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检索条件"任意字段=International Conference on Machine Learning and Computing"

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An Analysis of the Performance of Lightweight Gradient Boosting and Random Forest Methods for Producing Effective Nutritional Diet Recommendations 15

An Analysis of the Performance of Lightweight Gradient Boost...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, D. Mahesh Gnanajeyaraman, R. Dept of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350370249

The aim of the research is to recommend Efficient Nutrition and diet to patients using Light weighted gradient Boosting compared over Ensemble Random Forest to improve accuracy. In order to provide patients with food and nutrition recommendations, models were created that make use of a simple gradient boosting technique and Ensemble Random Forest. The suggested system is implemented with the help of a python programming tool. By using the Gpower 3.1 software (g power setting parameters alpha:0.05 and G power =0.8). Light Weighted gradient boosting is used for Group 1 and Naive Bayes with 10 sample sizes is used for Group 2. The outcome of the patients is monitoring the health conditions. The recommended light weighted gradient boosting algorithm classifier accuracy level is confirmed with 92.45%. Gradient Boosting Algorithm and Random Forest classifier’s processing time is also calculated. The recommended light weighted gradient boosting algorithm classifier accuracy level is confirmed with 92.45% with a significance of 0.001 (p ©2024 IEEE.

关键词： Diet Food Light Weighted Gradient Boosting machine learning Nutrition Random Forest Vitamins

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Research on computing Offloading Methods Based on Edge computing and Reinforcement learning in The Industrial Internet of Things

Research on Computing Offloading Methods Based on Edge Compu...

引用

international conference on Internet of Things and machine learning, IoTML 2023

作者： Wang, Xian Wan, Qiang Li, JianCheng Faculty of Intelligent Manufacturing Wuyi University Jiangmen529020 China School ofComputer Science and Engineering South China University of Technology Guangdong GuangZhou510000 China School of Computer Science and Technology Guangdong University of Technology GuangZhou528300 China

ISBN: (纸本)9781510671805

Most of the existing research focuses on a single MEC server scenario, and there are few studies on computing offloading and resource allocation in multi-MEC server scenarios. Therefore, this paper proposes a dynamic computation offloading and resource allocation algorithm based on hybrid decision-making deep reinforcement learning, which utilizes deep deterministic policy gradients and dueling dual deep Q-networks to enhance the Actor-Critic structure. By calling the Actor part of DDPG to deal with continuous power allocation, and then combining the Critc part of DDPG with D3QN, the discrete MID is associated with the MEC server to solve the mixed decision-making problem in multi-MEC server multi-user scenarios. From the simulation results, we can see that the proposed algorithm has faster convergence and better stability than baseline algorithms such as DQN. © 2023 SPIE.

关键词： Reinforcement learning

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Interview Bot Using Natural Language Processing and machine learning 5

Interview Bot Using Natural Language Processing and Machine ...

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5th international conference on Advancements in computing, ICAC 2023

作者： Caldera, Amitha Hettiarachchi, Sathira Bandara, H.M.R.M. Abeywickrama, Y.S. Fernando, B.D.R. Wijesuriya, I.M. Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350358131

In order to optimize the hiring process for organizations, this research article suggests using a system based on machine learning and natural language processing. The interview bot revolutionizes the recruitment process by using a combination of machine learning algorithms, natural language processing techniques and automated interview processes to streamline candidate selection. The bot can conduct multiple interviews simultaneously while capturing facial expressions and speech patterns of the candidates while their responses are compared against a diversified question bank made by training learning algorithms. A unique scoring mechanism is employed to evaluate the alignment with expected answers. The automated nature of the bot ensures consistent and standardized interview procedures, resulting in fair evaluations across all candidates. The interview bot's efficiency not only results in substantial time savings but also leads to a reduction in the financial resources typically expended on lengthy interview procedures, enabling the acquisition of toptier personnel. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhancing Operational Efficiency and Security in Multi-Sectioned Businesses with Edge AI through Real-Time Analytics

Enhancing Operational Efficiency and Security in Multi-Secti...

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2024 IEEE international conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Vijay, K. Jeyanth, V. Kamalesh, S. Dheva Prasath, D. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522988

The goal of this project is to disrupt the way businesses monitor their operations by integrating IoT and machine learning to gain a deeper understanding of customer behavior and improve operational efficiency. Traditional surveillance systems create lots of data to store and analyze, but they do not provide actionable intelligence. CCTV cameras and edge devices strategically placed throughout a facility capture real-time footage, identify visitor movement patterns, and represent critical heat zones on a heat map. This data-driven process enhances resource allocation, enhances customer experience, and enables proactive improvements in operational workflow. Edge devices enable real-time anomaly detection, such as theft or staff sitting, without compromising privacy, as the device retains all information and performs all processing locally. Our next-gen solution provides businesses with an all-inclusive monitoring system that solves today's most pressing security and operational issues, ultimately delivering better business results. © 2024 IEEE.

关键词： Resource allocation

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Genetic Optimization Techniques for Enhancing Web Attacks Classification in machine learning

Genetic Optimization Techniques for Enhancing Web Attacks Cl...

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2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Jaradat, Ameera S. Nasayreh, Ahmad Al-Na'amneh, Qais Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Yarmouk University Dep. of Computer Science Irbid Jordan Yarmouk University Dep. of Computer Science Irbid211633 Jordan Applied Science Private University Dep. of Cyber Security and Cloud Computing Amman Jordan Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya

ISBN: (纸本)9798350304602

Web-based applications are now the preferred approach for delivering a variety of services via the Internet. As a result of the globalization of commerce, web applications have been growing quickly and becoming increasingly complicated. Such applications have a significant security vulnerability in the online environment since they were developed with little experience and without testing or validation. Numerous attackers use this security vulnerability to take control of the program, modify the data, and steal the most crucial information. They may also access all internal, unauthorized items. This work presents a hybrid model that classifies website attacks as benign through the integration of four machine learning algorithms: gradient boost (GB), multilayer perceptron (MLP), logistic regression (LR) and K nearest neighbor (KNN). The work deployed Tree-based Pipeline Optimization Tool (TPOT) that utilizes the Genetic algorithm (GA) to extract the optimal parameter and consequently enhance the model performance. The model underwent evaluation utilizing a data set from the Canadian Institute 2023 that contains various types of attacks. Among these algorithms, GB achieved the best accuracy scores of 95%, 94% and 95% for accuracy, recall and F1-score, respectively. © 2023 IEEE.

关键词： Genetic algorithms

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Enhancing Cloud Security: A Comprehensive Review of machine learning Approaches 2

Enhancing Cloud Security: A Comprehensive Review of Machine ...

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2nd international conference on Cyber Resilience, ICCR 2024

作者： Safi, Wassim Ghwanmeh, Sameh Mahfuri, Mahmoud Al-Sit, Waleed T. Higher Institute For Applied Sciences And Technology-HIAST Damascus Syria American University In The Emirates Department Of Computer Science United Arab Emirates International University For Science And Technology Computer And Information Technology Department Damascus Syria College Of Engineering Mu'tah University Department Of Computer Engineering Al-karak Jordan Higher Colleges Of Technology Dubai United Arab Emirates

ISBN: (纸本)9798350394962

Cloud computing (CC) has ushered in a paradigm shift in the provisioning of IT resources, offering users enhanced cost-efficiency and streamlined infrastructure management. However, this growth in CC adoption has also brought forth a myriad of security challenges, encompassing threats to availability, integrity, and confidentiality. To combat these challenges, Cloud Service Providers (CSPs) are increasingly turning to machine learning (ML) to diminish the necessity for human intervention in the identification and resolution of security issues. ML's capacity to process extensive datasets and make highly accurate predictions holds the potential to revolutionize CSPs' approach to security. This paper delves into recent advancements in the realm of ML-based security within Cloud computing. It assesses the attributes and efficacy of various ML algorithms, underscoring their distinctive strengths and possible limitations. Our objective is to present a comprehensive overview of the current landscape of ML in cloud security, illuminating the promising prospects this emerging field holds. © 2024 IEEE.

关键词： Cloud security

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11th international conference on Intelligent computing and Applications, ICRTC 2023

11th International Conference on Intelligent Computing and A...

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11th international conference on Intelligent computing and Applications, ICRTC 2023

ISBN: (纸本)9789819717231

The proceedings contain 70 papers. The special focus in this conference is on Intelligent computing and Applications. The topics include: RNN learning for Dynamic Selection of Channel Access Scheme in FANETs;a Crime Knowledge Discovery Scheme Based on Entity Recognition, Relation Extraction, and Development of Criminal Profiling Using Modus Operandi;optimized Biometric Key Management System for Enhanced Security;human Posture Identification and Recognition Using Deep learning Techniques;twitter Sentiment Analysis Using Different machine learning Techniques;exploring Empirical Mode Decomposition for Music Genre Classification Using Deep learning;a Comparative Analysis of Fog computing’s Problems, Challenges and Future Directions;explainable Artificial Intelligence Insight: An Orderly Survey;deep learning-Based Sign Language Translator;Analysis of Arrhythmia from Electrocardiogram (ECG) Data Using ML Framework;brain Tumor Detection Using Quantum Neural Network;augmented Reality-Based Application for Indian Monuments;fog Obscurity Mitigation;a Load Balancing Using Multi-population Grasshopper Optimization Approach for Workflow Tasks in Clouds;parental Control for Techie Child Using Keylogger;modified Box Filter Design and Noise Analysis on Two-Dimensional Images;Change Detection in Remote Sensing SAR Image Using a Ratio-Based Operator;automatic Question Generation: A Comparative Analysis of Rule-Based and Neural Network-Based Models;mango Leaf Images Quality Improvement Techniques Using Subjective Approach of Image Enhancement;real-Time 3D Texture and Motion Analysis for Face Anti-spoofing Using Deep learning and Computer Vision;Hybrid Sentiment Polarity Prediction Scheme in Social Networks using Attention Mechanism and Improved CNN;survival Analysis of Heart Failure Patients with Advanced machine learning Models;a Differential Privacy Perturbation with Random Forest Classifier in Medical Database;smart Application for Early Detection of Rice Plant Disease Using Incept

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Symptom-Based Disease Prediction Using machine learning 14

Symptom-Based Disease Prediction Using Machine Learning

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14th international conference on Cloud computing, Data Science and Engineering, Confluence 2024

作者： Nishad, Divyansh Mishra, Anshika Goyal, Nidhi Kiet Group of Institutions Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350344837

Accurate prediction of disease is a major challenge in the current healthcare sector, where on-time and correct diagnosis can remarkably impact treatment outcomes. This research introduces a user-focused approach to predicting diseases, using machine learning along with a mobile application interface. Empowering individuals with the ability to assess their health and make well-informed decisions is the primary aim of this research. Our machine-learning model demonstrates exceptional accuracy, often achieving a 100% accuracy rate. This includes the incorporation of various algorithms such as Multinomial Naive Bayes, Decision Tree, and Random Forest. The cross-validation score serves to validate the robustness of the model. Furthermore, the high F1 score of 0.99 under-scores its effectiveness in providing accurate predictions for different classes of diseases. The integration of a user-friendly Flutter mobile application streamlines the procedure of entering symptoms and obtaining disease predictions, enhancing the user experience. In addition to disease prediction, our system enhances its utility by providing supplementary materials from external platforms such as YouTube video suggestions, doctor's blogs, and podcasts through third-party APIs, delivering a holistic understanding of predicted diseases. This research makes notable advancements in bridging the gap between individuals and crucial health information. Our system exhibits potential as a tool for early disease detection, improving health awareness, and contributing to public health and well-being. © 2024 IEEE.

关键词： Decision trees

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Chronic Illness Detection using Gradient Boosting Algorithm 15

Chronic Illness Detection using Gradient Boosting Algorithm

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Guhan, T. Bhavishya, S. Kalaiarasan, S. Lalith, K. Dhitchith, O.P. Karpagam College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331300579

The proposed work emphasizes the importance of accurate diagnosis of chronic kidney disease. The research helps refine proactive healthcare strategies and provides valuable insights into the prevention and management of chronic diseases. The goal of this project is to develop a framework based on Big Data analysis that predicts the likelihood of kidney failure, a chronic disease. The framework uses advanced machine learning techniques such as MEG (Mean Decrease Gini), MSE (Mean Square Error), Grid Search, K-fold cross validation to detect risk factors and correlations associated with chronic diseases. The models help healthcare professionals make informed decisions and detect high-risk individuals so that proactive strategies can be implemented to prevent or delay chronic diseases. The framework can be used to predict a wide range of chronic diseases beyond kidney failure. However, the risk factors and correlations can vary and need to be adapted accordingly. Further research and testing are needed to determine the effectiveness of the framework. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Aspect-based Sentiment Analysis (ABSA) using machine learning Algorithms 3

Aspect-based Sentiment Analysis (ABSA) using Machine Learnin...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Siddiqua, Ayesha Bindumathi, V. Raghu, G. Bhargav, Y. S. Vamsi Department Of Ece Nitte Meenakshi Institute Of Technology Bengaluru India

ISBN: (纸本)9798350318609

Aspect-Based Sentiment Analysis (ABSA) is a Natural Language Processing task that aims to identify and extract the sentiment of specific aspects or components of a product or service. ABSA typically involves a multi-step process that begins with identifying the aspects or features of the product or service that are being discussed in the text. This is followed by sentiment analysis, where the sentiment polarity (positive, negative, or neutral) is assigned to each aspect based on the context of the sentence or document. Finally, the results are aggregated to provide an overall sentiment for each aspect. The process involves training machine learning models to classify text sentiment (positive, negative, or neutral). First, we transform text data using Term Frequency-Inverse Document Frequency (TF-IDF), which assigns weights to words based on their importance within a document collection. This emphasizes informative terms. Then, these TF-IDF features are fed into both SVM and Logistic Regression models. SVM find a hyper plane that best separates sentiment classes, while Logistic Regression calculates the probability of a text belonging to a specific sentiment class. Extensive experiments have been conducted on datasets of covid vaccinations dataset and results show that the support vector machine model achieves excellent performance in terms of aspect extraction and sentiment classification. Sentiment on Twitter can be imbalanced, with more positive or negative tweets depending on the topic. This can affect the training process. Techniques like oversampling or undersampling the minority class might be necessary. This work investigates the performance of machine learning algorithms for a specific classification task. Support Vector machine (SVM) and Logistic Regression (LR) were compared. The results indicate that Support Vector machine achieved superior accuracy (87.34%) compared to Logistic Regression (84.64%), suggesting Support Vector machine as a more suitable opt

关键词： Sentiment analysis

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