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2024 international conference on advances in Computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Islam, Mohammad Tanvirul Ashraf, Kahakashan Hosen, Md.Hamid Nawar, Sadia Asgar, Safa East Delta University Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350350289

In today's developing world, anxiety is a common mental disorder among university students. In this work, we predict anxiety in university students using a voting classifier. We have applied explainable artificial intelligence (XAI), to gain a better understanding of the machine learning model, using a Google Form, the dataset was gathered from several public, private, and national universities in Bangladesh. We have compared machine learning algorithms using 20 selected features and without feature selection. By using the concept of voting, we have created a new model. In order to create our final model, we selected the best three ML algorithms based on their accuracy. The voting classifier has the highest accuracy of 96%, while F1 and Recall scores are 96%, and Precision is 97%. The LIME and SHAP models are used to explain model predictions instead of a black-box machine learning model. The study determines anxiety levels by particular observations, allowing for transparency and comprehension. The goal is to create prediction models to detect at-risk individuals and root causes of anxiety among university students, consequently reducing detrimental consequences for academic performance and well-being. © 2024 IEEE.

关键词： Lime

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Enhancing Traffic Incident Management with Large Language Models: A Hybrid machine learning Approach for Severity Classification

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international JOURNAL OF INTELLIGENT TRANSPORTATION systems RESEARCH 2024年第1期23卷 259-280页

作者： Grigorev, Artur Saleh, Khaled Ou, Yuming Mihaita, Adriana-Simona Univ Technol Sydney Fac Engn & IT Sydney Australia Univ Newcastle Coll Engn Sci & Environm Newcastle Australia

This research showcases the innovative integration of Large Language Models into machine learning workflows for traffic incident management, focusing on the classification of incident severity using accident reports. By leveraging features generated by modern language models alongside conventional data extracted from incident reports, our research demonstrates improvements in the accuracy of severity classification across several machine learning algorithms. Our contributions are threefold. First, we present an extensive comparison of various machine learning models paired with multiple large language models for feature extraction, aiming to identify the optimal combinations for accurate incident severity classification. Second, we contrast traditional feature engineering pipelines with those enhanced by language models, showcasing the superiority of language-based feature engineering in processing unstructured text. Third, our study illustrates how merging baseline features from accident reports with language-based features can improve the severity classification accuracy. This comprehensive approach not only advances the field of incident management but also highlights the cross-domain application potential of our methodology, particularly in contexts requiring the prediction of event outcomes from unstructured textual data or features translated into textual representation. Specifically, our novel methodology was applied to three distinct datasets originating from the United States, the United Kingdom, and Queensland, Australia. This cross-continental application underlines the robustness of our approach, suggesting its potential for widespread adoption in improving incident management processes globally. The code and data subsets are available by the link: https://***/Future-Mobility-Lab/LLM-Incident-Classification.

关键词： Traffic accident Incident severity classification machine learning Traffic management Large language models

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Advanced Detection of Malicious URLs using machine learning : A Comparative Analysis of SVM, Random Forest, and Logistic Regression 1

Advanced Detection of Malicious URLs using Machine Learning ...

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1st IEEE international conference on advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kesarwani, Vartika Rajesh, E. Galgotias University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

With the escalating threats in the digital landscape, recognizing bad URLs has grown to be a paramount concern for ensuring cybersecurity. This research introduces an innovative approach leveraging machine learning algorithms, specifically Support Vector machine (SVM), Random Forest, and Logistic Regression, for proactive detection of malicious URLs. The study gives a comparative evaluation of these algorithms, highlighting their efficacy in discerning malicious URLs from legitimate ones. The suggested framework achieves an astounding degree of precision of 96.6% using Random Forest, demonstrating its robustness in addressing the evolving challenges in web security. The study adds to the field by offering insights into the execution of various machine learning models, paving the path to improved cybersecurity measures. © 2024 IEEE.

关键词： Adversarial machine learning

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Intelligent Attack Detection in ROS-based systems

Intelligent Attack Detection in ROS-based Systems

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2023 IEEE international conference on Big Data, BigData 2023

作者： Zaim, Hande Cavsi Yolacan, Esra Nergis Yavanoglu, Uraz Eskişehir Osmangazi University Computer Engineering Eskişehir Turkey Gazi University Computer Engineering Ankara Turkey

ISBN: (纸本)9798350324457

Robotic systems, which were initially intended only for industrial work, have become a part of our daily lives in recent years. ROS is a popular middleware for developing applications in robotic systems. The security of ROS-based systems has gained importance with the expansion of robotic system usage. To enhance security, machine learning algorithms play an important role in robotic systems, especially in attack detection. This article provides an overview of the security of robotics by examining the machine learning based detection methods of cyber-attacks against ROS. We start by presenting security issues with a brief review of attack vectors in ROS. Next, we discuss the machine learning based attack detection approaches. Finally, we conclude by reviewing studies in the literature that use machine learning algorithms on attack detection in ROS. This survey aims to investigate the most popular and effective techniques applied to detect cyber-attacks, focusing on highlighting the latest advances in the field of machine learning in ROS attack detection. Our study will raise awareness among researchers in robotic system security and encourage researchers to upgrade attack detection in ROS-based systems. © 2023 IEEE.

关键词： Attack Detection Intelligent Detection machine learning Robot Operating System (ROS) Robotic systems

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AI Empowered Solar Energy: Reinforcement learning and Comparative Analysis for Grid-Connected Photovoltaic systems Optimization 28

AI Empowered Solar Energy: Reinforcement Learning and Compar...

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28th IEEE international conference on Intelligent engineering systems (INES)

作者： Marwa, Banawaz Ali, Zeddini Mohamed Hanen, Berriri Faouzi, Mimouni Mohamed LAS2E Elect Dept Monastir Tunisia

ISBN: (纸本)9798350367607;9798350367591

This study addresses the optimization of grid-connected photovoltaic (PV) systems, particularly focusing on overcoming challenges posed by shading conditions. Employing machine learning (ML) technology, specifically Reinforcement learning (RL), this research conducts a comparative analysis with traditional optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for Maximum Power Point Tracking (MPPT). Simulation results, executed using Simulink/Matlab, highlight RL's superior performance in terms of convergence speed and effectiveness compared to PSO and GA, without the need for prior system knowledge. This study contributes valuable insights into the application of ML-based algorithms in enhancing PV system efficiency, paving the way for advancements in renewable energy technologies.

关键词： Reinforcement learning Particle Swarm Optimization Genetic algorithm MPPT grid-connected photovoltaic system Shunt Active Power filter Multilayer Feed Forward Neural Network Fuzzy Logic

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machine learning on Public Intrusion Datasets: Academic Hype or Concrete advances in NIDS? 53

Machine Learning on Public Intrusion Datasets: Academic Hype...

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53rd Annual IEEE/IFIP international conference on Dependable systems and Networks (DSN)

作者： Catillo, Marta Pecchia, Antonio Villano, Umberto Univ Sannio Dipartimento Ingn Benevento Italy

ISBN: (纸本)9798350325454

The number of papers on network intrusion detection based on machine and deep learning is growing at an unprecedented rate. Most of these papers follow a well-consolidated pattern: (i) proposal of an intrusion detection system based on machine (deep) learning, (ii) learning-testing with one (more) public intrusion dataset(s), (iii) achievement of outstanding detection performance. Is the intrusion detection problem solved? Unfortunately, no. This paper shares a deep reflection on the major limitations of public intrusion datasets and related machine learning experiments, which greatly diminish the findings documented by the literature. At the end of the day, in spite of the academic hype and the increasingly-complex machine and deep learning exercises around, the role of public datasets in advancing intrusion detection of real-world networks remains questionable. The way existing intrusion datasets are collected, released and used by the community should be approached with extreme caution. This paper provides concrete hints for the construction of future intrusion detection datasets and more rigorous machine learning experiments.

关键词： deep learning intrusion detection NIDS public datasets cybersecurity

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Threat Modeling of ML-intensive systems: Research Proposal 24

Threat Modeling of ML-intensive Systems: Research Proposal

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IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Jedrzejewski, Felix Viktor Blekinge Inst Technol Karlskrona Blekinge Sweden

ISBN: (纸本)9798400705915

Context: The rise of Artificial Intelligence (AI) and machine learning (ML) applied in many software-intensive products and services introduces new opportunities but also new security challenges. Motivation: AI and ML will gain even more attention from industry in the future, but threats caused by already discovered attacks specifically targeting ML models are either overseen, ignored, or mishandled. Problem Statement: Current Software engineering security practices and tools are insufficient to detect and mitigate ML Threats systematically. Contribution: We will develop and evaluate a threat modeling technique for non-security experts assessing ML-intensive systems in close collaboration with industry and academia.

关键词： Adversarial machine learning Threat Modeling Industry

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Factory Floor Transformation for Achieving Operational Excellence with IoT and machine learning

Factory Floor Transformation for Achieving Operational Excel...

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2024 international conference on advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Nagalakshmi, T.J. Santhuja, P. Rangarajan, J. Muthukumarasamy, S. Mohankumar, N. Velmurugan, S. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Cmr Technical Campus Department Of Computer Science And Engineering Telangana Hyderabad India Department Oj Etectronics And Communication Engineering Tamil Nadu Rasipuram India S.A.Engineering College Department Of Computer Science And Engineering Tamil Nadu Chennai India Maharashtra Nagpur India T.J.S. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

In today's competitive industry, manufacturers seek novel approaches to improve operations and enhance efficiency. To achieve Operational Excellence, this abstract provides a Factory Floor Transformation plan that uses the Internet of Things (IoT), machine learning, and the Random Forest algorithm. The Factory Floor Transformation employs IoT sensors and devices throughout production. These sensors monitor machine operation, product quality, and energy usage in real-time. Using data preprocessing, feature engineering, and predictive modeling, a strong machine learning framework is created. The Random Forest algorithm, which handles complicated, multi-dimensional data, powers machine learning. Random Forest is appropriate for manufacturing complex problems because its ensemble learning technique yields reliable and accurate forecasts. Predictive maintenance, quality control, and resource optimization are enabled by the model's previous training and real-time updates. Factory Floor Transformation provides enterprises with a complete Operational Excellence solution. Companies may minimize production bottlenecks, downtime, defects, and resource allocation by using IoT data and machine learning. This abstract illustrates how this method may transform production and make it more efficient and competitive. © 2024 IEEE.

关键词： Sensor networks

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AI-Driven Enhancements in Indian Payment systems: A Futuristic Perspective on Financial Applications and Modifications 1

AI-Driven Enhancements in Indian Payment Systems: A Futurist...

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1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Swarna, T. Kulkarni, Shraddha Kumar, Mayank Choephel, Tenzin Mohan Raju, B.H.L. Gosavi, Shruti Gitam University Department of Finance Telangana Hyderabad India Maharashtra Pune India School of Business and Management Christ University Bengaluru India Rajiv Gandhi University Department of Management Arunachal Pradesh India Mallareddy college of engineering Department of MBA Telangana Hyderabad India Tilak Maharashtra Vidyapeeth Pune India

ISBN: (纸本)9798331518578

This study explores the application of machine learning (ML) to improve Indian payment systems, with a focus on AI-driven developments for tasks including fraud detection, transaction validation, and consumer behaviour research. We evaluate the effectiveness of several machine learning (ML) systems, including K-Nearest Neighbours (KNN), Random Forest (RF), Decision Trees (DT), Support Vector machine (SVM), and Logistic Regression (LR), using a variety of criteria. The results, which include precision, F1 score, accuracy, recall, and AUC, show how well Random Forest and Logistic Regression work to detect fraudulent transactions. Important elements including transaction amount, payment method, and user behaviour patterns are also revealed by the feature importance evaluation. Significant differences exist between models in terms of training times and hyperparameter optimisation outcomes. All things considered, this study highlights how ML models can spur innovation in Indian payment systems, enhancing security, effectiveness, and consumer satisfaction while offering a thorough assessment structure for potential future implementation in the fintech industry. © 2025 IEEE.

关键词： Smart manufacturing

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Breast Cancer Prediction Using Artificial Intelligence Technology 4

Breast Cancer Prediction Using Artificial Intelligence Techn...

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4th international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Akila, V. Christaline, J. Anita Srm Institute of Science and Technology Vadapalani Dept. of Electronics and Communication Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

The aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. Data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. The use of these strategies can help medical practitioners forecast the onset of breast cancer. © 2024 IEEE.

关键词： Algorithms Breast cancer Data mining machine learning

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