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检索条件"主题词=Machine Learning Algorithms"

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Personalized Federated learning Techniques: Empirical Analysis

Personalized Federated Learning Techniques: Empirical Analys...

引用

IEEE International Conference on Big Data

作者： Azal Ahmad Khan Ahmad Faraz Khan Haidar Ali Ali Anwar Department of Computer Science and Engineering University of Minnesota-Twin Cities Department of Computer Science Virginia Tech

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Personalized Federated learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy. However, achieving optimal performance in pFL often requires a careful balancing act between memory overhead costs and model accuracy. This paper delves into the trade-offs inherent in pFL, offering valuable insights for selecting the right algorithms for diverse real-world scenarios. We empirically evaluate ten prominent pFL techniques across various datasets and data splits, uncovering significant differences in their performance. Our study reveals interesting insights into how pFL methods that utilize personalized (local) aggregation exhibit the fastest convergence due to their efficiency in communication and computation. Conversely, fine-tuning methods face limitations in handling data heterogeneity and potential adversarial attacks while multi-objective learning methods achieve higher accuracy at the cost of additional training and resource consumption. Our study emphasizes the critical role of communication efficiency in scaling pFL, demonstrating how it can significantly affect resource usage in real-world deployments.

关键词： Training Measurement Costs Accuracy machine learning algorithms Federated learning Memory management Medical services Market research Data models

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Advanced machine learning for Disease Semantic Classification and Decision-Making

Advanced Machine Learning for Disease Semantic Classificatio...

引用

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

作者： Malasani Karthik Sourav Mondal Chidella Teja Myla Sowmya Punneswari Devi Kammila Hema Sri Uttej Kumar Department of CSE Vignan Foundation for Science Technology and Research Guntur India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The classification of diseases is of utmost significance in the field of healthcare as it facilitates the process of diagnosing, treating, and conducting medical research. This paper proposes that illness categorization based on machine learning may be achieved by utilizing a dataset that encompasses several kinds of diseases, including diseases with IDs, names, types, classes, and associated features. The initial step in data pre-processing is the encoding of category variables and the normalization of numerical features. Various machine learning methods such as XGBoost, LGBM, Random Forest, and Extra Tree Classifier are employed to assess and categorize illness semantic types. The performance is assessed using evaluation criteria such as accuracy, precision, recall, and F1-score. Furthermore, hyperparameter optimization and cross-validation techniques are employed to optimize the performance and generalization capabilities of the model. A model called Ensemble Voting Classifier will be created to utilize the unique capabilities of each model in order to enhance the accuracy of classification. To enhance the accuracy and reliability of disease semantic typing, ensembles of diverse classifiers are constructed. The experimental findings demonstrate that the suggested technique is highly successful, achieving an accuracy of up to 82% in classifying illness semantic kinds. Additionally, the results provide valuable insights into the role of various variables used in the classification process.

关键词： Accuracy machine learning algorithms Semantics Decision making Medical services Reliability Random forests Medical diagnostic imaging Tuning Diseases

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Investigating the Impact of machine learning in Personalized Education Systems

Investigating the Impact of Machine Learning in Personalized...

引用

Technological Innovations and Advance Computing (TIACOMP), International Conference on

作者： Bhupesh Rawat Yogesh Joshi Narendra Singh Bisht School of Computing Graphic Era Hill University Bhimtal India School of Computing Amrapali University Haldwani India School of Computing Pal College of Technology and Management Haldwani India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

The traditional education system or in-person classroom teaching operates on the concept of a uniform approach for all and does not take into account the varied needs of learners, which results in a decrease in student performance. Recognizing the limitations of a one-size-fits-all approach, personalized learning systems aim to tailor educational content to each student's unique needs. This has been significantly bolstered by the recent surge in machine learning integration within education. machine learning algorithms can now analyze student data and learning patterns, allowing educators to create highly personalized learning experiences that cater to individual strengths and weaknesses. This means learning content is designed and recommended to a learner according to the learners’ level. This research delves into the transformative impact of machine learning on traditional classroom teaching. By analyzing its implementation in personalized education systems, the paper explores how this technology can empower learning. However, it also critically examines the challenges that arise, such as data privacy, algorithmic bias, and ethical considerations. Ultimately, the goal is to ensure responsible and equitable access to this transformative technology within education.

关键词： learning systems Ethics Data privacy Technological innovation machine learning algorithms Heuristic algorithms Education machine learning Market research Surges

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Animal Care Application in Rehabilitation and Captivity using machine learning for Emotion Recognition

Animal Care Application in Rehabilitation and Captivity usin...

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IEEE Colombian Communications Conference (COLCOM)

作者： Washington Velasquez Guillermo Sanchez-Guzman Joshua Taranto-Usey Faculty of Electrical and Computer Engineering Escuela Superior Politecnica del Litoral ESPOL Guayaquil Ecuador

ISBN: (数字)9798331504724

ISBN: (纸本)9798331504731

This paper states an animal care app that uses machine learning to recognize emotions and monitor animal welfare in real-time. The app aims to help pet owners, veterinarians, and animal shelter keepers ensure animals’ safety and comfort. The paper describes the methodology used for the development and implementation and the preliminary results of the application analysis. The challenges of analyzing the emotions of animals and the ethical implications of using technology in animal care are also discussed. Finally, the application is expected to help improve the quality of life of animals in captivity or rehabilitation in the future.

关键词： Training Emotion recognition machine learning algorithms Animals Software algorithms machine learning Real-time systems Software reliability Time factors Monitoring

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Analysis of machine learning Methods for Solving Cumulative Vehicle Routing Problem

Analysis of Machine Learning Methods for Solving Cumulative ...

引用

International Conference on Informatics and Computing (ICIC)

作者： Muhammad Amin Syahril Efendi Mahyuddin Marischa Elveny Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Cumulative Vehicle Routing Problem (CVRP) is an important challenge in logistics that involves optimizing vehicle routes by considering cumulative time or distance. This research aims to evaluate the effectiveness of machine learning methods in solving CVRP and compare them with conventional methods. Using machine learning techniques such as deep learning and reinforcement learning algorithms, this study tests the ability of the model to provide a more efficient solution. The results show that machine learning methods can produce better and faster solutions than traditional methods, despite challenges related to data and model complexity. This study concludes that machine learning has significant potential to improve efficiency in solving CVRP, but still requires further development to overcome some of the obstacles.

关键词： Deep learning Training machine learning algorithms Vehicle routing Reinforcement learning Simulated annealing Prediction algorithms Space exploration Logistics Investment

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MEDIMIND MATE: The Healthcare ChatBot Based on machine learning and Natural Language Processing

MEDIMIND MATE: The Healthcare ChatBot Based on Machine Learn...

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International Conference on Advances in Information Technology (ICAIT)

作者： Aditi Choudhary Guru Prasad M S Anshika Rawat Khushi Upadhyay Mahima M Gowda Amith K Jain Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India School of Computing & Information Technology Reva University Bengaluru Department of Electronics and Communication Engineering SDM Institute of Technology Dehradun India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

While one's health is unquestionably a valuable asset in the modern world of hustle and bustle, it is also susceptible to several threats. Regular visits to healthcare specialists are frequently impeded by the demands of busy schedules. Healthcare chatbots have proven important in addressing this widespread issue. The proposed paper successfully solves this issue by evaluating four distinct algorithms for disease prediction, out of which the random forest classifier produced the highest accuracy of 99.6%, so the chatbot made use of the random forest model, which was trained on a dataset that included illnesses and the symptoms that went along with them. It then uses cosine similarity to predict symptoms accurately to identify possible illnesses. The chatbot also provides information about nearby doctor locations in addition to further specifics regarding the anticipated disease.

关键词： Schedules machine learning algorithms Accuracy Medical services Predictive models Chatbots Prediction algorithms

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Leveraging AI-Driven machine learning Approaches -SVM and RESNET-50 For Predicting Esophageal Cancer Detection

Leveraging AI-Driven Machine Learning Approaches -SVM and RE...

引用

IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R. Indumathi R. Jayaraj Department of CSE SRMIST Department of DSBS SRMIST

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Esophageal cancer, a very aggressive malignancy, exhibits a significant mortality rate attributed to late diagnosis and diverse patient responses to therapeutic interventions including chemotherapy, radiation therapy, and immunotherapy. Customized therapy suggestions are essential for enhancing patient outcomes; however, existing methodologies depend on generic models that fail to consider individual heterogeneity. This paper proposes an AI-driven approach utilizing machine learning algorithms, that includes Support Vector machines (SVM) ResNet-50 structure for deep convolutional neural networks and its objective is to classify the Barrett's esophagus and adenocarcinoma cancers. As multilayered perceptron's are the center of the approach, determined which maximal classification in ResNet-50 would be appropriate layers and trained them to cut to avoid redundancy. It was demonstrated that regional information and high-dimensional features are important to enhance classification. Also, maximal layer discrimination and differential effect can be important in Barrett's esophagus and adenocarcinoma classification training on ResNet50. The model improves precision through the incorporation of clinical data to plan individualized treatment pathways. It has shown more sensitivity in terms of the detection of neoplasia in Barrett's esophagus, reaching up to 90% as compared to conventional techniques. The experimental findings indicate enhanced prediction accuracy, precision, sensitivity relative to conventional approaches. This AI system offers a prospective option for the optimization of individualized treatment in esophageal cancer therapy.

关键词： Support vector machines Sensitivity machine learning algorithms Accuracy Computational modeling Data models Vectors Convolutional neural networks Cancer Esophagus

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Airfoil Self Noise Prediction Using machine learning and Explainable AI

Airfoil Self Noise Prediction Using Machine Learning and Exp...

引用

IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Honey Pandey Prateek Pandey Deepa Gupta Department Of Computer Science And Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

A major environmental challenge in the aerospace sector is presented by aircraft noise, particularly airfoil noise. Traditional methods for predicting airfoil noise involve complex and computationally intensive numerical simulations. This research explores the use of machine learning to enhance the efficiency and accuracy of airfoil noise prediction. By utilizing extensive datasets from wind tunnel experiments, encompassing a variety of airfoil geometries and flow conditions, supervised learning algorithms are employed to construct a predictive model. Diverse regression models, including random forests, support vector regression, CatBoost, XgBoost, Gradient Boosting and decision trees, are trained on the NACA 0012 airfoil dataset to uncover correlations between airfoil characteristics and noise emissions. Initial findings underscore the potential of machine learning, with Gradient Boosting regression achieving a noteworthy R2 score of 0.96625 and RMSE as 1.2622. This study not only equips engineers with a valuable tool for optimizing airfoil shapes to reduce noise but also establishes the groundwork for real-time noise prediction systems in aircraft operations.

关键词： machine learning algorithms Accuracy Atmospheric modeling Wind tunnels Wind speed Noise Boosting Prediction algorithms Aircraft Automotive components

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Patient Risk Classification based on Vital Signs using machine learning Algorithm

Patient Risk Classification based on Vital Signs using Machi...

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Intelligent Technologies (CONIT), International Conference on

作者： Savithri Prabhu G. Muralidhar Bairy Niranjana Sampathila Krishnaraj Chadaga Department of Biomedical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Infectious disease is always a serious issue in the world due to the impact of the spread of the disease and mortality. Now a days heart disease is also the most common disease. Heart disease, chronic disease and COVID19 reflected by changes in vital signs. The major vital signs are blood pressure, pulse rate and body temperature. The evolution in machine learning plays an important role to improve healthcare facilities and save human lives. The effective diagnosis of heart disease in a shorter time is very important to save human lives. At the same time COVD-19 is also affecting many people worldwide. machine learning techniques are used for diagnosis of disease. It uses datasets of earlier patient reports. This paper discusses machine algorithms to diagnose and differentiate infectious diseases, chronic disease, and heart disease. As a result, patients facing high risk can be shifted to the ICU and other patients to normal wards. These algorithms are effective in disease screening.

关键词： Heart Logistic regression machine learning algorithms Infectious diseases Forestry Nearest neighbor methods Blood pressure

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Revolutionizing Communication: A Hybrid Deep learning Framework for Enhanced Sign Language Recognition

Revolutionizing Communication: A Hybrid Deep Learning Framew...

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Data Science and Network Security (ICDSNS), International Conference on

作者： S. Prabu Sridhar T K Sridharan S Sukesh D Rajavel J Dept. of Electronics and communication Engineering Mahendra Institute of Technology Namakkal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Using cutting-edge computer vision and machine learning techniques, this research study suggests a system that can recognize American Sign Language (ASL) in real-time motions. The suggested approach classifies ASL gestures using a Long Short-Term Memory (LSTM) algorithm in conjunction with the Mediapipe library for feature extraction. The assessment results reveal that this approach exhibits an exceptional accuracy rate of 99.95% in recognizing all ASL alphabet signs, showcasing its suitability for integration into communication devices tailored for the deaf community. Furthermore, the suggested method is flexible enough to be used in other sign languages that have similar hand gestures, which could improve the lives of those who are deaf. This work significantly advances the fields of machine learning and computer vision by Showcasing the efficiency of utilizing Mediapipe and LSTM for the real-time detection of sign language.

关键词： Deep learning Sign language Computer vision machine learning algorithms Network security Data science Feature extraction Real-time systems Libraries Long short term memory

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