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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kanagaraju, P. Nadganndi, Swati Kedar Sawant, Tukaram N. Ekambaram, Saranya Keshwani, Palak Kumar, Yadavalli Suresh SRM Institute of Science and Technology Department of Computational Intelligence Chennai India Bharati Vidyapeeth College of Engineering Department of Instrumentation Navi Mumbai India Bharati Vidyapeeth College of Engineering Department of Electronics and Telecommunication Navi Mumbai India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore India FST the ICFAI University Raipur India Aditya College of Engineering Department OfCSE Surampalem India

ISBN: (纸本)9798350327533

Since intrusion detection has gained so much popularity, scientists continue to dedicate a lot of time and energy to studying it. Even after all these years of research, the intrusion detection community still has problems. Recognizing previously unseen attack patterns is hampered by a high rate of false positives, which is an ongoing pro blem. However, new research has shown that there are exits from this impasse. Intrusion detection relies heavily on the ability to spot anomalies, or unexpected behavior, because they indicate the presence of malicious activity. Data preparation, feature selection, and model training are all parts of the suggested approach. Machine learning algorithms can analyze the data for the development of a predictive model thanks to data preparation methods. For feature selection, the proposed approach relied on principal component analysis and iterative grading. After collecting the features, CNN -CRF is utilized to train the models. The proposed method is superior to the popular algorithms CNN and CRF. There is a 98.63% success rate when using this strategy. © 2024 IEEE.

关键词： Anomaly Detection Conditional Random Fields (CRF) Principal Component Analysis (PCA)

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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AcciRadar: Real Time Automatic Accident Detection and Reporting Framework

AcciRadar: Real Time Automatic Accident Detection and Report...

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International Conference on computer and Communications Technologies (ICCCT)

作者： M Fathima Mubarakkaa S. Hemavathi V. Kavitha Aarthi M Varshini K Janani Sindhu P Department of Computer Science and Engineering Sri Sairam Engineering College Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Every year, millions are injured or killed in road accidents, with a high economic and social cost to communities. Speeding, impaired driving (alcohol, drugs), distracted driving (texting, phone use), and adverse weather conditions significantly contribute to accidents. Accidents that occur in places with low population density, such as highways, can lead to loss of life due to the unavailability of emergency medical assistance and longer response times. Highways, where accidents often happen, now have more cameras. Therefore, utilizing these cameras can help automatically detect accidents. Thus, this proposal outlines a real-time automatic accident detection system using surveillance cameras and Convolutional Neural Networks (CNN) technology, combined with GPS for location tracking. The system would identify nearby hospitals and alert them via phone call and message, providing the accident location. This helps to save lives in danger and ensures that immediate medical care and treatment is provided to the injured person.

关键词： Road transportation Road accidents Hospitals Surveillance Cameras Real-time systems Convolutional neural networks Time factors Accidents Global Positioning System

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Exploring Image-Based Approaches for Effective Synthetic Time-Series Data Generation

Exploring Image-Based Approaches for Effective Synthetic Tim...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Oh, Samgwon Oh, Seungmin Lee, Junghoon Kim, Doyeon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligence Electronics Computer Engineering Gwangju Korea Republic of Chonnam National University Department of Intelligence Information Convergence Gwangju Korea Republic of Astana IT University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798331507022

The effective generation of synthetic time-series data is crucial for addressing data scarcity in various domains such as network monitoring, financial analysis, and healthcare. This study explores image-based approaches for transforming time-series data into visual representations, enabling the application of advanced image synthesis techniques. We systematically evaluate multiple transformation methods, including recurrence plots, Gramian Angular Fields (GAF), and Markov transition fields, to identify the most suitable techniques for preserving temporal and structural characteristics. The generated synthetic data are validated using statistical similarity metrics and tested on machine learning tasks, such as anomaly detection and forecasting, to assess their practical utility. Experimental results demonstrate that image-based synthetic data generation not only retains essential time-series features but also enhances model robustness under limited data conditions. This research contributes to the growing field of synthetic data generation by providing insights into the integration of time-series and image processing techniques. Future work will focus on refining these methods and exploring their scalability across diverse applications. © 2025 IEEE.

关键词： Photointerpretation

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An extension of the ADVISE Meta modeling framework and its application for an early-stage security analysis of a public transport supervision system

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Journal of Reliable Intelligent Environments 2023年第3期9卷 263-281页

作者： Mariotti, Francesco Bondavalli, Andrea Lollini, Paolo Montecchi, Leonardo Nardi, Simone Dipartimento di Matematica e Informatica ‘U. Dini’ University of Firenze Viale Morgagni 65 Firenze50134 Italy Department of Computer Science Norwegian University of Science and Technology Sem Sælands vei 7–9 Trondheim7034 Norway Computational Science Department MERMEC Engineering Srl Via Livornese 1019 San Piero a Grado Pisa56122 Italy

Early-stage security analysis can be used for a preliminary assessment of the security level of a system, thus providing useful insights to guide the whole system’s development. In this paper, we focus on a specific meta-level modeling framework for security analysis, ADVISE Meta, which allows representing a system using generic built-in blocks and relationships constituting the ontology of the framework, and to automatically derive complex low-level stochastic models representing attack steps and adversaries. In this paper, we extend the ADVISE Meta ontology to enlarge the variety of the possible attack paths and adversaries that can be represented in the framework, by modeling (i) attack patterns available in the CAPEC database, a comprehensive dictionary of known patterns of attack, and (ii) the adversaries’ profiles defined in the Threat Agent Library (TAL), a reference library which describes the characteristics of threat agents. The paper provides a detailed description of the whole process for extending the ADVISE Meta ontology, and the application of the extended modeling framework for an early-stage security analysis of a public transport supervision system. The framework enables a variety of security-oriented analyses, in particular to assess the probability that a given adversary can successfully reach a specific goal, to analyze the most probable attack path that adversaries can follow to reach a goal, to perform sensitivity analysis at varying of attack patterns and adversaries’ profiles, to compare different architectural solutions, and to identify the system’s components that can be more probably attacked by adversaries. © 2023, The Author(s).

关键词： Stochastic models

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EyeHelp: Predicting an AOI based on Eye Gaze for Patient Assistance

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Procedia computer science 2025年 258卷 1486-1495页

作者： Akshay S Dhanush S Aswin G Nath Amudha J Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus Karnataka India Computational Engineering Ruhr University Bochum Bochum Germany Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Bengaluru Karnataka India

In this research, we delve into the predictive capabilities of deep learning models for classifying Areas of Interest (AOIs) based on gaze patterns in patient assistance systems. Utilizing eye-tracking technology to capture gaze data consisting of x and y coordinates along with duration, we aim to discern the specific regions of interest on the screen. With 9 distinct AOIs identified through a 9-point calibration process, we leveraged Python to preprocess the dataset. Applying FFNN, LSTM, and GRU, EyeHelp achieved promising accuracies: 80% for FFNN, 96% for LSTM, and 93% for GRU. Comprehensive observations in terms of accuracy, recall, and F1-score metrics across AOI classes are provided. EyeHelp employs an open source integrated webcam; hence the approach is open and adaptable to various healthcare environments. By integrating this technology, our system can be seamlessly deployed in clinics, hospitals, and even home care environments. The utilization of integrated webcams facilitates cost-effective and non-intrusive monitoring, offering valuable insights into patient behavior and attention patterns.

关键词： Eye Tracking Assistance Deep Learning Prediction Area Of Interest(AOI) Eye Gaze Gaze Pattern

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Projective Exploration on Individual Stress Levels using Machine Learning 2

Projective Exploration on Individual Stress Levels using Mac...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Nagendra Prabhu, S. Singh, Amarjeet Bhavya, B. Sreeja, K. Bhargavi, P. Srm Institute of Science Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India New Horizon College of Engineering Department of Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9781665400527

Recently, Stress Prediction in every individual based on their profile and behaviour is a challenging task in the current sector. Current system is a manual process where it is difficult to identify the stress in the college students or employees. There is no automation for any stress prediction. System uses machine learning algorithms or AI algorithms to find out the stress levels, these technologies are used for application development. The purpose of this project is to reduce stress in students and employees. computer science has come a long way in the last few years. It is massive and multifaceted. It has been used in a variety of applications to meet the basic needs of human society. In the field of healthcare, machine learning has made significant progress. Stress is a deadly disease that kills a large number of people worldwide. How machine learning techniques can assist in reducing the risk of stress prediction, is examined in this study. © 2022 IEEE.

关键词： Personnel

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Transforming Diabetes Care with Predictive Machine Learning Techniques 4

Transforming Diabetes Care with Predictive Machine Learning ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Mariammal, G. Jasmine, R. Megiba Siva Rama Lingham, N. Poongothai, E. Prema, S. Sai Charan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Ponjesly College of Engineering Nagercoil India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331508845

Diabetes mellitus is a chronic metabolic disorder characterized by elevated blood sugar levels, affecting millions of individuals worldwide. Early detection and management of diabetes are crucial in preventing complications and improving patient outcomes. In recent years, machine learning techniques have shown promise in predicting diabetes risk based on various factors such as demographic information, medical history, and lifestyle habits. Verticaling through a not-too-well designed case study - This paper is an extensive examination and comparison of different ML techniques for diabetes prediction. It goes through the methodologies, datasets, features, performance metrics and challenges of current models that are based on machine learning. In addition, understand strengths and weaknesses of all the algorithms- SVM, Decision Trees, Random Forests, Logistic regression etc. Here the logistic regression algorithm is used to develop a predictive model for diabetes based on demographic and clinical features. After preprocessing the data, including handling missing values and scaling numerical features, trained a logistic regression model using a portion of the dataset. The model achieved a high accuracy of 90% on the testing set, indicating its effectiveness in distinguishing be- tween diabetic and non-diabetic individuals. © 2024 IEEE.

关键词： Logistic regression

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Approaches to the Development of an Effective Regulatory Ecosystem and Policies for Big Data Analytics in the Republic of Kazakhstan

Approaches to the Development of an Effective Regulatory Eco...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Akatov, Kamil Yedilkhan, Didar Urnaliyev, Alibi Astana It University Department Of Computational And Data Science Nur-Sultan Kazakhstan Astana It University Department Of Computer Engineering Nur-Sultan Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

This paper studies the Big Data ecosystem and regulatory approaches for the technological issues used in Kazakhstan. Key points are considered in terms of the importance and necessity of managing data at various levels for decision-making and facilitating their introduction into economic circulation, including an analysis of international approaches and cases in the use of data by authorities and organizations. The main aim of this paper is to make analysis and prepare model for improving policies for the development of Big Data policies included analytics at national level. © 2022 IEEE.

关键词： Information management

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Streamlining Attendance with Fast Face Recognition Model 8

Streamlining Attendance with Fast Face Recognition Model

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sundar, Koushik Ravikumar, S. Jeyalakshmi, J. Eugene Berna, I. Samuel, Prithi Citibank Na Jersey CityNJ United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Campus Chennai India

ISBN: (纸本)9798350376425

Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based solution to replace the manual attendance tracking process, streamlining the process and reducing human error. The proposed system integrates facial recognition, registration forms, and a Redis database to automate attendance tracking. By leveraging the ML search algorithm (similarity approach) and the Insight face Python module, the system can accurately identify individuals within a database. The system's implementation involves two phases: the initial phase focuses on integrating facial recognition, registration forms, and database access. The second phase involves utilizing software such as Streamlit and Dashboard to enhance user experience and visualization. This innovative solution offers a more efficient and accurate alternative to traditional manual attendance tracking methods, improving overall efficiency and reducing administrative burdens. By automating the process, the system contributes to a more streamlined and secure environment. © 2024 IEEE.

关键词： Database systems

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