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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drugs Training Proteins Measurement Message passing Prediction methods Graph neural networks

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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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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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Automatic segmentation, classification, and prediction of pelvic bone tumors using deep learning techniques

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Journal of engineering and Applied science 2024年第1期71卷 1-22页

作者： Shouman, Mona Rahouma, Kamel Hussein Hamed, Hesham F. A. Computer and System Engineering Department Faculty of Engineering Minia University Minia Egypt Faculty of Engineering Minia University Minia Egypt College of Artificial Intelligence Russian University Cairo Egypt

MRI and CT images are widely utilized for detecting tumors in internal organs. However, accurately distinguishing tumors using these images only, poses a challenge. This demands human expertise to effectively mark tumors in a given MRI or CT image, which is time-consuming. In this study, MONAI Label and MONAI Dynamic U-Net (DynU-Net) were used to segment tumors in any given 3D CT image of the pelvic bone, aiming to assist radiologists further. This method helps to run the model without needing a GPU which is better than traditional approaches. In addition, a convolutional neural network (CNN) was used to classify the tumors as benign or malignant and to predict three grades of tumors (low, medium, and high). The use of CNN in classification and prediction gives higher results than other studies. A dataset of 178 3D CT picture images was employed to feed the networks with the help of Adam optimizer and Categorical cross-entropy. We employ a set of 3D CT scans because of their greater contrast and spatial resolution which is better used for pelvic bone tumors. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) have been used to perform calculations in segmentation. The results show a DSC of 0.7660280 and an HD of 16.75480. A range of performance metrics, such as sensitivity, specification, and F1-score for classification and prediction methods, are used to evaluate the accuracy of the proposed system. The system has accuracy (99.4%) for classification and (97.8%) for prediction. These findings indicate that MONAI Label is effective for automatic segmentation of tumors in a given CT scan;with high accuracy. Moreover, CNN is useful for classification and prediction systems with high accuracy. However, achieving better results is possible with an abundance of training samples. © The Author(s) 2024.

关键词： computerized tomography

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

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computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

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Care4U: Integrated healthcare systems based on blockchain

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Blockchain(Research and Applications) 2023年第4期4卷 2-13页

作者： Randa Kamal Ezz El-Din Hemdan Nawal El-Fishway Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityEgypt

During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sickpeople, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of patientscan ensure remote healthcare at home using advanced technologies such as the Internet of Medical Things (IoMT).The IoMT can monitor and transmit sensitive health data;however, it may be vulnerable to various attacks. In thispaper, an efficient healthcare security system is proposed for IoMT applications. In the proposed system, themedical sensors can transmit sensed encrypted health data via a mobile application to the doctor for ***, three consortium blockchains are constructed for load balancing of transactions and reducing transactionlatency. They store the credentials of system entities, doctors' prescriptions and recommendations according to thedata transmitted via mobile applications, and the medical treatment process. Besides, cancelable biometrics areused for providing authentication and increasing the security of the proposed medical system. The investigationalresults show that the proposed system outperforms existing work where the proposed model consumed lessprocessing time by values of 18%, 22%, and 40%, and less energy for processing a 200 KB file by values of 9%,13%, and 17%. Finally, the proposed model consumed less memory usage by values of 7%, 7%, and 18.75%. Fromthese results, it is clear that the proposed system gives a very reliable and secure performance for efficientlysecuring medical applications.

关键词： Healthcare Blockchain Cyber security Internet of things Internet of Medical Things Cancelable biometrics

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