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检索条件"任意字段=4th International Conference on Machine Learning and Soft Computing, ICMLSC 2020"

共 564 条记录，以下是21-30 订阅

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soft kind of computing Based Least Resource Data finding which is Used for Analysis purpose 4

Soft kind of Computing Based Least Resource Data finding whi...

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4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Rani, L. Pattathu Surendran, S. Monica, D. Kumar, V. Senthil Rajaram, Gnanajeyaraman Dwibedi, Rajat Kumar Simats University Saveetha School of Engineering Chennai India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering India Aarupadai Veedu Institute of Technology Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

Deep learning (DL) approaches are utilized across three distinct contexts to showcase their applicability in commercial environments with restricted data for training. this study showcases the application of unsupervised DL with autoencoders that vary & illustrates how federation learning serves as a successful collaboration approach for machine learning (ML) that safeguards confidentiality of data. © 2024 IEEE.

关键词： Federated learning

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Early Detection: Deep learning-Driven ECG Image Analysis Forcardiovascular Screening 4

Early Detection: Deep Learning-Driven ECG Image Analysis For...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Jeny, J.R.V. Nikhil Goud, K. Leela Amrutha, Divya Sai Azmira, Kishore India

ISBN: (纸本)9798331515720

the heart is a very important organ in the circulatory system. Heart disease is extremely severe and can even lead to death, therefore it is essential to make an accurate and timely diagnosis. However, health data analysis may be complicated because there may not be any evident patterns. Cardiovascular diseases (CVDs) are the number one cause of death throughout the world. Early detection of them is vital for successful management. For this purpose, electrocardiograms (ECGs) are used which indicate electrical activities in our hearts;however, computers and machine learning systems are ways by which we could analyze patients' data more swiftly. It concentrates on using deep as well as machine learning techniques in assisting diagnosing diseases. the purpose was to evaluate how well deep learning could identify four significant heart conditions: arrhythmia, myocardial infarction or heart attacks, history of myocardial infractions (past heart attacks), and normal patterns. We utilized a real-world ECG dataset reflecting actual scenarios and complexities. the Proposed model performed even better than the existing ones with an accuracy of 98.23% implying that it could correctly identify heart issues in ECG images 98.23% times out of 100%. Also recall, precision and F1 score were very high which are other measures about how good a model is performing. By using machine learning Deep learning Algorithms like Support Vector machine, Efficient Net, Residual Neural Network, and *** Algorithms created their own deep learning model specifically for predicting heart problems. © 2024 IEEE.

关键词： Support vector machines

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the Effective Detection of Alzheimer in the System via ML Algorithm 4

The Effective Detection of Alzheimer in the System via ML Al...

引用

4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Kanna, R. Kishore Joshi, Kapil Chhabra, Gunjan Pacholi, Ambali Nimasajai, X.S. Blessing Menezes, Flosy R. Department of Biomedical Engineering Chennai India Uttaranchal University Uttaranchal Institute of Technology Department of Cse Dehradun India Department of Cse Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India Gwalior India King Khalid University College of Nursing Khamis Mushayt Saudi Arabia Bambolim India

ISBN: (纸本)9798350360165

Effective therapy for Alzheimer's disease depends on early diagnosis. Key results from four research that used machine learning to detect Alzheimer's disease during its initial level. Early research indicates that combining cognitive and neuropsychological data may enhance diagnosis. the second research interprets neuroimaging data using machine learning methods. Finding the first signs of illness is the objective here. In the third research, risk variables for Alzheimer's defect conditions are detected using machine learning algorithms. they stress the need of comprehensive data for precise prediction. A novel algorithm is presented in the fourth research. the soft voting classifier method increases recognition accuracy by operating on an averaging mechanism. these papers demonstrate how machine learning may enhance approaches for diagnosing Alzheimer's disease. To enhance therapeutic results, combine everything from cognitive evaluations to multimodal data. © 2024 IEEE.

关键词： Adversarial machine learning

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Predictive Modeling of Liver Diseases using Artificial Intelligence 4

Predictive Modeling of Liver Diseases using Artificial Intel...

引用

4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Srivastava, Sarthak Antal, Reena Chandigarh University Department of Mathematics Punjab Mohali India

ISBN: (纸本)9798331515720

Modern medical care generates large amounts of multimodal patient data, which many physicians find challenging to assess and synthesize into actionable knowledge. In the last few decades, AI has shown to be a helpful tool in this field. Hepatology is not an exception, as the application of AI techniques for the diagnosis and treatment of liver disease is the subject of an increasing amount of published research in the field. Artificial Intelligence (AI) has now enabled a faster access to fresh data on patients' chronic liver diseases, such as non-alcoholic fatty liver disease and liver fibrosis. this study determines the predictive capacity of machine learning, an application of artificial intelligence, for liver disease. the liver disease prediction (LDP) approach, which can be used by researchers, students, stakeholders, and health professionals to forecast liver illness, is introduced in this work. Random Forest, Linear Discriminant Analysis, Decision Tree, Classification and Regression Trees and BRDT are the five approaches that are selected. R and Python are used to assess the accuracy and determine which classification technique best predicts liver illness. the results showed that BRDT achieved the maximum accuracy of 93%, while the auto encoder network achieved an accuracy of 94.6%, which is more than acceptable and might be considered for the prediction of liver illness. © 2024 IEEE.

关键词： AI algorithms Data analysis liver disease machine learning prediction

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Robust Statistical Models for Identifying Inauthentic Online Reviews 4

Robust Statistical Models for Identifying Inauthentic Online...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Swapna, D. Shanmuga Sundari, M. Khadhyothi Sreeja, G. Sriya Varma, S. Rajeshwari, M. Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India

ISBN: (纸本)9798331515720

Review authenticity is a key factor in determining trustworthiness in the internet marketplace. However, the threat of fake reviews remains constant, endangering both fair competition and customer confidence. the rise of fake reviews undermines the credibility of these platforms and poses a significant challenge to users seeking genuine feedback. In response, this study proposes a machine learning (ML) approach for detecting fake *** research begins by collecting a dataset comprising both genuine and fake reviews from various online platforms. Features are extracted from the textual content of these reviews, including linguistic characteristics, sentiment analysis, and meta-data such as review length and rating distribution. Several ML models are employed for classification, such as Naive Bayes, Random Forest, and Support Vector machines. these algorithms are assessed using measures like accuracy, precision, recall, and F1-score after being trained on the dataset. In addition, methods like hyperparameter tuning and cross-validation are used to improve the performance of ***, the findings suggest that ML-based approaches hold promise for mitigating the problem of fake reviews, thereby enhancing the reliability of online review platforms and empowering consumers to make informed decisions. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence and machine learning Algorithms to Analyse Integrated Nutrient Management Strategies for Optimal Yield and Quality of Oilseeds Crops for Sustainable Development 4

Artificial Intelligence and Machine Learning Algorithms to A...

引用

4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Mithra, C. Dar, Showkat A. Preethi, A. Dept. of Computer Science and Engineering Chennai India Githam University Dept. of Computer Science and Engineeing India Dept. of Computer Science and Engineering Chidambaram India

ISBN: (纸本)9798331515720

the main objective of this research is to deduce the efficacy of integrated nutrient management (INM) technologies in production of oilseed crops for sustainable development. A great amount of experience is needed in selecting the most effective INM strategy. A new recommendation system to circumnavigate this issue is proposed. It lets farmers decide on the best INM strategy to maximize oilseed crop yield and quality. this system is built on the techniques of advanced machine learning (ML) and aritifical Intelligence (AI). Oilseed crop date in Tamil Nadu from 1961 to 2019 was used to develop the proposed algorithm. the proposed algorithm for crop yield prediction (CYP) which includes a soft Voting Ensemble Classifier with weights (SVECWW), a soft Voting Ensemble Classifier without weights (SVECWOW) along with the SVM technique are compared and contrasted with existing algorithms and also proves that SVECWW outperforms other ML algorithms with an accuracy rate of 97.2%. Furthermore, the Stacked Generalization Ensemble model is employed and compared with another Deep Neural Network (DNN) for the INM crop recommendation system which offers a simple graphical user interface (GUI) for farmers to use and received an accuracy of 97.5%. this GUI enables farmers to access valuable information such as the optimal timing for cultivating oilseed crops, the appropriate types and quantities of organic manures, inorganic fertilizers, and bio-fertilizers required for successful oilseed crop production. the study shows, on its whole, how to create tailored recommendation systems for farmers using GUI models with artificial intelligence and machine learning algorithms. Implementing these systems is expected to significantly improve oilseed crop production and quality significantly, benefiting the whole agricultural sector for sustainable development. Artificial intelligence (AI) makes a recommendation system more accurate and adaptable by looking through complex datasets and patterns

关键词： Adversarial machine learning

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Insight Evaluation on Traditional and CNN Features 2022

Insight Evaluation on Traditional and CNN Features

引用

6th international conference on machine learning and soft computing (icmlsc)

作者： Truong-Minh Le Nguyen-Phan-Long Le Nhat-Gia-Nghi Hua Nhien-Loc Bui Vinh Quang Dinh Univ Informat Technol VNU HCM Ho Chi Minh City Vietnam Vietnamese Germany Univ Binh Duong Vietnam

ISBN: (纸本)9781450387477

Feature extraction serves as the prerequisite for any intelligent-based applications. there are various methods to extract features from the initial data sets, namely traditional filters and deep learning components. However, current traditional feature extraction methods still have troubles in dealing with automatically updating the weights. this paper will propose a new feature representation method that shows more promising results than any previous methods. this proposed method investigates the benefits of features based on Convolutional Neural Network (CNN) to solve the above problem. CNN's purpose is to process multi-dimensional data, such as image and time-series data and crucial features are noticed and picked automatically without the need of being supervised by human-being. the proposed method has demonstrated significant improvement in terms of accuracy and stability compared to traditional methods of feature extraction. From that, we can easily understand why CNN-based algorithms have become a part of state-of-the-art algorithms these days.

关键词： Convolutional Neural Network Deep learning Feature Extraction Local Patterns Traditional Filters

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A Pragmatic Approach of Heart and Liver Disease Prediction using machine learning Classifiers 4

A Pragmatic Approach of Heart and Liver Disease Prediction u...

引用

4th international conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Pal, Karan Panwar, Sarthak Choudhury, Deepjyoti Manipal University Jaipur Dept. of Aiml Jaipur303007 India Manipal University Jaipur Dept. of Cce Jaipur303007 India

ISBN: (纸本)9798350349856

heart disease, also known as cardiovascular disease, can cause a heart attack by altering the body's blood flow. Liver disease contributes to a global death toll of about 2 million each year. the adaptation of Artificial Intelligence and machine learning has the latent capacity to fundamentally metamorphize the healthcare sector. this paper proposes undertaking a comparison analysis of different machine learning classifiers such as Random Forest, Logistic Regression, Support Vector, Naive Bayes, Decision Tree, and K-Nearest Neighbors. In our experiment, we employed four datasets, all sourced from Kaggle. In the Heart disease dataset, the best accuracy achieved was 82.35%. For the Heart Disease 2020 dataset, the highest accuracy was 74.59%. In the Framingham dataset, the top accuracy reached 68.6%. Lastly in the liver disease dataset, the highest accuracy was 83.33%. © 2024 IEEE.

关键词： Diseases

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Enhancing Bone Fracture Detection in Radiology: A machine learning and Explainable AI Approach 4

Enhancing Bone Fracture Detection in Radiology: A Machine Le...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Deka, Krishnav Rabha, Madhurjya Saikia, Deepratim Jena, Junali Jasmine Kumar Gourisaria, Mahendra Behera, Dayal Kumar School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar India

ISBN: (纸本)9798331515720

Bone fractures, caused by impacts, trauma or conditions such as osteoporosis, require timely and precise detection for effective medical care. Traditional approaches rely on radiologists' interpretations of X-rays, which are labor-intensive and prone to errors. machine learning (ML) presents a promising solution for enhancing fracture identification. However, the opacity of ML models presents challenges in their integration into healthcare, necessitating transparency and interpretability. this work examines the use of ML in fracture detection and incorporates Explainable AI (XAI) techniques to clarify decision-making processes and bolster model reliability. Here, Local Directional Pattern (LDP) and Local Binary Pattern (LBP) have been used for extracting the features and it is found that the Random Forest Classifier achieved the highest accuracy of 95 %. By assessing both the accuracy and interpretability of ML models in identifying fractures, this study aims to promote wider acceptance and application of AI-driven diagnostic tools in medical settings. © 2024 IEEE.

关键词： Radiology

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A Survey on Privacy-Preserving Communication Frameworks in machine learning for Cybersecurity threat Detection 4

A Survey on Privacy-Preserving Communication Frameworks in M...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Suneetha, Banothu Kesavan, R. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331529635

this survey covers the new trend of privacy-preserving communication frameworks in machine learning in the FL, DP and SMPC for cyber threat detection domains. the specific research performed between the year of 2020 and 2024 has been surveyed and included in this paper. the main contribution is monitoring cybersecurity threats using privacy-preserving frameworks in distributed systems such as IoT and industrial networks. 40 research papers have been reviewed and categorized to several related themes such as FL implementations, privacy-preserving methods, and cybersecurity threat detection. the relevance of privacy-preserving frameworks for the real-world cybersecurity scenario is illustrated with dataset CISA 2022 Vulnerability Dataset. the study of privacy-preserving communication shows that FL can be combined with DP and SMPC to achieve data privacy preservation while deduction accuracy of detecting cyber threats can be well-maintained. Moreover, the research gaps and challenges of scalability, communication overhead have been addressed and identified to facilitate the future research and development. © 2024 IEEE.

关键词： Federated learning

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