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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351-360 订阅

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Using OLAP Cubes as dataset for Neural Networks: Integrating Business Intelligence and Artificial Intelligence 3

Using OLAP Cubes as Dataset for Neural Networks: Integrating...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Abrarov, Rinat D. Khudaybergenov, Timur A. Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Department of Software Engineering Urgench Uzbekistan Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Department of Compyuter Engineering Urgench Uzbekistan

ISBN: (纸本)9798331516321

The integration of OLAP cubes and neural networks represents a significant advancement in business intelligence. OLAP cubes, with their multidimensional data structures, enable efficient analysis across multiple dimensions, helping businesses extract insights from their data. Neural networks excel at learning from large datasets and making accurate predictions. This paper explores how OLAP cubes can be used as datasets for training neural networks. The process includes extracting and preprocessing data to make it suitable for neural network training. The practical applications of this integration in business include improved forecasting, optimization, and strategic decision-making. By combining the analytical capabilities of OLAP cubes with the predictive strengths of neural networks, businesses can achieve more precise and actionable insights. Furthermore, the paper discusses future development and research possibilities in this area, emphasizing the potential for creating better business intelligence solutions. This integration opens new possibilities for enhancing data analysis, making it a promising area for future exploration. © 2024 IEEE.

关键词： Artificial intelligence Business analytics data integration data preprocessing Forecasting machine learning Multivariate analysis Neural networks OLAP-cubes Optimization

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Enhanced Plagiarism Detection in Online Assignments by Natural Language Processing and machine learning Techniques 3

Enhanced Plagiarism Detection in Online Assignments by Natur...

引用

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

作者： Hemandkumar, E. Varun Kumar, A. Gracewell, Jeffin Dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798331532420

As online projects become more common in the educational setting, ensuring academic integrity is a growing concern. Traditional plagiarism detection algorithms often have trouble meeting some of the more sophisticated practices that students adopt for avoiding detection such as paraphrasing or word substitution. We present a machine learning based approach that was designed to enhance the effectiveness and accuracy of online assignment plagiarism detection, the proposed system can also detect minor plagiarism cases like paraphrased information and cross-linguistically similar. Due to the fact that the model proposed is capable of learning complex patterns and it was trained on a large dataset of a marginally homogenous dataset (academic publications), it maintains relatively high rates of recall and precision. The system uses multiple approaches of information retrieval to combine the interactions of textual relationships and elements such that it captures the text semantics of how person is feeling through word embedding. Later, it trains supervised learning models to classify possible plagiarism cases, like neural networks and Support Vector machines (SVM). Also, the model can learn continuously from new data to improve its results. Our method improves the detection of traditional plagiarism and is also effective for more complex and subtle types of content reuse based on preliminary evaluation results. © 2024 IEEE.

关键词： Contrastive learning

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Improved EEG-Based Epileptic Seizure Classification Using Fusion of machine learning Model with Statistical Feature Integration 3

Improved EEG-Based Epileptic Seizure Classification Using Fu...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Sadangi, Rakesh Kumar Patra, Biswakalpa Gandhi, Tapan Kumar Sutar, Bijuni Charan Maity, Niladri Pratap Maity, Reshmi Mizoram Central University Dept. of Electronics and Communication Engineering Aizawl India Indian Institute of Technology Dept of Electrical Engineering New Delhi Delhi India Kmbb College of Engineering and Technology Dept. of Electronics and Communication Engineering Khordha India

ISBN: (纸本)9798331528201

Epilepsy is a neurological disorder characterized by recurrent seizures, often diagnosed through the analysis of electroencephalogram (EEG) signals. In this study, we propose a machine learning-based approach to classify EEG data into different brain states, focusing on epileptic seizure detection. Discrete Wavelet Transform (DWT) is employed to extract relevant time-frequency domain features from the EEG signals. Additionally, a set of statistical features - mean, standard deviation, variance, energy, entropy, coefficient of variation, root mean square, skewness, and kurtosis are incorporated to further enhance classification performance. Various machine learning models such as Random Forest, KNN, and LightGBM were evaluated. Results show that combining DWT with statistical features significantly improves the accuracy and robustness of the classification compared to using DWT alone. For example, the Random Forest model achieved an accuracy of 84.85% with DWT alone, which increased to 84.88% when combined with statistical features. Similarly, LightGBM improved from 84.87% to 85.10% in accuracy. This approach could serve as a valuable tool for aiding in the diagnosis and treatment of epilepsy. © 2024 IEEE.

关键词： Discrete wavelet transforms

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machine learning based Early Detection of Ongoing Cyber-Attacks 3

Machine Learning based Early Detection of Ongoing Cyber-Atta...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Bhagyalakshmi, A. Sruthi Laya, C.D. Yoga Preethikaa, A.M. Varsha, V. Easwari Engineering College Computer Science and Engineering Department India

ISBN: (纸本)9798350375190

The need for sophisticated detection and response systems to counteract cyber threats has grown in importance as the cybersecurity landscape changes. In order to improve cyberattack detection and response, this study proposes a novel combination of a Security Orchestration, Automation, and Response (SOAR) framework with a machine learning (ML) model. A Random Forest classifier was created and trained to distinguish between benign and potentially dangerous files using a dataset that included a variety of executable file properties. The classifier demonstrated a notable level of accuracy and precision. Then, a prediction API was established to enable real-time threat analysis by seamlessly integrating the learned machine learning model into a SOAR system. Through this integration, possible threats can be continuously identified by evaluating real-time data that has been retrieved from network traffic and endpoints. The SOAR system is activated when a suspicious file is found, which sets off a sequence of automatic events that include notifying security staff and starting response procedures. For a comprehensive and strong defence strategy, the system also integrates with already-in-use security solutions like firewalls and intrusion detection systems (IDS). The integration strengthens the overall cybersecurity posture by improving response time and increasing the effectiveness of cyberattack detection. This study presents the integrated approach's methodology, system architecture, and effectiveness, showcasing its potential as a comprehensive cybersecurity solution. © 2024 IEEE.

关键词： Cybersecurity

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Implementing the Power of Big data Analytics 3

Implementing the Power of Big Data Analytics

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

作者： Dandekar, Mayur Lote, Sukeshini Dandekar, Prajakta Maharashtra Wardha India

ISBN: (纸本)9798350390728

Big data analytics is growing as an innovator across multiple industries, helping companies to obtain useful information from large and unique datasets. This review paper thoroughly looks at the big data analytics principles, processes, uses, challenges, and possibilities. After providing a broad overview of big data, the essay discusses several analytics techniques, including descriptive, prescriptive, predictive, and diagnostic analytics. It covers Hadoop, Spark, machine learning methods, and further Big data analytics-supporting technologies. The evaluation also addresses the use of big data analytics in a variety of fields, such as cybersecurity, marketing, finance, and healthcare. Big data analytics-related ethical, security, and privacy issues are also discussed. The final section of this study discusses current developments and potential paths forward. The paper concludes by highlighting new developments and potential paths for the subject, providing insights into how data-driven decision-making is developing. © 2024 IEEE.

关键词： Adversarial machine learning

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Comprehensive Evaluations of machine learning enabled Trust and Reputation Models in Internet of Things 3

Comprehensive Evaluations of Machine Learning enabled Trust ...

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

作者： Kaur, Manpreet Verma, Vinod Kumar Sant Longowal Institute of Engineering and Technology Longowal Department of Computer Science and Engineering Punjab Sangrur148106 India

ISBN: (纸本)9798350372120

The Internet of Things (IoT) stands out as a pervasive technology, exerting a profound influence on various facets of our lives including social, commercial, and economic dimensions. IoT technologies hold immense potential for elevating human life. Despite these transformative benefits, the prevalence of cyber-attacks and threats poses considerable challenges to the security of IoT applications. Conventional standalone security measure (cryptography) have proven inadequate in heterogeneous and resource constraint IoT networks. To effectively tackle these challenges, machine learning (ML) techniques are applied in trust management for IoT security. This paper provides a comprehensive analysis of support vector machine (SVM) based models employed for trust management, to address challenges associated with trust evaluation in IoT. Furthermore, we examine existing research to demonstrate the effectiveness and applicability of ML in IoT trust management. The paper concludes with future research directions and potential advancements in this field. © 2024 IEEE.

关键词： Support vector machines

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machine learning (ML) based Electricity Price Prediction with Visualization 3

Machine Learning (ML) based Electricity Price Prediction wit...

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

作者： Abisha, D. Kumar, Prasanna D. Christy, Ida J. Evanjalin, Navedha R. Rajesh, P. Haris, Mohamed K. Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9798331532420

Electricity price prediction is crucial for businesses reliant on heavy machinery or computing services, as it aids in cost management and energy optimization. Accurate predictions allow these businesses to forecast annual electricity expenses, enhancing cost efficiency. This project aims to forecast electricity prices using machine learning techniques, specifically the Random Forest regression algorithm. The project utilizes historical data on electricity consumption and other influencing factors, including date and time, holiday indicators, day of the week, week of the year, forecasted and actual wind production, national load, temperature, wind speed, and CO2 intensity. The methodology involves converting string values to numerical values, handling missing data, and normalizing the dataset, identifying key features that significantly impact electricity prices, Splitting the data into training and testing sets, and training the model using the Random Forest regression algorithm and assessing the model's performance with appropriate metrics to ensure accuracy and reliability. The results demonstrate that the Random Forest model effectively predicts electricity prices with high accuracy. Visualizing these predictions in Power BI provides intuitive insights into the factors influencing electricity prices. These findings enable businesses to better anticipate electricity expenses, optimize energy usage, and improve financial planning. The comprehensive approach outlined in this project offers a valuable tool for understanding and forecasting electricity prices, thereby enhancing resource management and cost efficiency. © 2024 IEEE.

关键词： data Preprocessing Electricity Pricing Energy Cost Optimization machine learning Random Forest Regression

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Comparative Analysis of machine learning Algorithms for Disease Prediction using Medical datasets 3

Comparative Analysis of Machine Learning Algorithms for Dise...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Roopa, C. Latha, R.S. Dhavamani, M. Bommisrilekkhaa, G. Angel Shalini, P. Dhanyajothi, R. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Department of Artificial Intelligence Kongu Engineering College Tamilnadu Erode India Department of Mathematics Kongu Engineering College Tamilnadu Erode India

ISBN: (纸本)9798331527549

Application of machine learning in the analysis of medical data can be said to be one of the current transformations happening within the healthcare fraternity. This paper highlights several ways in which various machine learning techniques can be used for gaining valuable insight into disparate types of medical data, such as patient records or genetic information, toward further clinical decision-making with a view to better prediction of diseases or strategies related to personalized medicine. The study critically evaluated various algorithms by considering their performances, suitability with regard to certain medical datasets they would be set upon to analyze, and to interpret the performance, particularly within the supervised learning frameworks. It talks about methodologies through which feature selection can take place to make sure that there is effective model development. Consequential applications in early disease detection as well as the optimization of their treatments are further emphasized, representing great potential for massive improvements in their recovery outcomes. The results highlight how far reaching data-driven approaches can affect the future of healthcare. It is envisioned that, with the use of such methods, the paper foresees a paradigm shift in disease diagnosis and treatment for personalized medical care and improved health outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing Breast Cancer Detection with Neural Networks: A machine learning Approach for Improved Diagnostic Accuracy 3

Enhancing Breast Cancer Detection with Neural Networks: A Ma...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Aadiwal, Vikrant Sharma, Bhisham Yadav, D.P. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India G. L. A. University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350354171

This research paper presents a neural network model designed for breast cancer detection, which has demonstrated exceptional accuracy in distinguishing between malignant and benign cases. The model achieved an impressive 98.25% accuracy, with F1 Score, Precision, and Recall all reaching 0.9825, showcasing its high reliability. The confusion matrix underscores the model's effectiveness in minimizing both false positives and false negatives, while the close alignment between training and validation metrics suggests robust generalization to unseen data, mitigating concerns about *** findings underline the significant impact that advanced machine learning techniques can have in the realm of medical diagnostics, particularly in the early detection and treatment planning of breast cancer. Given its high accuracy and reliability, this model has the potential to become a valuable tool in clinical settings, assisting healthcare professionals in making well-informed decisions and ultimately improving patient outcomes. By advancing Good Health and Well-being and contributing to Reduced Inequalities, this research emphasizes the role of innovative technologies in enhancing healthcare delivery and ensuring equitable access to high-quality medical care. © 2024 IEEE.

关键词： Breast Cancer Detection machine learning Malignant and benign cases Neural Network

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Predictive Maintenance for An Industrial Robotic Arm using LoRa Technology 3

Predictive Maintenance for An Industrial Robotic Arm using L...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Pokhriyal, Vaibhav Prajwal, V Kannan, Vignesh Mahto, Dhanesh K. Acharya, Vadiraja PES Univ Comp Sci & Engn Bengaluru India

ISBN: (纸本)9798350372977;9798350372984

Industrial Internet of Things (IIoT) systems play a pivotal role in enabling real-time monitoring and analysis of equipment to prevent breakdowns and optimize performance. Predictive maintenance stands as a critical component of IIoT systems, allowing industries to anticipate equipment failures, minimize downtime, and enhance overall equipment effectiveness. This paper investigates the potential of Long Range (LoRa) technology in predictive maintenance applications within IIoT systems. Specifically, the study delves into the utilization of LoRa technology to bolster predictive maintenance capabilities, with a focus on a live prediction analysis demonstration conducted on an industrial robotic arm. The research presents a detailed test bed setup for conducting predictive maintenance, which involves capturing data from the robot, transmitting it to a LoRaWAN gateway, enabling prediction of normal or abnormal behavior in the robotic arm. This study provides insights into the efficacy of LoRa technology in enhancing predictive maintenance capabilities within IIoT systems.

关键词： Predictive Maintenance Industrial Internet of Things(IIoT) LoRa Technology machine learning

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