This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes. When individual models are used for classification, they are often prone to misclassification since they may not be able to learn the characteristics of all the classes adequately. This is due to the fact that each model may learn the characteristics of specific classes more efficiently than the other classes. We propose an ensemble model that leverages the predictions of three pre-trained models, DenseNet201, InceptionV3, and ResNet50 with accuracies of 94.54%, 88.38%, and 90.58%, respectively. The predictions of the base learners are combined using two methods: model averaging and weighted averaging. The performances of the models are evaluated, and the model averaging ensemble has an accuracy of 92.96% whereas the weighted average ensemble has an accuracy of 95.00%. The weighted average ensemble outperforms the model average ensemble and all individual models. The results from the evaluation demonstrate that utilizing an ensemble of base learners can successfully classify features that were incorrectly learned by individual base learners.

This article uses cutting‐edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time‐consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision‐based techniques for automatic damage detection have been developed. This paper’s civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning‐based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%.

Agriculture is the backbone of a country and plays a vital role in shaping its economic performance. Factors such as natural disasters, extreme weather changes, pests, and soil quality significantly impact productivity, often leading to economic losses. Accurate predictions in agricultural practices, particularly crop recommendations, can substantially boost productivity and resource management. This research aims to develop a robust crop recommendation system using ensemble learning (EL), which integrates multiple machine learning (ML) models for improved performance. This study utilizes two datasets: a real-time dataset available on Kaggle, collected using IoT sensors, and a synthetic dataset generated using CTGAN. These datasets provide crop recommendations for 22 different crops, based on key features like nitrogen, phosphorus, potassium, soil pH, humidity, and rainfall. The performance of various ML models—such as linear regression (LR), support vector machine (SVM), decision tree (DT), naïve Bayes (NB), K-nearest neighbor (KNN), random forest (RF), extra tree classifier, XGBoost, and gradient boost—is compared with that of EL models, including voting, bagging, boosting, and stacking ensemble techniques. The stacking ensemble model achieved the highest accuracy at 99.36% across all ensemble techniques. By further optimizing this model using the Optuna hyper-parameter tuning technique, the accuracy was improved to 99.43%.

Agriculture remains the backbone of many countries; it plays a pivotal role in shaping a country’s overall economy. Accurate prediction in agriculture practices, particularly crop recommendations, can greatly enhance productivity and resource management. IoT and AI technologies have great potential for enhancing precision farming; traditional machine learning (ML) and ensemble learning (EL) models rely primarily on the training data for predictions. When the training data is noisy or limited, these models can result in inaccurate or unrealistic predictions. These limitations are addressed by incorporating physical laws into the ML framework, thereby ensuring that the predictions remain physically plausible. In this study, we conducted a detailed analysis of ML and EL models, both with and without optimization, and compared their performance against a physics-informed ML model. In the proposed stacking physics-informed ML model, the optimal temperature and the pH for each crop (physics law) are provided as input during the training process in addition to the training data. The physics-informed model was trained to simultaneously satisfy two objectives: (1) fitting the data, and (2) adhering to the physics law. This was achieved by including a penalty term within its total loss function, forcing the model to make predictions that are both accurate and physically feasible. Our findings indicate that the proposed novel stacking physics-informed model achieved a highest accuracy of 99.50% when compared to ML and EL models with optimization.

This research focuses on developing models to accurately predict student’s cumulative grade point average (CGPA) in the early stages of their study to tackle the problem of dropout rates in educational institutions. The state-of-the-art methods address CGPA prediction as a classification problem, providing only an approximate prediction where precise prediction is essential. In this research, six regression models, namely linear regression, support vector regression (SVR), decision tree (DT), random forest (RF), lasso regression (LR), and ridge regression (RR) are developed without optimization and later fine-tuned using Bayesian optimization (BO) and GridSearchCV. BO efficiently searches the hyper-parameter space using probabilistic distribution’s function, whereas GridSearchCV exhaustively searches the hyper-parameter space. These techniques significantly improved the model's performance; SVR achieved an R² score of 94.11% through BO. Ensemble techniques, such as stacking, voting, and boosting, can further enhance the predictive capability of the model. The stacking ensemble model achieved the highest R² score of 94.45%, providing a 0.50% improvement in the R2 score. The findings of this study suggest that advanced optimization and ensemble techniques can substantially enhance the predictive capability of the model, thus enabling institutions to support students at risk of academic probation proactively.