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2022 OPJU international Technology conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Sahu, Hemlata Kashyap, Ramgopal Dewangan, Bhupesh Kumar Amity University Amity School of Engineering Raipur India Op Jindal University Department of Cse Raigarh India

ISBN: (纸本)9781665492942

One of the most promising fields in medicine is the application of artificial intelligence methods to medical imaging. Though annotating medical images is an expensive operation, most of the recent success in this field has relied heavily on vast amounts of meticulously annotated data. The best that we can tell, the method we present in this research, hybrid approach, is the first to use recent developments in semi-supervised learning (SSL) for medical imaging recognition. Due to the growing complexity of healthcare data, machine learning techniques like Deep Neural Network (DNN) models have become more and more popular. machine learning (ML) techniques can uncover hidden patterns and other important facts from the massive amount of health data that traditional analytics can't find in a reasonable length of time. These advancements will have a significant impact on medical imaging technology, medical healthcare data processing, medical illness diagnostics, and general healthcare. We have two goals: First to conduct a survey on DL techniques to medical pictures;and second proposed hybrid DL-based approaches for image classification. This paper mainly proposed hybrid methodology for medical imaging which can improve semi supervised model. © 2023 IEEE.

关键词： Medical imaging

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Comparative Study of machine learning Models for the Detection of Abusive Messages: Case of Wolof-French Codes Mixing data 7th

Comparative Study of Machine Learning Models for the Detecti...

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7th EAI international conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： Ndao, Ibrahima Dramé, Khadim Sambe, Gorgoumack Diallo, Gayo Laboratoire d’Informatique et d’Ingénierie pour l’Innovation Université Assane Seck de Ziguinchor Ziguinchor Diabir Senegal AHead Bordeaux Population Health - INSERM 1219 and LABRI University Bordeaux France

ISBN: (纸本)9783031864926

This paper presents a comparative study of machine learning models for detecting abusive messages, focusing on code-mixed data in Wolof and French languages. With the increasing use of digital platforms, there has been a surge in derogatory comments, necessitating effective detection strategies. The study introduces a meticulously annotated dataset of 2022 Twitter tweets, manually classified as abusive or not. Extensive experiments are conducted with various machine learning algorithms, including deep learning, with a focus on comparing their performance on the test dataset. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Deep learning

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EncoderMU: machine Unlearning in Contrastive learning 6th

EncoderMU: Machine Unlearning in Contrastive Learning

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Wang, Zixin Mi, Bing Chen, Kongyang Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Guangdong University of Finance and Economics Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9783031736988

machine unlearning is a complex process that necessitates the model to diminish the influence of the training data while keeping the loss of accuracy to a minimum. Despite the numerous studies on machine unlearning in recent years, the majority of them have primarily focused on supervised learning models, leaving research on contrastive learning models relatively underexplored. With the conviction that self-supervised learning harbors a promising potential, surpassing or rivaling that of supervised learning, we set out to investigate methods for machine unlearning centered around contrastive learning models. In this study, we introduce a novel gradient constraint-based approach for training the model to effectively achieve machine unlearning. Our method only necessitates a minimal number of training epochs and the identification of the data slated for unlearning. Remarkably, our approach demonstrates proficient performance not only on contrastive learning models but also on supervised learning models, showcasing its versatility and adaptability in various learning paradigms. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Contrastive learning

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Optimized FOREX Rate Prediction Using Hybrid machine learning Algorithm 1

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2nd international conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Latha, Challa Madhavi Bhuvaneswari, S. Soujanya, K.L.S. Department of Computer Science School of Engineering and Technology Pondicherry University Pondicherry Karaikal India Department of Information Technology CMR College of Engineering and Technology Medchal Hyderabad Kandlakoya India

ISBN: (数字)9789819754120

ISBN: (纸本)9789819754113

This research examines the application of fuzzy time series (FTS) models and different machine learning techniques to anticipate changes in foreign exchange (FOREX) data. The statistics used in this investigation include the actual FOREX prices and the high and low values of four different currencies. These statistics begin from January 2012 to November 2022;the data is available on the Reserve Bank of India website. This article investigates the significant flaws present in the literature information on FTS. This article identifies fundamental flaws in all FTS-based algorithms presently available in the literature and shows how to fix them. The study then describes the unique structure of improvised FTS. The Hybrid machine learning Algorithm is the name given to this structure. The purpose is to solve the identified issues present in FTS-based models previously published in the literature. The result was compared with the before and after optimization processes. After implementing the proposed hybrid machine learning algorithm, the result shows the better accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Exploring Enterprise Digital Transformation: The Perspective of Explainable machine learning 6

Exploring Enterprise Digital Transformation: The Perspective...

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6th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2024

作者： Tan, Zhonghao Peking University School of Software and Microelectronics Beijing China

ISBN: (纸本)9798331541798

Digital transformation (DT) significantly enhances enterprise efficiency, innovation, and competitiveness through cutting-edge technologies such as artificial intelligence (AI), cloud computing, and big data. This study employs interpretable machine learning (ML) models to investigate the determinants of DT outcomes. The primary aim is to assess the impact of these technologies on operational efficiency, innovation, and market competitiveness. Using the 2011-2022 CSMAR Enterprise Digital Transformation database, we performed regression analyses and SHAP value assessments to elucidate the contributions of various factors. Our findings highlight the criticality of early adoption and ongoing advancement of digital processes. AI and cloud computing substantially improve digital outcomes, whereas broader innovation initiatives, though crucial, exhibit more variable effects. Theoretically, this study enriches DT frameworks by integrating interpretable ML insights. Practically, it provides actionable strategies for enterprises to achieve superior digital outcomes and maintain a competitive edge. © 2024 IEEE.

关键词： Adversarial machine learning

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From data Science to Bioscience: Emerging era of bioinformatics applications, tools and challenges

From Data Science to Bioscience: Emerging era of bioinformat...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Iqbal, Naiyar Kumar, Pradeep Department of Computer Science and Information Technology Maulana Azad National Urdu University Telangana Hyderabad500032 India

With the gradual technological changes in the current era, data science using soft computing and machine learning methods has played a tremendous role in various areas, including the biomedical and health sectors. The etymology of bioinformatics with several types of omics, such as genomics, transcriptomics, proteomics, and metabolomics was coined in the late twentieth century in the direction of the investigation and finding solutions for informatics processing in biological decision-making systems. Similarly, the data science era began and played a vital role in solving a large domain, particularly in biological problem solutions. In this conceptual article, an integrated machine learning-based framework for the prediction of outbreaks is proposed, followed by omics biological data processing and dimension reduction. Subsequently, various prominent benchmark tools have been shown related to problem solutions in data science and bioinformatics applications. In this study, the role of data science and soft computing approaches in bioinformatics disciplines with various associated applications is explored. Various simple soft computing techniques such as fuzzy logic, artificial neural networks, support vector machines and evolutionary computation techniques have been explained. Furthermore, complex optimization and dimension reduction techniques, such as artificial bee colony, ant colony optimization, formal concept analysis, and principal component analysis have been presented. Finally, active research zones in bioinformatics, as well as their current trends and challenges have been highlighted which will lead to future research directions. © 2023 The Authors. Published by Elsevier B.V.

关键词： Gene expression

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Proposing machine learning as the technology to Deal with the Liver Disease

Proposing Machine Learning as the technology to Deal with th...

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2022 OPJU international Technology conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Yadav, Nageshwar Dev Singh, Vaibhav Kant Research & Development Product Specialist at Informatica Master Data Management Cloud Product Karnataka Bengaluru India A Central University School of Studies of Engineering and Technology Guru Ghasidas Vishwavidyalaya Department of Computer Science & Engineering Chhattisgarh Bilaspur India

ISBN: (纸本)9781665492942

In this work the Authors majorly focuses on the Disease that is Liver Disease and tried to work out a Strategy that could be handy to Deal with the Problem under Question. The implementation of the Work is solely in Python. The Major Python Libraries are used to come up with a Solution. There are various ML approaches Available to Handle the Problem. In our Implementation we made a Utilization of the data-Set received from Kaggle Repository. The Implementation makes usage of the Technology that is Support Vector machine. We in our work made utilization of the Four Kernels Available for Implementation in Python Programming. In the Implementation Section we will observe the Classification Report Generated in the Coding done by the Authors. SVM is a very nice approach to take care of the Medical Issues. © 2023 IEEE.

关键词： Python

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Federated learning on Non-IID data Silos: An Experimental Study 38

Federated Learning on Non-IID Data Silos: An Experimental St...

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38th IEEE international conference on data engineering (ICDE)

作者： Li, Qinbin Diao, Yiqun Chen, Quan He, Bing Sheng Natl Univ Singapore Singapore Singapore Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Due to the increasing privacy concerns and data regulations, training data have been increasingly fragmented, forming distributed databases of multiple "data silos" (e.g., within different organizations and countries). To develop effective machine learning services, there is a must to exploit data from such distributed databases without exchanging the raw data. Recently, federated learning (FL) has been a solution with growing interests, which enables multiple parties to collaboratively train a machine learning model without exchanging their local data. A key and common challenge on distributed databases is the heterogeneity of the data distribution among the parties. The data of different parties are usually non-independently and identically distributed (i.e., non-IID). There have been many FL algorithms to address the learning effectiveness under non-IID data settings. However, there lacks an experimental study on systematically understanding their advantages and disadvantages, as previous studies have very rigid data partitioning strategies among parties, which are hardly representative and thorough. In this paper, to help researchers better understand and study the non-IID data setting in federated learning, we propose comprehensive data partitioning strategies to cover the typical non-IID data cases. Moreover, we conduct extensive experiments to evaluate state-ofthe-art FL algorithms. We find that non-IID does bring significant challenges in learning accuracy of FL algorithms, and none of the existing state-of-the-art FL algorithms outperforms others in all cases. Our experiments provide insights for future studies of addressing the challenges in "data silos".

关键词： data privacy machine learning algorithms Distributed databases Training data machine learning Organizations Collaborative work

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Research on the Analysis and Prediction of College Physical Test Results Based on machine learning 3

Research on the Analysis and Prediction of College Physical ...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Liu, Qing Health College of Shandong University of Traditional Chinese Medicine Shandong Jinan250355 China

ISBN: (纸本)9781510664814

The physical fitness test of college students mainly evaluates the physical condition and training effect through the test results of various items of students. It is a very sound and effective strategy to urge college students to actively participate in sports training. By analyzing the data of physical fitness test scores in colleges and universities over the years, the students with different physical fitness are classified and managed. According to the actual physical condition of students, different training plans are given to assist teachers in formulating more reasonable teaching programs for students. In order to reduce manual calculation and increase prediction efficiency, this paper proposes a comprehensive score prediction model based on machine learning. First, principal component analysis is used to transform multiple attributes with strong correlation into independent attributes without correlation, and the time and space for model training are reduced by eliminating redundancy. Secondly, the back propagation neural network algorithm is used to establish the physical fitness test prediction model, and the model is applied to the test data set to evaluate the performance of the model. © 2023 SPIE.

关键词： Students

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Overview of machine learning applications in fusion plasma experiments on J-TEXT tokamak

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Plasma Science and Technology 2022年第12期24卷 27-38页

作者： Wei ZHENG Fengming XUE Chengshuo SHEN Yu ZHONG Xinkun AI Zhongyong CHEN Yonghua DING Ming ZHANG Zhoujun YANG Nengchao WANG Zhichao ZHANG Jiaolong DONG Chouyao TANG Yuan PAN International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China Institute of Artificial Intelligence Huazhong University of Science and TechnologyWuhan 430074People's Republic of China

machine learning research and applications in fusion plasma experiments are one of the main subjects on *** 2013,various kinds of traditional machine learning,as well as deep learning methods have been applied to fusion plasma *** applications in the real-time experimental environment have proved the feasibility and effectiveness of the *** disruption prediction,we started by predicting disruptions of limited classes with a short warning time that could not meet the requirements of the mitigation *** years of study,nowadays disruption prediction methods on J-TEXT are able to predict all kinds of disruptions with a high success rate and long enough warning ***,cross-device disruption prediction methods have obtained promising *** analysis of the models are *** diagnostics data processing,efforts have been made to reduce manual work in processing and to increase the robustness of the diagnostic *** based on both traditional machine learning and deep learning have been applied to real-time experimental *** models have been cooperating with the plasma control system and other systems,to make joint decisions to further support the experiments.

关键词： machine learning disruption prediction diagnostics data processing J-TEXT

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