Model Of Intelligent System Research Articles

Multimedia English teaching analysis based on deep learning speech enhancement algorithm and robust expression positioning

In multimedia English teaching, learners face such an indifferent computer screen without emotion and feel the fun of interaction and emotional stimulation, which will cause resentment and affect the learner’s learning effect. In order to improve the efficiency of multimedia English teaching, aiming at the lack of emotion in multimedia English education, this study proposes an intelligent network teaching system model based on deep learning speech enhancement and facial expression recognition. Moreover, this study uses emotional calculation as the theoretical basis and uses facial expression recognition as the core technology to judge and understand the emotional state by capturing and recognizing the facial expressions of online learners. In addition, this study has carried out experimental tests on the effect of the identification method of this paper and verified that the method has good detection effect on the real smile micro-expressions through two sets of experiments and can provide theoretical reference for subsequent related research.

AI Empowered Communication Systems for Intelligent Transportation Systems

Intelligent control of traffic has significant influence on the scheduling efficiency of urban traffic flow. Therefore, in order to improve the efficiency of vehicles at intersections, first, the Back Propagation (BP) neural network is used to propose a vehicle passing model at the intersection, and based on the intelligent traffic control system model, the Earliest Deadline First (EDF) dynamic scheduling algorithm is used to improve the Controller Area Network (CAN) communication network. Finally, the simulation test is used to evaluate the effectiveness of the proposed model and the improved CAN bus communication network. The results show that the neural network model can be used to predict the passage time of vehicles queuing at intersections with an error of less than 10%. The improved CAN bus communication can improve the data transmission rate, and the success rate of data transmission under different load rates is above 95%. In conclusion, the application of artificial intelligence technology in intelligent traffic system can improve the efficiency of vehicle scheduling and the efficiency of communication system. This research is of great significance to improve the communication performance of the transportation system and scheduling efficiency.

Intelligent health monitoring system modeling based on machine learning and agent technology

The use of machine learning approach in the field of health informatics improves the quality and effectiveness of decision-making process. Its particular integration into the Remote Health Monitoring Systems (RHMS) allows: collecting, analyzing and learning from real-time data, automatically gainin g knowledge and making predictions on the patients state. The RHMS represent an effective solution to control and monitor a growing number of dependent or elderly patients. They showed impressive results in healthcare applications. These systems are built around the sensors installed on the body of the patient and others embedded in their environments. In this context, we are confronted with the challenge of temporal aspect of data. To meet this challenge, we propose that RHMS combine machine learning for generating intelligent valuable information, and visual analytics for gaining insight the collected real-time data. The success of these applications is based on the quality of their design and development. For this reason, we propose to design a RHMS using multi-agent technology. The developed prototype was evaluated to verify our proposal feasibility.

PENGEMBANGAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KONSENTRASI BIDANG KEAHLIAN MAHASISWA DENGAN INTEREST INVENTORY

Universities are designed to prepare graduates who are ready to enter the workforce and are able to develop a professional attitude. Educational institutions such as the University need a form of decisions in determining the right concentration for students, so that the learning process can be achieved well. The decision is very influential on the process of handling the choice of alternative concentration, choosing an appropriate concentration of interest will also have an impact on the research focus for the final assignment of students. This research develops student concentration selection system in Electrical. Currently the concentration determination system is based only on academic assessment alone, regardless of student interest, so that it can impact on student learning outcomes. The system was developed by combining academic judgment and interest inventory with three criteria, ie, interest tests using interest inventory, prerequisite concentration course grades, and GPA. The system is built using an intelligent system model that is Fuzzy Multiple Attribute Decision Making (FMADM), which helps the Department in the selection process and helps the process of career guidance on students. With this selection system, the Department can be provide the most suitable concentration decisions with interest in student.

Optimization of process parameters using a hybrid intelligent system model and evaluation of physicochemical properties of microwave roasted Chironji (Buchanania lanzan) kernels

Microwave (MW) roasting of Chironji kernels was carried out at different combinations of microwave power and roasting time. The impact of MW roasting on bulk density, hardness, water absorption capacity (WAC), water activity (WA), browning index (BI), total phenolic content (TPC) and antioxidant activity (AA) were evaluated. Significant reduction in bulk density, hardness, and WA was noticed upon roasting, while a substantial increase in WAC, BI, TPC, and AA was found. A hybrid intelligent system model was used for achieving the optimal conditions of MW roasting parameters. The model comprised of several steps such as a general full factorial experimental design, computation of Taguchi’s quality loss function, principal component analysis, grey relational analysis, and process optimization using combined artificial neural network—genetic algorithm technique. Under optimal condition of 700 W power and 5.5 min of roasting time, decrease in bulk density of 8.02 ± 0.15%, hardness of 16.63 ± 0.17 N, WAC of 1.02 ± 0.06 g/g, WA of 0.0541 ± 0.0009, BI of 76.79 ± 1.23, TPC of 26.74 ± 0.32 mg GAE/g, and AA of 73.23 ± 0.86%, were recorded for roasted Chironji kernels. Further, the SEM analysis confirmed microstructural changes in the kernels roasted at optimal conditions.

Application of 3D Numerical Model and Intelligent Systems in Discharge Coefficient Estimation of Combined Weir-Gate

Combined weir-gate structure is one of the important structures which are control the water level, measure discharge and avoid sediment deposition behind the weir. In this study, first try to simulate four combined triangular weir-rectangular gates with different geometric conditions via 3D numerical software (Flow-3D) by using experimental data. Then, dimensionless analyses were done to find the non-dimension parameters affected the discharge coefficient of this structure. At the end, four different intelligent system models were used to estimate the discharge coefficient, evaluated and compared the results with each other. Results show that the Flow-3D software has a high capability to simulate the flow over the combined structure. Besides, the values of goodness of fit criteria show that the numerical solver, estimate the water head and discharge coefficient very well. Moreover, in all models, the results show that the discharge coefficient has an inverse relation with dimensionless parameters (h/b, h/d and h/y) and discharge coefficients in this study are between 0.3–0.9. On the other hand, results of accuracy analyses of four intelligent system models of MLP, RBF, GRNN and M5P show that the MLP model is the superior model in this study, and after that, the rest of models’ sort as M5P, RBF and GRNN in this study.

Supply chain finance credit risk evolving intelligent analysis system based on system dynamic model

Supply chain finance (SCF) plays a prominent role in solving the small-middle enterprises financing difficulties. But its credit risk is easy to conduct and magnify by the supply chain relation to develop into system risk. In the paper the main structure and causal relationship of SCF credit risk s ystem are analyzed. And then the intelligent analysis system model of SCF credit risk evolving mechanism is constructed based on systematic dynamic, which simulates how the key factors act to each other and how they function to SCF credit risk. The intelligent analysis results show that: (1) The sub-system of bank, core enterprise, small-mid enterprise and risk intermediary interact and constitute the SCF credit risk system. (2) The credit risk management strategy execution and the management infrastructure input of the bank sub-system can promote improving the management base of core enterprise and small-mid enterprises. (3) To increase the management efficiency of core enterprise and small-mid enterprises can improve enterprises operating level and solvency, and then decrease the SCF credit risk. And also, the core enterprise influences the SMEs’ solvency by actively function to the operation ability. (4) The increasing of risk intermediary can accelerate to the SCF loan quantity, and at the same time, reduce the credit risk.

A review on control systems hardware and software for robots of various scale and purpose. Part 3. Extreme robotics

A review of robotic systems is carried out. The paper analyzes applied hardware and software solutions and summarizes the most common block diagrams of control systems. The analysis of approaches to control systems scaling, the use of intelligent control, achieving of fault tolerance, reducing the weight and size of control system elements belonging to various classes of robotic systems is carried out. The goal of the review is finding common approaches used in various areas of robotics to build on their basis a uniform methodology for designing scalable intelligent control systems for robots with a given level of fault tolerance on a unified component base. This part is dedicated to extreme robotics and the generalization of the conclusions for the whole review. The paper notes the possibility and necessity of developing common approaches to the creation of robots of various sizes and purposes. It is noted that, in contrast to many foreign countries, in the Russian Federation developers must consider the serious limitations on the electronics components, which do not allow for the free conversion of technologies between civilian, military and space robotics. In this regard, it is proposed to analyze the interchangeable Russian and foreign microelectronic components, to find technical solutions in the field of the control systems and communication between them, which could be implemented both in foreign and Russian microelectronics, and to create on their basis a conceptual model of scalable intelligent control system with a required level of fault tolerance. The model should be based on a unified set of components.

A Smart Stretcher and Integrated Medical Intelligence System Model for Unconscious Person

The integration of health information technology into primary care includes a variety of electronic methods. This can improve the quality of care which makes the process quicker. The purpose of this project is to highlight the main features of an intelligent stretcher and a system of monitoring crucial signs of unconscious person. Arduino is the selected device for information processing which collectively obtains the heart rate and respiratory rate of the patient, updating directly to the intensive care unit via IoT. This further updates the biometric details to the police station informing about the accident. This system is developed by using affordable technology and merging different function to offer an expedient solution. Additionally, the stretcher is provided with automatic moving mechanism under the guidance of one person which reduces manpower requirement.

Deep learning based energy efficient optimal timetable rescheduling model for intelligent metro transportation systems

Due to the recent advances in intelligent transportation systems (ITS), Automatic Train System (ATS) gained significant attention among the research community. An effective ATS offers the whole railway network to operate in a safe, cost-effective and proficient manner against sudden disturbances like temporary platform blockages. Numerous Train Timetable Rescheduling (TTR) models have been presented for managing unforeseen events which might disturb the timetable. The main aim of an effective TTR model is to reduce power utilization by consuming the entire benefits of reproductive braking energy under a random situation. In this view, this paper presents a new TTR model to optimize the energy of metro systems by the incorporation of improved genetic algorithm (IGA) and long short term memory (LSTM) based recurrent neural network (RNN). The proposed method incorporates three different models, namely controller, timetable, and energy models. The proposed method requires minimum time to recompute a new schedule and offers effective solutions instantly after a random disturbance happens. The performance validation of the proposed IGSA-LSTM model is simulated using Chennai Metro Train Station. The proposed method significantly reduces the energy consumption of metro train and reaches to a minimum average energy utilization of 696 kWh.

Design and implementation path of intelligent transportation information system based on artificial intelligence technology

By using the concept, guiding ideology and design idea of artificial intelligence (AI) and Internet of Thing, this study puts forward the logistics intelligent transportation system (ITS) model, structure design and function design based on AI, focusing on the time value and information value of the intelligent logistics information system. The results show that the design of ITS model based on AI includes three modules: input, transformation and output of ITS. The ITS supply chain management platform and function design of freight vehicles with AI is feasible and prospective. The application of AI technology in ITS has great value and development prospect.

Deep Learning Neural Network for Unconventional Images Classification

The pornographic materials including videos and images are easily in reach for everyone, including under-age youths, allover Internet. It is also an aim for popular social network applications to contain no public pornographic materials. However, their frequent existence throughout all the Internet and huge amount of available images and videos there, make it impossible for manual monitoring to discriminate positive items (porn image or video) from benign images (non-porn image or video). Therefore, automatic detection techniques can be very useful here. But, the traditional machine learning models face many challenges. For example, they need to tune their many parameters, to select the suitable feature set, to select a suitable model. Therefore, this paper proposes an intelligent filtering system model based on a recent convolutional neural networks where it bypasses the aforementioned challenges. We show that the proposed model outperforms the recent machine learning based models. It also outperforms the state of the art deep learning based models.

Knowledge representation model for intelligent power supply control system of special purpose control machines

The problem of automating the process of reconfiguring the power supply system of onboard radio-electronic equipment in special-purpose control machines in different operation modes is considered. It covers power supply from different power sources, both while parked and when moving by developing and introducing the intellectual management to the structure of an electric power supply system. The analysis of the existing devices used to provide mobile objects with electrical energy of a direct current is carried out. Their advantages and drawbacks are highlighted. The conclusion is drawn on the necessity of new product development – the P910 power supply control panel with an integrated intellectual power supply management system. The model of representation of knowledge of the intellectual management system offered by authors is based on the classic productional model specified regarding the mechanism of forming and creating the database and rule base structures. The process of synthesizing managing influences that are based on analysis of information that describes a system status of control machines’ power supply is formalized. The operation algorithm of the intellectual power supply management system is developed and the chart illustrating its work is provided. The 1887BE7T domestic microcontroller is offered for the implementation of the model.

Agent Based Simulation of ITS for Urban Traffic Control Using GPS

Intelligent transportation systems (ITS), especially in metropolitan areas, can play a crucial role in reducing traffic and its flow and finally reducing the average travel time of vehicles. Due to the high capabilities of intelligent agents in modeling and simulation, they have been used significantly to model complex urban environments and traffic control systems in recent years. In fact, base operating systems are appropriate for modeling intelligent transportation systems, especially in changing urban spaces, and simulating related facilities and equipment. However, most researches in this area have not been comprehensive. As it was mentioned, in this research, a comprehensive agent-based modeling of intelligent urban transportation system is used to control and manage urban traffic using instantaneous traffic information of streets and finally various scenarios have been proposed and implemented. Also, all vehicles have been equipped with GPS and communication devices are simulated. In this study, more emphasis has been placed on intelligent traffic lights using traffic information than routing methods using traffic information and the results have shown high effect of the smartization of traffic lights compared to the change in vehicles routing methods in management and reduction of urban traffic in implemented scenarios.

A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

Recommender Systems are intelligent applications designed to assist the user in a decision-making process whereby user wants to choose one item among the potentially overwhelming set of alternative products or services. This work focused on using users’bank statements that explicitly shows inflow and outflow of funds. The data set used is real and reliable because the use of non-reliable data in a recommender system causes users lack of trust in the system. However, the data collected were anonymized for privacy reasons. The recommender system was developed as aweb application using Java programming language. Unlike other recommender systems, the graph-oriented database management system was used.In Google news, 38% of the total views are the result of recommendations; similarly, 60% of the rented movies from Netflix come from recommendations and more than that Amazon sales percentage due to recommendations are 35%. Successful integration of recommendation system by online companies like Amazon, eBay, Flipkart among others impelled the research community to avail similar benefits in Financial domain to recommend product and services. Therefore, recommendation systems are considered an expedient factor in business nowadays. The aim of all recommender systems is to provide recommendation that will be favorably evaluated and accepted by its users.This work provides detailed descriptions of methods employed to proffer solutions to intelligent recommender system with explicit feedback mechanism. The methodology of this research work refers to the research approach adopted by the researcher to tackle the research problem as stated in earlier chapter. Since the efficiency and maintainability of any application is solely dependent on how the designs are prepared, this chapter describes the various processes, methods and procedures used to achieve set objectives and the conceptual structure within which the research was conducted.

A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement

Modelling and control of fouling in submerged membrane bioreactor using neural network internal model control

Membrane bioreactor (MBR) is one of the best solutions for water and wastewater treatment systems in producing high quality effluent that meets its standard regulations. However, fouling is one of the main issues in membrane filtration for membrane bioreactor system. The prediction of fouling is crucial in the membrane bioreactor control system design. This paper presents an intelligence modeling system so called artificial neural network (ANN). The feedforward neural network (FFNN), radial basis function neural network (RBFNN) and nonlinear autoregressive exogenous neural network (NARXNN) are applied to model the submerged MBR filtration system. The simulation results show good predictions for all methods which the highest performance of the model given by RBFNN. Based on the developed models, the neural network internal model control (NNIMC) is implemented to control fouling development in membrane filtration process. Three different control structures of the NNIMC are proposed. The FFNN IMC, RBFNN IMC and NARXNN IMC controllers are compared to the conventional IMC. The RBFNN IMC has a superior performance both in tracking and disturbance rejections.

Intelligent tutoring system using expert knowledge and Kohonen maps with automated training

This paper presents an intelligent tutoring system (ITS) model that is capable of driving the didactic transposition of contents. Initially, the tutoring system reactions bases its behavior on rules defined by an expert teacher; after this, a neural network that learns from the student’s behavior when they are studying adjusts these rules. This way, the neural network improves the teacher’s rules and, consequently, defines a learning strategy that is more adaptive and reactive to the student’s profile. Thus, it is possible to offer the student a personalized and individualized education form. The model is able to guide the student throughout the didactic transposition of contents, aiding the consolidation of desired competencies established on educational propositions. This work shows the development process of the ITS, including the expert guidance system and the hybrid system, which improves the expert rules from SOM neural network use. The obtained results indicate that the application of hybrid technology in ITSs is feasible because, for defining the teaching strategies, it incorporates the teacher’s knowledge and by neural network use, it assimilates the students’ learning process behavior. The results show that proposed model has great agreement between the actions of the “ITS” and the students’ actions. The model showed satisfactory performance when compared to other systems proposed in the literature that use connectionist approach in its conception.

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO2 emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.

Research on Modeling and Simulation of Agent-based Intelligent Teaching System

With the development of information technology such as Artificial Intelligent and mobile computing, various kinds of education concepts or products have become a reality which great promote the development of educational innovation. Nowadays more and more attention are put on the reform of teaching mode, such as MOOC, SPOC, Micro-Course and so on. These teaching modes have tremendous advantages over traditional teaching on facilitating the students’ study, enhancing their learning interest and broadening their horizons. However, the problem of lacking of platform support for the integration of these teaching models still exists. In order to solve this problem, this article propose a model of intelligent teaching system, named ITS for short. The ITS could be regarded as a platform or a system but also a new generation of innovation teaching environment, consisting of software platform and hardware devices. Compared with the traditional classroom teaching, the intelligent teaching system gives emphasis on virtual teaching online and offline, in and out of class. In terms of the definition listed above, this article presents a modeling and simulation of intelligent teaching system basing on agent technique, targeting all components concerned, such as knowledge base, network education system, intelligent teaching platform and some hardware devices. The simulation proves that the modeling of the intelligent teaching system is an efficient way to enhance the quality of teaching and improve the teaching effect, and the intelligent teaching system has the potential to become an application product in the market.