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An Artificial Intelligence-based bio-Medical stroke Prediction and Analytical system Using a Machine Learning Approach

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Computational Intelligence and Neuroscience 2022年第0期2022卷 5489084页

作者： Pitchai, R. Dappuri, bhasker Pramila, P.v. vidhyalakshmi, M. shanthi, s. Alonazi, Wadi b. Almutairi, Khalid M. A. sundaram, R.s. beyene, Ibsa Department of Computer Science and Engineering B v Raju Institute of Technology Telangana Narsapur502313 India Department of Electronics and Communication Engineering CMR Engineering College Kandlakoya Telangana 501401 India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai600124 India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Tamilnadu Chennai600089 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai638060 India Health Administration Department College of Business Administration King Saud University P. O Box: 71115 Riyadh11587 Saudi Arabia Department of Community Health Sciences College of Applied Medical Sciences King Saud University P. O. Box 10219 Riyadh11433 Saudi Arabia Department of Health Sciences University of Texas TX United States Department of IT Mettu University Mettu Ethiopia

stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored;their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (svM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed svM than in other methods. © 2022 R. Pitchai et al.

关键词： Forecasting

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Detecting and Predicting Agitation Episodes in Dementia Patients Using IoT and Artificial Neural Networks

Detecting and Predicting Agitation Episodes in Dementia Pati...

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Emerging Technologies and Innovation for sustainability (EmergIN), International Conference on

作者： s. Pragadeeswaran N. Mohankumar R Puviarasi N. Nandhini Audithan sivaraman M. Rajmohan 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 Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Information Technology P. S. V College of Engineering and Technology Krishnagiri Tamilnadu Department of Information Technology Netaji Subhas university of Technology (Govt of NCT Delhi) New Delhi India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Dementia patients' agitation is a major problem for caregivers, who report greater rates of stress and costs connected to care. This research aims to determine if it can detect and predict agitation episodes in dementia patients by combining Internet of Things (IoT) devices with Artificial Neural Networks (ANNs). It continually monitors environmental and physiological variables like room temperature, noise levels, heart rate, and movement patterns by installing IoT sensors. A custom-built ANN is trained to detect patterns related to agitation episodes and feed the collected data. This method preprocesses raw sensor data using present techniques for signal processing to increase reliability and robustness. Data from multiple healthcare institutes helps the ANNs model recognize agitation. Early intervention may lessen the severity of these instances and forecast weight gain due to the model's ability to predict. The integration of IoT with ANNs may improve the quality of care for individuals with dementia and provide carers with actionable insights that enable enhanced, personalized therapy. This combination of technology has the potential to significantly enhance patient outcomes and optimize resource utilization in dementia care facilities.

关键词： Training Temperature sensors Accuracy Artificial neural networks Predictive models Data models sensors Internet of Things biomedical monitoring Dementia

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Detection of Nuclei Cell in Histopathological Images of Uterine Cancer: Adenocarcinoma of Endometrium 4

Detection of Nuclei Cell in Histopathological Images of Uter...

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4th International Conference on Trends in Electronics and Informatics, ICOEI 2020

作者： Patil, shoba. R Patil, veena. I Basaveshwar Engineering College Affiliated to Visvesvaraya Technological University Department of Information Science and Engineering Bagalkot India B.L.D.E.A's V. P. Dr. P. G. Halakatti College of Engineering and Technology Affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Vijayapur India

ISBN: (纸本)9781728155180

Pathologist visual inspection of Histopathological uterine tissue sample is still considered as confirmatory test for uterine cancer finding. Manual assessment depends upon the knowledge and experience of pathologist. Hence subject of concern is objective analysis. An effective way of diagnosis, grading and classification of endometrium adenocarcinoma is structural analysis of the tissue sample. The effectiveness of computer assisted diagnosis, grading and classification depends on glandular structure and detection of individual nuclei cell. Hematoxylin channel from HE stained image is extracted using color deconvolution algorithm. Morphological operation and thresholding are carried out in this work for structural analysis of glandular region and for detection of nuclei cell. To rise the effectiveness of the system elimination of cell nuclei from stroma is done before feature extraction is also described. © 2020 IEEE.

关键词： Cells

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Processing of Human Motions using Cost Effective EEG sensor and Machine Learning Approach 1

Processing of Human Motions using Cost Effective EEG Sensor ...

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1st International Conference on Artificial Intelligence and Data Analytics, CAIDA 2021

作者： Waheed, shaikh Abdul Revathi, s. Matheen, Mohammed Abdul Khan Lodhi, Amairullah Ashrafuddin, Mohammed Maboobatcha, G.s. B.S. Abdur Rahman Crescent Institute of Science Technology Dept. of Computer Science and Engineering Chennai India King Saud University Common First Year Dept. of SDS Riyadh Saudi Arabia Shadan College of Engineering and Technology Dept. of Electronics and Communication Engineering Hyderabad India King Saud University Common First Year Dept. of SDS Riyadh Saudi Arabia

ISBN: (纸本)9780738131771

Emotions are different biological states brought on by neurophysiological changes associated with the nervous system which are affected and results in thoughts, feelings, behavioral responses, and a degree of pleasure or displeasure. These emotions play a vital role in understanding the human response towards any means of action they experience. The system uses an EEG headband device made out of IoT and various machine learning algorithms to understand the human's emotion of any incident they undergo. In this paper, we propose a method to detect and recognize the emotional changes in a human who is exposed to various images. © 2021 IEEE.

关键词： Cost effectiveness

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Comprehensive analysis of faults and diagnosis techniques in cascaded multi-level inverters

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Heliyon 2024年第21期10卷 e39901页

作者： Gatla, Ranjith Kumar Kumar, Devineni Gireesh shashavali, Palthur Dsnm, Rao Kotb, Hossam Alkuhayli, Abdulaziz Ghadi, Yazeed Yasin Mbasso, Wulfran Fendzi Department of Electrical & Electronics Engineering Institute of Aeronautical Engineering Telangana Dundigal 500043 India Department of Electrical & Electronics Engineering B V Raju Institute of Technology Telangana Narsapur 502313 India Department of EEE S K University College of Engineering & Technology Andhra Pradesh Ananthapuramu 515003 India Department of EEE Gokaraju Rangaraju Institute of Engineering & Technology Telangana Bachupally 500090 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria 21544 Egypt Electrical Engineering Department College of Engineering King Saud University Riyadh 11421 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 15322 United Arab Emirates Laboratory of Technology and Applied Sciences University Institute of Technology University of Douala PO Box: 8698 Douala Cameroon

Reliability is a crucial factor to consider for multi-level inverters (MLIs) used in industrial applications. With the increasing number of power semiconductor devices, the potential for defects to significantly degrade the overall system is heightened. A highly effective fault-detection technique is required to minimize the impact of faults. This paper provides a comprehensive overview of the fundamental principles of multi-level inverters and the various sorts of faults that can occur in multi-level inverters. This study provides a comprehensive analysis of five-level cascaded H-bridge multilevel inverters (MLIs) under both normal and defective conditions. The paper outlines a fault-detection method that utilizes total harmonic distortion and a normalized output voltage factor. In addition, the paper discusses a fault-isolation strategy that relies on reducing amplitude modulation. This method leads to the development of a fault-tolerant inverter. The utilization of level-shifted pulse-width modulation (LsPWM) technology is employed for the purpose of switching operations. LsPWM is the most appropriate technique for MLIs that require a low amount of computational resources. The fault-diagnosis approach given is suitable for MLI-based drives, grid-connected operations, and other applications. This paper presents a comprehensive examination of the 5L-CMLI (5-Level Cascaded Multi-Level Inverter) under various fault scenarios in CMLI. subsequently, various fault diagnosis approaches will be examined, including their advantages and disadvantages. The paper discusses several defects that can occur in the Insulated Gate bipolar Transistor (IGbT) of a Current Mode Logic Inverter (CMLI), and also presents a design for a reliable fault diagnosis system. Furthermore, this analysis examines several fault detection strategies in CMLI, categorized according to open-loop and closed-loop dynamic systems fault classifications. © 2024 The Authors

关键词： Fault detection Multi-level inverters Open-loop and closed-loop systems Total harmonic distortion

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Precisely Constructing Asymmetric Triple-Atoms for Highly Efficient Electrocatalysis

SSRN

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ssRN 2024年

作者： Zhang, Jingyan song, Zhongxing Yao, Xiaozhang Guan, Yi Huo, Ziwei Chen, Ning Zhang, Lei sun, Xueliang Department of Mechanical and Materials Engineering University of Western Ontario LondonONN6A 5B9 Canada Eastern Institute for Advanced Study Eastern Institute of Technology Zhejiang Ningbo3150200 China College of Materials Science and Engineering Shenzhen University Shenzhen518060 China Science Division Canadian Light Source Inc. 44 Innovation Boulevard SaskatoonSKS7N 2V3 Canada

Triple-atom catalysts (TACs) are promising to surpass the activity of normal single-atom and dual-atom catalysts. However, the rational design and construction of the TACs remain challenging. Herein, we developed the asymmetric Pt-Ru-Co triple-atoms by selective atomic layer deposition technology. The asymmetric triple-atoms demonstrate superior electrocatalytic performance in both hydrogen evolution reaction (HER) and hydrogen oxidation reaction (HOR) compared to corresponding single-atoms and dual-atoms counterparts. Operando X-ray absorption spectroscopy (XAs) reveals that the heterogeneous metal atoms within the triple-atoms interact and function as an active center, synergistically accelerating reaction kinetics. Additionally, density functional theory (DFT) calculations indicate that the d-band centers of Pt and Ru in triple-atoms shift upward due to the strong electronic interaction with Co single atoms, resulting in enhanced adsorption activity for protons during the HER. This work constructed the asymmetric Pt-Ru-Co triple-atoms with excellent electrocatalytic activity and provided new insights into the catalytic mechanism of TACs. © 2024, The Authors. All rights reserved.

关键词： X ray absorption spectroscopy

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sCMM: Calibrating Cross-modal Representations for Text-based Person search

arXiv

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arXiv 2023年

作者： Liu, Jing Wei, Donglai Liu, Yang Zhang, sipeng Yang, Tong Leung, victor C.M. School of Information Science and Technology Fudan University Shanghai200433 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Academy for Engineering & Technology Fudan University Shanghai200433 China Department of Computer Science University of Toronto TorontoONM5S 1A1 Canada MEGVII Technology Beijing100096 China Academy of Artificial Intelligence SMBU Guangdong Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Text-based Person search (TbPs) is a crucial task in the Internet of Things (IoT) domain that enables accurate retrieval of target individuals from large-scale galleries with only given textual caption. For cross-modal TbPs tasks, it is critical to obtain well-distributed representation in the common embedding space to reduce the inter-modal gap. Furthermore, learning detailed image-text correspondences is essential to discriminate similar targets and enable fine-grained search. To address these challenges, we present a simple yet effective method named sew Calibration and Masked Modeling (sCMM) that calibrates cross-modal representations by learning compact and well-aligned embeddings. sCMM introduces two novel losses for fine-grained cross-modal representations: sew calibration loss that aligns image and text features based on textual caption quality, and Masked Caption Modeling (MCM) loss that establishes detailed relationships between textual and visual parts. This dual-pronged strategy enhances feature alignment and cross-modal correspondences, enabling accurate distinction of similar individuals while maintaining a streamlined dual-encoder architecture for real-time inference, which is essential for resource-limited sensors and IoT systems. Extensive experiments on three popular TbPs benchmarks demonstrate the superiority of sCMM, achieving 73.81%, 64.25%, and 57.35% Rank-1 accuracy on CUHK-PEDEs, ICFG-PEDEs, and RsTPReID, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Advancing Data science Using AI-Driven Processes

Advancing Data Science Using AI-Driven Processes

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： M Jahir Pasha Kumar Gaurav Amit Kumar bhanja Mohamed Dawood shamout Chinna babu Mupparaju Mahesh Manohar bhanushali Computer Science and Engineering G.Pullaiah College of Engineering and Technology Kurnool English School of Liberal and Creative Arts(Social Sciences and Languages) Lovely Professional University Phagwara Department of Management School of Management Sciences Varanasi Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Masters in Computer Science University of central Missouri warrensburg USA Management Studies University of Mumbai VPM's Dr.V.N.Bedekar Institute of Management Studies Thane

Advances in AI-pushed facts technological know-how can be seen in an expansion of different regions. Examples of those consist of herbal Language Processing (NLP), machine learning (ML), Deep gaining knowledge of (DL), pc vision (Cv), computerized data evaluation, and plenty *** technology is used to technique and extract significant records from textual content-based totally datasets. This era uses semantic algorithms, natural language processing tools, and rule-based totally era to apprehend and interpret textual content information. machine studying (ML) is now being used to automate positive obligations such as predicting monetary trends, figuring out consumer styles, and greater. ML technology can be used to identify relationships among facts points that have been previously no longer seen. Deep gaining knowledge of (DL) is a sub field of the device getting to know that applies layers of neural networks to identify patterns inside facts units. This generation gives an exceptionally powerful manner of analyzing records, allowing machines to stumble on complicated relationships that may have previously been too hard to come *** imaginative and prescient (Cv) is another region in which advances in AI-pushed information science are being v.s.ble. Cv generation lets in for computerized evaluation of snap shots and movies, making it viable to become aware of gadgets, come across tendencies, and discover anomalies. computerized facts analysis is one of the maximum powerful approaches.

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Comparative study on focusing wave interaction with cylinder using qale-fem and qalefoam 30

Comparative study on focusing wave interaction with cylinder...

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30th International Ocean and Polar engineering Conference, IsOPE 2020

作者： Yan, shiqiang Ma, Qingwei Asnim, Windiman sulaiman, Zana sun, Hanbing School of Mathematics Computer Science and Engineering University of London London United Kingdom GustoMSC B.V. SchiedamZuid Holland Netherlands College of Shipbuilding Engineering Harbin Engineering University HarbinHeilongjiang China

ISBN: (纸本)9781880653845

The paper presents a numerical comparative study on the interaction between focusing waves and a cylinder using a hybrid solver, qaleFOAM, which couples a fully nonlinear potential theory (FNPT) and an incompressible two-phase Navier-stokes model using a domain decomposition coupling approach. The former is solved by using the Quasi Lagrangian Eulerian Finite Element Method (QALE-FEM) and the latter is solved using the OpenFOAM by the finite volume method with the volume of fluid for identifying the free surface. In this paper, one-way coupling is used in qaleFOAM. For the purpose of comparison, the simulation using the QALE-FEM alone is also carried out. both results are compared with the experimental data, contributing to the numerical comparative study organized by IsOPE 2020. Good agreements are achieved. Furthermore, the significance of the viscous and turbulent effects is also discussed. © 2020 by the International society of Offshore and Polar Engineers (IsOPE).

关键词： Finite volume method

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A hybrid mental health prediction model using support vector Machine, Multilayer Perceptron, and Random Forest algorithms

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Healthcare Analytics 2023年 3卷 100185-100185页

作者： Mohamed, E. syed Naqishbandi, Tawseef Ahmad bukhari, syed Ahmad Chan Rauf, Insha sawrikar, vilas Hussain, Arshad Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai 600048 India Division of Computer Science Mathematics and Science Collins College of Professional Studies St. John's University NY United States Department of Psychiatry Government Medical College Srinagar Jammu and Kashmir 190010 India Department of Clinical and Health Psychology University of Edinburgh Edinburgh United Kingdom

The prevalence and burden of mental health disorders are on the rise in conflict zones, and if left untreated, they can lead to considerable lifetime disability. Following the repeal of Article 370, political unrest spread quickly, forcing the Indian government to impose safety precautions such as lockdowns and communication ban. Consequently, the region of Kashmir experienced a marked rise in anxiety as a result of these lifestyle changes. Machine learning has proven useful in the early diagnosis and prognosis of certain diseases. Therefore, this study aims to classify anxiety problems early by utilising a pre-clinical mental health dataset collected after the abrogation of article 370 in Kashmir. The first part of the paper aims at developing and implementing a prediction model based on classification into one of the five pre-clinical anxiety stages, i.e., stage 1: minimal anxiety, stage 2: mild anxiety, stage 3: moderate anxiety, stage 4: severe anxiety, and stage 5: very severe anxiety. The second part offers recommendations for those suffering from anxiety disorders. Feature selection and prediction are used to predict the correct stage of anxiety for best possible medical intervention. Three different algorithms: support vector Machine(svM), Multilayer Perceptron (MLP), and Random Forest (RF), are employed for predicting anxiety stages. Among them, random forest (RF) achieved 98.13% accuracy. A forecasted likelihood condition was assessed to provide a suitable recommendation. Further, accuracy and kappa statistics are used to assess the performance of the suggested model, which offers a significant addition to predicting anxiety early, and exhibits high prediction and recommendation accuracy. This study aims to assist mental health professionals and experts in making quick and accurate choices. © 2023 The Author(s)

关键词： Machine learning Mental health Multilayer Perceptron Prediction Random Forest support vector Machine

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