scholarly journals Towards understanding of artificial intelligence in accounting profession

2021 ◽  
pp. 1-5
Author(s):  
Aditya Prasad Sahoo Sahoo ◽  
Dr Yajnya Dutta Nayak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intelligent Systems ◽  
Feature Detection ◽  
Expert Knowledge ◽  
Object Identification ◽  
Extensive Experience ◽  
Efficiency And Effectiveness ◽  
Repetitive Activities

Accountants have embraced the emission of automation over many years to get better the efficiency and effectiveness of their work. But technology has not been able to replace the need for expert knowledge and decision-making. Earlier generations of ‘intelligent systems have usually demonstrated the progressing power of human expertise and the restrictions of machines. In the upcoming decades, intelligent systems must take over more and better decision-making tasks from humans. While accountant has been using technology for a lot of years to improve what they do and deliver more value to businesses, this is an opportunity to reimagine and radically improve the quality of business and investment decisions which is the ultimate purpose of the profession. Accountants, as expert decision-makers, use both ways of thinking they apply their knowledge to specific situations to make reasoned decisions, although also make quick intuitive decisions based on extensive experience in their field. Today, AI is being used for image recognition, object identification, detection, classification, and automated geophysical feature detection. These are underlying tasks that once required the input of a human. Focusing on how artificial intelligence will impact accountants, AI will very soon help the organization to automate much of the routine and repetitive activities that are undertaken on a daily, weekly or annual basis. It will also help the organization to empower quick decision-making to create smart insights examine huge quantities of data with ease.

Artificial Intelligence in Aviation Industries

2020 ◽  
pp. 1-35
Author(s):  
Tetiana Shmelova ◽  
Arnold Sterenharz ◽  
Serge Dolgikh
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Unmanned Aircraft ◽  
Innovative Technology ◽  
Sociotechnical System ◽  
Air Traffic Controllers ◽  
High Degree ◽  
Difficult Situations

This chapter presents opportunities to use Artificial Intelligence (AI) in aviation and aerospace industries. The AI used an innovative technology for improving the effectiveness of building aviation systems in each stage of the lifecycle for enhancing the security of aviation systems and the characteristic ability to learn, improve, and predict difficult situations. The AI is presented in Air Navigation Sociotechnical system (ANSTS) because the activity of ANSTS, is accompanied by a high degree of risk of causing catastrophic outcomes. The operator's models of decision making in AI systems are presented such as Expert Systems, Decision Support Systems for pilots of manned and unmanned aircraft, air traffic controllers, engineers, etc. The quality of operator's decisions depends on the development and use of innovative technology of AI and related fields (Big Data, Data Mining, Multicriteria Decision Analysis, Collaboration Decision Making, Blockchain, Artificial Neural Network, etc.).

ARTIFICIAL INTELLIGENCE AND "PREDICTED JUSTICE": FOREIGN EXPERIENCE

Lex Russica ◽  
2019 ◽  
pp. 79-87
Author(s):  
P. N. Biryukov
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Relevant Information ◽  
Final Decision ◽  
Legal Information ◽  
The Right ◽  
Task Interpretation ◽  
Short Time ◽  
Analytical Tools

The paper deals with the problems of application of artificial intelligence (AI) in the field of justice. Present day environment facilitates the use of AI in law. Technology has entered the market. As a result, "predicted justice" has become possible. Once an overview of the possible future process is obtained, it is easier for the professional to complete the task-interpretation and final decision-making (negotiations, litigation). It will take a lot of work to bring AI up to this standard. Legal information should be structured to make it not only readable, but also effective for decision-making. "Predicted justice" can help both the parties to the case and the judges in structuring information, and students and teachers seeking relevant information. The development of information technology has led to increased opportunities for "predicted justice" programs. They take advantage of new digital tools. The focus is on two advantages of the programs: a) improving the quality of services provided; b) simultaneously monitoring the operational costs of the justice system. "Predicted justice" provides algorithms for analyzing a huge number of situations in a short time, allowing you to predict the outcome of a dispute or at least assess the chances of success. It helps: choose the right way of defense, the most suitable arguments, estimate the expected amount of compensation, etc. Thus, it is not about justice itself, but only about analytical tools that would make it possible to predict future decisions in disputes similar to those that have been analyzed.

Multi Criteria Decision Making Enhancing Sustainable Energy Management

2015 ◽  
Vol 805 ◽  
pp. 32-37
Author(s):  
Johannes Boehner
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Energy Efficiency ◽  
Energy Management ◽  
Expert Knowledge ◽  
Energy Costs ◽  
Multi Criteria Decision Making ◽  
Manufacturing Costs ◽  
Discrete Manufacturing ◽  
Energy Optimisation

Establishing energy management in manufacturing major challenge means to increase the energy efficiency of machinery in existing and future processes leading to both, a reduction of energy costs as well as to a reduction of the manufacturing-process-related environmental impacts. Therefore we developed a procedure to prioritize existing machinery for detailed machine examination in order to create a sustainable approach for machine operating companies to prioritise its assets for energy optimisation projects. By using fuzzy logic as method of artificial intelligence nominal and utilisation machinery data as well as inhouse expert knowledge is considered to enhance multi criteria decision making both. Applying this methodology in a series of industrial case studies in discrete manufacturing costs savings of up to 40 percent were realised.

scholarly journals Methods and intelligent systems of analysis and synthesis new technical solution

2018 ◽  
Author(s):  
Александр Андрейчиков ◽  
Aleksandr Andreychikov
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Transition Economy ◽  
Self Regulation ◽  
Technical Solution ◽  
Quality Of Information ◽  
Knowledge Processing ◽  
Heterogeneous Information ◽  
Analysis And Synthesis

This monograph describes the basic methods and intelligent digital technologies of conceptual design, which allow to solve the most important problems of structural synthesis, decision-making and forecasting in the conditions of uncertainty in the early stages of design of technical objects. Conditions of uncertainty mean the presence of incomplete, inaccurate, non-quantitative, unreliable, fuzzy information involved in solving these problems. With the development of Informatization tools, the volume of data that can be used for the synthesis, analysis, forecasting and planning of solutions significantly increases. This, in turn, requires the development of tools for presenting and processing heterogeneous information, as well as effective methods for extracting the necessary information from powerful information flows. Every year, the time allocated for decision-making decreases, and the complexity of procedures increases due to the increase in the number of participants in these processes, with an increase in the amount of information involved, with a rapid and poorly predictable change in the conditions in which the formulation and solution of tasks, as well as the implementation of decisions. In a rapidly changing environment, traditional technologies for analyzing, synthesizing and predicting solutions are not always acceptable, mainly due to time constraints and insufficient quantity and quality of information. All this makes it necessary to obtain forecasts of possible changes in the environment of design tasks and to develop methods for assessing the consequences of decisions. The problem of planning solutions in crisis and unstable situations, which are typical for Russia in recent years, is of particular importance, because in the conditions of the transition economy, the mechanisms of self-regulation either do not work, or work extremely inefficiently, since the processes are unbalanced. Therefore, it is important to identify the types of tasks that occur in such situations and to develop approaches to their solution using information systems. Analysis of methods and computer systems used for the synthesis, prediction and decision-making leads to the conclusion that the most promising are the methods and systems based on the technology of knowledge processing.

Diabetes Diagnosis System Based on Support Vector Machines Trained by Vortex Optimization Algorithm

2018 ◽  
pp. 203-218 ◽  
Author(s):  
Sadi Fuat Cankaya ◽  
Ibrahim Arda Cankaya ◽  
Tuncay Yigit ◽  
Arif Koyun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Support Vector Machines ◽  
Optimization Algorithm ◽  
Intelligent Systems ◽  
Support Vector ◽  
Diabetes Diagnosis ◽  
Diagnosis System ◽  
Vector Machines ◽  
Expert Support

Artificial intelligence is widely enrolled in different types of real-world problems. In this context, developing diagnosis-based systems is one of the most popular research interests. Considering medical service purposes, using such systems has enabled doctors and other individuals taking roles in medical services to take instant, efficient expert support from computers. One cannot deny that intelligent systems are able to make diagnosis over any type of disease. That just depends on decision-making infrastructure of the formed intelligent diagnosis system. In the context of the explanations, this chapter introduces a diagnosis system formed by support vector machines (SVM) trained by vortex optimization algorithm (VOA). As a continuation of previously done works, the research considered here aims to diagnose diabetes. The chapter briefly gives information about details of the system and findings reached after using the developed system.

scholarly journals Sistem Tutorial Berbasis Kecerdasan Buatan Pada Proses Pengambilan Keputusan Perawatan dan Perbaikan Gitar

2019 ◽  
Vol 3 (1) ◽  
pp. 79-86
Author(s):  
Agusta Rakhmat Taufani ◽  
Harits Ar Ar Rosyid
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Repair Process ◽  
Musical Instrument ◽  
Certainty Factor ◽  
Maintenance And Repair ◽  
On Certainty ◽  
Total Percentage ◽  
The World

Guitar is a popular musical instrument in the world and is a metronome for every use in various music events and its correlation. As a metronome, the guitar must be well standardized on the quality of each part so that sound that comes in line with the user's expectations in this case is the guitarist. Damage to the guitar is something normal because of its intense use so it needs proper handling in the repair process. The easiest thing is to bring a broken guitar to the experts, but when there are not many guitar service experts or a long enough distance to reach it, then guitar repairs need to be done immediately. Therefore, it is necessary to develop a system that can act as a tutor in the maintenance and repair of guitars by utilizing artificial intelligence embedded in the system. With the help of artificial intelligence, it is expected that the system can assist in the decision making of guitar technicians in the process of making guitar repair decisions based on the symptoms that occur. Decision making used uses the certainty factor method based on certainty factors. After going through the equivalence partitioning testing process, in general this system produces a total percentage of 100% on the success of the item test by experts in the testing process of the 25 items tested. Thus the application meets the requirements for making the program, which is readable and valid.

scholarly journals Big Data and the Threat to Moral Responsibility in Healthcare

2021 ◽  
pp. 11-25
Author(s):  
Daniel W. Tigard
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Moral Responsibility ◽  
Intelligent Systems ◽  
Healthcare Systems ◽  
Ethical Challenges ◽  
Trade Offs ◽  
The Social ◽  
Healthcare Technologies

AbstractTechnological innovations in healthcare, perhaps now more than ever, are posing decisive opportunities for improvements in diagnostics, treatment, and overall quality of life. The use of artificial intelligence and big data processing, in particular, stands to revolutionize healthcare systems as we once knew them. But what effect do these technologies have on human agency and moral responsibility in healthcare? How can patients, practitioners, and the general public best respond to potential obscurities in responsibility? In this paper, I investigate the social and ethical challenges arising with newfound medical technologies, specifically the ways in which artificially intelligent systems may be threatening moral responsibility in the delivery of healthcare. I argue that if our ability to locate responsibility becomes threatened, we are left with a difficult choice of trade-offs. In short, it might seem that we should exercise extreme caution or even restraint in our use of state-of-the-art systems, but thereby lose out on such benefits as improved quality of care. Alternatively, we could embrace novel healthcare technologies but in doing so we might need to loosen our commitment to locating moral responsibility when patients come to harm; for even if harms are fewer – say, as a result of data-driven diagnostics – it may be unclear who or what is responsible when things go wrong. What is clear, at least, is that the shift toward artificial intelligence and big data calls for significant revisions in expectations on how, if at all, we might locate notions of responsibility in emerging models of healthcare.

scholarly journals ARTIFICIAL INTELLIGENCE TECHNOLOGY AND LAW: CHALLENGES OF OUR TIME

2020 ◽  
pp. 524-549
Author(s):  
Y. S. Kharitonova ◽  
◽  
V. S. Savina ◽  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intelligent Systems ◽  
Legal Regulation ◽  
Original Result ◽  
Legal Personality ◽  
Blockchain Technology ◽  
Legal Science ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Systems

Introduction: the article deals with the issues concerning the protection of the rights to digital content created with the use of artificial intelligence technology and neural networks. This topic is becoming increasingly important with the development of the technologies and the expansion of their application in various areas of life. The problems of protecting the rights and legitimate interests of developers have come to the fore in intellectual property law. With the help of intelligent systems, there can be created not only legally protectable content but also other data, relations about which are also subject to protection. In this regard, of particular importance are the issues concerning the standardization of requirements for procedures and means of storing big data used in the development, testing and operation of artificial intelligence systems, as well as the use of blockchain technology. Purpose: based on an analysis of Russian and foreign scientific sources, to form an idea of the areas of legal regulation and the prospects for the application of artificial intelligence technology from a legal perspective. Methods: empirical methods of comparison, description, interpretation; theoretical methods of formal and dialectical logic; special scientific methods (legal-dogmatic and the method of interpretation of legal norms). Results: analysis of the practice of using artificial intelligence systems has shown that today intelligent algorithms include a variety of technologies that are based on or related to intelligent systems, but not always fall under the concept of classical artificial intelligence. Strictly speaking, classic artificial intelligence is only one of the intelligent system technologies. The results created by autonomous artificial intelligence have features of works. At the same time, there are some issues of a public law nature that require resolution: obtaining consent to data processing from the subjects of this data, determining the legal personality of these persons, establishing legal liability in connection with the unfair use of data obtained for decision-making. Standardization in the sphere and application of blockchain technology could help in resolving these issues. Conclusions: in connection with the identified and constantly changing composition of high technologies that fall under the definition of artificial intelligence, there arise various issues, which can be divided into some groups. A number of issues of legal regulation in this area have already been resolved and are no longer of relevance for advanced legal science (legal personality of artificial intelligence technology); some issues can be resolved using existing legal mechanisms (analysis of personal data and other information in course of applying computational intelligence technology for decision-making); some other issues require new approaches from legal science (development of a sui generis legal regime for the results of artificial intelligence technology, provided that the original result is obtained).

Instructor Performance Prediction Model Using Artificial Intelligence for Higher Education Systems

2021 ◽  
Author(s):  
Shuping Xiao ◽  
A. Shanthini ◽  
Deepa Thilak
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Decision Making ◽  
Prediction Model ◽  
Education Systems ◽  
Higher Education Systems ◽  
Routine Tasks ◽  
Instructor Performance ◽  
The Individual

Recent advancements in Artificial Intelligence techniques, including machine learning models, have led to the expansion of prevailing and practical prediction simulations for various fields. The quality of teachers’ performance mainly influences the quality of educational services in universities. One of the major challenges of higher education institutions is the increase of data and how to utilize them to enhance the academic program’s quality and administrative decisions. Hence, in this paper, Artificial Intelligence assisted Multi-Objective Decision-Making model (AI-MODM) has been proposed to predict the instructor’s performance in the higher education systems. The proposed AI-assisted prediction model analyzes the numerical values on various elements allocated for a cluster of teachers to evaluate an overall quality evaluation representing the individual instructor’s performance level. Instead of replacing teachers, AI technologies would increase and motivate them. These technologies would reduce the time necessary for routine tasks to enable the faculty to focus on teaching and analysis. The usage for administrative decision-making of artificial intelligence and associated digital tools. The experimental results show that the suggested AI-MODM method enhances the accuracy (93.4%), instructor performance analysis (96.7%), specificity analysis (92.5%), RMSE (28.1 %), and precision ratio (97.9%) compared to other existing methods.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2018 ◽  
Vol 3 (2) ◽  
pp. 31-47 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Intelligent Systems ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

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