Improving the quality of strategic decision-making process in universities through employing expert systems: A case study from a developing country

Strategic decisions represent the fundamental core of the strategic planning process and strategic management in universities and they are essential in shaping the universities' policies and achieving their strategic goals. Without those strategic decisions, the universities stand unable to achieve their strategic goals and mission; therefore, specialists realized the critical importance of improving the quality of strategic decision-making in the current complex fast-changing environment that its dynamism continuously increases and which is based on the use of cutting-edge information and communications technology (ICT). Undoubtedly strategic decision-making process requires processing a huge amount of information with different robust smart methods and the extensive use of experts knowledge. There are many discussions about the uses and applications of expert systems (ESs), which are evolving rapidly in solving real problems in many fields that require experienced experts with deep sound experiences, and despite these many applications in many different fields and domains. Literature reveals that there is a scarcity of scientific research on how to employ expert systems to raise the quality of strategic decision-making processes in universities. Thus the purpose of the research is to fill this research gap by investigating how expert systems will enhance the quality of the strategic decision-making process in universities. The research design is a case study applied in Ain Shams University as a model of public universities in a developing country. This research makes a new research contribution by suggesting a futuristic proposal for improving the quality of the strategic decision-making process in universities through employing expert systems that are based on the theoretical framework of the research and the results of the field study.

TECHNOLOGIES OF EXPERT SYSTEMSCONSTRUCTION FOR ELECTRIC POWER

The technologies of construction diagnostic expert systems is considered. The strategy of the knowledge base structuring by diagnostic expert system is described

Beyond RoboDebt

Automated decision support systems with high stake decision processes are frequently controversial. The Online Compliance Intervention (herewith “OCI” or “RoboDebt”) is a system of compliance implemented with the intention to facilitate automatic issuance of statutory debt notices to individuals, taking a receipt of welfare payments and exceeding their entitlement. The system appears to employ rudimentary data scraping and expert systems to determine whether notices should be validly issued. However, many individuals that take receipt of debt notices assert that they were issued in error. The commentary on the system has resulted in a lot of conflation of the system with other system types and caused many to question the role of decision of support systems in public administration given the potentially deleterious impacts of such systems for the most vulnerable. The authors employ a taxonomy of Robotic Process Automation (RPA) issues, to review the OCI and RPA more generally. This paper identifies potential problems of bias, inconsistency, procedural fairness, and overall systematic error. This research also considers a series of RoboDebt specific issues regarding contractor arrangements and the potential impact of the system for Australia's Indigenous population. The authors offer a set of recommendations based on the observed challenges, emphasizing the importance of moderation, independent algorithmic audits, and ongoing reviews. Most notably, this paper emphasizes the need for greater transparency and a broadening of criteria to determine vulnerability that encompasses, temporal, geographic, and technological considerations.

The development of oil companies in the light of the evolution of digital platforms

In the article, the authors consider the formation and development of domestic digital platforms for the country's oil companies based on artificial intelligence. A brief analysis of the modern legal framework, which determines the priorities for the formation of the digital economy, is given, and the economic essence of the conceptual apparatus describing AI is revealed. Variants of practical application of expert systems of artificial intelligence are proposed.

Implementation of Hardware-Based Expert Systems and Comparison of Their Performance to Software-Based Expert Systems

Expert systems are a form of highly understandable artificial intelligence that allow humans to trace the decision-making processes that are used. While they are typically software implemented and use an iterative algorithm for rule-fact network processing, this is not the only possible implementation approach. This paper implements and evaluates the use of hardware-based expert systems. It shows that they work accurately and can be developed to parallel software implementations. It also compares the processing speed of software and hardware-based expert systems, showing that hardware-based systems typically operate two orders of magnitude faster than the software ones. The potential applications that hardware-based expert systems can be used for and the capabilities that they can provide are discussed.

Neurules and connectionist expert systems: Unexplored neuro-symbolic reasoning aspects

Neuro-symbolic approaches combine neural and symbolic methods. This paper explores aspects regarding the reasoning mechanisms of two neuro-symbolic approaches, that is, neurules and connectionist expert systems. Both provide reasoning and explanation facilities. Neurules are a type of neuro-symbolic rules tightly integrating the neural and symbolic components, giving pre-eminence to the symbolic component. Connectionist expert systems give pre-eminence to the connectionist component. This paper explores reasoning aspects about neurules and connectionist expert systems that have not been previously addressed. As far as neurules are concerned, an aspect playing a role in conflict resolution (i.e., order of neurules) is explored. Experimental results show an improvement in reasoning efficiency. As far as connectionist expert systems are concerned, variations of the reasoning mechanism are explored. Experimental results are presented for them as well showing that one of the variations generally performs better than the others.

Fuzzy logical conclusions and conclusions in expert systems of medical diagnostics

The main problems in making a correct diagnosis are: subjectivity and insufficient qualifications of the doctor, difficulties in correctly assessing the patient’s complaints, signs and symptoms of the disease observed in the patient, as well as individual manifestations of the symptoms of the disease. In publications on the use of expert systems for medical diagnostics using fuzzy logic, the main attention was paid to the medical features of the problem. In this work, for the first time, general methodological aspects of building such systems, creating databases, representing by fuzzy sets of real numbers, digital scales, linguistic and Boolean data of symptom values are formulated. The types of membership functions that are advisable to use to represent the symptoms of diseases are proposed. In fuzzy-logical conclusions, not only the values of the characteristic functions of the logical terms of individual symptoms, but also complex arithmetic functions of their values are used.