Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health.

Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

The Compton Spectrometer and Imager (COSI) is a NASA satellite mission under development designed to survey the entire sky at 0.2–5 MeV with a wide-field gamma-ray telescope. Its main instrument is a germanium detector array surrounded on the sides and bottom by bismuth germanium oxide scintillator active shields (the “Anticoincidence Subsystem” (ACS)) to reduce and monitor background and for detecting transients. COSI will have an onboard trigger algorithm to detect gamma-ray bursts (GRBs) in the ACS and send data to the ground for further analysis. In this paper, we present three localization methods that we evaluated for the localization of short GRBs (sGRBs) using the ACS light curves. The first method is the χ ² fit already used by the Fermi Gamma-ray Burst Monitor, which calculates the best fit between look-up tables and the GRB data. The second method is a maximum likelihood estimation fit implemented in bc-tools for the BurstCube mission that performs a fit between the instrument response function and the GRB data. The last method is based on deep learning techniques and consists of a neural network developed for the COSI mission and trained to perform a regression of the sGRB position, taking as input the count rates of each ACS panel. The localization errors obtained by analyzing simulated sGRBs with the three methods are consistent. Despite the theoretical similarity between the approaches, their consistency in results is noteworthy, as they differ substantially in their implementations and optimization processes. The bc-tools obtain the best localization accuracy.