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Machine Learning Model for Wind Power Forecasting Using Enhanced Multilayer Perceptron

computers, Materials & Continua 2025年第5期83卷 2287-2303页

作者： Ahmed A.Ewees Mohammed A.A.Al-Qaness Ali Alshahrani Mohamed Abd Elaziz Computer Science and Artificial Intelligence Department College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Department of Computer Damietta UniversityDamietta34517Egypt College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt

Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy *** enhances the efficiency and reliability of renewable energy *** approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy *** primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data *** research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original *** developed FHONO is then employed to optimize the MLP for enhanced wind power *** effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in *** results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all *** findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.

关键词： Wind power forecasting multilayer perceptron fire hawk optimizer non-monopolize search

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Detecting Advanced Persistent Threat Exfiltration with Ensemble Deep Learning Tree Models and Novel Detection Metrics

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IEEE Access 2025年 13卷 81803-81822页

作者： Cai, Xiaojuan Zhang, Haibo Ahmed, Chuadhry Mujeeb Koide, Hiroshi Kyushu University Faculty of Information Science and Electrical Engineering Department of Information Science and Technology Fukuoka819-0395 Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Department of Artificial Intelligence Fukuoka Iizuka820-8502 Japan Newcastle University School of Computing Secure and Resilient Systems Group Newcastle upon TyneNE1 7RU United Kingdom Kyushu University Section of Cyber Security for Information Systems Research Institute for Information Technology Fukuoka819-0395 Japan

Advanced Persistent Threats (APTs) involve attackers maintaining a long-term presence on victim systems, leading to the stealthy exfiltration of sensitive data during network transfers. Despite existing methods to detect and halt APT data exfiltration, these attacks continue to pose significant threats to sensitive information and result in substantial commercial losses. Current approaches primarily focus on preemptive measures, which are insufficient once early-stage detection fails due to a lack of continuous monitoring. We propose an effective and efficient network monitoring method to address this gap and detect APT exfiltration during data transfer. Our approach assumes the presence of an undetected APT attacker within the victim system. We examine data exfiltration across three exfiltration traffic environments: exfiltration over command control channels, exfiltration over transfer size limitations, and their combinations. We introduce two detection metrics: Package Transfer Rate and Byte Transfer Rate. Utilizing these metrics, we measure network traffic, categorize APT attack environments, and train deep neural network models, named EDXGB, using ensembled decision trees to predict APT exfiltration. Our method is validated on two public datasets and compared against six baseline methods. Additionally, we simulate real-world exfiltration scenarios by creating three exfiltration traffic environments for each dataset. The results demonstrate that our method effectively detects APT exfiltration across various network environments, enhancing data protection and secure transfer. The code is open source and available at https://***/cxjuan/EDXGB-for-APT. © 2013 IEEE.

关键词： Sensitive data

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Exploring information hiding in images – problems and measures: a survey

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Multimedia Tools and Applications 2025年 1-63页

作者： Khan, Ahmed Sarfaraz, Aaliya School of Computer Science Taylor’s University Subang Jaya Malaysia School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

The widespread adoption of digital communication technologies across various domains has led to a significant shift towards digitalization. However, this evolution has also introduced vulnerabilities, including unauthorized access, data manipulation, and fabrication by malicious entities, which severely impact the confidentiality and integrity of information. The proliferation of digital images on the internet has facilitated the use of these media for embedding confidential data in a way that prevents detection or extraction by unauthorized third parties. Despite numerous efforts, existing image steganography and watermarking techniques often lack robustness. This paper proposes several novel strategies to enhance the dynamism and imperceptibility of these techniques, integrating cryptographic methods by manipulating the entropy of encrypted images using minimal pixel value differences and applying unique image transformations. Additionally, the study highlights that implementing multi-layered security mechanisms can protect confidential information from modern statistical steganalysis attacks, thereby preserving both the integrity of hidden data and image quality. A comprehensive review of information hiding, specifically focusing on image watermarking and steganography, is provided. Furthermore, this survey outlines key problem statements and offers quantitative metrics for future research in this area. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Steganography

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The Role of Artificial Intelligence in Improving Histopathological Diagnosis of Prostate Cancer: A Review

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Journal of engineering and science in Medical Diagnostics and Therapy 2025年第2期8卷 020801页

作者： Afifi, Shereen Faragallah, M. Hamdy Taha, Radwa Baig, Mirza Ullah, Ehsan Gholam Hosseini, Hamid Hassanein, Sally I. Computer Science and Engineering Department Faculty of Media Engineering and Technology German University Cairo11835 Egypt Biochemistry Department Faculty of Science Helwan University Cairo11795 Egypt Digital Media and Engineering Department Faculty of Media Engineering and Technology German University Cairo11835 Egypt Digital Front Door Orion Health Auckland1010 New Zealand Department of Pathology and Laboratory Medicine Health New Zealand Te Toka Tumai Auckland1061 New Zealand School of Engineering Computer and Mathematical Sciences Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand Biochemistry Department Faculty of Pharmacy and Biotechnology German University Cairo11835 Egypt

This review investigates the effectiveness of exploiting the massive Artificial Intelligence (AI) technology in the diagnosis of prostate cancer histopathological images. It focuses on studying and analyzing the current state and practice for utilizing AI tools, including significant machine learning and deep learning models in the histopathological image analysis process. The preferred reporting items for systematic reviews and meta-Analyses (PRISMA) methodology was adopted for conducting this systematic review to include recent research articles that have been published since 2017. Leveraging novel deep learning models and advanced imaging techniques, AI demonstrates promising capabilities in improving accuracy and efficiency in detecting and classifying prostate cancer. A comprehensive comparison of existing works has been presented with in-depth discussions around current limitations and key challenges while proposing some future advancements. This study aims to pave the way for future research and further integration of AI into the diagnostic processes toward early detection, personalized treatment strategies, and enhanced patient outcomes in the context of a prostate cancer diagnosis. Copyright © 2025 by ASME.

关键词： Contrastive Learning

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Synthesis, physical, structural, mechanical, and nuclear radiation shielding properties in cerium dioxide reinforced lead-silicate glasses

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Ceramics International 2025年第9期51卷 12207-12217页

作者： Zakaly, Hesham M.H. Aloraini, Dalal Abdullah Wahab, E.A. Abdel Issa, Shams A.M. Tekin, H.O. Shaaban, Kh. S. Department of Physics Faculty of Science Al-Azhar University AssiutPO Box 71524 Egypt Institute of Physics and Technology Ural Federal University Yekaterinburg620002 Russia Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Physics Department Faculty of Science University of Tabuk Tabuk71451 Saudi Arabia Department of Medical Diagnostic Imaging College of Health Sciences University of Sharjah Sharjah27272 United Arab Emirates Istinye University Faculty of Engineering and Natural Sciences Computer Engineering Department Istanbul34396 Turkey Department of Chemistry Faculty of Science Al-Azhar University AssiutP.O. 71524 Egypt

Notably, cerium dioxide (CeO2)-containing lead silicate glasses have attracted considerable attention, which is attributed to their versatile properties and possible use in many domains of technology. This work investigates the effect of CeO2 doping on the structural, mechanical, and radiation shielding performance of 60SiO2-35Pb3O4-5ZnO glasses. Materials Glasses were prepared via the melt-quench approach and characterized with XRD, density and ultrasonic velocity measurements, and Monte Carlo simulations for radiation shielding evaluation. The results confirm that adding CeO2 to the glasses increases their density, elastic moduli, and microhardness, and, therefore, their mechanical strength and rigidity. The density of the glass samples grew from 5.91 g/cm³ for SPCZ0 to 6.463 g/cm³ for SPCZ4 with increasing CeO2 content. The VL increased from 4790 m/s for the undoped glass sample (SPCZ0) to 5290 m/s for the sample with the highest content of CeO2 (SPCZ4). The VT similarly increases from 2385 m/s for SPCZ0 to 2885 m/s for SPCZ4. Moreover, the results suppose that CeO2-doped glasses had higher attenuation coefficients against gamma-ray and neutron radiation in relation to undoped samples. These properties are of utmost importance for their deployment in components requiring high mechanical strength, durability, and efficient absorbing layers for radiation protection. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Gamma rays

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Load Balancing in Cloud Computing based on Ant Colony Optimization and Crow Search Algorithm 27

Load Balancing in Cloud Computing based on Ant Colony Optimi...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Alsheavi, Amar N. Wang, Xingfu Zhao, Wei Hawbani, Ammar Alhusaini, Naji Ahmed, Ibrahim Abdulrab Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China School of Computer Science and Technology Anhui University of Technology Anhui China School of Computer Science Shenyang Aerospace University Shenyang110136 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China Computer Department Applied College Najran University Najran66462 Saudi Arabia College of Life Sciences and Oceanography Shenzhen University Shenzhen518060 China Faculty of Science Zagazig University Zagazig44519 Egypt

ISBN: (纸本)9791188428137

With the rapid increase in cloud services and the increasing shift toward them, balancing the cloud load has become a critical research issue. The increasing demand from customers for technology services worldwide is largely due to its direct impact on performance quality. Therefore, to provide better service quality, it is essential to consider reducing response time and cost in load balancing. This paper addresses the load-balancing problem by optimizing response time and computing cost in cloud computing systems. Specifically, we first formulated the load balancing problem in cloud computing mathematically by designing an objective function that optimizes response time and cost, including constraints to ensure task assignment to a single virtual machine (VM) and resource limits are not exceeded. To solve this optimization problem, we proposed a hybrid algorithm, ACOCSA, that combines Ant Colony Optimization (ACO) and Crow Search Algorithm (CSA). We implemented the ACOCSA algorithm, evaluated the response time, cost, and load balancing metrics, and found that our algorithm performed better in terms of response time and computational cost, and showed good fairness among VMs. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Ant colony optimization

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A Type-1 Fuzzy Logic Framework for Early Risk Assessment of Gestational Diabetes Mellitus Using Clinical Risk Factors 2

A Type-1 Fuzzy Logic Framework for Early Risk Assessment of ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Suman, Saurabh Pacharaney, Utkarsha Gote, Pradnyawant M. Faculty of Engineering and - Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Gestational Diabetes Mellitus (GDM) is a condition that can put at risk both mother and fetus but early and proper risk assessment need to be carried out for effective management. This paper discloses a Mamdani-kind Fuzzy Inference System to evaluate GDM risk dependent on clinical parameters like age, parity, glucose levels, body mass index (BMI), and insulin levels. The system can simulate all the numerous risk factors in a more gentle way by the utilization of the triangular membership function that converts accurate clinical data into a fuzzy format. A clear rule base, got from the knowledge maps in the clinical field, is put together from the input data to determining the GDM risk levels. This is neither then defuzzied via the centroid method to generate a determinable score. Results show that this system can be used along with the experts, thus yielding the same results as them, meanwhile, it is capable of providing an intelligible output. It lends further credence to the role of fuzzy logic in uncertainties in medical data and their potential by decision support systems in clinical context. While reasonably beneficial results from the system are reported, the static nature of the membership functions implies, for the future, adaptability in methodologies for relative accuracy. This study still emphasizes that fuzzy logic finds application in settings of clinical delineation, which are distinctly more complex for their intended usefulness for conditions such as GDM here. © 2025 IEEE.

关键词： Risk assessment

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Multi-Feature Fusion-Based Speech Disorder Classification Using MobileNetV3-EfficientNetB7, Linformer-Performer, and SHAP-Aware XGBoost

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IEEE Access 2025年 13卷 83348-83360页

作者： Sait, Abdul Rahaman Wahab Sankaranarayanan, Suresh Gouthaman, P. King Faisal University Center of Documentation and Administrative Communication Department of Archives and Communication Al-Ahsa Al Hofuf31982 Saudi Arabia Department of Computer Science Al-Ahsa Al Hofuf31982 Saudi Arabia SRM Institute of Science and Technology Faculty of Engineering & Technology Department of Networking and Communications Kattankulathur Chennai603203 India

Traditional speech disorders (SD) detection relies on subjective analysis, resulting in inconsistent outcome. Direct voice classification lacks effective approaches to capture temporal dependencies. Machine learning (ML) models face challenges in extracting the complex temporal and spectral variations in speech signals. Advanced deep learning (DL) and transfer learning techniques offer a foundation for early screening of SD. However, the lack of interpretability reduces the generalization capabilities of these models. Addressing these shortcomings is essential in order to improve the accuracy of SD detection and clinical trustworthiness. Thus, the proposed study introduces a novel image-based SD classification model to classify healthy and pathological speech with high accuracy and robustness. The raw speech signals are transformed into Mel-Spectrograms to overcome the limitations of direct voice classification. To facilitate the model’s interpretability, the statistical and handcrafted acoustic features are extracted from the raw speech signals. Hybrid MobileNet V3-EfficientNet B7and Linformer-Performer are employed to extract diverse features from the Mel-Spectrograms. An attention-based feature fusion is used to identify critical features indicating the SD patterns from the extracted features. The XGBoost classifier is optimized using Bayesian Optimization and HyperBand (BOHB) to classify the healthy and pathological speech. SHapley Additive exPlanations (SHAP) values is employed to offer valuable insights into the model’s decisions. The proposed model obtains an exceptional performance on two benchmark datasets. On the Saarbruecken Voice Database (SVD), it achieves an accuracy of 98.1% with loss of 0.13. It yields a remarkable generalization accuracy of 98.2% on the VOICE dataset, outperforming the state-of-the-art models. In addition, it contributes a significant advancement in SD detection, setting the stage for future research endeavors. © 2013 IEEE.

关键词： Spectrographs

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Rethinking Transferable Adversarial Attacks With Double Adversarial Neuron Attribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 354-364页

作者： Zhu, Zhiyu Jin, Zhibo Wang, Xinyi Zhang, Jiayu Chen, Huaming Choo, Kim-Kwang Raymond University of Sydney School of Electrical and Computer Engineering DarlingtonNSW2008 Australia University of Malaya Faculty of Computer Science and Information Technology KualaLumpur50603 Malaysia SuZhou Yierqi Jiangsu Suzhou215000 China The University of Texas at San Antonio San AntonioTX78249 United States

Transferable adversarial attacks are a threat to deep neural networks, in particular, for black-box scenarios where access to model information is limited. One can, for example, exploit the intermediate layer neurons to generate transferable adversarial samples. However, current works show limitations in providing feature-level attack mechanisms across multiple victim models. In light of the attribution methods, in this article, we investigate the attribution similarity across different models. We leverage the similarity to incorporate different attribution properties to enhance the sample transferability, for the first time, formulating a novel neuron attribution-based transferable attack termed DANAA++. Specifically, we utilize a range of adversarial attack methods to generate different baseline points through adversarial training. The attribution results are thus obtained along both linear and nonlinear integration paths. In our experiments, the baseline points and integration paths significantly help improve the transferability of adversarial samples. Our approach provides novel insights for building effective attribution-based feature-level adversarial attacks. https://***/LMBTough/DANAAPP. © 2020 IEEE.

关键词： Iterative methods

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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging

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Alexandria engineering Journal 2025年 127卷 357-373页

作者： Alzughaibi, Arwa Alanazi, Adwan A. Alshahrani, Mohammed Hilali Jaghdam, Ines Hassaballa, Abaker A. Applied college Taibah University Madinah Saudi Arabia Department of Computer Science and Information College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Computer Sciences Applied College Najran University Najran66462 Saudi Arabia Department of Computer Science and Information Technology Applied College Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Mathematics Faculty of Science Northern Border University Arar73213 Saudi Arabia

The disease of Kidney stones is a risky disease for individuals all over the world. Many people with kidney stones in the early phase do not detect it as an illness, and it harms the organ gradually. Precise analysis of kidney illness is vital, as it is a major health concern that needs accurate detection for appropriate and effective treatment. CT scans are one of the most extensively accessible imaging models, and they are employed for effective diagnosis. Deep learning (DL) techniques are gradually identified as beneficial tools for analyzing illness in the medical field. However, present techniques employing deep networks often meet low accuracy and overfitting challenges, demanding further alteration for optimum performance. This study presents a Leveraging Flying Foxes Optimization with an Ensemble of Deep Learning for Accurate Kidney Stone Detection (LFFOEDL-AKSD) technique in CT scans. The presented LFFOEDL-AKSD technique mainly focuses on detecting kidney stones using CI imaging. At first, the presented LFFOEDL-AKSD technique applies the pre-processing phase, which involves image resizing for uniform CT scan dimensions and data augmentation through transformations like rotation and flipping to reduce overfitting, sobel filtering (SF) sharpens edges, and the data is separated into training, validation, and testing sets for model development. The presented LFFOEDL-AKSD technique employs the swin transformer (ST) model for the feature extraction method. Furthermore, the majority voting ensemble of three DL approaches, such as the graph convolutional network (GCN), temporal convolutional network (TCN), and three-dimensional convolutional autoencoder (3D-CAE) approaches, are employed to increase the precision and reliability of the kidney stone recognition. Finally, the presented LFFOEDL-AKSD technique implements the flying foxes optimization (FFO) approach for the hyperparameter tuning involved in the ensemble learning models. An extensive experiment is conduct

关键词： Flying foxes optimization Ensemble deep learning Kidney stone detection CI imaging Swin transformer

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