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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

computer Modeling in engineering & sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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The Challenges and Opportunities of Smart Cities Infrastructure

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SN computer science 2024年第6期5卷 1-12页

作者： Abuali, Amer Alharthi, Wejdan Althbyani, Mariam Ghabban, Fahad Ameerbakhsh, Omair Alfadli, Ibrahim Al-Shehri, Ashwaq Hasen Fallatah, Najmah Adel College of Computer Science and Engineering Information System Department Taibah University

Information communications technology (ICT) refers to the technology used for communication and managing information must be utilized to enhance the environment, economics, mobility, and governance, among other facets of urban life, in order to create smart cities. However this progress is frequently accompanied by a number of challenges and unfavorable outcomes. This study aims to uncover ICT difficulties related to smart city infrastructure through an extensive literature review. In addition, a survey was carried out among Saudi nationals to find out what they thought about, anticipated, and worried about in terms of smart city concepts and features. The study also looked at a literature checklist of ICT problems, which included dangers to information security, incompatibilities across systems, privacy concerns, and deficiencies in digital capabilities. Based on the findings it appears that privacy breaches and information security concerns are the most important issues. This is explained by heightened susceptibility and potential, cyberattacks and a pervasive ignorance of the protection of personal data. Because of the anticipated high costs and difficulties with adaption and utilization, the public is concerned about incompatibility between systems and services. Furthermore, older individuals and those with lesser educational achievement have different digital skills. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Compatibility ICT challenges Information insecurity Privacy Smart city Technology skills

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Accelerated discovery of eutectic compositionally complex alloys by generative machine learning

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npj Computational Materials 2024年第1期10卷 1091-1102页

作者： Z.Q.Chen Y.H.Shang X.D.Liu Y.Yang Department of Mechanical Engineering City University of Hong KongHong KongChina City University of Hong Kong(Dongguan) DongguanGuangdongChina College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Materials Science and Engineering City University of Hong KongKowloonChina Department of System Engineering College of EngineeringCity University of Hong KongKowloonChina

Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties,as well as their technological ***,the discovery of eutectic compositionally complex alloys(ECCAs)(*** entropy eutectic alloys)remains a formidable challenge in the vast and intricate compositional space,primarily due to the absence of readily available phase *** address this issue,we have developed an explainable machine learning(ML)framework that integrates conditional variational autoencoder(CVAE)and artificial neutral network(ANN)models,enabling direct generation of *** overcome the prevalent problem of data imbalance encountered in data-driven ECCA design,we have incorporated thermodynamicsderived data descriptors and employed K-means clustering methods for effective data *** our ML framework,we have successfully discovered dual-or even tri-phased ECCAs,spanning from quaternary to senary alloy systems,which have not been previously reported in the *** findings hold great promise and indicate that ourML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

关键词： eutectic alloys alloy

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Advancement in piezoelectric nanogenerators for acoustic energy harvesting

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Microsystems & Nanoengineering 2024年第6期10卷 21-54页

作者： Fandi Jean Muhammad Umair Khan Anas Alazzam Baker Mohammad Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 12778UAE System on Chip Lab Khalifa UniversityAbu Dhabi 12778UAE Department of Mechanical Engineering Khalifa UniversityAbu Dhabi 12778UAE

The demand for sustainable energy sources to power small electronics like IoT devices has led to exploring innovative solutions like acoustic energy harvesting using piezoelectric nanogenerators(PENGs).Acoustic energy harvesting leverages ambient noise,converting it into electrical energy through the piezoelectric effect,where certain materials generate an electric charge in response to mechanical stress or *** review paper provides a comprehensive analysis of the advancements in PENG technology,emphasizing their role in acoustic energy *** begin by discussing the essential principles of piezoelectricity and the design considerations for nanogenerators to optimize energy capture from sound *** discussion includes a detailed examination of various piezoelectric materials,such as polyvinylidene fluoride(PVDF),lead zirconate titanate(PZT),and zinc oxide(ZnO)nanowires,which are known for their superior piezoelectric properties.A critical aspect of this review is the exploration of innovative structural designs and resonance devices that enhance the efficiency of *** delve into the mechanisms and benefits of using Helmholtz resonators,quarter-wavelength tubes,and cantilever beams,which are instrumental in amplifying acoustic signals and improving energy conversion *** device’s design parameters and operational principles are scrutinized to highlight their contributions to the *** review addresses practical applications of PENGs in various *** monitoring systems,wearable electronics,and medical devices stand to benefit significantly from the continuous and sustainable power supplied by *** applications can reduce reliance on batteries and minimize maintenance by harnessing ambient acoustic energy,leading to more efficient and longer-lasting *** the promising potential of PENGs,several challenges remain,including material degradation,efficiency limitations,and integrating these devices i

关键词： piezoelectric ambient integrating

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Revolutionising facility layout: a case study of dynamic facility layout in cable production

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International Journal of Web engineering and Technology 2024年第3期19卷 267-309页

作者： Vineetha, G.R. Rasheedali, Shiyas Chekkot Division of Computer Science and Engineering Department of Mechanical Engineering Cochin University College of Engineering CUSAT Kuttanadu Kerala Alappuzha India

In the competitive landscape of globalised markets, businesses must prioritise cost reduction for sustained competitiveness. This study delves into the dynamic facility layout problem (DFLP) within a cable production company in Kerala, emphasising adaptability to changing production demands. Addressing material handling costs and rearrangement expenses, the research evaluates the efficacy of the current static layout and explores the benefits of transitioning to a dynamic layout. The case study reveals potential cost savings through the strategic restructuring of machine arrangements. The innovative machine learning-based genetic algorithm (ML-GA) integrates machine learning algorithms, genetic algorithms, and a local search method, offering a cutting-edge solution to dynamic facility layout challenges. By considering demand variability and relocation costs, the study provides insights for informed decision-making, emphasising the significance of material flow patterns. This research contributes to enhancing efficiency and profitability, providing practical implications for businesses navigating the complexities of modern manufacturing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Plant layout

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AMTLUS: Attention-guided multi-task learning with uncertainty estimation in skin lesion segmentation and classification

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Multimedia Tools and Applications 2024年第37期83卷 84885-84909页

作者： Kasukurthi, Aravinda Davuluri, Rajya Lakshmi Department of Computer Science & amp Engineering JNTU-GV Andhra Pradesh Vizianagaram India Department of Computer Science and Business System R.V.R. & ampJ.C. College of Engineering Andhra Pradesh Guntur India

Skin lesion segmentation and classification from dermoscopic images have emerged as pivotal research topics, playing vibrant role in early detection and diagnosis of skin diseases, including melanoma. Previous studies have employed various deep learning models for skin lesion segmentation and classification, enabling the automatic learning of complex and discriminative features from dermoscopic images. However, inherent challenges arise due to the variance in skin lesion shape, size, and contrast, leading to intrinsic limitations of former models, such as Isolated Representation Learning, Uniform Attention, Limited Model Generalization, Reduced Model Interpretability, and Uncertainty. To address these limitations and propel the field forward, this paper introduces a novel frameworkcalled AMTLUS that leverages Multi-Task Learning (MTL) in conjunction with deep Attention Mechanisms and Uncertainty Estimation. The integration of MTL facilitates joint training of segmentation and classification tasks, enabling shared representation learning and efficient utilization of data. Incorporating attention mechanisms dynamically focuses on informative regions within dermoscopic images, improving segmentation accuracy and feature extraction for classification. Uncertainty estimation techniques quantify model confidence, offering probabilistic interpretations for improved reliability and interpretability. Our widespread experiments conducted on the ISIC-2016 dataset demonstrate superior accuracy and reliability, showcasing the proposed model’s capability to identify challenging cases. This deep learning framework represents a significant advancement in automated skin lesion analysis, enhancing early detection and diagnosis of skin diseases, including melanoma. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Digital twinning of vertical centrifugal casting

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Concurrent engineering Research and Applications 2024年第1-4期32卷 78-89页

作者： Anadkat, Dhaval Dave, Anjali Sata, Amit Shukla, Minal Jarboui, Slaheddine Department of Mechanical Engineering Marwadi University Rajkot India Department of Mechanical Engineering Lukhdhirji Engineering College Morbi India MGL Group Rajkot India Deparment of Computer Engineering College of Computer Science King Khalid University KSA Abha Saudi Arabia

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation. © The Author(s) 2024.

关键词： Centrifugal casting

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Design and development of political rider competitive swarm optimiser enabled deep learning model for air quality detection

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International Journal of Intelligent systems Technologies and Applications 2024年第1期22卷 77-104页

作者： Patil, Deepika Dadasaheb Thanuja, T.C. Melinamath, Bhuvaneshwari C. Department of Computer Science and Engineering Vishveshvaraya Technological University Belgavi India Sanjay Ghodawat University Kolhapur India Department of VLSI and Embedded System Visvesvaraya Technological University Belagavi India Department of Computer Science and Engineering BMSIT&M College Bangalore India

The air quality prediction process is a more significant one for air pollution prevention and management because air pollution becomes crueller. The precise identification of air quality has become a more significant concern for controlling air pollution. Recently, the weight of particulate matter (PM) on the human physical condition has become an important research area. In this paper, the political rider competitive swarm optimiser (PRCSO)-based deep recurrent neural network (DRNN) algorithm is devised for air quality and carbon monoxide prediction. The missing value imputation scheme is employed to perform pre-processing. Moreover, technical indicators and location information are extracted for the prediction process. The DRNN is employed for prediction, which is trained by the PRCSO and the training process is performed based on every location independently. The PRCSO-based DRNN outperforms existing techniques in terms of mean square error (MSE) of 0.0313, and mean absolute percentage error (MAPE) of 3.08%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Carbon monoxide

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Experimental investigation on machining of titanium composite using artificial neural network

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Interactions 2025年第1期246卷 1-10页

作者： Perumal, B. Kannan, R. Arunkumar, R. Kumaresan, T. Subbiah, Ram Department of Mechanical Engineering Sri Venkateshwara College of Engineering Andhra Pradesh Tirupati India Department of Mechanical Engineering PSNA College of Engineering and Technology Dindigul India Department of Mechanical Engineering MRK Institute of Technology Tamilnadu Kattumannarkoil India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Mechanical Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

Manufacturers must be able to figure out the most suitable technique capable of generating rapid and accurate performance when developing a precise modelling approach for the development of an efficient machining process. This paper assesses the predictive capabilities of the Artificial Neural Network (ANN) and the response surface methodology in the titanium machining process with respect to tool tip temperature and tool wear. The adequacy of the ANN in modeling and forecasting reactions were rigorously examined and contrasted using data collected from properly prepared machining experiments. Both approaches performed admirably in terms of forecasting machining process responses. The coefficient of correlation (R2) obtained from the analysis verifies the ANN’s choice, with a maximum value of 99.9% and with the response surface methodology with a maximum value of 99.8% was found. The experiment also demonstrates that when the proper parameters are used, the ANN approach can produce the best results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Machining centers

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Automated Colonic Polyp Detection and Classification Enabled Northern Goshawk Optimization with Deep Learning

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computers, Materials & Continua 2023年第5期75卷 3677-3693页

作者： Mohammed Jasim Mohammed Jasim Bzar Khidir Hussan Subhi R.M.Zeebaree Zainab Salih Ageed Engineering College Al-Kitab UniversityKirkukIraq Information System Engineering Department Erbil Technical Engineering CollegeErbil Polytechnic UniversityErbilIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq

The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various *** specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer *** is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss *** automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical ***,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative *** manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)*** NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp *** accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing ***,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO ***,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC *** comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.

关键词： Biomedical imaging artificial intelligence colonic polyp classification medical image classification computer-aided diagnosis

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