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soft computing Algorithm-Based Intelligent Fuzzy Controller for Enhancing the Network Stability of IPS 2

Soft Computing Algorithm-Based Intelligent Fuzzy Controller ...

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2nd international conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Kalyan, Ch.Naga Sai Rathore, Rajkumar Singh Choudhury, Subhashree Bajaj, Mohit EEE Department Vasireddy Venkatadri Institute of Technology Guntur522 508 India Cardiff School of Technologies Cardiff Metropolitan University Llandaff Campus Western Avenue CardiffCF5 2YB United Kingdom Bhubaneswar751030 India Dehradun248002 India Graphic Era Hill University Dehradun248002 India Applied Science Research Center Applied Science Private University Amman11937 Jordan

The work furnished in this paper projects the application of fuzzy-aided tilt-integral-derivative (TID) with filter (TIDF) for regulating the dynamical behavior of the electrical interconnected power system (IPS) during uncertain conditions. The optimal design of the fuzzy TIDF is performed using the efficient soft computing technique of the water cycle algorithm (WCA). The suggested fuzzy TIDF controller performance is tested on the multi-area diverse source realistic (MADSR) IPS (MADSRIPS) subjected to control area 1 with step load disturbance (SLD) of 10%. Moreover, the analysis carried out on MADSRIPS is considered with the communication time delays (CTDs) for the investigation near to realistic environment. The efficacy of fuzzy-aided TIDF is contrasted with reported controllers available in literature, and the simulation analysis is acquired from the framework of MATLAB, which revealed the sovereignty of the suggested controller. Later, the MADSRIPS is operated with the integration of a HVDC line in conjunction with the tie-line to improve the performance. © 2024 Elsevier B.V.. All rights reserved.

关键词： Controllers

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Indo-Aryan Dialect Identification Using Deep Learning Ensemble Model 2

Indo-Aryan Dialect Identification Using Deep Learning Ensemb...

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2nd international conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Subhash, Paliwal Mohan Kavitha, C.R. Gupta, Deepa Kanjirangat, Vani Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Istituto Dalle Molle di Studi sull'Intelligenza Artificiale USI/SUPSI Switzerland

Language identification has become a critical challenge in NLP, particularly in multilingual countries like India. This study addresses the identification of closely related Indo-Aryan languages, proposing a robust deep-learning ensemble model with data augmentation. Pre-trained models (Indic BERT, Indic Transformer, DevBERT, L3Cube-HindBERT) and various embedding techniques (word2vec, Indic transformer, Keras embedding) are explored with LSTM, Bi-LSTM, GRU, Bi-GRU, and CNN-Bi-LSTM deep learning models. Keras embedding performs better as compared to other embedding methods. The four best deep learning models are selected, and ensemble methods (soft voting, hard voting, stacking with meta models Random Forest and SVM classifier) are employed. When utilizing data augmentation with the training data and fitting the models Bi-LSTM, GRU, Bi-GRU, and CNN-Bi-LSTM with Keras embedding, the best performance, a 93% F1-score, was achieved by their soft voting classifier as compared to the other models. The performance of the proposed model was compared with the existing baseline models on the same dataset. For the comparison of model performance, the macro F1 score, accuracy, recall, and precision were used. © 2024 Elsevier B.V.. All rights reserved.

关键词： Classification (of information)

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Object Detection in Shelf Image of the Retail store using Single Stage Detector 2

Object Detection in Shelf Image of the Retail store using Si...

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2nd IEEE international conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Bagyammal, T. Mahesh Madhav, K. Dhiraj Kumar, Y. Arthi Sri, N. Siva Srinivas, V. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354461

The primary objective is to develop a robust system for precise object identification on retail shelves, accurate item counting, and product categorization through class detection. This multifaceted approach directly tackles critical issues in retail industry, encompassing inventory management and optimized product placement. The ultimate goal is to boost operational efficiency and elevate customer satisfaction within the retail sector. The proposed methodology involves comprehensive steps, including data collection, annotation, pre-processing, and the deployment of advanced object detection models based on YOLOv5, with a particular emphasis on YOLO due to its real-time capabilities and being known as a single stage detector. By achieving these milestones, the proposed model promises to provide valuable solutions for the retail industry, reducing out-of-stock situations, minimizing manual labour, and delivering enhanced product organization. Thus, ensuring good performance compared to its other object detectors of the same use case. We have modified the architecture at different levels of the State-of-the-art YOLOv5 (Backbone, Head) and compared the models implemented for inference. We found out that soft-IoU layer added in the head has given the best performance in detecting object with mean average precision of 79.7 © 2024 IEEE.

关键词： Customer satisfaction

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Employing soft computing-Based GGA-MLP for Hyperparameter Optimization in COVID-19-Infected Lung Image data Classification 2nd

Employing Soft Computing-Based GGA-MLP for Hyperparameter Op...

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2nd international conference on Signal and data Processing, ICSDP 2022

作者： Sinha, Anurag Singh, N.K. Garg, Namit Verma, Amritansh Anum, Alisha Desai, Jay D. Department of Information Technology Amity University Jharkhand Ranchi India Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India Department of Maths and Computing Indian Institute of Technology Kanpur India Department of Civil Engineering Indian Institute of Technology Kanpur India School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research Gujarat Anand India

ISBN: (纸本)9789819914098

Evaluating the hyperparameters and layers of the number of layers, node counts, and activation functions in Multilayer Perceptron (MLP) is not possible through the conventional process of tuning because of its dense layer of architecture. Hence, it becomes very difficult to choose the ideal network architecture layer for testing the data in the ensemble method. Deep learning a subset of artificial is gaining huge popularity in health care and medical image processing due to its precision in the identification of objects within an image. Hence, in this paper, a modified GGA-MLP classifier is used for detecting COVID-19 using an X-ray image dataset. In this paper, a genetic algorithm has been implemented on MLP for optimization of the algorithm by analyzing the principles of network architecture. The goal of this study is to propose a new robust method for classifying COVID-19-infected lung data using a soft computing approach and formative image processing enhancement. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Development of a Programmed Generation of t-way Test cases Using an Improved Particle Swarm Optimization Strategy 2

Development of a Programmed Generation of t-way Test cases U...

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2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Prasad, M. Lakshmi Sastry, J.Kodanda Rama Mallikarjuna, Basetty Sitaramulu, V. Srinivasulu, Ch. Naib, Bharat Bhushan Hyderabad India Kl University Department of Ecse AP Vijayawada India School of Computing Science & Engineering Galgotias University UP 203201 India Institute of Aeronautical Engineering Department of CSE Hyderabad India

ISBN: (纸本)9781665437899

Extensive testing is constrained by a lack of resources and time limitations. As a result, producing appropriate testing data in real-time is critical for advancing the entire application process. A search-based optimization technique has been used to create application testing data since 1992, and interest and action in this area had also recently increased. According to the study, modifying the t-way parametric interaction could indeed drastically reduce the amount of testing data. Based on this idea, many t-way test data procedures have already emerged over the past decade. According to the latest research, utilizing synthetic intelligence-based searching to start generating test data can yield near-optimal results. Acquiring the finest test data, on either hand, appears to be tricky. As a result, generating the biggest selection of testing data for a methodology is practically impossible. This study proposed a swarm smart exploring technique for trying to generate relatively close test records based on the review of recent research of legitimate diverse search-based optimal control methodologies. The results obtained are compared to those of other well-known techniques. According to experiment findings, the suggested technique is extremely fair in terms of test data dimension. © 2022 IEEE.

关键词： soft computing

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An Ensemble Sentiment Classification System of Twitter data for Airline Services Analysis: Smart Use of soft computing 2

An Ensemble Sentiment Classification System of Twitter Data ...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Pokhriyal, Shubham Gairola, Shivansh Vats, Satvik Rawat, Ramesh Singh Raut, Rajesh B. Sharma, Vikrant Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798350369960

To perform the airline services analysis it is difficult to gather the data by means of questionnaire investigation as it is both labour and time consuming. Using information obtained from Twitter is a useful method for gathering customer feedback and enhancing services. Everyone uses social media today, therefore there are a lot of user perspectives available. Twitter social media data frequently employs confusing phrasing that makes it challenging to distinguish between positive and negative sentiments. More than a billion social media communications need to be properly kept in a database and analysed in order to interpret the data. In this study, the majority vote principle was used to guide the use of an ensemble sentiment classification technique that combined the C4.5 Decision Tree, SVM, Bayesian Network, Naive Bayes and Random Forest algorithms. The results show that the suggested ensemble classifier performs better than both majority voting ensemble classifiers and stand-alone classifiers. According to our findings, the ensemble approach might increase overall mining and sentiment classification performance for other services as well. © 2023 IEEE.

关键词： component formatting insert (key words) style styling

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Intrusion Detection System for Local Networks - A Review Study 2

Intrusion Detection System for Local Networks - A Review Stu...

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2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Giri, Aditi Laxmi Annamalai, S. School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665437899

Cyber security is the implementation of smart technologies to safeguard computer systems, mobile devices, communication networks or most importantly the sensitive and confidential data saved in those systems or devices from various types of cyber-attacks, unauthorized access, hackers or intruders. Cyber security can also be considered as a subset of information security because information security is a general term. It aims to protect a wider domain which includes all kinds of information assets either hard copy or soft copy. The recent accelerating rise in digitalization due to Covid-19 has brought in many new challenges. The amount of personal data present on the web due to the same has raised concerns among users. However, it's not only the personal data that is a matter of concern but also the dataset which is given as input to numerous machine learning and deep learning models. Local networks are prone to attacks and intrusion activities now more than ever. As a result, cyber security experts have been working on the development of more complex monitoring systems and algorithms for the detection and prevention of such activities. Various technologies like machine learning and deep learning might play a significant role in improving cyber security. It can help in analyzing patterns and improving the models for recognizing similar attacks in future. This research work aims to study intrusion detection systems in detail and differentiate between intrusion detection systems, intrusion prevention systems and firewalls as IDS and IPS are commonly regarded as the same thing. It also highlights the previous works related to this subject along with their suggested methods. © 2022 IEEE.

关键词： Cybersecurity

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soft computing Approaches for Medical Diagnosis and Prognosis of Heart Disease with Feature Analysis 2

Soft Computing Approaches for Medical Diagnosis and Prognosi...

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2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Ravichandran, S. Pradeepa, M. Padmaja, D Lakshmi Neetha, T. Venkatakiran, S. Gitam University School of Technology Department of Computer Science and Engineering Telangana Rudraram Hyderabad502 329 India Vellore Institute of Technology School of Information Technology and Engineering Tamil Nadu Vellore632 014 India Anurag University Department of Information Technology Telangana Hyderabad India Anurag University Department of Artificial Intelligence Telangana Hyderabad India Sri Venkatesa Perumal College of Engineering and Technology Department of Ece Andhra Pradesh Puttur India

ISBN: (纸本)9798350325799

In the field of medical diagnosis and prognosis, particularly for heart disease, deep learning techniques, specifically those utilizing convolutional neural networks (CNNs), have shown significant promise. With a focus on feature analysis to improve accuracy and interpretability, this abstract provides an overview of deep learning models using CNNs for the study of heart disease. This study aims to build reliable CNN-based models that can precisely diagnose and forecast the prognosis of heart disease using medical data. Large datasets containing various cardiac data, including patient demographics, medical imaging scans, and electrocardiograms (ECGs), are used to train the models to accomplish. The proposed deep learning methods heavily rely on feature analysis. The most instructive and pertinent features from the input data are extracted using feature extraction techniques, and these features are then fed into the CNN models. The CNN models are trained to learn complicated patterns and correlations contained in the data, enabling precise categorization and prediction of cardiac disease. Additionally, feature analysis improves the CNN models' interpretability. To comprehend the areas and aspects of the input data that contribute the most to the model's decision-making process, methods including saliency maps, gradient-based visualization, and class activation mapping are used. This improves clinical decision-making by giving doctors and researchers important insights into the diagnostic and predictive variables of cardiac disease. © 2023 IEEE.

关键词： Diagnosis

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Fault Detection in Wind Turbines using Deep Learning 2

Fault Detection in Wind Turbines using Deep Learning

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2nd international Mobile, Intelligent, and Ubiquitous computing conference, MIUCC 2022

作者： Ayman, Mahi Othman, Mariam Mahmoud, Nour Tamer, Zeina Sayed, Maha Hassan, Yomna M.I. Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9781665466776

Institutions have been redirecting investments away from fossil fuels, creating a path for clean energy generation. The wind industry has seen an exponential increase in recent years. Early fault detection creates an alternative for operation and maintenance (OM), allowing costs to be avoided before they reach a catastrophic stage, and improving turbine reliability. Predictive maintenance was the solution that presented itself for this problem, in which faults are detected before they occur and fixed accordingly. LSTM-Autoencoder and time-series data collected from SCADA sensors installed in wind turbines are used to detect anomalies in several components of the wind turbines that insinuate a major fault might occur. The dataset is collected from a wind farm in the West African Gulf of Guinea in 2016. Results have shown how PCA can be productive in identifying the features with the most influence on the prediction process, with the ability to predict faults 17.5 days prior on average. © 2022 IEEE.

关键词： Industries Fault detection Wind farms Ubiquitous computing Fossil fuels Wind turbines Sensors

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Study on The Importance of Computer Big data to Volleyball Match: Taking the data Analysis and Application of Women's Volleyball Video Challenge of 2020 Tokyo Olympics as an example 2

Study on The Importance of Computer Big Data to Volleyball M...

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2nd international conference on Statistics, Applied Mathematics, and computing science, CSAMCS 2022

作者： Liu, Chenxi Ding, Yi Wang, Jun Faculty for Physical Education Shanghai International Studies University Shanghai China Faculty for Physical Education Fudan University Shanghai China

ISBN: (纸本)9781510663183

The Video Challenge System (VCS) was officially introduced at the 2016 Rio Olympics. Utilizing multiple on-site officiating tracking cameras and processing the captured images with back-end computers, this system was proven to provide fair and effective resolution for disputes. This paper adopts the following research methods: VCS review, and statistical analysis of data from the Women's Volleyball competition at the 2020 Tokyo Olympics. The author obtained the following results. Limited simulation of flight trajectory when a volleyball is in a close range to the net and limited amount of available data to generalize research findings. According to the Fédération internationale de Volleyball (FIVB), the most common challenges include touch, ball in/out, followed by faulty antennae. 1) VCS can produce accurate and transparent results and help minimize human judgment errors in the court owing to its extremely high prediction accuracy, and it can create an instant, objective, and direct 3D animation. 2) Mass data collection is required;the VCS analysis and application in the competition need further improvement. 3) The digitalization and intelligence process in the sports industry are facilitated through the interactions between Hawk-eye technology innovation and volleyball rules. 4) Greater granularity in volleyball sport development is stimulated by creating new challenges for athletes, coaches, and referees. © 2023 SPIE.

关键词： Big data

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