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A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

computers, Materials & Continua 2023年第10期77卷 781-797页

作者： Yazeed Yasin Ghadi Mohammed SAlshehri Sultan Almakdi Oumaima Saidani Nazik Alturki Fawad Masood Muhammad Shahbaz Khan Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Institute of Space TechnologyIslamabad4400Pakistan School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK

Securing digital image data is a key concern in today’s information-driven *** encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption *** study introduces an innovative approach to creating S-boxes for encryption *** proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption *** nonlinearity measure of the proposed S-boxes is *** qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data ***,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption *** validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been *** addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are *** results indicate a 99.71%NPCR and 33.51%*** results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

关键词： S-box chaos artificial bee colony image encryption

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

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computers, Materials & Continua 2023年第2期74卷 3101-3115页

作者： Mohammed Maray Hamed Alqahtani Saud S.Alotaibi Fatma S.Alrayes Nuha Alshuqayran Mrim M.Alnfiai Amal S.Mehanna Mesfer Al Duhayyim Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective *** such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection *** of the computer-based systems that effectively protect the data include Cyber-Physical systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical *** software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the *** current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)*** presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud *** attain this,Zscore data normalization is utilized to scale the data into a uniform *** addition,BDDBN model is also exploited for recognition and categorization of *** effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is *** scenario enhances the classification results and also shows the novelty of current *** experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.

关键词： Malware detection security Internet of Things cloud computing machine learning parameter adjustment

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Student academic success prediction in multimedia-supported virtual learning system using ensemble learning approach

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Multimedia Tools and Applications 2024年第40期83卷 87553-87578页

作者： Saidani, Oumaima Umer, Muhammad Alshardan, Amal Alturki, Nazik Nappi, Michele Ashraf, Imran Department of Information Systems College of Computer and Information Sciences and Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano84084 Italy Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Multimedia systems and metaverse has been gaining increasing interest for education in virtual environments. With wide adoption of these technologies, the data is expected to grow exponentially. The increasing rise of educational data indicates that standard processing methods may be limited. As a result, constructing data mining-based academic performance assessment methods is becoming increasingly important. Students’ academic performance prediction can raise an alert for students at risk of failure and can help academia plan accordingly. This research suggests utilizing features derived from a convolutional neural network (CNN) in conjunction with machine learning models to enhance the overall performance of the model. By doing so, the necessity for manual feature extraction is eliminated, leading to superior outcomes compared to using machine learning and deep learning models separately. At the outset, nine machine learning models are employed to assess both the original features and the convoluted features. The top two individual models are later used to build an ensemble model using soft voting criteria. The ensemble model is built using random forest (RF) and support vector machine (SVM) for predicting student academic performance. The performance of the proposed approach is compared to existing models for validation indicating the superior performance of the former. With an accuracy of 98.99%, as well as precision, recall, and F1 scores of 99%, the proposed method achieved outstanding results for predicting student academic performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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A Survey on Smart Promo Creator: AI-Driven Dynamic Video Generation for Banners and Social Media Marketing 5

A Survey on Smart Promo Creator: AI-Driven Dynamic Video Gen...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Pardeshi, Yashini Alai, Smital Kumbhojkar, Pranav Chaudhari, Viraj Moholkar, Kavita JSPM's Rajarshi Shahu College of Engineering Department of Computer Science and Business Systems Pune India

ISBN: (纸本)9798331518097

Generative AI (GenAI) has emerged as a transformative technology across various sectors, particularly in marketing. The paper presents an innovative solution designed to revolutionize promotional content creation for small brands with limited marketing budgets. The proposed solution aims to overcome the research gap regarding lack of affordable, automated systems for generating high-quality and personalized promotional assets. By automating the generation of high-quality banners and engaging videos, GenAI reduces reliance on manual efforts while delivering visually appealing and relevant promotional assets. Utilizing advanced machine learning algorithms such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Natural Language Processing (NLP), the proposed system can create customized banners, posters, and videos tailored to specific inputs like product images, audience demographics, seasonal themes, and events. technology streamlines the content generation process, ensures consistency across various digital advertising channels, and enhances customer engagement and brand recognition overcoming. © 2024 IEEE.

关键词： Generative adversarial networks

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Career Center Application as Centralization of Career Development: An Empirical Investigation Using the Unified Theory of Acceptance and Use of Technology (UTAUT) Model 10

Career Center Application as Centralization of Career Develo...

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Pratama, Arista Mukaromah, Siti Idhom, Mohammad Ningtyas, Silvia Ayu Parlika, Rizky Suryantari, Putu Anggi University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Information Systems Surabaya Indonesia University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798331521295

The Career Center Application is one of the innovations in the application of technology used to assist in finding and identifying relevant career opportunities according to the interests and competencies of students and graduates of higher education institutions. This study aims to analyze the success factors in the acceptance and use of applications of the Career Center using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study used Warp-PLS version 7.0 with 348 respondents using the UPNVJT Career Center application using the UTAUT method. The results of the structural model indicate a significant value of the relationship between the variables processed using SEM/PLS analysis technique using Warp-PLS 7.0. The results of this study show that the effort expectancy variable produces a path coefficient or direct influence of 0.136 on behavioral intention. Social influence has a positive impact on the behavior intention variable, and the facilitating conditions create a direct influence. © 2024 IEEE.

关键词： Employment

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Tag Prediction of Stack Overflow Questions using NLP and ML 15

Tag Prediction of Stack Overflow Questions using NLP and ML

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Prasad, P. Yogendra Priyanka, Mylavarapu Amreen, Shaik Reddy, N. Desai Sreenivas Teja, Muppala Bhanu Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Technology is growing in great leaps and bounds and the number of innovations is increasing every day with major contributor being the advancement in the software development environment. Programming environment includes plethora of tools and support platforms that gives a complete assistance to the programming designers. These platforms can be question- answer based or coding based or tutorial based. Stack Overflow is a very popular online question- answer platform in the current generation and is growing to be the greatest question-answer gathering for programmers and software developers. The amount of information in this forum is so huge that one of the main functionalities of this platform would be tagging i.e., identifying the label or class to which the piece of information belongs to. Tagging helps in easy retrieval and organization of data. We propose a multi label classification system that labels all the questions on the forums with their appropriate tags autonomously. This auto-labelling framework makes it very easy for the users to find answers for the inquiries posted, given significant tags for the same and also helps the site in organizing their questions in a better way. © Grenze Scientific Society, 2024.

关键词： Software design

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Chameleon Optimization-based Path Selection and Improved Cryptography in Underwater Wireless Sensor Network 5

Chameleon Optimization-based Path Selection and Improved Cry...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Sajitha, L.P. Sathiyamoorthy, J. R.M.K Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350389609

The Underwater Wireless Sensor Network (UWSN) consists of a vast model where numerous active sensor nodes (SNs) surround each transmitting node. Secure communication encounters challenge due to the spatially and time changing characteristics of the UWSN medium. Moreover, there is a need for techniques for mitigating interference and better communication. To tackle this challenge, this work presents an optimal path and secure data transmission using Chameleon Search Optimization (CSO) and Enhanced Elliptical Curve Cryptography (EECC). The suggested model to transmit secure data transmission in UWSN on the basis of the optimal path has two phases. At first, nodes are chosen with respect to the energy and distance, and the optimal path is chosen by the CSO. Then, in the next phase, the encryption process is performed by the EECC for secure data transmission. The proposed CSO- EECC provided better communication effectiveness, security and robust solutions. The encryption time and decryption time achieved by the proposed CSO- EECC are 0.003 s and 0.0034 s respectively © 2024 IEEE.

关键词： Sensor nodes

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Improved Multi-Path Routing for QoS on MANET

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computer systems science & Engineering 2023年第6期45卷 2521-2536页

作者： M.Vargheese Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia

A Mobile Ad hoc NETwork(MANET)is a self-configuring network that is not reliant on *** paper introduces a new multipath routing method based on the Multi-Hop Routing(MHR)*** is the consecutive selection of suitable relay nodes to send information across nodes that are not within direct range of each *** to ensure good MHR leads to several negative consequences,ultimately causing unsuccessful data transmission in a *** research work consists of three *** first to attempt to propose an efficient MHR protocol is the design of Priority Based Dynamic Routing(PBDR)to adapt to the dynamic MANET environment by reducing Node Link Failures(NLF)in the *** is achieved by dynamically considering a node’s mobility parameters like relative velocity and link duration,which enable the next-hop *** method works more efficiently than the traditional *** the second stage is the Improved Multi-Path Dynamic Routing(IMPDR).The enhancement is mainly focused on further improving the Quality of Service(QoS)in MANETs by introducing a QoS timer at every node to help in the QoS routing of *** QoS is the most vital metric that assesses a protocol,its dynamic estimation has improved network performance *** method uses distance,linkability,trust,and QoS as the four parameters for the next-hop *** is compared against traditional routing *** Network Simulator-2(NS2)is used to conduct a simulation analysis of the protocols under *** proposed tests are assessed for the Packet Delivery Ratio(PDR),Packet Loss Rate(PLR),End-to-End Delay(EED),and Network Throughput(NT).

关键词： Multi-path routing quality of service node-link failure packet delivery ratio

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