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5th International Conference on computers, information Processing and Advanced Education, CIPAE 2024

作者： Liu, Zengguang Yin, Xiaochun Liu, Deyong Information Engineering College Shandong Vocational College of Science and Technology Weifang China Computer Science and Engineering College Weifang University of Science & Technology Shouguang China

ISBN: (纸本)9798331527662

Low-rate Distributed Denial-of-Service attacks, abbreviated as LDDoS, are experiencing an explosive and continuous growth in recent years. Meanwhile, people worked hard for making great contributions to prevent LDDoS attacks by deep learning methods. However, the existing surveys don’t give detailed summaries on all aspects of neural network for LDDoS attacks. This paper investigates the trend and the challenges of LDDoS attacks. And then, it classifies the neural network methods into DL-based cluster, RL-based cluster, FL-based cluster and GNN-based cluster. And it presents the comprehensive survey along with state-of-the-art detection solutions for each of them. At last, the future research directions about developing an LDDoS detection system with broad-spectrum capabilities, cost-effective mechanisms and robustness against adversarial attacks are given. © 2024 IEEE.

关键词： Federated learning

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Analysis of Watermarked Video Optimization and Training Based on Classification Using Deep Learning Techniques

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SN computer science 2024年第2期5卷 220页

作者： Muthulakshmi, K. Valarmathi, K. Department of Information Technology Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

The exchange of multimedia data is secured using watermarking. Typically, embedding a watermark involves optimising scheme parameters, frequently by using meta-heuristic optimization methods. Despite the fact that DNN have achieved significant advancements in field of multimedia representation, training neural methods takes a lot of time and data. This research propose novel method in analysing watermarked video optimization and training based on DL. Here input is collected as watermarked input video and optimize and train the watermarked input video data with enhanced security using swarm optimization-based clustering fuzzy greedy trust analysis. then the optimized video has been trained using variational temporal-based u-net convolutional encoder neural networks (VTU-net ENN). Experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, RMSE and NSE for various watermarked dataset. Accuracy attained by proposed technique is 92%, precision attained by proposed technique is 75%, recall attained is 62%, F-1 score attained 55%, RMSE obtained is 53%, NSE attained is 48% by proposed technique. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Optimization and training Securing multimedia Watermarked video

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A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

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computer Systems science & engineering 2023年第4期45卷 329-342页

作者： Nada M.Elfatih Elmustafa Sayed Ali Maha Abdelhaq Raed Alsaqour Rashid A.Saeed Department of Electrical and Electronics Engineering Red Sea UniversityPort Sudan33311Sudan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics Engineering Sudan University of Science and TechnologyKhartoum11111Sudan

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection *** optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold ***,these methods do not take into account the effect of sample size and its effect on improving CoR *** general,a large sample size results in more reliable detection,but takes longer sensing time and increases ***,the locally sensed sample size is an optimization ***,optimizing the local sample size for each cognitive user helps to improve CoR *** this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)*** the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

关键词： Cognitive radio spectrum sensing energy detection double threshold neural network machine learning optimization quality of service

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A method for determining the spatial pattern of forest trees based on the uniformity theory

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Journal of Forestry Research 2024年第6期35卷 148-159页

作者： Yeqiong Shi Xiulong Gao Chunling Lang Chuanwen Luo College of Computer Science and Information Engineering Harbin Normal UniversityHarbin 150040People’s Republic of China College of Vocation and Technical Education Guangxi Science and Technology Normal UniversityLaibin 546199People’s Republic of China College of Mathematics and Computer Science Guangxi Science and Technology Normal UniversityLaibin 546199People’s Republic of China College of Forestry Northeast Forestry UniversityHarbin 150040People’s Republic of China

The spatial pattern of trees is an important feature of forests,and different spatial patterns of trees exhibit different ecological *** has confirmed that natural forests with random patterns have higher biodiversity and stronger resistance to unstable factors such as pests and *** if they are disturbed or destroyed by unstable factors such as pests and diseases,they can still recover and rescue themselves;while artificial forests with uniform and clustered patterns have lower biodiversity and are susceptible to unstable factors such as pests and *** once pests and diseases occur,it’s more difficult for them to *** order to promote the healthy and stable develop-ment of the forestry industry and protect the diversity of the biological environment,it is necessary to protect the random pattern of natural forests from being destroyed in the process of forest management,while effectively transforming the spatial pattern of artificial forests into a random ***,in order to ensure the convenient and accurate determination of the type of forest spatial pattern,research on methods for determining forest spatial pattern has become particularly *** on the theory of uniformity,this study proposes definitions and related theories of included exclusive sphere,included exclusive body,included random pattern,and included *** the guidance of the definition of inclusion uniformity and related theories,and by using mathematical method,it is proved that the uniformity of inclusion(CL)is asymptotically subject to the Eq.18,Therefore,the relationship between the included uniformity(CL)and the number of trees in the sample plot was established,and the corresponding relationship formula was obtained,and then the determination of the spatial pattern type of trees was completed by using the corresponding relationship *** rigorous reasoning and case verification,the determination method of forest spat

关键词： Uniformity Forest spatial pattern Contained exclusive sphere Co correlation coefficient Included Uniformity

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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E-voting system using cloud-based hybrid blockchain technology

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Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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PBCounter:weighted model counting on pseudo-boolean formulas

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Frontiers of computer science 2025年第3期19卷 55-63页

作者： Yong LAI Zhenghang XU Minghao YIN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130017China

In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.

关键词： weighted model counting pseudo-boolean constraint algebraic decision diagrams preprocessing techniques

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A Novel Music Recommendation System Using Filtering Techniques

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Informatica (Slovenia) 2024年第4期48卷 595-610页

作者： Vashishtha, Srishti Varshney, Deepika Sarin, Eva Kaur, Simran Computer Science and Engineering Dept. Bharati Vidyapeeth’s College of Engineering GGSIPU New Delhi India Computer Science and Engineering Dept. Jaypee Institute of Information Technology Noida India

With the growth of the World Wide Web, a large amount of music data is available on the Internet. A large number of people listen to music online rather than downloading and listening offline. But only some sites provide personalized and accurately recommended songs while they listen to an auto-playing playlist. Hence the need for recommendation systems arises. Two approaches can be applied to the recommendation system: content based filtering and collaborative filtering. While in content-based filtering approach, analysis on the songs’ content which has been preferred by the user in history is done and the songs with relative similarity are recommended. While latter suggests songs that certain users of similar listening pattern have preferred. But collaborative filtering-based recommendation systems not only requires time to attain stability but also might recommend unsuitable music because it is not personalized to each user’s preference. Also, the latter requires the songs to be listened by a few users already to recommend it any other user. Hence for overall analysis, answers to few questions need to be implemented in recommendation systems: the very first one is how the properties should be analyzed, next one is how the analysis should be done and last is how the songs related to the user’s preference should be chosen. So, for suggesting the better system which solves these three questions as well as provides better and personalized recommendations, In this paper we build a collaborative filtering as well as content based filtering recommendation system, where various factors determine the essence of the songs, i.e. liveliness, keys used, loudness, pitch, valence, etc., are analyzed for comparison. From the experimental analysis in has been identified that content-based filtering technique performed best on KNN machine learning classifier with accuracy of 85%. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Collaborative filtering

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Sample-based relationship for assisting diagnosis of pneumonia in medical care

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Multimedia Tools and Applications 2025年第1期84卷 267-288页

作者： Chen, Hongkang Lu, Huijuan Zhu, Wenjie Zhou, Ye Yao, Yudong Wang, Renfeng College of Information Engineering China Jiliang University Zhejiang Hangzhou China Drore Information and Technology Co. LTD Zhejiang Hangzhou China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ United States College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China

Deep neural networks have succeeded in learning balanced and imbalanced data in the field of pneumonia diagnosis. However, both require separate model designs in their respective domains. The pneumonia recognition methods have poor transfer performance between the two types of image distribution data. This paper proposes BeFormer, which focuses on sample relationships. BeFormer introduces two stages, feature extraction and establishing sample relationships, to solve the problem. Specifically, in the feature extraction stage, a new backbone network called PANet based on Multi-Scale Frequency Domain Attention (FPSA) is proposed. In establishing sample relationships, Deformable Transformer is used in parallel to model the relationships between partial regions of interest among the same and different samples in the pneumonia dataset. These two modules are connected in series to form BeFormer. Without making any changes to the model, this paper conducted experiments using BeFormer on both balanced and imbalanced pneumonia datasets. As compared to traditional pneumonia recognition models, the proposed method achieved significant improvements in the two different data distributions, demonstrating outstanding transfer performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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ANNDRA-IoT:A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments

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computer Modeling in engineering & sciences 2025年第3期142卷 3155-3179页

作者： Abdullah M.Alqahtani Kamran Ahmad Awan Abdulaziz Almaleh Osama Aletri Department of Electrical and Electronic Engineering College of Engineering and Computer ScienceJazan UniversityJazan45142Saudi Arabia Department of Information Technology The University of HaripurHaripur22620Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Department of Computing College of Engineering and ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse *** study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing *** to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real *** training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent *** simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.

关键词： Internet of things resource optimization deep learning optimal resource allocation neural network efficiency

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