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检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

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An Empirical Investigation into machine learning Approaches for Breast Cancer Classification 2

An Empirical Investigation into Machine Learning Approaches ...

引用

2nd IEEE international conference on Recent advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Duggal, Anoushka Muhuri, Samya Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798350354461

Breast cancer was diagnosed in 2.3 million individuals worldwide in 2022, and was the cause of 670,000 deaths. It ranks as the second most common cancer overall and the most frequently diagnosed cancer in women. Since survival is highly dependent on early detection, the development of computational models for diagnosis is crucial. Among the various models available, we have chosen to focus on three supervised machine learning models in this study: Support Vector machine (SVM), K-Nearest Neighbor (KNN), and Feed-forward Neural Networks (FFNN). Their roles in breast cancer detection are investigated. We have pre-processed the data using normalization and feature selection to enhance these models' performances. They are trained on real-life documented patient data to predict breast cancer. It is determined that the FFNN is more accurate than both SVM and KNN. This paper provides a comprehensive analysis of the various machine learning models, highlighting their positive and negative aspects in diagnosing breast cancer. The results underscore the potential of advanced neural networks in medical diagnostics and pave the way for further research and development in this domain. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-Driven Retention: A Hybrid Approach to Employee Turnover Prediction 9

AI-Driven Retention: A Hybrid Approach to Employee Turnover ...

引用

9th international conference on Communication and Electronics systems, ICCES 2024

作者： Ponmalar, S. Affrin Fowmiya, N. Nandhini, C. Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798350377972

Predictive analytics by combining ensemble learning and deep learning techniques. The first step normalizes the feature space with MinMaxScaler, which improves the performance of the algorithms. To tackle class imbalances, the Synthetic Minority Over-sampling Technique (SMOTE) is used, ensuring strong predictive modeling. An empirical study shows the effectiveness of various algorithms, including ensemble methods like CatBoost, AdaBoost, LightGBM, and Gradient Boosting machine (GBM), whicheffectively capture complex interactions and nonlinear relationships in employee data. Furthermore, Long Short- Term Memory (LSTM) networks are employed to identify temporal dependencies in longitudinal records. The findings emphasize the approach's capability to detect subtle signals of potential turnover. By integrating these methodologies, this research provides HR professionals with valuable tools for proactive retention strategies. Ultimately, it advances HR analytics, assisting organizations in reducing attrition risks and promoting an engaged workforce. © 2024 IEEE.

关键词： Predictive analytics

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Predicting Course Grades for Students Using machine learning: Determine Early Prediction Time 3rd

Predicting Course Grades for Students Using Machine Learning...

引用

3rd international conference on advances in Information and Communication Technology, ICTA 2024

作者： Nguyen, Thi Hong Bui, Quang Vinh Nguyen, Viet Anh Do, Trung Kien Hanoi National University of Education Hanoi Viet Nam VNU University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9783031809422

Predicting learning outcomes (LO) is an important problem in educational data mining (EDM). Despite extensive research, there remains a gap in determining the optimal timing for predicting early LO with acceptable accuracy. This study proposes a methodology for developing a predictive model using machine learning techniques to identify the appropriate time to forecast students’ course scores in a blended learning environment. The scope of the research includes students participating in the subject ‘Introduction to Computer Science’ at Hanoi National University of Education (HNUE). The experimental data includes data from the learning management system (LMS) and the face-to-face classroom. The research model is built based on machine learning algorithms, including Linear Regression (LN), Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The research results show that the attributes most strongly influencing course scores are average practice score, midterm score, and major. The ANN model provides the highest efficiency with R2 = 0.6033. Early prediction of final exam scores can be made from week 7. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Comparative Analysis on a Diabetic Disease Prediction Considering Various machine learning Algorithms 4

A Comparative Analysis on a Diabetic Disease Prediction Cons...

引用

4th international conference on Sustainable Expert systems, ICSES 2024

作者： Das, Dilip Kumar Chowdhury, Suman Hossain, Mohammed Motaher International University of Business Agriculture and Technology Department of Mathematics Dhaka1230 Bangladesh International University of Business Agriculture and Technology Department of Electrical and Electronic Engineering Dhaka1230 Bangladesh International University of Business Agriculture and Technology Department of Quantitative Science Dhaka1230 Bangladesh

ISBN: (纸本)9798331540364

Diabetes is a serious health concern almost all over the countries as it impaired many organs in human body and rate of affected person is gradually increasing. Several significant factors have been incorporated for prediction of diabetic diseases through the machine learning attribute. In this study a non-communicable disease, diabetes, is predicted based on several machine learning approaches like Support vector machine (SVM), Decision tree (DT), Logistic regression (LR), K-Nearest Neighbours (KNN), Gaussian Naive Byes(GNB),Random Forest(RF). Data set of Sylhet Diabetic Hospital, Sylhet in Bangladesh containing 520 samples is collected from online source. Comparing the classifiers SVM shows the maximum accuracy 99%. Since a diabetic patient suffers immensely throughout the life, diagnosis along with proper medication is important. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing of 6G Network Performance Through machine learning by Allocating Resources 1

Optimizing of 6G Network Performance Through Machine Learnin...

引用

1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Agarwal, Ankita Thulasiram, Ramachandran Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India

ISBN: (纸本)9798331518578

The impediments with 6G network construction have encouraged the activity of machine learning Resource Allocation Algorithms (MLRA). In terms of flexibility and ability utilisation., MLRA outperforms more normal algorithms. Using data on how the network is functioning, the algorithms can resolve how to deliver resources most efficiently. To confirm that properties are delivered accurately and capably, MLRA will be decisive as 6G networks are employed to a greater extent. Quickly reallocating unaccustomed resources to more creative consumers or apps is entity the algorithms are fit achievement. The expected GB speeds and a 1000x increase in illiteracy from 6G networks are intense. Smart talent allocation algorithms that can exactly and expeditiously readjust to changeful consumer demands and behaviours are essential for this. The change to 6G networks and the facilities they supply will not be likely without MLRA. © 2025 IEEE.

关键词： Queueing networks

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Stress Level Detection using machine learning and Image Processing 15

Stress Level Detection using Machine Learning and Image Proc...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Hariharan, R. Valarmathi, K. Sriramkumar, R. Prasanth, G. Vairavap Sankarapandian, S. Dept of Electronics and communication Engineering PSR Engineering college Sivakasi India

ISBN: (纸本)9798331300579

Stress, a ubiquitous part of modern life, greatly affects an individual's mental and physical well- being. This work addresses the need for an efficient non-invasive system that can accurately detect stress levels in individuals. Leveraging advances in machine learning and simulation, this effort focuses on developing a sophisticated framework for facial expression-based stress analysis. The proposed system aims to overcome the limitations of traditional stress assessment methods by using sufficient data recorded in human facial expressions. Combining sophisticated computer vision techniques, the system captures facial images and extracts complex emotional signals of varying levels of stress machine learning algorithms play an important role in this task, enabling the system to recognize micro-patterns in stress-facial faces. The training process takes a large data set of labelled facial images to model complex stress-facial signals And, enables the power be easy to see and guess The integration of visualization and machine learning approaches contributes to the development of passive and objective stress assessment tools. The system's ability to analyze and interpret these facial cues makes it easier to detect stress levels in real time, and provide timely intervention and *** implications of this work extend to mental health policy, offering a new method for objectively measuring stress levels. The imagined system holds promise to be a valuable asset in clinical settings, workplaces, and everyday life, providing a means of active and human management. © Grenze Scientific Society, 2024.

关键词： Visualization

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Epileptic Seizure Detection and Analysis Using machine learning 2

Epileptic Seizure Detection and Analysis Using Machine Learn...

引用

2nd IEEE international conference on advances in Information Technology, ICAIT 2024

作者： Prabhu, Aniruddha B.P. Prasad, Guru M.S. Sharma, Tushar Bhatt, Chandradeep Taranath, N.L. Department of Computer Science and Engineering Alliance University Bangalore India University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India School of CSE Presidency University Bangalore Rajanukunte India

ISBN: (纸本)9798350383867

Epilepsy, a neurological illness marked by recurring seizures, presents a severe public health issue. Early and reliable diagnosis of epileptic seizures is critical for timely treatment and improved medical outcomes. This research focuses on employing machine learning techniques for the identification and analysis of epileptic seizures. The Electroencephalogram (EEG)dataset, obtained from the University of California Irvine (UCI) machine learning Repository, contains recordings of brain activity, each spanning 23.6 seconds. The study unfolds with an extensive data collection process, followed by a detailed exploratory data analysis to uncover inherent patterns. Subsequently, data visualization techniques are applied to provide insights into the unique characteristics of epileptic brain activity. Acknowledging the challenge of an imbalanced target variable (y), a critical feature in our dataset, we implement the Minority Random Oversampling technique during data preprocessing. We utilize the Light Gradient Boosted machine (LightGBM) model for our analysis. With the application of this sophisticated algorithm, our model attains an impressive 99.24% accuracy when evaluated on the testing *** study contributes not only to the area of epilepsy research but also highlights the efficiency of a balanced dataset and a powerful machine learning model in enhancing seizure detection accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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On deriving kernels for linear time-dependent systems 31

On deriving kernels for linear time-dependent systems

引用

31st international conference on Noise and Vibration engineering, ISMA 2024 and 10th international conference on Uncertainty in Structural Dynamics, USD 2024

作者： Jones, M.R. Cross, E.J. University of Sheffield Department of Mechanical Engineering Sir Frederick Mappin Building Mappin Street SheffieldS1 3JD United Kingdom

ISBN: (纸本)9789082893175

Embedding domain expertise within machine learning has allowed for hybrid models that possess both the expressivity of data-driven models whilst adhering to fundamental equations and constraints that ensure predictions are physically plausible. When forming physics-informed models, a question that is overlooked is what actually constitutes best practice for embedding prior knowledge that is held into the structure of the machine learner. Existing approaches largely ignore the influence of how physics is integrated into a model, and assumes that best practice is already being applied. To begin understanding whether the procedure of including domain expertise inside machine learning can be improved, we investigate physics-informed models that arise explicitly from a derivation path of the interaction between the physics and what remains unknown. Several case studies for modelling time-driven systems are considered, with the behaviour of the derived models compared to examples of what would likely be attempted in current practice. © 2024 Proceedings of ISMA 2024 - international conference on Noise and Vibration engineering and USD 2024 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

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A Review on the Applications of machine learning and Deep learning Techniques for Skin Cancer Detection 4

A Review on the Applications of Machine Learning and Deep Le...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through machine learning (ML) and Deep learning (DL) models. This review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. This article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 IEEE.

关键词： Adversarial machine learning

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Integrating machine learning in Air Quality Prediction systems 2

Integrating Machine Learning in Air Quality Prediction Syste...

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2nd international conference on advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Keerthika, S. Krishnasamy, Lalitha Priyanka, S. Sanjai, R. Sathyaa, S. Pooja, K. Kongu Engineering College Department of Artificial Intelligence TamilNadu Perundurai India Nandha Engineering College Department of Artificial Intelligence and Data Science TamilNadu Perundurai India Department of Artificial Intelligence Kongu Engineering College TamilNadu Perundurai India

ISBN: (纸本)9798331541217

Air pollution is described as the contains of dangerous substances in atmosphere that are emitted by natural sources or human activities and can harm humans, animals, and the environment. Sulfurous anhydride, carbon monoxide, Dioxide of nitrogen, trioxygen, carbon monoxide, and volatile organic compounds are some of the most common air contaminants. The development of tools for tracking and reducing air pollution is greatly supported by this analysis, which is important for both public health and environmental sustainability. To improve model performance, particular attention is paid to feature engineering and data pre-treatment methods. The study employs machine learning models, including Decision Tree Classifier, Logistic Regression, Naive Bayes, K-Nearest Neighbors, Random Forest, and SVM, to ascertain the most effective approach for accurate air quality forecasting. The key contribution of this work lies in its ability to provide a practical decision-making framework that can guide authorities in implementing focused interventions and optimizing resource allocation for pollution control. The hybrid approach utilized in this study surpasses traditional methods, offering a more precise tool for air quality forecasting. This model not only addresses the need for improved predictive performance but also acts as a useful tool for public health and environmental agencies seeking to minimize the harmful effects of air pollution. © 2024 IEEE.

关键词： Logistic regression

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