Abstract: Despite energy-related financial concerns and the growing demand for sustainability, many energy efficiency measures are not being implemented in industrial practice. There are a number of reasons for this, including a lack of knowledge about energy efficiency potentials and the assessment of energy savings as well as the high workloads of employees. This article describes the systematic development of an expert system, which offers a chance to overcome these obstacles and contribute significantly to increasing the energy efficiency of production machines. The system employs data-driven regression models to identify inefficient parameter settings, calculate achievable energy savings, and prioritize actions based on a fuzzy rule base. Proposed measures are first applied to an analytical real-time simulation model of a production machine to verify that the constraints required for the specified product quality are met. This provides the machine operator with the expert means to apply proposed energy efficiency measures to the physical entity. We demonstrate the development and application of the system for a throughput parts-cleaning machine in the metalworking industry.

Abstract: In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in patients' quality of life, this study focused on the development and evaluation of an expert system based on SWI Prolog. The main objective of this research was to provide an effective method for the preliminary diagnosis of ocular disorders, including cataract, trachoma, uveitis, glaucoma, and presbyopia. For the evaluation of the system, a confusion matrix was implemented and accuracy, sensitivity and specificity were calculated using a sample of 30 cases, of which 20 were positive and 10 negatives. The findings revealed an outstanding accuracy of 95%, with a sensitivity and specificity of 90%. This highlights the potential of the tool as an effective means of early detection of visual problems. In conclusion, this expert system represents a significant advance in ophthalmologic diagnosis, with important implications for clinical care and patients' quality of life, although expansion and validation of the tool in further clinical studies is suggested for its wider and more successful implementation in the field of ophthalmology.

Abstract: Expert systems (ESs) can be seen as a perspective method for risk analysis process automation, especially in the case of small- and medium-sized enterprises that lack internal security resources. Expert system practical applicability is limited by the fact that the creation of an expert system knowledge base requires a lot of manual work. External knowledge sources, such as attack trees, web pages, and ontologies, are already proven to be valuable sources for the automated creation of knowledge base rules, thus leading to more effective creation of specialized expert systems. This research proposes a new method of automated conversion of CVE data from the National Vulnerability Database (version CVSS 2) into the knowledge base of an expert system and flags CVE records that have higher risk due to already existing exploit tools. This manuscript also contains a description of the method for implementing software and a practical evaluation of conversion results. The uniqueness of the proposed method is incorporation of the records included in the Cybersecurity and Infrastructure Security Agency (CISA) Known Exploited Vulnerabilities Catalog.

Abstract: General background Eye diseases are a significant health issue and often go undiagnosed accurately. Specific background With advancements in technology, web-based expert systems offer solutions for more efficient and accurate diagnosis. Knowledge gap However, there are still limitations in the application of systematic diagnostic methods in the context of eye diseases. Aims This research aims to develop an expert system that can diagnose eye diseases using the Certainty Factor method. Results The designed system successfully identified the symptoms of eye diseases with a high accuracy percentage, providing relevant diagnosis results based on user input. Novelty This study introduces the use of the Certainty Factor method in an expert system specifically for eye diseases, which has not been widely applied before. Implications These findings are expected to help raise public awareness about eye health and encourage users to consult with specialists after receiving results from the expert system. Thus, this system not only functions as a diagnostic tool but also serves as an educational medium for users. Highlights: Development of an expert system for diagnosing eye diseases using the Certainty Factor method. The system demonstrates high accuracy in identifying symptoms based on user input. Provides an educational platform to raise awareness about eye health among users. Keywords: Expert system, Eye Disease, Certainty Factor, Web-Based Application, Diagnosis

Abstract: Fuzzy logic is crucial in water quality assessment for its capacity to handle imprecise data and model complex relationships among various parameters. Its application enables nuanced evaluations, aiding in effective decision-making for sustainable environmental management and conservation of water resources. This paper presents an intelligent approach to assess water quality in Vartur Lake using a Fuzzy Logic-based expert system which incorporates linguistic variables and fuzzy membership functions to provide a nuanced evaluation of water quality parameters across different seasons. Through meticulous definition of fuzzy membership functions and iterative rule base optimization, the proposed approach demonstrates the effectiveness of fuzzy logic in handling uncertainty and variability in water quality data, resulting in a robust and adaptable framework for water quality assessment. The results highlight the advantages of the Fuzzy Water Quality Index (FWQI) over traditional Water Quality Index (WQI) methods, showcasing its flexibility, adaptability to seasonal variations, and ability to provide a more comprehensive understanding of water quality conditions, thereby offering valuable insights for environmental management and decision-making. By leveraging Fuzzy Logic’s ability to handle imprecise information, the proposed approach offers a valuable tool for environmental monitoring and management, contributing to the sustainable preservation of Vartur Lake’s ecosystem and surrounding communities.

Abstract: Malaria, a widespread and potentially deadly disease affecting millions annually, underscores the urgent need for early detection to mitigate its impact on individuals and communities. Harnessing the ubiquitous presence of smartphones, an Android-based expert system emerges as a promising solution. This research endeavors to develop such a system using Android Studio, aimed at aiding healthcare professionals in malaria diagnosis.The Android-based expert system offers a timely and precise diagnosis by leveraging patient-reported symptoms and medical history. Additionally, it furnishes educational materials on malaria prevention and management, bolstering public awareness and proactive measures against the disease. Through rigorous evaluation, this study advocates for the widespread adoption of the malaria expert system, under the supervision of medical practitioners, to expedite diagnosis and streamline healthcare delivery. Future iterations of this research should delve into refining the system's capability to discern the specific type of malaria infection afflicting the user. In conclusion, the creation of an Android-based expert system for malaria detection is a noteworthy advancement in the use of technology to battle a widespread health risk. Embracing this innovative approach stands to enhance early detection efforts and ultimately alleviate the burden of malaria on global health systems and communities.

Abstract: In the field of pharmaceutical research, expert tool has become a game-changing tool that is altering many aspects of drug discovery and development. Understanding the physicochemical characteristics of drug candidates is essential for developing formulation methods, and this is where preformulation studies come into play. The incorporation of expert tools in preformulation research is thoroughly examined in this review, which also highlights the problems, applications, and future prospects of these techniques. Further, the present review focuses on role of SeDeM expert system to generate information about drug and excipients in association with Artificial Intelligence (AI). Researchers can identify safer and more effective therapies by utilizing AI-driven techniques to accelerate drug development processes, optimize formulations, and reduce risks related to drug delivery.

Abstract: This study introduces an innovative forward-chaining expert system aimed at the early detection of diabetes and providing decision support, utilizing a comprehensive dataset of 65 symptoms. The system employs a dynamic and iterative analysis methodology through forward chaining, creating a solid foundation for the prompt identification of diabetes, marked by its distinct clinical characteristics. Our experimental results highlight the system's high efficacy, demonstrating a 90% accuracy rate in symptom prediction among participants. This research challenges the conventional categorization of diabetes into types 1 and 2 by acknowledging the complexity of its diagnosis and the changing nature of age-related paradigms. Adopting a forward-chaining strategy transforms the landscape of diabetes diagnosis, offering a continually adaptive and dynamically evolving assessment of symptoms, which promises enhanced precision and effectiveness in the diagnostic procedure. Our findings reveal a remarkable 90% success rate in accurately predicting symptoms, underscoring the system's potential to alter the approach to decision-making in diabetes management significantly. This cutting-edge, dynamic methodology is poised to refine diagnostic practices and bolster health care decision support within the field of diabetes treatment.

Abstract: The article provides an innovative approach to improving the quality control and safety of meat products using neural networks that accurately simulate biological processes. The Expert-Diagnostic System (EDS) combines machine learning and computer vision to precisely detect meat's defects, such as mechanical damage, texture abnormalities, color changes, and bacterial infections. A key element is the multi-agent system, which incorporates specialized agents. The interaction of agents is arranged through a coordination committee and consensus algorithms, ensuring high accuracy of assessments. A mathematical model of agent interaction is presented. Strategies for overcoming challenges such as scalability and agent coordination are proposed.

Abstract: Tuberculosis is affected by mycobacterium tuberculosis. The bacteria commonly attack the lungs, but tuberculosis bacteria can violent any part of the body. This paper presents a rule-based system for Tuberculosis disease diagnosis to identify Tuberculosis disease timely. The system aims to deliver a monitor for health centers to assist the investigative process. A simple sampling technique is used for acquiring data. Data collection is acquired from document analysis, observation, and interviews to develop the proposed system. The acquired data is designed by using a decision tree structure to represent the diagnosis of tuberculosis disease. The proposed system is developed by SWI prolog and its interface is using Java programming language. The developed system has been evaluated by health centers' domain experts. The performance of the proposed system has been achieved by 86%. Therefore, the developed knowledge-based has useful for diagnosing tuberculosis disease.

Abstract: With the rapid development of internet and AI technologies, Agricultural Expert Systems (AESs) have become crucial for delivering technical support and decision-making in agricultural management. However, traditional natural language processing methods often struggle with specialized terminology and context, and they lack the adaptability to handle complex text classifications. The diversity and evolving nature of agricultural texts make deep semantic understanding and integration of contextual knowledge especially challenging. To tackle these challenges, this paper introduces a Bidirectional Encoder Recurrent Convolutional Neural Network (AES-BERCNN) tailored for short-text classification in agricultural expert systems. We designed an Agricultural Text Encoder (ATE) with a six-layer transformer architecture to capture both preceding and following word information. A recursive convolutional neural network based on Gated Recurrent Units (GRUs) was also developed to merge contextual information and learn complex semantic features, which are then combined with the ATE output and refined through max-pooling to form the final feature representation. The AES-BERCNN model was tested on a self-constructed agricultural dataset, achieving an accuracy of 99.63% in text classification. Its generalization ability was further verified on the Tsinghua News dataset. Compared to other models such as TextCNN, DPCNN, BiLSTM, and BERT-based models, the AES-BERCNN shows clear advantages in agricultural text classification. This work provides precise and timely technical support for intelligent agricultural expert systems.

Abstract: Precise lesion segmentation is essential in computer-aided diagnosis and treatment. Prevalent deep-learning-based approaches need high-quality annotations to achieve satisfying performance. However, blurring effects and infiltration hamper the accurate delineation of lesions. A common solution is to divide the annotation process into initial annotation by non-specialized personnel and subsequent modification by expert physicians, where the latent correction patterns and non-expert labels are seldom utilized effectively. To explore their helpfulness, we propose ReReNet, a recurrent refined network for lesion segmentation that learns from non-expert to expert. It achieves progressively refined segmentation results through multiple iterations with tailored discrepancy-aware supervision. During training, as the iterative process perceives discrepancies between refining and expert labels, the model gradually grasps the knowledge of turning the barely correct into the clinically accurate. We validate ReReNet’s capability on three medical image segmentation (MIS) datasets, including magnetic resonance (MR) and computed tomography (CT) modalities. Comparison results indicate that the proposed approach achieves superior performance by introducing the designed recurrent mechanism and outperforms mainstream methods, demonstrating the effectiveness of mining hidden correction patterns by utilizing non-expert information.

Abstract: Depression (major depressive disorder) is a common and serious medical illness that negatively affects how you feel, the way you think and how you act. Fortunately, it is also treatable. Depression causes feelings of sadness and/or a loss of interest in activities once enjoyed. It can lead to a variety of emotional and physical problems and can decrease a person’s ability to function at work and at home. Depression affects an estimated one in 15 adults (6.7%) in any given year. And one in six people (16.6%) will experience depression at some time in their life. Depression can strike at any time, but on average, first appears during the late teens to mid-20s. Women are more likely than men to experience depression. Some studies show that one- third of women will experience a major depressive episode in their lifetime. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment and give the appropriate method of treatment through several tips that concern the disease and how to treat it and we will see it through the application on the expert system. Methods: in this paper the design of the proposed Expert System which was produced to help Psychologist in diagnosing depression disease through its symptoms such as: a loss of energy, a change in appetite, sleeping more or less, anxiety, reduced concentration, indecisiveness, restlessness, feelings of worthlessness, guilt or hopelessness and thoughts of self-harm or suicide. The proposed expert system presents an overview about depression disease is given, the cause of diseases is outlined and the treatment of disease whenever possible is given out. SL5 Object Expert System language was used for designing and implementing the proposed expert system. Results: The proposed depression disease diagnosis expert system was evaluated by psychologist students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for psychologist, patients with depression and newly graduated psychologist.

Abstract: Opportunistic networks are one of the most popular categories of mobile Adhoc networks. These types of networks usually have to deal with intermittent disconnected path from source to the destination most of the time therefor suffers from numerous key challenges for successful custody transfer to be