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检索条件"主题词=Machine learning Algorithms"

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An Approach to Detect Fake News Based on machine learning

An Approach to Detect Fake News Based on Machine Learning

引用

International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Rohit Singh Himanshu Suyal Monia Digra Department of IoT & IS Manipal University Jaipur Rajasthan India School of CSET Bennett University Greater Noida UP India Jain (deemed-to-be) University Bangalore Karnataka

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In today's world, the proliferation of internet-connected devices is significantly increasing among users, creating a huge amount of information. It creates a big challenge to reliable information shared through these devices. The reliability of news is important to stop its negative side effects. Several fake news articles are broadcast publicly for the specific creator's small benefits. Therefore, a mechanism to identify such fake news is required so society is not stuck in counterfeit illusions. Nowadays, machine learning-based approaches are the most suitable way to recognize such news. A few researchers use machine learning to enhance the accuracy of predicting fake news. This paper also utilized the machine learning-based method to separate fake news from legitimate news sources. First, we analyze concepts of K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector machines (SVM), and Random Forest (RF) on a few important factors. Then, we propose an ensemble model to accurately identify fake news using counterfeit news detection datasets. The performance of the proposed method is proved through the experimental evaluation of how efficiently the method detects fake news. Finally, the proposed method detection findings are compared with a few important methods, showing the model's effectiveness.

关键词： Support vector machines Radio frequency learning systems Logistic regression machine learning algorithms Nearest neighbor methods Reliability Fake news Random forests Standards

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Improved Fraud Detection in Banking Systems through machine learning and Big Data Analytics with Management Key Components

Improved Fraud Detection in Banking Systems through Machine ...

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Kalaivani Balaji Niti Saxena Nihar Ranjan Behera M Kiran Kumar H K Prasad Pallavi Rahul Gedamkar MEASI Institute of Management Tamil Nadu India Management & Commerce Jagannath International Management School New Delhi India SSBM Geneva Swiss School of Business and Management GBC - Geneva Business Center Genève Switzerland JAIN(Deemed-to-be University) Seshadri Road Gandhi Nagar Bengaluru Karnataka India Jain College Bengaluru Karnataka India Dr. Vishwanath Karad MIT WPU Maharashtra India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The banking industry needs to set up strong detection systems to fight the continuing risk of fraud in order to keep people’s trust in financial systems and keep their cash safe. Problems often arise with traditional rule-based detection systems when they are put up against complicated fraud plans. It is possible to find fake activities more easily now that machine learning and big data analytics are becoming more popular. In this research, a complete approach is introduced that makes it easier to spot fraud in banking systems. The system has algorithms for machine learning, important management parts, and big data analytics. using “big data” technologies to collect and examine a lot of data from a lot of different sources, such as external data streams, internal transaction records, and profiles of customers. Fraud detection systems get better at telling the difference by picking out key features from preprocessed data. Researching on a system that will constantly watch all incoming transfers and send alerts right away if any suspicious activity is seen. Because of this, it is necessary to set limits, create automatic systems for sending out warnings, and come up with ways to spot anomalies. The financial industry must make sure that the methods they use to find and stop fraud are legal and meet their compliance responsibilities.

关键词： machine learning algorithms machine learning Banking Big Data Feature extraction Fraud Security

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Application of machine learning Techniques to Characterize AI Benchmarks Using Hardware Events

Application of Machine Learning Techniques to Characterize A...

引用

International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Rodriguez-Bahena Victor Pizano-Escalante Luis Longoria-Gandara Omar Electronics Systems and IT ITESO

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

Nowadays, in a dynamic data center landscape, effective distribution of computational resources is crucial to achieving peak performance and cost-efficiency. This study addresses the challenge of distinguishing artificial intelligence work-loads from other tasks within data centers, focusing on optimizing their execution on specialized AI accelerators like GPUs. By integrating machine learning algorithms with hardware performance counters, the study profiles AI workloads and develops predictive models. These models enable accurate detection and optimization of AI compute tasks, aiming to enhance resource allocation and overall performance in modern data center environments. The study achieves 100% precision in distinguishing AI / ML compute workloads from other benchmarks in the proposed experiments, underscoring its effectiveness in improving data center efficiency and productivity.

关键词： Data centers machine learning algorithms System performance machine learning Benchmark testing Robustness Real-time systems Resource management Artificial intelligence Unsupervised learning

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The Role of Linear Algebra in Developing machine learning Solutions

The Role of Linear Algebra in Developing Machine Learning So...

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Advancement in Technology (ICONAT), International Conference for

作者： Mayuri Mhaske Bhagwat Gidhad Kamalkishor Maniyar Gajanan Ghuge Dr. D. Y. Patil Institute of Technology Pimpri Pune India Savitribai Phule Pune University Pune India Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

The branch of mathematics known as “linear algebra” deals with vectors, matrices, and linear transformations. It is useful in a variety of disciplines, including computer science, physics, engineering, and machine learning, where it is essential to data analysis, classification, and machine learning algorithms. Additionally, machine learning is used to allow computer programmers to determine, without explicit programming, which of its applications—graphing, regression, and predicating is most closely related to mathematics, primarily linear algebra. Because machine learning is strongly related to statistics, we will briefly explain some statistical ideas before examining what linear algebra and machine learning are in this work. Afterwards, we demonstrate how linear algebra is used in support vector machines and linear regression, two types of machine learning algorithms.

关键词： Support vector machines Measurement Deep learning machine learning algorithms Linear regression machine learning Programming Vectors Mathematical models Physics

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Advancements in Hyperspectral Image Processing Using machine learning

Advancements in Hyperspectral Image Processing Using Machine...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Kasarapu Ramani P Yukitha Sake Karthik Karapakula Aravind Chowdary Mandapalli Akash Dept of Data Science Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of CSSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

In several domains, such as remote sensing, agriculture, and environmental monitoring, hyperspectral image processing is essential. In this work, the Indian Pines dataset is used to investigate hyperspectral picture classification. Five machine learning algorithms—Support Vector machines (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost) are assessed for performance. It improves classification accuracy for preprocessing methods such as feature extraction, PCA and noise reduction. The effectiveness of each method is evaluated using performance indicators such as kappa coefficient, average accuracy, and overall accuracy. Our results highlight how well XG Boost performs in obtaining the maximum accuracy of 96.12% when compared to other models.

关键词： Support vector machines Accuracy machine learning algorithms Vectors Classification algorithms Convolutional neural networks Environmental monitoring

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Enhanced Phishing URL Detection through Stacked machine learning Model

Enhanced Phishing URL Detection through Stacked Machine Lear...

引用

Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Prashant Meena Prashant Singla Pallavi Ranjan Department of Computer Engineering Delhi Technological University New Delhi India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Phishing remains a prevalent and evolving threat in cybersecurity. Phishing URL detection techniques have also continued to evolve to keep pace with the changing tactics of cybercriminals. According to the Anti-Phishing Working Group (APWG), the number of phishing attacks detected worldwide typically ranges from hundreds of thousands to millions each month. With the growing menace of phishing attacks in the cyberworld, it has become prudent to be digitally proactive. To detect phishing websites, this project proposes a robust solution for phishing website detection using a stacked classifier model that employs six algorithms: Random Forest, XGBoost, K Nearest Neighbors, Light Gradient Boosting machine, Logistic Regression, and Support Vector machine, keeping the Light Gradient Boosting machine as the final estimator (or meta classifier). The stacking ensemble achieves an accuracy of 99.31%. Further, compared to stand-alone algorithms, it performs better than various parameters such as recall, precision, and f1-score at 99% each. This enables the model to be used in real-time applications as well.

关键词： Uniform resource locators Support vector machines machine learning algorithms Accuracy Phishing Stacking Boosting

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Quantifying the Limits of Classical machine learning Models Using Contextuality

Quantifying the Limits of Classical Machine Learning Models ...

引用

Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Eric R. Anschuetz Mariesa Teo Willers Yang James Sud Christopher Kang Teague Tomesh Frederic T. Chong Institute for Quantum Information and Matter Caltech Pasadena CA Pritzker School of Molecular Engineering University of Chicago Chicago IL Dept. of Computer Science University of Chicago Chicago IL Infleqtion Chicago IL

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Classical machine learning models struggle with learning and prediction tasks on data sets exhibiting long-range correlations. To quantify this observation we introduce a new quantity we call strong k-contextuality, develop efficient algorithms to estimate the strong k-contextuality of an empirical data set, and prove that the presence of strong k-contextuality lower bounds the classical resources required to model the associated distribution. We also show that this correlation measure does not induce a similar resource lower bound for quantum generative models, and thus propose strong k-contextuality as an empirical measure for evaluating whether a given machine learning task may be better suited for quantum models than classical models.

关键词： Lower bound Quantum computing machine learning algorithms Correlation Hidden Markov models machine learning Predictive models Prediction algorithms Data models Context modeling

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MATLAB-Based Lightweight Workload Prediction via machine learning Models in Distributed Systems

MATLAB-Based Lightweight Workload Prediction via Machine Lea...

引用

IEEE Green Energy and Systems Conference (IGESC)

作者： Ghadeer Al Jufout Simon Zhang Hailu Xu Saleh Al Jufout Department of Informatics University of California Irvine Irvine CA USA Computer Engineering and Computer Science California State University Long Beach CA USA Electrical Engineering California State University Long Beach Long Beach CA USA

ISBN: (数字)9798350379617

ISBN: (纸本)9798350379624

This paper presents a dynamic selector model for data workload prediction. A main function responsible for selecting the most accurate machine learning Algorithm (e.g., Linear Regression, Support Vector Regression, and Random Forest Regression) has been developed. The selection of the algorithm is based on the runtime workload of the given data application ensuring an efficient and accurate development of the application. The selector model is parallelized and deployed in Docker containers which can speed up the runtime and improve the integrated performance of the system on a large scale. Evaluated results show significant improvements in scalability, versatility, and prediction accuracy by analyzing various data sets in different distributed environments.

关键词： Support vector machines Accuracy Runtime machine learning algorithms Predictive models Containers Mathematical models Data models MATLAB Random forests

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Optimized machine learning for Medical Data Analysis and Disease Prediction

Optimized Machine Learning for Medical Data Analysis and Dis...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Anil Kumar Dubey Raman Kapoor Mala Saraswat ABES Engineering College Ghaziabad INDIA Bennett University Greater Noida INDIA

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

In the context of healthcare and human-computer interaction., this research study provides a thorough analysis of sophisticated computational algorithms for data classification, picture processing, and disease prediction. For multi-disease prediction and intrusion detection, various works have been investigated to arrive at optimal hybrid learning methods and biological feature selection techniques. The role of revolutionary blockchain-based data storage systems and enhanced analysis techniques of EEG data for brain illness detection have been reviewed. Furthermore, an improved bidirectional long short-term memory (LSTM) for time-series data categorization is also discussed. Some noteworthy studies such as the analysis of medical picture defects, application of immersive technology to diagnose Alzheimer's illness and improvements in image resolution and segmentation are also reviewed in this work. Based on the reviewed literature, an improved method is also proposed which is expected to assist with early diagnosis and risk reduction.

关键词： Human computer interaction machine learning algorithms Intrusion detection Medical services machine learning Prediction algorithms Feature extraction Hybrid learning Medical diagnostic imaging Long short term memory

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Enhancing Network Security: A Comparative Analysis of Deep learning and machine learning Models for Intrusion Detection

Enhancing Network Security: A Comparative Analysis of Deep L...

引用

E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Arunima Ajeesh Tessy Mathew Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Thiruvananthapuram

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

The escalating reliance of computer networks on security systems exposes them to increasing threats. In response, an advanced security system is proposed, utilizing machine learning and deep learning techniques and leveraging the NSLKDD and CSE-CIC-IDS2018 datasets. Addressing the class imbalance challenges, the DSSTE algorithm is adopted to optimize model performance. Evaluation involves six, machine learning and deep learning methods: XGBoost, LSTM, Bi-LSTM, CNN, and VGG19, with a focus on assessing speed, false positive rate and detection efficiency. Experimental results underscore DSSTE's efficacy in mitigating class imbalance and enhancing overall intrusion detection. The proposed IDS, empowered by machine learning and DSSTE, ensures early detection and strengthens network security by countering intrusion attempts. Performance is compared with systems utilizing SMOTE and ADAASYN for data balancing.

关键词： Deep learning machine learning algorithms Power measurement Computational modeling Power control Intrusion detection Network security

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