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.

Artificial intelligence (AI) is an emerging field in library science, involving the programming of computers to execute tasks that typically require human intelligence. The overarching goal is to create computer systems capable of thinking and acting like humans, which holds profound implications for the field of librarianship. AI has found widespread application within libraries, with examples including expert systems for reference assistance, robots designed to assist with tasks like book sorting, and the integration of virtual reality for immersive learning experiences. While some may fear that AI implementation could distance librarians from their users, the prevailing view suggests that it will instead complement human expertise rather than replace it. By leveraging AI, libraries can enhance their service delivery, streamline operations, and adapt to the evolving demands of a digital society.

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%.

A chatbot is a computer program that uses general rules and Artificial Intelligence techniques to simulate human conversation. This paper highlights the different scenarios of human-computer interaction and the journey it has gone through from evolution to evolvement to innovation to the development of the technical era. Here, the main focus is on the ways humans interact with the computer and how it has changed day-to-day life and reduced human efforts in performing everyday activities. There is an impact of HCI (Human–Computer Interaction) on people and has consequences in the form of both advantages and disadvantages of this interaction. The various innovations and machines have given birth to human–computer interaction as well as technology interaction. The main objective is to style the interface amongst men as well with Personal Computers (PCs) as usual as the interface amid beings. The user can interact in this system using text or voice. As per way as interaction is concerned direct, indirect, and strategic interaction of humans with computers and the latest gadgets is possible. Dynamic intelligence makes it like real-time communication with an individual. It can handle the user request and offer relevant information that can be used as a friend one would seek for knowledge. The proposed system is developed using the Rasa of an open-source platform. Further, the article focuses on the features and role of chatbots in an educational context. High precision in sentence analysis is attained with the aid of the proposed method up to a 91% hit ratio. The hit rate for the similarity computation is high. The system can handle a broader variety of requests as a consequence of its ability to recognize many ways to phrase the same inquiry and map them to related results.