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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191-200 订阅

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Using machine learning Techniques in Business Analytics: Lessons from Agile Project Management 7

Using Machine Learning Techniques in Business Analytics: Les...

引用

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

作者： Kaur, Harjeet Sarkar, Tiyas Kaur, Baljinder Singh, Amanpreet Cheema, Gagandeep Singh Das, Sumitra Lovely Professional University School of Computer Science & Engineering Punjab Phagwara India Lovely Professional University School of Computer Science & Engineering Uttarakhand Dehradun India Lovely Professional University School Of Computer Science & Applications Punjab Phagwara India Lovely Professional University Mittal School of Business Punjab Jalandhar India

ISBN: (纸本)9798350350067

A potent technique for drawing conclusions from data and advancing corporate analytics is machine learning (ML). Although successful ML project implementation calls for more than simply academic know-how;it also calls for agile approaches, efficient project management, and strategic planning. This study examines the relationship between agile project management and machine learning adoption, offering suggestions for how to include ML throughout business data analysis projects. In the larger context of business analytics, we point out important factors, obstacles, and tactics for streamlining the value of machine learning initiatives by referencing agile methodology principles and best practices. This paper seeks to provide enterprises with the expertise and resources needed to effectively employ machine learning (ML) for business analysis and gain a competitive edge by outlining efficient implementation methodologies. © 2024 IEEE.

关键词： agile project management and implementation strategies business analysis data science predictive modelling Project success

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Prediction of Irrigation Water for Plants Using machine learning Algorithms 1

Prediction of Irrigation Water for Plants Using Machine Lear...

引用

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

作者： Arulraj, Srishivani Karthikeyan, B. School of Computing SASTRA Deemed University Thanjavur India

ISBN: (纸本)9798350365337

Water is essential for nurturing plants and ensuring high yields. With optimal plant growth being fundamental, managing irrigation challenges due to lifestyle changes is crucial. Crops have varying water needs, some constant, others minimal. Precision farming and automated irrigation systems are necessary to overcome setbacks. Leveraging predictive techniques and considering factors like soil moisture and temperature optimize water usage, increasing crop yield. machine learning methodologies like Support Vector machines (SVM), Random Forest, Stochastic Gradient Descent (SGD), Naive Bayes, and Decision Trees offer effective forecasting. Additionally, deep learning mechanisms, particularly Feed-Forward and Long Short-Term Memory, provide valuable insights into water needs of plants. While neural network models greatly impact prediction analysis, machine learning models like Random Forest and Decision Trees exhibit higher efficiency in swift forecasting. Through evaluation of metrics including accuracy, recall, precision, F1 score, execution time, and efficiency of water prediction can be assessed, thereby enhancing automated irrigation practices. © 2024 IEEE.

关键词： Irrigation

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Application of machine learning for Ensuring Water Quality in Aquaculture Ponds

Application of Machine Learning for Ensuring Water Quality i...

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2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Bharathi, S.V.Shri Sai, Paidipati Lakshman Ayyappa, Paidipati Charan Ram Reddy, Kakuru Jaya Praveen Reddy, Gangireddy Sai Preetham Rajesh, Katuri Madhava SRMIST- Ktr Department Of Data Science And Business Systems Tamil Nadu Chennai India Vit Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350352689

Aquaculture plays a pivotal role in meeting the growing global demand for seafood. However, ensuring optimal water quality within aquaculture ponds is a pressing challenge. This project proposes a paradigm shift by integrating machine learning to enhance water quality assurance. The system leverages real-time monitoring, predictive analytics, and adaptable algorithms, contributing to sustainability and responsible aquaculture practices. © 2024 IEEE.

关键词： Water quality

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Exploring machine learning Techniques for Accurate Heart Disease Detection: A Comprehensive Study

Exploring Machine Learning Techniques for Accurate Heart Dis...

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2024 IEEE international conference on Signal Processing and Advance Research in computing, SPARC 2024

作者： Singh, Pardeep Chaudhary, Vipin Kumar Bhatia, Arvind Kumar Sharma, Gaurav Kumbhawat, Amit Singh, Suchita Lovely Professional University Dept. of Computer Science Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350385199

Globally, heart disease poses a significant challenge to public health. Early identification is crucial for effectively managing and treating heart conditions. machine learning has shown promising results in predicting medical problems for the past few years. In this study, A system was developed that is capable of detecting cardiovascular diseases using machine learning methods. The project consists of data loading, Exploratory data analysis, preprocessing, training, evaluation and storage. A dataset was introduced that contained an array of characteristics related to the cardiovascular health status (such as age, gender, cholesterol level.). However, training measured the accuracy of 3 algorithms: K Nearest Neighbours (KNN), Decision Tree, and Random Forest in identifying heart ailment. The article also returns an ensemble graph that considers the forecasts of both these models together, saving the best GNB model for future use. Results showed that the machine learning methods are proficient in heart problem identification, as in this hybrid model, with a 97.32% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Application of machine learning Techniques to Characterize AI Benchmarks Using Hardware Events 21

Application of Machine Learning Techniques to Characterize A...

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

作者： Victor, Rodriguez-Bahena Luis, Pizano-Escalante Omar, Longoria-Gandara Electronics Systems IT ITESO Mexico

ISBN: (纸本)9798350377545

Nowadays, in a dynamic data center landscape, effective distribution of computational resources is crucial to achieving peak performance and cost-efficiency. This study addresses the challenge of distinguishing artificial intelligence work-loads from other tasks within data centers, focusing on optimizing their execution on specialized AI accelerators like GPUs. By integrating machine learning algorithms with hardware performance counters, the study profiles AI workloads and develops predictive models. These models enable accurate detection and optimization of AI compute tasks, aiming to enhance resource allocation and overall performance in modern data center environments. The study achieves 100% precision in distinguishing AI / ML compute workloads from other benchmarks in the proposed experiments, underscoring its effectiveness in improving data center efficiency and productivity. © 2024 IEEE.

关键词： Adversarial machine learning

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Improve the efficiency of network traffic in IoT using dimensionality reduction 6

Improve the efficiency of network traffic in IoT using dimen...

引用

6th international conference on computing and Informatics, ICCI 2024

作者： Mohamed, Ibrahim Ali Wassif, Khaled Bayomi, Hanaa Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt

ISBN: (纸本)9798350373875

IoT has changed our lives through the increased convenience of automating mundane tasks, enhancing home security systems, wearable devices to improve health and wellness, and improved connectivity. Vast volumes of data, such as images, are generated and then transmitted to the cloud server for processing or storage. This leads to an increase in latency and network traffic. To address these challenges, the concept of edge computing has been introduced as a computing paradigm that moves processing closer to the point of data generation. This paper includes the merging of cloud and edge computing in the realm of IoT. It explores techniques that utilize machine learning, employing the autoencoder-based deep learning method and principal component analysis, to perform image dimensionality reduction at the edge. Subsequently, the encoded data is transmitted to the cloud server, where it can be directly utilized for various machine learning tasks without significantly impacting the accuracy of the processed data in the cloud. The suggested approach has been assessed on an image classification task using a set of 20,580 images from the ILSVRC dataset. The results indicate that a 61% reduction in data did not significantly affect the accuracy of the image classification task. © 2024 IEEE.

关键词： Edge computing

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machine learning Approach towards Stratification of Opioid Patients Based on data Analysis - A Study 15

Machine Learning Approach towards Stratification of Opioid P...

引用

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

作者： Praneeth, A. Siva Ramakrishna Reddy, Ch. Bayapa Reddy, Ch. Vijaya Durga Sai, G. Revanth Sampath, Nalini Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

This work investigates machine learning and deep learning methods for drug categorization in order to address the urgent problem of opioid abuse. Because of the intricacy of the issue, many approaches to anticipate opioid prescribing have not produced adequate results in terms of accuracy. The goal of this study is to use structured and unstructured data from the MIMIC-III database to categorize patients who are opioiddependent. To distinguish between purposeful and inadvertent opioid users, 41 pertinent variables were included in both classic machine learning and deep learning models. The findings show that deliberate users from structured data and inadvertent users from unstructured data may be predicted with greater accuracy. Furthermore, encouraging outcomes were obtained from the integration of these models via knowledge distillation. Additional clarification on the categorization of opioid patients was provided by ablation analysis. Utilizing cutting-edge methods such as knowledge distillation and attention processes, this study improves the accuracy of drug categorization and provides insightful information for managing the opioid problem. © 2024 IEEE.

关键词： Ada Boost convolutional neural network Lstm Opioid Random forest XG Boost

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Map Reduce Techniques for machine learning in Big data Analytics 7

Map Reduce Techniques for Machine Learning in Big Data Analy...

引用

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

作者： Kukreti, Sanjeev Al-Jawahry, Hassan M. Shrivastava, Anurag Bansal, Saloni Raju, Hemanth Begum, Rowsonara Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Gla University Department of Computer Engineering and Applications Uttar Pradesh Mathura India New Horizon College of Engineering Department of Mechanical Engineering Karnataka Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

One of the most groundbreaking approaches to the processing and analysis of large datasets has been created as a result of the combination of MapReduce techniques and machine learning algorithms within the framework of big data analytics. The problems of traditional data processing in terms of handling the volume, velocity, and variety of big data are addressed by the distributed computing architecture introduced by MapReduce. This research investigates the ways in which MapReduce may be used to simplify the process of machine learning, with a particular focus on the capabilities of the framework to manage enormous volumes of data and to parallelize operations. By contrasting the performance of MapReduce with that of more traditional methods, the study solves the problems of scalability and computing efficiency that are brought about by massive amounts of data. Through an in-depth analysis, the paper highlights the optimization potential of MapReduce as well as its implications for next-generation machine learning applications across a variety of industries. © 2024 IEEE.

关键词： MapReduce

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On the Role of Re-Descending M-estimators in Resilient Anomaly Detection for Smart Living CPS 10

On the Role of Re-Descending M-estimators in Resilient Anoma...

引用

international conference on Smart computing (SMARTCOMP)

作者： Abedzadeh, Sahar Bhattacharjee, Shameek Western Michigan Univ Dept Comp Sci Kalamazoo MI 49008 USA

ISBN: (纸本)9798350349955;9798350349948

Anomaly-based attack detection methods that rely on learning the benign profile of operation are commonly used for identifying data falsification attacks and faults in cyber-physical systems. However, most works do not assume the presence of attacks while training the anomaly detectors- and their impact on eventual anomaly detection performance during the test set. Some robust learning methods overcompensate mitigation which leads to increased false positives in the absence of attacks/threats during training. To achieve this balance, this paper proposes a framework to enhance the robustness of previous anomaly detection frameworks in smart living applications, by introducing three profound design changes for threshold learning of time series anomaly detectors:(l) Tukey biweight loss function instead of square loss function (2) adding quantile weights to regression errors of Tukey (3) modifying the definition of empirical cost function from MSE to the harmonic mean of quantile weighted Tukey losses. We show that these changes mitigate performance degradation in anomaly detectors caused by untargeted poisoning attacks during training-while is simultaneously able to prevent false alarms in the absence of such training set attacks. We evaluate our work using a proof of concept that uses state-of-the-art anomaly detection in smart living CPS that detects false data injection in smart metering.

关键词： Explainable machine learning Smart Living CPS Resilient AI/ML data Poisoning attacks

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Symmetric Locality: Definition and Initial Results

Symmetric Locality: Definition and Initial Results

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Escalona, Giordan Mckellips, Dylan Ding, Chen University of Rochester Department of Computer Science RochesterNY United States

ISBN: (纸本)9798350355543

In this paper, we characterize symmetric locality. In designing algorithms, compilers, and systems, data movement is a common bottleneck in high-performance computation, in which we improve cache and memory performance. We study a special type of data reuse in the form of repeated traversals, or re-traversals, which are based on the symmetric group. The cyclic and sawtooth traces are previously known results in symmetric locality, and in this work, we would like to generalize this result for any re-traversal. Then, we also provide an abstract framework for applications in compiler design and machine learning models to improve the memory performance of certain programs. © 2024 IEEE.

关键词： algebraic topology algorithms cache locality machine learning poset complexes

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