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Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

computers, Materials & Continua 2022年第9期72卷 6091-6106页

作者： Medhat A.Tawfeek Saad Alanazi A.A.Abd El-Aziz Department of Computer Science College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA Department of Computer Science Faculty of Computers and InformationMenoufia UniversityEgypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySaudi Arabia(KSA) Department of Information Systems and Technology Faculty of Graduates Studies and ResearchCairo UniversityEgypt

The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very *** is a trade-off in the objectives in the existing techniques of MultipleSequence Alignment (MSA). The techniques that concern with speed ignoreaccuracy, whereas techniques that concern with accuracy ignore speed. Theterm alignment means to get the similarity in different sequences with highaccuracy. The more growing number of sequences leads to a very complexand complicated problem. Because of the emergence;rapid development;anddependence on gene sequencing, sequence alignment has become importantin every biological relationship analysis process. Calculating the numberof similar amino acids is the primary method for proving that there is arelationship between two sequences. The time is a main issue in any alignmenttechnique. In this paper, a more effective MSA method for handling themassive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method dependson Artificial Fish Swarm (AFS) algorithm that can break down the mostchallenges of MSA problems. The AFS is exploited to obtain high accuracyin adequate time. ASF has been increasing popularly in various applicationssuch as artificial intelligence, computer vision, machine learning, and dataintensive application. It basically mimics the behavior of fish trying to getthe food in nature. The proposed mechanisms of AFS that is like preying,swarming, following, moving, and leaping help in increasing the accuracy andconcerning the speed by decreasing execution time. The sense organs that aidthe artificial fishes to collect information and vision from the environmenthelp in concerning the accuracy. These features of the proposed AFS make thealignment operation more efficient and are suitable especially for large-scaledata. The implementation and experimental results put the proposed AFS as afirst choice in th

关键词： Multiple sequence alignment swarm intelligence artificial fish swarm protein sequences

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Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks

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Alexandria Engineering Journal 2025年 128卷 127-143页

作者： Islam, Shayla Topazal, S.M. Hasan, Mohammad Kamrul Kolandaisamy, Raenu AL Abbas, Huda Saleh Vo, Nguyen Thakkar, Rahul Khan, Muhammad Attique Gupta, Deepak AlHammadi, Dina Abdulaziz Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Department of computer science College of Computer Science and Engineering Taibah University Madina Saudi Arabia Victorian Institute of Technology Melbourne Australia Center of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Maharaja Agrasen Institute of Technology Delhi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia

The emergence of 5 G and 6 G networks promises significant advancements in the Internet of Medical Things (IoMT) by enabling real-time medical data transmission and continuous connectivity for mobile healthcare devices. However, traditional Handover (HO) mechanisms face latency, security, energy consumption, and scalability challenges when supporting fast-moving and dense IoMT environments. This paper presents a secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks, incorporating a predictive algorithm, Blockchain-Based (BC-B) decentralized authentication mechanism, and AI-driven anomaly detection. The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. Meanwhile, blockchain-based authentication ensures tamper-proof and decentralized security, reducing vulnerability to cyberattacks by 90 % compared to traditional methods. Additionally, the energy consumption during HO events is reduced by 20–35 %, dropping from 15 mJ in traditional schemes to 12 mJ in 5 G and further to 10 mJ in 6 G. The scheme also incorporates AI-driven anomaly detection, achieving an accuracy of 96.7 %, ensuring proactive identification and mitigation of potential security threats. The analyzed scheme maintains consistent throughput with less than 5 % reduction and scales effectively for up to 300 devices per cell. The simulation results demonstrate significant improvements across key performance metrics, including latency, security, energy efficiency, and scalability, making this approach highly suitable for critical IoMT applications such as telesurgery, remote patient monitoring, and innovative healthcare environments. These advancements position the analyzed framework as a cornerstone for next-generation (NG) healthcare networks. © 2025 The Authors

关键词： Remote patient monitoring

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Ensembles of Deep Learning Framework for Stomach Abnormalities Classification

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computers, Materials & Continua 2022年第3期70卷 4357-4372页

作者： Talha Saeed Chu Kiong Loo Muhammad Shahreeza Safiruz Kassim Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyUniversiti Malaya50603 Kuala LumpurMalaysia

Abnormalities of the gastrointestinal tract are widespread worldwide ***,an effective way to diagnose these life-threatening diseases is based on endoscopy,which comprises a vast number of ***,the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the ***,this led to the rise of studies on designingAI-based systems to assist physicians in the *** several medical imaging tasks,deep learning methods,especially convolutional neural networks(CNNs),have contributed to the stateof-the-art outcomes,where the complicated nonlinear relation between target classes and data can be learned and not limit to hand-crafted *** the other hand,hyperparameters are commonly set manually,which may take a long time and leave the risk of non-optimal hyperparameters for *** effective tool for tuning optimal hyperparameters of deep CNNis Bayesian ***,due to the complexity of the CNN,the network can be regarded as a black-box model where the information stored within it is hard to ***,Explainable Artificial Intelligence(XAI)techniques are applied to overcome this issue by interpreting the decisions of the CNNs in such wise the physicians can *** play an essential role in real-time medical diagnosis,CNN-based models need to be accurate and interpretable,while the uncertainty must be ***,a novel method comprising of three phases is proposed to classify these life-threatening *** first,hyperparameter tuning is performed using Bayesian optimization for two state-of-the-art deep CNNs,and then Darknet53 and InceptionV3 features are extracted from these fine-tunned ***,XAI techniques are used to interpret which part of the images CNN takes for feature *** last,the features are fused,and uncertainties are handled by selecting entropybased *** experimental results show that the

关键词： Gastrointestinal tract deep learning Bayesian optimization hyperparameters explainable AI uncertainty handling feature fusion

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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computer Systems science & Engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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Optimation Cavacity Vehicle Routing Problem with K-Nearest Neighbor in Classification of Goods Ditribution Route 7

Optimation Cavacity Vehicle Routing Problem with K-Nearest N...

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7th IEEE International Conference of computer science and information technology, ICOSNIKOM 2023

作者： Novelan, Muhammad Syahputra Efendi, Syahril Sihombing, Poltak Mawengkang, Herman Universitas Sumatera Utara Doctoral Program of Computer Science Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Department of Mathematics Medan Indonesia

ISBN: (纸本)9798350360752

Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification in the influence of the number of 'nodes' on vehicle routes with machine learning. The problem of imbalance in vehicle route classification has been a challenge in the classification process and attracted the attention of a number of researchers. The purpose of the model in general is to gain an understanding of the mechanism in the problem so as to classify the imbalanced vehicle route data based on JNE delivery routes. The solution method that will be used by applying k-nearest neighbor to determine the amount of carrying capacity of the goods will then determine the delivery location point with the vehicle routing problem. So that this model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods. © 2023 IEEE.

关键词： Machine learning

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Optimizing Clustering Approaches in Cloud Environments

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 70-94页

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kreishan, Yazeed Alsarhan, Ayoub Idhaim, Hasan Qahmash, Ayman Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Computer Science College King Khalid University Abha Saudi Arabia

This study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external needs. The study proposes a new approach to request processing in clusters, improving downtime, load distribution, and overall performance. A comparison of three clustering approaches is conducted: local single cluster, local multiple clusters, and multiple cloud clusters. Performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness are evaluated through experiments with 50 requests. All three approaches achieve a 100% success rate, but processing times vary. The local single cluster has the longest duration, while the local multiple clusters and multiple cloud clusters perform better and offer faster processing, scalability, fault tolerance, and availability. From a cost perspective, the local single cluster and local multiple clusters incur capital and operational expenses, while the multiple cloud clusters follow a pay-as-you-go model. Overall, the local multiple clusters and multiple cloud clusters outperform the local single cluster in terms of performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness. These findings provide valuable insights for selecting appropriate clustering strategies in cloud environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fault tolerance

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Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

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computers, Materials & Continua 2022年第8期72卷 2305-2321页

作者： Abdelaziz A.Abdelhamid Sultan R.Alotaibi Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Science and Human StudiesShaqra UniversitySaudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityEgypt

The design ofmicrostrip antennas is a complex and time-consuming process,especially the step of searching for the best design ***,the performance ofmicrostrip antennas can be improved usingmetamaterial,which results in a new class of antennas called metamaterial *** parameters affect the radiation loss and quality factor of this class of antennas,such as the antenna ***,the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning,which presents a better alternative to simulation tools and trialand-error ***,the prediction accuracy depends heavily on the quality of the machine learning *** this paper,and benefiting from the current advances in deep learning,we propose a deep network architecture to predict the bandwidth of metamaterial *** results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error(MSE).In addition,the proposed model is comparedwith current competing approaches that are based on support vector machines,multi-layer perceptron,K-nearest neighbors,and ensemble *** results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately.

关键词： Metamaterial antenna deep learning bandwidth prediction regression models

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Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

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computer Systems science & Engineering 2023年第11期47卷 1571-1591页

作者： Ghoroub Talal Bostaji Emad Sami Jaha Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy ***,biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral *** different viable biometric modalities,the human ear structure can offer unique and valuable discriminative characteristics for human recognition *** recent years,most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful ***,such traditional models can be sensitive to several unconstrained environmental *** such,some traits may be difficult to extract automatically but can still be semantically perceived as soft *** research proposes a new group of semantic features to be used as soft ear biometrics,mainly inspired by conventional descriptive traits used naturally by humans when identifying or describing each ***,the research study is focused on the fusion of the soft ear biometric traits with traditional(hard)ear biometric features to investigate their validity and efficacy in augmenting human identification *** proposed framework has two subsystems:first,a computer vision-based subsystem,extracting traditional(hard)ear biometric traits using principal component analysis(PCA)and local binary patterns(LBP),and second,a crowdsourcing-based subsystem,deriving semantic(soft)ear biometric *** feature-level fusion experiments were conducted using the AMI database to evaluate the proposed algorithm’s *** obtained results for both identification and verification showed that the proposed soft ear biometric information significantly improved the recognition performance of traditional ear biometrics,reaching up to 12%for LBP and 5%for PCA descriptors;when fusing all three capa

关键词： Ear biometrics soft biometrics human ear recognition semantic features feature-level fusion computer vision machine learning

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Metric-Based Resolvability of Quartz Structure

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computers, Materials & Continua 2022年第4期71卷 2053-2071页

作者： Muhammad Imran Ali Ahmad Muhammad Azeem Kashif Elahi Department of Mathematical Sciences United Arab Emirates UniversityAl AinUnited Arab Emirates College of Computer Science&Information Technology Jazan University JazanSaudi Arabia Department of Aerospace Engineering Faculty of EngineeringUniversiti Putra MalaysiaMalaysia Deanship of E-learning and Information Technology Jazan UniversityJazanSaudi Arabia

Silica has three major varieties of crystalline. Quartz is the main andabundant ingredient in the crust of our earth. While other varieties are formedby the heating of quartz. Silica quartz is a rich chemical structure containingenormous properties. Any chemical network or structure can be transformedinto a graph, where atoms become vertices and the bonds are converted toedges, between vertices. This makes a complex network easy to visualize towork on it. There are many concepts to work on chemical structures in termsof graph theory but the resolvability parameters of a graph are quite advanceand applicable topic. Resolvability parameters of a graph is a way to getting agraph into unique form, like each vertex or edge has a unique identification bymeans of some selected vertices, which depends on the distance of vertices andits pattern in a particular graph. We have dealt some resolvability parametersof SiO2 quartz. We computed the resolving set for quartz structure and itsvariants, wherein we proved that all the variants of resolvability parameters ofquartz structures are constant and do not depend on the order of the graph.

关键词： Quartz polycyclic aromatic hydrocarbon related structure metric dimension metric or distance-based resolvability parameters

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Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 375-386页

作者： Sazia Parvin Sonia Farhana Nimmy Md Sarwar Kamal Information Technology Melbourne PolytechnicMelbourne VIC 3072Australia Faculty of Economics and Business University of New South WalesSydney ACT 2612Australia School of Computer Science Faculty of Engineering and ITUniversity of Technology SydneySydney NSW 2007Australia

Alzheimer’s disease(AD)is a neurological disorder that predominantly affects the *** the coming years,it is expected to spread rapidly,with limited progress in diagnostic *** machine learning(ML)and artificial intelligence(AI)algorithms have been employed to detect AD using single-modality ***,recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD *** this study,we developed a framework that utilizes multimodal data(tabular data,magnetic resonance imaging(MRI)images,and genetic information)to classify *** part of the pre-processing phase,we generated a knowledge graph from the tabular data and MRI *** employed graph neural networks for knowledge graph creation,and region-based convolutional neural network approach for image-to-knowledge graph ***,we integrated various explainable AI(XAI)techniques to interpret and elucidate the prediction outcomes derived from multimodal ***-wise relevance propagation was used to explain the layer-wise outcomes in the MRI *** also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data *** expression values play a crucial role in AD *** used a graphical gene tree to identify genes associated with the ***,a dashboard was designed to display XAI outcomes,enabling experts and medical professionals to easily comprehend the predic-tion results.

关键词： Multimodal Region-based convolutional neural network Layer-wise relevance propagation Submodular pick local interpretable model-agnostic explanations Graphical genes tree Alzheimer’s disease

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