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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Third international conference on Communications, Information System, and data Science, CISDS 2024

Third International Conference on Communications, Informatio...

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3rd international conference on Communications, Information System, and data Science, CISDS 2024

ISBN: (纸本)9781510688315

The proceedings contain 18 papers. The topics discussed include: client dependability evaluation in federated learning framework;FPGA-based hardware optimization and implementation of YOLOv4-tiny;MTOClus: multi-type objects clustering in heterogeneous information networks;FPFS: federated privacy-preserving feature selection with privacy techniques for vertical federated learning;renal tumor classification and detection based on artificial intelligence;stat-net: spatio-temporal aggregation transformer network for skeleton-based few-shot action recognition;dynamic feedback-based vulnerability mining method for highly closed terminal protocols;a RevVIT-based discrimination model for concrete crack images;and acquisition of adaptive knowledge in case-based reasoning for the online set-point control of industrial process.

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Musical Instrument recognition Using machine learning Approaches 3

Musical Instrument Recognition Using Machine Learning Approa...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chethan, B.K. Bharath, K.N. Puttegowda, Kiran Manjula, A.V. Sunil Kumar, D.S. Anitha, K. Yathiraj, G.R. Mysuru India Dayananda Sagar Academy of Technology and Management Department of Ece Bengaluru India Vidyavardhaka College of Engineering Department of Ece Mysuru India The National Institute of Engineering Department of Ece Mysuru India East Point College of Higher Education Department of Computer Applications Bengaluru India Gsss Institute of Engineering Technology for Women Department of Ece Mysuru India Coorg Institute of Technology Department of Cse Ponnampet India

ISBN: (纸本)9798331508456

This work focuses on musical instrument recognition in miscellaneous audio sources, a fundamental issue of the machine auditory system. We contrast traditional machine learning techniques against deep learning strategies in a clinical dataset solved under a supervised multi-label, multi-output classification paradigm. To enhance the performance of the system, we adjust the analysis parameters for window size, hop size, and label threshold levels. Furthermore, we explore the benefits of auxiliary feature sets as alternative data representations, specifically: Auditory features are based on two categories: (1) four-band spectral decomposition of stereo sound data, left, right, mid, and side channels and (2) the harmonic and residual model of the audio signal. Two methods for fusing the models trained on these representations are suggested and we use the best of these methods to increase recognition accuracy. These enhancements lead to a vast improvement;with a 14.25 % increase M-F score for the macro average and a 24.17 % improvement in the exact match ratio overall compared to the baseline. © 2025 IEEE.

关键词： Deep learning

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An Analysis of Face based eCrime Identification using First Information Report 3

An Analysis of Face based eCrime Identification using First ...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Vanarasan, S. Dharani, S. Dharshini, R. Harani, S. Selvarathi, C. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Sri Sairam College of Engineering Department of Artificial Intelligence and Machine Learning Banglore562 106 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639 113 India

ISBN: (纸本)9798331509675

Face based e-crime identification initiative combines cutting-edge technologies to detect criminals in video footage, allowing for quick and effective online FIR (First Information Report) reporting. Furthermore, it uses blockchain technology to securely store and manage sensitive data, as well as to ensure the integrity of evidence and to facilitate robust facial recognition using the Inception V3 algorithm. The proliferation of electronic crimes (eCrimes) in today's digital age poses a huge challenge to law enforcement organizations worldwide. In the face of continuously shifting criminal strategies, traditional means of identifying and reporting offenders frequently fall short. The fundamental functionality of the technology is around identifying offenders using video footage. It makes use of the potent Inception V3 algorithm, a cutting-edge deep learning model noted for its efficient image recognition skills. This algorithm analyses video frames, detects and matches facial traits, and identifies suspects. This not only improves the accuracy of criminal identification but also speeds up the investigation. In addition to identifying criminals, the streamlines the FIR reporting process. Victims and witnesses can now conveniently file FIRs online, eliminating the need for physical visits to police stations. The integration of digital reporting platforms enhances the efficiency of law enforcement agencies, making the process more accessible to the public. Blockchain technology is used to provide data security and immutability. A blockchain-based database stores all data relevant to criminal identification and FIR reporting. The combination of improved facial recognition, online reporting, and secure blockchain data management promises to make identifying and reporting criminals more efficient and secure. © 2025 IEEE.

关键词： Deep learning

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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...

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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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Attack Detection System for Cloud-Based Wireless Sensor Networks Using the Proposed Fast Hyper Deep learning Model 3rd

Attack Detection System for Cloud-Based Wireless Sensor Netw...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Saleh, Hadeel M. Marouane, Hend Fakhfakh, Ahmed NTS’COM Laboratory Safax University Sfax Tunisia Continuing Education Center University of Anbar Ramadi Iraq University of Sfax Sfax Tunisia

ISBN: (纸本)9789819624676

Cyber security requires detecting and stopping wireless sensor network attacks. We introduce a fast super deep learning model for cloud-based WSN attack detection in this research. Our offline approach divides data into training and testing sets, preprocesses it, then reduces features using Singular Value Decomposition (SVD) and Principal Component Analysis. We then extract features using our ultrafast deep neural network and put them into a hyper deep-machine learning model. We evaluate our strategy using data mining metrics and preserve the model for further use. We also compare our model to other data mining and machine learning methods using numerous datasets to ensure accuracy and efficiency. We focus on a few key topics. First, we address generalization, complexity, and execution computation time. Second, many datasets increase model generalization. Third, our fast and accurate deep learning approach improves machine learning results. Our Oracle and Java cloud infrastructure lets us design an injection tool to produce and transmit packets to the intrusion detection model. Model predictions stored on an Oracle database offer complete attack information. Convolutional Neural Networks (CNN) and machine learning algorithms like Complement Naive Bayes (CNB) and Stochastic Gradient Descent are among our methods. We also study combining PCA and SVD with machine learning to improve results. Finally, deep feature selection improves the previously reported methods. Overall, our proposed technique for cloud-based WSN attack detection has high generalization, efficiency, and accuracy while addressing major cyber security challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Comprehensive Analysis of Feature Extraction Methods for EEG-based Emotion Classification 3

Comprehensive Analysis of Feature Extraction Methods for EEG...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Sivaranjani, S. Prithiga, V. Ragavi, S. Reena, S. Sanchana Sri, S. M. Kumarasamy College of Engineering Department of Ece Karur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Emotion recognition from EEG signals is widely applicable and a rapidly emerging field in human-computer interaction, mental health analysis, and affective computing. The main problem of addressing a novel enhanced method for EEG-based emotion classification using the Discrete Wavelet Transform (DWT) to extract its features into delta, theta, alpha, beta, and gamma frequency components, and essential time-frequency features could be extracted that can improve the accuracy of the classification. The features are fed into the machine learning classifiers, namely CNN, RNN, SVM, LDA, and Decision Trees, in order to obtain a classification accuracy of 96%. The experimental results indicate a progressive increase in accuracy and consistent reduction in loss over 20 epochs, showing the effectiveness of DWT-based feature extraction. The study evaluates model robustness by using key performance metrics like accuracy, sensitivity, and specificity. It shall improve in future, including optimized wavelet parameters, refinement in architectures of the neural network, real-time processing improvement, and multimodal data fusion to be incorporated for enhancement of system performance. © 2025 IEEE.

关键词： data fusion

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Enhancing Cloud Security with machine learning: Tackling data Breaches and Insider Threats 3

Enhancing Cloud Security with Machine Learning: Tackling Dat...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Nair, Sreejith Sreekandan Lakshmikanthan, Govindarajan Parthasarathy, J. Dinesh, P.S. Shanmugakani, Kavitha Jegajothi, B. Anna University Chennai India Jayalakshmi Institute of Technology Thoppur Dharmapuri India Sathyamangalam India Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College avadi India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331509675

Cloud computing remains susceptible to internal threats and data compromises, notwithstanding its foundational role in contemporary business operations within this age of automation. To mitigate this concern, a machine learning framework is suggested for the preemption of prospective threats. Most, breaches of organizational internal systems are concerns about user non-compliant behaviors aimed at messing up activity logs, which can be combatted using supervised models and change detection. There seems to be a class of threats that go undetected that the authors would refer to as behaviour patterns that do not follow standard operating behaviour for business. An interface of RF and LSTMs would be able to establish common patterns and knit rule-based threats to create business insights and acceptable threats. User and System exhaustion of RF was used to minimise covert system identifiers of users to examine access pattern data streams, which were unlimited in dimensional centers. Evaluation of detection of anomalous content, such as R- scoring, classification of log events as generic clustering, etc. It could prevail on the challenges of log records analysis and the checkpoints of Structural Semantic Communication principles. It can free up a range of security checkpoints by glaring at user access limits and ensuring multi-channel compliance. This approach would significantly strengthen data security on the Cloud by targeting high-risk actions across oriented sections of breaching data storage centers. © 2025 IEEE.

关键词： Cloud security

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A Study on machine learning-Based Methods for Early Gastric Cancer Screening 3rd

A Study on Machine Learning-Based Methods for Early Gastric ...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Xu, Jialun Wu, Xiayang Xu, Zhenyuan School of Statistics Nanjing Audit University Nanjing China School of Arts Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9789819624676

machine learning (ML), deep learning (DL), and artificial intelligence (AI) are increasingly employed in medical assistance and statistical analysis. These technologies enhance medical diagnostics by enabling precise image segmentation, feature extraction, and lesion localization. Such capabilities significantly improve the accuracy of auxiliary diagnoses and facilitate more effective treatment planning. In the field of medical statistics, machine learning (ML), deep learning (DL), and artificial intelligence (AI) can aid in large-scale analysis and mining of medical data to identify potential disease patterns and predict patient risks. For instance, doctors use ML and AI to integrate and analyze patients’ genomic and imaging data for personalized treatment plans. However, extensive research has not been carried out concerning physical examination data in this field. However, the early stages of numerous diseases are not objectively reflected in the current physical examination evaluation system. For instance, with gastric cancer, the significance of routine physical examination detection indexes is limited, making it challenging to diagnose precisely during consultations. This factor significantly complicates the early detection of gastric cancer. In our study, we introduce a method based on PSO and Stacking learning (PSOS) for predicting early-stage gastric cancer. This method leverages basic personal bioinformation and standard physical examination data to construct a predictive model. Designed to assist in the pre-gastroscopy clinical screening of gastric cancer, this model aims to identify potential cases before the need for invasive procedures such as gastroscopy and tissue biopsies. We trained the model using data from 22,304 actual patients and achieved a high success rate in early detection, demonstrating the model’s robust capability to identify gastric cancer at early stages. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2

关键词： Diseases

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Research on the Relationship Between data Visualization and Visualization data mining: Using the Iris dataset as an Example 3rd

Research on the Relationship Between Data Visualization and ...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Chen, Hongjun Liu, Yunxiang He, Xuansen Wang, Junhu Information Technology and Engineering School Guangzhou College of Commerce Guangzhou511363 China

ISBN: (纸本)9789819616978

data visualization and visual data mining are two crucial directions in data analysis domain, and both of which play pivotal roles in data exploring, patterns identifying, hypotheses validating, and interactive discovery. In this essay, we take the classic dataset Iris as an example to study the interconnectedness of the two. Firstly, the target of data mining is clearly analyzed, and then the Iris data set is visually explored for data dispersion distribution, data distribution frequency, data estimation probability density, and data key features and potential patterns. Secondly, research is focused on the data visualization of dispersion graph and KNN visual data mining, the interaction between Violin graph and decision tree, and the discovery of data mining algorithm models. Additionally, hypothesis validation for unsupervised clustering numbers in the Iris dataset is also explored. Through this essay, we aim to offer practical applications and insights into research methods pertinent to data visualization and visual data mining. We also hope to provide some application and reference for the research of data visualization and visualization mining. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Visual analytics

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Hand Mudras and Indian Classical Dances: Techniques, dataset and Future Direction 3

Hand Mudras and Indian Classical Dances: Techniques, Dataset...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Sarmah, Chayanika Sarma, Parismita Gauhati University Department of Information Technology Assam Guwahati India

ISBN: (纸本)9798331523893

Concept, purpose and feelings of a dance is expressed through performer's gesture. Proper interpretation of these signals requires skills acquired by different methods, artificial intelligence or human experts. In recent times, Dance Mudra recognition is a very prominent and a multidisciplinary research topic. This paper discusses extensive research work carried out on various Indian classical dances by different researchers and automated recognition system developed by them. Author's basic focus here is to study and discover an efficient intelligent method which will be capable of recognizing different hand mudras of Sattriya dance. A few efficient machine and deep learning methods are selected for working on our sattriya dance mudra dataset. The authors of this paper have used customized CNN model, ResNet50, ViT and YOLOV8 for training the data and were able to achieve very good accuracy. Till now no work is done using the above mentioned models for recognition of sattriya hand mudras. © 2025 IEEE.

关键词： deep learning hand gestures machine learning ResNet 50 sattriya dance mudra ViT YOLOV8

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