In this paper, a novel Siamese network-based lightweight framework is proposed for automatic tomato leaf disease recognition. This framework achieves the highest accuracy of 96.97% on the tomato subset obtained from the PlantVillage dataset and 95.48% on the Taiwan tomato leaf disease dataset. Experimental results further confirm that the proposed framework is effective with imbalanced and small data. The backbone network integrated with this framework is lightweight with approximately 2.9629 million trainable parameters, which is second to SqueezeNet and significantly lower than other lightweight deep networks. Automatic tomato disease recognition from leaf images is vital to avoid crop losses by applying control measures on time. Even though recent deep learning-based tomato disease recognition methods with classical training procedures showed promising recognition results, they demand large labeled data and involve expensive training. The traditional deep learning models proposed for tomato disease recognition also consume high memory and storage because of a high number of parameters. While lightweight networks overcome some of these issues to a certain extent, they continue to show low performance and struggle to handle imbalanced data.

The most prominent kind of entertainment in the present day is playing video games, which are experienced by players of all ages. When a player is fully engaged in a game, they are utterly unaware of their surroundings. The primary objective of this study is to prove the close relationship between excessive gaming and the negative effects of anxiety and depression. It also looks at how well machine learning techniques can be used to predict the level of anxiety and depression in people who have become dependent on video games. In order to predict anxiety and depression, previous research has mostly used cross-sectional data and standard-scale questionnaires. We collected information from an online survey and utilized the use of several machine-learning approaches in order to predict the anxiety and depression that persons who are dependent on video games will certainly suffer. Furthermore, we employed feature selection techniques to identify features that were most effective at predicting both anxiety and depression. When taking only into account the most important factors, the SVM classifier obtained a depression prediction accuracy of 93.48% whereas the k-NN classifier achieved an accuracy of 91.44% for anxiety. The study's findings offer a promising start by using machine learning to anticipate anxiety and depression in those who depend on video games.

Protracted droughts, defined as successive dry events occurring before full vegetation recovery, pose an emerging challenge to assessing ecosystem resilience. Conventional drought analyses often treat these events as isolated, overlooking the temporal dependencies that govern vegetation stress. This study addresses this limitation by proposing an integrated spatiotemporal drought assessment approach that links Standardised Precipitation Index (SPI) based identification of individual drought events with vegetation recovery dynamics derived from Normalised Difference Vegetation Index (NDVI) and Vapour Pressure Deficit (VPD), enabling the identification of protracted droughts across Queensland, Australia. Results reveal a noticeable seasonal pattern, with NDVI consistently decreasing following drought periods (SPI-1 < -1) and showing a negative-lagged relationship with VPD, indicating delayed vegetation recovery. The mean recovery duration across Queensland was estimated at 3.38 months, quantified as the time lag between drought termination (SPI ≥ -0.5) and the return of NDVI to baseline conditions. Based on this definition, region-specific recovery lags were derived and used to generate ground-truth labels for protracted drought events. Spatiotemporal drought patterns were analysed using a hybrid deep learning framework integrating a convolutional neural network (CNN) and recurrent layers, including long short-term memory (LSTM) and bidirectional-LSTM (BiLSTM), with and without multi-head attention (MHA). The CNN-LSTM-MHA model achieved the highest performance (accuracy 95.39% and F1-score 0.9569) in detecting protracted drought events across space and time, demonstrating the significance of integrating temporal memory with attention mechanisms. This study shows that coupling remote sensing indices with deep spatiotemporal learning enhances protracted drought detection by explicitly modelling vegetation recovery-dependent temporal dependencies, capturing lagged NDVI responses to atmospheric dryness and enabling more consistent identification of successive drought events before full recovery, thereby improving the interpretation of vegetation resilience and supporting adaptive management in climate-sensitive regions.