The global Internet of Medical Things (IoMT) market is growing at a Compound Annual Growth Rate (CAGR) of 17.8%, a testament to the increasing demand for IoMT in the health sector. However, more IoMT devices mean an increase in the volume and velocity of data received by healthcare decision-makers, leading many to develop Distributed Decision Support Systems (DDSSs) to help them make accurate and timely decisions. This research is a systematic review of DDSSs in healthcare using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The study explores how advanced technologies such as Artificial Intelligence (AI), IoMT, and blockchain enhance clinical decision-making processes. It highlights key innovations in DDSSs, including hybrid imaging techniques for comprehensive disease characterization. It also examines the role of Case-Based Reasoning (CBR) frameworks in improving personalized treatment strategies for chronic diseases like diabetes mellitus. It also presents challenges of applying DDSSs in the healthcare sector, such as security and privacy, system integration, and interoperability issues. Finally, it discusses open issues as future research directions in the field of DDSSs in the healthcare sector, including data structure standardization, alert fatigue for healthcare workers using DDSSs, and the lack of adherence of emerging technologies like blockchain to medical regulations.

The increasing demand for automation has led to the complexity of the design and operation of robotic systems. This paper presents a systematic literature review (SLR) focused on the applications and challenges of Data Distribution Service (DDS)-based middleware in robotics from 2006 to 2024. We explore the pivotal role of DDS in facilitating efficient communication across heterogeneous robotic systems, enabling seamless integration of actuators, sensors, and computational elements. Our review identifies key applications of DDS in various robotic domains, including multi-robot coordination, real-time data processing, and cloud–edge–end fusion architectures, which collectively enhance the performance and scalability of robotic operations. Furthermore, we identify several challenges associated with implementing DDS in robotic systems, such as security vulnerabilities, performance and scalability requirements, and the complexities of real-time data transmission. By analyzing recent advancements and case studies, we provide insights into the potential of DDS to overcome these challenges while ensuring robust and reliable communication in dynamic environments. This paper aims to contribute to the transformative impact of DDS-based middleware in robotics, offering a comprehensive overview of its benefits, applications, and security implications. Our findings underscore the necessity for continued research and development in this area, paving the way for more resilient and intelligent robotic systems that operate effectively in real-world scenarios. This review not only fills existing gaps in the literature but also serves as a foundational resource for researchers and practitioners seeking to leverage DDS in the design and implementation of next-generation robotic solutions.

Recently, researchers proposed automation of hydroponic systems to improve efficiency and minimize manpower requirements. Thus increasing profit and farm produce. However, a fully automated hydroponic system should be able to identify cases such as plant diseases, lack of nutrients, and inadequate water supply. Failure to detect these issues can lead to damage of crops and loss of capital. This paper presents an Internet of Things-based machine learning system for plant disease detection using Deep Convolutional Neural Network (DCNN). The model was trained on a data set of 54,309 instances containing 38 different classes of plant disease. The images were retrieved from a plant village database. The system achieved an Accuracy of 98.0% and AUC precision score of 88.0%.