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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161-170 订阅

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machine learning in Agriculture: A Land data Approach to Optimize Crop Choice with the LAGNet Model 6

Machine Learning in Agriculture: A Land Data Approach to Opt...

引用

6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Bala Kamatchi, C. Muthukumaravel, A. Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Faculty of Arts and Science Tamil Nadu Chennai India

ISBN: (纸本)9798350306613

Adopting machine learning in agriculture is essential for improving crop selections and increasing crop yield. Traditional crop selection methods sometimes rely on limited data, local knowledge, and outdated practices. These conventional techniques might result in inefficient crop selections, resource wastage, and reduced yields. In response to these limitations, introduce the Land Allocation and Crop Growth Network (LAGNet). This machine-learning model revolutionizes crop choice by integrating land and weather data with season-specific recommendations. The StudyStudy compares the LAGNet model to traditional techniques. The results show considerable increases in crop selection accuracy, with an average yield gain of 16%. LAGNet's powerful machine learning algorithms exceed conventional approaches, providing improved accuracy, sensitivity, specificity, and a higher F1 score. The LAGNet model delivers a complete and adaptable crop selection solution, empowering farmers with data-driven recommendations and eventually improving agricultural production, sustainability, and food security. The StudyStudy highlights LAGNet's transformational potential and role in modernizing precision agriculture. © 2024 IEEE.

关键词： Agriculture Crop Choice Optimization Crop Yield Improvement LAGNet Model Land data machine learning

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Flood Forecasting with Classical and Quantum machine learning Models: A Comparative Analysis

Flood Forecasting with Classical and Quantum Machine Learnin...

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2024 international conference on Intelligent computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Perla, Madhavi Jagadish, Sripelli Aggarwal, Alankrita Maram, Balajee Creesy, R. Das, Smritilekha GMRIT Department of CSE-AIML Andhra Pradesh Rajam India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India University Institute of Engineering Apex Institute of Technology Chandigarh University Gharuan Department of Computer Science and Engineering Punjab Mohali140413 India Tamil Nadu Chennai77 India Koneru Lakshmaiah Education Foundation Department of CSE AP Vaddeswaram India

ISBN: (纸本)9798350369175

Flood forecasting is one of the most disastrous natural phenomena;however, its prediction with sufficient accuracy can help to at least reduce the damage caused by this potentially disastrous event. Additionally, in this paper, a comparison of classical and quantum models for machine learning in flood forecasting is made. Traditional machine learning means have found wide use in view of this, particularly when there exist historical data and a number of environmental parameters. But with the strength that quantum computing holds, quantum machine learning models are poised for even better predictive accuracy and efficiency for the future, as they have the capability to process loads of data all at once. In this text, we implement both the classical models, such as the Support Vector machine (SVM) and Random Forests, alongside quantum machine learning models. We compare on prediction accuracy, computational efficiency, and scalability. The results will point out the strengths and limitations of each approach, from which it will be possible to derive how quantum machine learning can complement or surpass the performances of classical methods for the improved reliability and timeliness of predictions in flood forecasting. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-task learning Model for Location Estimation Using RSSI of Wireless LAN in NLoS Environment 24

Multi-task Learning Model for Location Estimation Using RSSI...

引用

16th international conference on machine learning and computing (ICMLC)

作者： Wei, Zehui Ding, Jianbing Wang, Zening Wang, Xidong Ye, Xiaozhou Ouyang, Ye AsiaInfo Technol China Inc Beijing Peoples R China

ISBN: (纸本)9798400709234

In 6G networks, applying native AI/ML techniques to user signal quality data to obtain high-precision location estimation is a typical application scenario. Recent advanced techniques using global positioning system (GPS) and traditional received signal strength-based localization approaches are model-based. However, their accuracy decreases under the influence of non-line-of-sight (NLoS) reception and multipath fading channel, which leads to demand for data-oriented localization techniques using received signal strength indicator (RSSI) obtained from the surrounding radio transmitters with the help of powerful AI/ML techniques. AI/ML-based localization technique is quite challenging in practice for data scarcity issues and how to capture and fully utilize the information for records. In this work, we conducted careful feature engineering and proposed a multi-task learning model to cope with these problems. The multi-task learning model improves the estimation through explicitly extracting the RSSI distance relation with shared layer and coping with latitude and longitude simultaneously. We chose three tree-based models as baselines. Numerical experiment shows that the multi-task learning model is promising in the RSSI-based location estimation.

关键词： location estimation machine learning multi-task learning NLOS RSSI

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Classification of Fetal Health on Cardiotocograph data using machine learning Techniques 15

Classification of Fetal Health on Cardiotocograph Data using...

引用

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

作者： Deepa, M.S. Sujithra, M. Department of Computing-Data Science Coimbatore Institute of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798331300579

Pregnancy and fetus development is an incredibly intricate biological process that can go wrong when the health of fetus or the mother gets ***, India has the highest number of stillbirths, with an estimated 5,92,100 deaths per year, and a WHO estimated rate of 22 per 1000 total *** is one procedure used to check whether the fetus is growing as *** diagnostic procedure measures the mother's uterine contractions and the fetus' heartbeat, usually in the third trimester of pregnancy when the fetus heart is fully *** paper aims to classify the results into one of three states (physiological, suspicious or abnormal) based on cardiotocographic data using machine learning algorithms and compare the accuracies of different ML classification models such as logistic regression, k-nearest neighbor, decision tree, random forest classifier and support vector machine. © Grenze Scientific Society, 2024.

关键词： Support vector machines

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Comparative Analysis of machine learning Models for Solar Power Generation Prediction 10

Comparative Analysis of Machine Learning Models for Solar Po...

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10th international conference on Smart computing and Communication, ICSCC 2024

作者： Orish, Goodness Chinaza Tackie, Samuel Nii Kayali, Devrim Alpan, Kezban Vafaei, Lida Ebrahimi Dimililer, Kamil Near East University Department of Electrical and Electronic Engineering Nicosia Cyprus Research Center for Science Technology and Engineering Near East University Department of Electrical and Electronic Engineering Nicosia Cyprus Research Center for Science Technology and Engineering Near East University Department of Software Engineering Nicosia Cyprus Research Center for Science Technology and Engineering Near East University Department of Alternative Energy Resources Technology Nicosia Cyprus Department of Electrical and Electronic Engineering Nicosia Cyprus

ISBN: (纸本)9798350363104

When constructing renewable energy facilities, it is important to predict the amount of solar electricity generated precisely. To make these precise solar power output forecasts, varying weather patterns should be considered. In this study, the effectiveness of three well-known machine learning methods: K nearest neighbour, Random Forest, and support vector regression was examined and assessed. According to the obtained R2 values, Random Forest performed better compared to the other techniques with 98% on training data and 88.7% on testing data. This shows that when compared to SVR and KNN, the generalization of Random Forest is better when capturing the connection between the input and output data. © 2024 IEEE.

关键词： Adversarial machine learning

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Fededrated learning: Research, Challenges, and Future Directions 5

Fededrated Learning: Research, Challenges, and Future Direct...

引用

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

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Yadav, Rishika Malik, Kiran Tripathi, Aanjey Mani Galgotias University School of Computing Science and Engineering UP Greater Noida India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation AP Guntur India Graphic Era Hills University Department of Computer Science and Engineering Uttarakhand Dehradun India Mriem Department of Computer Science and Engineering Rohtak India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Federated learning, a disruptive and novel aspect of machine learning, is at the forefront of decentralized, privacy-conscious data processing. This in-depth review study navigates the complex environment of Federated learning Ecosystems, attempting to extract the core of the topic while shining light on its research horizons, pressing difficulties, and promising approaches. Our trip begins with a look at the fundamental concepts of Federated learning, emphasizing its importance in the landscape of distributed machine learning paradigms. We track its development via historical milestones and key contributions, offering context for the field's increasing research. This study focuses on the many features of Federated learning research, including privacy-preserving techniques, communication protocols, aggregation approaches, and real-world applications. We aggregate major results and advances within these domains, highlighting notable authors and their contributions. However, the route to Federated learning's full fulfillment is plagued with difficulties, including questions about privacy, communication efficiency, and scalability. In the future, we will enlighten the growing paths of Federated learning research, forecasting trends, providing insights into overcoming present problems, and visualizing its integration into diverse fields. To summarize, this review article serves as a guidepost for scholars, practitioners, and enthusiasts wanting to understand the vast domain of Federated learning. It captures the core of this dynamic discipline, offers strategic counsel for resolving its obstacles, and encourages optimism about its bright future within the domain of distributed machine learning paradigms. © 2024 IEEE.

关键词： data Analysis Distributed Environments Edge computing Federated learning machine learning

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Modern Agriculture: A Review of Emerging Crop Yield Prediction Models Using machine learning 2

Modern Agriculture: A Review of Emerging Crop Yield Predicti...

引用

2nd IEEE international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Agiwal, Priyanka Gupta, Rajendra RNTU University Department of computer science and It M.P Bhopal India

ISBN: (纸本)9798350390179

Accurately predicting crop yields is a very critical component of global food safety and agricultural efficiency. Such is a multifaceted approach toward crop yield prediction explored in the present study. Anchoring on the Green Revolution and subsequent impact on crop diversity and resilience, it reviews how far a wide range of yield prediction models has evolved in terms of effectiveness, including regression models, machine learning, and hybrid models. Such methods would require advanced machine learning techniques, statistical methods, and integration of data sources like satellite imagery and meteorological data to assure the accuracy of yield predictions. This study analyzes strengths and limitations of the models in order to provide a fuller understanding of the current situation and identify opportunities for technological advances and improved implementation strategies in crop yield forecasting. © 2024 IEEE.

关键词： Prediction models

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An Energy-Efficient Ensemble Approach for Mitigating data Incompleteness in IoT Applications 20

An Energy-Efficient Ensemble Approach for Mitigating Data In...

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20th Annual international conference on Distributed computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： AlShehri, Yousef Ramaswamy, Lakshmish Univ Georgia Sch Comp Athens GA 30602 USA

ISBN: (纸本)9798350369458;9798350369441

The dynamic nature of many IoT ecosystems poses unique challenges to the efficacy of IoT ML-based applications. One such challenge is data incompleteness. Furthermore, most IoT systems are severely power-constrained. It is important that we build IoT-based ML systems that are robust against data incompleteness while simultaneously being energy efficient. This paper presents an empirical study of SECOE a recent technique for alleviating data incompleteness in IoT with respect to its energy bottlenecks. Towards addressing the energy bottlenecks of SECOE, we propose ENAMLE a proactive, energy-aware technique for mitigating the impact of concurrent missing data. ENAMLE is unique in the sense that it builds an energy-aware ensemble of sub-models, each trained with a subset of sensors chosen carefully based on their correlations. Furthermore, at inference time, ENAMLE adaptively alters the number of the ensemble of models based on the amount of missing data rate and the energy-accuracy trade-off. ENAMLE's design includes several novel mechanisms for minimizing energy consumption while maintaining accuracy. Through experimental studies, we demonstrate the energy efficiency of ENAMLE and its ability to alleviate sensor failures.

关键词： IoT Failure Resilience Energy-Aware machine learning data Incompleteness machine learning Ensemble

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Usage of Basic Geometrical Ideas and Principles of Symmetry for machine learning

Usage of Basic Geometrical Ideas and Principles of Symmetry ...

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2024 international conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Srivastava, Vatsal Subramanian, Sakthitharan School of Computing Srm Institute of Technology Department of Computational Intelligence Chennai India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331510022

The paper discusses the usage of geometric principles and theories in order to manipulate the position of decision boundary itself to classify data points belonging to two different classes. Moving away from the close relation between statistical approaches at classification, we will attempt to create a classifier that establishes a more direct connection, and works with the decision boundary. We explore binary classification problems using the dataset, compare convergence conditions and evaluate our results against some popular classifiers. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning and Cloud computing: Synergies And Innovations for data-Driven Insights 7

Machine Learning and Cloud Computing: Synergies And Innovati...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Sadhankar, Dileep S. Santhoshkumar, S.P. Sharma, M.K. Saxena, Pratha Reddy, Ch. Srinivasa Jain, Amit Kumar St. Francis De Sales College Department of Computer Science Maharashtra Nagpur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Avadi Chennai600062 India Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India Invertis university UP Bareilly India Department of Information Technology Duvvada Visakhapatnam India Poornima University Department of Electrical & Electronics Engineering Rajasthan Jaipur303905 India

ISBN: (纸本)9798350350067

This paper aims at discussing the interaction between cloud computing and machine learning in analyzing the data since they boost the analytical ability of insights through boosting the predictability and operational ability. The study employs Cybersecurity Attack data set from Kaggle together with Cloud data set and employs Random Forest and ARIMA algorithms together with cloud computationally intensive resources to analyze and interpret large and complicated datasets. It covers data preparation, building, and deploying of models that can analyze data in real-time with cloud support. Findings show that integrating machine learning with cloud computing has immensely improved threat detection, predictive analytics, and actions. This brings in to perspective the potential of such technologies in enhancing the performance of data processing and, forecast future research on the advanced machine learning algorithms and real-time data. © 2024 IEEE.

关键词： Adversarial machine learning

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