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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是4591-4600 订阅

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Predictive Modeling of Crime Types and Occurrences Using machine learning and Deep learning

Predictive Modeling of Crime Types and Occurrences Using Mac...

引用

Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Kaustav Mitra Ankit Sinha Ankit Anurag Senapati Anurag Ranjan Das Satyananda Champati Rai School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

This paper review focuses on the potential of applying machine learning algorithms such as K-Nearest Neighbours, Naïve Bayes, Random Forest, and Artificial Neural Network to analyze crime prediction by type and occurrence at specific locations and times and regarding historical data of similar scenarios. From these studies, it is possible to conclude that applying these predictive models can be beneficial when sharing knowledge with law enforcement agencies, particularly regarding optimized resource allocation and strategic security plans. Further research may focus on improving the prediction of new types of threats through stronger ensemble methods, transfer learning between cities, and real-time processing to adapt to changes in crime characteristics. It ultimately helps to reduce the risk to residents and prevent more crime incidents.

关键词： Accuracy machine learning algorithms Computational modeling Prevention and mitigation Transfer learning Predictive models Security Resource management Long short term memory Principal component analysis

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An automated security policy generation method based on rule-matching and machine-learning models

An automated security policy generation method based on rule...

引用

Advanced Control Systems and Automation Technologies (ACSAT), International Conference on

作者： Yue Wu Ruixia Lang Hao Yang Xiangze Li State Grid Jiangxi Electric Power Co. Ltd. Nanchang China University of Electronic Science and Technology of China Chengdu China State Grid Jiangxi Electric Power Research Institute Nanchang China

ISBN: (数字)9798331541668

ISBN: (纸本)9798331541675

With the increasing diversification of security threats and the expanding scale of industrial control system networks, traditional manual policy generation methods are inadequate for the swift response required and are prone to configuration errors. An automated security policy generation method is proposed in this paper, integrating rule-matching with machine learning models to dynamically adapt to network security conditions, thereby effectively enhancing response speed and accuracy. By minimizing human intervention, the method reduces the risks associated with misconfiguration errors and employs machine learning algorithms for precise identification and response to security threats, offering more robust security protection for industrial control systems.

关键词： Automation Accuracy machine learning algorithms Industrial control machine learning Manuals Network security Control systems Security Protection

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Traffic Classification in Software-Defined Networking (SDN): Application of machine learning Models for Flooding DDoS Attack Detection

Traffic Classification in Software-Defined Networking (SDN):...

引用

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Lucas Leal Bosi André Chaves Mendes Raphael Melo Guedes Ronaldo Moreira Salles Defense Engineering Military Institute of Engineering Rio de Janeiro Brasil VALORIZA - Research Centre for Endogenous Resource Valorization Campus Politécnico Portalegre Portugal Informatics and Computer Science Department State University of Rio de Janeiro Campus Maracanã Rio de Janeiro Brazil CIICESI ESTG Polytechnic of Porto Porto Portugal

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

Among the advantages offered by the SDN paradigm over traditional networks is the centralized implementation of comprehensive security policies. From this perspective, this study develops, trains, and tests several machine learning algorithms for traffic classification, distinguishing between legitimate traffic and three types of DDoS flooding attacks (HTTP, UDP, and TCP). The dataset used for training was employed both in its raw and preprocessed forms, and was generated in a reference study. The Random Forest and Gradient Boosting algorithms achieved the highest results in terms of accuracy and F1-score. Additionally, a comparison of the results of the studies is conducted.

关键词： Training machine learning algorithms Accuracy Heuristic algorithms Denial-of-service attack Classification algorithms Security Computer crime Software defined networking Tuning

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Interactive Sign Language learning System using Deep learning

Interactive Sign Language Learning System using Deep Learnin...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S Ravikumar P. Dimple Praharsha J. Sri Harsha Priya V. Naga Rohit Sai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tamil Nadu

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

The “Interactive Sign Language learning System” is a sophisticated application designed to facilitate the learning process of sign language learners. This comprehensive system encompasses several key features, including sign language alphabet and word recognition, text-to-action conversion for learners, multi-language support, and integrated voice output functionality. The system utilizes advanced algorithms for sign language recognition, employing techniques such as image processing and machine learning to accurately interpret hand gestures and movements. For sign language alphabet and word recognition, a combination of computer vision algorithms, possibly including convolutional neural networks (CNNs) or recurrent neural networks (RNNs), may be employed to analyze input images or video streams and classify them into corresponding sign language symbols or words. Text-to-action conversion involves mapping textual input to corresponding sign language actions or gestures, possibly using natural language processing (NLP) algorithms to understand the semantics of the text and generate appropriate sign language representations. The system’s accuracy, measured in terms of correctly recognized sign language symbols or words, would depend on the effectiveness of the algorithms employed and the quality of the training data. With rigorous development and training, the system aims to achieve high accuracy levels, in sign language recognition and text-to-action conversion tasks.

关键词： Sign language Accuracy machine learning algorithms Image recognition Symbols Training data Speech recognition Natural language processing Classification algorithms Convolutional neural networks

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A Review On Heart Disease Detection Using machine learning Techniques

A Review On Heart Disease Detection Using Machine Learning T...

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Amit Kumar Er. Reshma Khan Deepika ME-CSE Chandigarh University Mohali India Dept. of CSE Chandigarh University Mohali India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

In recent times, heart diseases have emerged as a notable factor contributing to mortality at a global level. Accurate prediction of heart disease is of utmost importance to improve patient outcomes and reduce mortality rates. Heart attacks occur due to coronary artery disease and chronic heart failure. Angiography is a common way to diagnose heart disease. Nowadays, researchers are focusing a lot on cardiovascular diseases. Using machine learning, a better diagnostic process is created. machine learning gives more accurate and efficient results, helping spot heart disease in the early stages. Through this review paper, we aim to provide a comprehensive literature survey on the topic of heart disease prediction using ML algorithms.

关键词： Heart Surveys Accuracy machine learning algorithms Reviews Focusing Prediction algorithms

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Extreme learning machine for Spammer Detection and Fake User Identification from Twitter

Extreme Learning Machine for Spammer Detection and Fake User...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Sai Yaswanth Modadugu S. Siva Nageswara Rao Dodda Venkata Reddy Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopet Andhra Pradesh India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

The proliferation of online social networking sites (OSNs) platforms like Twitter has led to an increase in spam and the presence of fake user accounts, posing significant challenges to user experience and platform resources. This research focuses on the detection of spammers on Twitter as well as the identification of fraudulent users. The research begins by pre-processing the Twitter dataset to remove noise and irrelevant information. Next, Multinomial Naive Bayes (MNB) is employed to extract informative features from the preprocessed dataset, considering various textual, linguistic, and user-specific characteristics. After the characteristics have been collected, they are utilized to train a classifier called an Extreme learning machine (ELM), which is able to efficiently differentiate between spam accounts and real user accounts. Extensive tests are run on an actual Twitter dataset to assess the effectiveness of the suggested methodology. The results demonstrate that the combination of MNB feature extraction and ELM classification achieves high accuracy, precision, recall, and F1-score in identifying spammers and fake users on Twitter.

关键词： machine learning algorithms Social networking (online) Extreme learning machines Communication systems Blogs Noise Linguistics

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Prediction of Particulate Matter in a Mining Environment via machine learning

Prediction of Particulate Matter in a Mining Environment via...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Milka C.I. Madahana John E.D. Ekoru School of Mining Engineering University of the Witwatersrand Johannesburg South Africa School of Electrical & Information Engineering University of the Witwatersrand Johannesburg South Africa

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

The objective of this research work is to present an investigation in the application of machine learning algorithms in the prediction of particulate matter concentration in an open cast coal mine. Three types of neural networks, Long Short Term Memory, Gated Recurrent Unit and Recurrent Neural Networks are used to estimate and forecast the particulate concentration in the mine. The neural networks are bench-marked using Linear Regression and support vector machines. The results indicate that the Recurrent Neural Network performs well under conditions with short time lags, whereas both the Long Short Term Memory and Gated Recurrent Unit neural networks perform well over a wider range of lag times. Future work includes the implementation of these algorithms in early warning and intervention systems.

关键词： Support vector machines Deep learning machine learning algorithms Automation Linear regression Logic gates Prediction algorithms Coal mining Long short term memory Robots

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Attention performance classification based on eye tracking and machine learning

Attention performance classification based on eye tracking a...

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IEEE International Scientific Conference on Informatics

作者： Adrian Vulpe-Grigorasi Zvetelina Kren Djordje Slijepčević Roman Schmied Vanessa Leung St. Pölten University of Applied Sciences St. Pölten Austria NOVA School of Science and Technology NOVA University of Lisbon Caparica Portugal Viewpointsystem GmbH Vienna Austria

ISBN: (数字)9798350387681

ISBN: (纸本)9798350387698

Visual attention represents a key concept in cognitive performance and visual information processing. The relationship that exists between cognition, problem-solving, and attention can be explained and modeled through the use of eye-tracking technologies. Novel technologies like machine learning can enhance cognitive performances by finding gaze patterns and using them to direct and guide attention in problem-solving scenarios. Our experiment aimed to classify attention into three performance classes: low, average, and high, using eye tracking data. For the task at hand, the most relevant eye-tracking features found were mean gaze velocity, saccade and fixation count, and average fixation duration. Our study showed that machine learning models trained on eye-tracking features can classify attention performance with over 81% accuracy.

关键词： Measurement Hands Visualization Accuracy machine learning algorithms Correlation Gaze tracking machine learning Problem-solving Informatics

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APAT-A Smart Tool for Screening of Academic learning Difficulties in Children

APAT-A Smart Tool for Screening of Academic Learning Difficu...

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Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE International Conference on

作者： Nara Kalyani Mandakini Chagamreddy Kaliki Sujitha CSE G.Narayanamma Institute of Technology &Science for Women Hyderabad Telangana India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

learning difficulties(LD), particularly in reading, mathematics, or language skills, can significantly hinder children’s academic progress. Identifying these difficulties early is essential for implementing effective interventions and preventing long-term educational setbacks. This study proposes an ML-based Academic Performance Assessment Tool (APAT) to enhance screening accuracy and efficiency. A review of existing screening methods was conducted, and a comprehensive dataset approved by psychologists was developed. This involved collaboration with schools, where teachers with at least 8 months of classroom experience provided valuable inputs for children aged 6 to 11 years (Grades 1-5). Questionnaires were used to assess potential LD in this age group. Additionally, academic performance data from students with and without LD was collected. Different machine learning(ML) models, including Support Vector machines (SVMs), 1D Convolutional Neural Networks (1D-CNNs), Random Forests (RFs), and Decision Trees (DTs), were evaluated for their effectiveness in predicting LD. Among these, the ID-CNN model achieved the highest accuracy, reaching 97%. Using the 1D-CNN model, APAT was developed. APAT allows teachers to input questionnaire data to identify learning difficulties in students, ensuring accurate assessments and timely interventions, providing a comprehensive and effective approach to supporting students with learning difficulties.

关键词： Support vector machines Radio frequency Accuracy machine learning algorithms Reviews Psychology Predictive models Mathematics Decision trees Random forests

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Early-Stage Diabetes Risk Prediction Using machine learning Techniques

Early-Stage Diabetes Risk Prediction Using Machine Learning ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Aparajita Raj Junali Jasmine Jena Debolina Ghosh Sonal Jain Tanvir Habib Sardar Mahendra Kumar Gourisaria School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India Department of Information Technology Manipal University Jaipur Jaipur Rajasthan India CSE School of Engineering Dayanand Sagar University Bangalore Karnataka India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Diabetes is a chronic and metabolic disease caused by an increment in glucose level in the blood, which leads overtime to major serious damage to the heart and kidneys. Millions of people are affected by diabetes in the world. The main aim of this study is the prediction of the risk of diabetes at an early stage. The data set contains information on the symptoms of newly diagnosed diabetic patients, those who are at risk of developing diabetes. The data was collected through direct questionnaire's administer to patients at the hospital in Bangladesh. In this work, 10 supervised machine learning algorithms such as Random Forest (RF), Naive Bayes (NB), etc. are implemented by taking accuracy, precision, F1 score etc. as evaluation criteria. This diabetic risk prediction model has a higher accuracy of 99.12% with Random Forest and Gradient boosting than other models.

关键词： Radio frequency Heart Accuracy machine learning algorithms Hospitals Predictive models Diabetes Internet of Things Random forests Kidney

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