Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. The present investigation is an attempt to study the trend in the production of total pulses in India using the autoregressive integrated moving average (ARIMA) method. For stochastic trend estimation, yearly data were used for the period from 1961 to 2019. On the basis of the performance of several goodness of model fit criteria, the most suitable ARIMA model is chosen to capture the trend of pulse production. Forecasting for the 10 years from 2020 to 2029 is done, and it is observed that India has the highest forecast value (31.03302 million tonnes) in 2029. This study will play an important role in determining the gap between production of and demand for pulses in the future.

Background: Lumpy Skin Disease (LSD) is an infectious disease induced by the Capripoxvirus, causing epidemics in Turkey and several countries worldwide and inducing significant economic losses. Although this disease occurs in Turkish cattle every year, it is a notifiable disease. In this study, LSD in Turkey was modelled using the Susceptible, Exposed, Infectious, and Recovered (SEIR) epidemiological model, and production losses were estimated with predictions of the course of the disease. The animal population was categorized into four groups: Susceptible, Exposed, Infectious, and Recovered, and model parameters were obtained. The SEIR model was formulated with an outbreak calculator simulator applied for demonstration purposes. Results: Production losses caused by the LSD epidemic and the SEIR model’s predictions on the disease’s course were evaluated. Although 1282 cases were identified in Turkey during the study period, the prevalence of LSD was calculated as 4.51%, and the mortality rate was 1.09%. The relationship between the disease duration and incubation period was emphasized in the simulated SEIR model to understand the dynamics of LSD. Early detection of the disease during the incubation period significantly affected the peak time of the disease. According to the model, if the disease was detected during the incubation period, the sick animal's time could transmit the disease (Tinf) was calculated as 2.66 days. Production loss from LSD infection was estimated at US $ 886.34 for dairy cattle and the US $ 1,066.61 for beef cattle per animal. Conclusion: Detection of LSD infection during the incubation period changes the course of the disease and may reduce the resulting economic loss.

Accurate automated classification of brain tumors from magnetic resonance imaging (MRI) is essential for early diagnosis and treatment. This study presents a hybrid framework combining Convolutional Neural Network (CNN) deep features, Large Margin Nearest Neighbor (LMNN) metric learning, and swarm-intelligence optimization for robust four-class classification. Five pretrained CNNs-DenseNet201, MobileNetV2, ResNet50, ResNet101, and InceptionV3-were evaluated on a dataset of 7,023 images categorized as glioma, meningioma, pituitary, healthy. Among these, DenseNet201 provided the highest baseline performance with 92.66% accuracy. LMNN improved feature separability, while Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) selected compact subsets. The selected features were classified using k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The DenseNet201-LMNN-GWO-KNN configuration, termed DenseWolf-K, achieved the best performance with 99.64% accuracy, establishing it as the optimal implementation of the framework. Robustness and generalizability were further confirmed using an independent external dataset. Model explainability was ensured through feature-level ranking of GWO-selected features and occlusion sensitivity maps, an Explainable Artifical Intelligence (XAI) method. Overall, the proposed DenseWolf-K framework delivers high accuracy, low false-negative rates, compact representation, and enhanced interpretability, representing a reliable and efficient solution for MRI-based brain tumor classification.

In the literature, it is well known that there is a bidirectional causality between economic growth and energy consumption. This is why it is crucial to forecast energy consumption. In this study, four deep learning models, i.e., Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), were used to forecast energy consumption in Brazil, Canada, and France. After a training test period, the performance evaluation criterion, i.e., R2, mean square error, root mean square error, mean absolute error, and mean absolute percentage error, was performed for the performance measure. It showed that GRU is the best model for Canada and France, while LSTM is the best model for Brazil. Therefore, the energy consumption prediction was made for the 12 months of the year 2017 using LSTM for Brazil and GRU for Canada and France. Based on the selected model, it was projected that the energy consumption in Brazil was 38 597.14–38 092.88, 63 900–4 800 000 GWh in Canada, and 50 999.72–32 747.01 GWh in France in 2017. The projected consumption in Canada was very high due to the country’s higher industrialization. The results obtained in this study confirmed that the nature of energy production will impact the complexity of the deep learning model.