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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 13410 条记录，以下是241-250 订阅

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Stacked Regressor for Crop Yield Prediction 1

Stacked Regressor for Crop Yield Prediction

引用

1st IEEE international conference on computing and data science, ICCDS 2024

作者： Gupta, Harsh Vincent, Rajiv Rajakumaran, Gayathri Sivaraman, Arun Kumar Rajesh, M. Sultana, Ajmery School of Computer Science and Engineering Vellore Institute of Technology Chennai India Digital Engineering Solution Center Photon Interactive Inc ONL6T 4A8 Canada School of Computer Science and Technology Algoma University ON Canada

ISBN: (纸本)9798350365337

A significant number of people in developing nations like India, when a primary source of income is farming, depend on the agriculture sector for their economy. The agricultural sector can be improved to make it more effective and lucrative for farmers by harnessing the prospects of data and analytics. This work suggests using deep learning and machine learning techniques to forecast crop yield throughout India. The work trains the models using a variety of input variables, including weather information, soil characteristics and crop character traits. The findings demonstrate that models based on machine learning and deep learning are effective at accurately predicting crop yield. Intelligent choices about crop production and management can be made using the proposed approach by farmers and policymakers. Using a stacked ensemble model of Random Forest, Gradient Boosting, Bagging regressor and RNN as the meta-learner layer, this work evaluates crop yield. © 2024 IEEE.

关键词： Recurrent neural networks

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AI-ML Trained Object Recognition System Development using Google Teachable machine with the Help of data sciences 15

AI-ML Trained Object Recognition System Development using Go...

引用

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

作者： Prasad, Rajneesh Pavithra, G. Manjunath, T.C. Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore560111 India

ISBN: (纸本)9798331300579

Object recognition is a crucial task in computer vision and artificial intelligence, as it plays a vital role in many applications such as autonomous vehicles, surveillance systems, and *** this paper, we present an overview of AI Trained object recognition using Google Teachable *** Teachable machine is a web-based platform that allows users to train machine learning models without any coding or programming *** explore the steps involved in training an object recognition model using Google Teachable machine and evaluate the performance of the model on a real-world *** results show that Google Teachable machine is a powerful and user-friendly tool for training object recognition models with high *** research focuses on the development of an AI-ML trained object recognition system using Google Teachable machine, enhanced by data science *** objective is to create a robust and efficient system capable of accurately identifying and classifying various objects in *** the user-friendly interface of Google Teachable machine, the system is trained with diverse datasets, incorporating advanced machine learning algorithms and data preprocessing *** approach ensures high accuracy and reliability in object recognition *** integration of data science principles allows for thorough analysis and optimization of the training data, improving the system's performance and *** resulting system demonstrates significant potential for applications in areas such as security, automation, and augmented reality, showcasing the synergy between AI, machine learning, and data sciences in solving complex recognition problems. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Lightweight Distributed Deep learning Model Based on MobileNet 9

Lightweight Distributed Deep Learning Model Based on MobileN...

引用

9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Gao, Jian Zhu, Li Liu, Bin College of Computer Science and Technology Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9798350376548

In order to solve the problem of high memory usage and large GPU computation in the case of a single machine. In this paper, MobileNet and Pytorch deep learning framework and Flink big data computing framework are deeply integrated through DepthWise Convolution technology, and a distributed deep learning model is designed and implemented. Firstly, Pytorch is used to quickly build a neural network to save memory, and then MobileNet is used to quickly improve the running speed of the neural network. Finally, Flink is used for parallel computing and cluster resource scheduling. The experimental comparison shows that the model not only effectively saves the memory occupation, but also greatly reduces the calculation amount of the convolute-onal layer of the neural network, and can maintain high prediction accuracy. © 2024 IEEE.

关键词： deep learning Flink Light-weight MobileNet Pytorch

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machine learning Approaches for Obesity Classification and Prediction: An Analysis of Demographic, Lifestyle, and Health Factors 2

Machine Learning Approaches for Obesity Classification and P...

引用

2nd international conference on computing and data Analytics, ICCDA 2024

作者： Azu, Joel Hasan, Raza Ahmad, Shakeel Mahmood, Salman Solent University Department of Science and Engineering Southampton United Kingdom Nazeer Hussain University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798331542573

The high level of obesity poses a serious public health problem across the globe, being a leading cause of various chronic diseases and substantial threats to the quality of people's lives. Given the ever-growing rates of obesity and its impact on individual well-being, the current study aims to explore numerous factors that drive obesity, using machine learning algorithms to solve classification tasks. By working on a rich dataset that contains a range of demographic, lifestyle, and health parameters, several classifiers were developed and tested, namely Decision Tree, Random Forest, Gradient Boosting, Support Vector machine, and K-Nearest Neighbors. The resulting outcomes indicated that both Random Forest and Gradient Boosting algorithms were highly accurate in the classification of obesity, with 95.0% and 95.3% accuracy rates, respectively. The obtained results confirm the critical role of various machine learning approaches in understanding obesity and developing predictions for more focused intervention. This study offers considerable input into obesity epidemiology literature and demonstrates the utility of advanced analytical appraisal in public health. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review on Quantum machine learning in Different Computer Vision Fields

A Review on Quantum Machine Learning in Different Computer V...

引用

2024 IEEE international Performance, computing, and Communications conference, IPCCC 2024

作者： Islam, Md Majedul He, Jing Selena Kennesaw State University Department of Computer Science Marietta United States

ISBN: (纸本)9798350367942

Quantum machine learning (QML) promises the transformative potential in computer vision by utilizing quantum computing to facilitate faster high-dimensional data processing. In this paper, we will go through some of the recent works that employ QML for computer vision problems such as Image Segmentation, Classification, and Generation. Demonstrations aimed at showing where QML methods beat the state of art techniques in particular applications like facial recognition, medical imaging, and satellite imagery. QML aspires to make pathbreaking changes in a field limited by current hardware capabilities. This poster abstract summarizes the important studies, methodologies and findings to inform further research in this developing field. © 2024 IEEE.

关键词： Generative adversarial networks

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Assessing Chronic Kidney Disease Prediction Models: A Comparative Analysis of Smart AI and Intelligent machine learning Approaches 2

Assessing Chronic Kidney Disease Prediction Models: A Compar...

引用

2nd international conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Thacker, Smit Vaghela, Ravirajsinh Ahmedabad India National Science Forensic University Department of Cyber Security and Digital Forensic Gandhinagar India

ISBN: (纸本)9798350370188

Millions of individuals worldwide suffer from chronic kidney disease (CKD). Early identification and treatment prevent CKD and improve patient outcomes. machine learning can analyze enormous volumes of medical data and find patterns that could help detect CKD early. The current study uses a massive dataset of clinical records, laboratory test results, and patient demographics. Feature selection and data processing improve data integrity. LR, SVM, RF, and deep neural networks have been built and tested using the dataset. Sensitivity, AUC-ROC, Accuracy, and specificity are used to evaluate CKD model performance. The results show that machine learning algorithms can accurately identify early CKD risk factors. Future studies may combine real-time data streams with personalized patient data to increase CKD detection algorithms' accuracy and therapeutic value. © 2024 IEEE.

关键词： Decision support systems

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AI-Powered Privacy Preservation: A Novel Framework for Adaptive data Protection 2

AI-Powered Privacy Preservation: A Novel Framework for Adapt...

引用

2nd international conference on computing and data Analytics, ICCDA 2024

作者： Kumar, R. Senthil Lokeshwari, J. Shanmugam, Selvanayaki Kolandapalayam Dr. N.G.P. Arts and Science College Department of Computer Science with Cognitive Systems Tamil Nadu Coimbatore India Ashland University Dept. of Mathematics and Computer Science AshlandOH United States

ISBN: (纸本)9798331542573

The enlargement of Artificial Intelligence (AI)-powered privacy preservation techniques leads to new research avenues and innovations. Moreover, in today's data driven world the privacy and data protection are increased. In order to balance both data utility and individual privacy rights. This research paper provides a unique AI-powered privacy preservation framework by using machine learning algorithm that adapts to diverse data scenarios. The proposed framework uses Adaptive Privacy-Preserving Ensemble learning (APPEL) machine learning algorithms to ensure the optimal data protection while maintaining both data utility and also privacy settings dynamically. Additionally, this paper evaluates the efficacy of data driven applications like Healthcare, social media and Online Platforms, Internet of Things (IoT) and smart devices. This study collected and pre-processed the data from data driven applications to determine trends, insights, and highlighting key challenges and opportunities for improvement. This framework's efficacy in real-world applications demonstrate the experimental results and provides an accurate and promising solution for privacy-preserving data analysis. This research contributes to the development of privacy-preserving AI solutions to safeguard sensitive data in data driven environment. © 2024 IEEE.

关键词： Adversarial machine learning

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An Automatic Prediction of Elevator Capacity Using ML to Customize the Total Capacity 5

An Automatic Prediction of Elevator Capacity Using ML to Cus...

引用

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

作者： Talwandi, Navjot Singh Tripathi, Anubhav Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Elevators serve as vital components in modern buildings, yet optimizing passengers' waiting time remains a crucial challenge. This study proposes a machine learning-based approach to enhance the efficiency of elevator systems by minimizing the waiting time experienced by passengers. By leveraging historical data and predictive models, this research aims to optimize elevator operations, considering factors such as traffic patterns, peak hours, and user demand. The implementation of machine learning algorithms enables the system to dynamically adjust elevator scheduling strategies, ultimately reducing waiting times and enhancing user experience. The findings highlight the potential of machine learning techniques in revolutionizing elevator management for improved passenger satisfaction and operational efficiency. © 2024 IEEE.

关键词： Arduino Elevator optimization kNN machine learning Operational efficiency Passenger waiting time Predictive modeling Traffic patterns User demand

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A Few-Shot learning-Focused Survey on Recent Named Entity Recognition and Relation Classification Models 13

A Few-Shot Learning-Focused Survey on Recent Named Entity Re...

引用

13th international conference on data science, Technology and Applications, data 2024

作者： Alqaaidi, Sakher Khalil Bozorgi, Elika Shams, Afsaneh Kochut, Krzysztof J. School of Computing University of Georgia AthensGA United States

ISBN: (纸本)9789897587078

Named Entity Recognition (NER) and Relation Classification (RC) are important tasks for extracting information from unstructured text and transforming it into a machine-readable format. Recently, the field of few-shot learning has gained increased interest due to its ability to enable models to generalize across multiple domains using minimal labeled data. However, no studies have addressed the recent achievements in the NER and RC fields within the few-shot learning paradigm. In this work, we aim to fill this gap by presenting a survey on recent few-shot learning models in the fields of NER and RC. Our survey provides a thorough introduction to these tasks, along with a summary of the latest approaches and achievements. We conclude with our observations on the current state of research in these domains. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Zero-shot learning

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machine learning Models for Sentiment Analysis: State of the Art and Applications 15

Machine Learning Models for Sentiment Analysis: State of the...

引用

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

作者： Anwar, Khalid Garg, Arushi Wasid, Mohammed Ahmad, Shahnawaz School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India

ISBN: (纸本)9798350370249

Sentiment Analysis (SA) is one of the prominent and emerging research area in the domain of natural language processing. This paper explores the field of sentiment analysis using machine learning (ML) models. It aims to provide a comprehensive overview of the current state of the art, applications, limitations, and future directions of SA. It identifies various ML models like Random Forest, Logistic Regression, Decision Tree, Support Vector machine, and Naïve Bayes used for SA. It also discusses various applications of SA across different domains like product reviews, social media monitoring, financial forecasting, recommender systems and crisis management. While acknowledging the limitations of SA like linguistic barriers, sarcasm detection, and noisy data, the paper proposes future research directions to address these challenges and improve the accuracy and efficiency of SA models. ©2024 IEEE.

关键词： Adversarial machine learning

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