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Ground-Glass Lung Nodules Recognition based on CatBoost Feature selection and stacking Ensemble learning

Journal of shanghai Jiaotong University (science) 2024年 1-10页

作者： Miao, Jun Chang, Yiru Chen, Chen Zhang, Maoxuan Liu, Yan Qi, Honggang Guo, Zhijun Xu, Qian School of Computer Science Beijing Information Science and Technology University Beijing100101 China Pathological Information Engineering Technology Center Jinan Supercomputing Technology Research Institute Jinan250100 China School of Computer Science and Technology University of the Chinese Academy of Sciences Beijing100049 China Department of Radiology Tianjin Kanghui Hospital Tianjin300385 China Department of Radiology Huabei Petroleum General Hospital Hebei Renqiu062550 China

Aimed at the issue of high feature dimensionality, excessive data redundancy, and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition, a recognition method based on CatBoost feature selection and stacking ensemble learning was proposed. First, the method uses a feature selection algorithm to filter important features and remove features with less impact, achieving the effect of data dimensionality reduction. second, random forests classifier, decision trees, K-nearest neighbor classifier, and light gradient boosting machine were used as base classifiers, and support vector machine was used as meta classifier to fuse and construct the ensemble learning model. This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers. The experimental results show that the recognition accuracy of the proposed method reaches 94.375%. Compared to the random forest algorithm with the best performance among single classifiers, the accuracy of the proposed method is increased by 1.875%. Compared to the recent deep learning methods (ResNet+GBM+Attention and MVCsNet) on ground-glass pulmonary nodule recognition, the proposed method’s performance is also better or comparative. Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules. © shanghai Jiao Tong University 2024.

关键词： Feature selection

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Graph-based Feature learning for Cross-project software Defect Prediction

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computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long short-Term Memory(LsTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LsTM,and defect *** evaluation using nine open-source Java projects from the PROMIsE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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Automation of Human-Machine Interface Design for Highly-Automated substations

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Power Technology and Engineering 2025年第5期58卷 872-876页

作者： Dobrokhotov, D.s. Trofimov, A.V. Trofimov, V.A. DEP Company" LLC Moscow Russia National Research University "Moscow Power Engineering Institute " Moscow Russia

The issues of automated generation of mnemonic diagrams for automated workstations used by the operators of electrical installations are considered. The algorithm is implemented based on the substation configuration description file, compiled in accordance with IEC 61850. The description of mnemonic diagrams is generated using the standard computer graphics format (sVG) based on conventional graphical symbols intended for the syTrack sCADA system developed by the DEP Company. The project management is performed within the framework of the computer-Aided Design system for secondary Circuits (CAD sC). © springer science+Business Media, LLC, part of springer Nature 2025.

关键词： project management

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sleep Posture Classification Using RGB and Thermal Cameras based on Deep learning Model

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computer Modeling in Engineering & sciences 2024年第8期140卷 1729-1755页

作者： Awais Khan Chomyong Kim Jung-Yeon Kim Ahsan Aziz Yunyoung Nam Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea ICT Convergence Research Center Soonchunhyang UniversityAsan31538Korea Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang UniversityAsan31538Korea

sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by *** posture assessment remains challenging because of the complex nature of the human body and variations in sleep ***,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and *** method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone *** study involves 10 participants under two conditions:with and without ***,the database is normalized into a video *** subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 *** the third phase,the extracted features are utilized for *** fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal ***,the fused vectors are passed to machine learning classifiers for final *** dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic support Vector Machine(QsVM)without the ***,the Linear sVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.

关键词： Human sleep posture VGG16 deep learning ResNet50 fusion machine learning

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Natural Image Matting with Attended Global Context

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Journal of computer science & Technology 2023年第3期38卷 659-673页

作者：张億一牛力 Yasushi Makihara 张健夫赵维杰 Yasushi Yagi 张丽清 MoE Key Lab of Artificial Intelligence Department of Computer Science and EngineeringShanghai Jiao Tong University Shanghai 200240China Department of Intelligent Media Institute of Scientific and Industrial ResearchOsaka UniversityOsaka 565-0871Japan

Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels, which is too big for a learning-based network to capture global contextual information due to the limit size of a receptive field. Although uniformly downsampling the matting image can alleviate this problem, it may result in the degradation of matting performance. To solve this problem, we introduce a natural image matting with the attended global context method to extract global contextual information from the whole image, and to condense them into a suitable size for learning-based network. specifically, we first leverage a deformable sampling layer to obtain condensed foreground and background attended images respectively. Then, we utilize a contextual attention layer to extract information related to unknown regions from condensed foreground and background images generated by a deformable sampling layer. Besides, our network predicts a background as well as the alpha matte to obtain more purified foreground, which contributes to better qualitative performance in composition. Comprehensive experiments show that our method achieves competitive performance on both Composition-1k and the *** benchmark quantitatively and qualitatively.

关键词： image matting global context deformable sampling

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Low-Cost Federated Broad learning for Privacy-Preserved Knowledge sharing in the RIs-Aided Internet of Vehicles

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Engineering 2024年第2期33卷 178-189页

作者： Xiaoming Yuan Jiahui Chen Ning Zhang Qiang(John)Ye Changle Li Chunsheng Zhu Xuemin sherman shen Qinhuangdao Branch Campus Northeastern UniversityQinhuangdao 066004China State Key Laboratory of Integrated Services Networks&the Research Institute of Smart Transportation Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada SUSTech Institute of Future Networks Southern University of Science and TechnologyShenzhen 518055China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility *** order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client *** enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIs)configuration optimization framework for *** problem is decoupled into two convex *** to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIs configuration *** on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their *** simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

关键词： Knowledge sharing Internet of Vehicles Federated learning Broad learning Reconfigurable intelligent surfaces Resource allocation

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Efficient Clustering Network based on Matrix Factorization

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computers, Materials & Continua 2024年第7期80卷 281-298页

作者： Jieren Cheng Jimei Li Faqiang Zeng Zhicong Tao and Yue Yang School of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Haikou570228China

Contrastive learning is a significant research direction in the field of deep ***,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing *** address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).specifically,we design a batched low-rank singular Value Decomposition(sVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the ***,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters *** experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.

关键词： Contrastive learning clustering matrix factorization

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Bidirectional LsTM-based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： sheik Mohideen shah Meganathan selvamani Mahesh Thyluru Ramakrishna surbhi Bhatia Khan shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long short-Term Memory(LsTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LsTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LsTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMsE and an 18.8%improvement in MAE over conventional unidirectional LsTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including sVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMsE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LsTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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A novel random forest-based approach for the non-destructive and explainable estimation of ammonia and chlorophyll in fresh-cut rocket leaves

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Information Processing in Agriculture 2024年第2期12卷 1-11页

作者： Polimena, stefano Pio, Gianvito Cefola, Maria Palumbo, Michela Ceci, Michelangelo Attolico, Giovanni Department of Computer Science University of Bari Aldo Moro Via E. Orabona 4 Bari70125 Italy Via Ariosto 25 Rome00185 Italy Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing National Research Council of Italy via Amendola 122/D Bari70126 Italy Department of Knowledge Technologies Jožef Stefan Institute Jamova cesta 39 1000 Ljubljana Slovenia Institute of Sciences of Food Production National Research Council of Italy c/o CS-DAT Via Michele Protano Foggia71121 Italy Department of Science of Agriculture Food and Environment University of Foggia Via Napoli 25 Foggia71122 Italy

The perceived visual quality of fruits and vegetables plays a central role in the choices made by retail customers. Machine learning (ML) approaches based on image analysis have been recently proposed to overcome the poor efficiency and subjectivity of human visual evaluation as well as the expensiveness and destructiveness of physical and chemical methods that measure internal indicators. In this paper, we propose a ML method based on Random Forests for estimating the chlorophyll and ammonia contents (considered, in the literature, reliable indicators of product freshness) from images of fresh-cut rocket leaves. Our approach copes with specific issues raised by (i) the non-uniform distributions of ammonia and chlorophyll values and (ii) the need to provide insights into the features that produce a particular model outcome, aiming to enhance its trustworthiness. Our experiments, performed on real images of fresh-cut rocket leaves, proved that the proposed approach significantly outperforms 7 competitor methods, obtaining an improvement of the RsE results of 6.6% for the prediction of the ammonia and of 10.4% for the prediction of the chlorophyll over its best competitor. Moreover, a specific analysis of the explainability of the predictions showed that the learned models are based on reasonable features, empowering their acceptance in real-world applications. © 2024 The Author(s)

关键词： Ammonia

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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

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computers, Materials & Continua 2024年第9期80卷 4895-4916页

作者： Brij Bhooshan Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPuneMaharashtra411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Ronin Institute MontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)HomantinHong KongChina

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection *** machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 *** WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation *** results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

关键词： Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization

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