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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6842 条记录，以下是501-510 订阅

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Enhancing the BERT Model for Qur’anic Question Answering System 3

Enhancing the BERT Model for Qur’anic Question Answering Sy...

引用

3rd international conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Alarabi, Ebtihal Ellabib, Issmail Computer Engineering Department University of Tripoli Tripoli Libya

ISBN: (纸本)9798331529222

machine Reading Comprehension is a rapidly evolving field that aims to bridge the gap between human-like understanding and machine text processing. With advancements in deep learning and the development of sophisticated models, MRC systems are becoming increasingly capable of understanding context and providing accurate answers. Driven by the increasing demand for advanced question-answering (QA) systems on the Holy Qur'an, the inaugural Qur'an Question Answering shared task (Qur'an QA 2022 OSACT 2022), the second edition in Arabic NLP 2023. The Qur'an QA 2023 encompasses two essential sub-tasks: Passage Retrieval (PR) and machine Reading Comprehension (MRC). The shared task aims to enhance the capabilities of QA systems and facilitate deeper engagement with the Qur'an. This research paper focuses on developing an improved BERT model, implemented using the KNIME platform, and tailored for Arabic. We aim to utilize this model for Qur'anic Question Answering, resulting in an Area under the Curve (AUC) of 0.5 and an accuracy rate of 0.997. ©2024 IEEE.

关键词： Deep learning

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An Evaluation and Comparison for Phishing Attack Detection using machine learning Approaches 3

An Evaluation and Comparison for Phishing Attack Detection u...

引用

3rd international conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Sharma, Ajeet Kumar Anushree Rakesh, Nitin Verma, Pawan Kumar Sharda University School of Engineering and Technology Dept. of Computer Science & Engineering Uttar Pradesh Greater Noida India GLA University Dept. of Computer Engineering & Applications Uttar Pradesh Mathura India Nagpur Campus Pune India

ISBN: (纸本)9798350309409

The persistent and evolving threat of phishing attacks demands effective and adaptive detection techniques. This research paper presents a comprehensive evaluation and comparison of various machine learning approaches to detect phishing attacks. We investigated five prominent algorithms: Logistic Regression, Support Vector machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes, and Extreme Gradient Boosting (XGBoost), to determine their efficacy in identifying phishing activities. Our methodology involved a systematic analysis using a large dataset of phishing and legitimate URLs, where each model was trained, tested, and validated to ensure robustness and reliability. The performance of each algorithm was assessed based on accuracy, precision, recall, and F1 score. Among the evaluated models, XGBoost demonstrated superior performance, achieving an exceptional accuracy of 99.75%. This result underscores the potential of XGBoost in phishing attack detection, offering a promising tool for cybersecurity applications. © 2024 IEEE.

关键词： Cybersecurity

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

3rd Odisha International Conference on Electrical Power Engi...

引用

3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

ISBN: (纸本)9798350354379

The proceedings contain 170 papers. The topics discussed include: a comparison of various task scheduling algorithm using cloudSim;CyberEdPlay - an IQ calculator for predicting mental health;Alzheimer’s disease detection using VGG19 Model on MRI scans;optimizing microgrid systems with energy storage using advanced machine learning techniques;leveraging Keras for early detection of diabetes: a multi-layer perceptron model;an efficient framework for EEG signals processing using backpropagation multilayer convolutional neural network for brain tumor diagnosis;hybrid MPPT controller for photovoltaic system under partial shading condition;and analytical performance comparison of IoT communication protocols MQTT and CoAP from security perspective.

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Driving into the Future: Exploring machine learning Approaches for Optimal Energy Management in Electric Vehicles: A Review of Challenges and Future Recommendations 3

Driving into the Future: Exploring Machine Learning Approach...

引用

3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Raj, Aby N Sakthivel, K. Bharath Institute of Higher Education and Research Department of EEE Chennai India

ISBN: (纸本)9798350390728

Electric vehicles (EVs) represent an essential and pivotal stride in the pursuit of sustainable transportation. Nonetheless, effective and efficient operation of EVs is critical for achieving optimal energy management. This research paper presents an extensive examination of machine learning (ML) methodologies utilized in EVs to achieve optimal energy management. It also tackles the obstacles encountered in this field and delineates potential future suggestions. In this study, we investigate a range of ML methodologies, such as reinforcement learning, deep learning, supervised and unsupervised learning, and their potential uses in improving vehicle performance, energy efficiency, and battery management. Critical obstacles such as data scarcity, computational complexity, and real-time implementation are identified in the review. Additionally, the integration of machine learning with autonomous driving and vehicle-to-grid technologies is underscored as an emerging trend. In spite of the fact that ML provides encouraging solutions for energy management in EVs, additional research is required to overcome the identified obstacles and maximize its potential, according to the study's findings. © 2024 IEEE.

关键词： Self-supervised learning

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Optimization of Deep learning Method for Automatic Recognition of Electric Energy Meter Manufacturers and Models 3

Optimization of Deep Learning Method for Automatic Recogniti...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Xiaoyi, Zhang Xuliang, Zhao Zhibo, Feng Keyi, Liu Xuan, Xuan State Grid Liaoning Marketing Service Center Liaoning Shenyang110000 China

ISBN: (纸本)9798350380989

The manufacturing and application of electricity meters are constantly expanding with the development and expansion of the power industry. In order to better utilize the function of electricity meters and avoid accidents and malfunctions, this article believed that it is crucial to confirm the manufacturer, model, and even more relevant information of electricity meters through automatic identification. Moreover, this article also used deep learning methods to assist the automatic recognition system in identifying targets. In the final experiment of this article, a comparative experiment was conducted on the accuracy of conventional recognition systems and intelligent recognition systems based on convolutional neural networks (a deep learning technology). The results showed that the average accuracy of the conventional system was 78.79%, while the average accuracy of the intelligent system was 89.05%. Therefore, this study found that deep learning can improve the recognition accuracy of the automatic recognition system for electricity meter related information. © 2024 IEEE.

关键词： Automatic Recognition System Convolutional Neural Network Deep learning Electricity Meter

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Hybrid machine learning Based Shill Bidding Fraud Detection System Using Recursive Feature Elimination Method 3

Hybrid Machine Learning Based Shill Bidding Fraud Detection ...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Amathi, Jayakrishna Cherukuri, Likhitha Krishna Ponnaganti, Krishna Priyanka Kasukurthi, Aravinda Yalamanchili, Sai Likhitha Meghana, Mannava RVRJCCE Dept. of CSBS Guntur India RVRJCCE Dept. of CSE Guntur India

ISBN: (纸本)9798350396157

Due to the significant surge in internet traffic and the increasing complexity of networks, manual monitoring of traffic flows and intrusion detection in large networks has become an overwhelming task for network analysts. Maintaining information security is of paramount importance, with fraud detection playing a crucial role in this endeavour. Recently, the adoption of machine learning (ML) methods for intrusion detection has also gained substantial popularity. The vast volume of data generated by network traffic often contains irrelevant features, resulting in slower detection processes and diminished system performance. To address this issue, feature elimination or selection is carried out before classification. In this research, a Shill Bidding Fraud Detection system leverages a Hybrid machine learning approach and incorporates a recursive feature elimination method. To ensure a thorough and effective analysis of network traffic for intrusion detection, we combined Support Vector machine (SVM) and Decision Tree (DT) machine learning models into a hybrid framework. Additionally, we established a multi-class classifier capable of not only distinguishing between malicious and benign traffic but also extending labels to categorize various forms of malicious data. To optimize system performance, This study has identified and eliminated irrelevant features using the Recursive Feature Elimination method. Experimental results demonstrate a significant enhancement in the performance of network intrusion detection. © 2024 IEEE.

关键词： Decision trees

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Keratoconus Detection Using machine learning 3

Keratoconus Detection Using Machine Learning

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sandhiya, R. Methun Chakkravthi, N. Naveen Vignesh, D. Subash Chander, R. Information Tcehnology Kongu Engineering College Tamilnadu Erode India

ISBN: (纸本)9798350372120

Keratoconus is a degenerative corneal illness that thins and distorts the cornea into a cone shape, impairing the affected person's eyesight and quality of life. It is essential to identify keratoconus promptly and accurately to take the necessary action and stop the condition from progressing. The goal of this study is to use artificial intelligence principles and cutting-edge technologies to diagnose keratoconus early and automatically. The goal is to effectively categorize data with multiple features using supervised machine learning algorithms with impressive accuracy ratings. Finding the most effective model and putting it into practice with a user interface that enables realtime testing to determine whether a patient has keratoconus and to forecast the disease's stage are additional goals. There are still issues to be resolved, such as developing standardized diagnostic standards and affordable, user-friendly diagnostic instruments. Enhancing artificial intelligence models and growing telemedicine applications for remote sensing should be the main goals of future research. Clearly, the key to maintaining eyesight and improving the general quality of life for those with keratoconus is early identification. Additionally, to guarantee the smooth incorporation of cutting-edge technology into clinical practice, this project seeks to promote collaboration between medical practitioners and AI experts. for huge datasets for reliable model training. © 2024 IEEE.

关键词： Support vector machines

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Youth Mobile Usage Monitoring: A Comprehensive Overview of Trends and Tools 3

Youth Mobile Usage Monitoring: A Comprehensive Overview of T...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rameshbhai, Patel Kartik Zalawadia, Khyati Dabhi, Vipulkumar CSE Department PIET Parul University Gujarat Vadodara India

ISBN: (纸本)9798331532420

The pervasive use of mobile devices among young people has significant implications for their social, emotional, and cognitive development. To effectively address the challenges and opportunities presented by this trend, it is crucial to understand youth mobile usage patterns. This review explores a variety of methodologies employed to monitor youth mobile usage, including focus groups, surveys, observational studies, and machine learning techniques. Each method offers unique advantages and limitations, and a multi-faceted approach is necessary to gain a comprehensive understanding. The review highlights the importance of balancing data privacy and ethical considerations with the need for rigorous research. By combining qualitative and quantitative methods, researchers can generate valuable insights into the complex relationship between technology and youth development. The findings of this study can inform the development of evidence-based interventions and policies to promote the positive use of mobile devices among young people. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Path Planning and Cruise Control Features In Self-Driving Cars Using ROS2 & machine learning 3

Efficient Path Planning and Cruise Control Features In Self-...

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3rd IEEE international conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hassan, Mumit Maliha, Mahmuda Haque Hossain, Jamil Talukder, Imnul Haque Ruman Hasan, Md. Mehedi Ahmed, Faisal Chittagong University of Engineering & Technology Department of Mechatronics & Industrial Engineering Chattogram4349 Bangladesh Chittagong University of Engineering & Technology Department of Electrical & Electronic Engineering Chattogram4349 Bangladesh Chittagong University of Engineering & Technology Department of Mechanical Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798331534400

This study proposes an integrated approach that uses machine learning and ROS2 as a combined tool to provide effective path planning and cruise control capabilities in self-driving cars. For object identification and environment perception, the suggested system makes use of Convolutional Neural Networks (CNN) and Haarcascade algorithm. As well as A∗ and Dijkstra algorithms are applied for finding optimal path and navigation through challenging situations such as T-junctions and cross intersections. Reliable autonomous navigation is demonstrated by the system's efficient integration of motion planning and lane assistance. The model can improve self-driving capabilities, leading to safer and more effective autonomous driving solutions, as demonstrated by experimental findings. © 2024 IEEE.

关键词： Convolutional neural networks

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Air Quality Management in Smart Cities by leveraging machine learning Techniques 3

Air Quality Management in Smart Cities by leveraging Machine...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Shree, A.N. Ramya Shankaramma Reddy, Charulatha Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India Dept. of CSE RNS Institute of Technology Bengaluru560098 India

ISBN: (纸本)9798331532420

In today’s world machine learning (ML) algorithms are actively employed to solve several real-world problems. It is used further to forecast the Air Quality Index (AQI) in smart cities, utilizing data from sensors to predict pollution levels and provide real-time information to the public. ML models are trained on well-processed datasets, and their effectiveness is assessed using standard evaluation metrics. The predictions generated by these ML models play a crucial role in air quality management strategies, such as issuing early warnings for days with poor air quality, informing policy decisions to mitigate environmental pollution, and guiding urban planning initiatives. This research underscores the importance and application of ML in predicting air quality within smart cities. © 2024 IEEE.

关键词： Sulfur dioxide

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