Computational Intelligence Systems Research Articles

Cognitive Computing Approaches A Comprehensive Performance Evaluation Using the Weighted Property Method

In an era of rapid technological advancement, cognitive computing is emerging as a transformative paradigm that integrates methods from psychology, biology, signal processing, and mathematics to create intelligent computational systems. This research uses the Weighted Property Methodology (WPM) to comprehensively evaluate various cognitive computing approaches, analyzing their performance on important dimensions including ethical and social implications, learning rate, usability, and decision support. This study systematically examines five cognitive computing approaches: brain-inspired, human-like, adaptive, intelligent, and intelligent computing. Through rigorous multi-criteria analysis, brain-inspired computing distinguished itself as the best-performing approach, achieving a maximum priority score of 0.86755. The ability of this approach to model computational processes after human neural structures demonstrates significant potential for advanced technological development. Key findings reveal that cognitive computing is not a single concept but a complex ecosystem of interconnected methods. Human-like computing came in second, emphasizing the critical role of human-centered design in technological innovation. Adaptive computing came in third, highlighting the importance of dynamic computational reconfiguration. The research underscores the critical role of cognitive computing in addressing contemporary challenges in domains such as healthcare, web development, and decision support systems. By simulating human-like cognitive processes, these approaches offer unprecedented capabilities in processing complex, multidimensional data and making intelligent, context-aware decisions. This comprehensive analysis provides valuable insights into the evolving landscape of cognitive computing, and provides a roadmap for future research and technological innovation in a data-driven world.

Design of a computational intelligence system for detection of multiple sclerosis with visual evoked potentials

In this study, a new approach for modification of membership functions of a fuzzy inference system (FIS) is demonstrated, in order to serve as a pattern recognition tool for classification of patients diagnosed with multiple sclerosis (MS) from healthy controls (HC) using their visually evoked potential (VEP) recordings. The new approach utilizes Krill Herd (KH) optimization algorithm to modify parameters associated with membership functions of both inputs and outputs of an initial Sugeno-type FIS, while making sure that the error corresponding to training of the network is minimized.This novel pattern recognition system is applied for classification of VEP signals in 11 MS patients and 11 HC's. A feature extraction routine was performed on the VEP signals, and later substantial features were selected in an optimized feature subset selection scheme employing Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms. This alone provided further information regarding clinical value of many previously unused VEP features as an aide for making the diagnosis. The newly designed computational intelligence system is shown to outperform popular classifiers (e.g., multilayer perceptron, support-vector machine, etc.) and was able to distinguish MS patients from HC's with an overall accuracy of 90%.

Comparative Analysis of Utilizing Popular Intelligent Computer Systems in Education

Intelligent computer systems have indeed experienced rapid development in recent years. The penetration of artificial intelligence in almost all spheres will certainly change some professions. In the educational sphere for the use of the artificial intelligence are also required new digital competencies and skills that must be mastered by teachers in order to be successfully used for learning and teaching. А comparative analysis with various indicators of popular intelligent computing systems are made in the report and the possibilities for their use in the educational process are discussed. The results from study of readiness and attitude to use artificial intelligence systems of professors, teachers, students and doctoral students are presented. The results of the survey, which confirm the need to raise awareness of the possibilities of AI and to conduct more practical training to acquire digital competences and skills to use AI in education, are discussed in the paper.

Brain Tumor Segmentation from Optimal MRI Slices Using a Lightweight U-Net

The timely detection and accurate localization of brain tumors is crucial in preserving people’s quality of life. Thankfully, intelligent computational systems have proven invaluable in addressing these challenges. In particular, the UNET model can extract essential pixel-level features to automatically identify the tumor’s location. However, known deep learning-based works usually directly feed the 3D volume into the model, which causes excessive computational complexity. This paper presents an approach to boost the UNET network, reducing computational workload while maintaining superior efficiency in locating brain tumors. This concept could benefit portable or embedded recognition systems with limited resources for operating in real time. This enhancement involves an automatic slice selection from the MRI T2 modality volumetric images containing the most relevant tumor information and implementing an adaptive learning rate to avoid local minima. Compared with the original model (7.7 M parameters), the proposed UNET model uses only 2 M parameters and was tested on the BraTS 2017, 2020, and 2021 datasets. Notably, the BraTS2021 dataset provided outstanding binary metric results: 0.7807 for the Intersection Over the Union (IoU), 0.860 for the Dice Similarity Coefficient (DSC), 0.656 for the Sensitivity, and 0.9964 for the Specificity compared to vanilla UNET.

Preface

The 2024 International Conference on Computational Intelligence and Communication System (CCICS2024), held in Wuhan, China from June 26-28, 2024, is a testament to the ever-evolving nature of fields of computational intelligence and communication technology, has attracted a diverse and brilliant array of researchers, engineers, and scholars from around the globe. CCICS2024 attracted more than 80 participants from around the world, which is a vibrant and engaging event, with a diverse program that included an opening ceremony, keynote speeches, oral presentations, poster sessions, Q&A discussions, and an ending ceremony. The keynote speakers, renowned experts worldwide in their respective fields, provided insightful overviews of the latest developments and trends in domains related to computational intelligence and communication technology. The oral presentations and poster sessions were equally enriching. Researchers and practitioners shared their latest findings, discussed challenges and opportunities, and explored potential collaborations. The lively discussions and interactions among the participants created a vibrant intellectual atmosphere that fostered innovation and knowledge sharing. The submissions received for this conference are vivid evidence of the creativity and innovation that flourishes in related areas. The manuscripts cover a wide range of topics, including computing for emerging technology, signal processing, sensor and detection technology, futuristic communication and network technologies, etc. The diversity and depth of these contributions reflect the vibrant and interdisciplinary nature of relevant industries. All papers submitted to the conference have been reviewed using a double-blind peer-review process. Each paper was tested for similarity with already published content using the Turnitin platform. All sources of similarities were checked and only original content was accepted. This volume of proceedings is beneficial for readers from academia and industry to understand and tackle the challenges efficiently and adopt appropriate solutions in the above-mentioned topics. Building on the success of this event, we hope CCICS 2024 will be one of the most spectacular events in those areas. We would like to thank the reviewers and the members of the international scientific committees. We also extend our sincere thanks to Prof. Joarder Kamruzzaman, Prof. Yangjun Chen, Prof. Oscar Eduardo Ruiz Salguero, Prof. Jie Yang, Prof. JingTao Yao and Dr. Iacovos Ioannou for their wonderful keynote talks. We are grateful to everyone who has contributed to the realization of CCICS 2024. We also look forward to the next conferences in this series, which will continue to build upon the foundation laid by this one. Organizing Committee of CCICS 2024 Prof. Yuriy S. Shmaliy (Conference Chair) IEEE Fellow, AAIA Fellow, University of Guanajuato, Mexico List of Conference Committee is available in this Pdf.

Estimating Power, Performance, and Area for On-Sensor Deployment of AR/VR Workloads Using an Analytical Framework

Augmented Reality and Virtual Reality have emerged as the next frontier of intelligent image sensors and computer systems. In these systems, 3D die stacking stands out as a compelling solution, enabling in situ processing capability of the sensory data for tasks such as image classification and object detection at low power, low latency, and a small form factor. These intelligent 3D CMOS Image Sensor (CIS) systems present a wide design space, encompassing multiple domains (e.g., computer vision algorithms, circuit design, system architecture, and semiconductor technology, including 3D stacking) that have not been explored in-depth so far. This article aims to fill this gap. We first present an analytical evaluation framework, STAR-3DSim, dedicated to rapid pre-RTL evaluation of 3D-CIS systems capturing the entire stack from the pixel layer to the on-sensor processor layer. With STAR-3DSim, we then propose several knobs for PPA (power, performance, area) improvement of the Deep Neural Network (DNN) accelerator that can provide up to 53%, 41%, and 63% reduction in energy, latency, and area, respectively, across a broad set of relevant AR/VR workloads. Last, we present full-system evaluation results by taking image sensing, cross-tier data transfer, and off-sensor communication into consideration.

RESEARCH ON THE CONSTRUCTION OF AI COMPOSITION SYSTEM BASED ON HMM

The Markov chain is an automatic accompaniment algorithm for intelligent computer systems, belonging to the interdisciplinary research field of musicology and computer science. Currently, there are many methods for AI music generation, but research on AI music generation based on the Hidden Markov Model (HMM) is relatively scarce. This paper proposes a method for constructing an AI composition system based on the HMM. This system achieves the goal of automatically generating accompaniment music from score data. The proposed system has achieved relatively stable results in the generation of musical elements such as form and harmony, accompaniment texture, and instrumentation, and has scored well in evaluation experiments.

Zwitterionic-hydrogel-based sensing system enables real-time ROS monitoring for ultra-long hypothermic cell preservation

Hypothermic preservation (HP) is highly desired for the maintenance of the viability of living cell specimens, e.g. rare cells in whole-blood samples or therapeutic cells, in an unfrozen state. However, the extension of the viable preservation time is a challenge because of the multiple injuries suffered by hypothermically preserved cells. Here, based on a dynamic bond crosslinked zwitterionic hydrogel, we established a sensing preservation system that could monitor the levels of reactive oxygen species (ROS) via real-time electronic signals and intelligent control of antioxidant addition, to completely prevent an excess of ROS in the whole-cell specimen. Furthermore, the hydrogel-based system can counter the extracellular-matrix-loss-induced anoikis of living cells. Based on the design aimed at affording protection against two primary HP injuries (i.e. ROS overproduction and anoikis) to cells, this system extended the preservation time of cell specimens under refrigerated conditions to 24 days. After preservation, the use of a mild cell retrieval process guaranteed the activity of the preserved living cells. This work not only possesses the potential to facilitate intelligent cell-based clinical applications, but also paves the way for the preparation of living materials that can host programmed cells with long-term survival. Statement of significanceAn intelligent system based on a zwitterionic sensing hydrogel is established, which can afford ultra-long hypothermic cell-preservation times of up to 24 days. The system enables the real-time monitoring of ROS overproduction and intelligent antioxidant addition, because of the merging of the smart hydrogel with a computer intelligent detection and control system. Furthermore, the automatic addition of an antioxidant according to the ROS-signal changes produced by the ZBA hydrogel effectively prevented HP lesions, including ROS over-production and ECM loss, in the preserved living cells. Subsequently, the system could also be gently dissociated, to retrieve the preserved cells. This work provides a solution for the real-time monitoring and long-term HP of living specimens, which holds the promise of benefiting cell-based medicine and the development of genetically programmed cell-based living materials.

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.

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.