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Engineering companies, which are concerned with manufacturing activities in forming and welding technologies, consider with such a degree of an automation of the production process that will bring production improvement, lower production time and also economic benefits with return of investment. In the present time, businesses depend on a market economy conditions. Therefore, it is necessary to design production systems as very flexible with easy response to changes in a product range. In forming and welding technologies, which belong mostly to an area of wasteless technologies, an application of systems with a certain level of intelligence means a wide use of robots that replace the physical potential of people. They have a positive effect on a production quality and also on an ecology. [Hasse 2020] [ABSTRACT FROM AUTHOR]

Copyright of Journal of Education & Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pancreatic cancer remains one of the most challenging malignancies to diagnose and treat effectively, resulting in poor patient outcomes due to late-stage detection and limited therapeutic options. The emergence of biosensors has revolutionized cancer diagnosis and therapy, providing new avenues for early detection and personalized treatment. This paper explores the development and integration of biosensors within a unique expert system for pancreatic cancer diagnosis and drug delivery automation. It discusses the principles, types, and applications of biosensors in pancreatic cancer diagnosis, their role in automating drug delivery, and the design of an expert system that leverages these technologies to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

Given the urgent need for early detection of learning disorders such as dysgraphia, dyslexia, and dyscalculia in children, this study aimed to evaluate an expert system developed in Python to facilitate early diagnosis of these disorders. The background highlights the importance of providing parents, educators, and health professionals with an effective tool for early detection of these disorders. In 21 simulated cases, the system showed impressive performance with an accuracy rate of 95%, a precision of 100%, a sensitivity of 93%, and a specificity of 100%. Furthermore, the acceptability evaluation, conducted with 15 parents selected by convenience sampling, showed a high level of satisfaction, with an overall mean of 4.78 and a standard deviation of 0.45, indicating consistency in responses. In conclusion, this study confirms the effectiveness of the expert system in the early diagnosis of learning disabilities, providing parents, educators, and health professionals with a valuable tool. Despite these encouraging results, the need for additional research is recognized to address limitations and improve the external validity of the system to ensure its widespread utility and adaptability in real clinical settings. [ABSTRACT FROM AUTHOR]

This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic (5VL) assigns five state values to the range of signal value changes, and these states are completely operational ({4}), incomplete ({3}), critical efficiency ({2}), and pre-fault efficiency ({1}). For the identical ranges of diagnostic signal values, all three of the applied state valence logics interpret failure as changes outside of their permitted ranges. Diagnostic procedures made use of an AI-based DIAG 2 system. This article's goal is to provide a comprehensive overview of the DIAG 2 intelligent diagnostic system, including its architecture, algorithm, and inference rules. Diagnosis with the DIAG 2 system is based on a well-established technique for comparing diagnostic signal vectors with reference signal vectors. A differential vector metric is born out of this examination of vectors. The input cells of the neural network implement the challenge of signal analysis and comparison. It is then possible to classify the object components' states in the neural network's output cells. Based on the condition of the object's constituent parts, this approach can signal whether those parts are working, broken, or urgently require replacement. [ABSTRACT FROM AUTHOR]

The object of this research is the decision-making process in the context of selecting recipients of social services using a semi-automated expert system. The main focus is on improving the mechanisms of assessment and selection of candidates eligible for assistance, in order to ensure a more efficient and objective allocation of resources. The problem addressed in this study is the need to improve the accuracy and efficiency of decision-making processes in social services through the implementation of semi-automated systems. In particular, reducing subjective influence in selection processes, as well as reducing the time and resources required to process applications. The study shows that the introduction of a semi-automated system can significantly reduce the response time to applications, increase the accuracy of candidate selection and ensure greater transparency in the decisionmaking process. The system, based on data analysis algorithms and production rules, is able to adapt to changing conditions and requirements, providing solutions based on up-to-date information. The effectiveness of the semi-automated system is due to the use of modern technologies for processing large volumes of data and the use of complex mathematical models for the analysis of this data. The implementation of a modular system with individually adjustable parameters allows the system to accurately evaluate each case based on the expected criteria, ensuring a high level of adaptability and accuracy. The results of the research can be applied in practice in various social security institutions, where there is a need to automate the processes of selection and decision-making. Important conditions for the effective implementation of the system are the availability of sufficient technical support, a high level of qualification of the personnel engaged in putting the system into operation, as well as a clear understanding of the rules and procedures that regulate social protection. In addition, to guarantee the successful application of the system, it is necessary to ensure compliance with all regulatory and legislative requirements, especially regarding the protection of personal data. [ABSTRACT FROM AUTHOR]

This paper introduces an expert system to decision-making. The expert system is linguistic summarization combined with prioritized operators. In the practical decision-making problems, the information of attributes is linguistic type and needs to be converted into numerical type. The validity of the linguistic summarization is recorded as the attribute value. We discuss how to calculate the validity of the linguistic summarization, and present three prioritized operators. Then the three prioritized operators are used to aggregate the attribute values. Finally, a practical example is given. In addition, we conduct a comparative analysis between the expert system method and another multi-attribute decision-making method by using a measure of specificity, and conclude that the expert system method is better. [ABSTRACT FROM AUTHOR]

The emergence of the highly contagious coronavirus disease has led to multiple pandemic waves, resulting in a significant number of hospitalizations and fatalities. Even outside of hospitals, general practitioners have faced serious challenges, stretching their resources and putting themselves at risk of infection. Telemonitoring systems based on Internet of things technology have emerged as valuable tools for remotely monitoring disease progression, facilitating rapid intervention, and reducing the risk of hospitalization and mortality. They allow for personalized monitoring strategies and tailored treatment plans, which are crucial for improving health outcomes. However, determining the appropriate monitoring intensity remains the responsibility of physicians, which poses challenges and impacts their workload, and thus, can hinder timely responses. To address these challenges, this paper proposes an expert system designed to recommend and adjust the monitoring intensity for COVID-19 patients receiving home treatment based on their medical history, vital signs, and reported symptoms. The system underwent initial validation using real-world cases, demonstrating a favorable performance (F1-score of 0.85). Subsequently, once integrated with an Internet of Things telemonitoring system, a clinical trial will assess the system's reliability in creating telemonitoring plans comparable with those of medics, evaluate its effectiveness in reducing medic–patient interactions or hospitalizations, and gauge patient satisfaction and safety. [ABSTRACT FROM AUTHOR]

Computer security consists in protecting access and manipulating system data by several mechanisms. However, conventional protection technologies are ineffective against current attacks. Thus, new tools have appeared, such as the intrusion detection and prediction systems which are important defense elements for network security since they detect the ongoing intrusions and predict the upcoming attacks. Besides, most of conventional protection technologies remain insufficient in terms of actions since they are all passive systems, unable to provide recommendations in order to block or stop the attacks. In this paper, a distributed detection and prediction system, composed of three major parts, is proposed. The first part deals with the detection of intrusions based on the decision tree learning algorithm. The second part deals with intrusions prediction using the chronicle algorithm. The third part proposes an expert system for security recommendations in response to detected intrusions, able to provide appropriate recommendations to stop the attacks. The proposed system gives good results in terms of accuracy and precision in detecting and predicting attacks, and efficiency in proposing the right recommendations to stop the attacks. [ABSTRACT FROM AUTHOR]

Stunting is a chronic nutritional problem that occurs in toddler due to lack of nutritional intake which results in impaired growth toddler. Usually, toddler who experience stunting are characterized by not increasing weight over a long period of time. Application utilization health which makes it easier for users to access information, one of which can be used to identify toddler who are stunted by selecting symptoms. The symptoms experienced by toddlers go through a system known as the system expert. In this research an expert system will be developed that is capable of early detection developmental disorders in toddlers using the Mamdani fuzzy method. The results obtained from this research are an expert system design for early detection of stunting using the Mamdani fuzzy method. The Mamdani fuzzy method was implemented to group the criteria for toddlers who fall into the stunting category or not from the initial data which is still gray because they are still unsure whether to categorize the toddler as having stunting or not. The detection accuracy rate using the Mamdani fuzzy method is 80.87% compared to expert diagnosis. [ABSTRACT FROM AUTHOR]

This paper aims to develop a framework for differentiating the selection of welding processes using specialized knowledge-based expert systems, which are particularly useful in dealing with issues that require complex decision-making. A program was created and programmed using the process information maps (PRIMAs) matrix in Visual Basic Access for selecting welding processes in a study. The program is in two stages. In the first stage, it excludes non-candidate operations according to several criteria, the most important of which is the type of metal and its thickness. The second stage is arranging operations using multi-criteria methods for decision-making through research to resolve complicated problems involving multiple factors. The hierarchical analysis process (AHP) was used. It is considered one of the most frequently used multi-criteria decision-making methods. The selection of analytic hierarchical process. AHP criteria depend on experience and knowledge rather than specific data for selecting alternatives and determining weights. The methodology is straightforward, easy to comprehend, and applicable to various domains needing intricate decision-making. The program results are compared with those of a previously published case to choose the best welding processes for a study case, which is a home radiator, and results matching the candidate processes were discovered.

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%. The system can also provide recommendations for the treatment of identified diseases. ESforRPD2 is available in Indonesian at http://esforrpd2.blog.unmul.ac.id

Cats are the most common pet in Indonesia with a cat ownership rate of 47% (Rakuten Insight, 2021). Cat owners need to know and recognize the signs and symptoms of the diseases that often occur in cats, especially respiratory problems and diseases. Although vaccines in cats can significantly reduce the incidence of respiratory diseases, they do not eliminate infectious disease pathogens. During the COVID-19 pandemic, it was necessary to adjust health consultations that could reduce the transmission of COVID-19, which is the contactless method. In animal health, there is an online consultation through the WhatsApp platform between veterinarians and cat owners. Cat owners manually type every symptom experienced by the cat. However, there are several shortcomings in the online consultation, including that the symptoms described by the cat owner are unclear, so the diagnosis data is lacking and the consultation fee is quite expensive. Based on the problems that have been mentioned, the purpose of this study is to create an expert system for diagnosing respiratory diseases in cats using the Certainty Factor method. The result of this study is the availability of an expert system that can be used to diagnose respiratory diseases in cats.

This study explores the contemporary landscape of integrating numerical algorithms, artificial intelligence (AI), and expert methodologies within the domain of nephrology. Focusing on automation and decision support, we scrutinize the impact of numerical algorithms on the precise evaluation of kidney function. Furthermore, we delve into the transformative potential of artificial intelligence, particularly in the realms of machine learning and deep learning applications, elucidating its role in early disease detection and the formulation of personalized treatment strategies. The synergy between computer-based tools and expert-driven approaches is examined, underscoring their collaborative role in enhancing the accuracy and dependability of diagnoses. Additionally, we address the ethical considerations and challenges associated with the incorporation of automation in nephrology. The paper provides a concise overview of in-depth analysis while illustrating the promising prospects of these innovative methodologies for reshaping nephrology research and patient care.

Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell “events”. Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human experts. However, such systems face one central challenge: their decisions have far-reaching consequences for the health and lives of people. Therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable to human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison with a selection of state-of-the-art XAI systems shows that ALPODS operates efficiently on known benchmark data and on everyday routine case data.

The manufacturing of hard disk drives involves the intricate assembly of numerous components, making the testing process time-consuming and resource intensive. To optimize the manufacturing process and increase testing efficiency, the development of a rule-based expert system is proposed. This system leverages predictive models constructed from assembly process data to identify potentially defective hard drives before undergoing extensive testing. By preemptively identifying defects, this approach substantially reduces testing time and enhances tester capacity. Given the categorical and imbalanced nature of assembly data, Decision Trees are employed as the prediction model. Specifically, three Decision Tree algorithms are explored: ID3, C4.5, and CART. In addition, four feature selection techniques, namely Information Gain, Gain Ratio, Chi-Square, and Symmetrical Uncertainty, are utilized to identify high-impact features. Our experimental findings reveal that Information Gain coupled with the C4.5 algorithm yields the most favorable results in terms of prediction accuracy, modeling efficiency, and rule generation. Moreover, our study establishes that setting the failure probability threshold between 0.15 and 0.70 provides the shortest total test time for the proposed process, as supported by a 95% confidence level. This achievement represents a statistically significant enhancement compared with the existing manufacturing process.

The paper involved conducting preliminary research to explore the identification and control of a multi-dimensional, non-linear, and non-stationary cement grinding process using artificial neural networks and various other non-linear models. The primary objective was to establish a precise model that accurately characterizes the functioning of the grinding system. Several model structures were employed, including NARX models based on feed-forward network, Elman, Jordan, and Layer-Recurrent Network (LRN) recurrent networks, as well as MTL (Multi-Task Learning) and traditional NARX non-linear models. It was observed that, in contrast to the linear models, the non-linear models exhibited significantly superior performance in the modeling of the system. Another notable outcome of this research is the proposal of a neurocontroller, functioning as an expert system, which can provide control signals to operators. The development and implementation of such a neurocontroller have the potential to enhance the quality, simplicity, and efficiency of cement grinding process control.

ObjectiveTo fully utilize the experience of nuclear power plant operation and management to assist nuclear power operators in fault diagnosis, a marine nuclear power plant fault diagnosis expert system is designed. MethodFirst, based on the logical consistency between fault trees and production rules, a method is proposed to transform fault tree knowledge into production rules. The knowledge base and inference machine of the expert system are then optimized by using a mixed forward and backward inference method, and a forward inference strategy is designed to simplify the inference process based on the minimum cut set and importance analysis results of the fault tree. Finally, based on the idea of manually judging the fault status, a status monitoring module is designed to collect key equipment parameters in real time and convert them into equipment information that can be recognized by expert systems. ResultsThe results show that the proposed method solves the problem of difficult knowledge acquisition in expert systems and improves inference efficiency while ensuring inference accuracy, thereby achieving the online fault diagnosis function of expert systems. ConclusionUsing the proposed method can enhance the knowledge acquisition ability and inference efficiency of expert systems, which is of great significance for ensuring the operational management safety of nuclear power plants.

Abstract Background Menopause is a period of women’s life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women’s related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the “Healthy Menopause” expert system on the management of menopausal symptoms. Methods/design A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women’s experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts’ opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves. Discussion This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system “Healthy Menopause”. This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner.

Background Nowadays, financial institutions face and solve challenges to optimise Information Management (IM), so the use of new technologies such as Expert Systems (ES) is indispensable. Therefore, the objective of this research is to implement an ES to improve IM in insurance companies. Method In terms of approach, the agile methodology SCRUM was chosen, which consists of five phases: initiation, planning and estimation, implementation, review and retrospective, launch, and thus decision making and optimisation of the IM process. In addition, the following technologies were chosen: ASP.NET as programming language, HTML as markup language, SQL Server as database management and CSS for design and visual styling. Results The results also showed a significant increase of 35% in user service, accompanied by a significant improvement of 44% in report delivery. Finally, a significant improvement of 24% was observed following the implementation of the expert system. This streamlines processes, reduces waiting times, improves the user experience in real time and optimises the management of large volumes of data in the insurance company. Conclusions The system demonstrated that this tool improves decision making, reduces errors in the issuing area and provides a user-friendly interface for information management.

The Pharma-safe Index Dataset comprises comprehensive information on several aspects of pharmaceuticals, including general medication data, usage guidelines, dosage, adverse effects, pricing, drug interactions, duration of use, composition, and contraindications. The dataset is provided in both CSV and JSON file formats, and it is available in both English and Bahasa Indonesia. By conducting interviews, distributing flyers, and using pharmacy books, the dataset was collected from over-the-counter medications that were sold at three pharmacies located in Yogyakarta, Indonesia. A medical professional performed cleansing, standardization, and validation on it before it was exported to JSON and CSV formats. The data collected on drug efficacy, safety, and patient outcomes in Indonesia can be utilized by researchers in order to uncover trends and developing patterns of prescription drug resistance. It is possible that this will direct future research, lead to improvements in drug formulations, treatment strategies, and public health policies, and expand our understanding of how drugs work and how they affect patient health.

In this work, we enhance the sustainability of an enterprise network (EN) by complementing it with an expert system that apprehends the explore‐exploit behavioural intelligence of Physarum to survive against the attractive‐adversarial nutritional environment. EN sustainability is dynamic since it depends on how well EN can react to an adversarial environment. We capture a reverse analogy to characterize EN's workload‐environment with Physarum's nutritive‐environment, where the high volume of workloads at the backbone network corresponds to a poor‐nutrient environment. The expert system explores EN to find out how to manage the workloads as Physarum handles its survivability, and exploits the users' workload patterns by grouping the highly communicating users together to redesign the network structure as Physarum's intelligence to exploit energy from rich‐ and poor‐nutrient food sources through redesigned tubular structures. We define two factors, such as nutrient‐intensity and chemo‐attractant to aid the redesign process. EN evolves through a set of redesigned clusters with an objective function to maximize its sustainability for a given set of explored workloads by minimizing the workloads through the backbone. EN evolution terminates when there is no change in the backbone utilization, resembling the organism's stay in a dormant state until it experiences a favourable environment. Our experimental results on an EN with a higher volume of workloads at the backbone producing 14.26 kWh energy consumption demonstrated that the developed expert system reduced the energy consumption to 11.27 kWh, thus enhanced the sustainability from 21% to 61%. [ABSTRACT FROM AUTHOR]

This study aims to measure the quality of distance university higher education in the Peruvian department of Junín, as well as to seek strategies of improvement. To meet this objective, a survey was carried out on students from the 4 universities located in this department, all with an institutional license. An intentional non-probabilistic sampling was used. The quality variable in university higher education is described in nine dimensions derived from the educational policies imposed by the National Superintendency of Higher University Education (SUNEDU in Spanish), which are: academic objectives, educational offer, infrastructure and equipment, research lines, qualified teacher, complementary services, job bank, transparency, and satisfaction. Among the results, it stands out that 59% of the students on average show satisfaction with the education received, which allows us to conclude that quality should be directed by a unit with its own name and established transversally in all administrative, academic, and academic procedures, investigative, social projection, and university extension. Transparency avoids any act of corruption, therefore it is important to associate it with the ethics of the authorities and workers of the university entity, strengthening timely communication with the student body. On the other hand, Kosko's Hidden Patterns Method was used to process a fuzzy cognitive map designed by education quality experts' opinions and in this way conclusions were reached about the strategies to follow for improving the quality of Peruvian non-face-to-face education in the department. [ABSTRACT FROM AUTHOR]

Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell "events". Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human experts. However, such systems face one central challenge: their decisions have far-reaching consequences for the health and lives of people. Therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable to human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison with a selection of state-of-the-art XAI systems shows that ALPODS operates efficiently on known benchmark data and on everyday routine case data. [ABSTRACT FROM AUTHOR]

Train operation monitoring device (LKJ) is one of the most widely used on-board equipment in Chinese current ordinary speed railway. With the continuous improvement of railway transportation demand, as the key equipment to ensure traffic safety and improve operation efficiency, LKJ equipment is becoming more and more important. In order to solve the problems such as heavy workload, low efficiency and difficulty in finding the fault in the manual test process of LKJ equipment, an LKJ automatic test platform is designed and key technologies are studied. The automatic test platform fulfills optimization based on the traditional manual test process, generates test cases in a graphical manner, designs signal adaptation units to simulate the operating conditions of the equipment, adds humancomputer interaction units to simplify the test operation, automatically executes the test based on the Robot Framework automatic test framework, calls on fault analysis expert system to give the cause of the fault, so as to realize the unattended automatic test. Through the verification of LKJ-15 equipment and laboratory simulation environment, the verification results show that the automatic test platform can replace some manual operations in the traditional simulation test, provide guidance for the analysis of fault phenomena, and effectively improve the test efficiency. [ABSTRACT FROM AUTHOR]

Since organic rice storage silos were faced with an insect problem, an owner solved this problem using the expert system (ES) in the controlled atmosphere process (CAP) under the required standard, fumigating insects with an N2, reducing O2 concentration to less than 2% for 21 days. This article presents the computational fluid dynamics (CFD) assisted ES successfully solved this problem. First, CFD was employed to determine the gas flow pattern, O2 concentration, proper operating conditions, and a correction factor (K) of silos. As expected, CFD results were consistent with the experimental results and theory, assuring the CFD's credibility. Significantly, CFD results revealed that the ES controlled N2 distribution throughout the silos and effectively reduced O2 concentration to meet the requirement. Next, the ES was developed based on the inference engine assisted by CFD results and the sweep-through purging principle, and it was implemented in the CAP. Last, the experiments evaluated CAP's efficacy in controlling O2 concentration and insect extermination in the actual silos. The experimental results and owner's feedback confirmed the excellent efficacy of ES implementation; therefore, the CAP is effective and practical. The novel aspect of this research is a CFD methodology to create the inference engine and the ES. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent speed adaption system for vehicles on conventional roads. The fuzzy logic based expert system outputs a recommended speed to ensure both safety and passenger comfort. This intelligent system includes geometrical features of the road, as well as subjective perceptions of the drivers. It has been developed and checked with real data that were measured with an instrumental system incorporated in a vehicle, on several two‐lane roads located in the Madrid Region, Spain. Along with the road geometrical characteristics, other input variables to the system are external factors, such as weather conditions, distance to the preceding vehicle, tire pressure, and other subjective criteria, such as the desired comfort level, selected by the driver. The expert system output is the most suitable speed for the specific road type, considering real factors that may modify the category of the road and thus, the appropriate speed. This information could be added to the adaptive cruise control of the vehicle. The recommended speed can be a very useful input for both, drivers and the autonomous vehicles, to improve safety on the road system. [ABSTRACT FROM AUTHOR]

Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model's enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system. [ABSTRACT FROM AUTHOR]

Background: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women's related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the "Healthy Menopause" expert system on the management of menopausal symptoms. Methods/design: A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women's experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts' opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves. Discussion: This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system "Healthy Menopause". This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner. Plain English summary: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, easy for use and affordable way for managing related problems and meeting related needs is important. Menopause is a period of women's life that has physical, psychological and social consequences. It is important to identify methods that are effective, practical and affordable. New technologies can increase women's ability to access educational information. This is the first study for designing, validating and evaluating of the expert system "Healthy Menopause". A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase (qualitative): The conventional content analysis method will be used. The second phase: Designing a healthy menopause expert system: It is based on the codes of women's challenges from the first phase, along with conducting interviews and literature review. The participatory approach (Participatory Design) through nominal group or if needed, Delphi method based on the categories of needs and solutions by considering the opinions of the participants, available experts related to this issue will be listed. It should be used to design a healthy menopause expert system at this stage. The third phase (quantitative): The evaluation of the healthy menopause expert system will be a randomized clinical trial that determine the effect of the healthy menopause expert system on the management of menopause symptoms. In the present study an expert system (ES) will be designed that can be installed on mobile phones and computers. This tool is not only educational but also interactively helps to adapt to continuous changes, so by asking questions about menopause the system will respond as if an expert (midwife or gynecologist) is giving advice. [ABSTRACT FROM AUTHOR]

This article discusses the development of an intelligent-based job applicants' assessment and recruitment system using artificial intelligence techniques. The system aims to automate the process of sorting resumes, assessing applicants, and notifying them of their status. The study highlights the advantages of using this system, such as speeding up the staffing process, automating tasks, and improving accuracy in matching applicants with the right jobs. The article also mentions the application of artificial intelligence in recruitment and the potential benefits and risks associated with it. Overall, the system offers a more efficient and cost-effective approach to recruitment, but it can still be upgraded in the future. [Extracted from the article]

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Addressing the challenges in diagnosing and classifying self-care difficulties in exceptional children's healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry. [ABSTRACT FROM AUTHOR]

This research is mostly about Guillain-Barre syndrome (GBS), a complicated neurological condition with many subtypes that make diagnosis and treatment hard, even though medical care is always getting better. The main goal of this study is to build and test an expert system that can correctly diagnose these subtypes, with a focus on early detection and personalized treatments. The evaluation of the system was carried out using a dataset composed of 20 cases (12 positive and 8 negative). A confusion matrix was used to evaluate key metrics such as precision, sensitivity, and specificity. The findings demonstrate precision and sensitivity of 83% and specificity of 75%. These findings unambiguously demonstrate the efficacy of the system in correctly identifying positive Guillain-Barre cases while substantially reducing false negatives. In conclusion, this expert system offers a potentially useful tool to improve the accuracy of the diagnosis and treatment of Guillain-Barre patients. However, to take advantage of its full potential in clinical practice, it should be used as diagnostic support and not replace the medical staff, and it should be updated periodically to reflect new findings in medicine. [ABSTRACT FROM AUTHOR]

X Balikpapan merupakan usaha yang bergerak di bidang produksi furniture. Berdasarkan data cacat produk pada Tahun 2022. Diketahui terdapat rata-rata gap antara persentase jumlah cacat produk yaitu 13,8 % dengan standar cacat produk yang telah ditetapkan pada sofa sebesar 10%. Tujuan penelitian ini adalah untuk menentukan penyebab kegagalan yang harus ditangani dan usulan upaya penanggulangan berdasarkan metode FMEA berbasis expert system. Metode yang digunakan adalah FMEA dan Expert System yang berlandandaskan pada Kecerdasan Buatan. Metode FMEA berupa basis data dalam mengidentifikasi dan menganalisis kegagalan yang menghasilkan nilai Risk Priority Number (RPN). Metode expert system sebagai sistem komputer algoritma untuk meniru kemampuan yang ditransfer dari sebuah ahli pakar ke komputer untuk disimpan dalam memori sehingga dapat berkonsultasi dengan komputer. Hasil dari penelitian ini berupa sebuah perancangan website sistem pakar berbasis metode FMEA dengan persentase keakuratan data sebesar 86,66% dari 30 data pengujian sistem berdasarkan kondisi lingkungan pada jenis kegagalan proses yang telah terjadi pada produksi sofa yang menghasilkan jenis kegagalan yang harus ditangani yaitu pada proses produksi assembly dan jenis kegagalan prosesnya yaitu plywood atau kayu pecah dengan nilai RPN paling tinggi sebesar 216 beserta rekomendasi upaya penanggulangannya adalah sebelum produk sofa dikirim kepada pelanggan, lakukan pemeriksaan akhir terhadap plywood dan kayu yang digunakan dengan cara uji ketahanan sofa dengan beban maksimal yang telah ditetapkan. Pastikan tidak ada kelemahan atau kerusakan yang dapat menyebabkan pecahnya plywood dan kayu.

Preservation of cultural relics is crucial for cultural sustainability, particularly in the case of cave temples, which are a unique and immovable part of our heritage, nested within mountains. These structures bear immense historical significance and possess considerable economic value, making them vulnerable to criminal activities, notably theft. Establishing a robust physical protection system (PPS) is imperative to safeguard these relics from potential damage. This paper proposes a novel approach for assessing the vulnerability of cave temples' PPS. Based on the fuzzy Petri net (FPN) principle, a comprehensive vulnerability assessment index system was developed within the PPS framework, considering the unique characteristics of cave temples. This study refines the formal definition of the FPN, enhancing its precision and effectiveness for vulnerability assessment. The practicality and effectiveness of the proposed method are verified through simulation experiments. An illustrative example is provided to demonstrate this approach.

Chronic Energy Deficiency in pregnancy is a condition where pregnant women suffer from a lack of food that lasts for a long time. Pregnant women with chronic energy deficiency have poor eating behavior, which is influenced by knowledge, perception, beliefs and health education. Health education provided by health workers can increase knowledge and change eating behavior and can be done better if assisted by an expert system. The aim of this activity is to train health workers (doctors, midwives, nutritionists) to carry out early detection of chronic energy deficiency in pregnancy using an expert system, increasing knowledge, attitudes and eating behavior of pregnant women. The method that will be carried out is training using the web-based expert system modelangraini.com for health workers, health education for pregnant women, and increasing knowledge and changing eating behavior of pregnant women. This activity was carried out for 1 day and took place in Faculty of Medicine, University of Lampung on Saturday 30 September 2023 with 45 family doctors, midwives and nutritionists in Bandar Lampung City participating. The results of the evaluation of the implementation of the service showed that there was an increase in participants' understanding by 91.1% to good understanding, 8.9% of participants had a fairly good understanding, and no participants had a poor understanding of the causes, risk factors, impacts, prevention methods, and early detection of chronic energy deficiency in pregnancy with techniques that are fast, easy and can help health workers in providing education and interventions on the eating behavior of pregnant women. This training activity can improve the skills of health workers in an effort to increase knowledge, attitudes and eating behavior of pregnant women so that they can prevent, overcome and help reduce the prevalence of chronic energy deficiency in pregnant women and prevent stunting in toddlers. Keywords: early detection, chronic energy deficiency, training, health workers

The control of the pH neutralization process has a decisive role in many industrial branches, as: wastewater treatment, biotechnology, pharmaceutical industry, chemical processing, etc. However, the pH neutralization process is a difficult one to control, due to time-varying process parameters, its high non-linearity and to the sudden variation of the pH around the equivalence point. The paper presents a device and an integrated application that uses artificial intelligence (IA) developed for dosage of the necessary reactants in wastewater pH control. The AI techniques were integrated into the application by developing an expert system (ES). For this purpose, were used the necessary if-then rules (rules that make up the inference engine (IE) of the ES) related to the flowrates of the reactants necessary for wastewater pH neutralization. The ES knowledge base (KB) was built based on the recorded pH values. The hardware component (the device) was created using the Arduino equipment.

Identification of inflammatory bowel disease quickly and accurately is motivated by the large number of patients who come with pain in the abdomen and receive minimal treatment because they are considered to be just ordinary abdominal pain. This study aims to identify inflammatory bowel disease which is still considered by some people as a common stomach ache quickly, and precisely and to recommend therapy that can be done as an initial treatment before getting medical action by medical personnel. The method used in this expert system research is a combination of forward chaining and certainty factors. The forward chaining method traces the disease forward starting from a set of facts adjusted to a hypothesis that leads to conclusions, while the certainty factor method is used to confirm a hypothesis by measuring the amount of trust in concluding the process of detecting inflammatory bowel disease. The results of this study are a conclusion from the process of identifying inflammatory bowel disease which begins with selecting the symptoms experienced by the patient so that the diagnosis results appear using forward chaining and certainty factor in the form of a percentage along with therapy that can be given to the patient to reduce pain in the abdomen. A comparison of the diagnosis results using the system and diagnosis by experts, in this case, specialist doctors, shows an accuracy rate of 82,18%, which means that the expert system diagnosis results can be accounted for and follow the expert diagnosis.

Abstract A tropical cyclone (TC) is extreme weather severely threatening coastal wind turbines. However, the non‐extreme wind speed period (nEWSP) before and after it can also bring considerable economic benefits to wind farms. Aiming at the working conditions of nEWSP‐TC, this paper analyzes the characteristics of the wind field based on historical actual TC data and constructs a pseudo‐Monte Carlo experiment, uses the experimental results to construct a knowledge base and an inference engine, and forms an expert system to guide the yaw control of wind turbines in nEWSP‐TC. The simulation results show that the yaw error has different effects on the fatigue load of the wind turbine under different working conditions of nEWSP‐TC, and the proposed improved yaw strategy can reduce the fatigue load of the wind turbine under the premise of limited power loss and improve safe operation capability of wind turbines under nEWSP‐TC conditions.

Despite being very old, the mining industry continues to be one of the major sources of pollution, with more people killed or injured than in all other industries. Prevention of incidents/accidents on machinery in mining pits and the issues of operator safety on mining machinery largely depend on the ergonomic adaptation of the workplace, compliance with safety procedures and policies, and organizational and other influential factors. Evidently, scarce consideration of those factors in the available literature has not given satisfactory results till now. The aim of this paper is to first set up a comprehensive model based on ergonomic factors and contextual theory, which takes into account all the influencing factors on the occurrence of incidents/accidents and represents a complex system of interdependence of influential variables of diverse, mostly stochastic nature, and then design a software solution on the given basis. In this research, based on the extensive data collected, a model was generated using the structural equations modelling methodology, which was then used to design the reasoning logic in the expert system for mitigating the risks of the operation of mining machines. An innovative solution incorporating a mathematical model of the interdependence of influential variables into the stored knowledge base offers a decision support system that provides recommendations for the maintenance of a particular mining machine, depending on the assessment of model factors in a specific decision-making situation at the higher organizational level and ergonomic suitability for the operator at the lower organizational level, and, in that manner, enables the mitigating of risky/unwanted events.

Background The vocational guidance process in educational institutions faces important challenges in managing trials and errors in diagnoses. Technological tools are identified as an effective solution to address these problems. This research seeks to improve career guidance in educational institutions through the implementation of an expert system. The main objective is to reduce test processing time and achieve greater efficiency in students’ self-knowledge regarding their interests, based on the personalities of the Holland Test. Methods The development of the expert system followed a six-model approach. First, an organisational model was created to assess the scope and feasibility of the project. Next, a task and agent model was developed to investigate the impact and look for improvements. A knowledge model was then developed to analyse the relevant knowledge bases. A communication model was also developed to evaluate the communication interface of the system. Next, a design model was created to provide guidelines for the implementation of the system. Finally, the implementation of the knowledge system was carried out to ensure its correct functioning. Results The implementation of the expert system has shown significant improvements in the vocational guidance process. It was possible to reduce the time needed to apply the test, thus optimising the psychologist’s time and allowing a greater capacity for analysis. In addition, an improvement in the effectiveness of the students’ self-knowledge in relation to their vocational interests based on the personalities of the Holland Test was observed. Conclusions This study contributes to career guidance in educational institutions by introducing an innovative expert system. This technological solution optimizes the career guidance process, benefiting psychologists administering tests and students seeking self-knowledge about their career interests.

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent days the manufacturing process have become more precise and cost efficient due to advancement in the field of computer technology. Information technology has been integrated with manufacturing practice and has resulted in time reduction from concept of a product to marketing of the product. Cutting force generated is the main manufacturing issue raised among industries as it clearly affects quality and cost of the final product. Hence using extensive literature and data base knowledge optimum cutting parameters are selected. Therefore, this paper focuses on a response surface methodology (RSM) based expert system that has been developed using JAVA programming with the help of response surface second order model to automatically generate values of cutting force during machining of Ti–6Al–4V alloy under minimum quantity lubrication (MQL) for different process input parameters. From RSM it has been observed that calculated value of F (20.36) was greater than the F-table value (3.02) and hence the model developed can be effectively used for machining of Ti–6Al–4V alloy. Further the developed RSM based expert system model can be successfully used to predict the force generated during cutting process while machining Ti–6Al–4V alloy under MQL conditions. [ABSTRACT FROM AUTHOR]