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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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Comparison of the SOLA model and ARIMA for Cloud computing Resources Allocations 4

Comparison of the SOLA model and ARIMA for Cloud Computing R...

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4th International Multidisciplinary information Technology and Engineering Conference, IMITEC 2024

作者： Prince Sekwatlakwatla, Sello Malele, Vusumuzi North-West University Unit for Data Science and Computing Department of Computer Science and Information Systems South Africa

ISBN: (纸本)9798350387988

Cloud computing solutions are becoming more and more popular as a way for organizations to improve productivity, save costs, and simplify procedures. The advantage of cloud services is that they enable users to store data remotely and access on-demand applications and services from a shared pool of configurable computing resources. Cloud computing resource allocation faces challenges in load balancing, effective resource management, and compliance with regulatory and legal requirements, requiring complex tools and techniques for optimal use. Most organizations are moving to cloud services and encouraging their clients to access services online. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times, and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA) and the individual method, which is autoregressive integrated moving average (ARIMA). The results show SGLA prediction accuracy increased with an average of 97.9%, and ARIMA prediction showed an accuracy of 71.5%. In this regard the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Bibliometric Analysis of Machine Learning Ethics 1

Bibliometric Analysis of Machine Learning Ethics

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1st International Conference on the Advancements of Artificial Intelligence in African Context, AAIAC 2023

作者： Sekwatlakwatla, Sello Prince Malele, Vusumuzi North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark South Africa

ISBN: (纸本)9798350330137

automating decision-making on a wide range of aspects are affecting humanity. There is great promise in machine learning (ML) and artificial intelligence (AI) algorithms for assisting with predicted and automated decision-making and transforming both societies and industries to improve African citizens' everyday lives. In a business context, platforms such as cloud computing can benefit a lot from AI and ML. Prediction models could be developed to predict the traffic flow of cloud computing resources, which could ease traffic flow. Although AI and ML cloud computing are powerful and vital, they are unfortunately not immune from ethical challenges. As well as improving the allocation of cloud computing resources with prediction models. In recent years, despite AI's powerful performance, in order to conduct responsible AI and ML modeling, it is important not to ignore ethical issues that might need serious investigation, particularly if human and classified data are concerned. In this regard, the objective of this paper is to determine the prevalence of AI and ML ethics research in Africa. In this paper, RStudio and the computing-related databases (IEEE Xplore, Web of service and Scopus) were used to conduct a bibliometric analysis on the prevalence of AI and ML ethics that relate to the cloud computing environment. The scope was minimized due to its prevalence in the African context. The findings show that AI and ML ethics are understudied on the African continent compared to the West. In this regard, there is a need to conduct more AI and ML ethics-related research in Africa because failure to do so will result in the West shaping the ethical discussion in areas of importance for African development, such as cloud computing. The latter will allow the West to slowly impose its perspective without considering African perspectives. Of note, this paper contributes to the doctoral study with an objective phrased as "to conduct bibliometric analysis to identify the preval

关键词： Machine learning

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Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

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Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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SYSTEM THINKING MEETS data science/ENGINEERING 67

SYSTEM THINKING MEETS DATA SCIENCE/ENGINEERING

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67th Annual Meeting of the International Society for the systems sciences, ISSS 2023

作者： Malele, Vusumuzi Unit for Data Science and Computing School of Computer Science and Information Systems North-West University Vanderbijlpark Campus South Africa

ISBN: (纸本)9781713893455

With the advent of data 3.0 and analytics 3.0, system thinkers are in the position to provide a bigger picture in data science and data engineering. In the data life cycle, a system thinking approach emphasises data-driven decision-making. A System Thinker approaches problem-solving by viewing the problems as part of a wider, data-resourced and dynamic system, and a data Practitioner supports the data life cycle by collecting, transforming, and analyzing data, and communicating results to inform and guide decision-making. This paper uses explanatory research and a pragmatic case study approach to look at the (i) What is the role of system thinking and data science/engineering skill in data-driven decision-making or organisation? (ii) Does the combination of system thinking and data science/engineering give rise to a new discipline? (iii) What are the skills needed in this new discipline? The research shows that the system thinking skills in the data life cycle are important. System thinkers meet data practitioners to provide a bigger picture of data-driven decision-making. The latter ascertains the position of a system thinker in any industrial revolution (i.e. industry 4.0. and industry 5.0). Furthermore, a data-System Thinker is proposed as a new career field. © Copyright, Author (Creative Commons), 2023.

关键词： systems thinking

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of computer science & Technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent unit-based Respiratory data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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

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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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Optimizing AES S-Box Implementation:A SAT-Based Approach with Tower Field Representations

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computers, Materials & Continua 2025年第4期83卷 1515-1531页

作者： Jingya Feng Ying Zhao Tao Ye Wei Feng School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Computing and Information Systems The University of MelbourneMelbourne3010Australia Guangxi Wangxin Information Technology Co. Ltd.Nanning530000China

The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data *** paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)*** research makes several significant contri-butions to the ***,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known ***,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local *** approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box ***,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior *** implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy *** approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy *** optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.

关键词： AES S-box SAT optimization tower field hardware implementation area efficiency energy consumption

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on information Communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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