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Design and Implementation of a data Encryption Framework Simulink Model for Protecting Sensitive Organizational data

Design and Implementation of a Data Encryption Framework Sim...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Veera Lakshmayya Patchigolla

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Internet-of-Medical-Things (IoMT) based sensitive data protection has become a critical problem lately. IoMT-sensitive data are used to connect patients with adults. Since every record, data, and healthcare signal is transmitted over these systems, they need strong and safe access methods for individuals to transmit their essential statistics or documents. Because of this, breaching these systems could have negative impacts on patients. An emerging pattern in biometric verification is to prevent using authentic biometrics for security purposes. Cancelable digital fingerprints can be created using a data encryption framework or non-invertible transformations. This study presents an integrated structure for cancelable ECG signal detection that is suitable for use in IoMT networking access steps. The suggested system uses fuzzy reasoning to perform a non-invertible transition on ECG data, altering its shifting spectrum. Cancellable biometric platforms aim to retrieve original ECG data from processing variants, but the procedure is non-invertible and hinders this goal. Next, customized trends are used to build compact encryption using XOR. This eliminates the complication of comprehensive encryption systems requiring significant computing resources. By combining encryption techniques and non-invertible transformations, the encryption phase improves the safety of cancelable biometrics features and permits the mixed nature of the suggested cancelable biometrics architecture. For the actual adoption of the suggested ECG-based cancelable biometrics identification structure, hardware adaptation is also shown. According to empirical findings, the suggested structure performs well, with an Equivalent Error Rate (EER) of 0.059%.

关键词： Software packages Internet of Medical Things Electrocardiography Market research Hardware Encryption Safety Security Signal detection Fuzzy reasoning

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Enhanced Security Through Honeywords and Video CAPTCHA: An Innovative Authentication and Tracking Mechanism

Enhanced Security Through Honeywords and Video CAPTCHA: An I...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Madhuri Gurale Komal Borate Rachana R. Sangitrao Garima Tiwari Information Technology Department D. Y. Patil College of Engineering Akurdi Pune India Artifical Intelligency and Data Science Department D. Y. Patil College of Engineering Akurdi Pune India Department of Instrumentation and Control D. Y. Patil College of Engineering Akurdi Pune India Department of Electronics & Communication D. Y. Patil College of Engineering Akurdi Pune India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

In recent years, activities across countries have increasingly been carried out digitally, with information being shared over networks to boost speed and efficiency. However, this digital transformation poses security threats. Cybercrime has consistently evolved, necessitating improved network security measures to combat digital scams. To maintain security, users are assigned unique identification passwords, which they are instructed to keep secret. Despite this, attackers often manage to steal passwords. To counter these threats, we propose using honeywords, which are decoy passwords created from the existing user password. If an attacker inputs a honeyword, an alert is activated for the administrator. Moreover, we implement IP and location monitoring for users and introduce a approach known as the video click-based CAPTCHA system. This scheme authenticates users by distinguishing between humans and bots, addressing issues in traditional graphic password CAPTCHA schemes. This architecture significantly enhances protection and security of data and applications over online networks.

关键词： Resistance Authentication Passwords Network security Reliability Protection Computer crime Usability Monitoring CAPTCHAs

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5G to 6G Shaping and Effect of the Wireless data Transmission

5G to 6G Shaping and Effect of the Wireless Data Transmissio...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Anita Walia Neeraj Das Department of CETT JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Digital advancement and smart production have been made possible via artificial intelligence (AI) and computing in the cloud (CC) because of the fifth-generation wireless network (5G), which is a key enabler of the fourth industrial revolution. Nevertheless, beyond 5G (B5G) is seen as a watershed moment that will radically alter current worldwide tendencies in mobile phone practices and impact the lives of many. Physical and digital worlds will eventually merge, according to B5G. The focus of this research is on the future of technology for wireless communications, specifically the evolution from 5G into 6G. Still, mankind hasn't arrived to the age of instantaneous contact with aliens, lightning-fast internet, plus zero latency, despite a number of advancements. In order to accomplish these more elevated goals, this article investigates the potential obstacles and hurdles that the shift from 5G to 6G may encounter. The article lays out the plan for 6G, the enabling infrastructures, the problems, and the research that will lead to the realization of the “technology for humanity” goal and improved assistance for the poor.

关键词： 6G mobile communication Technological innovation Quantum computing 5G mobile communication Wireless networks Watersheds Production Artificial intelligence Protection Terahertz communications

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Advanced Deep Learning Framework for Diagnosing Autism Spectrum Disorder Through Facial Expression Analysis

Advanced Deep Learning Framework for Diagnosing Autism Spect...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Jagadesh Balasubramani Surendran R Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Autism Spectrum Disorder is a neurodevelopmental disorder that presents with persistent deficits in social communication and social or emotional reciprocity. Timely intervention may result from an early and precise diagnosis of autism spectrum disorder (ASD). This study proposes a paradigm that uses deep learning systems to analyze facial expressions and emotions to classify children with autism spectrum disorder. The proposed framework utilizes a hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks (CNN-BiLSTM) model to learn and perform classification tasks, To enhance image quality, input data undergoes pre-processing techniques, including histogram equalization, which improves contrast, and adaptive median filtering, which reduces noise while preserving essential details. Model optimization is a self-adaptive Black-Winged kite optimization method for hyperparameter tuning, ensuring that the model's parameters are optimal for improved classification accuracy. Performance measurement results such as Accuracy, recall, precision, specificity, and confusion matrix shown from evaluating the framework in ASD datasets obtained from the Kaggle repository are implemented in Python. This approach enhances the speed and precision of Autism Spectrum Disorder diagnosis accordingly and gives a very powerful instrument for real-time clinical application.

关键词： Deep learning Image quality Autism Adaptation models Histograms Accuracy Filtering Real-time systems Tuning Long short term memory

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Optimization of Parameters of the OWC Wave Energy Converter Device Using a Machine Learning Approach

Optimization of Parameters of the OWC Wave Energy Converter ...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Kush Karnavat Nilansh Vadodaria Kshma Trivedi Prashant Patel School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Mathematics School of Advanced Sciences VIT-AP University Amaravati India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

In this study, the performance of an L-shaped front wall OWC (Oscillating Water Column) device is investigated in the presence of irregular incident waves. To efficiently explore the complex interactions between input features and the target variable, two supervised machine learning models: (i) a decision tree model and (ii) an XGBoost model are considered. The input parameters selected for this analysis are: OWC Front wall Draft, OWC Duct Length, Chamber Length, ripple Number, and ripple amplitude of the sinusoidal seabed. The output attribute is the maximum power extracted by the OWC device. The XG Boost model is used to identify critical input regions where power extraction peaks, while the decision tree model further aids in understanding the parameter influences on power extraction. A dataset of 3125 samples was generated for training, validation, and testing. By employing accumulated local effect plots with the XG Boost model, five specific regions in the input space were identified that maximize annual average power extraction. The results indicate that optimal power extraction is achieved for a specific value of each parameter, thereby validating the influence of each on OWC device performance.

关键词： Training Computational modeling Ducts Predictive models Feature extraction data models Decision trees Wave energy conversion Optimization Testing

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Multi-Camera Trajectory Based Vehicle Re-Identification for Robust Traffic Monitoring

Multi-Camera Trajectory Based Vehicle Re-Identification for ...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： R. Athilakshmi Emandi Venkata Sanjeevi Sai Prasanna Syed Yasir Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Vehicle Re-Identification (ReID) is the challenging task of identifying the same vehicle across multiple cameras in urban environments. In this paper, we propose a novel MultiCam TrajectoryID model that leverages trajectory-based features, addressing the limitations of single-image-based ReID methods. The system combines feature extraction methods like SIFT with the Kalman filter and You Only Look Once (YOLO) algorithms for trajectory prediction to guarantee accurate and effective tracking of objects across several camera views. This MultiCam TrajectoryID approach enhances traditional single-image vehicle ReID by focusing on the continuity of object movement, improving the robustness of identification in complex, real-world traffic environments. We evaluate the performance of our model on the challenging VeRi-776 dataset and compare it with state-of-the-art methods. The proposed model achieves a mean average Precision (maP) of 86% on VeRi-776 data, outperforming recent approaches such as Multi-Branch Enhanced Discriminative Network and Multi-Fine Grained Network models. This significant improvement demonstrates the effectiveness of trajectory-based re-identification in multi-camera setups and its contribution to vehicle ReID and real-time surveillance systems.

关键词： YOLO Adaptation models Accuracy Surveillance Computational modeling Feature extraction Real-time systems Robustness Trajectory Kalman filters

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6G Mobile Network Latency Mobile Edge computing Cloud Architecture

6G Mobile Network Latency Mobile Edge Computing Cloud Archit...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Preeti Naval V Pushparajesh Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Communication refers to the process by which one individual conveys information to another. Telecommunication and the instruments are made available by technological gadgets, which is now feasible. 5G mobile network standards are being used at present. It is thought that 6th-generation mobile network standards will address several user needs. 6G will be universally available by 2030. In a cloud computing setup, a specific hardware or geographic location is not required. The main aim of this method is to offer computing resources whenever they are needed. By fusing the two, mobile cloud computing enables more flexible and personalized solutions. Machine learning is finding its way into a wide variety of devices. With the use of AI., mobile network services (MNS) may improve the reliability and personalization of user experiences in areas like fraud detection, mobile transaction security, network operation monitoring, and management. A world without traffic accidents, high-tec, specialized healthcare, and criminal activity would be a reality if 6G mobile network services integrated cloud computing with artificial intelligence. The most important requirements for 6G standards are high data rate connection per user and the ability to manage huge record numbers. Research and development are underway for the next-generation mobile network. There are several intriguing aspects of this age. The most pressing problem that requires solving using suitable forensic methods is security. High-performance computers are needed to improve customer service. Two situations are created by combining technological, organizational, and cloud-hosted application-based research approaches in a high-performance computing setting: monitoring performance via remote desktop connection and real-time stream processing. This article seeks to narrow the designated worldwide spectators with a wide variety of empirical chances to allocate resources for trustworthy and defensible on-demand services.

关键词： 6G mobile communication Cloud computing High performance computing Forensics Machine learning Communications technology Fraud Security Standards Monitoring

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in Software Logs

Leveraging Sequential Deep Learning Models for Robust Compil...

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Intelligent data Communication technologies and Internet of Things (IDCIoT), international conference on

作者： Sahil Mishra Utkarsh Tiwari Kavitha C. R. Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, Fl-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI.

关键词： Deep learning Analytical models Explainable AI Computer bugs Software quality Feature extraction data models Internet of Things data communication Long short term memory

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G-Litter Marine Litter dataset Augmentation with Diffusion Models and Large Language Models on GPU Acceleration 33

G-Litter Marine Litter Dataset Augmentation with Diffusion M...

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33rd Euromicro international conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Mellone, Gennaro De Vita, Ciro Giuseppe Nardo, Emanuel Di Coviello, Giuseppe Luccio, Diana Di Aucelli, Pietro Patrizio Ciro Ciaramella, Angelo Montella, Raffaele University of Napoli Parthenope Department of Science and Technology Napoli Italy University of Naples Parthenope Dep. of Science and Technologies Naples Italy Integrated System NEC Laboratories America Inc PrincetonNJ United States

ISBN: (纸本)9798331524937

Marine litter detection is crucial for environmental monitoring, yet the imbalance in existing datasets limits model performance in identifying various types of waste accurately. This paper presents an efficient data augmentation pipeline that combines generative diffusion models (e.g., Stable Diffusion) and Large Language Models (LLMs) to expand the G-Litter dataset, a marine litter dataset designed for autonomous detection in heterogeneous environments. Leveraging scalable diffusion models for image generation and Alpaca LLMs for diverse prompt generation, our approach augments underrepresented classes by generating over 200 additional images per class, significantly improving the dataset's balance. Training G-Litter augmented dataset using YOLOv8 for object detection demonstrated an increase in detection performance, improving recall by 7.82% and mAP50 by 3.87% (compared with baseline results). This study emphasizes the potential for combining generative AI with HPC resources to automate data augmentation on large-scale, unstructured datasets, particularly in edge computing contexts for real-time marine monitoring. The models were tested on real videos captured during simulated missions, demonstrating a superior ability to detect submerged objects in dynamic scenarios. These results highlight the potential of generative AI techniques to improve dataset quality and detection model performance, laying the foundation for further expansion in real-time marine monitoring. © 2025 IEEE.

关键词： Image enhancement

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Integrating Blockchain and Artificial Intelligence for Securing Payment processes Against Fraudulent Transactions

Integrating Blockchain and Artificial Intelligence for Secur...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： J V Naga Ramesh Nusrat Hamid Shah Malik Bader Alazzam Divya Nimma V. Selvi A Balakumar Department of CSE Graphic Era Hill University Dehradun India Department of CSE Graphic Era Deemed To Be University Dehradun Uttarakhand India College of Engineering and Computer Science Jazan University Kingdom of Saudi Arabia Faculty of Information Technology Jadara University Irbid Jordan University of Southern Mississippi Data Analyst in UMMC USA Department of Computer Science Mother Teresa Women's University Kodaikanal India Department of ECE K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

In recent years, the fast growth of digital payment processes has raised large safety concerns, which include fraud and data breaches, prompting the need for robust safety mechanisms. Existing techniques, which include conventional rule-based systems, regularly fall quick in adaptability and accuracy, leading to multiplied vulnerabilities within the economic zone. This study proposes a unique integration of Blockchain technology with Artificial Intelligence (AI), specially making use of Generative Adversarial Networks (GANs), to enhance the security of monetary transactions. The technique includes growing a decentralized ledger device to make sure transparency and immutability, coupled with a GAN framework for real-time anomaly detection. Then behavior experiments the use of a complete dataset of monetary transactions to validate the effectiveness of the included technique. The consequences imply a considerable improvement in detecting fraudulent activities, accomplishing higher accuracy and decrease false positive rates in comparison to conventional methods. This research no longer most effective addresses the crucial safety gaps found in contemporary monetary transaction systems however additionally demonstrates the potential of integrating Blockchain and AI to create an extra stable monetary environment. The findings recommend that this integrated technique can serve as a scalable solution for enhancing transaction safety, providing valuable insights for both academia and industry practitioners.

关键词： Industries Economics Accuracy data breach Generative adversarial networks Real-time systems Safety Blockchains Fraud Artificial intelligence

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