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检索条件"任意字段=1st International Conference on Machine Learning Algorithms, ICMLA 2024"

共 3723 条记录，以下是81-90 订阅

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Spam Detection in YouTube Comments: A machine learning Approach 1

Spam Detection in YouTube Comments: A Machine Learning Appro...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yadav, Shristy Jena, Junali Jasmine Prakash Singh, Jay Gourisaria, Mahendra Kumar Jain, Sonal Kumar, Vivek Kiit Deemed University School of Computer Engineering Bhubaneswar India Manipal University Jaipur Department of Computer Science & Engineering Jaipur India Kiit Deemed to be University School of Economics and Commerce Bhubaneshwar India Manipal University Jaipur Dept of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798331529246

Due to the broad spectrum of content and evolving tactics of spammers, identifying spam in YouTube comments has become a challenging task. If it goes undetected it may hamper the security and integrity of the application. Automated spam detection techniques have found profound application in this field. This study offers an extensive approach for enhancing spam classification through the integration of cutting-edge machine learning algorithms. A multi-pronged paradigm is investigated, including SMOTE to address class imbalance, and TF-IDF vectorization for feature extraction and used for spam classification. A wide range of models is scrutinized in the study such as Gradient Boosting, AdaBoost, XGBoost, etc. The best-performing models, based on cross-validation scores, were the Support Vector Classifier (SVC) with an average accuracy of 96.42%, followed by Gradient Boosting, Voting Classifier and LightGBM, achieving an average accuracy of 94.63%, followed by Logistic Regression and Random Forest achieving an accuracy of 94.37%. © 2025 IEEE.

关键词： Decision trees

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Comparative Analysis of Infant Cry Classification using machine learning and Deep learning Techniques 1

Comparative Analysis of Infant Cry Classification using Mach...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ramulu, Kothuri Parashu Jogannagari, Pranathi Reddy Indur Institute of Engineeing & Technology Department of Computer Science and Engineering Siddipet India University of Alabama at Birmingham Department of Computer Science Birmingham United States

ISBN: (纸本)9798331529246

Infant Cry Classification is the process of analyzing acoustic signals from an infant's cry for extracting relevant features to determine the cause of crying, and assess the infant's health condition. The process is typically divided into four main stages: pre-processing, feature extraction, feature fusion, and classification. In the feature extraction stage, spectrograms along with frequency transforms such as the Wavelet Transform, Cosine Transform, and Fourier Transforms are employed to capture important characteristics of the cry. The feature fusion stage is critical as it integrates information from various scales and contexts obtained during feature extraction, allowing for a more comprehensive representation of the cry's features. In current times, both machine learning (ML) and Deep learning (DL) techniques are employed for classification. The ML techniques predominantly utilized in this process are: Multi-Class Support Vector machines (SVM), Hyperparameter-Optimized SVM, Kernel-based SVM, Ensemble Boosting methods, Random Forest classifiers, K-Nearest Neighbors (K-NN), and Bayesian Optimization. In the case of DL architectures, models such as style and Latent Guided Adversarial Networks (SLGAN), Multi-Layer Perceptron (MLP), Convolutional Neural Network - Sparse Convolution Net (CNN-SCNet), 1D CNN, and Dual-CNN are frequently employed to ensure high accuracy. The performance of these classification algorithms is evaluated in terms of the following metrics: sensitivity, specificity, accuracy, precision, and F-measure. © 2025 IEEE.

关键词： Cosine transforms

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Hyperbilirubinemia or jaundice detection using CNN in machine learning 1st

Hyperbilirubinemia or jaundice detection using CNN in machin...

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Gupta, Priyanka Upasna Rai, Vishal Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad India

ISBN: (纸本)9781032874036

Jaundice, also called congenital hyperbilirubinemia, is a common condition caused by alterations in an infant’s red blood cell metabolism within the initial week of life. There are many symptoms associated with this multifactor disorder, and we have systems available both invasive and non-invasive. A practical and precise method for monitoring neonatal jaundice using the CNN algorithm has been described in this paper. The method’s accuracy and viability are predicated on the different detection systems examined. The methodologies and detection techniques discussed offer practical insights. To find a linear regression function, the SVR algorithm was used to find linear relationships within an elevated feature space. An approaching nonlinear function was used to map the input data to a scope. The color card is an additional method of detection that allows for the rapid identification of disease fatalities based on skin color and eyes. The old invasive methods of diagnosing jaundice have several drawbacks, including the inability to monitor simultaneously, the length of time required, the trauma to the patient, and the thousands of dollars involved. To reduce the discomfort associated with these techniques, a non-intrusive approach has been devised to identify jaundice in the early months of life, utilizing the image processing technologies and CNN algorithm. © 2025 the Author(s).

关键词： Neonatal monitoring

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BugWatch: A Comprehensive Approach to Classification and Redundancy Detection in Software Testing 1

BugWatch: A Comprehensive Approach to Classification and Red...

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1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Shinde, Sandeep Chrungoo, Mahi Mane, Aditya N. Maske, Pavan R. Kulkarni, Sakshi Chat, Sachin Vishwakarma Institute of Technology Dept of Computer Engineering Pune India Dassault Medidata Software Testing California United States

ISBN: (纸本)9798350355611

Countless software programs are created daily, leading to a parallel increase in software bugs. Each institution typically maintains a dedicated quality assurance team tasked with ensuring the integrity of their developed products. However, the sheer volume of bug reports received on a daily basis presents a formidable challenge for these teams to manually address each issue. The primary aim of this research is to streamline the process of bug classification and mitigate redundancy within reported bug data. To achieve this goal, the authors explored the utilization of dif erent classification algorithms, specifically SGD, based on the issue summary of each bug. The approach involves categorizing bugs into two main groups: major and minor. Additionally, a redundancy removal process was implemented to preprocess the data, flagging sentences that exhibit significant similarity above a predetermined threshold as duplicates. The results of the study demonstrate promising outcomes, with classification accuracies of 75.93% (Feature Classification) and 90.02% (Redundancy detection) achieved using SGD classifier, which gave us the best accuracy. © 2025 IEEE.

关键词： Bug Report Classification Duplicate detection machine learning Natural Language Processing SGD classifier Software Testing

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Precision Bearing Fault Diagnosis Using Advanced machine learning Models and Comprehensive Vibration Signal Feature Extraction 1st

Precision Bearing Fault Diagnosis Using Advanced Machine Lea...

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1st international conference on Electrical Systems and Smart Technologies, ICESst 2024

作者： Aychour, Soufiane Ouachtouk, Ilias Mouchtachi, Ahmed - Hassan II University of Casablanca 150 Nile Blvd Casablanca20670 Morocco Moulay Ismail University of Meknes B.P 15290 Marjane II Meknes Morocco

ISBN: (纸本)9783031867040

Bearing fault diagnosis is crucial for machine reliability and cost reduction. This project applies machine learning to classify bearing faults via vibration signal analysis. Data, prepared by Dr. Eric Bechhoefer for machinery Failure Prevention Technology (MFPT), includes healthy bearings and those with inner and outer race faults under various loads. Key features like kurtosis, RMS frequency, and standard deviation were extracted, normalized, and ranked using a chi-squared test. machine learning models, including decision trees (DT), Support Vector machine (SVMs), and neural networks, were trained to classify bearing conditions. Performance was evaluated by accuracy, with discriminant analysis (DA) and ensemble models (EN) achieving high accuracy. This study demonstrates the effectiveness of combining vibration feature extraction with machine learning for precise bearing fault diagnosis, advancing predictive maintenance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Predictive maintenance

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

PhD Symposium, Posters, Demos, and A Web for more inclusive,...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

ISBN: (纸本)9789819614820

The proceedings contain 36 papers. The special focus in this conference is on Web Information Systems Engineering. The topics include: Effective Transparent Monitoring of Personal Data;iCNN-LstM: An Incremental CNN-LstM Based Ransomware Detection System;a Mini Review on Purchaser Security in the Metaverse: Challenges and Solutions;low-Resource Dataset Synthetic Generation for Hate Speech Detection;Web Open Data to SDG Indicators: Towards an LLM-Augmented Knowledge Graph Solution;leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval;time Distance Aware for Multi-component Graph Collaborative Filtering;scientific Documents Recommendation Based on Graph Convolutional Network;semantic Communication of Images Using Image Generation and Image Captioning Models;leveraging Optimization Techniques for Effective Arabic Query Expansion;DURLLCON: Deep Reinforcement learning for URLLC Optimization in Multi-edge Networks;FMM-RNS: A Fast HMM Map Matching Method Based on Road Network Simplification;Context-Aware Selection of machine learning as a Service (MLaaS) in IoT Environments;GraphTFD: A Fraud Detection System Based on Graph Transformer;MLGE-AC-UFD: Multi-level Graph Embedding and Approximate Computation for Unsupervised Fraud Detection;SeCORE: Quantitative Security Assurance and Evaluation Platform;developing Geospatial Web Applications Using Question Answering Engines, Knowledge Graphs and Linked Data Tools;A Visual Query Builder for DBpedia;FL-PPELA: Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning;ORCPM: An Online Regional Core Pattern Mining System;MTRM: A Web-Miner Multi-Threshold Mining Co-location Patterns to Mitigate Redundancy;constrained Path Optimization on Time-Dependent Road Networks;prompt strategies for Sarcastic Meme Detection: A Comparative Analysis;Deepfake Detection in Cancer Medical Imaging Using CNN Architectures;strengthening Cybersecurity: The Influence o

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Diagnosing Autism Spectrum Disorder in Children Using Various machine learning Methods: A Review 1st

Diagnosing Autism Spectrum Disorder in Children Using Variou...

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1st international conference on machine learning algorithms, icmla 2024

作者： Khurana, Robin Singh, Satyaveer India

ISBN: (纸本)9783031758607

Autism Spectrum Disorder (ASD) is a relatively rare condition between children;evaluations of the CDC's Autism and Developmental Disabilities Monitoring (ADDM) System suggest that approximately one in 36 kids is identified with ASD. Its symptoms are particularly prominent and observable in children aged 2–3 years. This review paper undertakes a broad analysis of ASD detection methods, harnessing the strength of both machine learning (ML) and neural network techniques. The review encompasses a systematic examination with a visual flowchart and a rigorous evaluation of various methodologies. These include traditional machine learning (ML) methods like Support Vector machines (SVM) and K-nearest neighbors (KNN), as well as advanced neural network approaches like Artificial Neural Networks (ANN). Furthermore, the analysis presents a comparative table that assesses the performance of these methods in terms of their accuracy on diverse datasets. This multifaceted approach provides a holistic understanding and offers valuable insights for scholars and physicians in the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Research on machine learning Assisted Hidden Danger Identification and Defect Analysis in Transmission Line Monitoring 1

Research on Machine Learning Assisted Hidden Danger Identifi...

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1st international conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Sun, Rongrong Wang, Chengsi Shenzhen Power Supply Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9781643685779

A machine learning-based method for detecting hazards and recognizing defects in transmission lines is proposed in this study. This method preprocesses the image data of transmission lines and employs an improved convolutional neural network model called attenalexnet to achieve precise localization of transmission towers, insulators, and conductors, as well as intelligent identification of defects such as icing, foreign objects, and damages. Large-scale testing on real transmission line image datasets demonstrates that this method reliably detects transmission line hazards with an accuracy of 96.7% and a detection speed of 203ms, providing strong support for intelligent operation and maintenance of transmission lines. Additionally, experimental results confirm the method's robustness, effectively coping with adverse weather and complex environmental factors. The research findings are expected to further promote the intelligent development of transmission line safety monitoring systems. © 2025 The Authors.

关键词： Convolutional neural networks

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Data-Driven Insights into student Performance: Benchmarking machine learning Models for Grade Prediction using Regression and Classification Approaches 1

Data-Driven Insights into Student Performance: Benchmarking ...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Christina, Sowmya Sowjanya, S. Lakshmhyma, Ch. Prathiba, L. Basha, Md Shaik Amzad Department of Professional Studies Bengaluru India Rajeev Gandhi Memorial College of Engineering Department of Management Studies Nandyal India Department of Business Administration Vijayawada India Kurnool India Gitam School of Business Hyderabad India

ISBN: (纸本)9798331529246

This research explores the effectiveness of 17 machine learning models in predicting student performance across Mathematics and Portuguese datasets. The primary goal of this study was to evaluate and compare regression and classification models to identify the most accurate predictors of student grades. A range of algorithms was tested, including linear models (Linear Regression, Elastic Net, Ridge, Lasso), tree-based models (Random Forest, Gradient Boosting, CatBoost, LightGBM), and advanced techniques (Neural Networks, SVM, XGBoost, Naive Bayes, SVR). The methodology involved data preprocessing, feature engineering, and splitting data into training and test sets. Base models were implemented, followed by hyperparameter tuning to optimize performance. Metrics like RMSE, MAE, MSE, R2 (for regression), and accuracy, precision, recall, F1 score (for classification) were used to assess performance. The study found that Gradient Boosting and Elastic Net consistently outperformed other models in regression tasks, achieving the highest R2 scores. For classification, Logistic Regression proved to be the most accurate, followed by Naive Bayes. These findings provide valuable insights for model selection in educational performance prediction, establishing Gradient Boosting and Logistic Regression as benchmark models. © 2025 IEEE.

关键词： Adversarial machine learning

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Evaluation of the Effective Online learning for College students using COOT Optimization Algorithm 1

Evaluation of the Effective Online Learning for College Stud...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Deng, Haibei Wang, Hefei Nanning University College of Art and Design Nanning China

ISBN: (纸本)9798331529246

The evaluation of the effectiveness of online learning for college students is a complex challenge that requires efficient methods to enhance student engagement and performance. However, one of the difficulties faced by educational platforms is the lack of transparency in the optimization processes for student performance prediction and the redundancy in features when dealing with large datasets. This research proposes a Fusing Cauchy Mutation and Operation - Based learning with COOT (FCMOBL-COOT) optimization algorithm is utilized for evaluating the learning outcomes of college students. The COOT optimization algorithm used for selecting most relevant features for both temporal and contextual analysis thereby improving the overall prediction accuracy for online learning success. Initially, student data obtained from a data is pre-processed using Min-Max Normalization technology, which helps to scale the data within a range. Feature selection through the COOT algorithm effectively addresses the high dimensionality of student learning data by identifying key features that contribute to the evaluation of student assessment performance. Feedback Mining-based Online Coarse (FMOC) address the challenges based on the feedback of the previous comment to enhance the effectiveness of the system. The proposed FCMOBL-COOT method achieves an accuracy of 98.32% when compared with existing methods namely stacked generalization for failure prediction to demonstrating its evaluation of the effectiveness of online learning for college students. © 2025 IEEE.

关键词： Optimization algorithms

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