highlights the potential of combining Distributed W-learning and DQN in multi-agent reinforcement learning to revolutionize traffic management systems and achieve scalable, adaptive, and efficient urban traffic control. © 2025 IEEE.

关键词： Traffic congestion

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Machine learning Integrated Health Advisory Chatbot System 3

Machine Learning Integrated Health Advisory Chatbot System

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3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jikar, Aman A. Mendhe, Samir Wardha India

ISBN: (纸本)9798331509675

The project aims to develop an intelligent health advisory system capable of predicting diseases based on user-provided symptoms. Utilizing a dataset-driven approach and advanced machine learning algorithms, the system integrates tools such as Decision Trees and Support Vector Machines (SVM) to deliver precise diagnoses. It employs labelled training datasets for symptom-prognosis mapping, enabling real-time identification of potential health conditions. The application further enhances user interaction with a conversational chatbot interface, guiding users through symptom entry, severity assessment, and personalized precautionary advice. Additionally, the model incorporates severity and description dictionaries to refine its predictive capabilities. This innovation is designed to empower individuals by providing early disease detection and advisory support, bridging gaps in access to immediate healthcare. © 2025 IEEE.

关键词： Random forests

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