Hybrid Intelligent System Research Articles

Design Hybrid Intelligent system for Cattle Diseases Diagnosis and Treatment

Cattle is one of the main concerns of people and companies in various countries of the world. This paper built the architecture of the cattle diseases by combining the characteristics of an expert system with a neural network Expert system focuses on diagnosing the possible diseases that affect the life of cattle through constructing knowledge bases. While the Radial base functions, a neural network is proposed for the cattle diseases treatments. A tightly coupled model is used to implement a hybrid intelligence technique for diagnosing and Treatment cattle diseases. The result generated by the system was enough to demonstrate its performance.

Transforming Healthcare with Artificial Intelligence: Innovations, Applications, and Future Challenges

Background: The integration of artificial intelligence (AI) in healthcare has significantly transformed clinical practices, offering substantial improvements in diagnosis, treatment planning, and patient outcome predictions. AI technologies, including artificial neural networks, fuzzy expert systems, and hybrid intelligent systems, are advancing the field of augmented medicine by combining AI with traditional healthcare practices. Methods: This study reviews the diverse applications of AI in healthcare, focusing on its impact on clinical procedures, disease detection, and healthcare management. The analysis covers the use of AI-driven tools such as surgical navigation systems, augmented reality for pain management, and machine learning algorithms for early disease detection and clinical documentation. Results: AI technologies like AccuVein and augmented reality headsets have enhanced clinical procedures such as intravenous placements and surgical interventions. Advances in machine learning, particularly neural networks and deep learning, have improved the detection of complex patterns in imaging data, facilitating early diagnosis of diseases like cancer and pneumonia. Natural language processing (NLP) has enhanced the analysis and classification of clinical documentation, while robotic process automation (RPA) has optimized administrative tasks. AI's role in managing infectious diseases, particularly during the COVID-19 pandemic, has been critical, demonstrating its potential in screening, diagnosis, and treatment surveillance. AI applications in oncology and laboratory medicine have also shown increased accuracy and efficiency in disease diagnosis and patient care. Conclusion: AI is revolutionizing healthcare by enhancing diagnostic accuracy, treatment efficacy, and patient care quality. Despite its transformative potential, challenges such as legal accountability and data bias must be addressed for successful integration into healthcare systems. Continued research and innovation in AI applications are essential to maximizing its benefits while minimizing associated risks.

WMN-PSODGA hybrid intelligent simulation system and its web GUI tool: performance evaluation considering different parameters of genetic algorithm

WMN-PSODGA hybrid intelligent simulation system and its web GUI tool: performance evaluation considering different parameters of genetic algorithm

Retracted] Personalized Education Based on Hybrid Intelligent Recommendation System

Differentiated pedagogy is a flexible and organized adaptation of teaching and learning as it argues that students, even those of the same age, have differences in learning readiness, interests, learning style, experiences, and living circumstances. These differences are important in the determination of requirements of their learning and the way of effective learning. In addition, the foundation for effective learning is the sense of community within the classroom, the authentic learning opportunities of using educational equipment, and the connection of the lesson with the experiences and interests of the students. In essence, the support of a teacher guides the pupils to learn to work on their own during a declining guidance policy, to improve their abilities and skills. Thus, the teachers are asked to modify their teaching methods instead of applying a similar way of teaching for all students. The modified teaching style should meet the different levels of readiness of students, the different ways they learn, and their different interests. In support of this specific task for teachers, the current work presents a personalized education system based on hybrid intelligent recommendations. Specifically, a hybrid framework of artificial intelligence is proposed, which focuses on the way to provide targeted recommendations for the implementation of integrated standard lesson plans, which will be the main tool for creating flexible differentiated pedagogical programs that will perfectly meet the personal needs and particularities of each student.

WMN-PSODGA hybrid intelligent simulation system and its web GUI tool: performance evaluation considering different parameters of genetic algorithm

WMN-PSODGA hybrid intelligent simulation system and its web GUI tool: performance evaluation considering different parameters of genetic algorithm

Diagnostics of psychological readiness of high school students for research activities in mathematics in the design context of a hybrid intellectual learning environment

Introduction. The problem of improving the process of organizing and supporting the project and research activities of schoolchildren through intelligent management for the purpose of self-organization of the individual, understanding and comprehending complex mathematical knowledge as a principle of personal development is relevant and far from solved. Intelligent systems provide the process of individualization of learning, the establishment of personalized and computerized feedback of cognitive and creative processes. The purpose of the article is to assess the student's readiness for research activities in the context of designing a hybrid intelligent learning environment. Materials and methods. The assessment of the student's psychological readiness for research activities in the conditions of using a hybrid intellectual environment was carried out on an experimental representative sample of students of 1-2 courses of secondary vocational education (n1=42) and students of the senior classes of secondary schools (n2=30). The diagnosis was carried out using the intelligence structure test of R. Amthauer, the creativity questionnaire of D. Johnson, the test "Individual styles of thinking" by A. Alekseev, L. Gromova, the methods of value orientations by M. Rokich, etc. The significance of the differences was established by means of Student's t-test, Fisher's angular transformation, χ2-test. The results of the study. The assessment of psychological readiness for research activities in mathematics was carried out on the basis of the developed nine parameters of scientific potential. The presented results allow us to pre-set the framework of boundary conditions in order to minimize the imprinting time of a hybrid intelligent system (including the selection of the neural network topology). For all three groups of criteria, differences by gender were established, for example, by the parameter "value orientations" (temp = 2.26 > tcr = 2.02); by the parameter "creativity" (χemp2 = 6,02 ≥ χcr2 (0,05;2) = 5,99). And also by the type of educational institution, for example, by the parameter “motivation to achieve the result” (φemp = 0,186 > φcr = 1,64). Conclusion. The results of the research are of practical value, as they serve as a technological basis for establishing the boundaries and boundary conditions of the most significant parameters for the effective realization of scientific potential, expressed in the work of a specialized web interface created with the student's personal account.

A web interface for WMN-PSODGA hybrid intelligent simulation system: performance evaluation for different mesh client distributions

PurposeThe purpose of this paper is to implement a web interface for a hybrid intelligent system. By using the implemented web interface, one can find optimal assignments of mesh routers in wireless mesh networks (WMNs). This study evaluates the implemented system considering three distributions of mesh clients to solve the node placement problem in WMNs.Design/methodology/approachThe node placement problem in WMNs is well known to be a computationally hard problem. Therefore, intelligent algorithms are used for solving this problem. The implemented system is a hybrid intelligent system based on meta-heuristics algorithms: particle swarm optimization (PSO) and distributed genetic algorithm (DGA). The proposed system is called WMN-PSODGA.FindingsThis study carried out simulations using the implemented simulation system. From the simulations results, it was found that the WMN-PSODGA system performs better for chi-square distribution of mesh clients compared with Weibull and exponential distributions.Research limitations/implicationsFor simulations, three different distributions of mesh clients were considered. In the future, other mesh client distributions, number of mesh nodes and communication distance need to be considered.Originality/valueThis research work, different from other research works, implemented a hybrid intelligent simulation system for WMNs. This study also implemented a web interface for the proposed system, which make the simulation system user-friendly.

Method of creation of a two-level neural network structure for solving problems in mechanical engineering

There is an industrial trend of increasing technological effectiveness of production and growing autonomy of factories, resulting in generation of huge amounts of raw data that can be used to improve efficiency and continuity of industrial plants operation. Big data collection, processing and analysis technologies are already being actively implemented. Further use of big data will help with a variety of tasks, such as optimization and process monitoring, quality control of equipment and produced parts, modeling and forecasting of the facility operation and other mechanical engineering challenges. A new method of creation of a two-level neural network structure is proposed, designed to solve a number of problems by training individual neural networks for each subset of data used in the task at hand. This method combines two levels of information processing: the first level of the neural network classifier and the second level, which includes several neural network analyzers. Depending on the specific subject area and the data sets available, it is possible to use the method to solve various problems in mechanical engineering. The method allows to add new neural network analyzers and expand the scope of application. The practical application of the method in solving the problem of text message sentiment analysis is shown and an example of the Python programming language software implementation of the two-level structure is given. Use cases for the two-level structure method in mechanical engineering tasks are proposed. In addition, the proposed method can be used as a part of the hybrid intelligent information system that includes mivar expert systems. Combining neural networks with mivar expert systems as part of a hybrid intelligent information system is a promising direction for the development of artificial intelligence for mechanical engineering.

Open Innovation Model of Student’s Research Activities

Abstract The study is aimed to develop the open innovation model of student’s research activities based on the adaptation of modern scientific achievements in the context of transition to sustainable development of the education system. It advances a general approach to the design of the structure and implementation of main components of hybrid intelligent system to integrating a production-frame model of knowledge and neural network algorithm for the formation of an individual learning scenario. The software implemented as a hybrid intellectual environment for organizing student’s research activities is conventionally represented as a combination of blocks: a graphical user interface; technological core of hybrid learning environment, including a neural network classifier and a training data bank; a system for interaction and activation of third-party software and software and hardware. The technological basis for the individualization of the learning trajectory is the automated construction, forecasting of development and interactive correction of competence-oriented and associated models of a subject area. As a technological core, it uses a hybrid system of interaction between an artificial neural network and an expert system functioning based on a database of research projects. The presented innovation algorithm makes it possible to implement the technology of student’s research activities organizing as a process of setting up the generalized constructions of complex knowledge. Further research prospects consist of the program implementation of hybrid intellectual learning environment for the development of student’s research activities and its preparation for trial operation.

IoT-Assisted Hybrid Intelligent Learning Architecture Based on Digital Education in a Diverse Society

The existing research paradigm has caused the research of educational theory to be criticized for lack of logic and scientificity. With the gradual deepening and refinement of the development of smart education platforms, the educational process can be quantified, and the research paradigm of educational science has moved from a sampling model to a full-sample model, and it has become a real empirical science. This paper combines the Internet of Things technology to construct a hybrid intelligent learning system and applies it to digital education in a diverse society to construct a functional structure of the extraction system. Moreover, starting from the actual situation of intelligent education, this paper combines a simulation system to implement an IoT-assisted hybrid intelligent learning architecture and evaluates the teaching effect of the system. Through experimental research, it can be known that the IoT-assisted hybrid intelligent learning framework based on social diversified digital education has certain effects and has a certain reference effect for the development of subsequent intelligent education.

A comparison study of chi-square and uniform distributions of mesh clients for different router replacement methods using WMN-PSODGA hybrid intelligent simulation system

Wireless Mesh Networks (WMNs) are gaining a lot of attention from researchers due to their advantages such as easy maintenance, low upfront cost and high robustness. Connectivity and stability directly affect the performance of WMNs. However, WMNs have some problems such as node placement problem, hidden terminal problem and so on. In our previous work, we implemented a simulation system to solve the node placement problem in WMNs considering Particle Swarm Optimization (PSO) and Distributed Genetic Algorithm (DGA), called WMN-PSODGA. In this paper, we compare chi-square and uniform distributions of mesh clients for different router replacement methods. The router replacement methods considered are Constriction Method (CM), Random Inertia Weight Method (RIWM), Linearly Decreasing Inertia Weight Method (LDIWM), Linearly Decreasing Vmax Method (LDVM) and Rational Decrement of Vmax Method (RDVM). The simulation results show that for chi-square distribution the mesh routers cover all mesh clients for all router replacement methods. In terms of load balancing, the method that achieves the best performance is RDVM. When using the uniform distribution, the mesh routers do not cover all mesh clients, but this distribution shows good load balancing for four router replacement methods, with RIWM showing the best performance. The only method that shows poor performance for this distribution is LDIWM. However, since not all mesh clients are covered when using uniform distribution, the best scenario is chi-square distribution of mesh clients with RDVM as a router replacement method.

Agents’ ontologies negotiation in cohesive hybrid intelligent multi-agent systems

The paper proposes the model for negotiating intelligent agents’ ontologies in cohesive hybrid intelligent multi-agent systems. Intelligent agent in this study will be called relatively autonomous software entity with developed domain models and goal-setting mechanisms. When such agents have to work together within single hybrid intelligent multi-agent systems to solve some problem, the working process “go wild”, if there are significant differences between the agents’ “points of view” on the domain, goals and rules of joint work. In this regard, in order to reduce labor costs for integrating intelligent agents into a single system, the concept of cohesive hybrid intelligent multi-agent systems was proposed that implement mechanisms for negotiating goals, domain models and building a protocol for solving the problems posed. The presence of these mechanisms is especially important when building intelligent systems from intelligent agents created by various independent development teams.

Knowledge graphs as tools for explainable machine learning: A survey

This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI has become a very active field of research by addressing the limitations of the latest machine learning solutions that often provide highly accurate, but hardly scrutable and interpretable decisions.An increasing interest has also been shown in the integration of Knowledge Representation techniques in Machine Learning applications, mostly motivated by the complementary strengths and weaknesses that could lead to a new generation of hybrid intelligent systems. Following this idea, we hypothesise that knowledge graphs, which naturally provide domain background knowledge in a machine-readable format, could be integrated in Explainable Machine Learning approaches to help them provide more meaningful, insightful and trustworthy explanations.Using a systematic literature review methodology we designed an analytical framework to explore the current landscape of Explainable Machine Learning. We focus particularly on the integration with structured knowledge at large scale, and use our framework to analyse a variety of Machine Learning domains, identifying the main characteristics of such knowledge-based, explainable systems from different perspectives. We then summarise the strengths of such hybrid systems, such as improved understandability, reactivity, and accuracy, as well as their limitations, e.g. in handling noise or extracting knowledge efficiently. We conclude by discussing a list of open challenges left for future research.

Method for synthesis of an intelligent automatic system for diagnosing combustion engine vibration of the power supply system of an unmanned vessel

The paper considers the implementation prospects and features of the synthesis of an intelligent automatic system for diagnosing the internal combustion engine vibration of a power supply system in terms of developing technologies for automatic or remote control of unmanned vessels. The paper addresses the issue of predicting the technical condition of a combustion engine using available diagnostic methods. The study focuses on the equipment required for monitoring and analyzing the technical condition of the engine of an unmanned vessel when operating in automatic or remotely controlled mode. An algorithm has been developed for collecting, storing and accumulating data on the condition of the engine components and units in the on-board information system of the vessel to exchange data on the condition of the engine and its components during remote monitoring and to determine fault codes. The approaches to the synthesis of an intelligent automatic system for diagnosing the combustion engine vibration are considered from a methodological point of view. Vibration diagnostics equipment used for the analysis of the technical condition of the engine is proposed. Some results of the classification of the technical condition of the engine are presented. The expediency of introducing and using hybrid intelligent systems based on artificial neural networks is exemplified by diagnostics of the MITSUBISHI S16R engine.

Genetic algorithm hybridized with eXtreme gradient boosting to predict axial compressive capacity of CCFST columns

The study aimed to propose a robust method for predicting the axial compressive capacity (Nu) of circular concrete-filled steel tube (CCFST) columns. For this purpose, a hybrid intelligent system was designed by fusing the eXtreme Gradient Boosting (XGB) model with the Genetic Algorithm (GA). The GA was utilized to determine an optimal set of XGB’s hyperparameters to improve accuracy. A dataset of 509 tests from available literature was compiled for training and testing phases, which covered five input variables considering geometric and material properties of CCFST columns. The objective predictive model (GA-XGB) was validated against a benchmark model, namely GA-ANN, an artificial neural network (ANN) model optimized by the same metaheuristic algorithm. The simulation results in terms of error range and statistical indices indicated that the GA-XGB was superior to the GA-ANN model and other established mathematical approaches in predicting Nu of CCFST columns. The relative importance of individual features was further investigated to find the most significant input variable. Finally, a web app has been developed to make the proposed GA-XGB model applicable to practical design. The results confirmed that GA-XGB can be used in the design and behavior prediction of CCFST columns as a reliable and precise tool.

A Hybrid Intelligent System Framework for Detecting and Predicting Risk of Cardiovascular Disease

Number of deaths is increasing everyday especially due to heart disease in the present-day world. The cardiovascular diseases can appear due to family history, obesity, smoking etc. Prevention of death due to heart failure and proper treatment requires on time and accurate diagnosis of the disease. In this study we predict heart disease using a system that is based on machine learning using heart disease dataset. In this paper we try analysing and predicting heart diseases occurring by applying algorithms such as K-Nearest Neighbour, Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, XG Boost. We have taken the dataset from UCI Machine Learning Repository. The algorithms results based on different factors like cholesterol, age, gender etc. The resulting performance provides efficiency in diagnosing the disease.

Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive

A reliable prediction of the soil properties mixed with recycled material is considered as an ultimate goal of many geotechnical laboratory works. In this study, after planning and conducting a series of laboratory works, some basic properties of marine clay treated with recycled tiles together with their unconfined compressive strength (UCS) values were obtained. Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. Actually, in these systems, respectively, the weights and biases of the artificial neural network (ANN) were optimized using the particle swarm optimization (PSO) and imperialism competitive algorithm (ICA) to get a higher accuracy compared to a pre-developed ANN model. The best neuro-swarm and neuro-imperialism models were selected based on several parametric studies on the most important and effective parameters of PSO and ICA. Afterward, these models were evaluated according to several well-known performance indices. It was found that the neuro-swarm predictive model provides a higher level of accuracy in predicting the UCS of clay soil samples treated with recycled tiles. However, both hybrid predictive models can be used in practice to predict the UCS values for initial design of geotechnical structures.

Hybrid intelligent telemedical monitoring and predictive systems

Healthcare systems need to overcome the high mortality rate associated with cardiovascular disease and improve patients’ health by using decision support models that are both quantitative and qualitative. However, existing models emphasize mathematical procedures, which are only good for analyzing quantitative decision variables and have failed to consider several relevant qualitative decision variables which cannot be simply quantified. In solving this problem, some models such as interval type-2 fuzzy logic (IT2FL) and flower pollination algorithm (FPA) have been used in isolation. IT2FL is a simplified version of T2FL, with a reduced computation complexity and additional design degrees of freedom, but it cannot naturally achieve the rules it uses in making decisions. FPA is a bio-inspired method based on the process of pollination, executed by the flowering plants, with the ability to learn, generalize and process numerous measurable data, but it is not able to describe how it reaches its decisions. The hybrid intelligent IT2FL-FPA system can conquer the constraints of individual approaches and strengthens their robustness to cope with healthcare data. This work develops a hybrid intelligent telemedical monitoring and predictive system using IT2FL and FPA. The main objective of this paper is to find the best membership functions (MFs) parameters of the IT2FL for an optimal solution. The FPA technique is employed to find the optimal parameters of the MFs used for IT2FLSs. The authors tested two data sets for the monitoring and prediction problems, namely: cardiovascular disease patients’ clinical and real-time datasets for shock-level monitoring and prediction.

Analysis and Recognition of Standards in Intelligent Hybrid Systems using Natural Computing

This work shows the application of one of the techniques of bioengineering, the perceptron network in the detection of system failures, and also allows the use of the perceptron network technique in choosing the location of the best sensor to be used in the dynamic system. The application of the perceptron network was adopted because it is considered the best binary linear classifier. This work is considered multidisciplinary and difficult to develop. The final result demonstrates a severe application of pre-processing and processing, until the classification and grouping of signals in the two phases of the work. Through the results found, this work can be considered successful and can be applied in several areas of engineering for structural analysis.

An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles

With the growing number of applications of artificial intelligence such as autonomous cars or smart industrial equipment, the inaccuracy of utilized machine learning algorithms could lead to catastrophic outcomes. Human-in-the-loop computing combines human and machine intelligence resulting in a hybrid intelligence of complementary strengths. Whereas machines are unbeatable in logic and computation speed, humans are contributing with their creative and dynamic minds. Hybrid intelligent systems are necessary to achieve high accuracy and reliability of machine learning algorithms. In a design science research project with a Swedish manufacturing company, this paper presents an application of human-in-the-loop computing to make operational processes more efficient. While conceptualizing a Smart Power Distribution for electric industrial equipment, this research presents a set of principles to design machine-learning algorithms for hybrid intelligence. From being AI-ready as an organization to clearly focusing on the customer benefits of a hybrid intelligent system, designers need to build and strengthen the trust in the human-AI relationship to make future applications successful and reliable. With the growing trends of technological advancements and incorporation of artificial intelligence in more and more applications, the alliance of humans and machines have become even more crucial.