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

作者： Shanmugasundaram, C. Umamaheswari, C. Vijayalakshmi, A. Varghese, Prabha Elizabeth Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering Puducherry India St. Thomas College of Arts and Science Department of Computer Science with Data Science Tamil Nadu India Sri Manakula Vinayagar Engineering College Department of Biomedical Engineering Puducherry India

ISBN: (纸本)9798331510022

The agriculture industry significantly contributes to the nation's economy by employing a large segment of the population and enhancing the GDP. It is a primary source of livelihood for many people, particularly in rural areas. India's agricultural sector is known for its diversity, producing a wide range of crops including rice, wheat pulses, sugarcane, cotton, fruits, and vegetables. This industry faces a significant challenge in predicting crop yield production and crop demand accurately. Efforts have been made to address these challenges through various government initiatives such as subsidies and continued investment in agricultural research and development. The traditional methods of predicting crop yield and market demand is cumbersome and time consuming and is susceptible to errors, resulting in inefficient resource utilization and revenue losses. There is a need for predicting the most efficient crop based on various influential environmental markers and forecasting the profitability. This project aims to develop an intelligent platform that uses machine learning algorithms to estimate the crop for cultivation in each area based on environmental factors. The project also aims to forecast market demand and pricing trends for the identified crop with the aim of increasing the profitability for the farmers and other stakeholders in the agriculture industry. © 2024 IEEE.

关键词： Agricultural economics

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Enhancement of Co-located Shared VR Experiences: Representing Non-HMD Observers on Both HMD and 2D Screens 23

Enhancement of Co-located Shared VR Experiences: Representin...

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23rd IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2024

作者： Guo, Zixuan Xu, Wenge Wang, Hongyu Wan, Tingjie Baghaei, Nilufar Lo, Cheng-Hung Liang, Hai-Ning Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China The University of Liverpool Department of Computer Science Liverpool United Kingdom Birmingham City University College of Computing Birmingham United Kingdom The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia Xi'an Jiaotong-Liverpool University Department of Industrial Design Suzhou China Computational Media and Arts Thrust Guangzhou China

ISBN: (纸本)9798331516475

Virtual reality (VR) not only allows head-mounted display (HMD) users to immerse themselves in virtual worlds but also to share them with others. When designed correctly, this shared experience can be enjoyable. However, in typical scenarios, HMD users are isolated by their devices, and non-HMD observers lack connection with the virtual world. To address this, our research investigates visually representing observers on both HMD and 2D screens to enhance shared experiences. The study, including five representation conditions, reveals that incorporating observer representation positively impacts both HMD users and observers. For how to design and represent them, our work shows that HMD users prefer methods displaying real-world visuals, while observers exhibit diverse preferences regarding being represented with real or virtual images. We provide design guidelines tailored to both displays, offering valuable insights to enhance co-located shared VR experiences for HMD users and non-HMD observers. © 2024 IEEE.

关键词： Virtual environments

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An Intelligent System for Furfural Estimation in the Power Transformers 2nd

An Intelligent System for Furfural Estimation in the Power T...

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2nd International Conference on Renewable Power, ICRP 2023

作者： Nezami, Md. Manzar Hashem, Hythem Equbal, Md. Danish Khan, Mohammad Junaid Ansari, Md. Fahim Mustafa, Elfatih Elmubarak Department of Electronics and Communication Engineering GLA University Mathura281406 India Department of Computer Science Faculty of Science and Arts at Belqarn University of Bisha Sabt-Al-Alaya61985 Saudi Arabia Department of Electrical Engineering Galgotias College of Engineering and Technology Greater Noida India Haryana Nuh India Department of Electrical Engineering Graphic Era University Dehradun India

ISBN: (纸本)9789819967483

Power transformers are supposed to be an expensive and critical component of a power system and so its schedule maintenance is an important aspect near the utilities. The cellulose paper used as the solid insulating material of the transformer deteriorates regularly due to progressive aging. As a result, it produces several degradation by-products of cellulose insulation into the transformer oil. Furfurals are among the major by-product of cellulose and are exploited to estimate the physical state of the transformer’s dielectric and the electrical insulation directly and noninvasively. In the present work, an intelligent system is proposed and developed that predicts the level of furfural in the transformer oil. The system makes predictions using easily quantifiable parameters, enabling utilities to avoid suffering financial losses. The proposed system employs the Adaptive Neuro Fuzzy Inference System (ANFIS) technique with temperature and moisture as the input and 2-Furfuraldehyde (2-FAL) as the output. An optimum arrangement of the developed model to find accurate outputs has been achieved by the suitable selection of number and types of the membership functions of the network. Also, an exhaustive study is made to optimize the training data points to achieve the accurate predictions at minimum iterations. The proposed system is validated with nine test conditions of temperature and moisture, and the output thus obtained is verified through existing life models in the literature. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Power transformers

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An Effective Forest Fire Prediction based on Contrastive Vision Transformer with Pool Former 2

An Effective Forest Fire Prediction based on Contrastive Vis...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. Husain, Saef Obad Ezhil, G. Indhumathi, V. Revathi, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Humanities Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Applications Sona College of Arts and Science Salem India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Since years, forest fires are a significant concern globally, causing damage to ecosystems and human settlements. For effective prevention and mitigation predicting forest fires is important, different factors contribute to forest fires which includes human activities, topography, climate change and weather conditions. Traditional approaches for predicting forest fires had faced significant challenges which include limited validation of prediction models and lack of standardization. Therefore, this paper proposes Contrastive Vision Transformer (CViT)-Pool Former for predicting forest fire which is employed on Forest Fire Big Data dataset which enabled better comparability. This dataset is preprocessed by using data augmentation, noise reduction and data enhancement techniques. Then, the features are extracted by using CViT which extracted optimal features based on weather conditions and captured contextual relationships by maximizing mutual information. After that, Pool Former light weight attention module is used for predicting fires which gave more accurate predictions by enabling targeted prevention and mitigation, minimized unwanted deployments and resource waste, boosted prediction accuracy by token fusion and enhanced representation learning. The proposed CViT-Pool Former method established better results including accuracy (92.8%), precision (89.4%), recall (91.4%) and F1-score (90.2%) when compared with existing Region Proposed Convolutional Neural Network (R-CNN). © 2024 IEEE.

关键词： Premixed flames

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Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

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computers, Materials & Continua 2023年第1期74卷 1673-1691页

作者： Sukhendra Singh Sur Singh Rawat Manoj Gupta B.K.Tripathi Faisal Alanzi Arnab Majumdar Pattaraporn Khuwuthyakorn Orawit Thinnukool Department of Information Technology JSS Academy of Technical EducationNoidaIndia JSS Academy of Technical Education NoidaIndia Department of Electronics and Communication Engineering JECRC University JaipurRajasthanIndia Harcourt Butler Technological University Kanpur India Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

In computer vision,object recognition and image categorization have proven to be difficult *** have,nevertheless,generated responses to a wide range of difficult issues from a variety of *** Neural Networks(CNNs)have recently been identified as the most widely proposed deep learning(DL)algorithms in the *** have unquestionably delivered cutting-edge achievements,particularly in the areas of image classification,speech recognition,and video ***,it has been noticed that the CNN-training assignment demands a large amount of data,which is in low supply,especially in the medical industry,and as a result,the training process takes *** this paper,we describe an attentionaware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned *** provide attention-aware properties to the Attention *** attentionaware features of various modules alter as the layers become *** a bottom-up top-down feedforward structure,the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention *** the present work,a deep neural network(DNN)is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray *** produce attention-aware features,the suggested networkwas built by merging channel and spatial attentionmodules in DNN *** this network,we worked on a publicly available Kaggle chest X-ray *** testing was carried out to validate the suggested *** the experimental results,we attained an accuracy of 95.47%and an F-score of 0.92,indicating that the suggested model outperformed against the baseline models.

关键词： Attention network image classification object detection residual networks deep neural network

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Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

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computers, Materials & Continua 2023年第2期74卷 3063-3083页

Currently,breast cancer has been amajor cause of deaths in women worldwide and the World Health Organization(WHO)has confirmed *** severity of this disease can be minimized to the large extend,if it is diagnosed properly at an early stage of the ***,the proper treatment of a patient having cancer can be processed in better way,if it can be diagnosed properly as early as possible using the better ***,it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast *** address the above said issues,this paper presents a hybrid model using the transfer learning to study the histopathological images,which help in detection and rectification of the disease at a low *** dataset experiments were carried out to validate the suggested hybrid model in this *** experimental results show that the proposed model outperformed the baseline methods,with F-scores of 0.81 for DenseNet+Logistic Regression hybrid model,(F-score:0.73)for Visual Geometry Group(VGG)+Logistic Regression hybrid model,(F-score:0.74)for VGG+Random Forest,(F-score:0.79)for DenseNet+Random Forest,and(F-score:0.79)for VGG+Densenet+Logistic Regression hybrid model on the dataset of histopathological images.

关键词： Histopathological deep neural network machine learning breast cancer binary classification transfer learning

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Protein-Ligand Interaction Prediction with Effective Method of Graph Attention Networks and Long Short-Term Memory 2

Protein-Ligand Interaction Prediction with Effective Method ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. AlFatlawy, Ramy Shashidhara, K.S. Revathi, R. Reka, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (纸本)9798331504960

Nowadays, the drug discovery field of molecules in protein-ligand interactions developing effective therapeutics for identifying potential drug candidates with spatial features to predict binding affinity. However, the existing Deep Learning (DL) based prediction approaches failed to interact and predict the binding affinity of protein-ligand precisely for drug design with binding affinity. To overcome this problem, a Graph Attention Networks with Long Short-Term Memory (GATN-LSTM) method is proposed with spatiotemporal forecasting for interaction between protein and ligand in binding affinity prediction for drug design. The proposed GATN-LSTM method is employed on DUD-E dataset consists of enhanced the prediction by capturing relationship (interaction) between protein and ligand in molecules for drug discovery using PubChem for prediction. Then, the preprocessed data of protein-ligand interaction is extracted using (CNN-GNN) for better generalization of both local and global features for predicting drug discovery. Finally, GATN-LSTM is employed for interaction of molecules with binding affinity which enhanced the drug design and also improved the prediction results. Experimental results of the proposed GATN-LSTM method attained accuracy (0.9992), F1-score (0.9985) are higher than existing prediction approaches such as Convolutional Neural Network (CNN) and Mutual Information-based Feature Selection (MIFS). © 2024 IEEE.

关键词： Graph neural networks

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Data-Driven Ground-Fault Location Method in Distribution Power System With Distributed Generation

arXiv

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arXiv 2024年

作者： Caporuscio, Mauro Dupuis, Antoine Löwe, Welf Department of Computer Science and Media Technology Linnaeus University Sweden Department of Electrical Engineering Uppsala University Sweden

The recent increase in renewable energy penetration at the distribution level introduces a multi-directional power flow that outdated traditional fault location techniques. To this extent, the development of new methods is needed to ensure fast and accurate fault localization and, hence, strengthen power system reliability. This paper proposes a data-driven ground fault location method for the power distribution system. An 11-bus 20 kV power system is modeled in Matlab/Simulink to simulate ground faults. The faults are generated at different locations and under various system operational states. Time-domain faulted three-phase voltages at the system substation are then analyzed with discrete wavelet transform. Statistical quantities of the processed data are eventually used to train an Artificial Neural Network (ANN) to find a mapping between computed voltage features and faults. Specifically, three ANNs allow the prediction of faulted phase, faulted branch, and fault distance from the system substation separately. According to the results, the method shows good potential, with a total relative error of 0,4% for fault distance prediction. The method is applied to datasets with unknown system states to test robustness. © 2024, CC BY.

关键词： Time domain analysis

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New 5G Kaiser-Based Windowing to Reduce Out of Band Emission

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computers, Materials and Continua 2022年第2期71卷 2721-2738页

作者： Hammoodi, Ahmed Audah, Lukman Al-Jobouri, Laith Mohammed, Mazin Abed Aljumaily, Mustafa S. Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Batu Pahat86400 Malaysia School of Engineering Arts Science and Technology University of Suffolk IP4 1QJ United Kingdom College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Electrical Engineering and Computer Science Department University of Tennessee KnoxvilleTN37901 United States

OFDM based waveforms are considered as the main part of the latest cellular communications standard (namely 5G). Many inherited problems from the OFDM-Based LTE are still under investigation. Getting rid of the out of band emissions is one of these problems. Ensuring low out of band emission (OOBE) is deemed as one of the most critical challenges to support development of future technologies such as 6G and beyond. Universal Filtered Multi Carrier (UFMC) has been considered as one of the candidate waveforms for the 5G communications due to its robustness against Inter Carrier Interference (ICI) and the Inter Symbol Interference (ISI). It is also a preferred option because it is the most appropriate for low latency scenarios. In this paper, a novel approach is proposed that makes use of modified Kaiser-Bessel filter-based pulse windowing instead of standard Dolph-Chebyshev filter for UFMC based waveform. The aim of proposing the new approach is to enable the reduction of spectral leakage into nearby sub-bands. A comprehensive study for the modified Kaiser-Bessel filters is performed and the results are presented in terms of several Key Performance Indicators (KPIs). Based on the results of the simulation, the UFMC Kaiser-Hankel window demonstrated lower sidebands and better power spectral density, when compared with the traditional Orthogonal frequency-division multiplexing (OFDM) and UFMC as well as the normal UFMC Kaiser window. In addition, the real test for the kaiser window with 5G waveform is lower OOBE than conventional 5G waveform (CP-OFDM and UFMC). The OOBE reduction of 31% of the Kaiser vs. the Dolph-Chebyshev filter, 68% of Kaiser Hankel over the Dolph-Chebyshev, and 20% of Kaiser Hankel over Kaiser filter have been reported in this paper. The Power Spectral Density has been improved accordingly. © 2022 Tech Science Press. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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