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Convolutional Neural Network Image Classification Based on Different Color Spaces

Tsinghua science and technology 2025年第1期30卷 402-417页

作者： Zixiang Xian Rubing Huang Dave Towey Chuan Yue School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The existing literature regarding the influence of color space use on the performance of CNNs is *** paper explores the impact of different color spaces on image classification using *** compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets,each converted to nine color *** find that color space selection can significantly affect classification accuracy,and that some classes are more sensitive to color space changes than *** color spaces may have different expression abilities for different image features,such as brightness,saturation,hue,*** leverage the complementary information from different color spaces,we propose a pseudo-Siamese network that fuses two color spaces without modifying the network *** experiments show that our proposed model can outperform the single-color-space models on most *** also find that our method is simple,flexible,and compatible with any CNN and image dataset.

关键词： color space Convolutional Neural Network(CNN) image classification pseudo-Siamese network

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Secure Transmission Scheme for Blocks in Blockchain-Based Unmanned Aerial Vehicle Communication Systems

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computers, Materials & Continua 2024年第11期81卷 2195-2217页

作者： Ting Chen Shuna Jiang Xin Fan Jianchuan Xia Xiujuan Zhang Chuanwen Luo Yi Hong School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer Science Qufu Normal UniversityRizhao276826China School of Information Science and Technology Beijing Forestry UniversityBeijing100083China

In blockchain-based unmanned aerial vehicle(UAV)communication systems,the length of a block affects the performance of the *** transmission performance of blocks in the form of finite character segments is also affected by the block ***,it is crucial to balance the transmission performance and blockchain performance of blockchain communication systems,especially in wireless environments involving *** paper investigates a secure transmission scheme for blocks in blockchain-based UAV communication systems to prevent the information contained in blocks from being completely eavesdropped during *** our scheme,using a friendly jamming UAV to emit jamming signals diminishes the quality of the eavesdropping channel,thus enhancing the communication security performance of the source *** the constraints of maneuverability and transmission power of the UAV,the joint design of UAV trajectories,transmission power,and block length are proposed to maximize the average minimum secrecy rate(AMSR).Since the optimization problem is non-convex and difficult to solve directly,we first decompose the optimization problem into subproblems of trajectory optimization,transmission power optimization,and block length ***,based on firstorder approximation techniques,these subproblems are reformulated as convex optimization ***,we utilize an alternating iteration algorithm based on the successive convex approximation(SCA)technique to solve these subproblems *** simulation results demonstrate that our proposed scheme can achieve secure transmission for blocks while maintaining the performance of the blockchain.

关键词： Unmanned aerial vehicles blockchain finite blocklength block transmission alternating optimization

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Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

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Journal of Electronic science and technology 2024年第1期22卷 53-69页

作者： Jia-Jun Zhong Yong Ma Xin-Zheng Niu Philippe Fournier-Viger Bing Wang Zu-kuan Wei School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China College of Computer Science&Software Engineering Shenzhen UniversityShenzhen518060China School of Computer Science Southwest Petroleum UniversityChengdu610500China

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

关键词： Graph neural network Multi-head attention mechanism Spatio-temporal dependency Traffic flow prediction

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Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

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computer Modeling in engineering & sciences 2024年第6期139卷 2605-2625页

作者： Jungpil Shin Md.Al Mehedi Hasan Abu Saleh Musa Miah Kota Suzuki Koki Hirooka School of Computer Science and Engineering The University of AizuAizuwakamatsu965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

关键词： Japanese Sign Language(JSL) hand gesture recognition geometric feature distance feature angle feature GoogleNet

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Border Sensitive Knowledge Distillation for Rice Panicle Detection in UAV Images

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computers, Materials & Continua 2024年第10期81卷 827-842页

作者： Anitha Ramachandran Sendhil Kumar K.S. School of Computer Science and Engineering VIT(Vellore Institute of Technology)VelloreVellore632014India

Research on panicle detection is one of the most important aspects of paddy phenotypic analysis.A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based ***,it entails many other challenges,including different illuminations,panicle sizes,shape distortions,partial occlusions,and complex *** detection algorithms are directly affected by these *** work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation(BSKD).It is designed to prioritize the preservation of knowledge in border areas through the use of feature *** feature-based knowledge distillation method allows us to compress the model without sacrificing its *** imitation mask is used to distinguish panicle-related foreground features from irrelevant background features.A significant improvement in Unmanned Aerial Vehicle(UAV)images is achieved when students imitate the teacher’s *** the UAV rice imagery dataset,the proposed BSKD model shows superior performance with 76.3%mAP,88.3%precision,90.1%recall and 92.6%F1 score.

关键词： Rice panicle detection UAV border sensitive method deep learning knowledge distillation

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Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

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computers, Materials & Continua 2024年第3期78卷 3469-3487页

作者： Sureka Sivavelu Venkatesh Palanisamy School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two sta

关键词： Software defect prediction feature selection nonparametric statistical Torgerson-Gower scaling technique quadratic censored regressive convolution deep neural network softstep activation function nelder-mead method

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Joint multiple resource allocation for offloading cost minimization in IRS-assisted MEC networks with NOMA

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Digital Communications and Networks 2023年第3期9卷 613-627页

作者： Guang Chen Yueyun Chen Zhiyuan Mai Conghui Hao Meijie Yang Shuangshuang Han Liping Du School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovition School of Unversity of Science and Technology Beijing

Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)***,it is challenging to balance the limited energy of MTs and the strict delay requirement of their *** this paper,in order to tackle the challenge,we jointly optimize the offloading delay and energy consumption of mobile terminals(MTs)to realize the delay-energy tradeoff in an IRS-assisted MEC network,in which non-orthogonal multiple access(NOMA)and multiantenna are applied to improve spectral *** achieve the optimal delay-energy tradeoff,an offloading cost minimization model is proposed,in which the edge computing resource allocation,signal detecting vector,uplink transmission power,and IRS phase shift coefficient are needed to be jointly *** optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension *** solve the intractable problem,we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable *** computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource ***,the wireless transmission subproblem is solved iteratively through decoupling the residual *** each iteration,the closed-form solution of residual variables is obtained through different successive convex approximation(SCA)*** verify the proposed algorithm can converge to an optimum with polynomial *** results indicate the proposed method achieves average saved costs of 65.64%,11.24%,and 9.49%over three benchmark methods respectively.

关键词： Mobile edge computing Intelligent reflective surface Task offloading Multiple resource allocation Non-orthogonal multiple access

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DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 1-13页

作者： S, Balasubramani D, John Aravindhar Renjith, P.N. Ramesh, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment. © 2023

关键词： K-means clustering

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of computer science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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Object Detection Model for Remote Sensing Images Based on YOLOv9

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 840-847页

作者： Hou, Donghao Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the field of object detection for remote sensing images, especially in applications such as environmental monitoring and urban planning, significant progress has been made. This paper addresses the common challenges faced by traditional object detection methods in remote sensing images, such as the large number of targets and complex backgrounds, by proposing a novel network based on YOLOv9. The network innovatively introduces the C3_CD_CGA module, an enhanced module based on Cascaded Group Attention, designed to reduce computational redundancy and increase attention diversity, and enhances the processing capability of multi-scale information through the CD module. The C3 module employs deep asymmetric convolution to mitigate information loss and increase the receptive field. Additionally, the network integrates DSConv with the RepNCSPELAN4 module to adaptively focus on and precisely capture the features of elongated and curved local structures, such as vehicles. The introduction of the CARAFE module further improves the spatial resolution of the feature maps, significantly enhancing performance across various visual tasks. Experimental results show that the improved YOLOv9 achieves a mean average precision (mAP) of 88% on the SIMD dataset, which is an improvement of 1.6% compared to the baseline YOLOv9 model and 1.5% higher than the state-of-the-art YOLO-SE model. This model not only achieves more effective multi-target recognition in complex backgrounds but also strikes a good balance between accuracy and efficiency. © (2025), (International Association of Engineers). All rights reserved.

关键词： Urban planning

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