Model Of Intelligent System Research Articles

A Study on the Utilization of Polyphonic Folk Songs in College Choral Teaching in the Context of Big Data

Abstract Introducing polyphonic folk songs into the choral teaching classroom in colleges and universities not only helps to carry forward the excellent traditional culture but also promotes the students’ emotional education and physical and mental health development. This paper analyzes the status quo and dilemma of the use of polyphonic folk songs in college choral teaching and puts forward the model of music choral intelligent tutoring system (ITS) and the method of spectral tone recognition as countermeasures. A collaborative filtering algorithm is used to construct the learning resource recommendation algorithm in the music chorus intelligent guided learning system model, which is based on the ITS system structure. The YIN algorithm is used to extract the audio, and the pitch template matching is accomplished with the help of the improved DTW algorithm to complete the choral ability assessment. An improved normal harmonic algorithm has been proposed to preprocess the audio signal and achieve spectral tone recognition for multi-part folk songs in the fundamental frequency extraction stage of spectral tone recognition. In a comprehensive university in Sichuan Province, China, the teaching practice of polyphonic folk song chorus was carried out. The students’ scores in the dimensions of learning interest and learning confidence increased by 0.976 and 0.698 on average after the experiment. The p-values were all less than 0.05, showing significant differences.

Research on evaluation system and method of MES system for intelligent manufacturing

Abstract With the development of informatization and digitalization, the transformation of intelligent manufacturing has become the focus. This paper builds an evaluation system based on the scientific evaluation method of the development level of smart manufacturing and the real demand situation. It establishes an evaluation model of intelligent manufacturing MES systems by combining the GA-BP algorithm. Then, a SeqGAN generative adversarial network is used to expand the real samples. The evaluation model was trained and validated by constructing a training model through the BP neural network, taking the sample data of evaluation indexes as the network input and the seven labels from Industry 1.0 to Industry 4.0 as the network output. The results show that the classification accuracy of the model is above 98%, and all of them have achieved good results. In the actual case evaluation, it is judged that the intelligent manufacturing MES system of Company Z is in the range of industry 3.0 to 3.5, and this result is very close to the manual evaluation of the factory field survey. The synthesis shows that the model is reasonable and effective and has important guiding significance for the evaluation of enterprise intelligent manufacturing MES systems.

Design and Evaluation of Trustworthy Knowledge Tracing Model for Intelligent Tutoring System

Design and Evaluation of Trustworthy Knowledge Tracing Model for Intelligent Tutoring System

Multi-cloud resource scheduling intelligent system with endogenous security

<abstract> <p>A secure and reliable intelligent multi-cloud resource scheduling system in cyberspace is especially important in some industry applications. However, this task has become exceedingly challenging due to the intricate nature of information, the variety of knowledge representations, the compatibility of diverse knowledge reasoning engines, and the numerous security threats found in cloud networks. In this paper, we applied the endogenous security theory to the multi-cloud resource scheduling intelligent system and presented a novel model of the system. The proposed model incorporates various knowledge representations and inference engines, resulting in a multi-cloud resource scheduling intelligent system that ensures endogenous security. In addition, we have devised a scheme for an intelligent system that schedules multi-cloud resources using dual-channels and has an endogenous security mechanism, which we have named Dynamic, Heterogeneous, and Redundant (DHR). Finally, we have used the multi-cloud resource scheduling intelligent run log database to carry out numerous experiments to validate the efficiency of the dual-channel redundant reasoning system with the endogenous security mechanism's DHR property. The results of the experiment demonstrated that the multi-cloud resource intelligent scheduling system model with an endogenous security mechanism was superior to the current single-channel inference system scheme in regards to security and reliability.</p> </abstract>

Knowledge Navigator: Revolutionizing Education through LLMs in Generative AI

The education landscape is shifting towards automation and digitalization to cater to the increasing demand for personalized learning experiences and more efficient teaching methods. In response to this trend, we propose the development of an integrated educational automation fusion platform that aims to overhaul educating and learning practices across various educational sectors. The integration of cutting-edge language models like GPT-3.5 and Gemini Pro in information retrieval and conversational AI has opened fresher opportunities, even within the realm of education. With its advanced features, LangChain, a powerful framework for large language models, enables seamless integration of AI-driven functionalities, including document analysis, question generation, and chatbot interaction, revolutionizing the educational landscape. Also, by harnessing the vast resources of the OpenAI API, our platform empowers educators and learners to engage in dynamic conversations with educational materials, generate personalized assessments, and gain deeper insights from complex datasets within a single forum. This single platform disseminates information on all facets of research and development in educational domain on the grounds of fusion practices and applications. This system is successful in combining multiple models for intelligent systems. On evaluation, our system was successful in generating decent performance compared to existing systems, even though they are singular modules. Overall, our platform aims to empower educators, students, and institutions to embrace the digital era of learning and unlock new avenues for fusion-based knowledge acquisition and innovation.

An empirical verification of the proposed distributor marketing intelligence system model

An empirical verification of the proposed distributor marketing intelligence system model

МОДЕЛИ И ФУНКЦИИ ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ ПОДДЕРЖКИ ПРИНЯТИЯ УПРАВЛЕНЧЕСКИХ РЕШЕНИЙ В ЭНЕРГОСИСТЕМАХ

The aim of the work is to study models and intelligent systems for supporting management decisions in energy systems. The article is devoted to solving the problem of adequate description of systems for supporting management decision-making in energy systems, as well as analysing models and functions of intelligent system models for supporting management decision-making in energy systems. As part of the research, methods of generalization and systematization of models, functions of an intelligent management decision support system, as well as systemic, evolutionary, categorical and modelling methods are used to form a conceptual model of the Transnistrian Moldavian Republic power systems which is necessary to further develop a mathematical model of intelligent system for supporting management decisions in energy systems. The novelty of the research lies in the fact that a conceptual model of a decision information system for the power supply control in the power systems of the Pridnestrovian Moldavian Republic is developed. The results of the study include analysing the subject area and developing a conceptual database model and the information support structure in the energy systems of the Transnistrian Moldavian Republic. Findings state that to develop an information system project, a system analysis of the energy industry, in particular information processes in the power supply is performed. The main aims and objectives of the project are determined, which become the basis for constructing an objective tree.

Stochastic model of the smart power grid section functioning

A feature of large systems, in particular electric power systems, is that the entire system physical state of the is affected by a large number of interacting elements that are randomly in different states. The work relevance is caused by to the fact that construction of mathematical models taking into account the structure and features of the power grid functioning allows us to solve important problems of the energy intellectualization. The goal of the study is due to the desire to show the advantages that arise when using the apparatus of the hidden Markov models theory to semi-Markov models of intelligent electrical systems. To do this, we build a semi-Markov model of a section of a distribution power grid (intelligent electrical grid). Using the theory of semi-Markov processes with a common phase space of states, it is possible to determine the temporal characteristics of the system reliability and obtain a more adequate model. However, territorial dispersion, inaccessibility of some sections of electrical networks and the widespread introduction of automation tools give rise to new problems. For a timely response to various factors, it becomes necessary to as-sess and forecast the states of the system (sections of the system) depending on the signals received in the course of its operation. This can be achieved by applying the hidden Markov models theory. Reliability characteristics are determined and, using the algorithm of stationary phase enlargement, an enlarged semi-Markov model of the section of the intelligent distribution grid is built, which allows passing to the finite state space of the model. Using the merged model, the parameters are determined and a hidden Markov model is developed, for which the most probable states corresponding to a given signal vector are found, the elements subsequent states of the modeled system and signals are predicted. The final part of the article provides an example of finding the reliability characteristics of the system and solving a number of problems in hidden Markov models theory.

Integrating an Intelligent Language Tutoring System in Teaching English Grammar

This paper describes a research project using an intelligent language tutoring system model in teaching grammar in English. The study aims to investigate the perceptions of a group of students regarding the effectiveness of this system and its service quality. This investigation is significant since it gives further insights into how Intelligent Language Tutoring Systems can help students learn grammar in a personalized manner based on their levels. Research has revealed that classroom teaching may fall short in providing customized instruction and feedback. Therefore, intelligent language tutoring systems can support adaptivity to individual learner needs. In order to assess the effectiveness of this system, the participants answered a survey questionnaire at the end of this study. The research project involves a class of adult mixed-ability students in a continuing education program at the National School of Applied Sciences in Fes, Morocco. The results demonstrate that these students have a positive attitude toward the merits of such technological tools. These advantages are mainly related to enhancing learner achievement and motivation. Based on these findings, some implications for both teaching and learning grammar can be drawn.

Knowledge Equivalence in Digital Twins of Intelligent Systems

A digital twin contains up-to-date data-driven models of the physical world being studied and can use simulation to optimise the physical world. However, the analysis made by the digital twin is valid and reliable only when the model is equivalent to the physical world. Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous. The paper focuses in particular on digital twin models of intelligent systems where the systems are knowledge-aware but with limited capability. The digital twin improves the acting of the physical system at a meta-level by accumulating more knowledge in the simulated environment. The modelling of such an intelligent physical system requires replicating the knowledge-awareness capability in the virtual space. Novel equivalence maintaining techniques are needed, especially in synchronising the knowledge between the model and the physical system. This paper proposes the notion of knowledge equivalence and an equivalence maintaining approach by knowledge comparison and updates. A quantitative analysis of the proposed approach confirms that compared to state equivalence, knowledge equivalence maintenance can tolerate deviation thus reducing unnecessary updates and achieve more Pareto efficient solutions for the trade-off between update overhead and simulation reliability.

Facile and optimal evaluation model of intelligent waste collection systems based on the Internet of Things: a new approach toward sustainability

Waste management is a difficult and complicated issue. Since this waste may constitute a threat to persons and the environment, it is vital to guarantee that it is adequately collected. Therefore, new waste collection technologies that adopt modern technology and the Internet of Things (IoT) are the appropriate alternative. Determining the optimal intelligent technology for waste management and tuning its priorities is a complicated task that requires taking into account the dimensions of environmental, economic, and social sustainability. Thus, this paper introduces a hybrid methodology for multi-criteria decision-making (MCDM) that assesses intelligent waste management technologies utilizing IoT, taking into account multiple criteria. First, eleven decision-making criteria are determined to give a realistic approach. Second, the researchers demonstrated the innovative decision approach established on the combination of the Measurement of Alternatives and Ranking according to the COmpromise Solution (MARCOS) method and the Indifference Threshold-based Attribute Ratio Analysis (ITARA) method, called T2NN-ITARA, under a type-2 neutrosophic numbers (T2NNs) environment. This approach has been used to define the criteria’s relative significance. Also, T2NN-MARCOS approach has been established to evaluate and classify intelligent waste management technologies based on IoT and to reveal the most sustainable solution. An illustrative case study evaluating four intelligent waste management technologies based on IoT is presented to prove the validity of the applied methodology. The findings show that the criteria of sustainability and standardization are the two most influential criteria in the evaluation and classification of intelligent waste management technologies based on IoT. It has also been determined that the RFID and GPRS blend for waste management is the most suitable intelligent technology for garbage management. Sensitivity and comparison analyzes were also accomplished to illustrate the stability, strength, and robustness of the suggested approach. The research provides significant information for government and waste practitioners.

Predicting the solubility of hydrogen in hydrocarbon fractions: Advanced data-driven machine learning approach and equation of state

BackgroundHydrogen is a free carbon source for energy that attracts massive interest to be utilized during energy transition period in the near future. Molecular hydrogen soon will be a key component in fuels productions. It can be produced through catalytic steam and natural gas. For such processes the accurate estimation of solubility of hydrogen in many alkanes’ solution can have a major impact on the design and optimization of such petrochemical processes. MethodsIn this study, advanced data-driven scheme consisting in a committee machine intelligent system (CMIS) model was proposed for predicting the solubility of hydrogen in many hydrocarbon liquids raging from light n-alkanes C1 to heavy n-alkanes of C46. The CMIS was gained by combining multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath a sole model using gene expression programming (GEP). A wide range database of hydrogen solubility in a binary system of hydrogen and various n-alkanes types was considered for developing the intelligent model. Besides, the reliability of the physics-based models, namely the Peng–Robinson equations of state (PR EOS) was checked for predicting the solubility of H2 in n-alkanes. Significant findingsThe main results demonstrated that the developed CMIS scheme can estimate the solubility of hydrogen in n-alkanes with a quite low computational cost and excellent performance, where the yielded overall RMSE and R2 values were 0.0033 and 0.9972, respectively. Besides, the established machine learning based model is capable of providing a good match against a wide range of experiment data. Furthermore, it is found that the implemented CMIS paradigm is superior to the existing intelligent and physics-based models in terms of accuracy. Lastly, the result of this work is vital for increasing the accuracy of hydrogen solubility in various hydrocarbon solvents required for process optimization of the hydrogen production units.

Smart microgrid construction in abandoned mines based on gravity energy storage

The share of new energy in China's energy consumption structure is expanding, posing serious challenges to the national grid's stability and reliability.As a result, it is critical to construct large-scale reliable energy storage infrastructure and smart microgrids. Based on the spatial resource endowment of abandoned mines' upper and lower wells and the principle characteristics of the gravity energy storage system, an intelligent microgrid system model for abandoned mines based on gravity energy storage is proposed, and the system's feasibility and key influencing factors are discussed and analyzed from the standpoint of economic benefits. The gravity energy storage system principle, system structure, subsurface powerhouse, underground storage, and transit system are all examined and analyzed.The viability of establishing intelligent microgrid systems in abandoned mines is proved using the resource conditions, technical conditions, economic advantages, and social benefits of Panyidong Mine in Huainan Coal Mine.The findings indicate that the project concept has good economic and social benefits as well as practical viability.Next, from the perspectives of technology, policy, and the ecological environment, several recommendations for the development of a smart microgrid system based on gravity energy storage power station are made. This study presents a novel concept for the advancement of energy storage technology and the reuse of abandoned mine resources, which is critical to the long-term development of abandoned mine resources and the advancement of energy storage technology.

Economic and Structural Development of Turkey's Tourism within Development Plans During a Century of The Republican Era

This study provides a comprehensive analysis of the impact of the five-year development plans implemented during the republican period on the tourism sector in Turkey. Although tourism revenues and the number of tourists in the five-year development plans implemented during the Republican Era have been in a continuous upward trend, Turkey has not fully achieved the targets set in the five-year development plans. The main purpose of this article is to analyze the development plans of Turkey during the Republican Era and to identify the frequently emphasized and under-emphasized targets. A mixed methodology was used in this study. Within the scope of the research, secondary data were collected from official institutions. These data were subjected to trend analysis, and the average number of tourists and tourism revenues were calculated for each five-year development plan period. In the second stage of the research, the development plans were analyzed through content analysis, and themes were identified. Create network maps and word clouds created. Finally, Chat GPT, an artificial intelligence modeling system, was asked questions about the future of the tourism sector in the Republic of Turkey. As a result, it was found that Turkey emphasizes “sustainability” the most in its five-year development plans. In addition, infrastructure, transportation and diversification of tourism opportunities are other themes emphasized in five-year development plans. However, it has been determined that targets such as technology, digitalization and artificial intelligence should be sufficiently emphasized.

Connected Intelligent Transportation System Model to Minimize Societal Cost of Travel in Urban Networks

The increasing societal cost of vehicle travel in urban networks is causing higher social and environmental impacts on road users and urban residents. The societal cost of travel can be reduced through implementation of more efficient traffic management solutions, deeper integration of connected vehicles in the traffic stream, and increased deployment of vehicle-to-infrastructure (V2I) systems. This work proposes an innovative traffic management solution, based on Urban Connected Intelligent Transport Systems. The solution dynamically manages traffic by controlling for speed and acceleration in connected vehicles through V2I to minimize societal cost in urban networks. This is achieved by minimizing all four components of societal cost of travel, i.e., traffic accidents, fuel consumption, pollutant emissions and travel time. By minimizing societal cost, this research contributes to safer, greener and more sustainable transport in urban networks, while reducing the adverse environmental and economic impact. Experimental and field data as well as data from simulation were used to test the proposed solution at an urban coastal area in Patras, Greece.

Evaluation model and algorithm optimization of intelligent manufacturing system on the basis of BP neural network

Intelligent manufacturing is a key direction for the development of the manufacturing industry. Since the level of intelligent manufacturing varies among enterprises, building a scientific and reliable evaluation model is the key to evaluate enterprises. The study optimizes the Back Propagation (BP) neural network based on the Firefly Algorithm (FA) and innovatively introduces the Sparrow Search Algorithm (SSA), which changes the search mechanism of the BP and improves the traditional BP affected by the local optimum. of the traditional BP. In the experimental results, the average error of the improved model is 1.34 % and 0.41 % under the optimal preset parameters, which is significantly better than other advanced algorithms. It is verified that the improved evaluation model in this study has lower error and better effect, which has better application value in the evaluation of intelligent manufacturing system.

Application Of Adaptive Neuro Fuzzy Inference System (ANFIS) In Predicting Thermal and Electrical Efficiency of a Photovoltaic Thermal (PvT) System

The use of photovoltaic (PV/T) system that converts solar radiation to electricity and provide thermal needs concurrently stands as one of the most effective means of utilizing renewable energy. In recent times, machine-learning techniques have been extensively used in solar system applications due to their high accuracy in predicting the performances without necessarily going through physical modelling. The main objective of this study is to implement an intelligent algorithm adaptive neuro-fuzzy inference system (ANFIS) model to simulate and predict the thermal and electrical efficiencies of a water-based photovoltaic-thermal (PV/T) system. Thorough experimentation for the fabricated set-ups (conventional PV and a water-based PV/T system) was carried out. The real experimental results was validated using ANFIS model. Base on the results obtained it was confirmed that there was an excellent agreement between the predicted model outputs and the actual experimental data. However, the ANFIS model gave the highest prediction accuracy with the lowest error margin of 0.00021, 0.0089 and 0.0459 for MSE, RMSE and MAE with strong correlation (R2) of 0.9998. Base on the results obtained it was concluded that this intelligent algorithm is a reliable tool in predicting the PV/T system performances.

Adversarial Deep Learning based Dampster–Shafer data fusion model for intelligent transportation system

Intelligent Transportation Systems (ITS) have revolutionized transportation by incorporating advanced technologies for efficient and safe mobility. However, these systems face challenges ensuring security and resilience against adversarial attacks. This research addresses these challenges and introduces a novel Dampster–Shafer data fusion-based Adversarial Deep Learning (DS-ADL) Model for ITS in fog cloud environments. Our proposed model focuses on three levels of adversarial attacks: original image level, feature level, and decision level. Adversarial examples are generated at each level to evaluate the system’s vulnerability comprehensively. To enhance the system’s capabilities, we leverage the power of several vital components. Firstly, we employ Dempster–Shafer-based Multimodal Sensor Fusion, enabling the fusion of information from multiple sensors for improved scene understanding. This fusion approach enhances the system’s perception and decision-making abilities. For feature extraction and classification, we utilize ResNet 101, a deep learning architecture known for its effectiveness in computer vision tasks. We introduced a novel Monomodal Multidimensional Gaussian Model (MMGM-DD) based Adversarial Detection approach to detect adversarial examples. This detection mechanism enhances the system’s ability to identify and mitigate adversarial attacks in real-time. Additionally, we incorporate the Defensive Distillation method for adversarial training, which trains the model to be robust against attacks by exposing it to adversarial examples during the training process. To evaluate the performance of our proposed model, we utilize two datasets: Google Speech Command version 0.01 and the German Traffic Sign Recognition Benchmark (GTSRB). Evaluation metrics include latency delay and computation time (fog–cloud), accuracy, MSE, loss, and F-score for attack detection and defense. The results and discussions demonstrate the effectiveness of our Dampster–Shafer data fusion-based Adversarial Deep Learning Model in enhancing the robustness and security of ITS in fog–cloud environments. The model’s ability to detect and defend against adversarial attacks while maintaining low-latency fog–cloud operations highlights its potential for real-world deployment in ITS.

A multi-attribute decision-making framework for enterprise competitive intelligence system evaluation with 2-tuple linguistic neutrosophic information

Big data is changing our lives and the way we understand the world, as well as the operational patterns of business and social organizations. Fully understanding the value of data and knowing how to use big data to provide a basis for business decision-making has gradually become the most basic thinking that business organizations should possess in the era of big data. Under the thinking mode of data-driven decision-making, many information science researchers have discussed the model, architecture, operation mechanism and other aspects of big data competitive intelligence system. At the same time, more and more enterprises, such as IBM, Amazon, Google, Microsoft, Wal Mart, etc., have begun to attach importance to the development and construction of big data competitive intelligence software systems, and have achieved certain results. The enterprise competitive intelligence system evaluation in the context of big data is regarded as multi-attribute decision-making (MADM). In this paper, the Hamy mean (HM) and the power average (PA) are connected with 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic numbers power HM (2TLNPHM) operator. Then, use the 2TLNPHM operator to handle MADM with 2TLNS. Finally, taking the enterprise competitive intelligence system evaluation in the context of big data as an example, the proposed method is explained. The main contributions of this study are summarized: the establishment of the 2TLNPHM operator; (2) The 2TLNPHM operator was developed to handle MADM with 2TLNS; (3) Through the empirical application of the enterprise competitive intelligence system evaluation, the proposed method is validated; (4) Some comparative studies have shown the rationality of the 2TLNPHM operator.

Semantic models and tools for designing adaptive user interfaces of intelligent systems

Objectives. Models and tools for designing adaptive user interfaces for intelligent systems are being developed. The relevance is determined by the need to reduce overhead costs and development time for user interfaces and to provide their adaptation to the specific characteristics of the user of the intelligent system.Methods. Existing approaches to designing user interfaces are being analyzed. A semantic model of an adaptive user interface for intelligent systems is proposed, implemented using a basic universal language for representing knowledge based on set theory and graph theory.Results. An adaptive user interface model for intelligent systems has been developed, which includes a knowledge base model of the user interface, an agent-oriented model of the user interface, and a library of reusable components that provide integration of the user interface into both individual intelligent systems and intelligent systems groups. A method of transferring user interface components within an intelligent systems group during the operation of an intelligent system has also been developed.Conclusion. Developed models and tools allow to simplify the reuse of user interface components together with knowledge base and problem-solving components in the design and development of individual intelligent systems, as well as in the design and development of a group of semantically compatible intelligent systems, ensuring automation of integration of user interfaces and their adaptation for each user. The developed set of user interface components has been included in a library of reusable user interface components. Further expansion of the set of components in the library and their integration into a group of semantically compatible intelligent systems is planned.