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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

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Deep Learning for Real-Time Malware Detection and Classification

Deep Learning for Real-Time Malware Detection and Classifica...

引用

Visual Analytics and Data Visualization (ICVADV), international conference on

作者： Shantanu Sudhir Gujar Department of Cyber Security Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The increased deep learning styles have been adopted to improve the identification and detection of malware in real time. This improves the detection and detection of malware. The research focuses on the utilization of Convolutional Neural Networks (CNNs) to recognize malware patterns. These techniques are utilized to accomplish the aforementioned goals. The incorporation of these styles results in an improvement in both the delicacy and the speed of operation when it comes to transmitting viruses. Additionally, the research highlights the application of DeepLearning4J (DL4J), which is an important framework for deep learning, to successfully deploy these models, which enables large-scale malware analysis operations. This is demonstrated by the research. During the research, significant advancements were made in the detection of malware in real-time, and strong results were obtained in the fight against developing cyber threats. One way in which these developments are displayed is through the combination of different approaches and technologies that are utilized.

关键词： Deep learning Recurrent neural networks Visual analytics Heuristic algorithms Autoencoders Malware Real-time systems pattern recognition Convolutional neural networks Long short term memory

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Physical Exergames Movements and pattern recognition using Convolutional Neural Network

Physical Exergames Movements and Pattern Recognition using C...

引用

international conference on Advancements in Computational Sciences (ICACS)

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

This paper proposes a novel framework for accurately recognizing physical exergames interaction in video sequences. The developed approach uses image processing and machine learning tools to correctly extract and classify the features. The framework comprises five main steps: Silhouette extraction, preprocessing, feature extraction, feature optimization and classification. Image contrast is improved by power law transformation, silhouette is extracted by the Multiple Object Tracking (MOT) algorithm and graph-based segmentation. We extract ORB and geometric skeleton based keypoints to capture discriminative features, and apply Fast Independent Component Analysis (FICA) to optimize feature representation. Thirdly, the Convolutional Neural Network algorithm is used to classify the optimized features, with a macro average accuracy of 0.85 and a weighted average accuracy of 0.87%. The proposed framework effectively recognizes physical exergames interaction and can be applied in many domains, such as human-human interaction and surveillance.

关键词： Image segmentation Machine learning algorithms Accuracy Surveillance Video sequences Independent component analysis Feature extraction Classification algorithms Convolutional neural networks Optimization

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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

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Visual Analytics and Data Visualization (ICVADV), international conference on

作者： Venkateswaran Radhakrishnan Vijayakumari Rodda S. Sowmiya Abdur Rahman Sarker R. Palanikumar R. Asokan Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Dhofar Sultanate of Oman Department of Computer Science Krishna University Rudravaram Machilipatnam Sri Eshwar College of Engineering COIMBATORE Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of ECE Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

With the rapid advancements in artificial intelligence, deep-learning-based cryptanalysis has emerged as a promising approach to evaluating the security of modern encryption algorithms. Traditional cryptanalysis methods, such as linear and differential attacks, often require extensive mathematical modelling and computational effort. In contrast, deep learning enables automated feature extraction and pattern recognition, potentially identifying cryptographic weaknesses more efficiently. This paper presents an advanced neural network-based cryptanalysis framework targeting modern encryption schemes, including AES, RSA, and lightweight block ciphers. We employ deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformer models to analyse ciphertext-plaintext relationships and recover cryptographic keys. Additionally, we explore the impact of data preprocessing, hyperparameter optimization, and model interpretability on cryptanalysis accuracy. Our experiments demonstrate that deep learning can achieve significant improvements in key recovery probability compared to conventional approaches. However, we also highlight the computational challenges and practical limitations of deep-learning-based cryptanalysis. This study provides insights into the applicability of AI-driven techniques in cryptanalysis and their implications for cryptographic security.

关键词： Deep learning Ciphers Accuracy Computational modeling Visual analytics Neural networks Transformers Encryption pattern recognition Convolutional neural networks

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SmartRecovery: A Deep Learning-Based System for Personalized Post-Cold Recovery Management in Diabetic Patients

SmartRecovery: A Deep Learning-Based System for Personalized...

引用

Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： G. Revathy J. Justina Princy Thilagavathy C Saranya M. Thangavel Department of CSE Srinivasa Ramunjan Centre Sastra Deemed University Kumbakonam Tamil Nadu Department of Computer App ications School of Computing VET Institute of Arts and Science (Co-education) College Thindal. Erode Tamil Nadu Department of Biomedical Engineering Nandha Engineering College Erode Tamil Nadu Department of MCA Erode Sengunthar Engineering College PerunduraiErode Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

People with diabetes frequently have a longer and more difficult time recovering from colds because of their weakened immune systems and altered metabolism. An method that is more individualized is required since traditional recovery regimens do not take into consideration lifestyle variables, pharmaceutical interactions, and individual differences in glucose levels. A deep learning-based solution called SmartRecovery is suggested in this study to help diabetics control their post-cold recovery as best they can. The system creates adaptive recovery plans by utilizing multimodal data, such as patient vitals, glucose monitoring, medication adherence, and symptom progression. Using a combination of recurrent neural networks (RNNs) for sequential health data analysis and convolutional neural networks (CNNs) for pattern recognition, SmartRecovery predicts potential complications and recommends personalized interventions. In order to ensure ongoing evaluation and feedback, the system incorporates real-time monitoring via wearable sensors and mobile health applications. Experimental assessments using actual diabetes patient datasets show how well the suggested strategy works to speed up recovery and reduce dangers to health. A revolutionary approach to post-illness care, SmartRecovery improves patient outcomes and lessens the strain on healthcare systems by utilizing AI-driven insights.

关键词： Deep learning Telemedicine Real-time systems Diabetes pattern recognition Glucose Monitoring Wearable sensors Standards Strain

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Automation Methods of Data Collection and Analysis for Managing Complex IT Infrastructures

Automation Methods of Data Collection and Analysis for Manag...

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international Russian Smart Industry conference (SmartIndustryCon)

作者： Anton Skalkin Anton Romanov Department of Information Systems Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article discusses an approach to the organization of monitoring and management of server equipment, aimed at ensuring the stable operation of the IT infrastructure. Modern methods based on threshold analysis cannot cope with the volume and variety of data, which requires automation of the collection, processing and analysis of metrics. A computing cluster of servers using the Proxmox VE virtualization platform is presented as an object of research. The features of time series storage and data processing using the InfluxDB database, which has a convenient query language called Flux, are described. Implemented software architecture for integrating monitoring and data analysis tools, including Prometheus PVE Exporter, Telegraf and Flux. The advantages of using Python for metric analysis and query development using the Builder pattern are shown, which simplifies working with the system. Methods of data preprocessing and visualization for anomaly analysis and forecasting with the aim of maintaining the stable operation of server equipment are discussed. The proposed approach ensures the efficiency and ease of monitoring, while also enhancing the accuracy of decision-making given the increasing complexity of IT infrastructure.

关键词： Measurement Automation software architecture Source coding soft sensors Time series analysis Virtual machines Servers Monitoring Python

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Wristband for Monitoring the Safety of Elderly People Using IoT and Deep Learning Algorithms: A Review 15th

Wristband for Monitoring the Safety of Elderly People Using ...

引用

15th international conference on soft computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Gidugu, Mansi Lakshmi Yanduri, Krishna Guptha Paliviri, Manoj Kumar Aluvalu, Rajanikanth Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad 500075 India Symbiosis Institute of Technology Hyderabad Campus Symbiosis International (Deemed University) Pune India

ISBN: (纸本)9783031810794

As the world’s population is aging continuously, the need for healthcare support services, particularly those related to fall-related injuries, is poised to surge. Effective fall detection technology can alleviate this strain on healthcare resources and reduce the burden on caregivers and family members. The increasing prevalence of health issues in the aged has highlighted the need for innovative healthcare solutions prioritizing safety, independence, and proactive health management. The risk of falling while walking is a significant concern among the many challenges elders face with certain medical conditions. Falling can lead to severe injuries and complications, making timely detection and response needed to safeguard individuals’ health. The significance of this problem is multifaceted and profound. Rapid detection and response to falling can mitigate these consequences, thereby enhancing the safety and health of individuals. In this article, we proposed wristband-integrated deep-learning techniques for wearable sensors. The use of mobile applications exemplifies the potential of technology to revolutionize healthcare. It underscores how AI-driven solutions can empower individuals to lead independent lives while providing comprehensive support for their health needs. The proposed device further seamlessly combines fall detection with medication reminders. This approach promotes proactive healthcare management. Adherence to medication regimens is critical for individuals with chronic conditions, and technology can simplify and enhance this aspect of self-care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aging Population AI-Driven Solutions Deep Learning Techniques Elderly Care Fall Detection Technology Health Tech Revolution Healthcare Innovation Healthcare Resource Optimization Independent Living Support Medication Adherence Mobile Applications Proactive Healthcare Management Wearable Sensors Wristband Devices

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Deep Neural Networks for Advanced Medication Security in IoT-Enabled Smart Robotic Dispensing Cabinets

Deep Neural Networks for Advanced Medication Security in IoT...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Prabhu V S Gurumoorthi Gurulakshmanan C. Chandravathi D. P. Sangeetha Vinoth Kumar V B Meenakshi Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Mphasis Corporation Texas USA Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Ensuring advanced medication security is of the utmost importance in smart robotic dispensing cabinets enabled by the Internet of Things (IoT). Strong pharmaceutical security is of the utmost importance in smart robotic dispensing cabinets that the IoT enables. To strengthen security measures in these systems, this research presents a new method that uses deep neural networks (DNNs). The proposed system improves the identification of potential medication mistakes and unauthorized access by using DNNs' anomaly detection and pattern recognition capabilities. A thorough security layer is created by incorporating DNN models into the IoT design of smart cabinets to address drug administration and dispensation weaknesses. Discovering security breaches or procedural problems entails training DNNs on large datasets to detect typical operating patterns and abnormalities. It shows that the strategy significantly improves security procedures and operational dependability in real-world conditions via simulations and validation experiments, proving its usefulness. Providing a strong answer to the problems of improving the effectiveness of drug administration in healthcare settings based on the IoT helps move healthcare technology forward. The technology improves pharmaceutical security and lays the groundwork for responsive and adaptable healthcare infrastructures. This study will make improving the dependability and safety of healthcare delivery systems possible.

关键词： Drugs Medical services Artificial neural networks Robot sensing systems pattern recognition Security Internet of Things Artificial intelligence Anomaly detection Intelligent sensors

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602315

The proceedings contain 158 papers. The special focus in this conference is on Life System Modeling and Simulation and Intelligent computing for Sustainable Energy and Environment. The topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater Image recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel Distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-Thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024