This abstract is a synopsis of the paper "An Expert System for Diagnosing Whooping Cough Using CLIPS." The bacterium Bordetella pertussis causes whooping cough, a highly infectious respiratory ailment with several phases of symptoms. An accurate and timely diagnosis is critical for effective treatment and the avoidance of future transmission. The construction of an expert system for detecting whooping cough using the CLIPS (C Language Integrated Production System) architecture is highlighted in this abstract. The expert system provides standardized and systematic (...) evaluation, which reduces the chance of misdiagnosis and improves patient outcomes. Maintenance and upgrades are required to keep the system current with growing medical knowledge. More study in this area has the potential to advance expert systems in identifying and managing a variety of medical disorders. (shrink)

A newborn baby is an infant within the first 28 days of birth. Diagnosis and treatment of infant diseases require specialized medical resources and expert knowledge. However, there is a shortage of such professionals globally, particularly in low-income countries. To address this challenge, a knowledge-based system was designed to aid in the diagnosis and treatment of neonatal diseases. The system utilizes both machine learning and health expert knowledge, and a hybrid data mining process model was used to extract knowledge from (...) a clinical dataset. The PART algorithm achieved the highest performance result with 98.06% accuracy under 10-fold cross-validation, and the generated rules were used to develop the knowledge-based system. The system achieved 90.9% accuracy in system performance testing and 89.2% in user acceptance testing, and is intended to serve as an assistant tool for healthcare experts. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...) it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)