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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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The Cubic Bipolar Neutrosophic Sets theory and Uncertainty Management in Environmental Data Analysis

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Neutrosophic Sets and systems 2024年 72卷 60-133页

作者： Salama, A.A. Khalid, Huda E. Mabrouk, Ahmed G. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dean of higher Institute of Computer Science and Information Systems Ministry of Higher Education October City Egypt Telafer University Telafer Iraq Higher Institute of Computer Science and Information Systems Ministry of Higher Education October City Egypt

Environmental data analysis often faces uncertainties in measurements. Cubic Bipolar Neutrosophic Sets (CBN Sets) provide a powerful framework to address this challenge. This paper explores the mathematical foundations of CBN Sets and highlights their practical applications through illustrative examples. We demonstrate how CBN Sets effectively capture varying degrees of certainty, possibility, and impermanence in environmental parameters. The concept of inclusion relations facilitates comparisons and information fusion. Fundamental set operations (intersection, union, complement) are explored for manipulating and analyzing uncertain environmental data. We present the application of CBN Sets in water quality assessment, highlighting their ability to analyze parameters while accounting for measurement uncertainties. The potential for air quality monitoring using CBN Sets is also discussed. Finally, distance measures and similarity coefficients are introduced to quantify relationships between air quality characteristics from different stations. By leveraging CBN Sets and their associated operations, researchers can gain a more nuanced understanding of environmental data, enabling informed decision-making for a healthier planet. © 2024, University of New Mexico. All rights reserved.

关键词： Environmental monitoring

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Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter

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Neural Computing and Applications 2025年第4期37卷 2211-2237页

作者： Elsedimy, E.I. Herajy, Mostafa Abohashish, Sara M. M. Department of Information Technology Management Faculty of Management Technology and Information Systems Port Said University Port Said Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt Cyber Secuirty Department Faculty of Science Al-Esraa University College Baghdad Iraq

Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of cloud data centers in a flexible and scalable manner. It allows placing multiple independent virtual machines (VMs) on a single server in order to improve resource utilization and energy efficiency. However, determining the optimal VM placement is crucial as it directly impacts load balancing, energy consumption, and performance degradation within the data center. Furthermore, deciding on VM placement based on a single factor is usually insufficient to improve data center performance because many factors must be considered, and ignoring them may be too expensive. This paper improves a new multi-objective VM placement (MVMP) algorithm using a quantum particle swarm optimization (QPSO) technique. We call it QPSO-MOVMP, and its objective is to find the Pareto optimal solution for the VM placement problem by balancing different goals. This algorithm generates Pareto optimal solutions that save power by minimizing the number of running physical machines, avoid performance degradation by maintaining service level agreement (SLA), and improve load balancing by keeping server loads at optimal utilization. The experimental results show that QPSO-MOVMP had superior performance in terms of power consumption and performance degradation compared to three other multi-objective algorithms and three conventional single-objective algorithms. Simulation results show that the proposed QPSO-MOVMP achieves a consumption of 2.4 × 104 watts in power. Furthermore, it outperformed the others, achieving a minimum of 12% SLA breaches while experiencing a significant surge in requests from VMs. Moreover, the proposed model generated Pareto solutions that had a better distribution than those derived from a comparative method. © The Author(s) 2025.

关键词： Particle swarm optimization (PSO)

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Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

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computers, Materials & Continua 2023年第3期74卷 5559-5572页

作者： Aliaa El-Gawady BenBella S.Tawfik Mohamed A.Makhlouf Department of Information Systems Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Faculty of Computer Science Nahda UniversityBeni SuefEgypt

Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many *** machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research ***,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity *** this work,we shed light on the prediction of AD from GE data accurately using *** approach consists of four phases:preprocessing,gene selection(GS),classification,and performance *** the preprocessing phase,gene columns are preprocessed *** the GS phase,a hybrid filtering method and embedded method are *** the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous *** final phase is to validate the performance of these classifiers using different *** crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of *** different datasets are used to achieve our *** predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one *** assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance *** metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for *** these results,the proposed approach outperforms recent interesting *** these results,the proposed approach outperforms recent interesting results.

关键词： Gene expression gene selection machine learning classification Alzheimer’s disease

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Exploring the Role of Sleep Quality, Obsessive–Compulsive Disorder, Fear of Missing Out, and Psychological Resilience on Problematic Instagram Use

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Journal of Technology in Behavioral science 2024年 1-11页

作者： Yilmaz, Ramazan Avci, Ummuhan Karaoglan Yilmaz, Fatma Gizem Department of Computer Technology & Information Systems Faculty of Science Bartin University Bartin Turkey Department of Management Information Systems Faculty of Economics and Administrative Sciences Bartin University Bartin Turkey

This study addresses a gap in the literature regarding the relationships between sleep quality, obsessive–compulsive disorder (OCD), fear of missing out (FoMO), psychological resilience, and problematic Instagram use (PIU) among university students. By examining these interrelated factors, the research aims to clarify the mechanisms behind Instagram addiction, support effective interventions, and contribute to academic knowledge. This study examined university students’ sleep quality, PIU, OCD, FoMO, and psychological resilience, as well as the structural relationships between these variables. The study encompassed a sample of 407 Turkish university students with an average age of 21.78. The age range of the participants was between 18 and 35 years;43% of them were female, and 57% were male. This study was conducted according to the correlational research method, and pathway analysis was used to conduct the research. Research findings show that OCD and FoMO positively affect PIU. In other words, as OCD and FoMO increase, Instagram addiction also increases. Psychological resilience has an indirect effect on PIU. OCD plays a mediating role between psychological resilience and PIU and between psychological resilience and FoMO. Therefore, as psychological resilience increases, PIU and FoMO decrease. A positive correlation was found between OCD and FoMO. As the OCD level increases, the FoMO level of the individuals also increases. Another study finding was that OCD, FoMO, psychological resilience, and PIU did not significantly affect sleep quality. Future research may focus on exploring cause-effect relationships among these variables alongside implementing school-based programs to enhance students’ psychological resilience through training, planning, and follow-up studies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Fear of missing out (FoMO) Obsessive–compulsive disorder Problematic Instagram use Psychological resilience Sleep quality

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A Holistic Anti-Counterfeiting Platform Using NFC and Blockchain Technologies

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computers, Materials & Continua 2025年第6期83卷 4257-4280页

作者： Rajendren Subramaniam Saaidal Razalli Azzuhri Teh Ying Wah Atif Mahmood Vimala Balakrishnan Department of Information System Faculty of Computer Science&Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia Department of Computer System&Technology Faculty of Computer Science&Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia Faculty of Data Science and Information Technology INTI International UniversityNilai71800Malaysia

Counterfeiting is still a pervasive global issue,affecting multiple industries and hindering industrial innovation,while causing substantial financial losses,reputational damage,and risks to consumer *** luxury goods and pharmaceuticals to electronics and automotive parts,counterfeit products infiltrate supply chains,leading to a loss of revenue for legitimate businesses and undermining consumer *** anti-counterfeiting measures,such as holograms,serial numbers,and barcodes,have proven to be insufficient as counterfeiters continuously develop more sophisticated replication *** a result,there is a growing need for more advanced,secure,and reliable methods to prevent *** paper presents a novel,holistic anti-counterfeiting platform that integrates Near Field Communication(NFC)-enabled mobile applications with blockchain technology to provide an innovative,secure,and consumer-friendly authentication *** approach addresses key gaps in existing solutions by incorporating dynamic product identifiers,which make replication significantly more *** system enables consumers to verify the authenticity of products instantly using their smartphones,enhancing transparency and trust in the supply *** technology plays a crucial role in our proposed solution by providing an immutable,decentralized ledger that records product authentication *** ensures that product verification records cannot be tampered with or altered,adding a layer of security that is absent in conventional ***,NFC technology enhances security by offering unique identification capabilities,enabling real-time product *** validate the effectiveness of the proposed system,real-world testing was conducted across different *** results demonstrated the platform’s ability to significantly reduce counterfeit products in the supply chain,offering businesses and consumers a more robust and reliable aut

关键词： Blockchain near field communication(NFC) anti-counterfeiting

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Tracking Dengue on Twitter Using Hybrid Filtration-Polarity and Apache Flume

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computer systems science & Engineering 2022年第3期40卷 913-926页

作者： Norjihan Binti Abdul Ghani Suraya Hamid Muneer Ahmad Younes Saadi N.Z.Jhanjhi Mohammed A.Alzain Mehedi Masud Department of Information Systems Faculty of Computer Science&Information TechnologyUniversiti Malaya50603Kuala LumpurMalaysia School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The world health organization(WHO)terms dengue as a serious illness that impacts almost half of the world’s population and carries no specific *** and accurate detection of spread in affected regions can save precious *** the severity of the disease,a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text ***,the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data,as reliable mechanisms to gather the data and suitable techniques to extract meaningful insights from the data are *** research study proposes a sentiment analysis polarity approach for collecting data and extracting relevant information about dengue via Apache *** method consists of two main parts:the first part collects data from social media using Apache Flume,while the second part focuses on querying and extracting relevant information via the hybrid filtration-polarity algorithm using Apache *** overcome the noisy and unstructured nature of the data,the process of extracting information is characterized by pre and post-filtration *** a result,only with the integration of Flume and Hive with filtration and polarity analysis,can a reliable sentiment analysis technique be offered to collect and process large-scale data from the social *** introduce how the Apache Hadoop ecosystem–Flume and Hive–can provide a sentiment analysis capability by storing and processing large amounts of *** important finding of this paper is that developing efficient sentiment analysis applications for detecting diseases can be more reliable through the use of the Hadoop ecosystem components than through the use of normal machines.

关键词： Big data analysis data filtration text analysis sentiment analysis social media event detection

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Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

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computers, Materials & Continua 2023年第5期75卷 3065-3089页

作者： Ali Arshad Muhammad Nadeem Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Zaid Alzaid Rana Alabdan Badr Almutairi Sultan Almotairi Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic university of MadinahMedinah42351Saudi Arabia Department of Information Systems Faculty of Computer and Information Sciences CollegeMajmaah University11952Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah 11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an *** cryptography is the best way to transmit data in a secure and reliable *** researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data *** algorithm has some data security *** some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original *** this paper,various data security techniques are developed,which can be used to protect the data from attackers ***,a customized American Standard Code for information Interchange(ASCII)table is *** value of each Index is defined in a customized ASCII *** an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the *** that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original *** the number of cipher values is double the original data,the attacker tries to decrypt each *** of getting the original data,the attacker gets such data that has no relation to the original *** that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain *** boundaries of each Hill text work up to that *** techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorit

关键词： Cryptography symmetric cipher text encryption matrix cipher encoding decoding hill matrix 64-bit encryption

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Organizational Data Breach:Building Conscious Care Behavior in Incident Response

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computer systems science & Engineering 2022年第2期40卷 505-515页

作者： Adlyn Adam Teoh Norjihan Binti Abdul Ghani Muneer Ahmad Nz Jhanjhi Mohammed A.Alzain Mehedi Masud Department of Information Systems Faculty of Computer Science&Information TechnologyUniversiti Malaya50603Kuala LumpurMalaysia School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Organizational and end user data breaches are highly implicated by the role of information security conscious care behavior in respective incident *** research study draws upon the literature in the areas of information security,incident response,theory of planned behaviour,and protection motivation theory to expand and empirically validate a modified framework of information security conscious care behaviour *** applicability of the theoretical framework is shown through a case study labelled as a cyber-attack of unprecedented scale and sophistication in Singapore’s history to-date,the 2018 SingHealth data *** single in-depth case study observed information security awareness,policy,experience,attitude,subjective norms,perceived behavioral control,threat appraisal and self-efficacy as emerging prominently in the framework’s applicability in incident *** data analysis did not support threat severity relationship with conscious care *** findings from the above-mentioned observations are presented as possible key drivers in the shaping information security conscious care behaviour in real-world cyber incident management.

关键词： End user computing organizational behavior incident response data breach computer emergency response team cyber-attack

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