Rice (Oryza sativa L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identification and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features.

Incorporating generative artificial intelligence (GAI) in education has become crucial in contemporary educational environments. This research article thoroughly investigates the ramifications of implementing GAI in the higher education context of Saudi Arabia, employing a blend of quantitative and qualitative research approaches. Survey-based quantitative data reveals a noteworthy correlation between educators’ awareness of GAI and the frequency of its application. Notably, around half of the surveyed educators are at stages characterized by understanding and familiarity with GAI integration, indicating a tangible readiness for its adoption. Moreover, the study’s quantitative findings underscore the perceived value and ease associated with integrating GAI, thus reinforcing the assumption that educators are motivated and inclined to integrate GAI tools like ChatGPT into their teaching methodologies. In addition to the quantitative analysis, qualitative insights from in-depth interviews with educators unveil a rich tapestry of perspectives. The qualitative data emphasizes GAI’s role as a catalyst for collaborative learning, contributing to professional development, and fostering innovative teaching practices.

Forecasting of sediment load (SL) is essential for reservoir operations, design of water resource structures, risk management, water resource planning and for preventing natural disasters in the river basin systems. Direct measurement of SL is difficult, labour intensive, and expensive. The development of an accurate and reliable model for forecasting the SL is required. Sediment transport is highly non-linear and is influenced by a variety of factors. Forecasting of the SL using various conventional methods is not highly accurate because of the association of various complex phenomena. In this study, major key factors such as rock type (RT), relief (R), rainfall (RF), water discharge (WD), temperature (T), catchment area (CA), and SL are recognized in developing the one-step-ahead SL forecasting model in the Mahanadi River (MR), which is among India’s largest rivers. Artificial neural networks (ANN) in conjunction with multi-objective genetic algorithm (ANN-MOGA)-based forecasting models were developed for forecasting the SL in the MR. The ANN-MOGA model was employed to optimize the two competing objective functions (bias and error variance) with simultaneous optimization of all associated ANN parameters. The performances of the proposed novel model were finally compared to other existing methods to verify the forecasting capability of the model. The ANN-MOGA model improved the performance by 12.81% and 10.19% compared to traditional AR and MAR regression models, respectively. The results suggested that hybrid ANN-MOGA models outperform traditional autoregressive and multivariate autoregressive forecasting models. Overall, hybrid ANN-MOGA intelligent techniques are recommended for the forecasting of SL in rivers because of their relatively better performance as compared to other existing models and simplicity of application.

What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research’s accuracy in determining volume percentages has significantly increased.

This research explores the application of machine learning in predicting student academic outcomes, analyzing data from 236 students enrolled at a Saudi university from 2021-2024. The comprehensive dataset encompassed multiple variables, including gender, academic history (previous GPA), assessment performance (quizzes, midterms, assignments, and final exams), engagement metrics (course hours), and attendance patterns. The study evaluated multiple machine learning approaches, comparing the effectiveness of Random Forest (RF), Support Vector Machines (SVM), Logistic Regression, and k-nearest Neighbors (kNN). Gradient Boosting demonstrated superior performance through tenfold cross-validation, achieving an AUC score of 0.987. While Random Forest and kNN also yielded strong results, Logistic Regression and SVM showed comparatively lower predictive accuracy. Feature importance analysis revealed that assignment performance and final examination scores were the most significant predictors of academic success. These findings provide educators with actionable insights to develop targeted interventions and optimize learning environments for enhanced student achievement according to the evidence shown in this study serving higher education institutions.Keywords: Machine Learning Approaches, Student Success Prediction, Higher Education.يستكشف هذا البحث تطبيق تعلم الآلة في التنبؤ بالنتائج الأكاديمية للطلاب، وذلك من خلال التطبيق على حزمة بيانات 236 طالبًا مسجلين في إحدى الجامعات السعودية خلال الفترة من 2021 إلى 2024. شملت حزمة البيانات المتكاملة العديد من المتغيرات، بما في ذلك الجنس، والسجل الأكاديمي (المعدل التراكمي السابق)، وأداءات التقييمات (الاختبارات القصيرة، والامتحانات النصفية، والواجبات، والامتحانات النهائية)، ومؤشرات التفاعل (ساعات الدراسة)، وأنماط الحضور. قام البحث بتقييم عدة نماذج من تقنيات تعلم الآلة، ومقارنة مدى فاعلية كل من الغابة العشوائية (Random Forest - RF) ، وآلات المتجهات الداعمة (Support Vector Machines – SVM)، والانحدار اللوجستي، وأقرب الجيران k-Nearest Neighbors -(kNN). وقد أظهر نموذج التعزيز التدريجي (Gradient Boosting) أداءً متفوقًا من خلال التحقق المتقاطع المكون من عشر طيات، حيث حقق درجة AUC بلغت 0.987. بينما قدمت نماذج الغابة العشوائية (RF) وأقرب الجيران (kNN) نتائج قوية...