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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ravisankar, B. Shankar, S. Abbas, G. Gopalakrishnan, M. Gugapriyan, S. Nadin, L. Kongu Engineering College Department of Electrical And Electronics Engineering Erode India Kongu Engineering College Department of Mechatronics Engineering Erode India Kongu Engineering College Department of Computer Technology Erode India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

In developing countries like India, several parts of works are carried out manually with the help of hand tools which may lead to health issues. Therefore, proper design is important for avoiding musculoskeletal disorder like arm vibration syndrome, tendonitis, carpal tunnel syndrome etc. This kind of health concerns can be reduced significantly by using hand tools designed from ergonomic concepts. The feeling of discomfort in the form of fatigue, experiencing pain during work can be caused due to prolonged usage of improperly designed hand tools. By introducing ergonomics issues in design of product, the customer's well-being and effectiveness of manual operations can be improved by reducing the musculoskeletal disorders. The aim of the proposed study is to analyze the effects of ergonomically designed holder used to uplift the water canes. Two holders which have been designed ergonomically with various holding position are used. Nearly six male participants are subjected to uplift the water canes using two different modified holders and standard holder. © 2024 IEEE.

关键词： Developing countries

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Logistic Regression based Sentiment Analysis System: Rectify

Logistic Regression based Sentiment Analysis System: Rectify

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Singh, Harsh Pratap Singh, Nagendra Mishra, Anuprita Sen, Santosh Kumar Swarnkar, Mamta Pandey, Deepak Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Computer Science and Engineering Indore India Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar India IES College of Technology Department of Electrical and Electronics Engineering Bhopal462044 India IES University Department of Electrical and Electronics Engineering Bhopal426044 India Corporate institute of Science and Technology Department of Computer Science and Engineering Bhopal505001 India MANIT Department of Computer Science and Engineering Bhopal India TIT Department of EEE Bhopal India

ISBN: (纸本)9798350374100

The detection of various reactions using computer vision, machine learning, and artificial intelligence is a rapidly growing field of research. In this paper, we present a sentiment analysis model based on the Python, NLTK (natural language toolkit) libraries, and machine learning algorithms that can detect multiple reactions from the public on a single platform. The proposed Sentiment Analysis System deals with the very usual problem faced by different companies, manufacturers, and sellers about knowing the customer review for their product. The model classifies the public reviews and the normal text as positive, negative, or neutral. This proposed system is categorised into two parts, one being a field-specific sentiment analysis and the other being a generalised system that can judge any particular word or sentence to be either positive, negative, or neutral. The proposed model is also capable of representing a huge dataset (input as a csv file) in the form of a graph, which can be easily understood by the desired person, and the graphical representation is possible with the Matplotlib library of Python. The model's performance is evaluated using several metrics, such as precision, recall, and others. The model's accuracy and efficiency make it a promising tool for sentiment analysis, which can be used by anyone in any field. In conclusion, the system can work for any specific field provided the data set, and the generalized way can help out with random sentences, and the model's potential can be boosted with some further research. © 2024 IEEE.

关键词： Python

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Identification of Medicinal Plants and Related Fungal Diseases based on Deep Learning Model 6

Identification of Medicinal Plants and Related Fungal Diseas...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kani, Mathina M. Mahalakshmi, R. Shunmugavel, V. Kareem, Abdul M. Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (纸本)9798331522667

In today’s technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. These readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multiplatform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms. © 2025 IEEE.

关键词： Medicinal chemistry

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LMI Based PI Controller Design for a Multivariable System with High-performance Tracking for Non-minimum Phase Systems 1

LMI Based PI Controller Design for a Multivariable System wi...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Nagarajapandian, M. Devan, P. Arun Mozhi Anitha, T. Srinivasan, K. Assaad, Maher Hussin, Fawnizu Azmadi Sri Ramakrishna Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Coimbatore India Universiti Teknologi Petronas Department of Electrical and Electronics Engineering Seri Iskandar32610 Malaysia Ajman University Department of Electrical and Computer Engineering Ajman United Arab Emirates

ISBN: (纸本)9798350348637

This study develops a modified Linear Matrix In-equality with a PI controller to manage the multivariable process and maintain the effective liquid level system. The proposed method is designed by converting the PI into state feedback, and then the problem is determined using the convex optimization problem. The controller synthesis is reduced to an equivalent static output feedback control problem. Further, access to model the auto-tuned PI controller to control a class of multivariable systems using ideal decoupler and linear matrix inequality. The proposed control technique is validated on the quadruple tank system containing flow ratios operating the structure in minimum and non-minimum phase systems. © 2024 IEEE.

关键词： Convex optimization

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Hybrid Fault Prediction and Recovery Framework for VANETs using AI and Federated IoT 7

Hybrid Fault Prediction and Recovery Framework for VANETs us...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Darney, P. Ebby Vallileka, N. Manoj, Sumitha Fernando, A. Vegi Krishnan, R. Santhana Prasath, S. Ram RajaRajeswari College of Engineering Department of Electrical And Electronics Engineering Bangalore India Psna College of Engineering And Technology Department of Computer Applications Dindigul India RajaRajeswari College of Engineering Department of Electronics And Communication Engineering Bangalore India Bangalore Harohalli India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

In contemporary vehicular communication systems, Vehicular Ad Hoc Networks (VANETs) serve a crucial role in enabling seamless data exchange among vehicles and infrastructure components. However, the dynamic and unpredictable nature of vehicular environments renders VANETs susceptible to various faults and disruptions, potentially compromising network performance and jeopardizing safety-critical applications. Therefore, the development of robust fault prediction and recovery mechanisms is imperative to ensure the reliability and resilience of VANETs. This research introduces a novel Hybrid Fault Prediction and Recovery Framework for VANETs utilizing the paradigm of Artificial Intelligence (AI) and Federated Internet of Things (IoT). The proposed algorithm, Deep Neural Network with Federated IoT Model (DNN-FIOT), is presented to enhance fault prediction and recovery efficacy in VANET environments. Extensive simulation analyses comparing DNN-FIOT with existing algorithms are conducted, employing suitable simulation metrics. Results demonstrate the superior performance of DNN-FIOT in terms of fault prediction accuracy, recovery time, and network stability. The proposed framework offers a promising solution for robust fault management in VANETs, ensuring uninterrupted vehicular communication and safety. © 2024 IEEE.

关键词： Internet of things

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Real Time Chest X-Ray Interpretation Applying Vision Transformer for Tuberculosis Detection

Real Time Chest X-Ray Interpretation Applying Vision Transfo...

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2024 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Irhamsyah, Muhammad Roslidar Yunidar Al Lail Qadrillah, M. Nasaruddin Arnia, Fitri Munadi, Khairul Universitas Syiah Kuala Post Graduate Program in Engineering Banda Aceh2311 Indonesia Faculty of Engineering Department of Electrical and Computer Engineering 2311 Indonesia

ISBN: (纸本)9798350368802

Tuberculosis (TB) is an infectious disease that can attack the lungs and other organs. In the diagnosis of TB, the interpretation of X-ray radiological images requires special skills from medical specialists. However, the lack of experience of medical personnel and focus on X-ray examinations can be a challenge for radiologists because it has the potential to cause diagnostic errors in image interpretation. Therefore, we proposed a web-based application of real-time chest X-ray interpreter by integrating the deep learning model of Vision Transformer (ViT) to interpret chest X-ray images. The inference model was trained and tested on 652 normal and 652 tuberculosis X-ray images. The data preprocessing of Contrast Adaptive Histogram Equalization (CLAHE) was implemented to increase chest X-ray image contrast as well as inference model performance. The proposed real time system can inteprete the tested chest x-ray images with an accuracy of 95% and with simulation duration time of 4.7 seconds. © 2024 IEEE.

关键词： Diagnosis

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A New Comparative Approach Based on Features of Subcomponents and Machine Learning Algorithms to Detect and Classify Power Quality Disturbances

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Electric Power Components and Systems 2024年第8期52卷 1269-1292页

作者： Akkaya, Sıtkı Yüksek, Emre Akgün, Hasan Metehan Department of Electrical and Electronics Engineering of Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas Turkey Department of Computer Engineering of Faculty of Engineering and Natural Sciences Sivas University of Science and Technology Sivas Turkey Department of Aircraft Technology of Sivas Vocational School Sivas University of Science and Technology Sivas Turkey

Current measurement systems based on the IEEE-1159 standard have some limitations and robustness problems under noisy and fast-changing conditions. Besides, applying different methods for each Power Quality Disturbance (PQD) to every window is required but time-consuming and not feasible. Therefore, different kinds of two-stage methods, Detection and Classification (D&C), have been improved in many studies. Then, the required measurement can be performed to define disturbance. For this purpose, a new approach based on features of subcomponents with Machine Learning Algorithms (MLAs) to detect and classify PQDs is proposed. 21-class dataset including single and multiple PQDs under different noisy conditions was prepared randomly. Of this dataset, determined features were extracted and some of these were selected. Then, selected features were trained and tested with some MLAs in a workstation. Results obtained from comparative MLAs and the other classification methods show that the best MLA with related features is Random Forest with 96.97% while LightGBM, k-Nearest Neighbors, and XGBoost 96.85%, 96.73%, and 92.82% accuracy, respectively. Because the selected features, optimized parameters, and the related MLA were obtained by investigating for features provided from the PQDs in the whole parameter space, this approach brings the advantages of high accuracy, low D&C complexity, and computing load. © 2023 Taylor & Francis Group, LLC.

关键词： Power quality

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems Science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Resiliency-oriented scheduling of a residential community with a shared electric vehicle parking lot 9

Resiliency-oriented scheduling of a residential community wi...

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9th International Conference on Technology and Energy Management, ICTEM 2024

作者： Akbari-Dibavar, Alireza Zare, Kazem Gozel, Tuba University of Tabriz Faculty of Computer and Electrical Engineering Tabriz Iran Gebze Technical University Department of Electronics Engineering Gebze Turkey

ISBN: (纸本)9798350329797

Interconnected residential buildings bring a new concept to resiliency-oriented scheduling of energy systems. The shared capacity among the buildings can be managed optimally to supply demands in the grid outage times. In this paper, the optimal scheduling of residential buildings has been studied focusing on the idea of a system-of-systems (SOS) in which the resources are shared between agents to withstand power shortages and enhance the total resilience. In the studied case, four buildings with different energy resources, and a shared electric vehicle parking lot are optimized to supply the critical loads of the buildings. The availability of the parking lot's energy capacity is time-dependent and has been modeled using stochastic programming in this paper. Concerning the feasibility constraints for the electric vehicle's departure and arrival time and associated state-of-charge, the proposed optimization scheme minimizes the total cost and curtailed load. The proposed resiliency-oriented problem is formulated as mixed integer linear programming and solved in the GAMS optimization package. The results confirmed the effectiveness and successful performance of the proposed SOS operation and the effective role of the shared electric vehicle parking lot in supplying the residential loads whereas the total curtailed load has been minimized in the grid outage times. © 2024 IEEE.

关键词： Stochastic systems

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