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检索条件"任意字段=3rd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2021"

共 2040 条记录，以下是41-50 订阅

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Efficient Brain Tumor Detection Method Using Feature Optimization and machine learning Algorithm 3rd

Efficient Brain Tumor Detection Method Using Feature Optimiz...

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3rd international conference on data science, machine learning and applications, icdsmla 2021

作者： Bhatt, Ashish Nigam, Vineeta Saxena UIT-RGPV Bhopal India

ISBN: (纸本)9789811959356

The computer-aided diagnosis set the cornerstone for brain tumor detection. However, early-stage brain tumor detection is a very complex approach by the clinical process. The abnormal growth of cells in the brain forms the issue of the tumor. Therefore, the mortality rate of brain tumor patients is very high due to the late diagnosis of the tumor. On the other hand, the early-stage tumor saves millions of lives worldwide. This paper proposed a multi-sage support vector machine algorithm for the detection of tumors. The multi-stage support vector machine maximizes the area of margin and covers full features of MRI images. For the selection and optimization of features of tumors, apply the firefly algorithm. For the processing of features, extraction applies discrete wavelet transform. The proposed algorithm was tested with a reputed dataset such as BRATS. Furthermore, the proposed algorithm is compared with existing algorithms such as SVM and CNN. The proposed algorithm improves the detection rate by 2–3% instead of CNN and SVM algorithms. The evaluation of results was done under MATLAB software environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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applications of data science and machine learning for Combating COVID-19 3rd

Applications of Data Science and Machine Learning for Combat...

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3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Tyagi, Shiva Sirohi, Shagun Singh, Yash Vishwakarma, Akash Siddhant Ajay Kumar Garg Engn Coll Dept Comp Sci & Engn Ghaziabad India

ISBN: (纸本)9783031640636;9783031640643

Global research efforts have increased dramatically as a result of the COVID-19 epidemic, with data science, machine learning, and deep learning techniques emerging as essential weapons in the fight against this crisis. In order to clarify the current research trends in the application of these cutting-edge technologies to battle COVID-19, we conduct an extensive scientometric analysis in this research paper that spans from January 2020 to April 2020. Our analysis not only identifies the main lines of inquiry, but also explores their broad ramifications and provides perceptive glimmers into the changing COVID-19 research scene. We identify the emerging topics, approaches, and applications that have proven essential in combating the pandemic by carefully examining a variety of scholarly articles. This report highlights the varied contributions of data science and machine learning to this global health problem, ranging from early detection models to vaccine development approaches. Additionally, our work goes beyond the current environment to set the way for additional research projects. We put up fresh ideas that make use of the strength of data-driven strategies to address the COVID-19's enduring problems. This research paper offers a roadmap for researchers, policymakers, and practitioners to use data science and machine learning in their ongoing efforts to combat COVID-19 and get ready for Upcoming pandemics by combining scientific rigor and computational innovation.

关键词： data science machine learning Deep learning COVID-19 Epidemic Scientometric Analysis Research Trends Pandemic Early Detection Models

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Malaria Detection with Flask Using Deep learning Model 3rd

Malaria Detection with Flask Using Deep Learning Model

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3rd international conference on data science, machine learning and applications, icdsmla 2021

作者： Sushant, Deshmukh Bhalchandra, Parag School of Computational Sciences S.R.T.M. University Maharashtra Nanded431606 India

ISBN: (纸本)9789811959356

Malaria disease is rapidly spreading and very dangerous disease. It is caused due to bite of mosquito. Numbers of peoples all over the world are infected by this disease. Traditionally, the microscopic method could be considered as a best method to detect the malaria from cell images, but it requires more time and it need skilled and expert person in that field so, this research is focused on developing CAD system to diagnose the malaria patient via deep learning model which gives automated accurate and faster results than traditional system. Objective of this paper is also too aware the people about symptoms, prevention techniques and treatment usually used to cure this disease. Early prevention and treatment may save number of deaths and infections. This research uses deep learning-based transfer learning technique to make CAD system with the help of flask app. Malaria detection is Web app that can be used anywhere any time and this method gives accuracy of 99.6% with fine-tuning approach of ResNet50 model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Heart disease Classification Utilising machine learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various data Mining and machine learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

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Comparative Analysis of machine learning Algorithms for Disease Prediction using Medical datasets 3

Comparative Analysis of Machine Learning Algorithms for Dise...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Roopa, C. Latha, R.S. Dhavamani, M. Bommisrilekkhaa, G. Angel Shalini, P. Dhanyajothi, R. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Department of Artificial Intelligence Kongu Engineering College Tamilnadu Erode India Department of Mathematics Kongu Engineering College Tamilnadu Erode India

ISBN: (纸本)9798331527549

Application of machine learning in the analysis of medical data can be said to be one of the current transformations happening within the healthcare fraternity. This paper highlights several ways in which various machine learning techniques can be used for gaining valuable insight into disparate types of medical data, such as patient records or genetic information, toward further clinical decision-making with a view to better prediction of diseases or strategies related to personalized medicine. The study critically evaluated various algorithms by considering their performances, suitability with regard to certain medical datasets they would be set upon to analyze, and to interpret the performance, particularly within the supervised learning frameworks. It talks about methodologies through which feature selection can take place to make sure that there is effective model development. Consequential applications in early disease detection as well as the optimization of their treatments are further emphasized, representing great potential for massive improvements in their recovery outcomes. The results highlight how far reaching data-driven approaches can affect the future of healthcare. It is envisioned that, with the use of such methods, the paper foresees a paradigm shift in disease diagnosis and treatment for personalized medical care and improved health outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Federated learning for Smart Agriculture: Challenges and Opportunities 3

Federated Learning for Smart Agriculture: Challenges and Opp...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kondaveeti, Hari Kishan Sai, Guduru Balamanikanta Athar, Shaik Asneem Vatsavayi, Valli Kumari Mitra, Alakananda Ananthachari, Preethi School Of Computer Science And Engineering VIT-AP University Andhra Pradesh India Andhra University Department Of Cs & Se Andhra Pradesh India University Of Nebraska-Lincoln Nebraska Water Center LincolnNE United States Endicott College Of International Studies Woosong University Ai & BigData Dept Daejeon Korea Republic of

ISBN: (纸本)9798350318609

FL is a machine learning approach that allows knowledge sharing with privacy maintenance and cost reduction. FL has the potential to revolutionize the smart agriculture sector by enabling farmers to train and deploy machine learning models on their own devices, without the need to share their data with a central server. In this article, we review the applications of FL in smart agriculture, such as disease diagnosis and severity assessment, the Internet of Agricultural Things (IoAT), and yield forecasting. We also highlight the challenges and opportunities of using FL for smart agriculture. © 2024 IEEE.

关键词： Cost reduction

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Advancing Fake News Detection: From Traditional machine learning to Transformer-based Approaches 3

Advancing Fake News Detection: From Traditional Machine Lear...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhinand, I.V. Ajal Krishna, K.P. Krishna, Ajay Jayadev, J. Reji Rahmath, K. Divya, T. Dept. of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannur A. P. J. Abdul Kalam Technological University kerala Thiruvananthapuram India

ISBN: (纸本)9798331527549

Fake news on online platforms is spreading at a rapid rate, posing great threats to social, political, and economic stability in linguistically diverse regions like Kerala, India. This paper reviews comprehensively the techniques of fake news detection from traditional machine learning methods to transformer-based models. Special attention is given to the detection of fake news in regional languages like Malayalam, characterized by complex morphology and syntax. The study investigates applications of ensemble-based learning as well as state-of-the-art transformer models, BERT, and XLNet, focusing on their ability to capture nuanced linguistic features and contextual relationships. Evaluation results show how significantly better the transformer-based models fare than traditional methods with respect to accuracy and robustness. This review also captures current limitations - such as resource constraints for regional languages - as well as future opportunities in developing scalable, multilingual fake news detection systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Psychological Distress Detection and Classification 3

Psychological Distress Detection and Classification

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Gujarathi, Parth Menon, Kartik Patel, Jai Ghane, Sunil Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India Sardar Patel Institute of Technology Department of Computer Science Mumbai India

ISBN: (纸本)9798350381931

This research paper introduces an innovative method for stress detection and classification by utilizing a combination of machine learning, deep learning techniques, and BERT. Today with the advancement of technology and techniques like artificial intelligence and machine learning various manual tasks can be replaced very easily. Hence in this research paper, we have proposed various machine learning algorithms, deep neural networks, and BERT pre-trained models for the detection of stress based on their social media posts and then classifying their stress levels based on their responses to the universally accepted DASS(Depression Anxiety Stress Scales) questionnaire. This study aims to develop an effective system that can accurately detect and classify stress levels based on textual data. The results reflect that our system outperforms existing methods for stress detection and classification and proposes a refined approach for the classification of stress levels. This system, therefore, has potential applications in various domains, including mental health diagnosis, social media monitoring, and personalized stress management. © 2024 IEEE.

关键词： Stresses

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TriBoost and Beyond: Advanced machine learning Approaches for Diabetes Risk Prediction 3

TriBoost and Beyond: Advanced Machine Learning Approaches fo...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Katlariwala, Soham Biren Gandhi, Vaibhav C. Patel, Nirav Parmar, Devendra Desai, Ayushi Indian Institute of Technology Madras B.S. Data Science and Applications Chennai India University Department of Computer Engineering Mbit Anand India Parul University Parul Institute of Technology Vadodara India

ISBN: (纸本)9798331509675

Diabetes is a tremendous world health issue and, in its management, and complications prevention, proper screening is vital. This paper examines the improvement of accurate diabetes risk prediction by some advanced machine learning methods. The study compares various models such as;Random Forest, AdaBoost, Support Vector machine, CatBoost, Decision Tree, LS-SVM, Logistic Regression, Naïve Bayes, k-Nearest Neighbors, LightGBM, XGBoost, and neural networks. Most emphasis is put on TriBoost that is a novel ensemble model which integrates XGBoost, CatBoost, and LightGBM. Lastly, the effectiveness of a model is determined using measures such as ROC AUC and accuracy. Evaluation of the results has shown that the proposed TriBoost had the highest accuracy at 94% and ROC AUC of 91% thereby outcompeting the use of individual classifiers as well as other ensemble methods. The results of the study confirm adequacy and possibilities of ensemble learning to enhance predictive precision and stability and their applicability for early detection of diabetes in clinics. Further work shall seek to increase the variability of the dataset, integrate fresh clinical data, and increase computational effectiveness for practical use. © 2025 IEEE.

关键词： Adversarial machine learning

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A Review on Meta-learning: How Artificial Intelligence and machine learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and machine learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive learning

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