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Sensor Fault Diagnosis Method based on One Dimensional Local Ternary pattern 5

Sensor Fault Diagnosis Method based on One Dimensional Local...

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5th IEEE Advanced Information Technology, Electronic and Automation Control conference, IAEAC 2021

作者： Zhang, Kun Xu, Feiyun Cao, Susheng Southeast University School of Mechanical Engineering Nanjing China

ISBN: (纸本)9781728180281

In order to solve the problem of sensor fault diagnosis, a fault diagnosis method based on one dimensional local ternary patterns and extreme learning machine is proposed. this method applies local ternary pattern used in the field of image recognition to the processing of one dimensional signals. Firstly, the sensor signal is subjected to six sliding maximum filters to obtain signals of different *** then one dimensional local ternary pattern is used to process the original signal and signals of different scales to obtain the feature vector. Finally, dimension-reduced feature vector processed by principal component analysis is classified by extreme learning machine to identify different types of sensor faults. Four kinds of sensor fault data is generated using simulation fault data model, and comparative experiments verify the effectiveness of this method. © 2021 IEEE.

关键词： Fault detection

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A Comprehensive System for Accurate Identification of Gemstones from Corundum, Topaz, and Chrysoberyl Families Using machine learning Techniques

A Comprehensive System for Accurate Identification of Gemsto...

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Advancements in Computing (ICAC), international conference on

作者： E.A.E. Kanjana Epitawatta Samadhi Rathnayake Pipuni Wijesiri Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

“Gemstones, mineral stones formed through geological processes with a hardness above 7 Mohs, hold significant value in the gemstone industry” [1]. this research introduces a meticulously designed software system to address challenges in the gemstone industry. the system excels in identifying gemstones from the Corundum, Topaz, and Chrysoberyl families, focusing on the key factors of gemstone valuation known as the “4 Cs” (color, clarity, cut, and carat weight). the research aims to provide an efficient and precise solution for gemstone identification, offering transformative approaches to industry concerns and enhancing the market desirability of these gemstones. Our system utilizes machine learning, specifically a pre-trained convolutional neural network (CNN), with extensive data and image processing techniques like cropping, resizing, flipping, and zooming. these enhance the CNN's training process for 722 previously unseen gemstone images, categorizing them by variety, color, and clarity. the system aims to efficiently and reliably identify and differentiate gemstones, with broad applications in gemology, the jewelry industry, and mineralogy. this research represents not only a technological advance but also a practical solution to streamline gemstone recognition and classification across various domains.

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A real time robust facial recognition model for masked face images using machine learning model 5

A real time robust facial recognition model for masked face ...

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5th international conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Laxminarayanamma, K. Deepthi, V. Ahmed, Md Faqaruddin Sowmya, G. Institute of Aeronautical Engineering Department of Information Technology Hyderabad India

ISBN: (纸本)9781665435246

Almost everybody is wearing a coronavirus mask to prevent the spread of COVID19 effectively. Conventional facial recognition technologies are often virtually ineffective, like Community access monitoring, facial access control, front attendance, and facial security inspections at railway stations. Consequently, recognition of the current masked face recognition technology must be improved as a matter of urgency. the most advanced approaches to facial recognition are based on a deep knowledge based on multiple facial samples. However, the public is currently unable to view any masked facial recognition datasets. In this study, 3 types of masked face recognition data sets are offered, including Masked Face data Sets (MFDDs) (SMFRD). RMFRD is considered as the largest masked face dataset in the world. these data sets are available to industry and academia to develop various applications on masked sides. the model developed, which masked multi-granularity, achieves 99% precision, and goes further than the industry's reporting results. © 2021 IEEE.

关键词： Face recognition

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Enhancing machine learning-Based Webpages Phishing Detection with Heterogeneous Distributed Ensemble Feature Selection

Enhancing Machine Learning-Based Webpages Phishing Detection...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Bamidele M. Olukoya Emmanuel Tunbosun Aderemi Prisca O. Olawoye Taiwo Olatunde Emmanuel Oluwatobi Asani Sodipo Q.B Department of Computing & Information Science Bamidele Olumilua University of Education Science and Technology Ikere Ekiti-State Nigeria Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Computer Science Landmark University Omu-Aran Nigeria Department of Computer Engineering Bells University of Technology Ota Ogun-State Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

the analysis of webpage phishing is a standout computational method used to distinguish phishing from authentic websites through URL examination. In data mining, classification is crucial for constructing machine learning models that facilitate accurate decision-making. Phishing, a significant cyber menace, has recently escalated, inflicting substantial financial damage on individuals, corporations, and the global economy. Phishers typically replicate legitimate websites to trick unsuspecting users and extract vital information. To enhance cybersecurity systems, it's vital to selectively eliminate irrelevant or redundant features, retaining only pertinent attributes to narrow down the search scope. Not all data features are critical for classification, and feature selection can be a complex task. this research introduces a new ensemble feature selection method called Heterogeneous Distributed Ensemble Feature Selection (HDEFS). the authors evaluated and contrasted the features derived from each component of the ensemble framework against baseline features in chosen models. Conducted experiments revealed that HDEFS enhanced the performance of both traditional and ensemble models, also simplifying model complexity relative to earlier studies. the boosted_SVM surpassed all other boosted models, achieving a 97.4% accuracy rate followed by boosted_LR with a detection rate of 94%.

关键词： Uniform resource locators Training Humanities Accuracy Biological system modeling Phishing Decision making machine learning Feature extraction data mining

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Development of Virtual Skill Trainers and their Validation Study Analysis Using machine learning 2021

Development of Virtual Skill Trainers and Their Validation S...

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5th international conference on Information System and data mining (ICISDM)

作者： Shedage, Seema Farmer, Jake Demirel, Doga Halic, Tansel Kockara, Sinan Arikatla, Venkata Sexton, Kevin Ahmadi, Shahryar Univ Cent Arkansas Conway AR 72035 USA Florida Polytech Univ Lakeland FL USA Kitware Inc Clifton Pk NY USA Univ Arkansas Med Sci Little Rock AR 72205 USA

ISBN: (纸本)9781450389549

Minimally invasive skills assessment is important in developing competent surgical simulators and executing reliable skills evaluation [9]. Arthroscopy and Laparoscopy surgeries are considered Minimally Invasive Surgeries (MIS). In MIS, the surgeon operates through small incisions with specialized narrow instruments, fiberoptic lights, and a monitor. Arthroscopy surgery is used to diagnose and treat joints problems, and Laparoscopic procedures are performed on the abdominal cavity. Due to non-natural hand-eye coordination, narrow field-of-view, and limited instrument control, MIS training is challenging to master. We are analyzing two simulators' data, Virtual Arthroscopic Tear Diagnosis and Evaluation Platform (VATDEP) and Gentleness Simulator. Both simulators went through the validation studies with human subjects. We recorded simulation data during the validation studies, such as tool motion, position, and task time. Recorded data went through the data preprocessing;after the data cleaning, we extracted the recoded data features and normalized them. Normalized features were used to input various machine learning algorithms, including K-nearest neighbor (KNN), Support vector machine (SVM), and Logistic regression (LR). the average accuracy was evaluated through k-fold cross-validation. the proposed methods validated using 10 subjects (5 experts, 5 novices) for the VATDEP simulator. 23 subjects (4 experts and 19 novices) for the Gentleness Simulator. the result shows a significant difference between the expert and novice population with the p < 0.05 using the Mann-Whitney U-test. the VATDEP simulator's classification algorithms' average accuracy is 74% and 80% for the Gentleness Simulator. the results show that the normalized features and with KNN, SVM, and LR classifiers can provide accurate classification of experts and novices. the evaluation technique proposed in this study can develop surgical training by providing appropriate feedback to trainees to evaluate

关键词： machine learning Virtual Reality Simulator Validation Study

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Prompt and Contrastive learning for Few-shot Sentiment Classification 5

Prompt and Contrastive Learning for Few-shot Sentiment Class...

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Wang, Fei Chen, Long Huang, Xiaohua Xu, Cai Zhao, Wei Guan, Ziyu Lu, Guangyue Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Sentiment classification is a hot topic in the field of natural language processing. Currently, state-of-the-art classification models follow two steps: pre-training a large language model on upstream tasks, and then using human-labeled data to fine-tune a task-related model. However, there is a large gap between the upstream tasks of the pre-trained model and the downstream tasks being performed, resulting in the need for more labeled data to achieve excellent performance. Manually annotating data is expensive. In this paper, we propose a few-shot sentiment classification method based on Prompt and Contrastive learning (PCL), which can significantly improve the performance of large-scale pre-trained language models in low-data and high-data regimes. Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter-class and intra-class distribution patterns of labeled data. thanks to the integration of the two strategies, PCL markedly exceeds baselines with low resources. Extensive experiments on three datasets show that our method has outstanding performance in the few-shot settings. © 2022 ACM.

关键词： Computational linguistics

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Enhanced Approaches to Kingfisher Species Identification Using machine learning and Deep learning

Enhanced Approaches to Kingfisher Species Identification Usi...

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Advanced Research in Computing (ICARC), international conference on

作者： C. S. Vidanagamachchi W. G. D Chathuranga L. L. G. Chathuranga K. T. A. S. Jayathilake R. B. Yeshani S. M. Vidanagamachchi Dept. of Computer Science University of Ruhuna Matara Sri Lanka Dept. of Zoology University of Ruhuna Matara Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

this study addresses the critical challenge of bird species recognition within the domains of deep learning, emphasizing its significant ecological and conservation implications. Focusing on Sri Lanka’s avian biodiversity, particularly the Kingfisher family, this research systematically analyzes bird identification methodologies. the methodology encompasses data collection, pre-processing, feature extraction, classification, and analysis of results. the study employs various deep learning models, including VGG16, VGG19, SVM, a custom CNN, and a hybrid CNN-SVM model. the custom CNN model, specifically optimized for kingfisher species identification, achieved a testing accuracy of 81.21%, outperforming traditional models. However, the hybrid CNN-SVM model demonstrated slightly lower testing accuracy at 79.35%. these findings highlight the effectiveness of the proposed deep learning models in identifying Kingfisher species, particularly when dealing with complex variations in plumage and environmental factors. the results indicate that the custom CNN model is highly reliable for ornithological applications, especially for nonspecialists. the research highlights the complexity of Kingfisher species identification and underscores the critical role of advanced machine learning techniques in addressing these challenges. the findings have significant implications for wildlife conservation, enabling enhanced monitoring and preservation of bird populations. Additionally, the results reveal the potential for developing a mobile application for bird species recognition, particularly for Kingfishers, addressing the lack of existing computer-based identification solutions.

关键词： Deep learning Support vector machines Accuracy Biological system modeling Streaming media Birds Feature extraction Real-time systems Mobile applications Testing

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PetVision: Improved MobileViTv3-Based Image Classification for Cat Breed Identification

PetVision: Improved MobileViTv3-Based Image Classification f...

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international conference on Artificial Intelligence and data Sciences (AiDAS)

作者： Jackson Ang Shannie Tannaris Andry Chowanda Anderies Anderies Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Numerous animals are kept as pets, and one of them is a cat. Cats have different breeds and each breed requires different treatment and food. However, only a few cat caretakers have the ability to identify the breed of their pet. this has caused misidentification and mistreatment leading to fatal consequences. Hence, technology can help pet owners recognize their pet's breed to avoid serious problems. this study seeks to identify the breed of cats utilizing the machine learning approach. Instead of using either a Convolutional Neural Network (CNN) or a Vision Transformer(ViT), a hybrid model delivers the best performance. the hybrid model, MobileViT, is applied in this study, specifically MobileViTv3. It was tested with the dataset that comprises images consisting of 12 breeds of cats. the methodology involves extracting the 12 Cat breeds in the Oxford IIIT Pet dataset. these images will undergo preprocessing and data augmentation to enhance the training process. Models including PetVision are then trained in this dataset and performance evaluation will conducted using the accuracy and parameter counts of each model. the model PetVision produced an accuracy of 75% with parameter counts of 1.2 million in 200 epoch runs. these results produced a satisfying result that can aid pet owners in providing the appropriate care for their pets.

关键词： Performance evaluation Training Accuracy Image recognition machine learning learning (artificial intelligence) Transformers data models Convolutional neural networks Optimization

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Morphological Similarity Based Livestock Prediction Using machine learning Technique

Morphological Similarity Based Livestock Prediction Using Ma...

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Recent Trends in Computer Science and Technology (ICRTCST), international conference on

作者： C. S. Anita P. Shobha Rani Masthan Lakshmi Sahithi Maddali Monika J Rubeena Shaik Department Of CSE R.M.D. Engineering College Tamil Nadu India Full Stack Developer Virtusa Private Limited Chennai Tamil Nadu India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

In the current technological world, detection of stock market through various software systems is in practice. Predicting stock prices through recorded information of previous years, has enhanced the future prediction. Stock value information is viewed as a common place time in time series. In the existing system, a recurrent neural network(RNN), Long momentary memory (LMM) and gated rent units(GRU) are considered for regularizing the stock values that deals with sort of information. Considering the individual examination of stocks, according to the relativity between the existing stock and the whole market price, the proposed system analyzes the strategy behind. the proposed model considers clustering strategy for mining comfortable stocks which consolidate morphological comparison comparability distance MSD and K-means clustering. Further the system uses hierarchal temporal memory (HTM) model to which high course of data is initially depicted to learn then using the design among the comparative stocks and its information construction of inadequate stock values are normalized. In terms of prediction accuracy the proposed KMSD-LSTM model is improved in the prediction of price. LSTM model neglects the patterns that are not significantly related and remembers the pattern that makes a change in the prediction process.

关键词： Training Accuracy Transfer learning Time series analysis Predictive models User interfaces Agriculture

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Edge Processing in machine learning for Arthritis Detection and Classification

Edge Processing in Machine Learning for Arthritis Detection ...

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data Intelligence and Cognitive Informatics (ICDICI), international conference on

作者： T. Kalavathi Devi K. N. Baluprithviraj M. Madhan Mohan S. Mohammed Yunoos K.S. Renuha Devi V. Vishalini Dept. of Electronics and Instrumentation Engineering Kongu Engineering College Perundurai Erode India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Arthritis is a persistent inflammatory disease that affects the joints. It is a major global health issue, especially for people over 40. However, arthritis frequently remains untreated in isolated locations with poor access to medical services, impairing mobility and causing excruciating agony for those who suffer from it. this programme, which is in line with the National Programme for the Health Care of the Elderly (NPHCE), focuses on the diagnosis and classification of arthritis to enhance medical care for this population. To tackle this urgent matter, It is suggested to create a tool that can identify and categorize arthritis in isolated locations. the device uses sophisticated image processing techniques and machine learning algorithms to effectively identify various types of arthritis by utilising inputs such as MRIs, blood tests, and X-rays. For testing and training purposes, classes are named and labelled through repeated processing of large datasets. To improve arthritis classification accuracy, the research further develops the CNN algorithm. It also tests the model on arbitrary datasets and plots accuracy metrics to assess its effectiveness. the ultimate purpose is to demonstrate how well the created tool can identify and categorise cases of arthritis, guaranteeing prompt medical care for those in need and achieving the NPHCE's goals.

关键词： Training Measurement machine learning algorithms Accuracy Neural networks X-rays Arthritis Object recognition Older adults Testing

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