Introduction: Agriculture underpins global food security by providing food, raw materials, and livelihoods, contributing 4% to global GDP and up to 25% in rural areas. Rice, a staple for more than half of the world’s population, is nutritionally vital but highly vulnerable to diseases such as Hispa, leaf blast, and brown spots, which significantly reduce yield and quality. Achieving Sustainable Development Goal (SDG) 2 requires innovative approaches to mitigate these threats. Artificial intelligence (AI), particularly computer vision and machine learning, offers promising tools for early disease detection.Methods: This study developed a convolutional neural network (CNN)–based model for rice leaf disease detection and classification. A publicly available dataset containing 3,355 labeled images across four categories—Brown Spot, Leaf Blast, Hispa, and Healthy leaves—was used to train and evaluate the model. To improve classification accuracy, the CNN was enhanced with spatial and channel attention mechanisms, enabling it to focus on the most discriminative image regions. The system was designed for modular deployment, allowing lightweight, real-time implementation on edge devices.Results: The enhanced CNN achieved high accuracy and robust performance metrics across all disease categories. Attention mechanisms significantly improved precision in identifying subtle disease patterns. The lightweight design ensured efficient operation on edge devices, demonstrating feasibility for real-world agricultural applications.Discussion and conclusion: The proposed AI-driven system provides reliable and scalable rice leaf disease detection, supporting timely intervention to reduce yield loss. By strengthening rice production and promoting sustainable practices, the model contributes to SDG 2 by advancing global food security. This research highlights AI’s transformative role in agriculture, fostering mechanization, ecological stability, and resilience in food systems.

Surgical waiting lists present significant challenges to healthcare systems, particularly in resource-constrained settings where equitable prioritization and efficient resource allocation are critical. We aim to address these issues by developing a novel, dynamic, and interpretable framework for prioritizing surgical patients. Our methodology integrates machine learning (ML), stochastic simulations, and explainable AI (XAI) to capture the temporal evolution of dynamic prioritization scores, $q_p\left(t\right)$, while ensuring transparency in decision making. Specifically, we employ the Light Gradient Boosting Machine (LightGBM) for predictive modeling, stochastic simulations to account for dynamic variables and competitive interactions, and SHapley Additive Explanations (SHAPs) to interpret model outputs at both the global and patient-specific levels. Our hybrid approach demonstrates strong predictive performance using a dataset of 205 patients from an otorhinolaryngology (ENT) unit of a high-complexity hospital in Chile. The LightGBM model achieved a mean squared error (MSE) of 0.00018 and a coefficient of determination ($R^2$) value of 0.96282, underscoring its high accuracy in estimating $q_p\left(t\right)$. Stochastic simulations effectively captured temporal changes, illustrating that Patient 1’s $q_p\left(t\right)$ increased from 0.50 (at $t=0$) to 1.026 (at $t=10$) due to the significant growth of dynamic variables such as severity and urgency. SHAP analyses identified severity (Sever) as the most influential variable, contributing substantially to $q_p\left(t\right)$, while non-clinical factors, such as the capacity to participate in family activities (Lfam), exerted a moderating influence. Additionally, our methodology achieves a reduction in waiting times by up to 26%, demonstrating its effectiveness in optimizing surgical prioritization. Finally, our strategy effectively combines adaptability and interpretability, ensuring dynamic and transparent prioritization that aligns with evolving patient needs and resource constraints.

Software systems are hard to complete as customers became hard to satisfy and their requirements are more complicated. In this paper, quality factors that affect software are discussed, with some well-known standards that improve and assist while planning for quality assurance. The testing strategies and types that contribute to improving quality are also highlighted. A development methodology was mentioned as it gives more support to the testing phase.

People spend a significant amount of time in their life finding parking whenever they visit shopping malls or travel to various places of their interest. Malaysia is known for its huge number of vehicles travelling every day during the morning rush and after work. Although many existing systems are in place to solve the parking problem, most of these systems lack coordination and on-the-spot navigation. Most of the time, the parking system is not well maintained, which sometimes leads to misleading information about the number of available parking spaces at the given time. This research aims to change how the current "smart parking system" works by implementing on-the-spot navigation based on RFID, which will always lead the user to the nearest parking space. As a result, it will reduce the time spent finding vacant parking lots.

This research focuses on the development of automated techniques for Bill of Materials list detection and extraction in isometric drawings. The objective is to This research focuses on the development of automated techniques for Bill of Materials list detection and extraction in isometric drawings. The objective is to