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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 13417 条记录，以下是231-240 订阅

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Enhancing Asthma Diagnosis Through machine learning: A Stacking Ensemble Approach 1

Enhancing Asthma Diagnosis Through Machine Learning: A Stack...

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Goel, Manas Gupta, Manya Jothikumar, C. Shanmugam, S. Ashokkumar, C. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Chennai603203 India

ISBN: (纸本)9798331518943

Asthma diagnosis remains a major clinical concern since it has diverse symptoms and is caused by various contributory causes. machine learning was applied in this study to solve these complications in order to analyze such a broad dataset of 2,392 patients with the capture of important variables such as demographics, environmental exposures, allergies, medical history, and clinical indicators. Several machine learning models were implemented, including those such as Random Forest, XGBoost, Decision Tree, LGBM, CatBoost, and AdaBoost. Next, the stacking ensemble strategy was taken to further optimize predictive performance, using the Random Forest model meta-classifier. Our approach proves that stable ensemble learning takes place with an amazing 97% accuracy rate when it comes to difficult clinical data. Recall, precision, and F1-score represent some of the most important measures for model performance evaluation. These results might point out the machine learning potential in improving asthma diagnosis accuracy and might be helpful for a doctor in coping with this complicated disease. © 2024 IEEE.

关键词： Adversarial machine learning

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Malware Detection in IoT Devices Using machine learning: A Review

Malware Detection in IoT Devices Using Machine Learning: A R...

引用

2024 international conference on Computational Intelligence and computing Applications, ICCICA 2024

作者： Singh, Deepika Khurana, Shiraz Sharda School Of Engineering And Technology Sharda University Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350306446

A thorough summary of recent studies on malware detection in Internet of Things environments can be found in the literature review section. The necessity for complex and scalable detection techniques is emphasized by examining various approaches, from signature-based to anomaly detection approaches. Furthermore, the discussion comprises the application of supervised, unsupervised, and semi-supervised machine learning algorithms to mitigate IoT malware threats. While unsupervised techniques like clustering algorithms show promise in detecting new threats, supervised learning techniques like support vector machines (SVM) and random forests (RF) effectively identify known malware patterns. Furthermore, semi-supervised learning techniques present a viable way to improve detection abilities by utilizing compactly labeled data. © 2024 IEEE.

关键词： Internet of things

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Improve Accuracy in Healthcare data Analysis using Competitive Ensemble Deep learning Model 18

Improve Accuracy in Healthcare Data Analysis using Competiti...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Sravanthi, Jakkula Reddy, Chada Sampath Mahendar, A. Kumar, V. Ravi Buragadda, Swathi Ghantasala, G. S. Pradeep Gupta, Gaurav Vnr Vignana Jyoti Institute of Engineering and Technology Department of Information Technology Telangana India Sree Chaitanya Institute of Technological Sciences Department of Computer Science Engineering Telangana Karimnagar India Telangana Hyderabad India Ace Engineering College Department of Computer Science Engineering Ghatkesar India Lakireddy Balireddy College of Engineering Department of Computer Science Engineering Kakinada India Alliance University Department of Computer Science and Engineering Bengaluru India Shoolini University Yogananda School of Artificial Intelligence Computers and Data Sciences Solan India

ISBN: (纸本)9789380544519

This paper discusses the significance of machine learning (ML) and Deep learning (DL) techniques for structured and unstructured healthcare data. As healthcare data is increasing tremendously, it is difficult to identify hidden patterns in huge amounts of data. DL handles a massive amount of clinical data and provides better outcomes. A novel competitive ensemble deep learning model has been proposed to improve the classification performance of structured data. However, dealing with unstructured data, the proposed work highlights a competitive DL model for Twitter sentiment analysis. In addition, this paper discusses the proposed Competitive Ensemble Deep learning (CEPL) algorithm for text data. The proposed model is compared with a traditional model to evaluate the model's performance in the range of 0.2%-0.5%. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Accuracy CEPL Deep learning Healthcare data machine learning Methods

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machine learning for Strategic Sales Forecasting in Retail Chains

Machine Learning for Strategic Sales Forecasting in Retail C...

引用

2024 international conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Rajaram, Tharanimathi Udhayamoorthi, M. Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore641042 India

ISBN: (纸本)9798331510299

Sales of an individual item record maintained by supermarket chains/big retailers like Big Mart help in estimating consumer demand and thereby enable them to do effective inventory management. Through the analysis of data stored in the data warehouses, they access the anomalies and trends informing decision-making processes. This will be invaluable information to the retailers, in this case Big Mart, in predicting the future sales volume using machine learning techniques. A predictive model was developed utilizing XGBoost, Linear Regression, Logistic Regression, and SVM techniques to forecast sales for businesses like Big Mart. It was found that this model outperforms existing ones, providing more accurate sales predictions and enabling better inventory management decisions. © 2024 IEEE.

关键词： Chains

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Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric machine learning Algorithms

Proactive Algorithms for Stabilizing the State of Technologi...

引用

2024 international Russian Automation conference, RusAutoCon 2024

作者： Musaev, A. Makshanov, A. Grigoriev, D. Saint-Petersburg Russia Admiral Makarov State University of Maritime and Inland Shipping Department of Computing Systems and Computer Science Saint-Petersburg Russia Saint-Petersburg Russia

The task of proactive stabilization of technological processes (TP), the evolution of the state of which is described by non-stationary random processes, is considered. Metric precedent analysis technologies, which belong to the class of machine learning tasks, are proposed as a mathematical tool. The training data used is a data set containing large arrays of retrospective data obtained during the monitoring of the state of the TP during its previous operation. Basic mathematical models of ongoing processes and the algorithmic apparatus of precedent data analysis used are presented. The results of numerical studies of technologies for forecasting the state of non-stationary TPs and the effectiveness of proactive stabilization algorithms built on their basis are given. © 2024 IEEE.

关键词： Adversarial machine learning

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Quantum-Inspired machine learning Models for Cyber Threat Intelligence 1

引用

1st international conference on Intelligent computing and Big data Analytics

作者： Reddy, Sana Pavan Kumar Dey, Niladri Sekhar SrujanGoud, A. Rakshitha, U. BV Raju Inst Technol Dept AI & DS Narsapur Telangana India

ISBN: (数字)9783031746826

ISBN: (纸本)9783031746819;9783031746826

This work presents an innovative method of using quantum-inspired approaches in machine learning to improve cyber threat intelligence. The current digital environment is experiencing a significant increase in cyber risks, which presents difficult challenges to traditional security approaches. This study presents an innovative framework that utilizes concepts from quantum computing to create and execute sophisticated machine learning models customized for cyber threat intelligence. Our suggested models utilize the inherent parallelism and computational complexity found in quantum systems to effectively analyze large amounts of diverse data sources. This allows us to identify patterns that suggest harmful activity with unparalleled accuracy. We explore the fundamental principles of quantum computing and explain how they may be utilized to create advanced algorithms that can effectively identify, categorize, and reduce various cyber risks with improved precision and effectiveness. By conducting empirical evaluations and comparative studies against standard machine learning methodologies, we provide evidence of the higher performance and robustness of our quantum-inspired models in several cybersecurity situations. Our research adds to the growing field of quantum computing applications in cybersecurity and highlights the potential for quantum-inspired machine learning to significantly change the landscape of cyber threat intelligence. This could lead to more robust and flexible defense mechanisms in the digital age.

关键词： Quantum Inspired machine learning Models Cyber Threat Intelligence Security

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Integrating Deep learning, machine learning, AI, IoT and data science for Future Innovations 4

Integrating Deep Learning, Machine Learning, AI, IoT and Dat...

引用

4th international conference on Soft computing for Security Applications, ICSCSA 2024

作者： Gupta, Shipra Kumar, Vijay Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Hill University Department of Physics Uttarakhand Dehradun India

ISBN: (纸本)9798331515720

machine learning (ML), Deep learning (DL), Internet of Things (IoT), Artificial Intelligence (AI) and data science are integrated to create innovative solutions that are powering industries today like never before. This paper reviews these technologies and how they can work together to transform industries like healthcare, finance, and manufacturing, as well as the immensely important space of smart cities. With ML and DL-driven analytics for predictions, IoT-based real-Time data capture mechanisms, AI making informed decisions along data science generating insights from the data makes organizations efficient in every aspect as well as reliable and innovative. Some example applications are predictive maintenance in manufacturing, personalized medicine in healthcare, algorithmic trading in finance and the development of smart/sustainable urban environments. While these potboilers offer a hopeful advantage, they also come with some hurdles, data privacy/security, integration complexity and the need for high-end technical skills being the topmost. In this manuscript, some case studies of success are highlighted in the belonging field and discuss the challenges for incorporation today along with future directions for seamless integration aimed at promoting even greater innovation. © 2024 IEEE.

关键词： Smart manufacturing

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Investigating the Impact of Feedback Loop Training on machine learning Model's Performance in Time Series data 12

Investigating the Impact of Feedback Loop Training on Machin...

引用

12th international conference on Current Trends in Advanced computing, ICCTAC 2024

作者： Kh, Alex Kalaiselvi, K. Thomas, Lijo.P. Department of Computer Science Bengaluru India

ISBN: (纸本)9798350395808

Ever since the development of Artificial Intelligence (AI) and machine learning (ML), the need for raw data has increased to mitigate this problem we investigate the impact of feedback loop training on an LSTM model's time series data and the performance of the model is investigated. We also employ traditional ML models like RandomForest Regression to perform a comparative study with the LSTM model. We seek to examine the effect on the accuracy and convergence behavior of the model by using its output as input for further iterations. A climate time-series dataset from Kaggle is used to train the LSTM model iteratively over ten iterations. According to our research, feedback loop training can have benefits like capturing intricate dependencies, but the cost outweighs the results as the model accuracy decreases drastically over several iterations of training. We have also compared the Model performance with traditional machine learning algorithms and other types of Neural Networks like ANNs. © 2024 IEEE.

关键词： Time series

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data-Driven Crop Recommendation System for Indian Agriculture: Leveraging machine learning for Sustainable Development

Data-Driven Crop Recommendation System for Indian Agricultur...

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2024 IEEE international conference on computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Raju, Ratheesh Thasleema, T.M. Central University of Kerala Department of Computer Science Kasaragod Periye India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517892

Agriculture is a critical sector for many of India's population, contributing significantly to the national economy. However, many farmers rely on traditional practices rather than data-driven insights for crop selection, which can lead to adverse financial and psychological outcomes. The study develops an ensemble of machine learning (ML) models that analyze multiple environmental variables to provide evidence-based crop recommendations to farmers. By integrating features such as rainfall, climate, humidity, soil nutrients, and sowing seasons, the proposed ML model ensemble is designed to recommend crops suited to specific farm conditions. The methodology leverages advanced algorithms to process a comprehensive dataset, including critical parameters like soil properties and climate. Results indicate that this data-driven approach provides accurate and tailored crop recommendations, supporting Precision Agriculture (PA). Adopting such technology promises to transform traditional farming practices, reduce risks, and promote sustainable agricultural development for Indian farmers. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Processor Performance using Bidirectional LSTM Memory Prefetching

Enhancing Processor Performance using Bidirectional LSTM Mem...

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2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Gade, Rachana Bornare, Aditya Jog, Saurabh Joshi, Amit Sawant, Suraj Department of Computer Science and Engineering School of Computational Sciences COEP Technological University Pune India

ISBN: (纸本)9798331533137

Memory prefetching plays a vital role in enhancing processor performance, with modern processors using various prefetching methods to manage data access patterns efficiently. Traditional prefetchers work well with predetermined patterns but struggle with the unpredictability of modern applications. Advanced techniques, especially those employing machine learning, aim to predict and prefetch data based on complex spatiotemporal patterns, yet they face significant challenges like the class explosion problem and labeling issues. This study proposes a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model to dynamically adapt to intricate access patterns, addressing these limitations by leveraging features, localization, and semantic locality to improve prediction accuracy and adaptability. By integrating insights from traditional and machine learning-based approaches, this model seeks to optimize data prefetching, advancing memory system efficiency and ultimately enhancing overall processor performance. These contributions provide a robust framework for designing adaptive and efficient processors that can meet the demands of modern computing environments. ©2024 IEEE.

关键词： Deep learning

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