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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2812 条记录，以下是91-100 订阅

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Hybrid Human Interaction recognition Using Feature Extraction and Deep Neural Network

Hybrid Human Interaction Recognition Using Feature Extractio...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Human recognition have become interested in human interactions in images. Interaction recognition is a hot topic in many research fields. In this study, we propose a system for recognizing activities using a Deep Neural Network (DNN). First, we improve the clarity of video frames by transforming them to HSI color. We also use Mean filters to remove any noise. We use Multiple Object Tracking (MOT) and Statistical Region Merging techniques to extract silhouettes. In the feature extraction process, we use Local Binary patterns (LBP), SIFT, and Local Phase Quantization (LPQ). We apply the Particle Swarm Optimization (PSO) algorithm to identify the most important features that best represent the data structure. We then input these selected features into the DNN for classification into different human interactions. We tested this approach on the SBU Interaction dataset, and achieved a recognition rate of $\mathbf{9 2 \%}$.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Biological system modeling Surveillance Artificial neural networks Feature extraction pattern recognition Classification algorithms

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Pixel-level Reconstruction Noisy Handwritten Characters Classification Based on Deep Progressively learning Model

Pixel-level Reconstruction Noisy Handwritten Characters Clas...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Murali G. Azhagiri M. Manikanda Devarajan N. thiruppathi M. Vinod Kumar D. Manojkumar V. Biomedical Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Computer Science & Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Electronics & Communication Engineering Malla Reddy Engineering College Medchal Telangana India Electronics & Communication Engineering Vivekanandha College of Engineering for Women Namakkal Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Handwritten character recognition (HCR) is still regarded as a difficult learning problem in pattern recognition, even after being studied in-depth for a few decades. Research on script independent models is also scarce. this can be linked to several things, especially character structure similarities, variances in handwriting styles, noisy datasets, script diversity, the conventional research's emphasis on manual feature extraction techniques, and the lack of publicly available datasets and code repositories to replicate the findings. However, deep learning offers from top to bottom learning together with has achieved great success in various pattern recognition domains, such as hand gesture recognition (HCR). Deep learning methods, on the other hand, are computationally costly, require a lot of data to train, and are limited to scripts. We have developed a novel generic Deep Progressively learning Model architecture for script independent handwritten character recognition, known as HCR-DPLM, to overcome the limitations. the foundation of HCR-DPLM is a unique transfer learning strategy for HCR that makes use of a pre-trained network's feature extraction layers in part. HCR DPLM offers better performance and better generalizations, faster and computationally efficient training, and the ability to work with small datasets because of Transferring Knowledge and Imagery augmentation. In this paper, we use a pixel level classifier to extract the character pixels and eliminate noise from handwritten character images.

关键词： Hands Handwriting recognition Text recognition Computational modeling Optical character recognition Noise Feature extraction Numerical models Noise measurement Character recognition

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Boosting Software Quality: A machine learning Technique for Predicting Defects

Boosting Software Quality: A Machine Learning Technique for ...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Akilan E. Daniel Alwin Infant P Jainish G R Computer science & Engineering Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Computer Science and Engineering Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. this work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. the objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. these indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. these algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. this approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges.

关键词： Computational modeling Software algorithms Software quality Predictive models Prediction algorithms data models Software reliability Decision trees Software measurement Embedded software

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Alzheimer's Disease Detection Using Various machine learning Algorithms

Alzheimer's Disease Detection Using Various Machine Learning...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Parthvi Patel Krishi thakkar Prince Valani Krishna Patel Parth Goel Vaishali Vadhavana Computer Science & Engineering Department Devang Patel Institude of Advance Technology and Research Charotar University of Science and Technology (CHARUSAT) Changa Anand India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Alzheimer's disease (AD) is a brain-degenerative mental illness defined by growing forgetfulness. It also includes the loss of cognitive and behavioural characteristics. this research aims at developing a machine learning based tool that can work on early recognition of AD. this paper intends to develop an accurate, non-invasive machine learning model in detecting AD. Classification for AD has been done using behavioural, clinical, and lifestyle characteristics. the idea is to get such patterns from the data in a model, which will help in early detection of AD. For the detection of the AD dataset has been taken from Kaggle. this paper delivers an in-depth comparison of various algorithms, including Support Vector machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Artificial Neural Network (ANN), with the goal of improving the detection of Alzheimer's disease (AD). the algorithms were evaluated using various performance metrics, such as F1 score, Precision, Accuracy, and Recall. the results indicate that the Random Forest algorithm attained the highest Accuracy, reaching 95%.

关键词： Support vector machines Measurement Logistic regression machine learning algorithms Accuracy Artificial neural networks Alzheimer's disease Informatics Random forests Mobile computing

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

6th International Conference on Computational Intelligence i...

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

ISBN: (纸本)9783031813351

the proceedings contain 82 papers. the special focus in this conference is on Computational Intelligence in Communications and Business Analytics. the topics include: Early Classification of Alzheimer’s Disease Using Deep learning Technique;spoken Metro Station Name Identification: A Deep learning-Based Approach;EDR-DT: A Novel Energy Function-Based Enhanced Decision Tree Model for Classification;harnessing Smart Farming to Combat Climate-Induced Agricultural Challenges Using machine learning;classification of Tomato Maturity Levels: An Efficient Approach with Statistical Features;ioT Based Multifunctional Agriculture Monitoring and Smart Irrigation System;COVID-CT-H-UNet: A Novel COVID-19 CT Segmentation Network Based on Attention Mechanism and Bi-Category Hybrid Loss;a Review on the Efficacy of Different data Augmentation Techniques for Deep learning;MLAEDenseNet: Multi-layer Attention Enhanced DenseNet for Efficient Video Action recognition;Word Sense Disambiguation for BODO Language Using Simplified LESK;smali Code-Based Fake Application Detection;from Haze and Smoke to Clarity: An Integration of Deep learning and Atmospheric Model;Integrating BiLSTM-BiGRU with Autoencoders for Enhanced Feature Representation and Deep Q-Networks for Clinical Event Classification in Medical Records;Agile Development Using a Hybrid Approach for Cost Estimation of IT Projects;lung Cancer Diagnosis Using Image Enhancement and machine learning Methods;An ML-Based Hybrid Model for IIoT Attack Classification in Industry 4.0 Ecosystem;Design of an Explainable AI Model with Q Convolutional Neural Networks for Patient Health Reporting;comparative Analysis of Deep learning Techniques for Prediction of Protein Structure;enhancing Visual Question Answering with Beam Search in Transformer Models;Analyzing the Effect of Coffee Consumption on Visual Pathway Using Visual Evoked Potential (VEP) Signals and machine learning Algorithms;analysis of Occupational Stress Effects and Coping Among Prison

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

6th International Conference on Computational Intelligence i...

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

ISBN: (纸本)9783031813382

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

6th International Conference on Computational Intelligence i...

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

ISBN: (纸本)9783031813412

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Deep learning for Solar Panel Fault Detection: Integrating GAN and ResNet Models

Deep Learning for Solar Panel Fault Detection: Integrating G...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： V. Manimegalai B. Oviya S.Umesh Kargvel P. L. Vilashini V. Mohanapriya A. Elakya Department of Electrical and Electronics Engineering Sri Krishna College of Technology Department of Electrical and Electronics Engineering Bannari Amman Institute of Technology

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

the rapid expansion of solar energy implementation has highlighted critical challenges related to temperature variations and environmental factors impacting photovoltaic (PV) systems. this paper addresses the need for continuous monitoring to ensure the efficiency and reliability of solar panels. PV panels frequently encounter operational issues including surface contamination, irregular shading patterns, and thermal irregularities, which can result in hotspot formation, leading to substantial power losses and accelerated panel deterioration. the machine learning algorithms play a crucial role in detecting defects in solar panels, such as cracks, shading, and hotspots, by analyzing both thermal and visible light images. the implementation of deep learning architectures, specifically ResNet for feature extraction and Generative Adversarial Networks (GANs) for pattern recognition, enables precise real-time fault detection. the demonstrated success of this integrated deep learning approach establishes a new benchmark for PV system monitoring, enhancing both the reliability and longevity of solar installations while minimizing operational downtime.

关键词： Deep learning Adaptation models Fault detection Generative adversarial networks Feature extraction Real-time systems Maintenance thermal analysis Solar panels Monitoring

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Disabled Rehabilitation Monitoring and Patients Healthcare recognition using machine learning

Disabled Rehabilitation Monitoring and Patients Healthcare R...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Muhammad Tayyab Ahmad Jalal Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Due to the growing population of patients, in accordance to the general population growth rate, it remains imperative that healthcare deliveries are not only timely but also of high quality. It should ideally be designed to track the patient’s activities, behaviors, timetable, and general well-being. therefore, this paper aims to describe the methods of monitoring patients and detecting their illnesses using machine learning. Subsequently, with the help of different angled camera views, mounted on different positions, we would like to design a health monitoring and surveillance system that will help identify possible medical complications in the long run. Physiological and behavioral patterns are examined by using machine learning algorithms, which would then determine the presence of certain health conditions. In our approach, we prioritize accuracy and real-time to apply the results in real cases. Initial outcomes indicate how it will improve the quality of patient care by obtaining timely medical information while relieving medical personnel’s workload. the present investigation opens the avenue for the development of more intricate, individualized, and preventive gerontological healthcare models. For this purpose, we are using two well-known datasets, ETRIActivity3D and NTU-RGB-D. the first dataset is for the patient care daily life activities and the second is for the medical condition. the data is well-preprocessed to reduce the computational cost and to focus on the main subject of the data. then a robust Multi-feature descriptors system has been used to get the gradient values of the features. these values are processed by using feature matching and optimization. In the end, the classifier gives us an accuracy of $87.5 \%$ and $89.5 \%$ in ETRI and NTU-RGB-D datasets respectively.

关键词： Medical conditions Accuracy machine learning algorithms Surveillance Computational modeling machine learning Cameras Real-time systems Computational efficiency Optimization

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Enhancing Breast Cancer Diagnosis and Prognosis through machine learning and Deep learning: A Comparative Analysis of Predictive Models 6th

Enhancing Breast Cancer Diagnosis and Prognosis Through Mach...

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Sharma, Tushar Mishra, Mahi Sharma, Shamneesh Chaubey, Ved Prakash Krishan, Kewal School of Computer Science Engineering and Technology Lovely Professional University Punjab India upGrad Campus upGrad Education Private Limited Karnataka Bangalore India

ISBN: (纸本)9783031840586

Breast cancer is the most frequent invasive cancer among women and the primary cause of cancer-related deaths among women worldwide. With the aid of developments in data mining and machine learning, recent research endeavors have attempted to gain a better understanding of and prevent its emergence. these technologies provide strong analytical tools for oncological complicated datasets, with potential uses in clustering, classification, and prediction. Four machine learning models—Random Forest, Neural Network, Random- izedSearchCV, and Logistic Regression—were thoroughly examined in this investigation. the goal was to categorize the Wisconsin Breast Cancer database and the Breast Cancer Coimbra dataset, two different datasets related to breast cancer, to find correlations between patient characteristics and breast cancer outcomes. Key measures, such as prediction accuracy and F-measure scores, were used in the study to evaluate the performance of the model. In the end, the results demonstrated the Random Forest algorithm’s superior performance and generalizability, indicating its potential to offer medical professionals clinically meaningful insights and decision support. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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