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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Approach to Operate Appliances: Remotely Enabled Communication System 14

A Novel Approach to Operate Appliances: Remotely Enabled Com...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Mishra, Sarang Krishnakumar Dahiwale, Karan Krishna Shende, Vinay Sanjay Hira, Swati Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science and Engineering S.B Jain Institute of Technology Management and Research Nagpur India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Research Nagpur India

In recent years, a lot of research work has been proposed on routing scheme in WSN. Remote Accessing is one of the most promising techniques due to its simplicity, scalability and efficiency. Under many research and development cell, it has been noted that establishing the wireless sensor nodes and let them work as a cluster, but the major problem with the wireless technology has limitation of range. The solution of this issue is to use Radio frequency as a way to communicate in a cluster. As REMS mainly focuses on the Agricultural sector, where the lands are present remotely with issues of Internet connectivity which is the main requirement for the present IOT devices. That is why we aim to develop a system which entirely runs on its own private network without any need of Internet connectivity. The system is easy to use and has a long range. The system also doesn’t require a smart phone as it is interface free, it can be controlled with a simple keypad phone. The cost effectiveness and the mentioned features makes REMS a system, which can independently survive in remote agricultural lands. Thus, the creation of this system will help farmers for controlling the devices which run on electricity (e.g Irrigation Motors) as the system is cost effective, has long range and a internet free technology. © Grenze Scientific Society, 2023.

关键词： Cost effectiveness

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QUALYST: Data Quality Assessment System for Thailand Open Government Data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government Data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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UAV Images Based Maize Crop Analysis Using Custom CNN Model 16

UAV Images Based Maize Crop Analysis Using Custom CNN Model

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Thejaswini, M. Kumar, Gaurav Aggarwal, Sumit Kumar Bagaria, Pravin Kumar Indian Institute of Information Technology Bhagalpur Department of Computer Science and Engineering Bhagalpur India ICAR-Indian Institute of Maize Research Plant Pathology Ludhiana India

ISBN: (纸本)9798331505264

Precision agriculture is undergoing revolution by the integration of modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Artificial Intelligence (AI) and Machine Learning (ML). In this paper, high resolution UAV images of maize crops is used and further split into low resolution sub-images for training Convolutional Neural Network (CNN) models. The paper work provides an practical approach of applying an lightweight custom CNN architecture on UAV images to analyse the maize crops health status by classifying them into healthy crops, Maydis Leaf Blight (MLB) disease crops and also weeds and soil back ground images. The proposed custom CNN model shows better performance by achieving 95% accuracy when compared to other considered CNN pre-trained models such as MobileNet-V2 and ResNet-50. © 2024 IEEE.

关键词： Convolutional neural networks

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Image to Audio Conversion to Aid Visually Impaired People by CNN 4

Image to Audio Conversion to Aid Visually Impaired People by...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sivaganesan, D. Venkateshwaran, M. Dhinesh, S.P. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Sri Krishna Arts and Science College Department of Computer Science Coimbatore India

ISBN: (纸本)9798350300093

This study suggests an innovative method for helping people who are blind or visually handicapped by turning visuals into sounds. In the proposed system, audio descriptions are produced in real-time together with significant features that are extracted from photos using deep learning algorithms. The proposed work is developed to be user-friendly, which includes a simple interface that aids blind individuals to easily capture and process images using a mobile device. A user research was undertaken to assess the efficiency of the suggested method, and the results were encouraging in terms of precision and usability. This initiative offers a promising technique to give people who are blind or visually impaired an alternate means of perceiving and interacting with their environment, therefore improving their quality of life. The suggested picture to audio converter system aims to overcome the drawbacks of current assistive devices that rely on braille or textual descriptions. Blind people can more easily interpret visual information that is necessary for daily life, such as recognising items, interpreting signs, or navigate unfamiliar situations, through offering audio descriptions of images. The system makes use of recent deep learning developments that have significantly improved picture identification as natural language processing. As a result, the suggested technique has the ability to offer audio descriptions that are more precise and comprehensive than current methods. This technology has the potential to be implemented into a variety of products, from cellphones to intelligent glasses, and could significantly improve the lives of people who are blind or visually impaired. © 2023 IEEE.

关键词： Convolutional neural networks

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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Power-Measurement-Based Channel Autocorrelation Estimation for IRS-Assisted Wideband Communications

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4647-4662页

作者： Sun, He Zhu, Lipeng Mei, Weidong Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications 611731 China The Chinese University of Hon g Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user’s reference signal received power (RSRP) measurements with time-varying random IRS training reflections. As RSRP is readily accessible in existing communication systems, our proposed channel estimation method does not require additional pilot transmission in IRS-aided wideband communication systems. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS re

关键词： Orthogonal frequency division multiplexing

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A Review on NFC for Secure Transaction its Fundamental Challenges and Future Directions

A Review on NFC for Secure Transaction its Fundamental Chall...

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Kartik Gupta Praveen Bhanodia Kamal Kumar Sethi Shivshankar Rajput Mukesh Patidar Vignesh Hariharan Iyer Department of Computer Science & Engg. Acropolis Institute of Technology and Research Indore India Independent Researcher Pune India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Near-Field Communication (NFC) payment solutions are contactless payment solutions which harness the short-range communication capabilities and existing RFID infrastructure. The increasing availability of NFC enabled devices indicates a potential increase in usage of NFC based payment solutions becoming more ubiquitous. In this review, we summarize the working of NFC based solutions, the past works, and concerns with respect to adoption of NFC based payments, the industry standards and how the present scenarios can help or be improved in-order to use NFC based payments on E-Commerce websites. This review aims at gathering input to build an enhanced NFC based payments solution that addresses the existing concerns, improves convenience and is also compliant with industry standards.

关键词： Industries Reviews Authentication Encryption Electronic commerce Security Standards Radiofrequency identification

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Multi-objective evolutionary optimization for hardware-aware neural network pruning

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Fundamental research 2024年第4期4卷 941-950页

作者： Wenjing Hong Guiying Li Shengcai Liu Peng Yang Ke Tang Guangdong Provincial Key Laboratory of Brain-Inspired Inteligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Research Institute of Trustworthy Autonomous Systems Southern University of Science and TechnologyShenzhen 518055China Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055China

Neural network pruning is a popular approach to reducing the computational complexity of deep neural *** recent years,as growing evidence shows that conventional network pruning methods employ inappropriate proxy metrics,and as new types of hardware become increasingly available,hardware-aware network pruning that incorporates hardware characteristics in the loop of network pruning has gained growing attention,Both network accuracy and hardware efficiency(latency,memory consumption,etc.)are critical objectives to the success of network pruning,but the conflict between the multiple objectives makes it impossible to find a single optimal *** studies mostly convert the hardware-aware network pruning to optimization problems with a single *** this paper,we propose to solve the hardware-aware network pruning problem with Multi-Objective Evolutionary Algorithms(MOEAs).Specifically,we formulate the problem as a multi-objective optimization problem,and propose a novel memetic MOEA,namely HAMP,that combines an efficient portfoliobased selection and a surrogate-assisted local search,to solve *** studies demonstrate the potential of MOEAs in providing simultaneously a set of alternative solutions and the superiority of HAMP compared to the state-of-the-art hardware-aware network pruning method.

关键词： Multi-objective optimization Evolutionary algorithm Neural network pruning Hardware-awaremachine learning Hardware efficiency

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