检索结果-内蒙古大学图书馆

International Conference on science technology engineering and Management (ICONSTEM)

作者： M. Swarna R. Geetha G. Saranya Shakthi Murugan K H C. Gnanaprakasam Department of Electronics and Communication Engineering Dr. M.G.R. Educational And Research Institute University Chennai Tamilnadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Department of Electronics and Communication Engineering Narayana Engineering College Gudur AndhraPradesh Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamilnadu India

In the year of 2019, a major destruction was raised to all over the Country called as Coronavirus, which is also termed as Covid-19. This is one of the serious threat and many people lost their lives due to the affection of Coronavirus disease. Due to the spread ratio and nature, nobody is interested to properly monitor the patients who are all affected with this disease. So, that a new technology is required to monitor the health conditions of patients and instantly reporting the details to the respective person and guardian as well. As technology advances, doctors are constantly on the lookout for new automated gadgets that might make it simpler to detect abnormalities in the human body. The Internet of Things makes it possible to create cutting-edge, transparent healthcare support structures. This paper introduced a new health monitoring scheme called Covid Patients Health Analyzer (CPHA), in which this methodology is associated with the help of trending technology called Internet of Things (IoT). This system introduces an Internet of Things assisted patient health surveillance technology, which aids medical professionals in remotely measuring the blood concentration, pulse rate, respiration level and temperature of the respective patient. Both the person's pulse rate and blood oxygen sufficiency levels are read using an intelligent sensor called MAX3100, whereas the individual's temperature is scanned with a powerful sensor called DS18B20. Using a humidity identification sensor, one may determine the relative humidity of a given space. All required information will be encoded and decoded by the ESP32 module before being processed. The patients Fingers are hooked up to sensors as well as readings are shown on a respective computer or smartphone. After putting the suggested system through its paces, the working is clearly tested and it delivers the desired results in good manner. By monitoring the process remotely, medical personnel no longer need to worry about c

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cloud-Based Foot Pressure Analysis for Diabetic Care Using ANN

Cloud-Based Foot Pressure Analysis for Diabetic Care Using A...

引用

data intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Pradeepa H Raveendra N Amarnath V G Sivakumar Thamizhamuthu R G Manikandan G Bhuvaneswari Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai Tamil Nadu India Project Lead Mphasis Campbell Dr Melissa Texas USA Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Tiruchirappalli Tamil Nadu India Hyderabad Telangana India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus-Chengalpattu Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science RMK Engineering College RSM Nagar Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

It introduces a new method for caring for diabetic feet combines smart pressure monitoring for the feet with wearable gear that is cloud-based and uses artificial neural networks (ANNs). It is essential to recognize diabetic foot ulcers early and monitor them continuously to avoid complications. Wearable pressure sensors record data on the user's foot pressure in real time make up the proposed system. ANNs trained to detect patterns recommend a possible risk of ulceration by analyzing this data once it is transferred to a cloud-based platform. By using cloud-based architecture, healthcare practitioners can remotely monitor patients and intervene as necessary. Clinical trials show the technology improves diabetic foot care by detecting irregularities in pressure distribution and lowering the risk of ulcers and their sequel. Improved patient outcomes and decreased healthcare expenses are achieved via the preventive treatment of diabetic feet made possible by this system, which makes use of breakthroughs in wearable technology and artificial intelligence (AI).

关键词： Pressure sensors Cloud computing Scalability artificial neural networks Real-time systems Diabetes Biomedical monitoring Wearable devices Monitoring Remote monitoring

来源：评论

学校读者我要写书评

暂无评论

A Intelligent Approach for Fault Detection in Solar Photovoltaic Systems based on BERT-BiGRU Network

A Intelligent Approach for Fault Detection in Solar Photovol...

引用

Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Mohammed Hafeezuddin Bhagath Garapati T Vijayakumar Priyanka G Amit Chauhan A. Vidya Department of EEE Geethanjali College of Engineering and Technology Hyderabad India Department of EEE Geethanjali College of Engineering and Technology Cheeryal Telangana India Department of Artificial Intelligence and Data Science Dr.Mahalingam College of Engineering and Technology Coimbatore India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Life Sciences School of Sciences CHRIST (Deemed to be university) Bengaluru Karnataka India Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Trichy India

Large-scale photovoltaic (PV) plant problem identification and diagnosis is expected to grow more difficult in the future as more and more plants of increasing capacity enter into existence. To keep large-scale PV installations safe, reliable, and productive, automatic identification and localization of any mal-operation among thousands of PV modules is necessary. In order to identify problems in PV plants, the suggested method compares the ‘residuals' (fault indicator signals) generated by each string to a predetermined threshold. The suggested method relies on three distinct processes: data preparation, feature extraction, and model training. Preprocessing employs the method of Transform Invariant Low-rank Textures (TILT). The most useful and efficient measurements from feature extraction are kept while less important ones are discarded in favor of the Reduced Kernel PCA technique. Let's move on to model training with BERT-BiGRU. The proposed method is clearly superior compared to the two leading options, BERT and GRU. The proposed method had a 97.36% success rate.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluation of The Mixing Performance in a Planar Passive Micromixer with T Micromixer with Square Chamber Mixing Units (SAR)

Evaluation of The Mixing Performance in a Planar Passive Mic...

引用

Electrical, Energy, and artificial intelligence (EICEEAI), International engineering Conference on

作者： Hani Attar Rahma S. Zekry Samer M. Sharfo Mohanad A. Deif Mohamed Hafez Ahmad Solyman Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Bioelectronics Modern University of Technology and Information (MTI) University Cairo Egypt Department of Artificial Intelligence College of Information Technology Misr University for Science & Technology (MUST) 6th of October City Egypt Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Microscale mixing methods are crucial in various disciplines, encompassing chemical reactions and biological investigations. The present study used simulation methodologies to investigate the operational efficiency of splitting recombination (SAR) micromixers. The study demonstrates that SAR micromixers offer a notable advantage in enhancing mixing efficiency. The advantage above is a consequence of the effective combination of splitting-recombination and chaotic advection processes within the micromixer architecture. An in-depth analysis of the micromixer's behavior demonstrates that its performance is supported by intricate fluid dynamics, which provide remarkably high mixing efficiency. It is worth noting that the micromixer exhibits its maximum mixing efficiency, which is roughly 99% when the Reynolds number (Re) is at or below 0.5. Nevertheless, it is seen that as the Reynolds number grows, there is a steady decrease in mixing efficiency. At a Reynolds number of 70, the measurement of mixing efficiency yields a value of 75%. However, when the Reynolds number is further increased to a range of 90-100, the efficiency decreases to its lowest value of approximately 60%. The results above highlight the exceptional mixing ability of the SAR micromixer, hence stressing its potential for various applications that demand improved mixing capabilities. The results emphasize the promise of SAR micromixers as a reliable solution for complex mixing processes in many applications, providing valuable insights that may contribute to future developments in microscale mixing technologies.

关键词： Electric potential Numerical analysis Fluid dynamics Ethanol Chemical reactions Biology Reliability

来源：评论

学校读者我要写书评

暂无评论

Improved Metaheuristics with Deep Autoencoders for COVID-19 Detection and Classification

Improved Metaheuristics with Deep Autoencoders for COVID-19 ...

引用

Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Manish K Assudani Nirmala B N. Kanimozhi Shanker Chandre K. K. Sunalini Anandbabu Gopatoti Department of Computer Science and Engineering G H Raisoni University Amravati Maharashtra India Department of Computer Science and Engineering Sri Ramachandra faculty of engineering and technology Sri Ramachandra Institute of Higher Education and Research Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of English Koneru Lakshmaiah Education Foundation Guntur Andhrapradesh India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India

A healthcare monitoring system requires the support of current technologies like big data, artificial intelligence (AI), and machine learning (ML), particularly during the outbreak of COVID19. This pandemic caused millions of death worldwide. ML and AI are used to detect and combat COVID19 in the initial phase. Deep learning refers to a subdivision of ML that utilizes NN for making predictions and learning from data. In the context of COVID19 detection, deep learning (DL) methods can be trained for examining medical images like CT scans and chest X-rays and identify the presence of virus. This study presents an improved whale optimization algorithm with deep autoencoder (IWOA-DAE) algorithm for COVID-19 detection and classification. The presented IWOA-DAE technique makes use of DL model with metaheuristic hyperparameter optimizer for COVID-19 classification. To accomplish this, the IWOA-DAE technique employs bilateral filtering (BF) process to eliminate noisy data. Next, the IWOA-DAE technique uses NASNetLarge model for feature vector generation purposes. For COVID-19 detection, the IWOA-DAE technique uses deep autoencoder (DAE) model with IWOA based optimal hyperparameter adjustment process. To highlight the improved performance of the IWOA-DAE algorithm, a wide-ranging simulation analysis takes place. The simulation outcomes highlighted the enhanced performance of the IWOA-DAE technique over other methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Compounded Burr Probability Distribution for Fitting Heavy-Tailed data with Applications to Biological Networks

arXiv

引用

arXiv 2024年

作者： Chakraborty, Tanujit Chattopadhyay, Swarup Das, Suchismita Naik, Shraddha M. Hens, Chittaranjan SAFIR Sorbonne University Abu Dhabi United Arab Emirates Sorbonne Center for Artificial Intelligence Sorbonne University Paris France Department of Computer Science & Engineering XIM University Bhubaneswar India Department of Data Science SP Jain School of Global Management Mumbai India Department of Computer Science Khalifa University United Arab Emirates Center for Computational Natural Science and Bioinformatics International Institute of Information Technology Hyderabad India

Complex biological networks, encompassing metabolic pathways, gene regulatory systems, and protein–protein interaction networks, often exhibit scale-free structures characterized by heavy-tailed degree distributions. However, empirical studies reveal significant deviations from ideal power-law behavior, underscoring the need for more flexible and accurate probabilistic models. In this work, we propose the Compounded Burr (CBurr) distribution, a novel four-parameter family derived by compounding the Burr distribution with a discrete mixing process. This model is specifically designed to capture both the body and tail behavior of real-world network degree distributions with applications to biological networks. We rigorously derive its statistical properties, including moments, hazard and risk functions, and tail behavior, and develop an efficient maximum likelihood estimation framework. The CBurr model demonstrates broad applicability to networks with complex connectivity patterns, particularly in biological, social, and technological domains. Extensive experiments on large-scale biological network datasets show that CBurr consistently outperforms classical power-law, log-normal, and other heavy-tailed models across the full degree spectrum. By providing a statistically grounded and interpretable framework, the CBurr model enhances our ability to characterize the structural heterogeneity of biological networks. © 2024, CC BY.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

The Comparative Experimental Study of Rehabilitation Program Decision for Spinal Cord Injury Based on Electronic Medical Records

SSRN

引用

SSRN 2024年

作者： Qie, Botao Guo, Xin Chen, Wei Yu, Suiran Wang, Zhengtao School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300401 China Qinhuangdao Research Institute National Research Center for Rehabilitation Technical Aids Qinhuangdao066004 China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin300401 China National Research Center for Rehabilitation Technical Aids Beijing100176 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China

Objective: Electronic medical records (EMRs) contain patients' medical and health information. The Utilization of EMRs for assisted diagnosis is of significant importance for the rehabilitation of spinal cord injury (SCI) patients. Therefore, this study proposes a decision-making model for rehabilitation programs of SCI patients based on EMRs. Methods: First, an Electronic Medical Records (EMR) dataset comprising 1252 Spinal Cord Injury (SCI) patients was constructed, and data preprocessing was completed. Second, the Random Forest (RF) feature extraction algorithm was utilized to select case features with high contribution levels. Then, to address the imbalance issue in EMRs, a multi-label learning framework based on the improved MLSMOTE was adopted. Finally, seven multi-label classification models were employed to predict patients' physical therapy (PT) prescriptions. Results: The proposed improved MLSMOTE multi-label learning framework can solve the problem of class imbalance. Compared with the other six models, the CC model has improved significantly in many metrics. Its hamming loss and ranking loss were 0.1388 and 0.2467, and precision, recall, and F1-score were 83.33%, 81.20%, and 79.82% respectively. Conclusions: The improved MLSMOTE multi-label learning framework proposed in this study can make full use of the information in EMRs and effectively improve the decision-making accuracy of rehabilitation treatment programs. © 2024, The Authors. All rights reserved.

关键词： Patient rehabilitation

来源：评论

学校读者我要写书评

暂无评论

Research on Voiceprint recognition method of buried drainage pipe based on MFCC and GMM-HMM

Research on Voiceprint recognition method of buried drainage...

引用

第33届中国控制与决策会议

作者： Jiarui Yang Zao Feng Jiande Wu Yugang Fan Faculty of Information Engineering & Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology

An voiceprint recognition model based on short-term energy and Gaussian Mixed Hidden Markov models was proposed in this paper aiming to solve the problem that the number and position of the of the blockages,pipe components and pipe end in the buried drainage pipeline are uncertain and difficult to *** method first performs wavelet threshold de-noising method on the sound pressure signal collected in the operating pipeline,and then uses the short-term energy as the discriminant parameter for endpoint detection,then divide into segmentations obtain the sound pressure signal corresponding to different types of individuals in the pipeline,and then extracts the Mel-frequency Cepstrum coefficient to constructs feature vector sets that can characterize the blockage,lateral connection and the pipe end,and finally the feature vector sets are used to train the Gaussian Mixed hidden Markov model to obtain the classification *** experimental results have shown that the proposed method can effectively identify blockages,pipe fittings such as lateral connection and pipe end,furthermore,it avoids the problems of low recognition rate of new samples caused by insufficient known conditions included in the training samples.

关键词： Buried drainage pipeline voiceprint recognition Mel-frequency Cepstrum coefficient Gaussian mixture hidden Markov model

来源：评论

学校读者我要写书评

暂无评论

Neural Networks for Fault diagnosis in Electrical Machine

Neural Networks for Fault diagnosis in Electrical Machine

引用

Intelligent Technologies (ACOIT), Asian Conference on

作者： R. Vinston Raja J. Raja Sreenivasulu Gogula Atul Katiyar T. Thilagam V. S. Bhagavan Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering (Data Science) Vardhaman College of Engineering Hyderabad Telangana India Department of Electrical Engineering Mahatma Jyotiba Phule Rohilkhand University Bareilly Uttar Pradesh India Department of Science and Engineering S.A Engineering College Chennai Tamil Nadu India Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Finding electrical equipment problems quickly is essential for minimising downtime and ensuring ongoing functioning. When it comes to early defect identification, traditional methods like human inspection or sensor-based surveillance might not cut it. Another alternative to employing machine learning algorithms to analyse complex patterns in sensor data is neural networks. With an emphasis on motor drives, this article lays forth a method for identifying electrical equipment issues using neural networks. The network has been trained to identify various issues, such as imbalanced rotors, broken stator windings, and failing bearings, by examining historical data. The trial findings prove that the suggested method may effectively find issues, make things more reliable, and cut down on maintenance expenses.

关键词： Fault diagnosis Motor drives Machine learning algorithms Surveillance Neural networks Stator windings Rotors Machine learning Maintenance Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Rapid Detection technology of Sewing Thread Based on Deblurgan-Bsv3 Defuzzification Algorithm and St-Fpn Detection Algorithm

SSRN

引用

SSRN 2024年

作者： Zheng, Xiaohu Chen, Yufan Liu, Bing Xu, Xiuliang Institute of Artificial Intelligence Donghua University Shanghai201620 China Shanghai Industrial Big Data and Intelligent Systems Engineering Technology Center Shanghai201620 China College of Information Science and Technology Donghua University Shanghai201620 China Hangzhou Zhongfu Technology&Innovation Research Institute Co. Ltd Shanghai201620 China Precise Machinery Scientific & Technology Co. Ltd Shanghai201599 China

Stitch defect detection is an important means to ensure the quality of stitched fabrics. To realize the intellectualization of the sewing process, a rapid defect detection method based on machine vision was proposed. In order to solve the problem of motion blur caused by sewing speed, an image DeblurGAN-BSV3 algorithm is proposed. Firstly, different attention fusion mechanisms are introduced to achieve accurate extraction of image features. Secondly, the depth-separable convolution of the trunk network is replaced by blueprint convolution to ensure the processing speed of the detection algorithm and the clarity of the output image. In order to solve the difficulty of rapid detection of pin defects, a student-teacher feature pyramid matching method was proposed. The abnormal region interpretation technology was used to explicitly visualize the defect area of the identified abnormal instances, and then the abnormal detection score was performed on the detected images to make a quality judgment. © 2024, The Authors. All rights reserved.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：