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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13394 条记录，以下是151-160 订阅

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Gesture Recognition Technology in Smart Gloves Enhanced by machine learning 4

Gesture Recognition Technology in Smart Gloves Enhanced by M...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Thangam, S. Kandi, Nithin Charan, Dorai Sai Gosu, Vijay Kumari, J. Jesy Janet Virupaxappa, G. Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529635

Gesture recognition systems are changing how people interact with devices, promising natural and intuitive hands-free control in various applications. Based on the ESP32 microcontroller and machine learning techniques, the proposed work is developing a compact, power-efficient system for recognizing hand gestures from data gathered by flex sensors and accelerometers. The entire system fits within memory constraints, as lightweight machine learning models are first trained on a computer using Python and then ideally deployed in optimized versions onto the ESP32. In this respect, the stream of pre-processed sensor data happens to be fed to the ML model for inference, sending back results to the ESP32, which hence triggers specific actions or commands. This fundamentally new approach shows the feasibility of machine learning on resource-constrained platforms, opening the road for wearable technologies that will be more clever and reactive. © 2024 IEEE.

关键词： Accelerometers

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machine learning: The Driving Force Behind Intelligent Systems and Predictive Analytics 2

Machine Learning: The Driving Force Behind Intelligent Syste...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Virmani, Deepali Atheeq Sultan Ghori, Md. Tyagi, Nishant Ambilwade, R.P. Rajesh Patil, Priyanka Sharma, M.K. Vivekananda Institute of Professional Studies-Technical Campus School of Engineering and Technology India Telangana University Nizamabad Department of Computer Science and Engineering Telangana State India Management G.L. Bajaj Institute of Management and Research Greater Noida India Computer Science National Defence Academy Pune India Prof Ram Meghe College of Engineering and Management Department of Electronics and Telecommunication Engineering Badnera India Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

ISBN: (纸本)9798350384277

This technical analysis explores the profound impact of machine learning on intelligent systems and predictive analytics. It examines algorithmic fundamentals and explores models such as linear regression, support vector machines and neural networks, which are the pillars of supervised learning. The research explores practical applications in natural language processing, where advanced neural network architectures such as Transformers redefine, language understanding and computer vision using convolutional neural networks for image recognition and object recognition. In addition, the analysis covers the basic steps of data processing, including data cleaning, transformation and refinement, as well as feature design and model optimization. In addition, the research addresses the challenges of machine learning, emphasizing the importance of reducing bias and ethical considerations. The study concludes with a look into the future of machine learning, envisioning advances in quantum computing to solve complex problems, combinatorial learning for distributed data problems, and synthetic data generation as a solution to data protection problems. This comprehensive study provides an overview of the current state of machine learning, challenges and promising developments in intelligent system innovation and predictive analytics. © 2024 IEEE.

关键词： Federated learning

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Enhancing Anxiety Prediction: Leveraging machine learning with Functional Design Metrics 21

Enhancing Anxiety Prediction: Leveraging Machine Learning wi...

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21st international conference on Electrical Engineering, computing science and Automatic Control, CCE 2024

作者： Javed, Abqa Ajmal, Sahar Tajammal, Taliah Aslam, Muhammad Martinez-Enriquez, A.M. Sheikh, Usman Sadiq University of Engineering and Technology Department of Computer Science Lahore Pakistan National University of Computer and Emerging Sciences Department of Software Engineering Pakistan Virtual University of Pakistan Department of Computer Science and Information Technology Lahore Pakistan CINVESTAV-IPN D.F. Department of Computer Science Mexico Medicine Bronglais General Hospital Clinical Fellow in Respiratory SY23 1ER United Kingdom

ISBN: (纸本)9798350377545

Fear, nervousness, and social phobia all contribute to anxiety episodes reaching their peak in seconds. Using data collected from patient questionnaires, this study seeks to anticipate and diagnose anxiety disorders effectively and precisely using machine learning approaches. However, current research offers standardized metrics for assessing machine learning approaches throughout the design process. This work presents a systematic method for efficiently extracting literature from digital sources and emphasizes the need for data engineers to evaluate the efficiency of machine learning approaches to assure forecast quality. The international Organization for Standardization (ISO) 9126 standard is used to assess machine learning approaches for anxiety prediction to address this gap. This evaluation considers parameters such as functionality, accuracy, resilience, throughput, explainability, privacy, and correctness. These defined characteristics serve as the benchmark to measure the efficacy of machine learning approaches in predicting anxiety disorders. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review on Students' Performance Prediction using learning Analytics 15

A Review on Students' Performance Prediction using Learning ...

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

作者： Jyoti Kumar, Dharminder Dhingra, Sakshi Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

learning Analytics is a research area that is growing rapidly. It deals with selecting, analyzing, and reporting educational data collected from various learning environments and finding relevant patterns in students' behavior. Different online learning platforms like Massive Open Online Courses (edX, Udemy, Udacity, etc.), learning Management Systems (Moodle, Blackboard, Classroom, etc.), and Virtual learning Environments (DEEDS, Coursera, etc.). learning analytics can be viewed as a multidisciplinary field that includes machine learning (ML) Techniques, Educational data Mining (EDM), Statistics, Social Network Analysis, and Natural Language Processing. This paper comprises the study of learning Analytics and its different techniques used in predicting students' performance. Further, it consists of an analysis of previous studies described in tabular form for better understanding. A basic learning analytics model is described which consists of the steps involved in creating any project. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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The Prediction of Mental Health Using machine learning 15

The Prediction of Mental Health Using Machine Learning

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

作者： Dhawale, Hrutuja Thakare, Dhanashree Morris, Nekita Chavan Agrawal, Rahul Dhule, Chetan G.H Raisoni College of Engineering Department of Data Science IOT Cyber Security Nagpur India

ISBN: (纸本)9798350370249

Depression is the mental health condition that affects many people around the world. In order to find and treat depression and other mental health conditions, it is important to find those who are at risk so that they can be helped as soon as possible. The early diagnosis and prevention have been the help of the machine learning techniques to the prediction of depression. The different machine learning models and datasets used, and the current literature on depression prediction have been thoroughly inspected. In the project, we gathered data from employees and visualized it using KNN algorithm. Finally, the layout of the heatmap, the confusion matrix, pie chart, bar plots and the forest of the data was drawn. After that, we used KNN and logistic regression to the data and the accuracy was 0.825 and 0.73333, respectively. The KNN algorithm was more accurate, i.e. the number was 0.825 (82. 5%). © 2024 IEEE.

关键词： Health data Clustering Health data Visualization K-Nearest Neighbors (KNN) Logistic Regression machine learning in Healthcare Mental Health Predictive Modeling in Psychology

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Revolutionizing healthcare: Unleashing the power of machine learning in remote diagnosis and health management 4th

Revolutionizing healthcare: Unleashing the power of machine ...

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Puneet, Parth Shukla, Prakhar Parashar, Gaurav KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (纸本)9781032911571

The medical industry has increasingly used artificial intelligence (AI) as technology progresses. The primary advantage of machine learning comes in its capacity to derive significant insights from the immense volumes of health data gathered daily. The progress in the Internet of Things (IoT), machine learning, big data, and high-performance computing has created exceptional possibilities to enhance public health by maximizing scarce human resources. This work presents a diabetes prediction model that categorizes diabetes based on common parameters such as blood sugar, body mass index (BMI), age, insulin, and other relevant factors, as well as external factors that contribute to the development of diabetes. Our analysis utilizes the PIMA India and Sylhet datasets. machine learning techniques, such as the Support Vector machine (SVM), Naive Bayes classifier (NB), and decision tree, are used in the study. © 2025 the Author(s).

关键词： Electronic health record

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Deep learning Techniques for Alzheimer's Disease Prediction 5

Deep Learning Techniques for Alzheimer's Disease Prediction

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mahesh, T.R. Agarwal, Ankita Nair, Amrutha V. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350383522

Deep learning, a cutting-edge approach of machine learning, surpasses classical machine learning when it comes to detecting particular structures in complicated, high-dimensional data in the area of computer vision. The fast improvement of neuroimaging technologies and the associated creation of huge volumes of multimodal neuroimaging data have led in a noteworthy surge in interest in the application of deep learning for automated categorization and early diagnosis of Alzheimer's disease (AD). The best classification results were achieved utilizing multimodal neuroimaging in conjunction with fluid biomarkers. © 2024 IEEE.

关键词： and Alzheimer's disease (AD) Deep learning feature selection gradient descent multimodal neuroimaging data performance evaluation repeatability

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SMART UI: Crop Classification in Karnataka Nippani Using Ensemble Random Forest 2

SMART UI: Crop Classification in Karnataka Nippani Using Ens...

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2nd international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Susanth, Jagarlamudi Sivakumar, Soubraylu Shanmugam, Selvanayaki Kolandapalayam Sivakumar, R. Sridhar, S.S. Ganesh Kumar, S. School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Ashland University OH United States SRM Institute of Science and Technology Kattankulathur Chennai India School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331541217

This study helped in enhancing agricultural management, predicting yields, tracking crop health which are all dependent on accurate crop classification. But the conventional methods are labor intensive and time consuming. The goal of this study is to automate crop classification system that uses machine learning and satellite image to accurately classify the crops. Ground Truth data is gathered from *** website and Random Forest classifier is used to train on data. To be more accurate the algorithm is verified using dataset from specific regions. The Random Forest effectively classified crops with an accuracy of 79%. The outcome shows how well crop classification works when used with machine learning and satellite image together. This study provides scalable algorithm for crop classification, which helps in various agricultural related works like crop health. The future studies should investigate alternative use of real-time data for dynamic classification. © 2024 IEEE.

关键词： Adversarial machine learning

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An Empirical Study of Utility and Disclosure Risk for Tabular data Synthesis Models: In-Depth Analysis and Interesting Findings

An Empirical Study of Utility and Disclosure Risk for Tabula...

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international conference on Big data and Smart computing (BigComp)

作者： Park, Dae-Young Ko, In-Young Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Financial Secur Inst FSI Seoul South Korea

ISBN: (纸本)9798350370027;9798350370034

The ever-growing accumulation of data in various applications has spurred research into privacy-enhancing technologies. Synthetic data, in particular, has gained significant attention for enhancing machine learning model performance while preserving personal information. Although synthetic data studies have been on the rise, there are no clear criteria for how to measure the utility and disclosure risk of synthetic data. Furthermore, although many existing studies have primarily concentrated on image data synthesis models, there's a notable scarcity of research on tabular data synthesis models, particularly concerning disclosure risk. This is crucial in domains such as finance, which heavily rely on tabular datasets containing sensitive information. In this paper, we perform in-depth analysis of utility and disclosure risk index from classical to state-of-the-art tabular data synthesis models in terms of different metrics and various types of datasets. Our interesting findings can be summarized as follows: (1) Synthetic data's utility tends to increase as the proportion of continuous attributes in the original data decreases, (2) Conversely, disclosure risk rises with a lower proportion of continuous attributes in the original data, (3) As the volume of synthetic data grows, both utility and disclosure risk metrics generally increase, (4) An inverse relationship is observed between the sparsity of original data and a specific utility metric, and (5) Notably, we discover that Targeted Correct Attribution Probability (TCAP), a widely-used disclosure risk metric, fails to measure certain outlier records that are potential vulnerabilities for malicious attacks.

关键词： artificial intelligence synthetic data deep learning machine learning data utility data privacy privacy-enhancing technology

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Twitter News Classification Using machine learning

Twitter News Classification Using Machine Learning

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2024 international conference on Engineering and computing, ICECT 2024

作者： Memoon, Muhammad Umar, Muhammad Rani, Sabha Khalid, Muhammad Shaheen, Saima HITEC University Taxila Department of Computer Science Rawalpindi Pakistan

ISBN: (纸本)9798350349719

In the era of technology several social media platforms are generating massive data. Social media platforms like Twitter are also generating data in the form of text. Classification of this text is a challenging task because of the diversity in the text, contextual understanding, language complexity and Ambiguity. Classification of news data on Twitter is also an important task in Natural Language Processing (NLP). The news classification task helps us to categorize the Twitter news especially when a topic is trending. In this research, we implement a machine learning algorithm on a new self-created Twitter news dataset. Our methodology involves data collection, preprocessing, feature extraction, and then classification. We evaluate machine learning models including SVM, Random Forest, Naïve Bayes, Logistic regression, and gradient boosting. Experiments show that Random Forest achieved the highest accuracy of 92% on our Los Angeles Twitter News dataset. This study also provides a new dataset of Twitter news for more advanced text classification approaches. © 2024 IEEE.

关键词： Classification (of information)

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