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An intelligent Security systems in Cloud computing using Cryptography Techniques 5

An Intelligent Security Systems in Cloud Computing using Cry...

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5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Srilakshmi, V. Chetana, V. Lakshmi Dornala, Raghunadha Reddi Vallabaneni, S. Parvathi Ramanjaiah, Ganji School of Computer Science and Engineering Vit -AP University Andhra Pradesh Amaravati522237 India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amaravati Campus India Walgreens United States Pvp Siddhartha Institute of Technology Department of It Vijayawada India R v Institute of Technology Dept.of Cse Guntur India

ISBN: (纸本)9798350379945

Delivering computing resources over the internet has seen a paradigm shift towards cloud computing. However, there are serious security risks because cloud environments are distributed and shared. intelligent Security systems (ISS) that use cryptographic techniques are now necessary to overcome these issues, such as data confidentiality, integrity, and authenticity;this study developed an ISS for cloud computing, concentrating on cryptographic techniques. The RSA-OAEP (Optimal Asymmetric Encryption Padding) and X.509 algorithms are combined to create the ISS and further cover a variety of cryptographic methods, emphasizing their functions in protecting cloud-based systems, including encryption, digital signatures, and key management. It also looks at improving security systems' threat detection and response capabilities by integrating artificial intelligence (AI) algorithms like Grubbs' Test. In addition, several issues and potential paths for intelligent security systems in cloud computing are examined, highlighting the necessity of ongoing study and development to remain ahead of developing cyber threats. © 2024 ieee.

关键词： Cloud security

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Neurodynamic Mapping for Cybersickness Prediction in 3D Game Immersion 5

Neurodynamic Mapping for Cybersickness Prediction in 3D Game...

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5th international conference on Emerging systems and intelligent computing, ESIC 2025

作者： Balaji, C. Mishra, Shruti Satapathy, Sandeep Kumar Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331522100

The rapid expansion of virtual reality (VR) applications in gaming and training has highlighted cybersickness, a condition marked by symptoms such as nausea, dizziness, and disorientation. Traditional assessment methods often rely on subjective reporting, which hampers timely intervention. This study aims to develop a novel approach for predicting the onset of cybersickness through neurodynamic mapping of electroencephalogram (EEG) data and heart rate variability (HRV) in immersive 3D VR environments. Spiking neural network (SNN) architecture to analyze integrated EEG and HRV data collected from participants engaged in 3D VR games. Key EEG channels, particularly F7, Cz, and O2, were identified as critical biomarkers for susceptibility to cybersickness. Real-time monitoring of brainwave frequencies (delta, theta, alpha, and beta) enabled tracking of changes associated with symptom onset. SNN model achieved a prediction accuracy of 85.9% at resting baseline conditions and detected cybersickness events with an accuracy of 76.6%. The dynamic characteristics of the identified biomarkers suggest potential for enhancing prediction models over time. This research introduces a groundbreaking method for predicting cybersickness in VR gaming by leveraging neurodynamic mapping and SNNs. Our findings provide insights into the neural mechanisms underlying VR-induced motion sickness and have significant implications for improving user experience across various applications, including gaming and virtual therapy. The integration of neurodynamic feedback systems could foster more immersive and user-friendly VR environments by facilitating timely interventions to mitigate cybersickness. © 2025 ieee.

关键词： Volume rendering

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Improving Sleep Health Through data-Driven Machine Learning Insights 3

Improving Sleep Health Through Data-Driven Machine Learning ...

引用

3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Kapoor, Ananya Suyal, Priyanka Bhardwaj, Rakhi Gola, Kamal Kumar Kumar, Mohit Verma, Aman College of Engineering Roorkee Dept. of CSE Roorkee India COER University Dept. of AI-ML Roorkee India COER University Dept. of CSE Roorkee India College of Engineering Roorkee Dept. of AI-ML Roorkee India

ISBN: (纸本)9798331523893

Sleep is a critical component of overall health and well-being, yet sleep disorders and poor sleep quality affect millions worldwide. Traditional approaches to sleep assessment, often reliant on self-reports and polysomnography, can be costly and impractical for long-term monitoring. Our approach includes Machine learning algorithms, including random forests, gradient boosting, and data visualization techniques, are employed to predict sleep stages and identify factors influencing sleep quality. Experimental results demonstrate the efficacy of the proposed models, achieving high accuracy in sleep stage classification and actionable insights for sleep improvement. This work underscores the potential of machine learning to revolutionize sleep health management by providing scalable, data-driven, and personalized solutions. © 2025 ieee.

关键词： Sleep research

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Recent Developments in Robotic Grasping Detection 1

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international conference on advanced intelligent systems for Sustainable Development, AI2SD 2023

作者： Beguiel Bergor, Brahim Hadj Baraka, Ibrahim Zardoua, Yassir El Mourabit, Anouar Faculty of Science and Techniques Abdelmalek Essaadi University Ziaten Tangier Morocco

ISBN: (数字)9783031542886

ISBN: (纸本)9783031542879

Robotic grasping detection, a specialized domain in robotics, focuses on enabling robots to autonomously identify optimal object - grasping positions through sensory data analysis. This innovation has transformative implications for robotic interactions in diverse environments, including agriculture. The integration of deep learning techniques has significantly advanced this field, allowing robots to learn from extensive datasets and adapt to varied contexts, enhancing their versatility. This article provides a comprehensive survey of methodologies for estimating grasping configurations, explores diverse sensor modalities for data acquisition, and categorizes robotic grasping into two paradigms: known and unknown objects, based on familiarity. The study culminates in outlining critical considerations for developing robust grasping detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： data acquisition

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data Caching in Edge computing: A Survey 4

Data Caching in Edge Computing: A Survey

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4th ieee international conference on Smart Information systems and Technologies, SIST 2024

作者： Kara, Meliha Çağla Benlakehal, Mohamed Elamine Shayea, Ibraheem Tussupov, Akhmet Rzayeva, Leila Electronics and Communication Engineering Department Faculty of Electrical and Electronics Engineering Istanbul Technical University Istanbul34467 Turkey Laboratoire d’ingénierie des Systèmes et Télécommunication Department of Electrical engineering systems University of M’hamed Bougara Boumerdes Boumerdes35000 Algeria Department of Intelligent Systems and Cybersecurity Astana IT University Astana010000 Kazakhstan

ISBN: (纸本)9798350374865

As the Internet of Things (IoT) generates ever-increasing data streams, traditional cloud-centric architectures face crippling challenges in network latency, bandwidth consumption, and resource constraints. This paper explores how data caching in edge computing environments emerges as a potent solution, significantly impacting latency reduction, network efficiency, and overall system performance. We comprehensively review the landscape of edge IoT and data caching, analyze caching benefits and complexities, and delve into architectural integration, caching strategies, and algorithms tailored to address specific IoT challenges. Through case studies in chosen application domains, we quantify the performance improvements enabled by effective caching and pave the way for future research exploring novel caching methodologies and optimization techniques in the dynamic world of edge IoT. © 2024 ieee.

关键词： Mobile edge computing

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Traffic Sign Health Quality Assessment 27

Traffic Sign Health Quality Assessment

引用

27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Ahmed, Fahim Grimm, Donald K Bai, Fan General Motors WarrenMI48092 United States

ISBN: (纸本)9798331505929

Modern advanced Driver Assistance systems (ADAS) provide emerging capabilities for detecting the quality of roadway infrastructure assets to enable new customer features and analytics capabilities for road management authorities. This paper proposes measures to assess traffic sign health based on crowd sourced vehicle perception data, which to our knowledge has not been previously explored and for which there are no existing measures. We propose the following measures: (1) Euclidean Distance measurement of paint color deviation compared to the ideal condition, (2) Contrast Ratio assessment of visibility contrast of detected traffic signs colors and (3) Environmental Contrast Ratio to assess visibility contrast of detected traffic signs with respect to the background. We validated our approach against FHWA recommendation guidelines and the federally recommended CIE Chromacity diagram. © 2024 ieee.

关键词： advanced driver assistance systems

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Multilingual Racism Detection: Effects of CNN Architecture Complexity on Classification Performance 6

Multilingual Racism Detection: Effects of CNN Architecture C...

引用

6th international conference on intelligent computing in data Sciences, ICDS 2024

作者： El Miqdadi, Ikram Hourri, Soufiane Hayati, Assia El Idrysy, Fatima Zahra Namir, Yassine Kharroubi, Jamal University of Sidi Mohamed Ben Abdellah Laboratory of Intelligent Systems and Applications Fez Morocco University of Cadi Ayyad Laboratory of Process Industrial Signals and Computer Science Marrakech-Safi Morocco

ISBN: (纸本)9798350351200

This paper investigates the impact of Convolutional Neural Network (CNN) architecture complexity on multilingual racism detection through text classification. In a previous study, we used self-Training to annotate a database using the CNN as a classifier. This study explores various CNN models with increasing architectural depth to discern their effect on classification performance across English, French, and Arabic datasets extracted from social media platforms. We systematically evaluate a range of CNN configurations, varying parameters such as filter sizes and depths of convolutional layers. The experimental results demonstrate that while increasing the complexity of CNN architectures leads to marginal improvements in performance for some languages, the benefits are not uniform across all languages. This study provides valuable insights into optimizing CNN architectures for improved performance in multilingual text classification for racism detection. © 2024 ieee.

关键词： Convolutional neural networks

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Hybrid AI Approach Combining Decision Trees and SVM for intelligent Tutoring systems in STEM Education 10

Hybrid AI Approach Combining Decision Trees and SVM for Inte...

引用

10th international conference on advanced computing and Communication systems, ICACCS 2024

作者： Rao, B. Hanumantha Rengarajan, Manikandan Changala, Ravindra Santosh, Kathari Aarif, Mohd Ponni Valavan, M. Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering & Technology Department of Cse Andhra Pradesh Vijayawada India Vel Tech Rangarajan Sagunthala R&d Institute of Science and Technology Chennai Tamil Nadu Avadi India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Cmr Institute of Technology Department of Mba Bengaluru India Global Research Network Noida India K.Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350384369

intelligent Tutoring systems (ITS) play a crucial role in STEM education by providing personalized and adaptive learning experiences. However, enhancing student engagement and performance requires systems that can effectively recognize and respond to students' emotional states. This research introduces a pioneering Hybrid AI Approach that seamlessly integrates Decision Trees and Support Vector Machines (SVM) to achieve this goal. Leveraging Kaggle's Student data Emotion Recognition dataset, the proposed model is designed to understand and respond to students' emotional states during the learning process. To optimize facial expression data processing, Contrast Stretching is applied for data preprocessing, and Recursive Feature Elimination is employed for feature selection. The hybridization of Decision Trees and SVM allows for a balance between interpretability and high-dimensional processing capabilities, contributing to the model's success. Remarkably, the proposed approach outperforms existing methods, including Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and the combination of Histogram of Oriented Gradients (HOG) and the C4.5 decision tree algorithm (HOG+C4.5)., achieving an outstanding accuracy of 98.7%• The robust discriminatory power of the model is further confirmed through ROC curve analysis. Implemented in Python, the approach showcases the practicality and versatility of the proposed model within a widely used software environment. This study not only provides a comprehensive methodology and promising results but also sets the stage for further exploration, refinement, and practical implementation of emotion-aware ITS in STEM education. By offering a personalized and intelligent tutoring system tailored to the intricacies of STEM education, the hybrid model's adaptability and efficiency promise to significantly enhance the educational experience for students in these disciplines. © 2024 ieee.

关键词： Support vector machines

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A Nouvelle Optimal intelligent Learning (OIL) Framework for Secured data Transmission and Link Quality Analysis in UAV systems 3

A Nouvelle Optimal Intelligent Learning (OIL) Framework for ...

引用

international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Devi, B. Renuka Sarveshwaran, Velliangiri SRM Inst Sci & Technol Dept Computat Intelligence Chennai India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Unmanned Aerial Vehicles (UAVs), also known as drones, have gained popularity recently and are being used for a number of reasons. They are common aerial vehicles that have connections with the internet and are capable of identifying themselves and interact with other devices by transmitting information through intelligent gadgets. UAVs are flying vehicles that can fly on their own or with manned operator assistance. For certain crucial applications, the security of UAVs and their wireless communications is crucial. This research presents a new framework, the Optimal intelligent Learning (OIL) model that facilitates successful data exchange in UAV systems. In order to provide reliable data transfer with the least amount of information loss, a Deep Attention Predictor (DAP) methodology is applied in this instance to precisely detect the links between communication devices. The Heap Optimized Routing Model (HORM) is then used to determine plausible, reliable routes from the source to the destination. In addition, the Recurrent Auto Encoding based Assault Detector (RAE-AD) method looks at a number of characteristics, including travel duration, signal strength, packet size, delivery rate, connection quality, and data transmission priority, in order to identify malicious nodes inside the communication network. Moreover, three d's such as delivery rate, detection rate, and latency-are used to compare and contrast the simulation results and conclusions of the suggested OIL framework.

关键词： Unmanned Aerial Vehicle (UAV) Cyber Security Deep Learning Artificial Intelligence (AI) Optimization and Link Prediction

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Pioneering Real-Time Forest Fire Detection: A Comprehensive Examination of advanced Machine Learning Techniques in IoT-Integrated systems for Enhanced Environmental Adaptability 1st

Pioneering Real-Time Forest Fire Detection: A Comprehensive ...

引用

1st international conference on intelligent computing for Sustainable Development, ICICSD 2023

作者： Arun Prasad, M. Department of Electronics and Communication Systems KG College of Arts and Science Coimbatore India

ISBN: (纸本)9783031612862

The purpose of this research is to revolutionize forest fire detection by leveraging the integration of Internet of Things (IoT) technology, specifically sensors and Raspberry Pi, and advanced machine learning algorithms like AutoML. Aiming to address the need for adaptable systems to various environmental conditions, the primary objectives of this study include developing real-time prediction algorithms, employing multi-sensor data acquisition, and implementing intelligent response mechanisms to enhance accuracy, scalability, and responsiveness. The originality of this work lies in the novel combination of advanced computational models with real-time sensor data, setting a new paradigm in environmental protection and forest management. This paper begins with an in-depth analysis of existing IoT-based wildfire detection systems, particularly focusing on their machine learning components, an aspect often overlooked in the literature. Based on this analysis, a framework for a real-time forest fire detection system is proposed, integrating robust IoT infrastructure, including sensors and Raspberry Pi, with sophisticated machine learning techniques such as AutoML. Rigorous evaluation reveals the framework’s outstanding performance in terms of detection accuracy, computational efficiency, and adaptability to various environmental conditions. The results firmly indicate that the proposed system, which leverages the power of AutoML and the flexibility of IoT devices, represents a superior and innovative solution to existing methods, thus contributing significantly to the field of environmental protection and forest management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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