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Signet Ring Cell Detection from Histological Images Using Deep Learning

computers, Materials & Continua 2022年第9期72卷 5985-5997页

作者： Muhammad Faheem Saleem Syed Muhammad Adnan Shah Tahira Nazir Awais Mehmood Marriam Nawaz Muhammad Attique Khan Seifedine Kadry Arnab Majumdar Orawit Thinnukool Department of Computer Science UETTaxila47050Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Civil Engineering Imperial College LondonLondon SW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai 50200Thailand

Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous *** and diagnosis of SRC carcinoma at earlier stages is a challenging,laborious,and costly *** detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of *** the presented framework,we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning(DL)technique named Mask Region-based Convolutional Neural Network(Mask-RCNN).In the first step,the input image is fed to Resnet-101 for feature *** extracted feature maps are conveyed to Region Proposal Network(RPN)for the generation of the region of interest(RoI)proposals as well as they are directly conveyed to ***,RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected(FC)network and performs classification along with Bounding Box(bb)generation by using FC *** annotations are developed from ground truth(GT)images to perform experimentation on our developed *** introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical *** aim to release the employed database soon to assist the improvement in the SRC recognition research area.

关键词： Mask RCNN deep learning SRC segmentation

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Exams Item Analysis, Online Examination, and Education Evaluation System Via Internet 20

Exams Item Analysis, Online Examination, and Education Evalu...

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20th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Chaidee, Kamonlak Boontasri, Kanchana Chaidee, Ekkachai Punthura, Kittichai Kumtip, Narong Pankham, Yotsanan Rajamangala University of Technology Lanna Chiangrai Faculty of Business Administration and Liberal Arts Department of Computer Information System Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Science and Agricultural Technology Department of Information Technology Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Electrical Engineering Department of Electrical Engineering Chiang Rai Thailand

ISBN: (纸本)9798350310467

The objective of this study is to develop an exams item bank management system on the internet. The exam item analysis system is used to determine the reliability of the exams using K-R 20 and K-R 21 formulas. The difficulty and power of discrimination values are determined using a choice analysis and percentages of high and lower groups technique. The create exams set system can be performed randomly or identified through the difficulty level. The examination can be performed online and the exam results can be immediately announced. The grading system is used to support the decision-making to evaluate learning achievement. The system's accuracy was tested and found to be accurate compared with the hand calculation results. The system was tested with a sample group and satisfaction was assessed through questionnaires. The satisfaction of teachers and students was highest, while that of administrators was high. The developed exams item bank management system operates as a web application with a security system for supporting in learning activity. © 2023 IEEE.

关键词： Decision making

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Dual-Stage Interleaved On-board Charger for Electric Vehicle using Snow Geese Algorithm 4

Dual-Stage Interleaved On-board Charger for Electric Vehicle...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chandrashekhar, A. Mallikarjuna, B. Thiyagesan, M. Umasankar, L. Ezhil, G. Devi, Rukmani Icfai Foundation for Higher Education Faculty of Science and Technology Department of Mechanical Engineering Telangana Hyderabad501203 India Rns Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bengaluru560098 India R.M.K Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai601206 India S. A. Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai600077 India Bannari Amman Institute of Technology Department of English Tamil Nadu Sathyamangalam638401 India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

The growing demand for electric vehicles (EVs) has highlighted the need for efficient and reliable on-board chargers (OBCs) to reduce charging time and enhance battery life. Nevertheless, there are some issues in classic OBC designs: harmonic distortion may become too large, electromagnetic interference increases, and thermal management is not proper;all of which directly affects other elements of the system. As an optimization approach to enhance the performance of the dual-stage interleaved on-board charger (OBC) for electric vehicles (EVs), this work introduces the Snow Geese Algorithm (SGA). The objective of the proposed method is to enhance charging efficiency and minimize cost. The proposed SGA is used to optimize the dual-stage interleaved converter, focusing on reliable performance and effective operation within EV charging systems. The suggested approach is then put into practice on the MATLAB platform and contrasted with a number of current techniques, including Artificial Bee Colony (ABC), Simulated Annealing (SA), Multi-objective Genetic Algorithm (MOGA), and Particle Swarm Optimization (PSO). The proposed SGO method for dual-stage interleaved EV chargers achieves a cost of 1,050.80 and an efficiency of 97.2 %, demonstrating its effectiveness in reducing expenses while maximizing optimization performance in charging systems. © 2025 IEEE.

关键词： Simulated annealing

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Self-Adaptive Electronics using Artificial Neural Networks 9

Self-Adaptive Electronics using Artificial Neural Networks

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Anita, S. Joon, Rakesh Kumar Devi, S. Rukmani Khandan, K. Lakshmi Howard, Eric Rajendiran, M. R.M.K Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Ganga Institute of Technology And Management Department of Electronics And Communication Engineering Haryana Kablana124104 India Saveetha College of Liberal Arts And Sciences Simats Deemed To Be University Department of Computer Science Tamil Nadu Chennai600124 India Madanapalle Institute of Technology & Science Department of Electrical And Electronics Engineering Andhra Pradesh Madanapalle517325 India Macquarie University Department of Physics And Astronomy Australia Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Electronic gadgets and systems are growing rapidly, requiring new adaptability methods. This research uses Artificial Neural Networks to self-adapt electronic systems. ANNs, inspired by the brain, can optimize electronic circuits and devices in real ***-adaptive electronics uses ANNs for system control and decision-making. ANNs learn to adapt to changing operational circumstances, environmental factors, and user preferences through supervised and reinforcement learning. Feedback systems let neural networks improve energy usage, system parameters, and performance without operator interaction.A sensor array, artificial neural network-based control unit, and actuators make up the self-adaptive electronics system. The system smoothly integrates sensors and computing. Self-adaptive electronics could be used in loT, wearable, and autonomous systems, the research says. Electronic systems operate in unpredictable and dynamic contexts, thus the architecture is ***, self-adaptive electronics outperform static systems in performance, energy efficiency, and adaptability. Artificial neural networks can enable smarter, more responsive, and autonomous gadgets, according to the findings. Finally, self-adapting artificial neural networks in electronics could lead to intelligent systems. This research advances adaptive electronics, enabling new applications in the ever-changing electronics and technology environment. © 2024 IEEE.

关键词： Neural networks

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Cluster Head Selection for Single and Multiple data Sinks in Heterogeneous WSN using Wild Horse Optimization 2

Cluster Head Selection for Single and Multiple data Sinks in...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Kumar, Gotte Ranjith Nayana, B.P. Venkatramulu, S. Saranya, N. Naga Universiti Tun Hussein Onn Malaysia Electrical and Electronic Engineering Johor Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq SR University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India Kakatiya Institute of Technology and Science CSE Dept Telangana Warangal India Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Enhancing a network lifetime by balancing energy as well as minimizing delay is a significant aim of Wireless Sensor Network (WSN). Besides, number of clustering approaches are developed to this process through selecting the optimum Cluster Heads (CHs) in heterogeneous WSN, but it has a significant challenge. In this research, the Wild Horse Optimization (WHO)-based Optimized Clustering approach for single and multiple data sinks is proposed for optimizing CH selection through combining the parameters of sink distance, residual energy in its formulated fitness function. The proposed method achieves better results and it is evaluated by various performance metrices like residual energy, network lifetime, throughput, alive nodes, FND as well as LND and it achieves the values of about each as 61.37, 98.36, 7.8, 96.39, 6129 and 8347 respectively when compared to the existing methods such as Energy-Efficient CH selection utilizing Improved version of Grey Wolf Optimization-based (EECHIGWO) and Enhanced Pelican Optimization Algorithm for the CH Selection (EPOA-CHS). © 2024 IEEE.

关键词： Cluster Head selection Multiple data sinks Network lifetime Wild Horse Optimization and Wireless Sensor Network

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Deploying Hybrid MAELM Approach for Human Emotion Detection Through Speech and Facial Expressions 3

Deploying Hybrid MAELM Approach for Human Emotion Detection ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Senthil Kumar, K. Rukmani Devi, S. Ranjan, Nidhi Rath, Gitika Indira, G. Nishant, Neerav SRM Institute of Science and Technology School of Computing Department of Networking and Communications College of Engineering and Technology Chennai India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India Computer Engineering Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai India Marri Laxman Reddy Institute of Technology and Management Department of CSE Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Babu Banarasi das University School of Engineering Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350343632

Emotion recognition is one of the most fascinating emerging fields of research. It's useful in a lot of different contexts. Some of the most exciting applications of this technology involve robots' ability to see and communicate with one another. Human emotions can be recognized through both visual and audible cues. Facial expressions are one of the most accurate indicators of a person's emotional state. Data preprocessing, feature extraction, and model training are the first three steps of the proposed methodology. Image resizing, a median filter, noise removal, and histogram equalization are all components of preprocessing. Gaussian mixture models and the gray level co-occurrence matrix are being used for feature extraction. After the extraction of features, models with those features are trained using MA-ELM. The proposed method outperforms PCA and ELM, two of the most popular alternatives. © 2023 IEEE.

关键词： Gaussian Mixture Models(GMM) Gray Level Co-Occurrence Matrix (GLCM) Principal Component Analysis (PCA)

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Building the metaverse using digital twins at all scales,states,and relations

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Virtual Reality & Intelligent Hardware 2022年第6期4卷 459-470页

作者： Zhihan LV Shuxuan XIE Yuxi LI M.Shamim HOSSAIN Abdulmotaleb El SADDIK Department of Game Design Faculty of ArtsUppsala UniversitySweden College of Computer Science and Technology Qingdao UniversityQingdaoChina Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Electrical Engineering and Computer Science(EECS) University of OttawaOttawaOntarioCanada

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the *** objects can be at the micro-,meso-,or *** Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain ***,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social ***,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

关键词： Digital twins Metaverse Sensor

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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Facial Emotion Analysis using Deep Learning for Seamless Human-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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An Examination of Machine Learning Techniques for Automating and Optimizing VLSI Design

An Examination of Machine Learning Techniques for Automating...

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2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Jeyarohini, R. Sathya, R. Sellapaandi, S.P. Kavitha, P. Raja Ramesh, D. Mukherjee, Aparajita Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul624622 India Department of Computer Applications Alpha Arts and Science College Tamil Nadu Chennai600 116 India Department of Electrical and Electronics Engineering R.M.K. Engineering College Tamil Nadu Kavaraipettai601206 India Andhra Pradesh Vizianagaram535005 India Department of Computer Science and Engineering IEM-UEM Kolkata700160 India

ISBN: (纸本)9798350376913

This research examines the use of machine learning (ML) techniques to automate and optimise Very Large Scale Integration (VLSI) design procedures. Very large scale integrated circuits (VLSI) require a number of laborious and complex processes to be designed, such as placement, routing, and logic synthesis. Historically, manual involvement and specialised knowledge have been needed to complete these jobs. The goal of this research is to use machine learning approaches to increase chip performance, improve design efficiency, and streamline the workflows involved in VLSI design. Numerous machine learning (ML) techniques, including evolutionary algorithms, reinforcement learning, and supervised learning, are being investigated to determine their efficacy in automating design tasks, optimising design parameters, and cutting down on design cycle durations. This research shows that incorporating machine learning techniques into VLSI design processes is not only possible but also advantageous through extensive tests and case studies. The results of this study are highly relevant to the semiconductor industry in this era of sophisticated microelectronics. They could open the door to more scalable and effective VLSI design processes. © 2024 IEEE.

关键词： Learning algorithms

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