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检索条件"任意字段=2nd IEEE Conference on Information Technology and Data Science, CITDS 2022"

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Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm for Social Media Content Classification and Community Detection 2

Stereoscopic Gated Graph Scalable Quantum Capsule Convolutio...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Elumalai, A. Devi, S. Rukmani Victoria, D. Rosy Salomi Jagadeeswaran, A. Mukherjee, Aparajita Vinoth, K. R.M.K. Engineering College Department of Mathematics Tamil Nadu Chennai601206 India Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Department of Computer Science Tamil Nadu Chennai602105 India Chennai Institute of Technology Department of Information Technology Tamil Nadu Chennai600069 India Annapoorana Engineering College Department of Electronics and Communication Engineering Tamil Nadu Salem636308 India University of Engineering and Management Department of Computer Science and Engineering West Bengal Kolkata700160 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331505745

Social Media Content Classification and Community Detection (SMCCCD) classify content and identify communities through deep learning, and NLP. Traditional models are weak in scalability and interpretability. To solve all these problems, this paper introduces a Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm (SGGSQCCN2Nets+AHA) for social content classification and community detection. data inputs are accessed from the Twitter dataset and are initially preprocessed by the Natural Language (NL) method. After preprocessing, content classification and community detection are done using the Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks (SGGSQCCN2Nets) and optimization is then performed with the Artificial Hummingbird Algorithm (AHA). The performance of the new SGGSQCCN2Nets+AHA model is tested on a Twitter dataset with a high accuracy rate of 99.9% and specificity of 99.8%. The proposed model is developed by utilizing Python programming language. The outcome of the proposed SGGSQCCN2Nets+AHA model enhances the performance of content classification and improves user community detection accuracy. It is effective in addressing scalability issues, obtaining complex features, and offering more interpretability. The model is also resilient against noisy data dynamically adapts to evolving social media trends and outperforms traditional deep learning techniques in real-time analysis. © 2025 ieee.

关键词： Tweets

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Video Deepfake Detection: Current Trends, Methodologies, and Future Directions 2

Video Deepfake Detection: Current Trends, Methodologies, and...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Ansar, H. Kumar, Mathusoothana S Noorul Islam Centre for Higher Education Dept. of Computer Science & Engineering Tamil Nadu India Noorul Islam Centre for Higher Education Department of Information Technology Tamil Nadu India

ISBN: (纸本)9798331505745

Deepfake technology has emerged as a significant challenge in the domain of multimedia processing, particularly due to its capability to create highly realistic manipulated videos that can mislead viewers and compromise security, privacy, and public trust. While deepfake technologies have legitimate applications in fields such as entertainment, education, and healthcare, their misuse for malicious purposes such as misinformation and identity fraud necessitate the development of robust detection methods. This survey provides a comprehensive analysis of the state-of-the-art approaches in deepfake face detection within video data, categorizing them into three major sections: Hybrid Methods, Ensemble Methods, and Recurrent Methods. Hybrid methods integrate multiple machine learning models to enhance the accuracy of spatial and temporal feature extraction, offering a balanced approach to detecting subtle manipulations in deepfake videos. Ensemble methods leverage the combined strengths of diverse classifiers to improve robustness and adaptability in varying video contexts. Recurrent methods utilize recurrent neural networks to capture temporal dependencies across video frames, addressing challenges in detecting complex and sequential manipulations. The review highlights that while these approaches demonstrate significant progress, challenges remain in generalizing detection models to novel and highly advanced deepfake techniques. This work concludes by identifying the limitations in existing research and suggesting future directions for improving the robustness and scalability of deepfake detection systems. © 2025 ieee.

关键词： Recurrent neural networks

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Privacy-Preserving Real-Time Action Recognition on Mobile Devices Using Edge Computing and CNNs 2

Privacy-Preserving Real-Time Action Recognition on Mobile De...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Minu, M.S. Selvi, K. Dhanasakkaravarthi Mohanaprakash, T.A. Darshan, R. Dasan, Abhijith Ashvyn Kumar, K. SRM Institute Of Science and Technology Department CSE Chennai India R.M.K Engineering College Department of Information Technology Chennai India Agni College of Technology Department of Mechanical Engineering Chennai India CMR University Department CSE Bengaluru India

ISBN: (纸本)9798331505745

The increasing use of mobile devices has created a demand for efficient, real-time video monitoring solutions optimized for mobile platforms. This study presents an innovative application leveraging Convolutional Neural Networks (CNNs) to classify human actions in videos captured on mobile devices. Trained on the UCF 101 dataset, encompassing 101 diverse action categories, the application delivers accurate recognition of a wide range of activities. By processing video data locally on the device, the solution eliminates the reliance on centralized servers, addressing latency challenges and enhancing data privacy. This approach ensures real-time performance while maintaining user confidentiality, making it ideal for sensitive applications. Key use cases include healthcare, where therapists can track patient progress during physical therapy, and sports, where the application provides real-time insights into athlete performance. The app's lightweight design and efficient resource utilization ensure compatibility with a wide range of mobile devices, supporting widespread adoption. This work advances the integration of video analysis and edge computing, delivering a scalable and privacy-preserving solution for real-time action recognition on mobile platforms. © 2025 ieee.

关键词： Convolutional neural networks

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High-Performance Secure File Sharing with Dual Server Access and Time-Sensitive Retrieval 2

High-Performance Secure File Sharing with Dual Server Access...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Ranjithsingh, K. Sudharsan, A. Head Of the Department Department of Information Technology karpagam academy of higher education Coimbatore India Karpagam academy of higher education Department of Computer Science Coimbatore India

ISBN: (纸本)9798331505745

Cloud computing is emerging as a predominant interactive framework for accessing users' data stored remotely on an Internet cloud server. Cloud services provide substantial benefits, allowing the customers to utilize on-demand apps irrespective of their local infrastructure limitations. During data access, several users are collaborated. It makes data sharing essential for realizing productive advantages. This project provides a collaborative governance paradigm for data sharing to address the previously noted privacy issues in cloud storage. The project achieves shared access authority via a method for matching anonymous access requests, addressing security and privacy concerns. data access is granted only with the approval from cloud infrastructure and file owner. The shared access authority obtains and distributes the data solicited by users. This project enhancement entails a time-server access mechanism for secure data retrieval in the cloud, with files encrypted before storage. The encryption process utilizes symmetric key cryptography. The user secures authorization from a primary server to access the file directory or locate a specific file. The primary server recognizes the user's request and permits to select a file from the list. Then, the file owner receives an access request and upon acceptance, grants the secret key to the requesting user. It allows in viewing and downloading the material. If the user fails to submit a request within seconds, the primary server redirect to the mainpage. If the users need access to the file list again, the users have to obtain authorization from the primary server. © 2025 ieee.

关键词： Sensitive data

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Diabetic Retinopathy Image Classification using Quasi Cross UNet Discrete Transform with Gold Rush Optimizer 2

Diabetic Retinopathy Image Classification using Quasi Cross ...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Ramkumar, M.Siva Nivetha, R. Kannaiyan, Mathi Kanan, Mohammad Shankar, S. Giri, Jayant SNS College of Technology Dept of ECE India Kumaraguru College of Technology lakshitha Computer Science and Engineering India University college of Kancheepuram Anna University kancheepuram campus department of Mechanical Engineer Kanchipuram631552 India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Cambridge Institute of Technology Department of Mechanical Engineering Bengaluru560086 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

The diagnosis of diabetic retinopathy faces three main challenges which include inconsistent image clarity and weak detection of small visual impairments and little ability to share information between different sets of data. The model suffers from two issues which limit its practical use: high expenses for operations as well as low decision accuracy because of complex tissue dynamics. Using the MESSIDOR dataset as input, this paper illustrates the application of the Quasi Cross UNet Discrete Transform with Gold Rush Optimizer (QCUNet-DT-GRO) algorithm for diabetic retinopathy picture categorization. The Unet Swin Transformer (UST) approach is used to precisely segment areas afflicted by diabetic retinopathy, while the Quasi-Cross Bilateral Filtering (QCBF) methodology improves image quality. The One-Dimensional Discrete Transform (ODDT) is used to extract features. The Gold Rush Optimizer (GdRO) does optimization, while the Gates-Controlled Deep Unfolding Network (GCDUNet) handles classification. The system detects diabetic retinopathy along with distinguishing between normal and diabetic retina structures. According to experimental results, QCUNet-DT-GRO performs better than current techniques, with 99.9% accuracy and 99.8% sensitivity. This method is shown to be a viable substitute for the manual diagnostic processes that are now in use, enabling robotic diagnosis systems in subsequent applications. © 2025 ieee.

关键词： Image segmentation

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Traffic Surveillance System Through Capsule Gated Graph Attention Network for IoT/IIoT Cyberthreat Detection and Mitigation

Traffic Surveillance System Through Capsule Gated Graph Atte...

引用

2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Mahesh Prasanna, K. Marotka, Devashree Muralidharan, J. Adhvaryu, Rachit Vani, V. Maranan, Ramya Vivekananda College of Engineering and Technology Department of Electronics and Communication Engineering Puttur India G H Raisoni College of Engineering and Management Department of Electronics and Telecommunication Engineering Maharashtra Nagpur India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Bangalore Institute of Technology Department of Information Science and Engineering Karnataka Bangalore India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Internet of Things and Industrial Internet of Things have seen rapid adoption and have transformed industries through smart automation, constant monitoring and better connected systems. The progressive inclusion of such solutions has contributed to the growth in exposure to complex and changing global threats. The primary challenge lies in accurately detecting and classifying cyber threats from high-volume, unstructured network traffic data in real-time. Existing methods are not very accurate and cannot be easily scaled due to constraints in computational resources and the nature of IIoT network systems. This paper introduces Traffic Surveillance System through Capsule Gated Graph Attention Network for IoT/IIoT Cyberthreat Detection and Mitigation (Cyber-CapG). The system uses raw traffic data from Edge-IIoTset and uses the Tunable Transformer for feature extraction. The extracted features are passed through the Capsule Gated Graph Attention Network to model the traffic as a graph based on communication hierarchy and being selective to essential traffic patterns. Furthermore, weights and loss functions are tuned based on the Progressive Crested Porcupine Optimization so as to achieve maximum optimization. The proposed system exhibits outstanding performance with 99.21 % detection rate, 99.07% precision, 99.15% recall, 99.18% F1 measure, and it provides an effective way of identifying threats like DDoS, SQL Injection, and Ransomware. In conclusion, Cyber-CapG provides a solid solution for traffic surveillance in IoT/IIoT, providing the high detection and prevention of various cyber threats, as well as optimizing the cybersecurity and reliability of industrial networks. © 2025 ieee.

关键词： Industrial internet of things (IIoT)

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Social Media Multi-Emotion Detection 2

Social Media Multi-Emotion Detection

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2nd International conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ibrahiem, Soha S. El-Sayed Mohammed, Bahy Ibrahim El Sayed Ahmed, Mohammed Ahmed Elbahnasy, Khaled Menofia University Computality R&D System Analyst & Nlp Researcher Faculty of Science Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Jeddah International College Computer Science and Information Technology Department Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states;however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection. © 2025 ieee.

关键词： Tweets

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An Optimized YOLO-based License Plate Recognition System with Integrated Privacy Safeguards 2

An Optimized YOLO-based License Plate Recognition System wit...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Putluru, Aravinda Reddy Pannuru, Vivekananda Reddy Ashokkumar, C. Kumar, C. Jothi School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India

ISBN: (纸本)9798331505745

Efficient and accurate license plate recognition (LPR) is crucial for intelligent transportation systems, surveillance, and security applications. This work proposes a Powered Smart License Plate Recognition System utilizing YOLOv8 for real-time detection and Optical Character Recognition (OCR) for character extraction. The system integrates publicly available datasets, including OpenALPR, CCPD, and UFPR-ALPR, along with custom-annotated data to enhance robustness. data preprocessing involves normalization, augmentation, and privacy protection measures such as Gaussian blurring and encryption to ensure compliance with data protection regulations. The architecture is optimized using convolutional layers for feature extraction and attention mechanisms for improved character recognition. A privacy protection module ensures sensitive information masking, while Edge AI deployment on Jetson Nano or Raspberry Pi enables real-time inference with minimal latency. Model training employs transfer learning with pre-trained YOLOv8 weights, fine-tuned on license plate datasets. Optimizers such as Adam and SGD, along with IoU loss, enhance bounding box accuracy, ensuring precise detection. Performance evaluation demonstrates high mean Average Precision (mAP) exceeding 95% and OCR accuracy surpassing 90%, validating the system's efficiency across diverse environmental conditions. Applications include automated toll collection, traffic monitoring, vehicle tracking, and secure entry management in smart cities. Law enforcement agencies can utilize the system for stolen vehicle identification and traffic rule enforcement. The proposed approach offers a scalable, privacy-aware, and high-accuracy solution for modern LPR applications, ensuring compliance with security and regulatory standards. © 2025 ieee.

关键词： Optical character recognition

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A Hybrid Deep Learning Model for Optimizing Electric Vehicle Battery and Navigation Systems

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Arul Jose, R. Ebby Darney, P. Santhana Krishnan, R. Relin Francis Raj, J. Jansi Rani, D. Sundaravadivel, P. Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bengaluru India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency. © 2025 ieee.

关键词： State of charge

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CityShield: An IoT-Driven and AI-Based Threat Detection System for Smart City Operations 2

CityShield: An IoT-Driven and AI-Based Threat Detection Syst...

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2nd International conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Sarker Emon, Md. Asif Abdullah Al Sohan, Md. Faruk Hossain Munna, Md. Maruf Ahmed, Mustakim Redwan, Kazi American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331506995

In this era of technology, smart cities are becoming more common, but their growing connectivity exposes them to continuous cybersecurity threats, putting their operations and security at risk. This research focuses on enhancing cybersecurity in smart cities by implementing a real-time threat detection and response system, with key metrics such as faster detection speeds, reduced false alarms, and improved system adaptability to evolving threats. Robust safety protocols are required because attacks by ransomware, breaches of information, and weak IoT devices endanger vital services. This research proposes a system that presents the implementation of real-time data collection through IoT devices and a streamlined data flow leveraging Kafka for rapid ingestion, HDFS for scalable long-term storage, and Apache Spark for efficient processing. The proposed system uses Autoencoder algorithms to detect anomalies and DQN to make fast, intelligent decisions responding to detected threats. The proposed system aims to strengthen clever city defenses by continuously monitoring various city services. Robust and adaptable threat detection and response systems that can re-duce vulnerabilities are guaranteed through this technological integration. This versatile system protects vital infrastructure by changing it to counter new cyber threats. It enhances city safety and strengthens smart city cybersecurity by detecting threats and safeguarding critical information and infrastructure for a secure future. With this proposed strategy, smart cities could become safer, more technologically advanced, and more resilient. This proposed solution allows smart cities to become technologically advanced, secure, and resilient. © 2025 ieee.

关键词： Computer system firewalls

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