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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Pongiannan, R.K. Franklin, J. Richard Pravin, A. Maria, Marissa Mishra, Sarvottam Kumar Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India Computer Science Engineering Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331510022

BlindSpace is an innovative application aimed at improving the lives of visually challenged individuals. Leveraging cutting-edge image captioning and object detection technologies, the app allows users to capture images using their smartphone's camera, providing detailed scene descriptions and audibly identifying objects within those images. This not only enhances users' understanding of their environment but also fosters independence and confidence. With voice commands and text-to-speech technology, the application offers a user-friendly experience and can be customized to individual preferences. Operating efficiently offline, BlindSpace stands as a significant leap in accessibility technology, bridging the gap between the visual and non-visual worlds for visually challenged individuals and promoting inclusivity and autonomy. © 2024 IEEE.

关键词： Smartphones

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Explorative Study on Motor Interference During Synchronous Human and Robot Arm Movements Under Varied Presence of a Robot Head

Explorative Study on Motor Interference During Synchronous H...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Kaya, Mertcan Kuhnlenz, Kolja Coburg University of Applied Sciences and Arts Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg Germany

ISBN: (纸本)9781665481090

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI. © 2024 IEEE.

关键词： Robotic arms

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Prediction of Cardiac Arrest using Generalized Soft computing Techniques 18

Prediction of Cardiac Arrest using Generalized Soft Computin...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Hota, Radhanath Dash, Sachikant Mishra, Sujogya Das, Subhalaxmi Lenka, S. Pradhan, G. Giet University Department of Computer Science Engineering Odisha Gunupur India Gandhi Institute for Education and Technology Department of Computer Science Engineering Odisha Gunupur India Odisha University of Technology and Research Department of Mathematics Bhubaneswar India Odisha University of Technology and Research Dept. of Computer Science and Engineering Bhubaneswar India Utkal University Department of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9789380544519

Cardiac-related diseases always lead to fatal consequences around the globe. This research includes vague data analysis of the data related to cardiac anomalies. This study proposes a method to develop a dimension reduction model using vague data related to cardiac diseases using rough set theory (RST). The overall research is divided into two different sections. The first section of this study proposes analyses of the raw cardiac-related data using RST, and the second section calculates the performance accuracy using Principal Component Analysis (PCA) applied to the universal data set;the performance measures also include a combination of two Universal data sets. The above claims are verified using statistical tests © 2024 Bharati Vidyapeeth, New Delhi.

关键词： PCA Performance and accuracy Rough Set Theory Statistical method Vague data

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Dynamic edge server placement in mobile edge computing using modified red deer optimization algorithm and Markov game theory

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Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 12297-12315页

作者： Asghari, Ali Vahdani, Alireza Azgomi, Hossein Forestiero, Agostino Department of Computer Engineering Shafagh Institute of Higher Education Tonekabon Iran Department of Computer Engineering Islamic Azad University Rasht Branch Rasht Iran Institute for High-Performance Computing and Networking National Research Council of Italy c/o University of Calabria CS Rende Italy

Today, the development of intelligent mobile communication equipment, online applications such as online games, e-commerce, e-learning, and multimedia applications, and also the advent of the fifth generation of mobile communication networks have drawn mobile cloud services providers' special attention to this area. Cloud resources are brought to the edge of the network and adjacent to cloud users to increase the quality of the service provided. Proper placement of cloud resources reduces the resource access delay and balances the workload. Numerous studies have been conducted in this field. In most studies, it is assumed that the candidate location of the resources is fixed. The resources' area is also considered flat. Since the requested services of cloud users vary at different times of the day and in different geographical areas, the resources do not have the same efficiency during different times of the same day. For example, some administrative and business areas are active during the day, and at other times the demand decreases sharply. Thus, the mobility of cloud servers can be considered a suitable solution. In the proposed method, a two-stage static (SSP-RM) and dynamic (DSP-RM) model of cloud resource placement using Red deer (RD) and Markov game (MG) algorithms is introduced. Some cloud resources will be portable according to temporal and spatial requirements so that they can be transferred to different geographical areas according to different demands to increase their efficiency and distribute a more balanced workload on them while reducing latency. In the proposed method, by clustering the cloud resource deployment area and using the RD algorithm, the complexity of the cloud resource placement problem is reduced, and by using the Markov game model, the global convergence of resource deployment is obtained. Finally, dynamic cloud resource placement is performed using the Q-Learning (QL) algorithm. The results of experiments show that the proposed algor

关键词： computer games

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Industrial IoT Smart Manufacturing Systems based on Cloud computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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A Novel Handwritten Prescription Recognition with Stochastic Gradient Descent Using Adaptive Momentum Learning 6th

A Novel Handwritten Prescription Recognition with Stochastic...

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Anbazhagan, E. Sophiya, E. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819734412

Personalized medicine relies heavily on handwritten prescriptions since they provide patients with instructions that are customized to them. In the medical industry, accurately interpreting handwritten prescriptions poses a serious difficulty that affects patient safety and the standard of treatment due to the illegible handwriting by doctors. This research presents "MediScriptNet", a novel model that improves handwritten medical prescription detection by utilizing the advantages of both Bidirectional Long Short-Term Memory networks (BiLSTM) and Depthwise Separable Convolutional Networks (DSConvoNet). MediScriptNet establishes a new benchmark for handwritten prescription decoding complex cursive scripts by merging the sequence prediction expertise of BiLSTM with the lightweight and effective feature extraction skills of DSConvoNet. We introduce a novel architecture with an adaptable momentum learning rate that is optimized by stochastic gradient descent (SGD) and Particle Swarm Optimization (PSO). More stable and quicker convergence can be achieved by using the adaptive momentum to enable subtle adjustments to the learning rate based on the complexity of the recognition patterns and the model’s historical learning curve. Validation on a wide range of prescription datasets shows that MediScriptNet performs much better than current models in terms of accuracy, speed, and computing efficiency with performance of 94.5% accuracy as adaptive learning enhances the SGD optimization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medical informatics

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Parkinson's Disease Severity Assessment: A Comparative Study & Interpretability Analysis 5

Parkinson's Disease Severity Assessment: A Comparative Study...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Tiwari, Utkarsh Jahanve, P. Reddy Karna, Siwani Anand, M. Pati, Peeta Basa Prakash, Bhanu K.N. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Bioinformatics Institute A*STAR Singapore

ISBN: (纸本)9798350361155

Parkinson's disease (PD) is a progressive neurological disorder that mainly affects people over the age of sixty. Currently, there is no such cure for PD. However, Treatments are available to help relieve the symptoms related to PD. A rating tool called MDS-UPDRS III is used to gauge the severity and progression and stages of disease severity are measured by the Hoehn and Yahr (NHY) staging system. This study performs a comparative analysis of the effectiveness of various machine learning models such as AdaBoost, XGBoost, Gradient Boosting Classifier (GBC), Decision Trees (DT), K-Nearest Neighbor (KNN), Logistic regression (LR), and Gaussian Naïve Bayes (GNB) on a dataset containing a variety of PD-related features obtained from 2861 persons. Local Interpretable Model-agnostic Explanations (LIME) is employed to perform the analysis of the strength of each feature toward the outcome prediction for various classification tasks. It is observed that the AdaBoost classifier delivers superior performance of 93.2% accuracy and 84.2% F1-Score for the dataset. © 2024 IEEE.

关键词： Adaptive boosting

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QoS Based Efficient Link and Consistent Routing in Wireless Sensor Network

QoS Based Efficient Link and Consistent Routing in Wireless ...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Sivakumar, S. Anusuya, R. Nagaraju, V. Narendruni, Lakshmi Priya Thamizhamuthu, R. Department of Computer Science Tamil Nadu Perambalur India Modern Institute of Technology and Research Centre Department of Computer Science Engineering Rajasthan Alwar India Department of Computer Science Engineering Tamil Nadu Chennai India Nalla Malla Reddy Engineering College Department of Computer Science Engineering Telangana Hyderabad India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9781665462631

Wireless sensor networks, also known as WSN s, are an essential component for a wide variety of applications, particularly those in which a sizeable number of nodes are scattered all over the region being watched. Evaluation of the accuracy and quality of each connection is necessary for WSN s. When the mapping models between the physical layer and link quality characteristics are used, it is possible to forecast the outcomes, which comes with the advantages of greater vigilance and lower overhead. Traditional link quality estimator parameters, on the other hand, are unable to capitalize on connection quality information and instead deliver inconsistent routing. Simultaneously, the shortest data packet routing action is considered the most energy utilization, and it creates inconsistent routing. QoS-based Efficient Link and Consistent Routing (QLCR) in WSN are proposed to solve these issues. Here, the node Received Signal Strength Indication, Consistency Achievement, Forecasted Consistency, Communication Rate, and Left-over Energy parameters are used to measure the prediction of the link QoS factor. The forward nodes are chosen from the nodes with the greatest QF score. A thorough evaluation of the link's quality may guarantee that data will be sent, improve network performance, and extend the lifespan of the network. The Wireless Sensor Network, often known as WSN, is an important component for a variety of applications in which a sizeable number of nodes are spread out throughout the region being monitored. © 2023 IEEE.

关键词： Sensor nodes

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