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Improving the Ambient Intelligence Living Using Deep Learning Classifier

computers, Materials & Continua 2022年第10期73卷 1037-1053页

作者： Yazeed Yasin Ghadi Mouazma Batool Munkhjargal Gochoo Suliman AAlsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engieering United Arab Emirates UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Tech University of KoreaGyeonggi-do15073Korea

Over the last decade,there is a surge of attention in establishing ambient assisted living(AAL)solutions to assist individuals live *** a social and economic perspective,the demographic shift toward an elderly population has brought new challenges to today’s *** can offer a variety of solutions for increasing people’s quality of life,allowing them to live healthier and more independently for *** this paper,we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks(BiLSTM)and convolutional neural network(CNN)*** first pre-processed the signal data,then used timefrequency features such as signal energy,signal variance,signal frequency,empirical mode,and empirical mode *** convolutional neural network-bidirectional long-term and short-term memory(CNN-biLSTM)classifier with dimensional reduction isomap algorithm was then used to select ideal *** assessed the performance of our proposed system on the publicly accessible human gait database(HuGaDB)benchmark dataset and achieved an accuracy rates of 93.95 percent,*** reveal that hybrid method gives more accuracy than single classifier in AAL *** suggested system can assists persons with impairments,assisting carers and medical personnel.

关键词： Ambient assisted living convolutional neural network dimensionality reduction frequency-time features wearable technology

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CardioDiverse: A Balanced ECG Dataset for Improved Association of Heart Diseases with Relevant Leads

CardioDiverse: A Balanced ECG Dataset for Improved Associati...

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Artificial Intelligence and Internet of Things (GCAIoT), IEEE Global Conference

作者： Ahmad Mousa Khalid Elgazzar IoT Research Laboratory Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

This study introduces CardioDiverse, a new dataset that has been constructed to address the scarcity of balanced data in existing electrocardiogram (ECG) datasets. CardioDiverse is a combination of multiple datasets integrated to provide rich information to support the association between the different ECG leads and CVD subclasses. The dataset is available to the public through GitHub. Our findings illustrate that particular leads provide clear manifestation to certain CVD subclasses. For instance, leads II, AVF, V3, V4, V5, and V6 were predominantly associated with ST-T wave abnormality, whereas leads II, III, AVF, V1, V5, and V6 were more affiliated with hypertrophy. Furthermore, by extending the analysis to include specific subclasses within the PTB-XL dataset, our study shows promising results in categorizing these subclasses using single-lead ECGs. CardioDiverse is an invaluable resource for researchers, as it provides a more balanced dataset and includes essential data analysis tools within the repository. The data and insights obtained through this study pave the way for future research to investigate the use of these leads for the early detection and diagnosis of cardiovascular diseases.

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Artificial Intelligence and Features Investigating to Detect Neuropsychiatric Symptoms in Patients with Dementia: A Pilot Study

Artificial Intelligence and Features Investigating to Detect...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Badawi, Abeer Choudhury, Samira Elgazzar, Khalid Burhan, Amer M. Ontario Tech University Electrical and Computer Engineering OshawaON Canada Ontario Shores Centre for Mental Health Sciences WhitbyON Canada University of Toronto Temerty Faculty of Medicine TorontoON Canada

ISBN: (纸本)9781665430654

Dementia is a chronic and irreversible condition characterized by progressive cognitive and functional decline, along with non-cognitive Neuropsychiatric Symptoms (NPS) that significantly affect patients' quality of life. This study explores the application of artificial intelligence using wearable sensor data collected from dementia patients (PwD) to develop an AI system for detecting NPS, particularly episodes of agitation, in an institutional setting. We present preliminary results from a real-world study at the ontario Shores Center for Mental Health Sciences. The study outcomes indicate that by employing sequential feature selection, we are able to improve detection accuracy while reducing the number of features from an initial set of 198. Further, the Extra Trees classification model outper-formed other algorithms in accurately classifying non-agitated normal events from agitated events. The study also shows that personalized models yielded superior results, with an average accuracy improvement of 5-10% compared to models trained on all patient data combined. © 2023 IEEE.

关键词： Agitation Dementia Machine Learning Neuropsychiatric Symptoms Wearable sensors

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Selective Lead Integration: Enhancing ECG Classification for Effective Cardiac Monitoring

Selective Lead Integration: Enhancing ECG Classification for...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Ahmad Mousa Khalid Elgazzar IoT Research Laboratory Electrical Computer and Software Engineering Ontario Tech university Oshawa Canada

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

The advent of telehealth technology has ushered in advanced avenues for remote, non-invasive health monitoring, precipitating a surge in the development and application of remote health monitoring platforms. Such platforms are increasingly integrated with sophisticated algorithms to enable timely detection and diagnostic alerts. Prominently, electrocardiogram (ECG) monitoring systems have become pivotal in diagnosing a myriad of cardiac conditions. However, modern cardiac monitoring paradigms exhibit a deficiency in understanding the nexus between diseases and the corresponding leads. This oversight not only diminishes the diagnostic accuracy but also incurs superfluous consumption of computational resources and data bandwidth, thus challenging the efficacy and sustainability of the monitoring process. This research introduces an innovative model tailored for the concurrent multiclass classification of ECG signals by leveraging a minimal and highly correlated set of leads. A performance assessment delineates that the proposed model attains a Receiver Operating Characteristic (ROC) range of 92.8%-99.7% in all 23 categories using only six leads, sur-passing existing ECG classification strategies that employ 12-lead ECG signals. Furthermore, empirical results underscore that our proposed strategy, even with its reduced lead count, achieves superior accuracy for three cardiac conditions and maintains equivalent accuracy for an additional 17 conditions according to the testing data set.

关键词： Computational modeling Telemedicine Receivers Electrocardiography Lead Sustainable development Surges

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SegIt: Empowering Sensor Data Labeling with Enhanced Efficiency and Security 24

SegIt: Empowering Sensor Data Labeling with Enhanced Efficie...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Zhang, Zhen Abraham, Samuel Lee, Alex Li, Yichen Morota, Gota Ha, Dong Sam Shin, Sook BlacksburgVA United States The Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China School of Animal Sciences Virginia Tech BlacksburgVA United States

ISBN: (纸本)9798400717833

SegIt is a novel, user-friendly, and highly efficient sensor data labeling tool designed to tackle critical challenges such as data privacy, synchronization accuracy, and memory efficiency inherent in existing labeling tools. While many current sensor data labeling tools provide free online services, they typically necessitate users to upload unlabeled sensor data, alongside video or audio references, to cloud storage for labeling. Nevertheless, such third-party storage exposes user data to potential security risks. SegIt, an innovative open-source tool, provides a software solution for tagging unlabeled sensor data directly on a local computer, ensuring enhanced accuracy, convenience, and, most importantly, data security. © 2024 Copyright held by the owner/author(s).

关键词： Data accuracy

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Critical Thermal Characterization of Next-Generation Solid-State Batteries for Automotive Battery Management Systems

Critical Thermal Characterization of Next-Generation Solid-S...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Chandan Chetri Sheldon Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa Canada

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Solid-state batteries (SSBs) are at the forefront of next-generation energy storage technologies, promising enhanced safety, energy density, and longevity compared to traditional lithium-ion batteries. However, managing the thermal behavior of SSBs during charging is critical to ensuring their safe and reliable operation. This paper presents an in-depth analysis of the thermal characteristics of SSBs under various charging conditions, based on laboratory experiments conducted in controlled environments. The study specifically evaluates the thermal performance of SSBs in comparison to traditional lithium nickel cobalt aluminum oxide (NCA) batteries, with an emphasis on their potential application in e-mobility. The insights gained from this research are expected to inform the development of advanced thermal management strategies, contributing to the design of safer and more efficient solid-state battery technologies for e-mobility applications.

关键词： Thermal management of electronics Battery management systems Amplitude modulation Thermal management Lithium Batteries Solid state batteries Thermal analysis Next generation networking Automotive engineering

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Six Leads Are All You Need for Efficient Cardiac Analysis

Six Leads Are All You Need for Efficient Cardiac Analysis

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Artificial Intelligence and Internet of Things (GCAIoT), IEEE Global Conference

作者： Ahmad Mousa Khalid Elgazzar IoT Research Laboratory Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

Electrocardiography (ECG) serves as a noninvasive method of monitoring the electrical activity of the heart by employing electrodes attached to the skin. These electrodes detect the minuscule electrical signals generated by the heart’s activity. A conventional ECG, known as a 12-lead ECG, necessitates the positioning of 12 electrodes across the body to yield a detailed perspective of the heart’s electrical function. Our study introduces an innovative approach to real-time ECG signal classification, using a maximum of six leads, with the aim of reducing the time and resources dedicated to data acquisition and signal processing without compromising accuracy. We introduce a technique for grouping leads and assess the performance of the classification of ECG signals on various leads. Our findings indicate that implementing lead-wise grouping with up to six leads results in a 93.67% reduction in sampling time, a 50% decrease in data size on the acquisition device, and an 84.72% cut in signal processing time, all while experiencing a minimal accuracy decrease of 0.08%. The efficacy of the proposed lead grouping has been assessed through the Kafka real-time platform.

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Re-engineering higher education by models suitable for COVID-19 and students with disabilities 16

Re-engineering higher education by models suitable for COVID...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Saniei, Namdar Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9781665414685

Thanks to computers and the internet, since the rise of COVID-19, different methods of online teaching have replaced the face-to-face teaching;however there has not been a viable method of assessment that is fully capable of replacing in class assessments. It is hard, if not impossible, to maintain the integrity of the exams, when conducted remotely. Even prior to the COVID attack, arranging and conducting exams for students, who depend on Students Accessibility Services (SAS), were becoming cumbersome and a burden for universities and instructors. If there were no exams required, as an assessment tool, both the abovementioned issues, would not be a problem. In this paper a few theoretical common solutions to both the issues mentioned above is theorized, proposing elimination or reduction of the exams, as major assessment tool for undergraduate students. The model in which all the exams are eliminated is referred to as Stress-free, and the one with reduced exams is called Stress-less model. Implementing the models discussed in this paper, demand an overhaul of the entire higher education system. © 2021 IEEE.

关键词： Students

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Big Data Bot with a Special Reference to Bioinformatics

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computers, Materials & Continua 2023年第5期75卷 4155-4173页

作者： Ahmad M.Al-Omari Shefa M.Tawalbeh Yazan H.Akkam Mohammad Al-Tawalbeh Shima’a Younis Abdullah A.Mustafa Jonathan Arnold Biomedical Systems and Informatics Engineering Department Yarmouk UniversityIrbid21163Jordan Department of Medicinal Chemistry and Pharmacognosy Yarmouk UniversityIrbid21163Jordan Department of Electrical Computer and Software EngineeringUniversity of Ontario Institute of TechnologyOshawaL1H7K4Canada Department of Mechanical Engineering University of MosulMosul41001Iraq Genetics Department University of GeorgiaAthens30602GAUSA

There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential *** scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough *** data should go through several processes,including mining,data processing,analysis,and *** study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human *** software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line *** software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel ***,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data *** a result,data collection from the web will become faster and less expensive,with no need for manual data *** software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.

关键词： Bioinformatics big data data extraction bot drug design

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Detection of Ophthalmic Disorders Through Deep Feature Fusion

Detection of Ophthalmic Disorders Through Deep Feature Fusio...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Hussain, Muhammad Toheed Faraz, Muhammad Aziz, Sumair Iqtidar, Khushbakht Khan, Muhammad Umar Montes, Gabriel Axel University of Engg. and Tech. Taxila Department of Electronics Engineering Taxila Pakistan University of Canberra Faculty of Science and Technology CanberraACT Australia National University of Sciences and Tech. Dept. of Computer and Software Engineering Islamabad Pakistan University of Newcastle CallaghanNSW Australia

ISBN: (纸本)9798350349719

Given the rising prevalence of ophthalmic disorders, this study aims to meet the urgent need for an automated technique capable of diagnosing and treating these conditions. Our approach combines deep learning and machine learning techniques to develop a diagnostic system that can detect and classify seven distinct ophthalmic disorders. Manual diagnosis of ophthalmic disorders is labor-intensive and slow, worsened by a shortage of medical experts. In this paper, we propose a medical diagnostic system that utilizes deep features extracted from a ResNet-50 CNN, which has been trained on retinal images enhanced through contrast-limited adaptive histogram equalization (CLAHE). These deep features serve as input for the machine learning model. To address the class imbalance problem, we employed ADASYN to generate synthetic features for all classes. An Ensemble subspace KNN classifier, trained on these deep features, achieved a remarkable mean accuracy of 99.18% compared to other classifiers evaluated in this study. The proposed technique has the potential to assist medical experts in achieving timely and accurate diagnoses of ophthalmic diseases. © 2024 IEEE.

关键词： Ophthalmology

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