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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131-140 订阅

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Time-Series-Based Stock Market Analysis using machine learning 15

Time-Series-Based Stock Market Analysis using Machine Learni...

引用

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

作者： Amruth, S. Jaya Nigelesh, T.M. Shruthik, V. Sai Reddy, Valluru Sateesh Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The stock market is a very complex and volatile entity. Predicting it is a very challenging task but the emergence of machine learning and the availability of large-scale stock market data provide an opportunity to leverage data-driven approaches for improving prediction accuracy. In this paper, a framework for stock market analysis using up-to-date financial data extracted from the Yahoo Finance library and machine learning models is proposed. This paper's approach is to capture the complex relationships and patterns in the data by using machine learning techniques. Specifically, machine learning algorithms like XGBoost, Gradient Boost, and Random Forests are employed. By analyzing diverse data sources from various companies, the aim is to extract valuable insights for stock prediction. The experiments involve processing contemporary stock data, implementing machine learning models on it, and comparing the outcomes. The predominant motives are to enhance stock prediction accuracy, manage inherent risks, enable real-time decision-making, and maximize profitability by providing valuable insights for investors that help optimize their portfolios. However, the results are real-time and the optimal algorithm for a particular stock is decided on a dynamic basis due to the stock market being volatile and unpredictable in nature. © 2024 IEEE.

关键词： Decision Tree Gradient Boost machine learning Random Forest Stock Market Analysis XGBoost Yahoo Finance

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Targeted Transfer learning: Leveraging Optimal Transport for Enhanced Knowledge Transfer

Targeted Transfer Learning: Leveraging Optimal Transport for...

引用

international conference on computing, Networking and Communications (ICNC)

作者： Ahamed, Sayyed Farid Islam, Kazi Aminul Shetty, Sachin Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA Kennesaw State Univ Dept Comp Sci Marietta GA USA

ISBN: (纸本)9798350371000;9798350370997

machine learning models are often built based on the notion that the labeled training data follows the same underlying distribution of the testing dataset. Often this assumption does not hold and the trained model's performance becomes limited during the deployment in the target environment. To address this problem, we present a transfer learning-based targeted transfer learning (TTL) approach that employs the optimal transport distance (OTD) metric to facilitate knowledge transfer between the source and target domains. TTL method uses a target-side adaptation methodology by intentionally modifying the source domain data using a small number of target domain samples to improve transfer performance between source and target datasets. Experimental results on the benchmark datasets show the effectiveness of the proposed approach without re-training the source model on the target environment, which contributes to the selection of an appropriate source model and improves the performance of the target model.

关键词： Transfer learning Targeted Transfer learning Domain Adaptation and Optimal Transport

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Exploring Hyperparameter Tuning Strategies for Optimizing Model Performance 2

Exploring Hyperparameter Tuning Strategies for Optimizing Mo...

引用

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

作者： Julian, Anitha Devipriya, R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

The proposed work explores different machine learning hyperparameter tuning techniques to maximize model performance. By systematically adjusting hyperparameters, such as learning rates, regularization strengths, and network architectures, models can better capture underlying patterns in data and improve predictive accuracy. The study looks on commonly utilized hyperparameter tuning techniques. The benefits, drawbacks, and suitability for various situations of each technique are examined. Furthermore, the efficiency and scalability of automated frameworks for hyperparameter tuning are assessed to maximize model performance. The information gathered from this study provides practitioners and researchers with helpful guidance for optimizing their machine learning models through hyperparameter adjustments. © 2024 IEEE.

关键词： Adversarial machine learning

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Review of Movie Recommendation System using machine learning 15

Review of Movie Recommendation System using Machine Learning

引用

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

作者： Yeole, Akash Sakhare, Apeksha Department of Computer Science and Engineering G H Raisoni College of Engineering Nagpur India

ISBN: (纸本)9798331300579

A recommendation predicts a consumer's preference for a product that has not yet been recommendations is to predict the interests of users and recommend product they want. Movie recommendations uses a variety of filters and techniques to help users find the most relevant videos. From a business perspective, the more important information or videos a user sees on a platform, the higher their engagement and therefore their revenue. Content based filtering, collaborative filtering, and hybrid filtering are the most commonly used subcategories of machine learning algorithms for video *** can build models where can use machine learning to recommend videos based on historical data. In this article, we examine various machine learning techniques that can be used for movie recommendations. © Grenze Scientific Society, 2024.

关键词： Collaborative filtering

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Empowering Women's Health: machine learning for PCOS Detection and Prediction 2

Empowering Women's Health: Machine Learning for PCOS Detecti...

引用

2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Avasthi, Vasu Kumar, Ashish Bhardwaj, Aditya Jain, Tarun Manipal University Jaipur Department of Computer Science & Engineering Jaipur India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350382952

This research delves into the prompt identification and prediction of Polycystic Ovary Syndrome utilizing machine learning, specifically focusing on the XGBoost algorithm. Through an examination of data gathered from 541 women, we pinpointed seven crucial clinical and metabolic markers from a pool of 44 features. Notably, the XGBoost algorithm showcased remarkable efficacy, achieving a testing accuracy rate of 96%, surpassing alternative methodologies. This underscores the potential of machine learning in enhancing PCOS diagnosis, facilitating prompt interventions to mitigate associated complications. By bolstering early detection capabilities, our methodology significantly contributes to enhancing patient outcomes and underscores the significance of data-driven strategies in managing PCOS. However, further validation and refinement are imperative to optimize its clinical applicability and broader influence on healthcare practices. © 2024 IEEE.

关键词： Logistic regression

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Masked Contrastive Representation learning for Self-supervised Visual Pre-training 11

Masked Contrastive Representation Learning for Self-supervis...

引用

IEEE 11th international conference on data science and Advanced Analytics (DSAA)

作者： Yao, Yuchong Desai, Nandakishor Palaniswami, Marimuthu Univ Melbourne Dept Elect & Elect Engn Melbourne Vic Australia

ISBN: (纸本)9798350364941;9798350364958

Self-supervised learning has achieved state-of-the-art performance in various tasks and applications. In computer vision, self-supervised learning often employs contrastive learning and masked image modeling, each with its limitations: contrastive learning heavily relies on strong data augmentation and large batch sizes, etc., while masked image modeling struggles to capture high-level semantics and discrimination. In this work, we introduce MAsked Contrastive Representation learning (MACRL), a novel framework that integrates both paradigms through an asymmetric siamese network design. The online and momentum branches of the network receive asymmetric data augmentation operations and extract features through their encoders. The decoder in the online branch reconstructs the original image, while the projectors in both branches compute the contrastive loss. The online branch and the momentum branch are updated through gradient backpropagation and exponential moving average, respectively. MACRL jointly optimizes the reconstruction and the contrastive objectives to encourage representations with enhanced discrimination and semantics. Experimental results show that MACRL achieves competitive performance in downstream vision tasks, including image classification and semantic segmentation. Moreover, it demonstrates consistent performance across both large-scale and small-scale datasets.

关键词： Self-supervised learning Unsupervised learning Computer Vision Deep learning machine learning

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Enhancing Indoor BLE Distance Estimation Through machine and Deep learning Utilizing Anchor Information 10

Enhancing Indoor BLE Distance Estimation Through Machine and...

引用

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

作者： Al Qathrady, Mimonah College of Computer Science and Information Systems Najran University Information Systems Department Najran Saudi Arabia

ISBN: (纸本)9798350363104

This study introduces a comprehensive framework to enhance distance estimation models in indoor environments by incorporating Bluetooth Low Energy (BLE) Anchor information along with Received Signal Strength Indicator (RSSI) and channel data. Utilizing a publicly available dataset, our research systematically implements and evaluates various machine and deep learning algorithms. These include ensemble models, linear models, neural and deep network models, as well as other machine learning models, analyzing their performance with and without the integration of Anchor data. Our results demonstrate that the inclusion of Anchor data significantly improves the accuracy of distance estimations, with error reductions observed on more than 80% of the models, achieving enhancements up to 67% in terms of error reduction. This finding has profound implications for indoor distance estimation and navigation systems, potentially revolutionizing IoT applications that rely on precise distance measurements, such as infection tracing, crowd monitoring, and various contact tracing applications. © 2024 IEEE.

关键词： Adversarial machine learning

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The State of the Art in machine learning-Based Information Security: Identifying the Gaps 6

The State of the Art in Machine Learning-Based Information S...

引用

6th international conference on Intelligent computing in data sciences, ICDS 2024

作者： Langa, Thobeka Venter, Hein S. University of Pretoria Dep. of Computer Science Pretoria South Africa

ISBN: (纸本)9798350351200

machine learning (ML) has emerged as a constantly evolving and powerful technology with the potential to transform the field of information security. Its ability to handle enormous quantities of data at high speeds, detect abnormalities, and automate efficient responses to security risks makes ML very promising in this domain. However, its application in the field of information security has not been extensively investigated, and a comprehensive understanding of existing research gaps in ML-based information security is still lacking. To accomplish this objective, the authors use previous publications to conduct an analysis of the machine learning techniques that have been adopted or explored within the field of information security. The outcome is then used to produce a state-of-The-Art paper, identifying areas where more research is required. © 2024 IEEE.

关键词： Adversarial machine learning

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A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving machine learning Through Hybrid Homomorphic Encryption 22

A Pervasive, Efficient and Private Future: Realizing Privacy...

引用

2024 IEEE conference on Dependable, Autonomic and Secure computing

作者： Khoa Nguyen Budzys, Mindaugas Frimpong, Eugene Khan, Tanveer Michalas, Antonis Tampere Univ Dept Comp Sci Tampere Finland RISE Res Inst Sweden Gothenburg Sweden

ISBN: (纸本)9798331522735;9798331522728

machine learning (ML) has become one of the most impactful fields of data science in recent years. However, a significant concern with ML is its privacy risks due to rising attacks against ML models. Privacy-Preserving machine learning (PPML) methods have been proposed to mitigate the privacy and security risks of ML models. A popular approach to achieving PPML uses Homomorphic Encryption (HE). However, the highly publicized inefficiencies of HE make it unsuitable for highly scalable scenarios with resource-constrained devices. Hence, Hybrid Homomorphic Encryption (HHE) - a modern encryption scheme that combines symmetric cryptography with HE - has recently been introduced to overcome these challenges. HHE potentially provides a foundation to build new efficient and privacy-preserving services that transfer expensive HE operations to the cloud. This work introduces HHE to the ML field by proposing resource-friendly PPML protocols for edge devices. More precisely, we utilize HHE as the primary building block of our PPML protocols. We assess the performance of our protocols by first extensively evaluating each party's communication and computational cost on a dummy dataset and show the efficiency of our protocols by comparing them with similar protocols implemented using plain BFV. Subsequently, we demonstrate the real-world applicability of our construction by building an actual PPML application that uses HHE as its foundation to classify heart disease based on sensitive ECG data.

关键词： Hybrid Homomorphic Encryption machine learning as a Service Privacy-Preserving machine learning

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Artificial Intelligence in Radar Technology and Communication Networks

Artificial Intelligence in Radar Technology and Communicatio...

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2024 MIT Art, Design and Technology School of computing international conference, MITADTSoCiCon 2024

作者： Dutta, Ananya Misra, Aradhana Deka, Surajit Sarma, Kandarpa Kumar Gauhati University India

ISBN: (纸本)9798350362879

Artificial intelligence, together with machine learning, Big data analysis, data science, Mathematical modeling, Optimal control theory, and Simulation techniques, play very significant roles in the cutting-edge research fields of Radar technology and Communication networks. In this article, we review some recent research articles/ papers in this field, discuss their core findings, highlight some gaps and open problems, and elaborate the hierarchy of methodology by applying the principles PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses.) and PICOS (Population, Intervention, Comparison, Outcome, Study design), describe the rationale of outcomes, and finally draw the conclusion with the broader future spectrum of Radar technology and Communication networks. © 2024 IEEE.

关键词： machine learning

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