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Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

computers, Materials & Continua 2022年第7期72卷 125-140页

作者： Dibyendu Mukherjee Shivnath Ghosh Souvik Pal D.Akila N.Z.Jhanjhi Mehedi Masud Mohammed A.AlZain Department of Computer Science and Engineering Brainware UniversityBarasat700125India Department of Computer Science and Engineering Global Institute of Management and TechnologyKrishnanagar741102India Department of Information Technology School of Computing SciencesVels Institute of ScienceTechnology&Advanced StudiesChennai600117India School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short *** growing energy usage related to the migration of data into the cloud is one of the biggest *** computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of ***,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge *** the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT *** initial stage is followed by the reconstructing and the processing *** processed data is transmitted to the nodes through controlled deep learning *** entire stage of data collection,transmission and data reduction between edge devices uses less *** experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive *** consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES *** accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.

关键词： Energy efficient internet of things transmission performance cloud computing edge devices

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Object Detection Enhancement Based on Super-Resolution Mapping

Object Detection Enhancement Based on Super-Resolution Mappi...

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Iranian Conference on electrical engineering (ICEE)

作者： Danial Abyazi Dadfar Abyazi Mehran Yazdi Department of Computer Engineering Institute for Advanced Studies in basic Science University Zanjan Iran Department of Electrical and Computer Engineering Shiraz University Shiraz Iran School of Electrical and Computer Engineering Shiraz Uniersity Shiraz Iran

Object detection, while being a key step in many applications, has remained challenging, mainly due to the different resolutions of objects in an image. On the other hand, Super-Resolution (SR) approaches have recently been developed to improve the resolution of an image. In this paper, we utilize SR to enhance the quality of images before applying them to a detection model and propose a structure where the detection performance is improved. Furthermore, we remove the image noises by SR methods. Comparisons depict the superior visual performance of the proposed method compared to the conventional detection models. We evaluate our proposed method for detection of solar panels in two countries. To this aim, we created a dataset of satellite images needed for implementing detection models. As a result, we can simply evaluate the usage of solar panels in countries.

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Spiking Neural Network-Based Control of an Unmanned Aerial System Implemented on a Customized Neural Flight Simulation Environment

Spiking Neural Network-Based Control of an Unmanned Aerial S...

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2024 American Control Conference, ACC 2024

作者： Garcia A, Omar A. Arana, Diego Chavez Espinoza, Eduardo S. Scola, Ignacio Rubio Garcia Carrillo, Luis Rodolfo Sornborger, Andrew T. Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Mexico Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Conahcyt Mexico Conicet and National University of Rosario Department of Industrial Products Engineering of the National Institute of Industrial Technology Argentina Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350382655

A prototyping environment for the development of Spiking Neural Networks (SNN) is integrated with a physics-based flight simulator with the objective of stabilizing a quad rotorcraft Unmanned Aerial System (UAS) via neuromorphic controllers. Making use of the Neural engineering Framework (NEF), SNN-based Proportional+Derivative (PD) controllers are designed for the translational and rotational dynamics of the UAS. An online Model in the Loop (MIL) evaluation scenario was implemented, showing that the proposed neuromorphic controllers are capable of stabilizing the quad rotorcraft UAS in both regulation and trajectory tracking tasks. © 2024 AACC.

关键词： Flight simulators

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Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Preetha P A. Packialatha Department of Computer Science Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics.

关键词： Measurement Heart Filtering Data preprocessing Data acquisition Predictive models Feature extraction Sensors Monitoring Diseases

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Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbreak: A Comprehensive study in Healthcare

Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbrea...

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MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)

作者： Pritam Ramesh Ahire K. Ulaga Priya Computer Science & Engineering Department Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

A comprehensive health monitoring & analyzing system that provides personalized recommendations, goal setting, and tracking based on relevant factors related to BMI & COVID-19. System aims to analyze attack obesity through prediction, prevention and ongoing monitoring through appropriate predictive analytical system. The system's Risk Assessment component categorizes individuals by their risk levels using factors like age, gender, medical history, and behavior related to pre & post covid-19 related to BMI, then relates appropriate connections in human body. The wellness solution includes features such as fitness tracking, data integration, personalized metrics, and predictive analytics to create a holistic approach for predictive analytics health management. The system healthcare monitoring and analyzing system is a groundbreaking solution that leverages predictive analytics to promote a healthy lifestyle, combat obesity, and enhance overall well-being. By considering a range of factors and employing cutting-edge technology, we empower individuals to take control of their health, achieve their fitness objectives, and mitigate health risks.

关键词： COVID-19 Obesity Pandemics Urban areas Sociology Medical services Mental health

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Using Deep Reinforcement Learning in Optimal Energy Management for Residential House Aggregators with Uncertain User Behaviors 7

Using Deep Reinforcement Learning in Optimal Energy Manageme...

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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Lin, Yujun Yan, Linfang Hui, Hongxun Chen, Yin Chen, Xia Wen, Jinyu School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan China City & Energy Research Institute Suzhou China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China University of Strathclyde Department of Electronic and Electrical Engineering Glasgow United Kingdom

ISBN: (纸本)9798350366945

In this study, the home energy management problem, which can be regarded as a high-dimensional optimization problem, for numerous residential houses, is addressed. The concept of the aggregator is utilized to reduce the state and action space and to handle the high dimensionality. A two-stage deep reinforcement learning (DRL)-based approach is proposed for the aggregators to track the schedule from a superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of an electric vehicle's charging demand is quantitatively expressed based on the driver's experience. An aggregate anxiety concept is introduced to characterize the driver's anxiety on the electric vehicle's range and uncertain events. Finally, simulations are conducted to verify the effectiveness of the proposed approach under dynamic user behaviors, and comparisons show the superiority of the proposed approach over other benchmark methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication

Advancements in Speech Recognition: Exploring Feature Extrac...

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Advances in electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： R. Bhavani Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy.

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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The effect of electrical stress and thermal annealing on swift heavy ion irradiated SiGe HBTs

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Nuclear and Particle Physics Proceedings 2023年 336-338卷 37-40页

作者： Hegde, Vinayakprasanna N. Ceressler, J.D. Gnana Prakash, A.P. Department of Physics Vidyavardhaka College of Engineering Mysuru 570002 India School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta 30303 United States Department of Studies in Physics University of Mysore Manasagangotri Mysuru 570006 India

The nano-engineered silicon–germanium heterojunction bipolar transistors (SiGe HBTs) were irradiated with 100 MeV phosphorous ions up to a high total dose of 100 Mrad. The irradiated SiGe HBTs were subjected to mixed mode (MM) electrical stress and isochronal annealing. The DC electrical characteristics were studied for ion irradiated, electrically stressed and thermally annealed SiGe HBTs. The important parameters such as base current (I_B), current gain (h_FE) and output characteristics of the irradiated SiGe HBTs were significantly degraded after 100 Mrad of the total dose. This is due to high energy ion induced damages in Emitter-Base (E-B) and Shallow Trench Isolation (STI) oxides. A significant recovery in device parameters was observed after both MM stress and isochronal annealing. The recovery in electrical characteristics of the irradiated SiGe HBTs is mainly due to the raise in the junction temperature and de-trapping of trapped charges. © 2023 Elsevier B.V.

关键词： Ion irradiation Isochronal annealing Mixed-mode electrical stress SiGe HBTs

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Empowered Artificial Intelligence Approach using Intuitionistic Fuzzy based Deep Neural Network for Chronic Diseases Prediction

Empowered Artificial Intelligence Approach using Intuitionis...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： D. Karthik S. Arun Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Managing the increasing prevalence of chronic illnesses is a major worldwide health concern. It's important to understand the medical problem, its stages, types, and available treatments. The most important component in the early diagnosis of chronic diseases might enhance the diagnostic procedure more successfully. Medical professionals can diagnose a process with the help of numerous artificial intelligence tools. However, ambiguity, irrelevance, and inconsistency in the disease dataset have an impact on the classification accuracy. Conventional machine learning techniques are inadequate for managing scenarios of uncertainty that arise in datasets related to chronic illnesses. To anticipate chronic diseases, each characteristic in the suggested work is represented with a hesitation index. In order to compute the hesitation degree as a more informative procedure in determining the association among features, the belongingness and non-belongingness aspects of the intuitionistic fuzzy concept are induced in the prediction process. This work effectively manages the large chronic illness dataset by developing an intuitionistic fuzzy hesitation index-based similarity measure to weed out unimportant and strongly associated characteristics and enhance the classification model. Utilizing the scored feature information, the deep neural network modifies its hyper parameter to address the overfitting issues that frequently arise in DNNs. The performance of the empowered intuitionistic fuzzy-based deep neural network (EIF-DNN) is analyzed using five distinct chronic conditions, including hypertension, diabetes, hepatitis, cancer, and stroke. Comparing the proposed EIF-DNN against the current versions of multi-layered networks, the simulation results show that it produces promising results in predicting chronic diseases at an early stage.

关键词： Training Uncertainty Accuracy Artificial neural networks Stroke (medical condition) Prediction algorithms Indexes

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