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Investigating the Impact of Hyper Parameters on Intrusion Detection system Using Deep Learning Based Data Augmentation

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 523-532页

作者： Iftikhar, Umar Ali, Syed Abbas Department of Computer & Information System Engineering NED University of Engineering & Technology Karachi Pakistan

The effects of changing learning rates, data augmentation percentage and numbers of epochs on the performance of Wasserstein Generative Adversarial Networks with Gradient Penalties (WGAN-GP) are evaluated in this study. The purpose of this research is to find out how they affect the data augmentation to enhance stability during training. In this research, the degree of system performance is measured using the Classification Model Utility approach. For this reason, this study aims to determine the interaction between learning rate, augmentation percentage and epoch value when using WGAN-GP to generate synthetic data for the recognition of the system performance. The results will provide the indications on how some of the hyper parameters can be adjusted up or down for having positive or negative consequences on the generation process for further research and use of WGAN-GP. It also provides insights into how the generative model is trained, and how that affects stability and quality of the result in various settings such as image synthesis or other generative tasks. © (2025), (Science and Information Organization). All rights reserved.

关键词： Deep learning

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Optimization of Magnetization State Manipulation in Variable-Flux PMSMs

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IEEE Transactions on Industry Applications 2025年第3期61卷 3862-3876页

作者： Orviz, Marcos Tiemblo, Javier G. Laborda, Diego F. Reigosa, David Guerrero, Juan Manuel University of Oviedo Department of Electrical Electronic Computer and System Engineering Gijon33204 Spain

Variable-flux permanent magnet synchronous machines (VF-PMSMs) were proposed to avoid the additional losses of conventional PMSMs during flux weakening (FW) operation at high speeds, as they allow dynamic manipulation of the magnetization state (MS) of the permanent magnets (PMs). However, the effect of eddy currents induced in the PMs during MS manipulation has not yet been analyzed;this issue is evaluated in this paper. Different current waveforms (i.e., pulse, trapezoidal, and half-sinusoidal signals) will be compared regarding MS manipulation capabilities and energy consumption considering PMs eddy current effects, to identify the most advantageous one. © 1972-2012 IEEE.

关键词： Permanent magnets

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

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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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A Low Resource Multi-lingual Simultaneous Script Identification and Text Recognition Model

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SN computer Science 2024年第6期5卷 740页

作者： Mukherjee, Jayati Roy, Utpal Computer Science and Engineering Academy of Technology Department of computer and system sciences Visva-bharati

In this paper, we have proposed a multi-task learning model for multi-lingual Optical Character Recognition. Our model does the script identification and text recognition simultaneously of offline machine printed documents. We have extracted the spatial and temporal features of a line image by the combination of several CNN and BLSTM layers. The feature is shared between the script identification and text recognition modules. Fully connected layer and softmax identify the script. The identified script works as a case selector for the text recognizer which is a CTC layer. Finally, the text is identified by the text recognizer. The model is applied to two public datasets: ISIDDI, RETAS containing Bengali degraded, and English pages. We have created a dataset of Devnagari/Hindi and Tamil scripts to test our model. The model has achieved 99.2% accuracy for script recognition. The achieved text recognition accuracy on the scripts Bengali, English, Hindi, and Tamil are respectively 91.68%, 97.07%, 95.68% and 92.27%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Multi-task learning Optical character recognition

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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 713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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Robust CNN for facial emotion recognition and real-time GUI

AIMS Electronics and Electrical Engineering

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AIMS Electronics and Electrical engineering 2024年第2期8卷 217-236页

作者： Ali, Imad Ghaffar, Faisal Department of Computer Science University of Swat KP Swat Pakistan System Design Engineering Department University of Waterloo Waterloo Canada

computer vision is witnessing a surge of interest in machines accurately recognizing and interpreting human emotions through facial expression analysis. However, variations in image properties such as brightness, contrast, and resolution make it harder for models to predict the underlying emotion accurately. Utilizing a robust architecture of a convolutional neural network (CNN), we designed an efficacious framework for facial emotion recognition that predicts emotions and assigns corresponding probabilities to each fundamental human emotion. Each image is processed with various pre-processing steps before inputting it to the CNN to enhance the visibility and clarity of facial features, enabling the CNN to learn more effectively from the data. As CNNs entail a large amount of data for training, we used a data augmentation technique that helps to enhance the model's generalization capabilities, enabling it to effectively handle previously unseen data. To train the model, we joined the datasets, namely JAFFE and KDEF. We allocated 90% of the data for training, reserving the remaining 10% for testing purposes. The results of the CCN framework demonstrated a peak accuracy of 78.1%, which was achieved with the joint dataset. This accuracy indicated the model's capability to recognize facial emotions with a promising level of performance. Additionally, we developed an application with a graphical user interface for real-time facial emotion classification. This application allows users to classify emotions from still images and live video feeds, making it practical and user-friendly. The real-time application further demonstrates the system's practicality and potential for various real-world applications involving facial emotion analysis. © 2024 the Author(s).

关键词： Graphical user interfaces

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Optimal SOC Headroom of Pump Storage Hydropower for Maximizing Joint Revenue from Day-ahead and Real-time Markets Under Regional Transmission Organization Dispatch

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Journal of Modern Power systems and Clean Energy 2024年第1期12卷 238-250页

作者： Yikui Liu Bing Huang Yang Lin Yonghong Chen Lei Wu Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenNJ 07030USA Midcontinent Independent System Operator Inc.CarmelIN 46032USA

In response to the increasing penetration of volatile and uncertain renewable energy,the regional transmission organizations(RTOs)have been recently focusing on enhancing the models of pump storage hydropower(PSH)plants,which are one of the key flexibility assets in the day-ahead(DA)and real-time(RT)markets,to further boost their flexibility provision *** by the recent research works that explored the potential benefits of excluding PSHs’cost-related terms from the objective functions of the DA market clearing model,this paper completes a rolling RT market scheme that is compatible with the DA ***,with the vision that PSHs could be permitted to submit state-of-charge(SOC)headrooms in the DA market and to release them in the RT market,this paper uncovers that PSHs could increase the total revenues from the two markets by optimizing their SOC headrooms,assisted by the proposed tri-level optimal SOC headroom ***,in the proposed tri-level model,the middle and lower levels respectively mimic the DA and RT scheduling processes of PSHs,and the upper level determines the optimal headrooms to be submitted to the RTO for maximizing the total revenue from the two *** case studies quantify the profitability of the optimal SOC headroom submissions as well as the associated financial risks.

关键词： Pump storage hydropower energy market state-of-charge(SOC) headroom market revenue tri-level problem

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Practical autoencoder based anomaly detection by using vector reconstruction error

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Cybersecurity 2023年第2期6卷 1-13页

作者： Hasan Torabi Seyedeh Leili Mirtaheri Sergio Greco Department of Electrical and Computer Engineering Faculty of EngineeringKharazmi UniversityTehranIran Department of Informatics ModelingElectronics and System EngineeringUniversity of CalabriaArcavacataItaly.

Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score

关键词： Cloud Practical Anomaly detection Autoencoder Reconstruction error Machine learning

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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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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