Artificial Intelligence Expert Systems Research Articles

The Impact of Applying Artificial Intelligence on Human Resources Management in Jordanian Banks

The study aims to investigate artificial intelligence (expert systems, knowledge representation and reasoning, automatic learning and effectiveness) on human resources management in Jordanian banks. The descriptive analytical method was used by developing a digital questionnaire that was distributed to a purposive sample of the study population, which includes employees of directors of human resources departments and workers in the human resources department. To answer the study’s questions and hypotheses, the data was analyzed using the SPSS program. It was found that artificial intelligence variables (expert systems, knowledge representation and reasoning, automatic learning, and effectiveness) are associated with a positive, statistically significant relationship with training and development. ? It was also found that artificial intelligence variables (expert systems, knowledge representation and reasoning, automatic learning, and effectiveness) are associated with a positive, statistically significant relationship with selection and appointment. Finally, the results concluded that artificial intelligence explains a large portion of the variance in incentives, which indicates a positive and statistically significant relationship. The study recommended that the expanding the use of artificial intelligence techniques in ?human ?resources ?management?, and conduct more future studies about the relationship between the use of artificial intelligence technologies and other human resources practices such as: (performance evaluation, wages and compensation, talent management). Received: 11 July 2024 / Accepted: 31 October 2024 / Published: 05 November 2024

The role of AI and emerging technologies in global trade compliance

Artificial intelligence (AI) in global trade compliance is evolving rapidly, notably in classification, with the potential to unlock productivity, expand expertise and improve speed, reliability and accuracy. Due to its potential, AI is met with high expectations and sometimes apprehension, as there is a lot of uncertainty about what constitutes AI and how it will affect global trade stakeholders. The aim of this paper is to demonstrate how AI will likely affect customs agencies, importers/exporters, service providers and compliance managers. By using examples and case studies, the paper explains: 1) the role of AI expert systems (ES) that use machine learning (ML) and natural language processing (NLP); 2) AI’s potential and limitations in sourcing compliance knowledge for streamlining and automating global trade compliance activities; 3) the importance of upskilling compliance managers and compliance teams to successfully deploy AI, mitigate risks and better manage the global trade compliance process; and 4) our six steps for successfully implementing AI in your global trade compliance department. You will learn how AI can be valuable in performing your job and meeting your goals, if it is kept within its range of capabilities. By such measures, you will be able to obtain the advantages presented by the use of AI, while mitigating its very present risks, some of which are known and many of which are unknown.

Algorithm for the classification of phases and stages of sleep in patients with chronic disorders of consciousness based on logical artificial intelligence

BACKGROUND: The analysis of sleep patterns in patients with chronic disorders of consciousness is attracting increasing attention in terms of diagnosis, prognosis, and treatment of severe brain damage. The study describes a software package based on an artificial intelligence (AI) expert system designed to classify the phases and stages of sleep, taking into account the characteristics of impaired cortical rhythm in such patients.
 AIM: To develop a specialized AI-based software package focused on patients with chronic impairment of consciousness for automatic classification of sleep phases and stages, with an emphasis on identifying sleep spindles and non-rapid eye movement (REM) and REM sleep phases.
 MATERIALS AND METHODS: To ensure the correct operation of the software package, receiver operating characteristic (ROC) curves were analyzed considering the binary classification of slow sleep, REM sleep, and wakefulness.
 RESULTS: The average sensitivity and specificity of the algorithm were 87.9 and 70.1, respectively. The average area under the ROC curve was 0.790. The algorithm for determining the REM phase demonstrates low specificity with high sensitivity, and its graph was similar to that of wakefulness, as well as the irregularity of the presence of REMs in the REM sleep phase in patients with CNS and the frequent presence of nystagmus in the waking state. Information about the presence of nystagmus, entered at the start of the program, allowed us to slightly increase the efficiency of the algorithm; however, this aspect probably needs further improvement.
 CONCLUSION: A software package that takes into account the features of electroencephalography of patients with chronic disorders of consciousness and analyzes sleep and wakefulness automatically is not only useful as a diagnostic tool for neurologists and somnologists but also contributes to a wider dissemination of this technique in clinical practice.

Design of Artificial Intelligence-Based English Network Teaching (AI-ENT) System

The English teaching network system uses the Internet to provide distance education. There are several challenges in teaching, including a lack of knowledge and expertise, expectations from students, lack of facilities, and unfavourable opinions about the process of English teaching and learning. In the era of information, education combines the advantages of both online and offline learning, including conventional and networked learning. The production of test pattern paper is an important part of the English teaching network since it helps students study independently. In this study, the artificial intelligence-based English network teaching (AI-ENT) method has been suggested to enhance the student’s performance in distance education. There are strong indicators that the methods of teaching and learning and the teaching tools used by machine learning can be fundamentally altered by AI. With AI technology tools and teacher coaching, students can make significant progress in their English learning experiences. Artificial intelligence expert system thinking is reflected in this design. Teachers and students can improve their English proficiency by gaining access to relevant data from a wide range of sources. The system’s test application reveals that it can assist students in increasing their learning efficiency and making learning information more relevant.

Diagnostic Value of Specialist Systems in Sports Knee Injuries.

In order to explore the diagnostic effect of the expert system in knee sports injury, a method of diagnostic value of expert system in sports knee sports injury is proposed. This paper mainly takes 200 professional football players in sports higher vocational colleges as the research object. There are 146 male athletes and 44 female athletes; we establish a football injury ontology knowledge base and use a reasoning engine to build an intelligent expert system diagnosis system, allowing users to quickly discover diseases, accurately diagnose injuries, and obtain the best means of rehabilitation. Through the investigation, it can be seen that the body parts caused by football injuries are more complex, and the types of injuries in each part are also different. Therefore, it is particularly important to establish an intelligent retrieval system with convenient query and clear diagnosis by the expert system. With the birth and development of computer and artificial intelligence technology, the development of artificial intelligence expert systems in the medical field has become a reality. The construction of this system will have theoretical and practical significance and application value.

Innovative Research on English Teaching Model Based on Artificial Intelligence and Wireless Communication

It is a network system for teaching English through a wireless communication (WC) premised distance teaching system. This is a process of education that is capable of encouraging students’ concerns to acquire knowledge voluntarily. The paper is designed to develop and implement an online intelligent English training system using artificial intelligence (AI) that helps students improve their English learning efficiency in line with knowledge and personality. The system’s numerous sensor nodes may create a variety of topologies. The gathered information is transmitted over the global system for mobile communication (GSM) network to the user interface. The operator can manage the remote sensor node via the GSM network. Nevertheless, there are certain derivative aspects such as the absence of verbal judgment, the actual evaluation and signaling system, the interactive educational platform teachers and learners need. The paper is based on the above issues. It contains a whole talk-based system where teachers, students, and English teaching can be revised together — AIWC (ET-AIWC) systems are designed to improve and advance the genetic algorithm based on an encoding technique for dynamic parameter adjustment of the iterative process based on these problems. In combination with an AI expert system, suitable learning techniques were created to enable students to double the learning effect by half the amount of work. An online teaching assistant system was designed to monitor, regulate, and engage with students throughout the learning process and a modified scoring system that provides real-time evaluation of student speakers to improve students’ oral competence in English better and more efficiently, achieving 95.2%.

Application of Artificial Intelligence System Based on Wireless Sensor Network in Enterprise Management.

With the improvement of the ability to acquire natural information, wireless sensor networks also need to transmit corresponding information in terms of collecting information. Wireless sensor nodes have great application prospects as a key component of wireless sensors. Therefore, different wireless sensors play an important decisive role in the operation of wireless network applications. With the continuous development of wireless sensor networks, existing wireless sensor network nodes exhibit limitations and shortcomings such as inflexible structure, low variability, and low versatility. Specifically, the learning and neural networks obtained by different artificial intelligence expert systems in computing technology are different. On the one hand, it can meet the needs of users for information systems to a certain extent, and on the other hand, it can also help accelerate the development of computer science. At present, the new generation of information technology industry is listed in the seven emerging strategic industries of the country. The new cloud computing technology has gradually expanded to important corporate governance capabilities in terms of information technology. The intelligent application of cloud computing technology replaces traditional enterprise management technology. Efficiency management and risk management can improve the quality and business capabilities of the entire enterprise, improve system applications according to the actual situation of the enterprise, improve system applications, and implement health and the sustainable development of the enterprise, thereby promoting the sustainable development of the computer technology industry.

The development of artificial intelligence in the digital economy

In the article, the author highlights the development of the country's digital economy based on the use of artificial intelligence (AI), general artificial intelligence. The growth potential of the high-tech sector in the context of economic sanctions is shown. An analysis of the modern legal framework is given, which determines the strategy for the implementation of AI, and the economic essence of the conceptual apparatus that describes various types of artificial intelligence is revealed. The variants of practical application of artificial intelligence expert systems in Russia are considered.

Mapping and Visualizing Global Knowledge on Intermittent Water Supply Systems

Intermittent water supply systems (IWSSs) are prevalent in most developing countries and some developed ones. Their usage is driven by necessity rather than as a principal objective, mostly due to technical and economic deficiencies. Major health risks and socio-economic inequities are associated with such systems. Their impacts are aggravated by climate changes and the COVID-19 crisis. These are likely to have profound implications on progress toward advancing sustainable development goals (SDGs). Motivated by providing a comprehensive overview of global knowledge on IWSSs, the present work proposed to track and analyze research works on IWSSs utilizing bibliometric techniques and visual mapping tools. This includes investigating the trends and growth trajectories of research works on IWSSs and analyzing the various approaches proposed to expand our understanding with respect to the management, modeling, optimization, and impacts of IWSSs. The national and international contributions and collaboration figures are further analyzed at country, institution, author, and source levels. This analysis indicates that research works conducted on IWSSs have certain expectations in terms of productivity (total global productivity; 197 documents). The United States was the best country in terms of productivity (58 documents; 29.4%), while the Water Switzerland journal was the most productive journal (19 documents; 9.6%). The impacts of IWSSs on health and well-being have attracted considerable attention. The outcomes showed deep and justified worries in relation to the transition from intermittent to continuous supply, equity, and mitigating the health risks associated with IWSSs in the foreseen future. The utilization of artificial intelligence techniques and expert systems will drive and shape future IWSS-related research activities. Therefore, investments in this regard are crucial.

The Optimization Method of Mechanical Fault Diagnosis Based on Artificial Intelligence Technology

In order to overcome the problems existing in the traditional mechanical fault diagnosis, and to improve the accuracy of mechanical fault diagnosis, an optimization method of mechanical fault diagnosis based on artificial intelligence technology is proposed in this paper. The optimization method combines a variety of current popular high and new technologies, such as artificial intelligence technology, expert system and artificial neural network technology. at the same time, the optimization method also breaks the traditional mechanical fault diagnosis mode based on fuzzy set theory. Based on this, the optimization method also makes an in-depth analysis and research on artificial intelligence technology, which promotes the application and development of artificial intelligence technology, and expands the application scope and application field of artificial intelligence technology. The research results show that the optimization method can scientifically overcome the problems existing in the traditional mechanical fault diagnosis, and can improve the accuracy and scientificalness in the process of mechanical fault diagnosis, so it has strong feasibility.

Empowering the public during the COVID pandemic through interactive social media platform

During the COVID-19 pandemic, healthcare professionals and academic facilities are called to provide leadership in disseminating accurate and timely information through approaches that meet the needs of the public. Graduate students from a university in Taiwan collaborated with experts to provide interactive live broadcasting sessions on the COVID-related topics to the public through the Facebook platform. The broadcasting sessions also trained the students to communicate COVID-related information through succinct and interactive presentations. Twelve broadcasting sessions were conducted twice a week for three weeks in May 2020. Upon completion of the broadcasting sessions, students demonstrated growth in professional confidence, assessment of the public's knowledge gaps and needs, and preparation and delivery of professional live broadcasts. We recommend creating a live broadcast training application through an artificial intelligence (AI) expert system. Multidisciplinary academic-practice collaboration in preparing for the broadcasting and engaging in dialogues with the public is recommended.

Estimation of adhesive wear behavior of the glass fiber reinforced polyester composite materials using ANFIS model

Glass fiber reinforced polyester (GFRP) composite materials are widely used in various applications. The prediction of wear values for composite materials is very complex and nonlinear phenomena. Artificial intelligence methods (AI) and expert systems such as artificial neural networks (ANNs) and fuzzy inference systems (FIS) have a series of properties on modeling nonlinear systems. In some situations, ANNs are insufficient under abrupt changes in input variables. Adaptive Neuro Fuzzy Inference System (ANFIS) is capable of integrating the linguistic expressions of FIS with the adaptation and learning skills of the ANNs. The aim of this study is to determine the optimum material content and working conditions in terms of wear resistance. This study proposes an ANFIS sub-clustering based prediction model for estimation of wear behavior of GFRP composites within various concentrations of materials and under diverse loads and speeds. Proposed ANFIS model extracted optimum concentrations and operating parameters to obtain the minimum wear rate. Due to the wear rate estimation model, optimum wear rate value is reached to 25.0013 (mm3/Nm)*10−6 at CaCO3, polystyrene, glass fiber, glass bead, alumina, load and speed values of 49%, 0%, 11%, 10%, 0.8%, 10 N and 100 rpm respectively. A high estimation capability (R2 = 0.964) has been achieved using ANFIS Model.

An artificial intelligence based referral application to optimize orthodontic referrals in a public oral healthcare system

An artificial intelligence expert system called the Computational Formulation of Orthodontic referral Decisions (CFOD) was created in order to provide decision-making for orthodontic diagnosis and referral in the Emirates public oral healthcare system. Purpose: The study purposes were to validate and implement the CFOD system, and to quantify GP and pediatric dentist referral patterns to the orthodontist specialist before and after implementation of the CFOD system. Materials & Methods: The CFOD system was created using eight mandatory malocclusion variables and a technology and programming stack including PHP, Codelgniter, MySQL and WampServer. The CFOD system was implemented and clinical dentists were trained following validation by 15 experienced orthodontist experts. Pre- and post CFOD system orthodontic referral patterns were analyzed. Results: Seven of eight mandatory malocclusion variables (except crowding) were directly correlated (r = 1) with orthodontist expert evaluation which validated the CFOD system. A comparison of referral patterns of orthodontic patients before and after implementation and training of the CFOD system demonstrated a 10-fold increase for GP referrals and 2-fold for pediatric dentist referrals. Conclusions: An artificial intelligence expert system for orthodontic patient referral can improve the efficiency of a large public oral healthcare system such as found in the United Arab Emirates.

Research on intelligent Diagnosis method of Electric vehicle Charging Fault based on artificial intelligence expert system

To realize charging electric vehicle safety, this paper studies the latest artificial intelligence learning algorithm, combined with the characteristics in the process of electric vehicle charging, based on the deep learning algorithm of charge-discharge failure correlation method, combining with the characteristics of the expert system, design of electric vehicle charging process of the fault intelligent diagnosis system structure, characteristic information database, knowledge base, reasoning machine, and for battery, charging pile, power supply equipment failure correlation between the integration of online diagnosis, which can effectively improve the electric car battery safety, promote the healthy development of electric vehicles.

Design of online intelligent English teaching platform based on artificial intelligence techniques

AbstractArtificial intelligence education (AIEd) is defined in the field of education as the utilization of artificial intelligence. There are currently many AIEd‐driven applications in schools and universities. This paper applies an artificial intelligence module combined with the knowledge recommendation to the system and develops an online English teaching system in comparison with the common teaching auxiliary system. The method of English teaching is useful in investigating the potential internal connections between evaluation outcomes and various factors. This article develops deep learning‐assisted online intelligent English teaching system that utilizes to create a modern tool platform to help students improve their English language teaching efficiency in line with their mastery of knowledge and personality. The decision tree algorithm and neural networks have been used and to generate an English teaching assessment implementation model based on decision tree technologies. It provides valuable data from extensive information, summarizes rules and data, and helps teachers to improve their education and the English scores of students. This system reflects the thinking of the artificial intelligence expert system. Test application demonstrates that the system can help students improve their learning efficiency and will make learning content more relevant. Besides, the system provides an example model with similar methods and has a referential definition.

Evaluation of Block Chain and Artificial Intelligence using SPSS Statistics Analysis

Block chain is a peer-to-peer network of all transactions in is a decentralized ledger. This generation By using, individuals have a mandatory settlement authority Transactions can be confirmed without needing to Possible applications might also consist of monetary transactions, settlement trades, voting, and lots of other troubles. Block chain Defined: Block chain is a shared, immutable ledger is, in an enterprise network Recording of transactions and assets Simplifies the monitoring process An asset can be tangible (a house, car, money land) or intangible (hyper properties, patents, copyrights, branding). Bit coin and Ethereum are popular examples of block chains. Everyone is authorized to connect with the block chain and transact on it. Most intelligence procedures by Using machines, especially computers systems. In particular sets of AI Expert systems, herbal language Includes processing, speech recognition and gadget imaginative and prescient. Unlike trendy databases that store records in a centralized, relational desk, block chain is an open, peer-to-peer (P2P) network that supports communal hobby as opposed to a centralized controlling entity. In block chain, facts is accumulated in organizations known as blocks Artificial Intelligence (AI) allows machines to Research from Experience, New Adaptation to inputs and Performing human duties Most AI examples you listen approximately these days From chess-gambling computers to self-driving deep knowledge – up to cars acquisition and natural language Highly dependent on processing.. The selections AI takes at each step are determined by using previously accumulated data and a selected set of rules. If planned properly, those errors may be reduced to 0.

Artificial Intelligence in Indian Irrigation

Agriculture Automation is the major concern for India owing to the agricultural complexities resulting in decreased production of the crop and farmers commit suicide due to the heavy loss incurred . India is considered to be an agricultural country where farming and agriculture is the main occupation throughout. With the increase of population, the food demand is ought to increase manifold, thereby, the need of advanced agricultural systems has been elevated. It is imperative to apply artificial intelligence in irrigation process to increase the production. Various implementations have been made with the help of Artificial Intelligence and other Expert Systems. This research paper focuses on the use of Artificial Intelligence in Indian Irrigation. Further it focusses on the blending of embedded technology and Artificial Intelligence in agricultural sector. There are some areas which create hindrance in the agricultural fields like storage management, water management, control of pesticides and fertilizers, crop diseases, and lack of irrigation. These are some of the major problems that are faced by the Indian agricultural sector. India is facing a lot of issues of availability of water for irrigation and government is working for the same in order to implement automation in agricultural sector. Agricultural Automation concentrating more on efficient irrigation can be achieved by making use of the latest technologies like Artificial Intelligence and Machine Learning. Arduino and Raspberry devices can be embedded with moisture and temperature sensors along with the help of Machine Learning algorithms to attain advancement in irrigation. The world is getting advanced in terms of technology and the data storage as well as management of sensors is shifted towards online cloud. This makes it easy for the users to access the data from anywhere at any moment. Automatic irrigation is a vital topic for the evapotranspiration process and the crop prediction. Evapotranspiration process is the process of water management and the hydrologic cycle to develop a sustainable irrigation method. The paper discusses the application of Artificial Intelligence and other embedded systems in the agricultural sector by keeping in mind the past breakthroughs. The problem of water usage that is faced by the farmers will be solved with the help of the smart irrigation system. It is a fully automated irrigation system that is easily accessible and very beneficial for the future of agriculture.

Artificial intelligence design research on the cyber security penetration testing of power grid enterprises

This paper introduces the key components of Artificial Intelligence (AI) expert system. It studies the main application of AI technology in network security. It deeply analyzes the network security situation of power grid enterprises, as well as the main problems in the troubleshooting and verification of vulnerability. The AI penetration test expert system is designed, and the implementation of the system is elaborated.

Guide Teaching System Based on Artificial Intelligence

To improve the development and deployment efficiency of the system, this paper combined the software system with Java and AI language Prolog to achieve the guide teaching system based on artificial intel-ligence (AI). The system creatively adopted the theory of artificial intelligence expert system, at the same time, built a Struts+Spring+Hibernate lightweight JavaEE framework. The coupling degree of each module in the system was greatly reduced to facilitate the expansion of future functions. Based on the development principle of the artificial intelligence expert system, the system diagnosed the learner's mastery of each point of knowledge. It classified students' learning effect and evaluated the knowledge points. Making full use of the learning state of students and combining it with artificial intelligence expert system theory, the system developed a suitable learning strategy to help students improve their learning with less efforts. In addition, the system took the forgetting rule of human brain into account, which periodically presented trainees’ knowledge points assessment and avoided students wasting time. The purpose was to help students improve their learning effect. Finally, the system was tested. The test results showed that the system is applicable and useful. It is concluded that the artificial intelligence system provides an example for the same method and has certain reference significance.

Analysis of artificial intelligence expert systems for power transformer condition monitoring and diagnostics

A large amount of data is generated through monitoring, maintenance, repair and diagnostics of power transformer. However, all these data cannot preindicate the exact type and probability of failure. To overcome the problem this paper presents artificial intelligence based methodology for power transformers fault detection and classification. The possibility of presented monitoring methodology is to assist the operator’s engineers in decision making about urgency of intervention and type of maintenance of power transformer. The article analyzes the application of Mamdani-model and Sugeno-model in fuzzy expert system for fault diagnosis based on the current state of the power transformer. Paper presents two case studies with one unique and five separate controllers. In the first case inputs of controller are results of on-line and off-line transformer tests: age, the overheating temperature of the hot spot, frequency response analysis, temperature of insulation, dissolved gas-in-oil analysis, tgδ and polarization index. Second case study in addition to the existing inputs includes previous measurements. A fuzzy controller (FC) is designed to characterize the operating condition and to determine the urgency of intervention with possibility to indicate probability of specific type of failure. Cumulative probability of occurrence of the faults is also observed in second case study. FCs are tested based on real measurements from Serbian transmission system. The results show acceptable effectiveness in detecting different faults and might serve as a good orientation in the power transformer condition monitoring.