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Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

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computers, Materials & Continua 2024年第2期78卷 1897-1914页

作者： Sang-min Lee Namgi Kim department of computer Science Kyonggi UniversitySuwonKorea

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the *** Convolution Network(GCN)algorithms have been employed to implement the RIS ***,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning *** address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)*** proposed method involves multiplying the embedding results with different weights for each hop layer during graph *** applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding *** approach facilitates the learning of more hop layers within the GCN *** efficacy of the WF-GCN was demonstrated through its application to various *** the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,***,in the *** dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,***,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

关键词： Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems

Advances in Manta Ray Foraging Optimization:A Comprehensive Survey

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Journal of Bionic Engineering 2024年第2期21卷 953-990页

作者： Farhad Soleimanian Gharehchopogh Shafi Ghafouri Mohammad Namazi Bahman Arasteh department of computer Engineering Urmia BranchIslamic Azad UniversityUrmiaIran department of computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran department of computer Engineering Maybod BranchIslamic Azad UniversityMaybodIran department of Software Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbulTurkey

This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic *** in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault *** biologically inspired strategies allow for effective solutions to intricate physical *** its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization *** utility and benefits have found traction in numerous academic *** its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference *** paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization *** trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.

关键词： Manta ray foraging optimization Metaheuristic algorithms Hybridization Improved Optimization

A New Double Layer Multi-Secret Sharing Scheme

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China Communications 2024年第1期21卷 297-309页

作者： Elavarasi Gunasekaran Vanitha Muthuraman department of computer Applications Alagappa UniversityKaraikudiIndia

Cryptography is deemed to be the optimum strategy to secure the data privacy in which the data is encoded ahead of time before sharing *** Secret Sharing(VSS)is an encryption method in which the secret message is split into at least two trivial images called’shares’to cover ***,such message are always targeted by hackers or dishonest members who attempt to decrypt the *** can be avoided by not uncovering the secret message without the universal share when it is presented and is typically taken care of,by the trusted ***,in this paper,an optimal and secure double-layered secret image sharing scheme is *** proposed share creation process contains two layers such as threshold-based secret sharing in the first layer and universal share based secret sharing in the second *** first layer,Genetic Algorithm(GA)is applied to find the optimal threshold value based on the randomness of the created ***,in the second layer,a novel design of universal share-based secret share creation method is ***,Opposition Whale Optimization Algorithm(OWOA)-based optimal key was generated for rectange block cipher to secure each *** helped in producing high quality reconstruction *** researcher achieved average experimental outcomes in terms of PSNR and MSE values equal to 55.154225 and 0.79365625 *** average PSNRwas less(49.134475)and average MSE was high(1)in case of existing methods.

关键词： genetic algorithm oppositional whale optimization algorithm rectangle block cipher secret sharing scheme shares universal share

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Developing a Model for Parkinson’s Disease Detection Using Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 4945-4962页

作者： Naif Al Mudawi department of computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia

Parkinson’s disease(PD)is a chronic neurological condition that progresses over *** start to have trouble speaking,writing,walking,or performing other basic skills as dopamine-generating neurons in some brain regions are injured or *** patient’s symptoms become more severe due to the worsening of their signs over *** this study,we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk *** research worked on the publicly available dataset on PD,and the dataset consists of a set of significant characteristics of *** aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD *** research methodology involves developing a model using a machine learning *** the model selection,eight different machine learning techniques were adopted:Namely,Random Forest(RF),Decision Tree(DT),Support Vector Machine(SVM),Naïve Bayes(NB),Light Gradient Boosting Machine(LightGBM),K-Nearest Neighbours(KNN),Extreme Gradient Boosting(XGBoost),and Logistic Regression(LR).Subsequently,the concentrated models were validated through 10-fold Cross-Validation and Receiver Operating Characteristic(ROC)—Area Under the Curve(AUC).In addition,GridSearchCV was utilised to measure each algorithm’s best parameter;eventually,the models were trained through the hyperparameter tuning *** 98%accuracy,LightGBM had the highest accuracy in this ***,KNN,and SVM came in second with 96%***,the performance scores of NB and LR were recorded to be 76%and 83%,*** is to be mentioned that after applying 10-fold cross-validation,the average performance score of LightGBM accounted for 93%.At the same time,the percentage of ROC-AUC appeared at 0.92,which indicates that this LightGBM model reached a satisfactory ***,we extracted meaningful insights and figured out potential gaps on top of *** extracting meaningful in

关键词： Light GBM cross-validation ROC-AUC Parkinson’s disease(PD) SVM and XGBoost

KurdSet: A Kurdish Handwritten Characters Recognition Dataset Using Convolutional Neural Network

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computers, Materials & Continua 2024年第4期79卷 429-448页

作者： Sardar Hasen Ali Maiwan Bahjat Abdulrazzaq computer Science department University of ZakhoDuhokIraq

Handwritten character recognition(HCR)involves identifying characters in images,documents,and various sources such as forms surveys,questionnaires,and signatures,and transforming them into a machine-readable format for subsequent *** recognizing complex and intricately shaped handwritten characters remains a significant *** use of convolutional neural network(CNN)in recent developments has notably advanced HCR,leveraging the ability to extract discriminative features from extensive sets of raw *** of the absence of pre-existing datasets in the Kurdish language,we created a Kurdish handwritten dataset called(KurdSet).The dataset consists of Kurdish characters,digits,texts,and *** dataset consists of 1560 participants and contains 45,240 *** this study,we chose characters only from our *** utilized a Kurdish dataset for handwritten character *** study also utilizes various models,including InceptionV3,Xception,DenseNet121,and a *** show the performance of the KurdSet dataset,we compared it to Arabic handwritten character recognition dataset(AHCD).We applied the models to both datasets to show the performance of our ***,the performance of the models is evaluated using test accuracy,which measures the percentage of correctly classified characters in the evaluation *** models performed well in the training phase,DenseNet121 exhibited the highest accuracy among the models,achieving a high accuracy of 99.80%on the Kurdish *** Xception model achieved 98.66%using the Arabic dataset.

关键词： CNN models Kurdish handwritten recognition KurdSet dataset Arabic handwritten recognition DenseNet121 model InceptionV3 model Xception model

Unbalanced private set intersection with linear communication complexity

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Science China(Information Sciences) 2024年第3期67卷 79-93页

作者： Quanyu ZHAO Bingbing JIANG Yuan ZHANG Heng WANG Yunlong MAO Sheng ZHONG department of computer Science and Technology Nanjing University Purple Mountain Lab

The private set intersection(PSI) protocol allows two parties holding a set of integers to compute the intersection of their sets without revealing any additional information to each other. The unbalanced PSI schemes consider a specific setting where a client holds a small set of the size n and a server holds a much larger set of the size m(n ■ m). The communication overhead of state-of-the-art balanced PSI schemes is O(m + n) and the unbalanced PSI schemes are O(nlogm). In this paper, we propose a novel secure unbalanced PSI protocol based on a hash proof system. The communication complexity of our protocol grows only linearly with the size of the small set. In other words, our protocol achieves communication overhead of O(n). We test the performance on a personal computer(PC) machine with a local area network(LAN)setting for the network. The experimental results demonstrate that the client only takes 2.01 s of online computation, 4.27 MB of round trip communication to intersect 1600 pieces of 32-bit integers with 220pieces of 32-bit integers with the security parameter λ = 512. Our protocol is efficient and can be applied to resource-constrained devices, such as cell phones.

关键词： unbalanced private set intersection Hash proof system linear communication complexity small set resource-constrained devices

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of computer Science Shaanxi Normal University Faculty of computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences department of computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Proteins RNA Neural networks Encoding Robustness Decoding Convolutional neural networks

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer Science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India department of computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India department of computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

On optimal streaming kernelization algorithms

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Science China(Information Sciences) 2024年第8期67卷 337-338页

作者： Hao FENG Wei YANG Jianer CHEN School of computer Science Guangzhou University department of computer Science and Engineering Texas A&M University

The streaming model has been a popular model in big data computation. Streaming kernelization algorithms can be regarded as data compression processes on streaming data. In this study, we give a general method for developing computational lower bounds for streaming kernelization algorithms that is applicable to a large class of computational problems. As an example, we use the method to prove computational lower bounds for the well-known problem d-Hi TTin GSET.

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