In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and -mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with -mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.

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.

Tomatoes are the most common vegetable crop widely cultivated in the agricultural fields in India. The tropical climate is ideal for its growth, however certain climatic conditions and various other factors affect the normal growth of tomato plants. Apart from these climatic conditions and natural disasters, plant disease is a major crisis in crop production and results in economic loss. The traditional disease detection methods for tomato crops could not produce the expected outcome and the detection period for diseases was slow. The early detection of diseases can give better results than the existing detection models. Thus, computer vision-based technology deep learning techniques could be implemented for earlier disease detection. This paper introduces a comprehensive analysis of the disease classification and detection techniques implied for tomato leaf disease identification. This paper also reviews the merits and drawbacks of the methodologies proposed. This paper finally proposes the early disease detection technique to identify tomato leaf disease using hybrid deep-learning architecture.

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.

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%.