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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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Computer-Aided Diagnosis of Dyslexia Using machine learning 1

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2nd international conference on advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Gaikwad, Aaishwarya Ashish Raut, Roshani Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Department of Information Technology Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

Dyslexia is identified as a typical learning disorder which affects children due to which they develop difficulty in pronouncing words, writing with the correct spellings, etc. The numerical dataset includes results from a group of dyslexic people and a control group of people who can read normally. The objective here is to test how dyslexia can be diagnosed using machine learning effectively. We build models that can precisely categorize people as dyslexic or not using a variety of ML algorithms, such as random forest, LR, kernel SVM, multilayer perceptron, etc. We assess each model's performance using metrics including recall, accuracy, and precision. Based on test results, our findings show that ML algorithms are capable of accurately identifying people who have dyslexia. The model with the best results had an accuracy of 90% achieved by Naïve Bayes and logistic regression. These results imply that ML-based techniques have the potential to support early dyslexia detection, resulting in more prompt interventions and better outcomes for dyslexics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer aided instruction

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The Role of AI in Modern Classroom Management 2

The Role of AI in Modern Classroom Management

引用

2nd international conference on machine learning and Autonomous systems, ICMLAS 2025

作者： Gayaki, Unnati Daronde, Subodh Datta Meghe Institute of Higher Education and Research Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Biomedical Engineering Faculty of Engineering and Technology Maharashtra Wardha India

ISBN: (纸本)9798331505745

Artificial Intelligence is changing the way classrooms are managed, encouraging new ideas, making things easier, and helping students learn in their own way. Adaptive platforms, smart tutoring systems, and tools like I-MINDS customize learning experiences to fit different needs while making it easier for teachers. It takes care of jobs like grading, tracking attendance, and analyzing behavior, allowing teachers to concentrate on important thinking skills and emotional growth. AI also enhances teamwork through group tools, promotes fairness, and assists in real-time decision-making. Technologies like VR, AR, and MR allow people to learn through experience, especially with hard subjects. Incorporating AI learning into school curricula readies students for a future driven by AI. Despite issues with data privacy, biased algorithms, and ethical questions, AI is making learning environments inclusive, dynamic, and just. New technology, combined with ways that are focused on people, is how AI seeks to transform education into a holistic and flexible system for learners and teachers alike. © 2025 IEEE.

关键词： Decision making

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An empirical study on the application of KANs for classification 24

An empirical study on the application of KANs for classifica...

引用

8th international conference on advances in Artificial Intelligence, ICAAI 2024

作者： Costa, Samuel Sampaio Pato, Matilde Datia, Nuno Politécnico de Lisboa ISEL Lisbon Lisbon Portugal LASIGE Faculdade de Ciencias Universidade de Lisboa LASIGE Lisbon Lisbon Portugal NOVA LINCS NOVA School of Science and Technology NOVA LINCS Setubal Caparica Portugal

ISBN: (纸本)9798400718014

Kolmogorov-Arnold Networks (KANs) represent a breakthrough in deep learning, diverging from Multi-Layer Perceptrons (MLPs) by generalizing the Kolmogorov-Arnold representation theorem (KAT) to networks of arbitrary depth and width. This theorem facilitates the decomposition of multivariate functions into constituent one-dimensional elements, with learnable activation functions on weights and the sum operator on nodes. KANs have been shown to exhibit robust performance in function approximation, validated across mathematical, physical, and practical domains such as traffic prediction and medical diagnostics. Our study investigates KANs’ efficacy through comprehensive evaluations on OpenML, Kaggle and UCI datasets, with a focus on enhancing Human Activity Recognition systems. They demonstrate high classification performance compared to conventional machine learning approaches and MLPs. These findings underscore KANs’ potential as scalable, interpretable tools in modern machine learning applications given their favorable neural scaling laws. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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A Study on Integrating machine learning Techniques for Waste Management 7

A Study on Integrating Machine Learning Techniques for Waste...

引用

7th international conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Vaidya, Ruchika Dattangire, Rahul Biradar, Divya Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Data Engineering Publicis Sapient HoustonTX77002 United States University of Texas Department of Computer Science ArlingtonTX76019 United States

ISBN: (纸本)9798350372816

The current study focuses on how various machine learning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a broad writing survey, the paper features the meaning of incorporating these advances into waste management practices. The system segment subtleties the information assortment, preprocessing, model determination, and assessment measurements utilized in the review. The accuracy and performance metrics of various classifiers, including K Nearest Neighbors, Linear Support Vector Classifier, Logistic Regression, Decision Tree, Multi-Layer Perceptron, and Random Forest, are demonstrated by the results of experiments with them. Especially noteworthy is the MLP classifier's 91.81% accuracy. The discoveries highlight the viability of ML approaches in ssmart waste management. In addition, a comparison table that summarizes the classifiers' accuracy scores provides insight into their suitability for applications in waste management. © 2024 IEEE.

关键词： Adversarial machine learning

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From Data to Decision: Exploring machine learning's Impact on Shaping Smart Cities 4th

From Data to Decision: Exploring Machine Learning's Impact o...

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4th international conference on Digital Technologies and Applications (ICDTA)

作者： Adraoui, Meriem Diop, El Bachir Azmi, Rida Chenal, Jerome Abdem, Seyid Abdellahi Ebnou Mohammed VI Polytech Univ UM6P Ctr Urban Syst CUS Benguerir Morocco Ecole Polytech Fed Lausanne EPFL Urban & Reg Planning Community CEAT Lausanne Switzerland

ISBN: (纸本)9783031686528;9783031686535

In today's era of rapid urbanization, smart cities are emerging as a beacon of efficiency and sustainability. At the heart of this evolution is the integration of machine learning (ML), which can transform vast streams of data into informed decisions. This article examines through a literature review the transformative potential of machine learning in urban environments, highlighting advances and challenges in the key smart city domains: Infrastructure, Environment, Mobility and Transport. The review highlights both the promising breakthroughs achieved and the challenges ahead in this interdisciplinary field. Our analysis aims to provide readers with a comprehensive understanding of how machine learning not only improves data processing capabilities, but also leads to actionable and impactful decisions in the urban context. By tracing the path from raw data to informed urban solutions, this paper highlights the central role of machine learning in shaping the smart cities of the future.

关键词： Smart Cities machine learning Infrastructure Environment Mobility and Transport Sustainability Urban Data

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The application of large-scale MIMO detection algorithm in 5G and beyond 5G 4

The application of large-scale MIMO detection algorithm in 5...

引用

4th international conference on Signal Processing and machine learning, CONF-SPML 2024

作者： Tang, Qiteng Harbin Institute of Technology Shenzhen Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9781510674721

In recent years, the rapid advancement of 5G technology has brought to the forefront the pivotal role of Multiple-Input Multiple-Output (MIMO) system algorithms. This paper delves into a comprehensive exploration of two distinct algorithmic approaches within the context of 5G applications for massive MIMO systems. These two approaches are matrix transformation and machine learning, and the following paragraphs will shed light on their respective attributes and intricacies. Matrix transformation is a fundamental technique in MIMO systems, which aims to optimize the transmission of signals by manipulating the channel matrices. This method, while established and reliable, exhibits certain limitations in accommodating the dynamic and complex nature of 5G environments. On the other hand, machine learning algorithms, with their adaptability and capacity for self-improvement, have gained prominence in recent years. They offer a promising avenue for addressing the challenges presented by 5G MIMO systems, such as handling interference and optimizing resource allocation. In this paper, we provide concrete examples to analyze the strengths and weaknesses of both matrix transformation and machine learning in the context of 5G applications. Furthermore, we explore potential directions for the application of these algorithms and propose areas for improvement, with the ultimate goal of enhancing the efficiency and performance of massive MIMO systems in the evolving landscape of 5G technology. © 2024 SPIE.

关键词： machine learning

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Identification of Credit Card Frauds Using machine learning Techniques 9

Identification of Credit Card Frauds Using Machine Learning ...

引用

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

作者： Jain, Shaily Rani, Neetu Srivastava, Prakash Prabha, Chander Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Department of Computer Science and Engineering Punjab Ghruan India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350377972

Nowadays, most of the transactions are online due to the high involvement of online sales and purchase purposes. Although these transactions provide convenience to their end user, meanwhile these are also the main cause of fraud in online transactions of credit cards. Nowadays, online fraud transactions hiked in some cities generating interest in this topic of utmost security concern and needs to be addressed. Recent research studies proposed many machine learning techniques to resolve such kinds of issues but still their accuracy needs to be improved. In this research, the Kaggle dataset was used to solve the textual data problem. On the dataset downloaded from Kaggle, few machine learning algorithms have been implemented for research validation. An accuracy of 99.87% was achieved by the proposed research work. The proposed model detects the fraud transaction in earlier stages and will be more beneficial for business firms. © 2024 IEEE.

关键词： Adversarial machine learning

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Brain Stroke Detection Through Advanced machine learning and Enhanced Algorithms

Brain Stroke Detection Through Advanced Machine Learning and...

引用

2024 international conference on Recent advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Yogendra Prasad, P. Ramu, M. Anitha, Komali Lalasa, Kothapalli Hasritha, Dasari Reddy, Boggula Aarthi Mohan babu university Erstwhile Sree Vidyanikethan Engineering College Department of CSSE Tirupati India Annamacharya Institute of Technology & Sciences Deparmentt of CSE Tirupati India

ISBN: (纸本)9798350354539

Stroke, the second leading cause of mortality globally, demands timely and accurate prediction for effective intervention. This study explores advanced machine learning techniques to enhance stroke prediction models. Initially employing Decision Tree (DT), Random Forest (RF), and Support Vector machine (SVM) classifiers, later the research introduced eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting machine (LGBM) algorithms to bolster predictive capabilities. Evaluation metrics including accuracy, sensitivity, error rates, and log loss were utilized to assess model performance. Findings highlight the efficacy of machine learning algorithms, with XGBoost achieving remarkable accuracy of 98%. Complementarily, LGBM significantly contributed to overall accuracy. These results emphasize the pivotal role of advanced machine learning techniques in improving stroke prediction. Utilizing cutting-edge predictive models driven by advanced algorithms, this study advocates for their seamless integration into clinical practice. By doing so, it aims to expedite precise diagnoses, thereby enhancing patient care and pushing the boundaries of stroke detection forward. © 2024 IEEE.

关键词： Decision trees

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Enhanced and Effective Model for Heart Disease Prediction Using Hybridization of Random Forest and AdaBoost (HRFAB) 2

Enhanced and Effective Model for Heart Disease Prediction Us...

引用

2nd international conference on machine learning and Autonomous systems, ICMLAS 2025

作者： Shinde, Priti V. Sanghavi, Mahesh R. MET's IOE BKC Department of Computer Engineering Maharashtra Nashik India SNJB's KBJCOE Department Computer Engineering Maharashtra Chandwad India

ISBN: (纸本)9798331505745

Coronary artery disease or heart disease is the leading cause of death worldwide. In the field of data analysis and clinical decision making, one of the challenging tasks is the prediction of heart disease. In today's era, large amount of data is getting generated and machine learning is able to analyse, predict this data. We have studied ML techniques which are mostly used in medical field. In this view, various research techniques have been conducted by researchers using ML classifiers. In this paper, we have implemented a good ensemble & bagging and boosting techniques i.e. Random Forest and AdaBoost respectively to predict the heart disease. To generate prediction model, data preprocessing techniques are applied on well-known dataset extended Z Alizadeh Sani. We achieved 95% prediction accuracy for hybrid of Random Forest and AdaBoost model (HRFAB). It gave us better accuracy for heart disease forecast. © 2025 IEEE.

关键词： bagging-boosting Ensemble models Heart disease machine learning

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Improving Smart Home Safety with Face Recognition using machine learning 1

Improving Smart Home Safety with Face Recognition using Mach...

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1st IEEE international conference on advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Vinoth Kumar, M. Ajina, A. Deepak, D.J. Rv Institute of Technology and Management Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798350348057

In this paper, machine learning (ML) Algorithms are used to implement the proposed approach to identify social distance, face masks, drowsiness detection, age-gender detection, and emotion detection. While dealing with social distancing initially, we need to detect humans' faces, which are available by using COCO (Common Objects in Context) datasets, and later on, polygon-shaped ROI (Rectangular-region of Interest) is warped with a rectangle, which helps to find the distance from each centroid (person). Similarly, we predict the facemask, age-gender, emotion, and drowsiness altogether using frontal-face detection and eye-detection via haarcascade dataset loaded into Convolutional Neural Network (CNN) to train and test the models on colour mapped images. In the proposed model, we are using machine-learning techniques such as linear discriminant Analysis (LDA), Independent Component Analysis (ICA), Principal Component Analysis (PCA) for age-gender detection and emotion detection, K-Nearest Neighbours (KNN) for Social Distancing, and Support Vector machine (SVM) for facemask detection and drowsiness detection. The accuracy of proposed system depends on frame (i.e., 88.2%, 89.7%, 95.1% and 98.3% in 0~0.2s, 0.2~0.6s, 0.6~1s, >1s time windows respectively). The accuracy even depends upon the distance away from the camera (i.e., 60.4%, 73.9%, 89.3%, 95.2%, and 62.2% in >15, 15~10, 10~6, 6~0.5, © 2023 IEEE.

关键词： Face recognition

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