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检索条件"任意字段=4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023"

共 108 条记录，以下是51-60 订阅

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Averting Frustration in Geriatric Patients through Identification, 2D Mapping and Navigation Using machine learning 4

Averting Frustration in Geriatric Patients through Identific...

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4th international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2022

作者： Malhotra, Alekha Bhatia, Stuti Jain, Reetu Class of 2023 Cathedral and John Connon School 6 Purushottamdas Thakurdas Rd Marg Azad Maidan Fort Maharashtra Mumbai400001 India On My Own Technology Pvt. Ltd. 2A Pace House 7 Swastik Society Gulmohar Road Vile Parle West Mumbai400049 India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Alzheimer's patients experience agitation and frequently display aggressive behaviors due to their inability to retain simple information, such as people's names, and often misplace items, among other causes. the incurable nature of this disease creates the need for assistive technology to help patients. this proposed methodology, MemoryMap, consists of a pair of glasses, a camera, hardware for audio output and input and a microprocessor. the algorithm uses image-processing and machine learning to perform object and facial recognition, map out the locations of both the object and patient and provides a navigation system to guide patients to any object they have misplaced. this prototype continually records the patient's view through the camera and performs real-time analysis on the video feed using OpenCV, allowing the identification of the object or face desired by the user through a NumPy model. Each object is recognised and entered into a database in the form of an excel sheet using XLWT and XLRT to maintain the location and time frame. Visual feedback of the navigation route along with audio feedback for recognition enables the patient to become more self-sufficient. this prototype is targeted toward patients in the Mild Stage of Alzheimer's and works towards relieving their irritation caused due to memory loss. © 2022 ieee.

关键词： Cameras

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ML Algorithm-Based Detection of Leaf Diseases 4

ML Algorithm-Based Detection of Leaf Diseases

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Kathiravan, M. Priya, K. Hari Sreesubha, S. Irumporai, A. Kumar, V. Sukesh Reddy, Vishnu Vardhan Hindustan Institute of Technology and Science Deemed to be University Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9781665401180

In day-to-day life, while the plant is important for every human being to get rich vitamins of vegetables, the plant disease rate is increasing proportionally. the disease on the plant causes severe suffering to farmers economically. So, the plant disease is either to be controlled or predicted before it leads to big damages. To solve this issue, machine learning algorithms have been proposed to analyze the root causes of the disease and to prescribe suitable pesticides to cure the plant. though many methods have been encountered to detect the disease of a crop, the existing methods failed to give accurate results. While many existing methods failed to apply image processing techniques and databases to store the crop data, the proposed CNN algorithm adopted these features by creating web applications to know the different types of disease that have occurred on the plant. the result witnesses that the proposed algorithms have shown improved accuracy (93.3%) which helps the farmer to raise the productivity of a crop. the developed app also helps young farmers to read and select suitable image options to improve the good yielding of their agriculture. © 2022 ieee

关键词： pattern recognition

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Data analysis of Global Perceived Stress Scores during Covid-19 4

Data analysis of Global Perceived Stress Scores during Covid...

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4th international conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Muttaraju, Chakita Kabber, Anusha Parimala, S. Anagani, Prajwal Srinivasa, Gowri PES University Department of Computer Science and Engineering Bengaluru India PES University PES Center for Pattern Recognition Dept. of CSE Bengaluru India

ISBN: (纸本)9798350397475

the Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. this work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. machine learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 2022 ieee.

关键词： Decision trees

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Research and Application of Fault Diagnosis and Localization Techniques for Power Communication Optical Cables Based on Multimodal Data Analysis

Research and Application of Fault Diagnosis and Localization...

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Data Science and Computer Application (ICDSCA), ieee international conference on

作者： Xuguang Zhang Bo Zhang Bo Guo Hang Ren Wanxin Wang Chao Ma State Grid Jilin Electric Power Co. Ltd. Tonghua Power Supply Company Jilin China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Aiming at the problems of low accuracy of power cable fault pattern recognition and large error of fault location, a method of power cable fault diagnosis and location based on Beluga Whale Optimization-Extreme learning machine (BWO-ELM) algorithm and GIS system is proposed. First, the beluga whale optimization (BWO) algorithm is used to optimize the initial parameters of the extreme learning machine (ELM), and the BWO-ELM multi classification Optical Time Domain Reflectometry (OTDR) curve analysis fault pattern recognition method is constructed, obtain the straight-line distance and type of the optical fiber fault point to lay the foundation for fault location. Secondly, the accurate positioning method of optical cable fault based on GIS is proposed, which converts the straight-line distance of the optical fiber fault point into the optical cable distance, and matches it with the actual geographical location to obtain the coordinates of the actual fault point, which are intuitively displayed in the GIS system. Finally, the application effect of the proposed method is verified by simulation experiments. the results show that the proposed method achieves a fault pattern recognition accuracy of about 98.66%, the fault location error is floating up and down 3M, and the average error is 1.481%. It has higher performance and accuracy than other recognition models and fault location methods.

关键词： Optical fibers Integrated optics Accuracy Power cables Optical fiber cables Fault location Adaptive optics pattern recognition Optical reflection Geographic information systems

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Employing machine learning algorithms to discover risk factors of glaucoma 4

Employing machine learning algorithms to discover risk facto...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Al-Shamiri, Abdulkawi Yahya Radman School of Computer Science and Engineering Hodeidah University Department of Computer Science Al-Hodaidah Yemen

ISBN: (纸本)9781665413220

the statistics of the World Health Organization indicated that blindness or loss of vision in one of the eyes is one of the many threats and challenges in the world due to the increasing number of people infected annually. One of the many serious eye diseases is glaucoma, which leads to the loss of vision in the affected eye. Most studies on glaucoma are based on clinical trials and thus lack insights into the risk factors of glaucoma;these factors could be obtained from larger number of doctors and patients and analyzed sufficiently. With the recent development of technology, the need of utilizing machine learning algorithms in the medical field has increased to achieve a better and accurate health care system. thus, this study aims to effectively discover the hidden risk factors of glaucoma by machine learning algorithms using real data collected from doctors and patients. the author used one of decision tree algorithms so-called J48 and one of association rule algorithm socalled apriori to analyze the collected data. the study shows that some of the risk factors of glaucoma which have been proven by medical trials are also obtained by means of the two algorithms. therefore, machine learning algorithms can be used to effectively determine causes of any disease. the results not only provide a novel method to discover the risk factors of glaucoma but also reveal new risk factors of glaucoma which have not been discovered previously by studies based on medical trials such as discovery the relation chronic constipation and stomach upset with glaucoma as reasons leading to glaucoma. this study will contribute to avoiding blindness and preventing it by determining the recognition factors of glaucoma. © 2021 ieee.

关键词： pattern recognition

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Metagenomic Face: Applying machine learning to Facial Microbiome Data for pattern recognition and Disease Classification

Metagenomic Face: Applying Machine Learning to Facial Microb...

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Intelligent Technologies (CONIT), international conference on

作者： M S Guru Prasad Raghavendra M Devadas Vani Hiremani Afsar Jahan J Praveen Gujjar Aryan Dahiya Department of Computer Science and Engg Graphic Era (Deemed to be University) Dehradun India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India Department of Computer Applications Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

this innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. this framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. the investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. these findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. the Support Vector machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. these additional insights contribute to a comprehensive understanding of the Metagenomic Face framework’s efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis.

关键词： Support vector machines Measurement Logistic regression Metagenomics Microorganisms Accuracy machine learning algorithms

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Comparative Analysis of machine learning Techniques for Concrete Compressive Strength Prediction

Comparative Analysis of Machine Learning Techniques for Conc...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Fuad Musleh Ranyah Taha Abdel Rahman Musleh Civil Engineering Department University of Bahrain Isa Town Kingdom of Bahrain Computer Department Al Iman School Isa Town Kingdom of Bahrain Electrical Engineering Department University of Bahrain Isa Town Kingdom of Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

the compressive strength of concrete is of utmost importance in ensuring the structural stability and integrity of construction projects. With increasing importance placed on this aspect, there is a demand for intelligent and efficient techniques to accurately predict and evaluate compressive strength. machine learning methods (ML) present a promising approach to developing accurate models for assessing the compressive strength of concrete. By leveraging the power of data analysis and pattern recognition, these techniques enable the construction industry to obtain reliable and robust concrete for a wide range of applications. ML models can identify the key factors influencing compressive strength, allowing for more informed decision-making, and ensuring the construction of structurally sound and durable projects. this article explores the assessment and comparison of four machine learning (ML) methods, namely Gradient Boosting, Categorical Gradient Boosting CatBoost, Random Forest (RF), and extreme gradient boosting (XGBoost) regressors, for predicting concrete compressive strength. Among these techniques, the CatBoost Regressor demonstrated superior performance in terms of accuracy, R-squared coefficient of determination, and Root Mean Square Error RMSE, surpassing the other three methods.

关键词： Radio frequency Data analysis Accuracy Boosting Concrete Structural engineering pattern recognition

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Real Time Fake news Detection Web App Enhanced by machine learning Algorithms

Real Time Fake news Detection Web App Enhanced by Machine Le...

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Intelligent Technologies (CONIT), international conference on

作者： V Gunasekaran D Karthikeyan School of Information and Technology Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In this era, the pervasive proliferation of fabricated news has come to light as a formidable adversary, corroding the credibility of journalistic sources, sowing confusion, and distorting public discourse. To counteract this escalating predicament, this research introduces a pioneering and comprehensive solution-an interactive web application engineered to detect and mitigate the impact of fabricated news. through the strategic integration of sophisticated machine learning algorithms, this platform not only identifies spurious news content but also operates in real time, enabling swift responses to the proclamation of misinformation. the key aim of this study is to elevate the precision and efficiency of fabricated news identification. this is achieved through a meticulously devised amalgamation of advanced natural language processing (NLP) techniques, meticulous feature engineering strategies, and the robust framework of ensemble learning methods. the fusion of these techniques equips the system with an arsenal of tools to scrutinize news story, disentangling the intricate web of linguistic nuances that often obscure deceptive content. the real-time facet of the application is pivotal, ensuring that the detection process transpires in sync with the constant influx of news story across digital platforms. this immediacy minimizes the potential amplification of false narratives, thereby curbing the negative impact of fabricated news proclamation. the heart of the system lies in its fabricated news detection capability, fortified by the intricacies of modern machine learning. By training on vast repositories of verified news data and contrasting it with known instances of false information, the algorithms learn to recognize patterns that distinguish authentic news from falsified narratives. Accessed through standard web browsers, the web application interface offers an intuitive and accessible medium for users to engage with the detection process.

关键词： Training Heart machine learning algorithms Linguistics Real-time systems Natural language processing pattern recognition

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Partial Discharge pattern recognition in High Voltage Transmission Cable Project

Partial Discharge Pattern Recognition in High Voltage Transm...

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Electronic Technology, Communication and Information (ICETCI), ieee international conference on

作者： Guangxiuyuan Zhu Shengjie Wang Yongsheng Zhang Ling Jiang Qiuyang Li Daping Zhang Electric Power Research Institute of State Grid Qinghai Electric Power Company Xining China Qinghai Key Laboratory of High Altitude Electric Power Research Xining China Qinghai High Altitude Power Engineering Technology Research Center Xining China State Grid Qinghai Electric Power Company Xining China Xining Power Supply Company of State Grid Qinghai Electric Power Company Xining China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

this paper aims to improve the reliability and safety of cables, prolong the service life of cables and reduce the operating cost of power systems. the paper examines in detail how partial discharge (PD) patterns are identified in high-voltage transmission cable projects. this paper tests the performance of several layers of a convolutional neural network (CNN) to increase recognition efficiency and accuracy. During the experiment, unified network parameters are set, including the use of maximum pooling mode, ReLU activation function, and 32 33-size convolution kernels in each layer. By contrasting the recognition accuracy of 1-layer, 2-layer, 3-layer, 4-layer, 6-layer, and 9-layer CNN, the paper’s findings indicate that: (1) CNN’s recognition accuracy peaks at 90.77% when there are two network layers. However, as the number of layers increases, the accuracy of recognition gradually declines. this result reveals that moderately increasing the network depth is helpful to improve the performance in PD pattern recognition of high voltage transmission cables, but too deep network may lead to over-fitting. (2) CNN is stable in the detection of most defect types, while Support Vector machine (SVM) is the best in some specific defects. Back propagation neural network (BPNN) performs well in some cases, but its overall performance fluctuates greatly. these results are influenced by many factors, such as defect characteristics, model parameters and data set size. therefore, in practical application, it is necessary to select a suitable model for defect detection according to specific scenarios and requirements. this paper provides a useful reference for the follow-up research in high voltage transmission cable project.

关键词： Support vector machines Partial discharges Accuracy Transfer learning High-voltage techniques pattern recognition Safety

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Wood Species recognition Using machine learning 4

Wood Species Recognition Using Machine Learning

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4th international conference on Microelectronics, Signals and Systems, ICMSS 2021

作者： Neethu, S. Baby Syla, L. A P J Abdul Kalam Technical University Department of Computer Applications CET Trivandrum India

ISBN: (纸本)9781665448857

Although a computer vision-based automated wood species detection system is not extensively employed today, it is in high demand in a range of industries. Because each species of wood has distinct features and traits that determine how it is used and implemented. Human experts have traditionally been used nearly entirely in the identifying process, which is based on a variety of features of wood, such as texture, structure, and colour. Even competent scientists have difficulties distinguishing between different species of wood found in nature. the most difficult aspect of wood recognition is that some wood species have a texture that is identical to that of others, hence feature extraction is the most important step in the process. So in this work, two statistical feature extraction techniques are used 1) Local Binary pattern (LBP) 2) Gray Level Co-Occurrence Matrix (GLCM). Features extracted from both methods are combined to see whether they increase classification accuracy or not. Multi-SVM is used for classification. For this study, a new dataset is created which includes the three most common tree species in Indian territory. this study found that integrating LBP and GLCM features enhanced classification accuracy. Proposed system produces a result of 97.2%, which is better than individual approaches. © 2021 ieee.

关键词： Local binary pattern

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