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Generating labeled samples for hyperspectral image classification using correlation of spectral bands

Frontiers of computer science 2016年第2期10卷 292-301页

作者： Lu YU Jun XIE Songcan CHEN Lei ZHU Institute of Communications Engineering PLA University of Science and Technology Nanjing 210007 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China College of Command Information System PLA University of Science and Technology Nanjing 210007 China

Because the labor needed to manually label a huge training sample set is usually not available, the problem of hyperspectral image classification often suffers from a lack of labeled training samples. At the same time, hyperspectral data represented in a large number of bands are usually highly correlated. In this paper, to overcome the small sample problem in hyperspectral image classification, correlation of spectral bands is fully utilized to generate multiple new sub-samples from each original sample. The number of labeled training samples is thus increased several times. Experiment results demonstrate that the proposed method has an obvious advantage when the number of labeled samples is small.

关键词： hyperspectral image remote sensing image classification small sample problem

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Sc-Decorated WS_2 Nanoribbons as Hydrogen Storage Media

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Chinese Physics Letters 2016年第1期33卷 104-108页

作者：徐斌王玉生宋纳红张静李梦易林 College of Mathematics and Information Science North China University of Water Resources and Electric Power Department of Physics Huazhong University of Science and Technology College of Computer and Information Engineering Henan University of Economics and Law College of Science Zhongyuan University of Technology

The hydrogen storage behavior of So-decorated WS2 monolayer and WS2 nanoribbons is systematically studied by using first principles calculations based on the density functional theory. The present results indicate that an Sedecorated WS2 monolayer is not suitable for storing hydrogen due to the weak interaction between the monolayer WS2 sheet and the Sc atoms. It is found that both the hybridization meeh^nism and the Coulomb attraction make the Sc atoms stably adsorb on the edges of WS2 nanoribbons without clustering. The 2Sc/WS2NRs system can adsorb at most eight H.2 molecules with average adsorption energy of 0.20 eV/H2. The results show that the desorption of H2 is possible by lowering the pressure or by increasing the temperature.

关键词： WS on NRS SC it is Sc-Decorated WS2 Nanoribbons as Hydrogen Storage Media of

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Beyond boundaries: AI-optimized global landslide susceptibility mapping

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Geomatics, Natural Hazards and Risk 2025年第1期16卷

作者： Panahi, Mahdi Rezaie, Fatemeh Khosravi, Khabat Kalantari, Zahra Bateni, Sayed M. Lee, Jeong-A Department of Computer Engineering Chosun University Gwangju Korea Republic of Department of Geophysical Exploration Korea University of Science and Technology Daejeon Korea Republic of Department of Natural Resources College of Agriculture and Natural Resources Razi University Kermanshah Iran Department of Sustainable Development Environmental Science and Engineering KTH Royal Institute of Technology Stockholm Sweden Department of Physical Geography and Bolin Centre for Climate Research Stockholm University Stockholm Sweden Department of Civil Environmental and Construction Engineering and Water Resources Research Center University of Hawai‘i at Manoa HonoluluHI United States

Landslides pose a significant global threat, causing extensive loss of life, economic damage and environmental degradation. Despite advancements in landslide susceptibility mapping, existing methods often lack global-scale applicability and fail to incorporate robust optimization strategies for improved predictive accuracy. This study addresses these gaps by developing an optimized framework using support vector regression (SVR) enhanced with meta-heuristic algorithms (grey wolf optimizer [GWO] and bat algorithm) to refine model hyper-parameters. It integrates a globally representative data set of 37,984 landslide and non-landslide locations, ensuring broader applicability and generalizability. The information gain ratio method assessed the relative importance of 12 geo-environmental factors influencing landslide. The results indicated that all models achieved good predictive performance during the testing phase, as evidenced by an area under the receiver operating characteristic curve (AUC) value exceeding 0.8, but the SVR-GWO model exhibited the highest prediction accuracy (AUC = 0.92), making it suitable for large-scale hazard assessment. Plan curvature emerged as the most influential factor, surpassing slope, land use, and rainfall that are dominant at regional or local scales. The five countries with the highest landslide-prone areas were Russia, Canada, USA, China, and Brazil. The results support policymakers and urban planners in developing efficient strategies to minimize landslide risks. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Mapping

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Granular-ball fuzzy information-based outlier detector

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International Journal of Approximate Reasoning 2025年 185卷

作者： Li, Qilin Yuan, Zhong Peng, Dezhong Song, Xiaomin Zheng, Huiming Su, Xinyu Marketing Service Center of State Grid Sichuan Electric Power Corporation Chengdu610045 China College of Computer Science Sichuan University Chengdu610065 China Sichuan National Innovation New Vision UHD Video Technology Co. Ltd Chengdu610095 China Tianfu Jincheng Laboratory Chengdu610093 China National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation Chengdu610207 China

Outlier detection is an important part of the process of carrying out data mining and analysis and has been applied to many fields. Existing methods are typically anchored in a single-sample processing paradigm, where the processing unit is each individual and single-granularity sample. This processing paradigm is inefficient and ignores the multi-granularity features inherent in data. In addition, these methods often overlook the uncertainty information present in the data. To remedy the above-mentioned shortcomings, we propose an unsupervised outlier detection method based on Granular-Ball Fuzzy Granules (GBFG). GBFG adopts a granular-ball-based computing paradigm, where the fundamental processing units are granular-balls. This shift from individual samples to granular-balls enables GBFG to capture the overall data structure from a multi-granularity perspective and improve the performance of outlier detection. Subsequently, we calculate the outlier factor based on the outlier degrees of the granular-ball fuzzy granules to which the sample belongs, serving as a measure of the outlier degrees of samples. The experimental results prove that GBFG has a remarkable performance compared with the existing excellent algorithms. The code of GBFG is publicly available on https://***/Mxeron/GBFG. © 2025 Elsevier Inc.

关键词： Granular computing

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SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments

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Heliyon 2025年第1期11卷 e40874页

作者： Awan, Kamran Ahmad Ud Din, Ikram Almogren, Ahmad Nawaz, Ali Khan, Muhammad Yasar Altameem, Ayman Department of Information Technology The University of Haripur Khyber Pakhtunkhwa Haripur 22620 Pakistan Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11633 Saudi Arabia College of Information Technology United Arab Emirates University Al Ain 15551 United Arab Emirates SFI-Funded E-Governance Unit Insight Centre for Data Analytics University of Galway Galway H91 CF50 Ireland Department of Natural and Engineering Sciences College of Applied Studies and Community Services King Saud University Riyadh 11633 Saudi Arabia

The rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a novel deep learning framework, SecEdge, designed to enhance real-time cybersecurity in mobile IoT environments. The SecEdge framework integrates transformer-based models for efficient handling of long-range dependencies and Graph Neural Networks (GNNs) for modeling relational data, coupled with federated learning to ensure data privacy and reduce latency. The adaptive learning mechanism continuously updates model parameters to counter evolving cyber threats. The framework's performance was evaluated in a simulation environment using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. Results demonstrated that SecEdge outperformed state-of-the-art methods with a detection rate of 98.8% for DoS attacks on NSL-KDD, 98.5% for MitM attacks on UNSW-NB15, and 98.7% for data injection attacks on CICIDS2017. © 2024

关键词： Anamoly detection Cyber security Deep learning Federated learning Graph neural network Internet of things Mobile computing

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A Sustainable WSN System with Heuristic Schemes in IIoT

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computers, Materials & Continua 2022年第9期72卷 4215-4231页

作者： Wenjun Li Siyang Zhang Guangwei Wu Aldosary Saad Amr Tolba Gwang-jun Kim School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Computer and Information Engineering Central South University of Forestry and TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Mathematics and Computer Science Department Faculty of ScienceMenoufia UniversityEgypt Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Recently, the development of Industrial Internet of Things hastaken the advantage of 5G network to be more powerful and more ***, the upgrading of 5G network will cause a variety of issues increase,one of them is the increased cost of coverage. In this paper, we proposea sustainable wireless sensor networks system, which avoids the problemsbrought by 5G network system to some extent. In this system, deployingrelays and selecting routing are for the sake of communication and *** main aim is to minimize the total energy-cost of communication underthe precondition, where each terminal with low-power should be charged byat least one relay. Furthermore, from the perspective of graph theory, weextract a combinatorial optimization problem from this system. After that,as to four different cases, there are corresponding different versions of theproblem. We give the proofs of computational complexity for these problems,and two heuristic algorithms for one of them are proposed. Finally, theextensive experiments compare and demonstrate the performances of thesetwo algorithms.

关键词： Industrial Internet of Things sustainable wireless sensor network system combinatorial optimization problem heuristic algorithms

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Intrusion Detection System for Big Data Analytics in IoT Environment

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computer Systems science & engineering 2022年第10期43卷 381-396页

作者： M.Anuradha G.Mani T.Shanthi N.R.Nagarajan P.Suresh C.Bharatiraja Department of Computer Science and Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering University College of Engineering ArniThatchur632326India Department of Electronics&Communication Engineering Kings College of EngineeringPudukkottai613303India Department of Electronics&Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal *** IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging *** intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and ***,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT *** proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data *** manage big data,Hadoop ecosystem is ***,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection ***,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced *** BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification *** validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct *** resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

关键词： Big data cybersecurity IoT networks intrusion detection deep learning metaheuristics intelligent systems

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A generalized model of TiOx-based memristive devices and its application for image processing

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Chinese Physics B 2017年第9期26卷 70-81页

作者：张江伟汤振森许诺王耀孙红辉王之元方粮 State Key Laboratory of High Performance Computing National University of Defense Technology School of Computer National University of Defense Technology Department of Electrical and Computer Engineering University of Pittsburgh Department of Material Science and Engineering College of EngineeringSeoul National University School of Computer National University of Defense Technology

Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.

关键词： memristor modeling memristor-based network gray sketching edge detection

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Few-shot learning of initial coulombic efficiency using adaptive combination kernel deep Gaussian process regression

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Journal of Energy Storage 2025年 128卷

作者： Hai Guo Hongcheng Zhang Xiaofeng Lv Xiaoxu Liu Tianyi Ji School of Data Science and Artificial Intelligence Wenzhou University of Technology 325035 Wenzhou China College of Computer Science and Engineering Dalian Minzu University Dalian 116600 China School of Material Science and Engineering Shaanxi University of Science and Technology Xi'an 710021 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China

The initial coulombic efficiency (ICE) is critical for minimizing active ion loss and enhancing battery stability. However, the complex interplay between the multiscale structure of electrode materials and manufacturing processes obscures the underlying optimization mechanisms, hindering the development of high-performance batteries. Herein, a machine learning method based on adaptive combinatorial kernel deep Gaussian process (AKC-DGPR) is proposed on 110 sets of experimentally collected datasets, including the preparation process, structural parameters, and ICEs of hard carbon. A few-shot learning framework is developed especially for laboratory environments where data are collected manually. The kernel functions are first collected and organized to construct the kernel function list. The global optimal solution is gradually approached through iteration, and the SHapley Additive exPlanations (SHAP) method is used for model interpretation. The results show that the R 2 of the model is 0.74, and the RMSE is 0.13. The proposed model has high accuracy and reliability in predicting ICE, and the SHAP method explains the effect of input features on ICE and makes the model's decision-making process transparent. An inverse design idea is further proposed based on this model, which provides solid support for few-shot data-driven SIB material design and performance optimization.

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TRS Scheduling for Improved QoS Performance in Cloud System

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computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

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