Research Title: Advanced solar radiation prediction using combined satellite imagery and tabular data processing

Author: Amgad Monir Mohaned Elsayed, Published Year: 2025

Scientific Reports, 15

Faculty: Information Technology

Abstract: Accurate solar radiation prediction is crucial for optimizing solar energy systems. There are two types of data that can be used to predict solar radiation, such as satellite images and tabular satellite data. This research focuses on enhancing solar radiation prediction by integrating data from two distinct sources: satellite imagery and ground-based measurements. By combining these datasets, the study improves the accuracy of solar radiation forecasts, which is crucial for renewable energy applications. This research presents a hybrid methodology to predict the solar radiation from both satellite images and satellite data. The methodology basis on two datasets; the first data set contains tabular data, and the second dataset contains satellite images. The framework divides into two paths; the first path take the input as the satellite images; this stages contains three steps; the first step is removing noise using latent diffusion model, the second step is about pixel imputation using a modified RF + Identity GAN (this model contains two modification the first modification is adding the identity block to solve mode collapse problem in the GANs and the second modification is to add the 8-connected pixel to generate a value of missing pixel near to the real missed pixel. The third step in the first path is about using the self-organizing map to identify the special informative in the satellite image. The second path take the input as tabular data and use the diffusion model to impute the missing data in the tabulated data. Finally, we merge the two path and use feature selection to be as input for the LSTM for solar radiation predictions. The experiments done prove the efficiency of the used stage such as missing pixel imputation, removing noise, missing data imputation and prediction using LSTM when compared with other available techniques. The experiments also prove the enhancement of all prediction model after adding two paths before the prediction step.

Keywords: Solar radiation; LSTM; GANs; Identity block; Latent diffusion model

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

Author: Amgad Monir Mohaned Elsayed, Published Year: 2025

Neural Computing and Applications, 37

Faculty: Information Technology

Abstract: 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 reliable solar energy resource management by offering enhanced DHI forecasts across various spatial and temporal scales.

Keywords: Solar irradiation; LSTM; GANs; Identity Block; Latent diffusion model

Research Title: A hybrid deep learning approach for accurate water body segmentation in satellite imagery

Author: Amgad Monir Mohaned Elsayed, Published Year: 2025

Earth Science Informatics , 18

Faculty: Information Technology

Abstract: Precise water body segmentation in satellite imagery plays a vital role in environmental monitoring, water resource management, and disaster prevention. This study introduces a high-performance segmentation framework leveraging Sentinel-2 imagery, integrating advanced methodologies to enhance data integrity and segmentation accuracy. To increase dataset diversity and model generalization, StyleGAN3-based augmentation was implemented, yielding a 5?curacy improvement over conventional methods. An Attention-Guided Denoising Autoencoder with Skip Connection (AG-DAES) was utilized for noise reduction, effectively preserving spatial details and strengthening segmentation robustness. To address missing and corrupted pixels, Bi-ConvRNN was employed for pixel restoration, significantly boosting performance. Additionally, the Particle Swarm Dandelion Optimization (PSDO) algorithm was used for hyperparameter tuning, contributing to an additional 3–5?curacy gain. Feature extraction was refined through the Multiscale Strip Convolution Module (MSSCM), enhancing spatial-spectral representation and leading to a 6–8?curacy increase. The segmentation process was executed using the Map U-Net model, which, after integrating all proposed improvements, achieved state-of-the-art accuracy exceeding 99%. A comparative study demonstrated that the proposed framework outperforms existing methods, particularly in complex scenarios involving vegetation interference, occlusions, and mixed land–water transitions. This adaptable and scalable approach sets a new standard for water body segmentation in satellite image analysis, offering a powerful tool for future research in the field.

Keywords: Water body segmentation; Satellite imagery; Deep learning; Noise reduction; Pixel imputation

Research Title: Dynamic Classification Ensembles for Handling Imbalanced Multiclass Drifted Data Streams

Author: Amgad Monir Mohaned Elsayed, Published Year: 2024

Information Sciences, 670

Faculty: Information Technology

Abstract: Machine learning models often encounter significant difficulties when dealing with multiclass imbalanced data streams in nonstationary environments. These challenges can lead to biased and unreliable predictions, which ultimately impact the overall performance of the models. To address these issues, we propose an innovative approach that integrates dynamic ensemble selection, an adaptive technique for managing imbalanced multiclass data streams, with a concept drift detector for recognizing stream changes and the K-nearest neighbor (KNN) algorithm to tackle issues related to class overlap. The primary objective was to improve the classification of imbalanced multiclass drifted data streams. The adaptive oversampling method generates synthetic samples to mitigate the issues associated with imbalanced data streams. This method utilizes KNN to ensure that the generated samples do not overlap. To handle incoming data streams, a drift detector assists in deciding whether to retain the existing classifiers or create a new one. Dynamic Ensemble Selection (DES) was utilized to select the most appropriate classifier for incoming data, aiming to optimize the performance of the classification task. The proposed method offers an effective solution for achieving an accurate and resilient classification in the context of imbalanced multiclass drifted data streams. To evaluate the effectiveness of our proposal, we conducted experiments on a variety of datasets, including benchmark datasets, real application stream datasets, and synthetic data streams. The experimental results demonstrate the superiority of our contribution in addressing the challenges posed by imbalanced multiclass drifted data streams.

Keywords: Dynamic ensemble selection; Concept drift detection; Imbalanced data classification

Research Title: Training Set Selection and Swarm Intelligence For Enhanced Integration In Multiple Classifier Systems

Author: Amgad Monir Mohaned Elsayed, Published Year: 2020

Applied Soft Computing, 95

Faculty: Information Technology

Abstract: Multiple classifier systems (MCSs) constitute one of the most competitive paradigms for obtaining more accurate predictions in the field of machine learning. Systems of this type should be designed efficiently in all of their stages, from data preprocessing to multioutput decision fusion. In this article, we present a framework for utilizing the power of instance selection methods and the search capabilities of swarm intelligence to train learning models and to aggregate their decisions. The process consists of three steps: First, the essence of the complete training data set is captured in a reduced set via the application of intelligent data sampling. Second, the reduced set is used to train a group of heterogeneous classifiers using bagging and distance-based feature sampling. Finally, swarm intelligence techniques are applied to identify a pattern among multiple decisions to enhance the fusion process by assigning class-specific weights for each classifier. The proposed methodology yielded competitive results in experiments that were conducted on 25 benchmark datasets. The Matthews correlation coefficient (MCC) is regarded as the objective to be maximized by various nature-inspired metaheuristics, which include the moth-flame optimization algorithm (MFO), the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA).

Keywords: Combination rule; Swarm intelligence Optimization; Data reduction; Classifier integration; Multiple classifier systems; Big data; Machine learning; Classifier ensemble

Research Title: Selective ensemble of classifiers trained on selective samples

Author: Amgad Monir Mohaned Elsayed, Published Year: 2021

Neurocomputing,

Faculty: Information Technology

Abstract: Classifier ensembles are characterized by the high quality of classification, thanks to their generalizing ability. Most existing ensemble algorithms use all learning samples to learn the base classifiers that may negatively impact the ensemble’s diversity. Also, the existing ensemble pruning algorithms often return suboptimal solutions that are biased by the selection criteria. In this work, we present a proposal to alleviate these drawbacks. We employ an instance selection method to query a reduced training set that reduces both the space complexity of the formed ensemble members and the time complexity to classify an instance. Additionally, we propose a guided search-based pruning schema that perfectly explores large-size ensembles and brings on a near-optimal subensemble with less computational requirements in reduced memory space and improved prediction time. We show experimentally how the proposed method could be an alternative to large-size ensembles. We demonstrate how to form less-complex, small-size, and high-accurate ensembles through our proposal. Experiments on 25 datasets show that the proposed method can produce effective ensembles better than Random Forest and baseline classifier pruning methods. Moreover, our proposition is comparable with the Extreme Gradient Boosting Algorithm in terms of accuracy.

Keywords: Meta-heuristics; Ensemble selection; Data reduction; Ensemble pruning; Multiple classifier systems; Big data; Machine learning; Difficult samples; Ordering-based pruning

Research Title: An analysis of heuristic metrics for classifier ensemble pruning based on ordered aggregation

Author: Amgad Monir Mohaned Elsayed, Published Year: 2022

Pattern Recognition, 124

Faculty: Information Technology

Abstract: Classifier ensemble pruning is a strategy through which a subensemble can be identified via optimizing a predefined performance criterion. Choosing the optimum or suboptimum subensemble decreases the initial ensemble size and increases its predictive performance. In this article, a set of heuristic metrics will be analyzed to guide the pruning process. The analyzed metrics are based on modifying the order of the classifiers in the bagging algorithm, with selecting the first set in the queue. Some of these criteria include general accuracy, the complementarity of decisions, ensemble diversity, the margin of samples, minimum redundancy, discriminant classifiers, and margin hybrid diversity. The efficacy of those metrics is affected by the original ensemble size, the required subensemble size, the kind of individual classifiers, and the number of classes. While the efficiency is measured in terms of the computational cost and the memory space requirements. The performance of those metrics is assessed over fifteen binary and fifteen multiclass benchmark classification tasks, respectively. In addition, the behavior of those metrics against randomness is measured in terms of the distribution of their accuracy around the median. Results show that ordered aggregation is an efficient strategy to generate subensembles that improve both predictive performance as well as computational and memory complexities of the whole bagging ensemble.

Keywords: Heuristic optimization; Ensemble selection; Ensemble pruning; Classifier ensemble; Machine learning; Difficult samples; Ordering-based pruning; Classifier complementariness

Research Title: Quantum Crossover Based Quantum Genetic Algorithm for Solving Non-linear Programming

Author: Amgad Monir Mohaned Elsayed, Published Year: 2012

Faculty: Information Technology

Abstract: Quantum computing proved good results and performance when applied to solving optimization problems. This paper proposes a quantum crossover-based quantum genetic algorithm (QXQGA) for solving non-linear programming. Due to the significant role of mutation function on the QXQGA’s quality, a number of quantum crossover and quantum mutation operators are presented for improving the capabilities of searching, overcoming premature convergence, and keeping diversity of population. For calibrating the QXQGA, the quantum crossover and mutation operators are evaluated using relative percentage deviation for selecting the best combination. In addition, a set of non-linear problems is used as benchmark functions to illustrate the effectiveness of optimizing the complexities with different dimensions, and the performance of the proposed QXQGA algorithm is compared with the quantum inspired evolutionary algorithm to demonstrate its superiority.

Keywords: Quantum Computing; Quantum Evolutionary Algorithms; Quantum Crossover operator; Quantum Mutation operator; Convergence; Non-linear optimization

Research Title: Virtual Reality Training for Balance in Patients with Chronic Low Back Pain: A Systematic Review and Meta-Analysis

Author: Fuad Abdul Rahman Taha Abdulla, Published Year: 2025

Faculty: Allied Medical Sciences

Abstract: Background: Chronic low back pain is often associated with impaired balance and reduced functional mobility. Recent studies suggest that virtual reality-based interventions may be effective in improving balance outcomes in individuals with chronic low back pain. Objective: In this systematic review and meta-analysis, we aimed to investigate the impact of virtual reality training on static and dynamic balance outcomes in patients with chronic low back pain. Methods: Two independent reviewers searched English-language studies from inception to 1 July 2024, using the following databases: PubMed, Web of Science, Scopus, Dimensions, Semantic Scholar, and ProQuest. Randomized clinical trials with a PEDro score of ≥6 were included. Fixed- and random-effects meta-analyses were conducted on eligible trials. Results: Of 3172 records screened, 13 trials were eligible. Meta-analyses of six trials (n = 183) across diverse adults using 2–8 week interventions showed that virtual reality training improved dynamic balance: timed up and go (mean difference: −2.29 s; 95% confidence interval: −2.91 to −1.66; I2 = 0%; p < 0.00001) and forward reach (mean difference: 7.80 cm; 95% confidence interval: 2.08 to 13.52; I2 = 0%; p = 0.008). However, no significant effects were found for static balance, single-leg stance, center of pressure medio-lateral displacement, or center of pressure velocity, compared with controls. Conclusions: Virtual reality-based training seems to be more effective than control interventions in improving dynamic and functional balance, but not static balance, in patients with chronic low back pain.

Keywords: Dynamic; rehabilitation; randomized clinical trial; static; video games

Research Title: pH-responsive chlorhexidine LbL coated silica nanoparticles for managing skin wound infections

Author: Yazan Mohammad Al-Thaher, Published Year: 2025

Colloids and Surfaces A: Physicochemical and Engineering Aspects, Volume 726, Part 1,

Faculty: Pharmacy

Abstract: Skin wound infections pose a significant challenge for clinical treatment due to the development of biofilm. In this study, a wound dressing was employed to accelerate healing by enhancing the release and effectiveness of antimicrobial agents. pH-sensitive silica nanoparticles (SiNP) were designed to enable targeted drug delivery in both acidic and neutral wound environments, optimizing drug delivery. Chlorhexidine (CHX), a well-known antiseptic, was incorporated into SiNP using a layer-by-layer (LbL) coating method. The nanoparticles were characterized for size (TEM), surface charge (zeta potential), FTIR, TGA, CHX release. The CHX-loaded SiNP (CHX-SiNP) exhibited a 2–3 times higher release at pH 5 compared to pH 7.4. Additionally, CHX-SiNP demonstrated strong antibacterial activity against both Gram-negative and Gram-positive bacteria, without showing cytotoxicity in cell viability tests. To enhance usability, CHX-SiNP were incorporated into alginate hydrogels. Their antibacterial efficacy was further evaluated using artificial wounds created in an ex vivo human skin, where alginate-formulated CHX-SiNP treatment resulted in decrease in viable bacterial cells, compared to negative controls. These findings confirm that CHX-SiNP enable effective pH-responsive drug release, ensuring strong antibacterial performance. Furthermore, this study highlights the clinical potential of CHX-SiNP in treating wound infections.

Keywords: PH-response, Silica nanoparticles, Controlled drug release, Wound infection, Ex vivo skin.