Computational Intelligence Systems Research Articles

Fuzzy inference system for forecasting non-aeronautical revenues considering the customer experience

Non-aeronautical revenues are associated with commercial activities at the airport, in which the fundamental factor in managing these revenues is the customers’ experience. This study numerically analyzed the influence of passenger satisfaction and waiting time on consumption made in the commercial facilities of the Airport Passenger Terminal, operating predominantly with domestic flights. This research was made through a structured questionnaire and the passengers in the airport to support the definition of the behavior of the Fuzzy Inference System. The results show that the greater the passenger satisfaction and waiting time, the greater the chances of significantly shopping. Furthermore, this study presents fuzzy modeling of a rule-based computational intelligence system capable of forecasting non-aeronautical revenues referring to shopping and dining experience, with significant contributions to the literature and airport management.

ChatGPT as a Tool for Medical Education and Clinical Decision-Making on the Wards: Case Study.

Large language models (LLMs) are computational artificial intelligence systems with advanced natural language processing capabilities that have recently been popularized among health care students and educators due to their ability to provide real-time access to a vast amount of medical knowledge. The adoption of LLM technology into medical education and training has varied, and little empirical evidence exists to support its use in clinical teaching environments. The aim of the study is to identify and qualitatively evaluate potential use cases and limitations of LLM technology for real-time ward-based educational contexts. A brief, single-site exploratory evaluation of the publicly available ChatGPT-3.5 (OpenAI) was conducted by implementing the tool into the daily attending rounds of a general internal medicine inpatient service at a large urban academic medical center. ChatGPT was integrated into rounds via both structured and organic use, using the web-based "chatbot" style interface to interact with the LLM through conversational free-text and discrete queries. A qualitative approach using phenomenological inquiry was used to identify key insights related to the use of ChatGPT through analysis of ChatGPT conversation logs and associated shorthand notes from the clinical sessions. Identified use cases for ChatGPT integration included addressing medical knowledge gaps through discrete medical knowledge inquiries, building differential diagnoses and engaging dual-process thinking, challenging medical axioms, using cognitive aids to support acute care decision-making, and improving complex care management by facilitating conversations with subspecialties. Potential additional uses included engaging in difficult conversations with patients, exploring ethical challenges and general medical ethics teaching, personal continuing medical education resources, developing ward-based teaching tools, supporting and automating clinical documentation, and supporting productivity and task management. LLM biases, misinformation, ethics, and health equity were identified as areas of concern and potential limitations to clinical and training use. A code of conduct on ethical and appropriate use was also developed to guide team usage on the wards. Overall, ChatGPT offers a novel tool to enhance ward-based learning through rapid information querying, second-order content exploration, and engaged team discussion regarding generated responses. More research is needed to fully understand contexts for educational use, particularly regarding the risks and limitations of the tool in clinical settings and its impacts on trainee development.

Simulation of hybrid boiling nano fluid flow with convective boundary conditions through a porous stretching sheet through Levenberg Marquardt artificial neural networks approach

In the domain of artificial neural networks, the Levenberg-Marquardt technique stands out for its innovative approach and convergent stability. It generates a numerical approach for the progression of a hybrid Cu–Al2CO3/water nanofluid on a porous stretched sheet (HNF-PSS), employing regression plots, state transition measures, histogram representations, and mean squared errors. This work explores the analysis of the fluid flow problem associated with HNF-PSS, introducing a novel application of an intelligent computing system that utilizes the effectiveness of neural networks trained by the Levenberg-Marquardt algorithm (NN-TLMA). The initial mathematical expression, expressed in terms of partial differential equations (PDEs) for HNF-PSS, undergoes transformation into dimensionless nonlinear ordinary differential equations (ODEs). The collection of data for the envisioned NN-TLMA involves parameters influencing the velocity of the HNF-PSS system model, generated using the Lobatto IIIa formula. The efficacy of the suggested NN-TLMA for solving the HNF-PSS is robustly verified by examinations of state transition dynamics, regression analyses, mean square error, and error histogram studies. The consistent alignment of the obtained results with corresponding solutions attests to the legitimacy of the structure, achieving a high level of accuracy within the range of 10–6 to 10–8. Insightful findings demonstrate that the thermal Biot number contributes significantly to an upsurge in fluid temperature. Furthermore, an augmentation in nanofluid concentration and thermal profile is found to increase the porosity strength of the medium. Nevertheless, an attenuation in the velocity profile is noted under these conditions. The application of this simulation work can be found in optimizing the designs of heat exchanger, in biomedical application and in renewable energy processes.

Prediction mechanism of depression tendency among college students under computer intelligent systems

Abstract In response to the existing problems in the prediction accuracy, data collection, real-time monitoring, and consideration of factors leading to depression in the current mechanism for predicting depression among college students, this article used computer intelligent systems to study the prediction mechanism of depression among college students. This article conducted a survey on students at University A using a survey questionnaire to understand the main reasons that affect their tendency to develop depression. It processed and analyzed the data using the Beck Depression Scale and Statistical Product and Service Solution 21.0 (SPSS 21.0). Meanwhile, natural language processing techniques in computer intelligence systems can be utilized. This article combines emotional dictionaries and word frequency-inverse document frequency to construct a prediction mechanism model for depression tendencies among college students, improving the accuracy of predicting student depression tendencies. The experiment shows that the average accuracy of the depression tendency prediction mechanism model constructed based on Natural Language Processing technology after 50 experiments was 97.02%, which was 5.33% higher than the model constructed based on neural network calculations. Overall, research on the prediction mechanism of depression tendency among college students based on computer intelligence systems can provide more effective mental health support and intervention measures for schools, helping students improve their psychological state, academic achievement, and quality of life.

Intelligent Computer Systems of New Generation and Complex Technology of Their Development, Application and Modernization

Intelligent Computer Systems of New Generation and Complex Technology of Their Development, Application and Modernization

DYNAMIC PROGRAMMING FOR SOLVE THE INVENTORY MANAGEMENT PROBLEM IN LOGISTICS

Currently, there is a problem of methods insufficient efficiency for finding solutions to the inventory management problem. The research object is the process of solving inventory management problems. The research subject is methods for finding a solution to the inventory management problem based on dynamic programming. The research goal is to increase the efficiency of finding a solution to the inventory management problem through dynamic programming. A method based on deterministic dynamic programming, a method based on stochastic dynamic programming, a method based on Q-learning, and a method based on SARSA were applicated for the inventory management problem. There are advantages of the methods. of Methods modification of deterministic and stochastic dynamic programming, Q-learning, and SARSA due to dynamic parameters makes it possible to increase the learning speed while maintaining the root-mean-square error of the method. The numerical study made it possible to evaluate the methods (for modifying the deterministic and stochastic dynamic programming methods, the number of iterations is close to the number of stages; for both methods of deterministic and stochastic dynamic programming, the root mean square error was 0.02; for modifying the Q-learning and SARSA methods, the number of iterations was 300, for both methods of Q-learning and SARSA, the root mean square error was 0.05). These methods make it possible to expand the scope of dynamic programming, which is confirmed by their adaptation to the inventory management problem and helps to increase the intelligent computer systems efficiency for general and special purposes. The application of these methods for a wide class of artificial intelligence problems are the prospects for further research.

Overview of clinical and laboratory evaluation results of the effectiveness of digital methods for knowledge-based fabrication of complete removable acrylic dentures

This article provides an overview of the conceptual studies of modern digital technologies for the fabrication of complete removable acrylic dentures. Analysis of data of subtractive and additive methods of digital manufacturing shows the advantages of digital CAD/CAM methods over traditional methods. The use of known prepolymerized acrylic blocks in the subtractive milling method has enabled achieving high physical and mechanical properties, optimal spatial accuracy, and minimal thickness of the fabricated dentures. Moreover, residual monomer volume reduction in solid polymer blocks has allowed the high biological inertness and safety of the fabricated structures for prosthetic bed tissues and the patient’s body. Layer-by-layer printing of removable dentures by fused polymer deposition modeling technology results in high spatial accuracy of constructing structures of any degree of complexity and reduced total production time of removable dentures.
 Despite the intensity of implementation of digital CAD/CAM technologies in the practice of complete removable denture manufacturing, the search for alternative methods of modernization of the known traditional means and methods continues. However, we believe that automation and digitalization of the clinical and laboratory manufacturing of complete removable acrylic dentures has clear prospects for the total replacement of compression pressing and hot polymerization techniques of acrylates. In summary, the potential prospect in the future is a global reassessment of traditional concepts for the fabrication of complete removable dentures, considering the formation of an innovative digital CAD/CAM philosophy and the introduction of computer intelligent systems.

Methodological Problems and Strategic Goals of the Work on Creation of the Theory and Technology of New Generation Intelligent Computer Systems

The main methodological problems and strategic goals of work on creating the theory and technology of intelligent computer systems of a new generation are considered. The problems of the current state of work in the field of artificial intelligence and their causes are studied, and the need for a transition to a new generation of intelligent computer systems is substantiated. The basic principles underlying such systems, as well as the corresponding complex technology, are presented. The transition from local automation when solving particular problems to complex automation of various areas of human activity is justified. The concept of an ecosystem of interoperable intelligent computer systems is proposed, which is the basis for complex automation. The architecture of the ecosystem, the directions of its evolution, and the classes of systems included in the ecosystem are considered.

Principles of representation of innovative models of piece intelligence in intelligent computer measures for energy systems.

An analysis of the problem of innovative redesign of distributed energy systems based on the methods of piece intelligence for the intelligentization of fluid technological processes has been carried out. The methodology for representing innovative mathematical models to human intelligence in intelligent computer systems has been proposed. The set of principles for the formation of intelligent mathematical models of advanced intellectual complexity and dimension for. Methods for creating cognitive models and methods for simulating creative activity for identifying and forming new knowledge have been suggested. A number of differential mathematical models and methods for the development, in the field of differential images, of the totality of spectral and correlation analysis of anomalous processes, which are traditionally assigned to the creative class, have been proposed. Bible.8.

Interactive Human 3D Model with Conventional AI

Abstract: The convergence of interactive human 3D models with conventional artificial intelligence (AI) represents a groundbreaking fusion of computer graphics and intelligent systems. This interdisciplinary field combines advanced 3D modeling techniques with classical AI methodologies to create interactive virtual representations of humans. Rigorous studies in 3D modeling and animation form the basis for lifelike avatars, while conventional AI empowers these models with the ability to understand user inputs, make contextually relevant decisions, and engage in dynamic and responsive interactions. At the heart of this innovation is the emphasis on human-computer interaction (HCI), leveraging principles such as gesture recognition, voice commands, and user-friendly interfaces to enhance user experiences. The integration extends into virtual and augmented reality (VR/AR), providing users with immersive and engaging environments where interactive 3D human models come to life. Applications of this technology span a wide array of fields, including virtual companionship, education, healthcare simulations, entertainment, and training scenarios. The impact is transformative, offering new possibilities for human-machine interactions and redefining traditional approaches to AI applications. However, the burgeoning field is not without challenges. Privacy concerns, ethical considerations related to realistic human representation, and the societal impact of interactive 3D models necessitate careful examination. This abstract provides a glimpse into the technical intricacies of creating lifelike models, the intelligence driving their interactive capabilities, and the broader implications for diverse applications and industries. As research and development progress, the collaboration of computer graphics and AI not only opens new avenues but also prompts a thoughtful reevaluation of the evolving relationship between humans and intelligent, interactive 3D representations.

The Possibilities of Using Artificial Intelligence in Decision-Making in the Corporate Management System

The expansion of the use of intelligent computer systems based on artificial intelligence makes it possible to operate with a large amount of input data, generate a greater number of alternatives based on previous experience and competitive analysis, select and implement the best one for current market conditions. Such opportunities are especially in demand when solving non-standard, complex, unstructured management tasks. The result of artificial intelligence application in managerial decision-making is to increase the flexibility and efficiency of business processes, the implementation of the best complex options in a limited time and resource mode. The article is devoted to the consideration of research and successful cases related to the use of artificial intelligence technology in corporate governance. The methodological basis is represented by general scientific methods of cognition: comparative analysis, systematization, etc. As a result, based on the analysis of management practice, general requirements for the use of artificial intelligence in the decision-making process in corporate management are determined.

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing technology. This paper introduces the background of colorectal cancer diagnosis and monitoring, and then carries out academic research on the medical imaging artificial intelligence of colorectal cancer diagnosis and monitoring and machine learning, and finally summarizes it with the advanced computational intelligence system for the application of safe medical imaging.In the experimental part, this paper wants to carry out the staging preparation stage. It was concluded that the staging preparation stage of group Y was higher than that of group X and the difference was statistically significant. Then the overall accuracy rate of multimodal medical image fusion was 69.5% through pathological staging comparison. Finally, the diagnostic rate, the number of patients with effective treatment and satisfaction were analyzed. Finally, the average diagnostic rate of the new diagnosis method was 8.75% higher than that of the traditional diagnosis method. With the development of computer science and technology, the application field was expanding constantly. Computer aided diagnosis technology combining computer and medical images has become a research hotspot.

A preference group consensus method with three-way decisions and regret theory under multi-scale information systems

The foundation of intelligent computing and expert systems relies on the data processing conducted within the realm of multi-scale information systems (MSISs). Data processing within MSISs caters to diverse analytical needs, where the preference hierarchy among various factors plays a vital role in the consensus reaching process (CRP). Additionally, the three-way decisions (TWD) theory serves as an efficient tool, while regret theory (RT) quantifies decision-makers' risk inclinations associated with various psychological behaviors. Thus, the paper aims to introduce an innovative approach known as the CRP-TWD-RT-MSIS method. The study draws inspiration from spatial geometry, incorporating concepts like points, lines, surfaces, and bodies to create MSISs. This process generates a matrix of fuzzy preference relations (FPRs) among distinct decision-makers through data preprocessing. In addition, the paper explores feedback mechanisms based on identification and directional rules, as well as those rooted in minimal adjustments or cost considerations. Simultaneously, it takes negotiation and discussion time into account, culminating in the development of a personalized adjustment optimization model that focuses on minimizing time costs. In the end, the method's effectiveness and superiority are validated through a case study and a comparative analysis of real data from the Chinese Weather and Meteorological Bureau's website.

Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

CONNECTIONIST-METAHEURISTIC APPROACH TO THE ANALYSIS OF THE GLOBAL ECONOMY’S INVESTMENT ENVIRONMENT INDICATORS

The connectionist-metaheuristic approach solved the urgent task of using new approaches to analyze the foreign direct investment and macroeconomic indicators that affect the volume of their attraction to a particular country in the world economy. The proposed connectionist-metaheuristic system makes it possible to improve the quality of the approximation due to: the simplification of structural identification through the use of only one hidden layer of neural network models; reduction of the computational complexity of parametric identification and the ensuring good scalability through the use of batch mode for non-recurrent neural network models and multi-agent metaheuristics for recurrent neural network models; descriptions of non-linear dependencies through the use of neural network models; high approximation accuracy due to the use of recurrent neural network models; resistance to data incompleteness and data noise due to the use of metaheuristics for parametric identification of recurrent neural network models; lack of requirements for knowledge of distribution, homogeneity, weak correlation, and optimal factors’ choice. In the case of a GPU, an LSTM-based neural network with the highest approximation accuracy should be chosen. For LSTM, the coefficient of determination using the gradient method is 0.785, and using metaheuristics (modified wasp colony optimization) is 0.835. The proposed approach makes it possible to expand the scope of approximation methods’ application based on artificial neural networks and metaheuristics, which is confirmed by its adaptation for an economic problem and contributes to an increase in intelligent computer systems efficiency for general and special purposes.

Frequency modulation of terahertz microcavity via strong coupling with plasmonic resonators.

Tunable terahertz (THz) microcavities are crucial for the compact on-chip THz devices, aiming to future cloud-based computing, and artificial-intelligence technologies. However, the solutions to effectively modulate THz microcavities remain elusive. Strong coupling has been widely demonstrated in many configurations at different ambient conditions to date and may serve as a promising tool to modulate THz microcavities. Here, we schematically design a microcavity-plasmon hybrid system, and propose an effective approach to modulating the resonant frequencies of THz microcavities by the microcavity-resonator strong coupling. In this case, we observed the strongly coupling states, where the resultant two-polariton branches exhibit an anti-crossing splitting in the frequency domain, experimentally exhibiting a ∼6.2% frequency modulation to the microcavity compared to the uncoupled case. This work provides an efficient approach to modulating chip-scale THz microcavities, thereby facilitating the development and application of compact THz integrated devices, further empowering the evolution of future information processing and intelligent computing system.

Maker Culture: dissemination of knowledge and development of skills and competencies for the 21st century

The investigation of 21st century skills, in the context of Maker Culture, revealed the importance of interpersonal and intrapersonal skills, in addition to technical skills. These socio-emotional skills play a fundamental role in promoting the integral development of individuals. This study contributes to a deeper understanding of Maker Culture, its essential competencies and its influence in various areas. It highlights the importance of recognizing this culture as a transformative force that shapes the present and future of education, innovation and the dissemination of knowledge in an inclusive and collaborative way. The research adopted a methodology that included the selection of articles in the Scopus database, quantitative analysis and data visualization with the help of specialized software. The investigation revealed a constant increase in academic production on Maker Culture, with notable growth from 2017 onwards, indicating a growing and continuous interest in this movement, in addition to its relevance in areas of knowledge such as computer science, education and intelligent systems, highlighting its interdisciplinarity and applicability in different areas. Therefore, from this research, it is suggested that Maker Culture is not only a production movement, but also a training movement, preparing 21st century makers for the challenges of the contemporary era.

Automatic Video Traffic Surveillance System with Number Plate Character Recognition Using Hybrid Optimization-Based YOLOv3 and Improved CNN

Recently, the increased count of surveillance cameras has manipulated the demand criteria for a higher effective video coding process. Moreover, the ultra-modern video coding standards have appreciably enhanced the efficiency of video coding, which has been developed for gathering common videos over surveillance videos. Various vehicle recognition techniques have provided a challenging and promising role in computer vision applications and intelligent transport systems. In this case, most of the conventional techniques have recognized the vehicles along with bounding box depiction and thus failed to provide the proper locations of the vehicles. Moreover, the position details have been vigorous in terms of various real-time applications trajectory of vehicle’s motion on the road as well as movement estimation. Numerous advancements have been offered throughout the years in the traffic surveillance area through the random propagation of intelligent traffic video surveillance techniques. The ultimate goal of this model is to design and enhance intelligent traffic video surveillance techniques by utilizing the developed deep learning techniques. This model has the ability to handle video traffic surveillance by measuring the speed of vehicles and recognizing their number plates. The initial process is considered the data collection, in which the traffic video data is gathered. Furthermore, the vehicle detection is performed by the Optimized YOLOv3 deep learning classifier, in which the parameter optimization is performed by using the newly recommended Modified Coyote Spider Monkey Optimization (MCSMO), which is the combination of Coyote Optimization Algorithm (COA) and Spider Monkey Optimization (SMO). Furthermore, the speed of the vehicles has been measured from each frame. For high-speed vehicles, the same Optimized YOLOv3 is used for detecting the number plates. Once the number plates are detected, plate character recognition is performed by the Improved Convolutional Neural Network (ICNN). Thus, the information about the vehicles, which are violating the traffic rules, can be conveyed to the vehicle owners and Regional Transport Office (RTO) to take further action to avoid accidents. From the experimental validation, the accuracy and precision rate of the designed method achieves 97.53% and 96.83%. Experimental results show that the proposed method achieves enhanced performance when compared to conventional models, thus ensuring the security of the transport system.

Implementasi Profil Pelajar Pancasila melalui Kegiatan Ekstrakurikuler Robotik dan Smart Farmer di SMAS Santo Tarcisius Dumai

Santo Tarcisius High School is the first driving school in Dumai City, so Santo Tarcisius High School has the responsibility to implement the Pancasila Student Profile for its students. One of the characteristics of the Pancasila Student Profile is creative, namely being able to modify and produce something original, meaningful, useful and impactful. To realize the Pancasila Student Profile, an effort is needed to provide knowledge and skills to students in an interesting way, one of which is through robotics and smart farmer extracurricular learning activities. Based on the results of discussions with the school principal, the problems faced in implementing robotics and smart farming extracurricular activities include 1) Students have never had an understanding of computer and robot-based intelligent farming system technology, 2) They do not yet have teachers who are competent in the field of robotics, 3) Limited costs purchasing robotic electronic equipment and installing smart farmers at schools. Based on the problems above, the aim of this activity is to realize the Pancasila Profile, especially in the IT sector, through education on making Line Follower robots and smart farming systems for hydroponic plants. The implementation method is by dividing students into several groups to make Line Follower robots. The number of students in this activity is 150 students/class XI SMA Santo Tarcisius. Success in Beginner Community Service (PMP) is to improve students' skills in making robots and intelligent systems which is measured through the final product results, pre test and post test questionnaires, the product results from this PMP are line follower robots and smart farmer systems, the conclusion of the PMP This is to increase efficiency, productivity and sustainability in the agricultural sector through the application of the latest technology.

Development of an intelligent computing system using neural networks for modeling bioconvection flow of second-grade nanofluid with gyrotactic microorganisms

Nanoparticles are carried in bioconvective fluid flow by convective motion caused by living tissues. This flow has important applications in cell and tissue engineering because it demonstrates the mechanics of particle transfer between cells and fluids. This type of flow is used in medicine delivery systems that particularly target cancer cells in real life. Nanofluids are crucial suspensions that allow nanomaterials to disperse and behave in a homogeneous and stable environment. The bioconvective second-grade nanofluid flow, on the other hand, is distinguished by a more complex process that permits nanoparticle motion to be controlled by external fields and pressures. This type of flow has numerous applications, including biology, the environment, and energy. It is particularly useful in medical imaging, cancer hyperthermia treatment, and nanodrug delivery systems. The primary purpose of this research is to use an artificial neural network to examine the rate of heat, mass, and motile microbe movement in the convective flow of magnetohydrodynamic second-grade nanofluid toward vertical surface. Suspended nanoparticles are effectively stabilized by the action of microorganisms, facilitated through bioconvection. This process is influenced by both nanoparticle attributes and buoyancy forces. In addition to thermophoretic dynamics and Brownian motion, the model considers radiation and Newtonian heating effects. Nonlinear equation systems are obtained using appropriate transformations. The non-linear simplified equations underwent numerical calculations utilizing the fourth-order Runge-Kutta shooting method. The Sherwood number, Nusselt number, and density of motile microorganism coefficient were determined using various parameters, and three distinct artificial neural networks were built employing the findings.