Implementation Of Intelligent System Research Articles

The Effect of Business Intelligence System Implementation on Organizational Performance in the Digital Age

The implementation of Business Intelligence systems has emerged as one of the key strategies to enhance organizational performance in the digital era. This study systematically reviews 17 peer-reviewed articles from the Scopus database to examine the impact of BI systems on various performance dimensions, including operational efficiency, decision-making, financial outcomes, and customer satisfaction. Results indicated that BI systems enhance organizational agility and competitiveness, while data quality, management support, user training, and strong IT infrastructure were among the factors that drive their success. Challenges such as high implementation costs, resistance to change, and integration complexities remain. Emerging trends in AI integration, cloud-based solutions, and real-time analytics reflect the evolving possibilities of BI systems. This research contributes to the literature by synthesizing critical insights and offering actionable recommendations for practitioners and researchers aiming to leverage BI systems for sustained organizational growth.

Exploring Antibiotic Resistance Through Artificial Intelligence: A Novel Perspective

Microbial resistance has long been linked to antibiotic resistance, a serious worldwide health issue. But as technology advances quickly in this day and age, a related phenomenon in the field of artificial intelligence [AI] is beginning to take shape. In order to better understand the idea of "antibiotic resistance" in the context of artificial intelligence [AI], this study will compare and contrast the evolution of bacterial resistance with potential obstacles in the design and implementation of intelligent systems. The increasing prevalence of AI systems across several industries highlights the striking similarities between their capacity to adapt and withstand hostile attacks and changing surroundings, and the biological resistance mechanisms seen in bacteria. This study explores the causes behind AI resistance, looking at how data drift, adversarial manipulations, and changing user behavior might cause machine learning systems to lose their effectiveness over time. The paper also examines the ethical ramifications of AI resistance, addressing issues with biases, unforeseen outcomes, and the influence of intelligent systems on society that are resistant to change or intervention. The area of antibiotic stewardship in medicine serves as an inspiration for the paper's discussion of potential mitigation techniques for AI resistance. Through the identification of parallels between AI resistance and antibiotic resistance in bacteria, this study adds to a better comprehension of the difficulties pertaining to the long-term viability and efficiency of intelligent systems. Since AI will continue to be a major influence on the future, it is critical to address the problem of "antibiotic resistance" in this context in order to ensure that AI is developed responsibly and ethically.

Enhancing Human Resource Competency in Indonesia through the Framework of Artificial Intelligence System Strategy

In the increasingly evolving era of digitalization, technology plays a crucial role. The world is currently undergoing the technological era, commonly known as the fourth industrial revolution or Industry 4.0. This condition is directly marked by the utilization of digital machines and the internet, leading to rapid and significant changes across various sectors of human life, facilitating people in performing various tasks. The digital transformation era is a part of a larger technological process, representing the application of digital technology in different aspects of societal life. Artificial intelligence is a field of study that explores how to make computers perform tasks similarly to humans. A framework for artificial intelligence systems is required for application in the development of knowledge, technology, and the arts in Indonesia. This paper aims to discuss the Case-Based Reasoning (CBR) framework in the development of artificial intelligence systems. It is expected that this framework can serve as a reference for the implementation of intelligent systems in Indonesia.

ПРИМЕНЕНИЕ ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМ УПРАВЛЕНИЯ ОСВЕЩЕНИЕМ ДЛЯ ПОВЫШЕНИЯ ЭНЕРГОЭФФЕКТИВНОСТИ

The article discusses the use of intelligent lighting control systems to improve energy efficiency. It describes the main components of such systems, modern technologies and methods of lighting control, as well as examples of their application in various industries. It analyzes methods for assessing energy efficiency and economic benefits from the implementation of intelligent systems. Particular attention is paid to technical and technological challenges, ethical and social aspects of using such systems. A forecast is made for the development of intelligent lighting control systems and possible innovations in this area are considered.

ЭЛЕКТРОННЫЕ СИСТЕМЫ ДЛЯ МОНИТОРИНГА И УПРАВЛЕНИЯ В УМНЫХ ЭНЕРГОСЕТЯХ

The article examines the role of electronic systems in monitoring and controlling smart grids. The key functions of these systems, such as real-time data collection and analysis, consumption forecasting, and energy distribution management, are described. Technologies and tools used to improve the efficiency and sustainability of power grids are discussed. Examples of successful implementation of intelligent systems in various regions are presented, and the main problems and prospects for their further development are considered.

Ethical and legal aspects of artificial intelligence systems implementation: balancing technological progress and fundamental human rights in the context of society’s digital transformation

Ethical and legal aspects of artificial intelligence systems implementation: balancing technological progress and fundamental human rights in the context of society’s digital transformation

Special features of using neuronetwork technolo-ies to control the work processes of bulldozers with a gnss-intensifier

Abstract. Problem. The specificity of modern road construction works requires evaluation of the situation; forecast of the machine's normal operation and the development of emergency situations; synthesis and evaluation of possible actions of the operator and selection of the best ones. That is, there is a problem of effective support of the road car, whose solution is transferred to the intelligence of the car itself. A distinctive feature of intelligent systems is the ability to plan behaviour, adapt and learn. An intelligent system automatically changes the algorithms of its functioning and the structure in order to preserve or achieve an optimal state when external conditions change. The integration of algorithmic methods of controlling complex objects and methods of artificial intelligence is aimed at solving the tasks with uncertainty of the source information. The implementation of intelligent systems shows their prospects in the field of road construction works. Thus, optimization of road machine work processes, increasing the efficiency of road-construction machines (RCM) due to an intelligent control system is an urgent task. Studying the structure of the intelligent system of the road machine is relevant as well as developing a mathematical model of intelligent optimization of work processes based on the complex of readings of integrated GNSS sensors. Goal. The purpose of the research is to increase the efficiency of construction machines through their intellectualization, development of scientific algorithms for creating mathematical models of the working bodies of the RCM with SNS-intensifier of work processes of interaction with the soil environment. Methodology. In the paper the scientific methods of system analysis and decision-making theory for the structuring and synthesis of intelligent control systems are used; mathematical modeling methods for building and researching hydraulic and dynamic models of road vehicles; theories of automatic control under conditions of uncertainty; methods of object-oriented programming for simulation modeling of a road machine work process during operations. Results. For the first time, the methods of solving the problem of controlling the hydraulic drive of a road machine were offered, which, unlike traditional ones, take into account the execution of digging operations in conditions of a short decision-making time, which ensures the movement of the working body along an arbitrary trajectory in a short time with high accuracy. Originality. This paper is one of the first in Ukraine in which the scientific problem of increasing the efficiency of RCM at the interaction of working bodies with the soil environment in conditions of limited time for decision-making was formulated and solved. Practical value. Time of work is cut due to the reduced number of passes during soil development and increasing the productivity of RCM, which will have a positive effect on financial and labour costs, ensuring the possibility of continuous operation, which increases the productivity of RCM.

Recent Updates on Intelligent System for Talent Management: Does That Become a Helpful System?

The use of information technology in human management today has increased, along with the implementation of intelligent systems that generally use artificial intelligence (AI) in their application. One of the areas of human management that has adopted AI is talent management (TM). TM is crucial for companies to identify, manage, determine, assess, and recommend talent (in this case, it can be employees) for their company's sustainability. The application of family planning in TM is not as extensive as thought, but this study tries to review the latest research that adapts AI to a very complex TM process. The results of this review are at least 11 articles involved in the use of family planning. These 11 articles certainly discuss one or more processes in TM, such as talent identification, talent matching or mapping, and talent recommendations. Some critical studies in the future are that in practice, AI needs to be widely used, especially to handle large-scale data management (data intelligence), in addition to intelligent system methods, and AI can be used for all processes in TM, proven to be accurate, efficient, safe, and fast in practice.

Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics.

The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.

An ontological model for planning urban areas with a given level of economic efficiency

The paper presents an ontological model for planning urban areas with a given level of economic efficiency, the use of which can provide optimal configurations of future neighbourhoods for all participants in their construction and operation. The implementation of intelligent systems based on the algorithms of the proposed model can provide optimal configurations of future neighborhoods for all parties involved in their construction and operation

Machine-Learning Assisted Handwriting Recognition Using Graphene Oxide-Based Hydrogel.

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of the currently reported handwriting recognition systems are lacking in flexible sensing and machine learning capabilities, both of which are essential for implementation of intelligent systems. Herein, assisted by machine learning, we develop a new handwriting recognition system, which can be applied as both a recognizer for written texts and an encryptor for confidential information. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide-based hydrogel sensors. It offers fast response and good sensitivity and allows high-precision recognition of handwritten content from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from seven participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (∼91.30%). Our developed handwriting recognition system has great potential in advanced human-machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality.

Artificial Intelligence and Cognitive Computing in Companies in Portugal: An Outcome of Partial Least Squares—Structural Equations Modeling

Artificial intelligence (AI) and cognitive computing (CC) are different, which is why each technology has its advantages and disadvantages, depending on the task/operation that a business wants to optimize. Nowadays, it is easy to confuse both by simply associating CC with the widespread theme of AI. This way, companies that want to implement AI know that what they want, in most cases, are the features provided by CC. It is important in these situations to know how to differentiate them, so that it is possible to identify in which circumstance one is more suitable than another, to get more out of the benefits that each has to offer. This project focuses on highlighting the capabilities of both technologies, more specifically in business contexts in which the implementation of intelligent systems and the interest of companies in them is favourable. It also identifies which aspects of these technologies are most interesting for companies. Based on this information, it is evaluated whether these aspects are relevant in decision making. Data analysis is carried out by employing partial least squares structural equations modelling (PLS-SEM) and descriptive statistical techniques.

Justification of the need to improve emergency control systems for preventing disturbances of voltage stability in power systems

This article is aimed at justifying the need to modernize the existing systems that ensure voltage stability in the Unified Power System of Russia under the conditions of its permanent development, structural complication and active implementation of distributed generation. To this end, an analysis of the existing systems of emergency control used in the Unified Power System of Russia was conducted by considering their efficiency during major system accidents, including those in 2005 and 2017. In addition, specific features of operating power system facilities in the northern part of the Irkutsk Oblast were investigated from the standpoint of voltage stability. The conducted analysis of the operation of emergency control systems in the context of system accidents, including possible incorrect actions, identified their following disadvantages: insufficient fault tolerance, weak adaptability to emergency disturbances (including abnormal situations), as well as a lack of coordination between local devices and the discreteness of regulation. These shortcomings can be eliminated by introducing intelligent automation systems based on artificial neural networks and machine learning, as well as high-performance multi-agent systems into the structure of the emergency management of the Unified Power System of Russia. The results obtained indicate the need to modernize the existing voltage stability systems operated in the Unified Power System of Russia, both their software and hardware components. The proposed implementation of intelligent systems is expected to improve the existing systems at the same time as maintaining the current effective hierarchical principles of emergency management.

Python Implementation of intelligent system for Quality Control of Argo Floats using Alpha Convex Hull

In each research field, quality control is the crucial part in order to ensure that the data collected is trustworthy. Quality control management helps in detecting abnormal behavior, monitor the health of detecting instruments. Argo Float is an ocean instrument which gives a lot of information regarding the conditions of the ocean. The information obtained has its value only if the quality of the parameter measuring procedure involves good quality tools. So, maintaining the quality of the procedure is as important as analyzing the obtained data. The salinity and the temperature data from Argo floats which collect data from in situ regions of ocean need to be scrutinized for anomalous values and examined to find out the reason behind the abnormal values recorded using a new method proposed by using Alpha convex hull. The data collected is used to construct an alpha shape polygon which is fine tuned to obtain best possible results. This method segregates by representing good data inside the polygon and the bad data outside the polygon. The proposed intelligent system is observed to detect the anomalous data occurring due to various problems such as sensor corrosion, bias ,spikes etc. This method aims to perform quality control for oceanographic data of argos at present but aims to work on different sources. The main advantages of the proposed system can be seen as the ability to identify anomalous data from in situ bulk data in minimal steps and to be applicable for cumulative data of various depths together.

Алгоритмізація бази знань при обслуговуванні легкових автомобілів

The article considers the architecture of conceptual modeling of the knowledge base, which creates a model of the subject area in the form of many concepts and relationships between them. This approach is based on the concept of a mobile software agent, which is implemented and functions as an independent specialized computer program or an element of artificial intelligence. Ensuring the use of subject area knowledge has become one of the driving forces of the recent surge in the study of artificial intelligence. For example, for models of many different subject areas it is necessary to formulate the concept of time. This representation includes the concepts of time intervals, time points, relative measures of time, etc. If one group of scientists develops a detailed knowledge base, others can simply reuse it in their subject areas using their own database. Creating explicit assumptions in the subject area, which underlie the implementation, makes it easy to change the assumptions when changing our knowledge of the subject area. The process of conceptualization of TO and P, first of all, involves the development of databases in research areas for the formalization and systematization of knowledge about the characteristics of this area of entities and phenomena. That is, the use of concepts in the field of maintenance in a consistent manner in relation to theories of knowledge. Ultimately, the paper updated mathematical modeling, algorithmization and implementation of intelligent systems in the field of maintenance, which will help automate the process of diagnosis and inspection of all car systems, facilitate fault prevention and improve the maintenance process and modernize the maintenance system itself. The approach of algorithmization of the base of knowledge of a condition of the car in each moment of time considered in work gives the chance to reduce time of stay of the car in the service center and to reduce considerably expenses for passing of MOT at service of cars.

On the issue of ethical aspects of the artificial intelligence systems implementation in healthcare

The article analyzes ethical issues inherent to different life-cycle stages of artificial intelligence systems and provides up-to-date information about global and domestic trends in this area. The international and national experience concerning ethical issues of artificial intelligence systems use in healthcare is described. In addition, the international and national strategies for the development of artificial intelligence in healthcare are analyzed focusing on the national development; and the main trends, similarities, and differences between strategies are identified. Furthermore, ethical components of the process of clinical trials to evaluate the safety and efficacy of artificial intelligence systems in Russia are described. Domestic state-of-the-art and globally unique experience in technical regulation of artificial intelligence systems are shown on unification papers and standardization of requirements for the development, testing, and operation of artificial intelligence systems in healthcare are presented, and unparalleled Russian experience in terms of certification requirements for artificial intelligence-based medical devices is demonstrated.
 The article also summarizes the main conclusions and emphasizes the importance of a strong successful healthcare system based on artificial intelligence technologies that build trust and compliance with ethical standards.

Effictive technologies for automation and robotization of the agro-industrial complex

The issues of increasing the productivity and competitiveness of the agricultural sector through the transition to digital technologies are considered. Despite certain successes in the agricultural sector associated with state support for the digitalization of agriculture, there are still a number of unsolved technological problems. Today, the main trend in the use of digital technologies in this industry is to automate all processes of growing crops from sowing to harvesting. The article presents the results of the analysis of automation and robotization of the agro-industrial business and identifies the main directions of competitive technologies. The functional of intelligent control information systems is considered. Special attention is paid to the possibilities of using unmanned aerial vehicles. Along with its undeniable advantages, agricultural robotics is characterized by high expectations and exaggerated expectations and has a number of disadvantages. The issues of legal regulation of the process of digitalization of the agro-industrial complex are considered. The analysis of the market and technologies of the agricultural sector revealed the key methods of increasing the efficiency of agriculture. Among them, one can single out the development of fundamentally new methods and approaches to agriculture, greening, design and implementation of intelligent systems and the development of robotics, training of new specialists for the development and implementation of innovative technology.

The implementation of intelligent systems in automating vehicle detection on the road

<span lang="EN-US">Indonesia is a country with a high population, especially in big cities. The road always crowded with various types of vehicles. Sometimes the growth of vehicles is not matched by road construction. During peak hours, too many vehicles can cause traffic jams on the road. The road is needed to be widened to accommodate the number of vehicles that pass each day. In order for road widening to be precise at locations that frequently occur in traffic jams, data on the number and classification of vehicles passing is required. Therefore, a system that can calculate and recognize the type of vehicle that passes is needed. The development of various studies on artificial intelligence especially about object detection can classify and calculate the type of vehicle. In this study, the authors used the you only look once (YOLO) object detection system using a convolution neural network (CNN) method to classify and count vehicles that pass automatically. The author uses a dataset of 600 images with 4 classes which are car, truck, bus, and motorbikes that pass through the road. The results showed that the YOLO object detection system can recognize objects consistently with accuracy more than 80% on CCTV video that installed on the road.</span>

Assessment of Barriers to the Implementation of Intelligent (Smart) Logistics on the Example of Selected Systems Among Manufacturing Companies in Poland

Purpose: The development of intelligent logistics is the result of implementations made under the idea known as Industry 4.0. Intelligent logistics includes many systems whose task is to improve the efficiency of logistics processes. Despite many advantages, Polish enterprises are not willing to apply these solutions in practice. This is due to the existence of barriers that effectively limit the implementation of these systems. The article aims to identify barriers and assess their negative significance in the process of ISL implementation in the context of intelligent logistics. Design/methodology/approach: The research was conducted in 2020 on a sample of 2,500 enterprises operating in Poland. An original questionnaire prepared for this study was used. The researchers used statistical summaries of structure indices to answer the researchers' questions. The Kruskal-Wallis test and the t-Student test were used to verify the hypotheses. Findings: The results show that the barriers have a important negative impact on the decisions of enterprises regarding the implementation of intelligent systems in logistics. To a large extent, the level of impact depends on many factors (the size and age of the enterprise, the type of barrier or the number of implemented systems). Internal barriers are more negative than external ones. The conclusions clearly indicate that the greatest obstacles exist inside the entities - the importance of external barriers, i.e., those coming from the environment, are less important. Practical Implications: The posted results are important for scientists and practitioners dealing with logistics or directly related to the use of cyber-physical systems in production and logistics processes. Originality/value: Recognition of the negative importance of barriers in the implementation of intelligent systems in logistics among Polish manufacturing companies.

Understanding the Evolution and Applications of Intelligent Systems via a Tri-X Intelligence (TI) Model

The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence formed by ternary-interaction (e.g., a human-cyber-physical system) is at the dynamic level of intelligence (Type III). Representative cases are analyzed to deepen the understanding of intelligent systems and their future implementation, such as in intelligent manufacturing. This work provides a systematic scheme, and technical supports, to understand and develop intelligent systems.