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MCI-GAN: a novel GAN with identity blocks inspired by menstrual cycle behavior for missing pixel imputation

Neural Computing and Applications 2025年第16期37卷 9669-9703页

作者： Marie, Hanaa Salem Elbaz, Mostafa Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Department of Computer Science Faculty of Computers and Informatics Kafrelsheikh University Kafrelsheikh Egypt

This paper presents MCI-GAN, a novel menstrual cycle imputation (MCI) and generative adversarial network (GAN) framework designed to address the challenge of missing pixel imputation in medical images. Inspired by the intelligent behavior of the endometrial lining during the menstrual cycle, our method introduces four key innovations. First, we propose a novel metaheuristic algorithm that assigns weights to surround pixels based on menstrual cycle behavior, ensuring that the imputed pixels maintain structural integrity and coherence with their neighbors, thus preserving overall image quality. Second, to enhance the learning capability of the GAN, identity blocks are integrated into the network architecture, improving the network’s ability to capture complex spatial relationships and leading to more accurate and consistent imputation of missing pixels. Third, we introduce an adaptive loss function that dynamically adjusts the penalty for pixel discrepancies based on local image context, allowing the model to focus on areas where accurate imputation is most critical and thereby enhancing overall image fidelity. Fourth, the framework incorporates a multi-scale feature extraction mechanism, enabling the GAN to process and combine information at various levels of detail, ensuring that both fine-grained textures and larger structural patterns are accurately captured during the imputation process. The efficacy of MCI-GAN is demonstrated across three diverse medical imaging datasets: mammograms, magnetic resonance imaging (MRI) scans, and skin lesion images. Our results show that the proposed method significantly outperforms existing approaches in terms of accuracy and structural coherence, offering a robust solution for missing pixel imputation in medical imaging. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Machine Learning Models with Neutrosophic Numbers for Cloud Security Analysis and Smart Grid Control of Renewable Energy

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Neutrosophic Sets and Systems 2025年 85卷 960-973页

作者： Fakhry, Ahmed E. Gomaa, Mamdouh Metwaly, Ahmed A. Abdelhafeez, Ahmed Computer Science Department Faculty of Information Systems and Computer Science October 6th University Giza12585 Egypt Department of Computer Science Faculty of Information Technology Amman Arab University Amman11953 Jordan Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt

Machine learning (ML) enables difficult tasks to be completed independently. In a smart grid (SG), computers and mobile devices may make it easier to monitor security, control interior temperature, and perform routine maintenance. The ability of smart grids to identify cyberattacks is essential for assessing the operation's reliability and availability. This essay emphasizes the integrity of cyberattacks using fake data in the physical layers of smart grids (SGs). This paper analyzes data transmission security and proposes a novel approach to smart grid energy management. Here, renewable energy sources are used to assess the smart grid network's energy efficiency, and the network has been monitored for cloud computing security assessments. We use an interval trapezoidal neutrosophic number model to deal with uncertainty information and rank the best ML model under different evaluation matrices. We use the k-nearest neighbor with k= 3 to 12. The CoCoSo method is used to rank ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Learning to rank

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A NEW STEPSIZE STRATEGY FOR KORPELEVICH’S ALGORITHM SOLVING VARIATIONAL INEQUALITIES

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2024年第3期6卷 309-317页

作者： Trinh, Ngoc Hai Tran, Trung Duc Faculty of Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam College of engineering and computer science VinUniversity Hanoi Viet Nam

In this paper, we propose two self-adaptive extragradient-like algorithms for solving pseudomonotone variational inequalities. We consider two cases: the mapping is Lipschitz continuous (with unknown modulus) and is not Lipschitz continuous. The step size in our algorithms can either increase or decrease at each step. This feature makes our algorithms more efficient. We also give some numerical examples to compare the performance of our algorithm with the existing one. ©2024 Applied Set-Valued Analysis and Optimization.

关键词： Variational techniques

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Data-Driven Decision-Making for Bank Target Marketing Using Supervised Learning Classifiers on Imbalanced Big Data

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computers, Materials & Continua 2024年第10期81卷 1703-1728页

作者： Fahim Nasir Abdulghani Ali Ahmed Mehmet Sabir Kiraz Iryna Yevseyeva Mubarak Saif School of Computer Science and Informatics De Montfort UniversityLeicesterLE19BHUK Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia

Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance ***,imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics,limiting their overall *** study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers(SLCs)and evaluates their performance in data-driven *** evaluation uses various metrics,with a particular focus on the Harmonic Mean Score(F-1 score)on an imbalanced real-world bank target marketing *** findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve,while Extreme Gradient Boosting(XGBoost)outperforms other traditional classifiers in terms of F-1 *** oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost,delivering superior results across all metrics,particularly when using the SMOTE variant known as the BorderlineSMOTE2 *** study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced *** factors include the importance of selecting appropriate datasets for training and testing,choosing the right classifiers,employing effective techniques for processing and handling imbalanced datasets,and identifying suitable metrics for performance ***,factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.

关键词： Big data machine learning data mining data visualization label encoding imbalanced dataset sampling techniques

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A hybrid model for Arabic character recognition using CNN and Kolmogorov Arnold Networks (KANs)

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Multimedia Tools and Applications 2025年 1-24页

作者： Alsayed, Alhag Li, Chunlin Abdalsalam, Mohammed Fat’hAlalim, Ahmed School of Computer Science and Artificial Intelligence Wuhan University of Technology Hubei Wuhan China School of Computer Science and Information Technology University of Gadarif Gadarif Sudan Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland School of Computer Science and Information Technology Holy Quran University Omdurman Sudan

Optical Character Recognition (OCR) is a significant technological advancement that turns scanned documents and pictures with text into machine-readable formats. While OCR has reached high accuracy rates for Latin-based languages, such as English, there are still significant challenges for right-to-left languages, including Arabic. Improving the capabilities of Arabic Handwriting Recognition (AHR) is critical to enhancing access to digital content in the Arabic language, given that there are well over 400 million speakers. Arabic OCR faces unique challenges regarding the nature of the language, where letter forms change according to their position within a word with or without dots and connections between intricate characters. Moreover, diacritical marks and curved shapes present additional challenges. This paper presents a hybrid model for Arabic character recognition. It is based on Convolutional Neural Networks (CNNs) and various machine learning classifiers such as Support Vector Machines (SVM), k-nearest Neighbors (KNN), Random Forest (RF), Long Short-Term Memory (LSTM), and Kolmogorov Arnold Networks (KANs). The proposed CNN+KANs model exploits the ability of CNNs to extract features and the ability of KANs to approximate complex functions to achieve high recognition accuracy. The model is tested over two datasets, including diverse handwriting styles, such as the Arabic Handwritten Characters Dataset (AHCD) and Arabic letters written by children, which we call Hijja. Experimental results demonstrate that the CNN+KANs model achieves state-of-the-art performance, with testing accuracies of 97.71% on AHCD and 91.32% on Hijja, outperforming traditional models like CNN+SVM and CNN+KNN. The study shows that the CNN+KANs hybrid model is strong and can adjust well, which makes it a good choice for recognizing Arabic handwritten characters. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Optical character recognition

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A new CNN-based watermarking method for color medical images in a fusion domain

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Neural Computing and Applications 2025年第18期37卷 12611-12629页

作者： Hosny, Khalid M. Abdel-Aziz, Mostafa M. Lashin, Nabil A. Hamza, Hanaa M. Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Department of Information Technology Faculty of Information Technology and Computer Science Sinai University Arish Campus Al-Arish45518 Egypt

The transmission of medical images via medical agencies raises security concerns, necessitating increased security measures to ensure integrity and security. However, many watermarking algorithms overlook equipoise;the relation between robustness, invisibility, and payload capacity results in a less satisfactory performance. To bridge this gap, we propose a new blind watermarking method for securing medical images based on a convolutional neural network in a fusion domain. First, we transmute the host color image using a phase-only transform (PHOT) to detect the surface pattern. We feed the detected surface pattern into a pre-trained VGG19 model to impeccably extract the stable feature vector. We encrypt the extracted image features using a hybrid Chirikov map to enhance their robustness and reliability. The synergy between the pre-trained VGG19 model and PHOT in an integrated domain effectively captures additional native and localized image features. A hybrid Fibonacci Q-matrix method scrambles the binary watermark to enhance security. Integrating double encryption into the proposed method markedly enhances its resistance against diverse countermeasures. Experimental outcomes indicate that the proposed scheme performs efficiently in robustness and invisibility. The obtained PSNR value was up to 60.15 dB, which is optimal for human perception. The embedded watermark can be retrieved without any warping. The retrieved watermark seems to be authentic, exhibiting ideal BER and NC values. In almost all attack circumstances, the BER values approached zero while the NC values got closer. The proposed method demonstrates notable enhancements in robustness and invisibility compared to prior methods. © The Author(s) 2025.

关键词： Watermarking

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Transfer function adaptation for effective feature selection with the side-blotched lizard algorithm

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Multimedia Tools and Applications 2024年 1-21页

作者： Eisa, Mohamed Abdel-lah, Marwa Farouk, Fifi Abdel-aal, Amr System and Information Technology Department Faculty of Management Technology and Information System Port Said University Port Said Egypt Department of Computer Science Faculty of Computer and Informatics Zagazig University Zagazig Egypt

Feature selection is a crucial preprocessing step in data mining and machine learning, enhancing model performance and computational efficiency. This paper investigates the effectiveness of the Side-Blotched Lizard Optimization Algorithm (SBLA) for feature selection by developing six novel variants: Sbla-s1, Sbla-s2, Sbla-s3, Sbla-v1, Sbla-v2, and Sbla-v3, each employing distinct S-shaped or V-shaped transfer functions to convert the continuous search space to a binary format. These variants were rigorously evaluated on nineteen benchmark datasets from the UCI repository, comparing their performance based on average classification accuracy, average number of selected features, and average fitness value. The results demonstrated the superiority of Sbla-s3, achieving an average classification accuracy of 92.8% across all datasets, a mean number of selected features of 20, and an average fitness value of 0.08. Furthermore, Sbla-s3 consistently outperformed six other state-of-the-art metaheuristic algorithms, achieving the highest average accuracy on sixteen out of nineteen datasets. These findings establish Sbla-s3 as a promising and effective approach for feature selection, capable of identifying relevant features while maintaining high classification accuracy, potentially leading to improved model performance in various machine learning applications. © The Author(s) 2024.

关键词： Heuristic algorithms

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High performance of Dengue shock syndrome detection using extreme gradient boosting with ANOVA feature selection

Journal of Biotech Research

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Journal of Biotech Research 2024年 16卷 22-31页

作者： Muflikhah, Lailil Iskandar, Agustin Yudistira, Novanto Nadlori, Isbat Uzzin Dewanto, Bambang Nur Faculty of Computer Science Brawijaya University Malang India Faculty of Medicine Brawijaya University Malang India Department of Informatics Engineering and Computer Politeknik Elektronika Negeri Surabaya India Faculty of Technology Information Merdeka University Malang India

Dengue shock syndrome (DSS) is an infectious disease that affects millions of people every year all over the world. Early detection of DSS is essential for providing effective therapy and promoting patient recovery. In this work, we proposed a method to enhance DSS detection by combining the Extreme Gradient Boosting (XGBoost) algorithm with variance (ANOVA) feature selection analysis. We used a clinical dataset that contained important information gleaned from people with dengue virus infection. The dataset used for the research was collected from patients at Syaiful Anwar Hospital and consisted of 501 instances. Of these, 401 cases were related to DSS, while the other instances were unrelated to this specific medical condition. An analysis of variance (ANOVA) evaluated the most significant factors that distinguished persons with DSS from those with other dengue diseases. After that, the XGBoost model gave the characteristics of the selected features. For evaluation, we split the data into 80% and 20% for training and testing, respectively. The experimental result showed that the created model had a high degree of performance evaluation in detecting DSS. The highest performance achieved an accuracy of 0.839, precision of 0.875, recall of 0.92, and f1-score of 0.897. Most importantly, the model could detect DSS early on, enabling more proactive therapy and faster responses for individuals at risk of developing the condition. © (2024), (Bio Tech System). All Rights Reserved.

关键词： Patient treatment

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