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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1-10 订阅

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icmlsc 2020 - Proceedings of the 4th international conference on machine learning and soft computing

ICMLSC 2020 - Proceedings of the 4th International Conferenc...

引用

4th international conference on machine learning and soft computing, icmlsc 2020

ISBN: (纸本)9781450376310

the proceedings contain 32 papers. the topics discussed include: reasoning algorithms for complex matching features;named entity recognition method for fault knowledge based on deep learning;regression model for better generalization and regression analysis;implementing IoT-adaptive fuzzy neural network model enabling service for supporting fashion retail;multimodal sentiment analysis based on multi-head attention mechanism;valence-arousal model based emotion recognition using EEG, peripheral physiological signals and facial expression;cyber physical system: achievements and challenges;and energy expenditure estimation based on artificial intelligence and microservice architecture.

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Adverse Drug Reaction (ADR) Prediction using Integrated machine learning and Deep learning Approaches 4

Adverse Drug Reaction (ADR) Prediction using Integrated Mach...

引用

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

作者： Prabhu, K. Kanagasabapathy, T. Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Pollachi India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

the prediction of adverse drug reactions (ADRs) is paramount in mitigating risks to patient safety and enhancing pharmacovigilance efforts. While both machine learning (ML) and deep learning (DL) techniques have demonstrated efficacy in individual ADR prediction tasks, they possess distinct advantages and limitations. this study proposes an integrated approach that harnesses the synergies between ML and DL to improve ADR prediction accuracy and feature representation. Our hypothesis posits that by integrating traditional ML models with DL's feature extraction capabilities, we can achieve enhanced ADR risk assessment and gain deeper insights into the complex relationships among drug-related factors. this integrated framework holds promise for revolutionizing ADR prediction and proactive pharmacovigilance initiatives. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

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Malware Detection and Classification for URLs using Ensemble learning 4

Malware Detection and Classification for URLs using Ensemble...

引用

4th international conference on Pervasive computing and Social Networking (ICPCSN)

作者： Uke, Shailaja Bassan, Inderdeep Gite, Gayatri Hirkani, Haider Raghvani, Isha Vishwakarma Inst Technol Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9798350386356;9798350386349

the advent of the digital age has brought about previously unheard levels of connectedness and information availability, as well as an increase in cyber threats. Unauthorized Uniform Resource Locators (URLs) are now a common threat and a preferred avenue for cyberattacks. Because of the inadequacy of traditional blacklist-based solutions to counteract shifting strategies, machine learning (ML) is being investigated for proactive and predictive cybersecurity measures. the goal of this research work is to improve cybersecurity defenses against URL-based vulnerabilities by utilizing ML and ensemble learning. Several machine learning methods are used, such as Random Forest, Decision Tree, Logistic Regression, XGBoost, SVM, and Naive Bayes. the intricacy of the digital threat landscape is addressed by combining the strengths of different models through the use of ensemble approaches like Weighted Voting and soft Voting. this study highlights the significance of Weighted Voting and shows how well ensemble techniques may combine different machine learning model and emphasizes how important it is to keep improving cybersecurity defenses in line with changing threats. the results enable the possibilities for further advancements in the sector and offer workable answers to the emerging cybersecurity issues.

关键词： Malware Detection Uniform Resource Locator (URL) Classification Ensemble learning Majority Voting soft Voting Weighted Voting Cybersecurity Phishing Detection

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Survey of Deep learning Techniques for Malware Detection: Insights, Challenges, and Future Directions 4

Survey of Deep Learning Techniques for Malware Detection: In...

引用

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

作者： Sree Bai, Kshatriya Vinaya thirumaran, M. Department of Computer Science and Engineering Tamilnadu Puducherry India

ISBN: (纸本)9798331515720

Android and Windows become the main platforms for mobile and personal computing, the threat from advanced malware targeting these systems is increasing. Traditional machine learning methods are struggling to keep up with these sophisticated threats, so deep learning techniques, which have shown better results, are needed. this paper reviews various deep learning methods for detecting and classifying malware across different environments, like IoT and web platforms. We examined 40 research articles from major digital libraries, focusing on different deep learning models like CNNs, LSTMs, DBNs, and *** analysis highlights the strengths and weaknesses of these models, such as high false positive rates and difficulties with detecting hidden malware. In order to decrease false positives and increase detection accuracy, we provide a novel framework that integrates many deep learning methods. this survey offers a comprehensive guide for new researchers and practical insights for improving malware detection with deep learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Design and Development of Attendance System Using Face Recognition 4

Design and Development of Attendance System Using Face Recog...

引用

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

作者： Krishna, Abhay Manoj, Nandana Subbulakshmi, T. Vellore Institute of Technology Bachelor in Computer Science Chennai India Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331515720

the face identification technology-based attendance system represented in this paper is a novel design. the new system is an alternative to conventional more reliable, secure, and user-friendly. methods the key components are a robust face detection and recognition algorithm, a database management system, and an intuitive interface. the system achieves high accuracy, scalability, and privacy compliance by utilizing machine learning. the real-Time performance testing results show that it can be successfully used in various scenarios, thus becoming a significant breakthrough in the automated attendance management system. © 2024 IEEE.

关键词： attendance system educational institutions face detection machine learning web-based

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Transformative Potential of Consumer Insights into Sustainable Business Growth: machine learning Integrated with Marketing Analytics 4

Transformative Potential of Consumer Insights into Sustainab...

引用

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

作者： Bist, Ayush Singh, Gurpreet Srivastava, Shilpee Chandigarh University Department of Mathematics Chandigarh India Chandigarh University Department of CSE Chandigarh India

ISBN: (纸本)9798331515720

machine learning and marketing analytics have emerged as a well-known tool for transforming consumer insights into substantial business growth. through the consumption of advanced procedures and data analytics methodologies, enterprises gain the ability to extract invaluable insights from extensive consumer data sets, leading to refined marketing strategies and enriched customer experiences. this research study analyzes the synergy between machine learning and marketing analytics, emphasizing their profound impact on consumer insights and subsequent business expansion. It explains a range of machine learning algorithms and data analytics tools pertinent to marketing endeavors, including predictive modeling, customer segmentation, and recommendation systems. providing businesses with robust tools for decision-making and strategy formulation. Additionally, this research study explores the real-world examples showcasing the transformative potential of machine learning and marketing analytics in optimizing marketing campaigns, personalizing customer interactions, and enabling sustainable growth. By integrating machine learning and marketing analytics, organizations can capitalize on unparalleled opportunities to gain a competitive advantage in today's data-driven scenario, translating consumer insights into actionable strategies with unprecedented accuracy and efficacy. © 2024 IEEE.

关键词： Adversarial machine learning

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A Robust Methodology Design to Predict Student Educational Performance by using Hybrid Deep learning Strategy 4

A Robust Methodology Design to Predict Student Educational P...

引用

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

作者： Gayathri, J. Vijayalakshmi, K. Kalpana, P. Sri Bhavani, N. Saveetha School of Law Chennai600 077 India

ISBN: (纸本)9798331515720

Predicting student educational performance has become an essential task for educational institutions aiming to enhance learning outcomes and provide timely interventions. Traditional machine learning models have been widely used in this domain;however, they often struggle with the high-dimensional and complex nature of educational data. this study presents a robust methodology for predicting student educational performance using a Hybrid Deep learning Model Design (HDLMD) that integrates Cascaded ResNet with AutoEncoder. the model is developed to address the complexities of educational data, leveraging the deep feature extraction capabilities of ResNet and the dimensionality reduction power of AutoEncoders. the proposed model was tested against nine other machine learning models, demonstrating superior performance with an accuracy of 98.11%. the HDLMD outperformed traditional models such as Random Forest, SVM, and Gradient Boosting, showcasing its effectiveness in capturing complex patterns within student data. the results indicate that the HDLMD is a powerful tool for educational institutions seeking to predict and improve student outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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A Study of Optimizations in Rainfall Prediction using machine learning Models 4

A Study of Optimizations in Rainfall Prediction using Machin...

引用

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

作者： Sinha, Chaitanya Kumar Sharma, Udit Maragatharajan, M. Manikandan, J. VIT Bhopal University School of Computing Science and Engineering Madhya Pradesh India

ISBN: (纸本)9798331515720

Rainfall prediction is essential for many industries, such as agriculture, water resource management, and disaster relief. Recently, there has been a lot of interest in machine learning techniques to increase the accuracy of rainfall forecasts. To improve rainfall prediction models' accuracy, we provide a hybrid strategy in this study that combines conventional algorithms with cutting-edge optimization techniques. We utilize the XG Boost, Random Forest, and Linear Regression algorithms as baseline models. these are then combined with the Crow Search Algorithm (CSA) optimization technique. Crow collective behavior inspired the nature-inspired metaheuristic algorithm, CSA, which has demonstrated promising results in solving complicated problems. this study intends to improve the baseline models' performance by dynamically adjusting their parameters through the use of CSA. the proposed approach incorporates CSA with the chosen machine learning algorithms, which adaptively modify parameters to increase prediction accuracy. We validate the suggested approach with historical rainfall data and conduct extensive experimentation to show its efficacy. © 2024 IEEE.

关键词： Prediction models

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A Comprehensive Analysis on Integrating machine learning in Agriculture Practices 4

A Comprehensive Analysis on Integrating Machine Learning in ...

引用

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

作者： Mahendran, K. Madhavan, R. Sakthivel, M.S. Saveetha Engineering College Department of ECE India

ISBN: (纸本)9798331515720

this research investigates the integration of machine learning (ML) into traditional agricultural practices to address the challenges posed by conventional methods. the study begins by examining the limitations of traditional farming techniques, such as labour-intensive processes, low efficiency, and susceptibility to environmental factors. Subsequently, this study reviews the existing research that has successfully implemented technology in various agricultural domains, including precision farming, crop monitoring, and yield prediction. the core focus of the research lies in exploring the potential of ML to automate agricultural tasks. A detailed explanation is provided on how ML models can be developed and deployed to optimize agriculture processes such as irrigation scheduling, monitoring and harvesting. the study highlights the benefits of ML-driven automation, including increased productivity, reduced costs, and improved sustainability. the research concludes by highlighting the transformative potential of ML in reshaping the agriculture and ensuring food security for future generations. © 2024 IEEE.

关键词： Smart agriculture

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Critical Disaster Prediction using machine learning Algorithms 4

Critical Disaster Prediction using Machine Learning Algorith...

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

作者： Nirmala, E. Suresh, M. Maragatharajan, M. Dadlani, Riya VIT Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India VIT Bhopal University School of Computing Sciences and Artificial Intelligence Madhya Pradesh Kothrikalan India

ISBN: (纸本)9798331515720

Prediction of earthquake is a challenging factor in the early warning system. there are a number of geographical locations that cause earthquake very often due to climatic changes. Earthquake is one among the natural disaster that root cause for substantial damage and risk to human life. this article discusses in detail about the earthquake prediction scheme constructed through machine language with automated algorithmic implementation over regression, random forest and SVM model. this accuracy prediction with comparative analysis determines the suitable model for prediction accuracy of earthquake. the objective of this research study predicts the accuracy based on magnitude, magnitude and location or region, number of region and depth of earthquake parameters from the Kaggle dataset of 18029 values. Hence, prediction with three different model generates the possible accurate prediction of 99.9% to predict the magnitude and probability of earthquake occurring in a precise area (California) from the significant data of that county using numerous machine learning (ML) models. © 2024 IEEE.

关键词： Prediction models

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