scholarly journals Research on the Artificial Intelligence Teaching System Model for Online Teaching of Classical Music under the Support of Wireless Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jipeng Yang
Keyword(s):  
Artificial Intelligence ◽  
Online Teaching ◽  
Classical Music ◽  
Online Courses ◽  
Learning Algorithm ◽  
Research Work ◽  
Q Learning ◽  
Chinese Music ◽  
Virtual Classes ◽  
Computer Based

For the past year, everyone has been facing difficulties due to the fast spreading of the Corona Virus. As an extension, students, parents, and teachers are handling the challenges in the education sector. Since the COVID days, the schools and colleges were closed, and hence, the students were lagging in their subjects. As an alternative to this scenario, offline classes are converted to online courses, otherwise called virtual classes with virtual classrooms. Due to this conversion, the teaching has become a little more advanced by incorporating various computer-based technologies. The technologies like artificial intelligence, cloud computing, and machine learning paved the way for exploring concepts in data transmission in terms of timely delivery of content, less error rate, and nontechnical terms like making the classes interactive and understanding the subject concepts. In this research work, the online teaching class on music is considered. To be specific, traditional Chinese music is taken for the study. An artificial intelligence model is designed with the aid of wireless sensor networks for the online class on the musical subject. Q-learning algorithm, which is an artificial intelligence-based reinforcement learning algorithm, is implemented. The aim of the Q-learning algorithm in this online teaching of classical music is to check the frequency level of the music that aids in the automatic transfer of another wavelength inside the dataset.

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

scholarly journals Faultless Decision Making for False Information in Online: A Systematic Approach

2021 ◽  
Vol 2 (4) ◽  
pp. 226-235
Author(s):  
Yasir Babiker Hamdan ◽  
Sathish
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Systematic Approach ◽  
Learning Algorithm ◽  
Research Work ◽  
High Accuracy ◽  
Fake News ◽  
False Information ◽  
Q Learning ◽  
Deep Learning Algorithm

An identifying the news are real or fake instantly with high accuracy is a challenging work. The deep learning algorithm is implementing here to acquire very accurate separation of real and fake news rather than other methods. This research work constructs naïve bayes and CNN classifiers with Q-learning decision making. The two different approaches detect fake news in online and it gives to decision making section which is designed at tail in our research. The deep decision making section compares the input and make the decision wisely and it provides the more accurate output rather than single classifiers in deep learning. This research work comprises compare between our proposed works with single classifiers.

scholarly journals Choice of cargo delivery option in multimodal connection based on reinforcement learning

2021 ◽  
Vol 2131 (3) ◽  
pp. 032103
Author(s):  
A P Badetskii ◽  
O A Medved
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Digital Technologies ◽  
Qualitative Assessment ◽  
Q Learning ◽  
Cargo Delivery ◽  
Route Option

Abstract The article discusses the issues of choosing a route and an option of cargo flows in multimodal connection in modern conditions. Taking into account active development of artificial intelligence and digital technologies in all types of production activities, it is proposed to use reinforcement learning algorithms to solve the problem. An analysis of the existing algorithms has been carried out, on the basis of which it was found that when choosing a route option for cargo in a multimodal connection, it would be useful to have a qualitative assessment of terminal states. To obtain such an estimate, the Q-learning algorithm was applied in the article, which showed sufficient convergence and efficiency.

scholarly journals Learning Obstacle Avoidance with an Operant Behavior Model

2004 ◽  
Vol 10 (1) ◽  
pp. 65-81 ◽  
Author(s):  
D. A. Gutnisky ◽  
B. S. Zanutto
Keyword(s):  
Artificial Intelligence ◽  
Mobile Agents ◽  
Learning Algorithm ◽  
Robot Learning ◽  
Operant Learning ◽  
Avoidance Task ◽  
Q Learning ◽  
Proposed Model ◽  
Learning Research ◽  
High Flexibility

Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to be used in controlling mobile agents. Robot learning research, as well as research in biological systems, face many similar problems in order to display high flexibility in performing a variety of tasks. In this work, the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) is studied. An environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles is simulated. The results were compared with the Q-Learning algorithm, and the proposed model had better performance. In this way a new artificial intelligence agent inspired by neurobiology, psychology, and ethology research is proposed.

scholarly journals Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum

2020 ◽  
pp. 7-25
Author(s):  
Alla Evseenko ◽  
◽  
Dmitrii Romannikov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Training Time ◽  
Q Learning ◽  
Research Areas ◽  
Wide Range

Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is mainly carried out to solve problems in Atari 2600 games or in other similar ones. In this article, reinforcement training will be applied to one of the dynamic objects – an inverted pendulum. As a model of this object, we consider a model of an inverted pendulum on a cart taken from the Gym library, which contains many models that are used to test and analyze reinforcement learning algorithms. The article describes the implementation and study of two algorithms from this approach, Deep Q-learning and Double Deep Q-learning. As a result, training, testing and training time graphs for each algorithm are presented, on the basis of which it is concluded that it is desirable to use the Double Deep Q-learning algorithm, because the training time is approximately 2 minutes and provides the best control for the model of an inverted pendulum on a cart.

scholarly journals Reinforcement Learning Technique in Multiple Motorway Access Control Strategy Design

2012 ◽  
Vol 22 (2) ◽  
pp. 117-123 ◽  
Author(s):  
Kostandina Veljanovska ◽  
Kristi M. Bombol ◽  
Tomaž Maher
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Access Control ◽  
Travel Time ◽  
Control Strategy ◽  
Learning Algorithm ◽  
Traffic Flows ◽  
Traffic Demand ◽  
Q Learning ◽  
Total Travel Time

An appropriately designed motorway access control can decrease the total travel time spent in the system up to 30% and consequently increase the merging operations safety. To date, implemented traffic responsive motorway access control systems have been of local or regulatory type and not truly adaptive in the real sense of the meaning. Hence, traffic flow can be influenced positively by numerous intelligent transportation system (ITS) techniques. In this paper a contemporary approach is presented. It considers the design philosophy of an optimal and adaptive closed-loop multiple motorway access control strategy. The methodology proposed uses the artificial intelligence technique - known as reinforcement learning (RL) with multiple agents, and applies the Q-learning algorithm. One segment of the motorway network with three lanes in each direction and three motorway entries was designed. The detectors and traffic signals were placed at the entries (ramps). Traffic flows and traffic occupancy on the main line as well as the traffic demand on the motorway entries were taken as input model variables. The output variables referred to the travel speed on the corridor, the total travel time, and the total stop time. VISSIM micro-simulator and direct programming of the simulator functions were used in order to implement the RL technique. The peak hour was chosen for the time of simulation. The model was tested in two phases. Its effectiveness was compared to ALINEA. It was observed that the proposed strategy was capable of responding both to dynamic sensory inputs from the environment and to dynamically changing environment. The model of the environment and supervision were not required. The control policy changed as response to the inherent system characteristic changes. It was confirmed that the strategy was truly adaptive and real-time responsive to the traffic demand on the corridor. KEY WORDS: motorway access, traffic flows, control, strategy, artificial intelligence, Q-Learning, simulation

scholarly journals An English Network Teaching Method Supported by Artificial Intelligence Technology and WBIETS System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Haibao Du
Keyword(s):  
Artificial Intelligence ◽  
Online Teaching ◽  
Teaching Method ◽  
Learning Algorithm ◽  
System Function ◽  
Teaching Resources ◽  
Teaching System ◽  
Network Teaching ◽  
English Teaching ◽  
Network Teaching System

The traditional English teaching system has certain problems in the acquisition of teaching resources and the innovation of teaching models. In order to improve the effect of subsequent English online teaching, this paper improves the machine learning algorithm to make it a core algorithm that can be used by artificial intelligence systems. Moreover, this paper combines the WBIETS system to expand the system function, analyzes the needs of the English network teaching system, and constructs the system function modules and logical structure. The data layer, logic layer, and presentation layer in the system constructed in this paper are independent of each other and can be effectively expanded when subsequent requirements change. In addition, this paper solves the problem of acquiring English teaching resources through the WBIETS system. To evaluate the performance of the English network teaching system, this paper performs comprehensive mathematical and experimental analysis. The experimental results show that the system constructed in this paper basically meets the actual teaching requirements.

A Computer-based Clinical Information System

1987 ◽  
Vol 26 (04) ◽  
pp. 189-194
Author(s):  
S. S. El-Gamal
Keyword(s):  
Information System ◽  
Hospital Information System ◽  
Clinical Information System ◽  
Research Work ◽  
Clinical Information ◽  
Hospital Staff ◽  
Vital Role ◽  
Daily Routine ◽  
Specialized Hospital ◽  
Computer Based

SummaryModern information technology offers new opportunities for the storage and manipulation of hospital information. A computer-based hospital information system, dedicated to urology and nephrology, was designed and developed in our center. It involves in principle the employment of a program that allows the analysis of non-restricted, non-codified texts for the retrieval and processing of clinical data and its operation by non-computer-specialized hospital staff.This Hospital Information System now plays a vital role in the efficient provision of a good quality service and is used in daily routine and research work in this hospital. This paper describes this specialized Hospital Information System.

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