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computer aided detection of mammographic mass using exact Gaussian–Hermite moments

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1139-1147页

作者： Eltoukhy, Mohamed Meselhy Elhoseny, Mohamed Hosny, Khalid M. Singh, Amit Kumar Computer Science Department Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Faculty of Computers and Information Mansoura University El Mansûra Egypt Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig Egypt Waknaghat Himachal Pradesh Solan India

Breast cancer is one of the common cancer deaths in women worldwide. Early detection is the key to reduce the mortality rate. Clinical trials have shown that computer aided systems (CAD) have improved the accuracy of breast cancer detection. This paper proposed a highly accurate CAD system based on extracting highly significant features using exact Gaussian–Hermite moments. The obtained feature vector is presented to K-NN, random forests and AdaBoost classifiers. The proposed system is evaluated using two different datasets namely IRMA and MIAS. The evaluation metrics of accuracy, TP, FP and area under ROC curve using 10-fold cross-validation are calculated. The results indicate the usefulness of the proposed exact Gaussian–Hermite moments features for distinguishing between normal and abnormal lesions and the superiority of the moments features compared with the conventional methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Medical informatics

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BeGrading: large language models for enhanced feedback in programming education

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Neural Computing and Applications 2025年第2期37卷 1027-1040页

作者： Yousef, Mina Mohamed, Kareem Medhat, Walaa Mohamed, Ensaf Hussein Khoriba, Ghada Arafa, Tamer Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt

In recent years, large language models (LLMs) have gained significant traction across various domains, including education. This paper explores the application of LLMs in grading programming assignments. By leveraging data collected from existing programming assignments and their corresponding grades, we aim to develop a robust LLM-based grading system. We also incorporate augmented data representing various grading scenarios to enhance the model’s performance and ensure comprehensive coverage across all grading levels. Our approach involves training the LLM on this combined dataset to enable accurate and consistent evaluation of programming assignments. The proposed model, BeGrading, aims to reduce the grading burden on educators and provide timely and objective feedback to students. Compared to the Codestral model, our proposed model demonstrates an absolute difference rate of 19%, equivalent to ±0.95 out of 5. This is acceptable for using a small, fine-tuned model with optimized data. Additionally, the Codestral model compared to the dataset optimized score shows a difference of 15% equivalent to a margin of ±0.75 out of 5. Preliminary results demonstrate the potential of LLMs to perform grading tasks with a high degree of reliability, opening avenues for further research and practical applications in automated education systems. © The Author(s) 2024.

关键词： Digital elevation model

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Automating skin cancer screening: a deep learning

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Journal of Engineering and Applied science 2025年第1期72卷 1-18页

作者： Rashad, Nada M. Abdelnapi, Noha MM. Seddik, Ahmed F. Sayedelahl, M.A. Department of Computer Science Faculty of Computers and Information Suez University Suez43221 Egypt Biomedical Engineering Department Faculty of Engineering Helwan University Cairo11795 Egypt Department of Computer Science Faculty of Computers and Information Damanhour University Damanhour22511 Egypt

Skin cancer presents in various forms, including squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and melanoma. Established risk factors include ultraviolet (UV) radiation exposure from solar or artificial sources, lighter skin pigmentation, a history of sunburns, and a family history of the disease. Early detection and prompt intervention are crucial for achieving a favorable prognosis. Traditionally, treatment modalities include surgery, radiation therapy, and chemotherapy. Recent advancements in immunotherapy have revolutionized skin cancer diagnosis, but manual identification remains time-consuming. Artificial intelligence (AI) has shown potential in skin cancer classification, leading to automated screening methods. To support dermatologists, we improved the model for classifying images. This model is able to recognize seven different kinds of skin lesions. On the ISIC dataset, an analysis has been done. This study offers a novel approach to early skin cancer diagnosis based on image processing. Our approach leverages the high accuracy of a specific convolutional neural network architecture, utilizing transfer learning with pre-trained data to further enhance detection performance. Our findings demonstrate that the employed ResNet-50 transfer learning model achieves a remarkable accuracy of 97%, while ResNet50 without augmentation gives an accuracy of 81.57% and an F1-score of 75.75%. © The Author(s) 2025.

关键词： Convolutional neural networks

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Smart real-time detection of risky roads using vehicles trajectories for intelligent transportation

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive Computing in Engineering 2025年 6卷 370-379页

作者： Eldawy, Eman O. Abdalla, Mohammed Mokhtar, Hoda M.O. Hendawi, Abdeltawab AbdelAziz, Amr M. Minia Egypt Faculty of Computers and Information Minia University Egypt Faculty of Computers and Artificial Intelligence Beni-Suef University Egypt Egypt University of Informatics Egypt Department of Computer Science and Statistics University of Rhode Island United States

Indeed, risky roads have a negative impact on traffic by causing road injuries with fatalities, which can lead to negative emotional, social, and economic influences on humans, countries, and the world. Additionally, taxi and rideshare passengers prefer to move on familiar and safe roads. Therefore, to ensure the high quality of transportation services, it is required to follow secure roads to avoid poorly maintained roads, areas with high incidences of car accidents, neighborhoods with high crime rates, and places with a history of terrorist attacks or civil unrest. In this regard, discovering risky roads is a need. This paper introduces a real-time framework, named RiskyMove, that helps drivers and passengers to follow safe roads and avoid risky once that are not safe for travel. Mainly, the RiskyMove framework employs a probabilistic method based on a Minimum Adaptive Viterbi (MAV) algorithm to identify risky paths during the trip and alarm the drivers to take precautions. An experimental evaluation of the RiskyMove with a real dataset of the movement of cabs in San Francisco illustrates the effectiveness of the proposed framework. © 2025 The Authors

关键词： Quality of service

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Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

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computers, Materials & Continua 2023年第3期74卷 5559-5572页

作者： Aliaa El-Gawady BenBella S.Tawfik Mohamed A.Makhlouf Department of Information Systems Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Faculty of Computer Science Nahda UniversityBeni SuefEgypt

Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many *** machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research ***,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity *** this work,we shed light on the prediction of AD from GE data accurately using *** approach consists of four phases:preprocessing,gene selection(GS),classification,and performance *** the preprocessing phase,gene columns are preprocessed *** the GS phase,a hybrid filtering method and embedded method are *** the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous *** final phase is to validate the performance of these classifiers using different *** crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of *** different datasets are used to achieve our *** predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one *** assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance *** metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for *** these results,the proposed approach outperforms recent interesting *** these results,the proposed approach outperforms recent interesting results.

关键词： Gene expression gene selection machine learning classification Alzheimer’s disease

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EEG emotion recognition framework based on invariant wavelet scattering convolution network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2181-2199页

作者： Elrefaiy, Ahmed Tawfik, Nahed Zayed, Nourhan Elhenawy, Ibrahim Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computers and Systems Department Electronics Research Institute Cairo Egypt Mechanical Engineering Department The British University in Egypt Cairo Egypt

EEG signals for real-time emotion identification are crucial for affective computing and human-computer interaction. The current emotion recognition models, which rely on a small number of emotion classes and stimuli like music and images in controlled lab conditions, have poor ecological validity. Furthermore, identifying relevant EEG signal features is crucial for efficient emotion identification. According to the complexity, non-stationarity, and variation nature of EEG signals, which make it challenging to identify relevant features to categorize and identify emotions, a novel approach for feature extraction and classification concerning EEG signals is suggested based on invariant wavelet scattering transform (WST) and support vector machine algorithm (SVM). The WST is a new time-frequency domain equivalent to a deep convolutional network. It produces scattering feature matrix representations that are stable against time-warping deformations, noise-resistant, and time-shift invariant existing in EEG signals. So, small, difficult-to-measure variations in the amplitude and duration of EEG signals can be captured. As a result, it addresses the limitations of the previous feature extraction approaches, which are unstable and sensitive to time-shift variations. In this paper, the zero, first, and second order features from DEAP datasets are obtained by performing the WST with two deep layers. Then, the PCA method is used for dimensionality reduction. Finally, the extracted features are fed as inputs for different classifiers. In the classification step, the SVM classifier is utilized with different classification algorithms such as k-nearest neighbours (KNN), random forest (RF), and AdaBoost classifier. This research employs a principal component analysis (PCA) approach to reduce the high dimensionality of scattering characteristics and increase the computational efficiency of our classifiers. The proposed method is performed across four different emotional classific

关键词： Support vector machines

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Neutrosophic Multi-Criteria Decision Making for Sustainable Procurement in Food Business

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Neutrosophic Sets and Systems 2023年 57卷 283-294页

作者： Ismail, Mahmoud Ali, Ahmed M. Abdelhafeez, Ahmed EI-Douh, Ahmed Abdel-Rahim Ibrahim, Mahmoud Abdel-aziem, Ayman H. Department of Operations Research Faculty of Computers and Informatics Zagazig University Egypt Faculty of Computers and Informatics Zagazig University Sharqiyah Zagazig44519 Egypt Faculty of Information Systems and Computer Science October 6th University Cairo Egypt Faculty of computers and Informatics Zagazig University Zagazig44519 Egypt

Purchasing food in a way that minimizes negative effects on the environment, society, and the economy is a growing trend in the food industry. Sustainable procurement is discussed in this study, along with its significance, important criteria, and advantages in the food business. Businesses may aid sustainable development, lessen their impact on the environment, provide aid to local communities, and keep up with shifting consumer expectations for sustainably and ethically produced food when they prioritize responsible sourcing practices. To effectively implement sustainable procurement in the food sector, this article stresses the need for teamwork, openness, and a long-term commitment to sustainability. So, this paper ranks the best supplier in sustainable procurement in the food business to achieve sustainability. The concept of multi-criteria decision-making (MCDM) is used in this paper to deal with the various criteria. This paper used the TOPSIS method as an MCDM tool to compute the weights of criteria and rank the suppliers. The TOPSIS method is integrated with the single-valued neutrosophic set to deal with uncertain and vague information. There are seven criteria and 10 suppliers in the food business are evaluated and ranked in this study. We obtained the environmental impacts as the best criteria in seven criteria. The goal of environmental impact prioritizing suppliers and products that minimize negative environmental impact. © (2023). All Rights Reserved.

关键词： Sustainable development

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Revolutionizing online shopping with FITMI: a realistic virtual try-on solution

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Neural Computing and Applications 2025年第8期37卷 6125-6144页

作者： Samy, Tassneam M. Asham, Beshoy I. Slim, Salwa O. Abohany, Amr A. Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Computers and Information Damanhour University Damanhour Egypt Department of Information Systems Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

In today’s digital age, consumers increasingly rely on online shopping for convenience and accessibility. However, a significant drawback of online shopping is the inability to physically try on clothing before purchasing. This limitation often leads to uncertainty regarding fit and style, resulting in customer post-purchase dissatisfaction and higher return rates. Research indicates that online items are three times more likely to be returned than in-store ones, especially during the pandemic. To address this challenge, we propose a virtual try-on method called FITMI, an enhanced Latent Diffusion Textual Inversion model for virtual try-on purposes. The proposed architecture aims to bridge the gap between traditional in-store try-ons and online shopping by offering users a realistic and interactive virtual try-on experience. Although virtual try-on solutions already exist, recent advancements in artificial intelligence have significantly enhanced their capabilities, enabling more sophisticated and realistic virtual try-on experiences than ever before. Building on these advancements, FITMI surpasses ordinary virtual try-ons relying on generative adversarial networks, often producing unrealistic outputs. Instead, FITMI utilizes latent diffusion models to generate high-quality images with detailed textures. As a web application, FITMI facilitates virtual try-ons by seamlessly integrating images of users with garments from catalogs, providing a true-to-life representation of how the items would look. This approach differentiates us from competitors. FITMI is validated using two widely recognized benchmarks: the Dress-Code and Viton-HD datasets. Additionally, FITMI acts as a trusted style advisor, enhancing the shopping experience by recommending complementary items to elevate the chosen garment and suggesting similar options based on user preferences. © The Author(s) 2025.

关键词： Generative adversarial networks

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Conjugation of Hypersoft Set and Triangular Neutrosophic to Evaluate Cognitive Digital Twins' Contribution to Smart Manufacturing

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Neutrosophic Sets and Systems 2025年 80卷 632-644页

作者： Mohamed, Mona Alaa, Nurhan Higher Technological Institute 10th of Ramadan City44629 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt

The latest innovation in digital twin technology is called Cognitive Digital Twins (CDT). The sophisticated and autonomous activities made possible by this technology have the potential to revolutionize manufacturing. An overview of CDT in manufacturing is given in this paper, along with an examination of their main features, components, and possible uses. CDT can learn from data, reason about difficult circumstances, and make well-informed judgments by combining artificial intelligence, machine learning, and knowledge representation approaches. The advantages of CDT in smart manufacturing are covered in the paper. Hence, the objective of this paper is evaluating the smart manufacturing that adopts CDT in its operations and practices to be smart. Multi-Criteria Decision Making (MCDM) as CRiteria Importance Through Intercriteria Correlation and multi-objective optimization based on simple ratio analysis (MOOSRA) are leveraged to construct soft decision models. This model can trat with vague and incomplete information through harnessing uncertainty theory especially Triangular Neurosophic Number (TriNN). Also, Hypersoft set is utilized with MOOSRA to rank alternatives of smart manufacturing. © 2025, University of New Mexico. All rights reserved.

关键词： Smart manufacturing

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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

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Neutrosophic Sets and Systems 2025年 82卷 781-799页

作者： Abdellah, Mohamed Refaat Oudah, Hasan H. Badawy, Ahmed Mohamed Ahmed Mohamed, Abdelfattah Abdelfattah M. Bedier, Shady Ahmed Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Cybersecurity Department Faculty of Computers and Artificial Intelligence Helwan University Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Malware detection is one of the critical tasks of cybersecurity, especially considering the growing popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to identify malware quickly and precisely, as security hacks against these devices become increasingly frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. This research addresses these challenges by proposing a novel model, called "CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The main goal of this work is to create a global classifier for Android malware detection that is highly accurate and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number of clients and number of rounds. The results show the best alternative has a larger number of clients and rounds. © 2025, University of New Mexico. All rights reserved.

关键词： Android malware

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