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A hybrid deep learning framework for solar irradiation prediction based on regional satellite images and data

Neural Computing and Applications 2025年 1-37页

作者： Attya, Mohammed Abo-Seida, Osama Mohamed, Hatem Mohammed, Amgad Department of Information System Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt Department of Computer Science Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

Accurate prediction of solar irradiation, particularly Diffuse Horizontal Irradiance (DHI), is essential for optimizing solar energy systems. This paper presents a hybrid framework that integrates satellite imagery and satellite-derived data to improve the precision of DHI forecasts. Two key datasets were employed: ground-based Solar Irradiation Measurement (SRM) and satellite-based Solar Irradiation (SSR). The proposed methodology utilizes machine learning models along two paths. The first path processes satellite imagery using advanced techniques, including pixel imputation with a modified Random Forest (RF) and Generative Adversarial Network (GAN), noisy region detection with Self-Organizing Maps (SOM), and noise removal through a Latent Diffusion Model (LDM). The second path handles tabular data through a diffusion probabilistic model designed for missing data imputation. These features are then merged and fed into a Long Short-Term Memory (LSTM) network, enhanced to capture seasonality and trends for accurate DHI predictions. The paper makes significant contributions by introducing a robust hybrid framework that leverages both satellite imagery and tabular data, incorporating novel preprocessing methods such as GAN-based pixel imputation and diffusion-based noise removal. The LSTM model is further adapted to handle seasonal and trend components, resulting in enhanced DHI forecasting accuracy. The proposed model demonstrates superior performance, achieving a Mean Squared Error (MSE) of 8.170 W/m2, Root Mean Squared Error (RMSE) of 1.749 W/m2, Mean Absolute Error (MAE) of 0.693 W/m2, and an R-squared value of 2.574, significantly outperforming conventional methods such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), and traditional LSTM models. Additionally, the model records a low Mean Absolute Percentage Error (MAPE) of 0.8183% and a computation time of 35.4 s, highlighting its efficiency. These results contribute to more accurate and reli

关键词： Landsat

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Hybrid Approach of LDA, ROS-NCL, and CNN for Topic Mining in Fintech User Reviews

Hybrid Approach of LDA, ROS-NCL, and CNN for Topic Mining in...

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2025 International Conference on Advancement in Data science, E-learning and information system, ICADEIS 2025

作者： Hartono, Pieter Chang Widiantoro, Albertus Dwiyoga Harnadi, Bernadinus Nugroho, Erdhi Widyarto Faculty of Computer Science Semarang Information System Department Indonesia

ISBN: (纸本)9798331513320

The development of digital payment technology, such as Gopay, has provided convenience for the Indonesian public. To understand user satisfaction and identify key issues, this study analyzes Gopay user reviews using a hybrid approach. The main issues addressed are the imbalance in review data and the lack of studies integrating Latent Dirichlet Allocation (LDA) and Convolutional Neural Network (CNN) models for topic analysis. This research encompasses three main stages: topic identification using LDA, data balancing with Random Over Sampling and Neighborhood Cleaning Rule (ROS-NCL), and topic classification using CNN. The model is designed to accurately identify and classify key topics. Furthermore, this method is compared with other approaches to assess its advantages. The research findings show that this hybrid approach is effective in addressing data imbalance, achieving a topic classification accuracy of 96%. These findings provide valuable insights for service providers to improve the quality of Gopay services based on topic analysis derived from user feedback. © 2025 IEEE.

关键词： Convolutional neural networks

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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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A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

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computers, Materials & Continua 2022年第1期70卷 1381-1399页

作者： Ahmed Reda Sherif Barakat Amira Rezk Department of Information System Faculty of Computer and Information ScienceMansoura UniversityMansouraEgypt

Many respiratory infections around the world have been caused by ***-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival *** is a high need for computer-assisted diagnostics(CAD)in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud *** learning(ML)has been used to examine chest X-ray *** this paper,a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes,a pneumonia patient,a COVID-19 patient,or a normal ***,three different pre-trainedConvolutionalNeuralNetwork(CNN)models(resnet18,resnet25,densenet201)are employed for deep feature ***,each feature vector is passed through the binary Butterfly optimization algorithm(bBOA)to reduce the redundant features and extract the most representative ones,and enhance the performance of the CNN *** selective features are then passed to an improved Extreme learning machine(ELM)using a BOA to classify the chest X-ray *** proposed paradigm achieves a 99.48%accuracy in detecting covid-19 cases.

关键词： Butterfly optimization algorithm(BOA) covid-19 chest X-ray images convolutional neural network(CNN) extreme learning machine(ELM) feature selection

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An Efficient Indoor Localization Based on Deep Attention Learning Model

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computer systems science & Engineering 2023年第8期46卷 2637-2650页

作者： Amr Abozeid Ahmed I.Taloba Rasha M.Abd El-Aziz Alhanoof Faiz Alwaghid Mostafa Salem Ahmed Elhadad Department of Computer Science College of Science and ArtsJouf UniversityQurayyatSaudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityEgypt Computer Science Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

Indoor localization methods can help many sectors,such as healthcare centers,smart homes,museums,warehouses,and retail malls,improve their service *** a result,it is crucial to look for low-cost methods that can provide exact localization in indoor *** this context,imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an ***-based localization faces many issues,such as image scale and rotation ***,image-based localization’s accuracy and speed(latency)are two critical *** paper proposes an efficient 6-DoF deep-learning model for image-based *** model incorporates the channel attention module and the Scale PyramidModule(SPM).It not only enhances accuracy but also ensures the model’s real-time *** complex scenes,a channel attention module is employed to distinguish between the textures of the foregrounds and *** model adapted an SPM,a feature pyramid module for dealing with image scale and rotation variance ***,the proposed model employs two regressions(two fully connected layers),one for position and the other for orientation,which increases outcome *** on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error(MSE)for both position and *** the indoor 7-Scenes dataset,the MSE for the position is reduced to 0.19 m and 6.25°for the ***,on the outdoor Cambridge landmarks dataset,the MSE for the position is reduced to 0.63 m and 2.03°for the *** to the findings,the proposed approach is superior and more successful than the baseline methods.

关键词： Image-based localization computer vision deep learning attention module VGG-16

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3D Object Detection for Autonomous Driving: A Comprehensive Review 6

3D Object Detection for Autonomous Driving: A Comprehensive ...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Nagiub, Ahmed Soliman Ghoniemy, Said Fayez, Mahmoud Khaled, Heba Computer System Department Faculty of Computer and information Science Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

accurate perception is a critical component in enabling autonomous vehicles to understand their driving environment. The acquisition of 3D information about objects, including their location and pose, is essential for achieving this understanding. This survey paper presents a comprehensive review of 3D object detection techniques specifically tailored for autonomous vehicles. The survey begins with an introduction to 3D object detection, elucidating the significance of the third dimension in perceiving the driving environment. It explores the types of sensors utilized in this context and the corresponding data extracted from these sensors. Additionally, the survey investigates the different types of datasets employed, including their formats, sizes, and provides a comparative analysis. Furthermore, the paper categorizes and thoroughly examines the perception methods employed for 3D object detection, based on the diverse range of sensors utilized. Each method is evaluated based on its effectiveness in accurately detecting objects in a three-dimensional space. Additionally, the evaluation metrics used to assess the performance of these methods are discussed. By offering a comprehensive overview of 3D object detection techniques for autonomous vehicles, this survey aims to advance the field of perception systems. It serves as a valuable resource for researchers and practitioners, providing insights into the techniques, sensors, and evaluation metrics employed in 3D object detection for autonomous vehicles. © 2024 IEEE.

关键词： Object detection

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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A Framework for Food Availability Forecasting Using Temporal Knowledge Graph

A Framework for Food Availability Forecasting Using Temporal...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Eltaki, Nada Nagaty, Khaled Elzanfaly, Doaa Maghawry, Noura The British University in Egypt Faculty of Informatics and Computer Science Department of Information System Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt Helwan University Faculty of Informatics and Computer Science Department of Information System Cairo Egypt

ISBN: (纸本)9798350367560

The entire world encounters a significant problem which is food availability. 'Zero-hunger' is the second Sustainable Development Goal (SDG) of the goals established by the United Nations in 2015. With the increasing number of people and various factors affecting food availability such as climate and country-based food production rate, food forecasting models have become increasingly essential. However, traditional forecasting techniques are limited in dealing with the data and identifying the relationships between them within a specific timestamp. This paper proposes a framework for an intelligent food availability forecasting model built using deep learning techniques and based on a temporal knowledge graph where different timestamps are taken into consideration. The proposed framework helps in enhancing and overcoming the challenges and limitations of the recent forecasting techniques leading to a dynamic, scalable, intelligent forecasting model. © 2024 IEEE.

关键词： Prediction models

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A systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords

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IEEE Access 2024年 12卷 53408-53423页

作者： Por, Lip Yee Ng, Ian Ouii Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core objective of this paper is to identify existing security threats to graphical password schemes and the corresponding countermeasures developed to mitigate these attacks. The study process begins by identifying the usable databases and search engines to identify all the relevant resources. The inclusion and exclusion criteria were carefully selected to prioritize the study, focusing mostly on attacks and countermeasures related to graphical password schemes between 2009 and 2023. After thorough identification and selection progress, 59 studies met all the criteria. Among these studies, 47 mentioned shoulder surfing as a threat to graphical password schemes, while 20 discussed brute force attacks. Additionally, there were 21 papers on dictionary attacks, 13 on smudge attacks, spyware attacks, and social engineering, and 19 that discussed guessing attacks as threats to graphical password schemes. Furthermore, the papers identified several other attacks, including frequency of occurrence analysis attacks, video recording, eavesdropping, computer vision, sonar, and image gallery attacks, with the corresponding numbers of papers being 9, 17, 5, 2, 2, and 1, respectively. The results also highlight the countermeasures proposed in the study papers to mitigate the aforementioned attacks. Among the various countermeasures identified, most revolve around randomization, obfuscation, and password space complexity as the most commonly used techniques for enhancing the security of graphical password schemes. © 2013 IEEE.

关键词： Authentication

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Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset

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IEEE Access 2024年 12卷 9369-9381页

作者： Ainan, Ummey Hany Por, Lip Yee Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces the innovative hybrid model XGBoost+ANN, designed to leverage the strengths of both ensemble learning and artificial neural networks. This model integrates a comprehensive set of features with parameters optimized through genetic algorithms, eschewing traditional feature selection approaches. Our research focuses on an unbalanced dataset of Polish companies and reveals that the XGBoost+ANN model, in particular, exhibits outstanding performance. Optimized using genetic algorithms and without feature selection, this model achieved the highest AUC (0.958), sensitivity (0.752), and accuracy (0.983) scores, surpassing other models in our study. This remarkable outperformance, along with the robust results, marks a substantial advancement in the field of bankruptcy prediction. It underscores the efficacy of our approach in addressing the persistent challenge of data imbalance, offering a more reliable and accurate solution for financial risk assessment. © 2013 IEEE.

关键词： Feature extraction

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