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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171-180 订阅

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Utilizing Deep learning and Advanced machine learning Methods in Economic data Analysis 1

Utilizing Deep Learning and Advanced Machine Learning Method...

引用

1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Sharma, Himanshu Garg, Ankit Singhal, Ritesh Kumar Priya Gaur, Manu Sharma, Harsh Sharma, Nikhil Ajay Kumar Garg Institute of Management Ghaziabad India Deloitte Usi India

ISBN: (纸本)9798350367720

This extensive research explores the most recent developments in data science as they relate to developing nations. It carefully looks at four different categories: In an effort to find the cutting edge of technological innovation, collaborative models, mixed neural architectures, neural networks, and combination machine learning approaches are used. These innovative approaches are applied in a wide range of economic domains, including corporate banking, cryptocurrency, e-commerce, stock markets, and marketing. The study uses the Prisma method, a systematic literature review technique, to guarantee the highest level of rigor and accuracy in its findings. After further examination, a pattern becomes apparent that makes hybrid models the preferred option over conventional learning algorithms. The better performance of these hybrid models highlights a trajectory of rising complexity in the area and points to a trajectory towards more sophisticated hybrid deep learning architectures. © 2024 IEEE.

关键词： Adversarial machine learning

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I. Federated learning: Pushing Forward machine learning Without Scarifising Privacy

I. Federated Learning: Pushing Forward Machine Learning With...

引用

2024 international conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Ghade, Om Narad, Supriya School of Allied Sciences DMIHER Faculty of Science & Technology Maharshtra Wardha India

ISBN: (纸本)9798331517953

Federated learning is a new family of privacy-preserving machine learning, tackling at the same time the most demanding challenges set by data-driven systems. In this paper, we attempt to present a study on federated learning topics concerning the principles, architecture, and mechanisms for preserving privacy and the protection of information. We survey the challenges and bounds of FL, its communication efficiency, heterogeneity-driven issues, and tradeoffs between privacy and utility. We also relate real-world FL applications to healthcare, finance, and mobile computing. We continue to investigate current research and future directions of the federated learning research landscape, showing in this way that federated learning is really changing the machine learning field as it was known before, that it allows collaborative training of models without violating people's sensitive data. © 2024 IEEE.

关键词： Federated learning

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Comparison of machine learning Models for Prediction of In-Hospital Adverse Outcomes after Percutaneous Coronary Intervention 9

Comparison of Machine Learning Models for Prediction of In-H...

引用

9th IEEE/ACIS international conference on Big data, Cloud computing, and data science, BCD 2024

作者： Chanmanacharoen, I-Ruk Jampa-Ngern, Sira Senavongse, Wongwit The Graduate School Srinakharinwirot University Department of Biomedical Engineering Nakhon Nayok Thailand Srinakharinwirot University Faculty of engineering Department of Biomedical Engineering Nakhon Nayok Thailand

ISBN: (纸本)9798350394191

Cardiovascular disease is a non-communicable disease that is one of the leading causes of mortality worldwide, and its prevalence is growing year after year. Most cardiovascular diseases require percutaneous coronary intervention (PCI) to remove blood vessel blockages, However, this therapy carries risks of adverse outcomes, such as in-hospital bleeding and mortality. Currently, machine learning is widely used in prediction and decision-making support for clinicians. According to the above reasons, this study demonstrates using and comparing four common machine learning algorithms to predict in-hospital bleeding and in-hospital mortality outcomes. Finally, this study found that the XGBoost model accomplished the most outstanding in-hospital bleeding and mortality results. The performance of in-hospital bleeding shows Accuracy of 0.987 (95% CI: 0.980 - 0.994). In-hospital mortality shows Accuracy of 0.972 (95% CI: 0.962 - 0.982). © 2024 IEEE.

关键词： Contrastive learning

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Ten Years of Research on Cardiac Imaging in Diagnosing Heart Diseases 15

Ten Years of Research on Cardiac Imaging in Diagnosing Heart...

引用

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

作者： Chaithra, C.S. Siddesha, S. Niranjan, S.K. Department of Computer Applications JSS Science and Technology University Mysuru India

ISBN: (纸本)9798331300579

Cardiac images play a vital role in early detection and diagnosing the heart diseases. Due to the advancement in machine learning techniques many tools are emerging for early detection of heart disease and its diagnosis. Image based diagnosis requires previous clinical data which is extracted from hidden image patterns. machine learning algorithms with cardiac images gaining prominence in healthcare by automating calculations and finding new patterns in data and provide accurate results for diagnosing patients in early stages. This work provides the detailed 10 years of studies on different cardiac images for diagnosing the heart diseases using various machine learning methods. © Grenze Scientific Society, 2024.

关键词： Magnetic resonance imaging

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A Review on Alzheimer Prediction using machine learning 1

A Review on Alzheimer Prediction using Machine Learning

引用

1st international conference on Technological Innovations and Advance computing, TIACOMP 2024

作者： Rampal, Aakash Kapoor, Nitika Chandigarh University Computer Science and Engineering Department Mohali India

ISBN: (纸本)9798350392111

Alzheimer's disease (AD) is a devastating neurological disorder that progressively worsens cognitive function and memory. Early detection is critical to avoiding worse symptoms. While computational approaches have been used to identify Alzheimer's disease, they do have drawbacks. Predicting AD in its early stages is challenging but critical for effective therapy. machine learning (ML) offers the potential for improving AD. We want to learn more about how machine learning might help with early AD diagnosis, perhaps revolutionizing patient treatment, by reviewing significant studies. ML provides hope by analysing complicated data patterns and improving our capacity to detect AD sooner, resulting in better patient outcomes. Out of 432 total papers in total selected 15 papers were for this review analysis from different databases like IEEE, Web of science, etc. using keywords like "Alzheimer", "machine learning"etc. In this paper, a case study is also being added for the Support Vector machine which has given an accuracy of 89%. © 2024 IEEE.

关键词： Adversarial machine learning

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Study of machine learning Tactics for Missing data Imputation and Classification

Study of Machine Learning Tactics for Missing Data Imputatio...

引用

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

作者： Kapare, Suresh Anu, Maria Sathyabama Institute of Science and Technology CSE Department Chennai India MIT ADT Univerisity Pune India Vellore Institute of Technology CSE Departement Vellore India

ISBN: (纸本)9798350362879

Different data analysis techniques are used to perform data analysis on big data. Results of the analysis are affected by missing data values generated due to data generation sources. The general approach is to discard missing data values or use complex algorithms which leads to standard errors. In many applications like health, space, defense, etc., errors beyond the desirable threshold are not acceptable. This paper studies the different traditional and advanced data imputation methods available as per specific applications. The qualitative and optimized error analysis of these methods is discussed. Furthermore, a comparison of several machine learning approaches used to the task of imputation of missing data demonstrates the significance of the Iterative Robust Semi-Supervised based Imputation Algorithm (IRSSI) method in conjunction with the Credal Transfer learning (CTL) classification method. © 2024 IEEE.

关键词： Iterative methods

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machine learning in Particle Physics 11th

Machine Learning in Particle Physics

引用

11th international conference on Big data Analytics in Astronomy, science, and Engineering (BDA)

作者： Purohit, Milind, V Univ South Carolina Dept Phys & Astron Columbia SC 29208 USA

ISBN: (纸本)9783031585012;9783031585029

This note surveys developments in particle physics due to advances made in the fields of statistics, machine learning, and artificial intelligence. With the aid of examples and recent work, this article attempts to give a flavor of the effect of these advances on particle physics, including brief mention of cloud computing, classic machine learning techniques, statistics applications, new ML/AI techniques, reinforcement learning, and other advances. Suggestions are made regarding the future.

关键词： particle physics machine learning statistics simulation optimal transport physics

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Natural Language Processing and machine learning Based Automated Diagnosis of Epilepsy in Electronic Health Record 1

Natural Language Processing and Machine Learning Based Autom...

引用

1st international conference on computing, sciences and Communications, ICCSC 2024

作者： Leeshma, K. Praveena, M. Dr.S.N.S Rajalakshmi College of Arts and Science Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350353648

To promptly obtain an accurate diagnosis to effectively manage and treat epilepsy. This research introduces a novel method for automating the epilepsy diagnostic process in EHRs using Natural Language Processing (NLP) and machine learning (ML). Therefore, natural language processing (NLP) techniques may be employed to extract medically relevant information from both structured data contained in electronic health records (EHRs) and unstructured clinical notes. Next, machine learning techniques will be employed to predict the presence or absence of epilepsy. The approach's high overall accuracy, specificity, and sensitivity, as assessed on a comprehensive dataset, make it a promising candidate for clinical applications. This diagnostic automation strategy is designed to achieve a more precise diagnosis, reduced anxiety for physicians, and improved health outcomes for patients. The results illustrate the practicality and utility of incorporating advanced data analysis techniques into clinical procedures, and the technology shows potential for the detection of neurological disorders such as epilepsy. © 2024 IEEE.

关键词： Electronic health record

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Comparative Study Of Different Model Used On Genetic Disease Prediction System 1

Comparative Study Of Different Model Used On Genetic Disease...

引用

1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sinha, Aditya Mayank Tyagi, Shivani Aggarwal, Yash Gupta, Prateek Singh, Gautam SRMIST Delhi NCR Campus Modinagar Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350356816

This study explores the pivotal role of machine learning models in predicting genetic diseases, revolutionizing the landscape of genomic medicine. Genetic diseases, often arising from mutations in DNA sequences, pose significant challenges to diagnosis and treatment. Leveraging vast genomic datasets, machine learning algorithms offer unprecedented accuracy and speed in identifying disease-causing variants, enabling timely intervention and personalized healthcare. By analyzing genetic data with intricate patterns, these models empower clinicians to make informed decisions, potentially mitigating the impact of genetic disorders on individuals and families. This paper delves into the synergy between machine learning and genetics, illuminating the path towards precision medicine. © 2024 IEEE.

关键词： Adversarial machine learning

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A machine learning based Depression Detection via Questionnaires and Video 15

A Machine Learning based Depression Detection via Questionna...

引用

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

作者： Singh, Vrishank Gupta, Vishnu Kant Tripathi, Vansh Singh, Khushboo Pandey, Arti Kiet Group of Institutions Computer Science and Information Technology Ghaziabad India

ISBN: (纸本)9798350370249

This study aims to detect depression by integrating machine learning with facial analysis. It features two main components: a ten-question multiple-choice questionnaire (MCQ) to assess depression aspects, and a video analysis module that records participants' facial and verbal responses. Speech-to-text conversion and attention mechanisms analyze verbal responses, while facial expressions are examined for depressive symptoms. machine learning techniques such as neural networks, decision trees, and regression analyze data from both segments to identify depression symptoms. Validation against established diagnostic standards and a focus on consent and data confidentiality underscore the system's promise for early intervention and support. © 2024 IEEE.

关键词： Attention mechanism Bi-LSTM Facial analysis LSTM Speech-to-text conversion

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