Development of a Mobile-based expert system for rice disease diagnosis using forward chaining and Real-time data integration

Automatic disease diagnosis has been human concern for a long time. Since people are so busy and the doctors visit expenses areso expensive, a lot of different attempts have been done in the field of design of expert system for disease diagnosis. This paper describes aproject work aiming to develop a web based fuzzy expert system for human disease diagnosis. This program models the thinking pattern andhuman activity and leads to close the expert system and human action method. In this paper we have consulted with different physicians andanalyzed the diagnosis procedure and modeled them with a fuzzy expert system. This project is based on development of a web-based clinicaltool designed to improve the quality of the exchange of health information between health care professionals and patients . This system hasbeen tested on five diseases with sort throat symptom such as mononucleosis, scarlet fever, pharyngitis or tonsillitis, common cold and virusinfection and exhibited satisfactory results.

The increasing number of patients seeking medical consultation at primary healthcare facilities often leads to prolonged waiting times and delayed preliminary diagnosis, particularly for common diseases with overlapping symptoms. At Puskesmas Karanganyar, diagnostic procedures remain largely dependent on direct consultation, limiting service efficiency under high patient volumes. This study aims to design and implement a web-based expert system to support early diagnosis by simulating clinical reasoning. The system employs a rule-based inference mechanism using Forward Chaining to identify potential diseases from patient-selected symptoms, while the Certainty Factor method is integrated to quantify diagnostic confidence. Unlike prior rule-based diagnostic systems, this study contributes an integrated multi-disease diagnostic framework tailored to primary healthcare workflows, combining transparent rule traceability with graded confidence representation to enhance interpretability and practical usability. Knowledge acquisition was conducted through expert interviews and literature review, resulting in a knowledge base of ten diseases and forty symptoms. The system was implemented using PHP and MySQL and is accessible across devices. Empirical evaluation through structured functional testing and user-oriented validation indicates that the system achieves consistent diagnostic outputs with 100% functional success across tested scenarios, average response time below 2 seconds, and positive usability feedback from healthcare staff, demonstrating operational reliability in real-world settings. The findings suggest that the proposed system provides fast, consistent, and informative preliminary diagnoses, supporting more efficient decision-making in primary healthcare services.

The Aedes aegypti mosquito transmits the dengue, zika, and chikungunya viruses, endangering the health and lives of people in affected countries due to a lack of timely diagnosis. The objective of this study is to design and evaluate the feasibility of a mobile application based on an expert system for early diagnosis of diseases transmitted by the Aedes aegypti mosquito. The Buchanan methodology was used to develop the application. The results obtained show that the proposed mobile application has a diagnostic accuracy of 83%, a sensitivity of 91%, a specificity of 63%, and an error rate of 17%. The technical aspects of the application were also evaluated through a questionnaire administered to five computer experts. The results showed that the technical aspects of the application received an average rating of 3.91 out of a maximum of 5, with a standard deviation of 0.482. In addition, the usability of the application was evaluated using the standardized System Usability Scale (SUS), which was administered to a total of 15 users. The results of this evaluation showed that the application received an average score of 83 on the SUS scale, indicating a positive level of usability. In conclusion, the results support the effectiveness and potential of the application for the early diagnosis of diseases transmitted by the Aedes aegypti mosquito, providing a useful tool for the rapid detection of these diseases. Although it requires more attention to specificity and error rate to improve the accuracy of the diagnosis.

<p>This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.</p>

Expert systems have become an essential solution in providing efficient disease diagnosis services. This research aims to develop an Expert System for Disease Diagnosis at UPT Puskesmas Belawan using the Forward ChainingMethod.This method is employed to create a system capable of diagnosing diseases based on symptoms inputted by users.Literature review was conducted to understand the concepts of expert systems, Forward Chaining method, and disease diagnosis.Testing was carried out to evaluate the accuracy and performance of the system.The evaluation results indicate that the system is able to provide accurate diagnoses and can be used as a tool to assist in disease diagnosis services at UPT Puskesmas Belawan.

One of the factors causing rice production disturbance in Indonesia is that farmers lack knowledge of early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this problem by providing an Expert System that helps farmers to make an early diagnosis of rice plant diseases. Data of rice plant pests and diseases in 2016 were taken from Samarinda, East Kalimantan, Indonesia using an in-depth survey, and rice experts from the Department of Food Crops and Horticulture of East Kalimantan Province were recruited for the project. The Expert System for Rice Plant Disease Diagnosis, ESforRPD2, was developed based on the pest and disease experiences of the rice experts and uses a Waterfall Paradigm and Unified Modeling Language. This Expert System can detect 48 symptoms and 8 types of diseases of rice plants from 16 data tests with a sensitivity of 87.5%. ESforRPD2 is available in Indonesian at http://esforrpd2.blog.unmul.ac.id

Peer Review Report For: ESforRPD2: Expert System for Rice Plant Disease Diagnosis [version 1; peer review: 1 approved with reservations

A trust-based global expert system for disease diagnosis using hierarchical federated learning

Enterprise Resource Planning (ERP) systems have emerged as essential tools in streamlining financial reporting and improving management efficiency across various industries, including large-scale energy operations. This theoretical review explores how ERP systems enhance financial reporting by integrating real-time data, automating financial processes, and fostering transparency. In energy operations, where financial activities are complex and data-intensive, ERP systems centralize financial data, reducing errors, improving compliance, and supporting decision-making through enhanced visibility and accuracy. By automating tasks such as budgeting, forecasting, and financial statement generation, ERP systems enable energy companies to efficiently manage their financial resources, thus optimizing cost control and profitability. Additionally, ERP systems provide significant benefits for management efficiency, particularly in large-scale operations. The integration of financial, operational, and managerial processes within a single platform allows for better resource allocation, improved communication across departments, and faster response times to market fluctuations. These capabilities are crucial in the energy sector, where companies face volatile market conditions, regulatory pressures, and high operational risks. ERP systems enhance managerial decision-making by providing real-time insights into key performance indicators (KPIs), enabling executives to make data-driven decisions that support long-term strategic goals. However, implementing ERP systems in large-scale energy operations presents challenges, including high costs, lengthy implementation periods, and the need for employee training. This review also examines these barriers and suggests strategies for successful ERP adoption, including phased implementation, stakeholder engagement, and customization to industry-specific needs. In conclusion, ERP systems hold the potential to revolutionize financial reporting and management efficiency in large-scale energy operations. By leveraging ERP's real-time data integration and process automation, energy companies can significantly improve their financial management, enhance decision-making, and adapt more quickly to market changes. Keywords: ERP Systems, Financial Reporting, Management Efficiency, Large-Scale Energy Operations, Process Automation, Real-Time Data Integration, Decision-Making, Cost Control, Market Fluctuations, Energy Sector.

Real-time data integration from sources becomes critical in today's world to make efficient decisions for industries across finance, health, and retail. Informatica PowerCenter is considered one of the fastest and most fully featured Transform, Load (ETL) tools that face many challenges in real-time cloud integrations through Snowflake and Microsoft Azure, among many more. This assignment looks at a study of how real-time cloud integration is enabled by Informatica PowerCenter, as well as its architecture, features, and applicability [8]. With the key features of Change Data Capture (CDC), parallel processing, and workflow automation, PowerCenter manages high-frequency data ingestion with minimum latency and data consistency. We discuss some of the critical challenges around latency, security, and cost management and review the functionality of PowerCenter in the cloud, which is a scalable platform that allows compliance with industry standards. These findings show that with the integration of cloud platforms, PowerCenter can take the real-time processing of data further, thereby providing a scalable solution to organizations for pursuing analytics on the cloud. Many case studies in the financial and healthcare industries focus on how the PowerCenter toolset can drive operational efficiencies and timely decision-making. Therefore, this suggests that future advanced automation and machine learning integration would be recommended in this sphere of research at PowerCenter, optimizing real-time data integration.

The efficient direct integration of real-time medical device data is a promising approach to improve patient care enabling a direct and eminent intervention. This study presents a comprehensive approach for integrating real-time medical device data into clinical environments using the HL7® FHIR® standards and IEEE 11073 Service-Oriented Device Connectivity (SDC). The study proposes a conceptual framework and an opensource proof-of-concept implementation for real-time data integration within the Medical Data Integration Center (MeDIC) at UKSH. Key components include a selective recording mechanism to mitigate storage issues and ensure accurate data capture. Our robust network architecture utilizes Kafka brokers for seamless data transfer in isolated networks. The study demonstrates the selective capturing of real-time data within a clinical setting to enable medical device data for a down-stream processing and analysis.

Road accidents are still a prominent urban issue, worsened by a growing population and uncertain weather patterns. Conventional accident prediction models use static historical data, which does not enable them to be responsive to real-time traffic patterns. This research is responding to the demand for predictive models that are adaptive, smart, and can facilitate smart city infrastructure through real-time data integration. Machine learning models—Multilayer Perceptron, Gradient Boosting, Random Forest, Support Vector Machine, and K-Nearest Neighbors—were tested with both historical and real-time traffic data. The models were optimized and trained on varied datasets to improve prediction accuracy. Of these, Gradient Boosting recorded the highest accuracy at 88.1%, followed by Random Forest at 83.73%, showing the power of ensemble learning techniques in predicting accidents. This research emphasizes the significance of real-time data integration for accident prediction and prevention. Merging environmental elements like weather conditions and traffic congestion improves prediction quality, allowing proactive prevention of accidents. By leveraging these models, cities can shift towards data-based road management, infrastructure planning, and congestion control. The results show that real-time models of accident prediction have the potential to enhance urban safety, opening the door to smarter and more efficient traffic management systems.

This research investigates the role of big data analytics and machine learning in optimizing drilling operations, with a specific focus on predicting optimal drilling parameters to mitigate unplanned downtime (UDT). Conducted over two years at various oil drilling sites in Canada, the study highlights the integration of Logging While Drilling (LWD) and Measurement While Drilling (MWD) data into predictive models. The findings demonstrate a significant reduction in UDT through the development of machine learning algorithms that analyze historical drilling data to forecast and optimize the Rate of Penetration (ROP). Despite the advancements, challenges such as real-time data integration and anomaly detection were identified, emphasizing the need for enhanced data quality and management frameworks. The implications of this research underscore the necessity for drilling companies to adopt data-driven strategies and invest in workforce training to fully realize the potential of predictive analytics. By providing actionable insights, this study contributes to the ongoing evolution of drilling practices, paving the way for more efficient and resilient ope rations in the oil and gas industry.

This paper discusses the NVIDIA Omniverse platform for visualizing and integrating digital twins of fusion power plants, focusing on enhancing the design, simulation, and operational workflows of complex fusion systems. As global fusion projects such as ITER, STEP, and CFETR move toward realizing practical fusion energy, sophisticated tools to model, simulate, and optimize such systems become increasingly critical. Digital twins are virtual replicas of physical systems that act as a “single source of truth” by integrating scientific analysis, engineering design, and real-time data integration. This paper outlines the workflow for creating and visualizing a digital twin of a tokamak, demonstrating the integration of computer-aided design models, simulation data, and material properties into Omniverse. It also provides a view of extending Omniverse with real-time data visualization, Python scripting, and generative AI in applications that make the digital twin even more functional and interactive, allowing seamless collaboration across teams and stakeholders. The interoperability challenges that limit the adoption of Omniverse in fusion research are also discussed. In conclusion, the paper outlines how Omniverse can create a comprehensive, immersive, and interactive environment that optimizes the design and operation of a fusion power plant. In the future, this approach may extend to developing more efficient and reliable fusion energy systems.

Dr. Flynxz – A First Aid Mamdani-Sugeno-type fuzzy expert system for differential symptoms-based diagnosis