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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Begum, Almas Rajendra Thilahar, C. Monikaa, R. Gopinath, M. Ruth Naveena, N. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Information Technology Chennai India Hindustan Institute of Technology and Science Department of Mathematics Chennai India

ISBN: (纸本)9798331522988

Vehicle communication systems facilitate both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connections, playing a crucial role in Intelligent Transportation Systems. Accurate estimation of signal-to-noise ratio (SNR) is necessary for modern communication systems to be reliable and efficacious. This study aims to identify which machine learning model achieves the highest accuracy in predicting SNR. The analysis focuses on three different models: Random Forest, Gradient Boosting, and Linear Regression. The models were built using a comprehensive dataset containing wireless measurements from the iV2V industrial testbeds. The iV2I+ dataset records data from an industrial site where it acquires around 16∼h of the autonomous cleaning robot connected to a private cellular network, meanwhile, the iV2V has accumulated about 10 hours in side-link situations between three Automated Guided Vehicles (AGVs). It is an appropriate dataset for machine learning purposes because it provides information on positional data, wireless Quality of Service (QoS) measurements, and physical layer characteristics in detail. The models were analyzed based on performance measures including R2 Score, Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). The Random Forest model outperformed all other models we tested with an MSE of 0.0058, which translates to an RMSE of 0.0762 and an R2 score over the roof.9997 Yes, they performed well also but Random Forest outperformed Gradient Boosting and Linear Regression in all the metrics. Random Forest was the good model that this study could get which is constructed by other models as well, depicting its goodness for designing machine learning algorithms to forecast SNR over a vehicular communication system. Results provide means to develop methods for enhanced communication efficiency and reliability that can be helpful while building safer, more effective transportation systems where ITS plays a significant role. © 2024 IEEE.

关键词： Vehicle to infrastructure

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LightGBM empowered by whale optimization for thyroid disease detection

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International Journal of information Technology (Singapore) 2023年第4期15卷 2053-2062页

作者： Sinha, Bam Bahadur Ahsan, Mohammad Dhanalakshmi, R. Department of Computer Science Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi 834010 India Informatics Cluster School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Department of Computer Science Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620009 India

Thyroid disorder is a significant source of formulation in medical classification and prognosis, with onset being a challenging assumption in medical study. The thyroid gland is a vital organ of our body. Thyroid hormone releases are responsible for metabolic regulation. Hyperthyroidism and hypothyroidism are two of the most prevalent thyroid disorders that affect the thyroid’s ability to regulate the pace of the body’s metabolism. Data cleansing procedures were used to prepare the data to the point where analytics could be performed to determine the risk of individuals developing thyroid disease. In this paper, an optimal framework design employing LightGBM (Light Gradient Boosting Machine), Sequential Backward Selection (SBS), and a metaheuristic technique termed Whale Optimization (WO) plays a decisive role in detecting Thyroid disease. The objective of this research is to provide an impeccably clean and rational way to detecting thyroid disorder in humans. Numerous processes have been applied to thyroid data sets with astounding results, yet the data used for thyroid disease detection is redundant, inconsistent and contains missing feature values, as per our literature survey. The proposed effort would develop an expert advisory system based on the Opti-LightGBM architecture for the purpose of detecting thyroid disorders and initiating necessary medication promptly. The proposed Opti-LightGBM model yields an accuracy of 99.75% on Thyroid dataset and outperforms several state-of-art comparative models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Classification Feature selection LightGBM Optimization Thyroid

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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, J.E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications 15772 Greece Keio University Department of Information and Computer Science Kanagawa 2238522 Japan Tohoku University International Research Institute of Disaster Science Sendai9808576 Japan

As synthetic aperture radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this article proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network. First, employing Multiscale Superpixel Reconstructed Difference Image effectively enhances the edge information of the images. Second, a two-stage Weighted Center Constrained Fuzzy C-Means clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Third, a Convolutional Squeeze-and-Excitation Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model is introduced into the CSWNN to expand pseudolabeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2% –9% better than the classical algorithm. © 2008-2012 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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Deep Learning-Based Prediction of Daily COVID-19 Cases Using X (Twitter) Data

Deep Learning-Based Prediction of Daily COVID-19 Cases Using...

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34th medical informatics Europe Conference, MIE 2024

作者： Ahmed, Nourhan Saeed, Khansa Rodrigues, Jeevitha Lora Naeem, Maha Correa, Andrea Sanabboon, Chairungroj Rostam Niakan Kalhori, Sharareh Deserno, Thomas M. Information Systems and Machine Learning Lab Department of Mathematics Natural Science Economics and Computer Science Institute of Computer Science University of Hildesheim Germany Peter L. Reichertz Institute for Medical Informatics TU Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran

ISBN: (纸本)9781643685335

Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer. We develop and evaluate a time series mixer (TSMixer) predictive model for multivariate time series. The mean squared error (MSE) loss on the test dataset was 0.0063 for 24-month Gvalue prediction when using the MinMax normalization with recursive feature elimination (RFE) and average or min aggregation method. Our findings illuminate the potential of integrating social media data to enhance daily COVID-19 case predictions and are applicable also for epidemiological forecasting purposes. © 2024 The Authors.

关键词： Tweets

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FusFormer: global and detail feature fusion transformer for semantic segmentation of small objects

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Multimedia Tools and Applications 2024年第41期83卷 88717-88744页

作者： Li, Zheng Chen, Houjin Li, Jupeng Peng, Song Zhang, Zhenhao Wang, Baozheng Wang, Changyong School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Beijing Institute of Basic Medical Sciences Beijing100850 China

Improving the segmentation accuracy of small objects is essential for tasks such as autono-mous driving and remote sensing. However, the current main semantic segmentation methods are inadequate for small objects. To improve the segmentation accuracy of small objects, long-range global information and fine local details are needed, and neither pure Convolutional Neural Networks (CNNs) nor Vision Transformers (ViTs) can effectively provide these two different types of information simultaneously. In this paper, we introduce a novel model FusFormer for finding a solution, which contains a global branch and a detailed branch to fully capture the long-range features and spatial detail features from the input image. The global branch is based on MiT-B2 to efficiently acquire global context, while the detailed branch acquires rich local detail information by Spatial Prior Module (SPM) and Multi-scale Module (MSM). Feature Interaction Module (FIM) is proposed to perform information fusion across features at a dual scale. In addition, Multi-scale Edge Extraction Module (MSEEM) is utilized to supplement the missing edge information during model training, helping the model to better enhance the intra-class consistency of small objects. Extensive experiments on Cityscapes, ADE20K and PASCAL VOC 2012 show that our model achieves competitive overall segmentation accuracy, especially on small objects. FusFormer achieves 82.6%, 47.3% and 82.4% mIoU on the Cityscapes, ADE20K and PASCAL VOC 2012 validation sets, compared with other state-of-the-art methods, the proposed model significantly improves the IoU on small objects by 2%-4%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantic Segmentation

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RecStitchNet:Learning to stitch images with rectangular boundaries

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Computational Visual Media 2024年第4期10卷 687-703页

作者： Yun Zhang Yu-Kun Lai Lang Nie Fang-Lue Zhang Lin Xu Key Lab of Film and TV Media Technology of Zhejiang Province College of Media EngineeringCommunication University of ZhejiangHangzhou 310018China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK Institute of Information Science Beijing Jiaotong UniversityBeijing 100091China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand STEM University of South AustraliaAdelaide 5095Australia

Irregular boundaries in image stitching naturally occur due to freely moving *** deal with this problem,existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit ***,previous methods always depend on hand-crafted features(e.g.,keypoints and line segments).Thus,failures often happen in overlapping regions without distinctive *** this paper,we address this problem by proposing RecStitchNet,a reasonable and effective network for image stitching with rectangular *** that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework,we propose a three-step progressive learning based strategy,which not only simplifies this task,but gradually achieves a good balance between stitching and imposing *** the first step,we perform initial stitching by a pre-trained state-of-the-art image stitching model,to produce initially warped stitching results without considering the boundary ***,we use a regression network with a comprehensive objective regarding mesh,perception,and shape to further encourage the stitched meshes to have rectangular boundaries with high content ***,we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively,which can effectively improve the stitching results in terms of feature alignment,as well as boundary and structure *** to the lack of stitching datasets and the difficulty of label generation,we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels,and the performance upper bound induced from the it can be broken by our unsupervised *** and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.

关键词： image stitching boundaries convolutional neural network

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Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis

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IEEE Journal of Biomedical and Health informatics 2024年第7期28卷 4084-4093页

作者： Dorjsembe, Zolnamar Pao, Hsing-Kuo Odonchimed, Sodtavilan Xiao, Furen National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan University of Tokyo Faculty of Engineering Tokyo113-8654 Japan College of Medicine Institute of Medical Device and Imaging National Taiwan University Taipei100 Taiwan

Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 dice score of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of the proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues. © 2013 IEEE.

关键词： Semantics

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Improving Disease Detection in Tomato Leaves Using Visual Geometry Group 19 Compared to the Artificial Neural Network Algorithm 2

Improving Disease Detection in Tomato Leaves Using Visual Ge...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Jothika, G. Khilar, Rashmita Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science Engineering Thandalam Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Information Technology Thandalam Chennai India

ISBN: (纸本)9798350306415

By comparing the efficiency of the Artificial Neural Network (ANN) technique and the Novel Visual Geometry Group 19 (VGG19) algorithm, this research aimed to enhance the precision of the detection of tomato leaf diseases. Using a dataset consisting of 19,167 photographs, each of which measured 171 rows and 171 columns, the Artificial Neural Network method and Visual Geometry Group 16 were both used in order to identify tomato leaf disease. ClinCalc was used to pick a sample size of ten, providing a pretest power of 0.8 and an alpha of 0.05. This was done in order to assess the effectiveness of each method. Following 10 rounds, the performance of each algorithm was evaluated based on its degrees of accuracy. An average precision of 92.12% was achieved by the Novel Visual Geometry Group 19, which was superior to the mean accuracy of 84.43% that the Artificial Neural Network Algorithm achieved. The two algorithms were subjected to a statistical analysis revealing a statistically significant difference. The p-value for the independent sample T-test was 0.014, indicating a difference between the two algorithms. Regarding the detection of tomato leaf disease, the Novel Visual Geometry Group 19 technique performed much better than the Artificial Neural Network approach. Overall, the method achieved a higher average accuracy. © 2024 IEEE.

关键词： Artificial Neural Network Algorithm Detection Disease Healthy Novel Visual Geometry Group19 Performance Tomato leaves

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