Artificial Intelligence-Based Quality Management and Detection System for Personalized Learning

2021 ◽  
Author(s):  
Haixia Yu ◽  
Jidong Wang ◽  
Mohanraj Murugesan ◽  
A. B. M. Salman Rahman
Keyword(s):  
Artificial Intelligence ◽  
Quality Management ◽  
Teaching And Learning ◽  
Detection System ◽  
Personalized Learning ◽  
Heuristic Approach ◽  
Performance Ratio ◽  
Student Profile ◽  
Detection Systems ◽  
Learning Path

Recently, the teaching and learning method in the conventional engineering education system needs a group of learners with personalized learning paths. The introduction of technologies like Artificial Intelligence will aid the learners to identify and detect learning opportunities utilizing historical information, present student profile and success data from an institution, and recommend learning measures to enhance their performance. This study proposes an Artificial Intelligence-based Meta-Heuristic Approach (AIMHA) for personalized learning detection systems and quality management. The proposed model has been utilized to optimize learning effectiveness by considering the nature of the learning path and the number of simultaneous visits to every learning action. In addition, a quality resolution can be determined by a meta-heuristic approach. The simulation findings of the learning actions have been utilized to examine the efficiency of the suggested method. The proposed method is evaluated learning activities achieved an efficiency ratio of 92.3%, sensitivity analysis ratio of 88.4%, performance ratio of 92.3%, precision ratio of 94.3% compared to other existing models.

scholarly journals Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Transfer ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Fire Detection System

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

scholarly journals Suspicious Human Activity Detection System

2020 ◽  
Vol 2 (4) ◽  
pp. 216-221
Author(s):  
Pankaj Bhambri ◽  
Sachin Bagga ◽  
Dhanuka Priya ◽  
Harnoor Singh ◽  
Harleen Kaur Dhiman
Keyword(s):  
Artificial Intelligence ◽  
Anomaly Detection ◽  
Detection System ◽  
Behavioral Analysis ◽  
Activity Detection ◽  
Credit Score ◽  
Detection Systems ◽  
Score Card ◽  
Fitness Tracking ◽  
Human Activity Detection

In collaboration with machine learning and artificial intelligence, anomaly detection systems are vastly used in behavioral analysis so that you can help in identity and prediction of prevalence of anomalies. It has applications in enterprise, from intrusion detection to system fitness tracking, and from fraud detection in credit score card transactions to fault detection in running environments. With the growing crime charges and human lack of confidence globally, majority of the countries are adopting precise anomaly detection systems to approach closer to a comfy area. Visualizing the Indian crime index which stands at 42. 38, the adoption of anomaly detection structures is an alarming want of time. Our own cannot be prevented with the aid of CCTV installations. These systems not simplest lead to identification on my own, but their optimized versions can help in prediction of unusual activities as properly.

scholarly journals Comparative Study of Vehicle Detection using SSD and Faster RCNN

2021 ◽  
Vol 10 (7) ◽  
pp. 28-33
Author(s):  
K. Mohan Krishna ◽  
A. Sowmya ◽  
D. Jerusha ◽  
D. Susmitha
Keyword(s):  
Artificial Intelligence ◽  
Law Enforcement ◽  
Traffic Management ◽  
Detection System ◽  
Vehicle Detection ◽  
Single Shot ◽  
Accuracy Score ◽  
Efficient System ◽  
Detection Systems ◽  
Recent Developments

With the recent developments in Artificial Intelligence, vehicle detection systems have become an essential part of many sectors like transport, automobile, security, law enforcement, and traffic management. This increased the requirement for an efficient system for vehicle detection. The main focus of our work is to find the best algorithm which can be used to design a vehicle detection system. For this, we compare two well-known deep learning algorithms which are Faster R-CNN and Single Shot Detector (SSD) algorithms. Both of the Pre-trained models of Tensorflow were tested on a dataset of hundred images with cars in them. It was found that Faster R-CNN is better with an accuracy score of 82.75 but was slower than SSD, whereas SSD had an accuracy score of 80.58 but was faster compared to Faster R-CNN.

Modern Art Education and Teaching Based on Artificial Intelligence

2021 ◽  
pp. 2141005
Author(s):  
Zhang Wen ◽  
Achyut Shankar ◽  
A. Antonidoss
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Graphical Representation ◽  
Assessment Model ◽  
Performance Ratio ◽  
Art Instruction ◽  
Art Teaching ◽  
Art Design ◽  
Smart Technologies ◽  
The Impact

The rapid advancement of artificial intelligence has been intensely employed in art teaching and learning. Including the advancement of smart technologies, there are various difficulties in improving the teaching capability of technical art design courses, including the impact of several variables and the absence of quantitative study, and the imperfection in the index system. The paper proposes the Artificial Intelligence assisted Effective Art Teaching Framework (AIEATF) to expand the ability to adapt to AI-oriented art instruction, develop intelligent teaching styles, and enhance AI-oriented art teaching art knowledge and environment. The potential of improving AI’s effects on major art courses’ teaching effect has been illustrated in detail. On this basis, an assessment model has been developed to consider the enhancing effects. The study’s findings include a valuable guide for educators in art design to strengthen their teaching ability. The experimental results have shown that Modern Painting Perfection Ratio is 87.66%, Computer graphical representation ratio is 88.77%, Photographical Design Percentage ratio is 84.50%, Performance of Carving in Sculpture Ratio is 82.26%, Construction Development Ratio is 93.83%, Expressive Musical Performing Ratio is 92.70%, Energized Dance Performance Ratio is 84.26%, and overall performance ratio is 92.30%.

An Artificial Intelligence-based Intrusion Detection System

2021 ◽  
Vol 07 (02) ◽  
pp. 95-111
Author(s):  
Thani Almuhairi ◽  
◽  
Ahmad Almarri ◽  
Khalid Hokal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Learning Approaches ◽  
Malicious Attacks ◽  
Medical Systems ◽  
Detection Systems

Intrusion detection systems have been used in many systems to avoid malicious attacks. Traditionally, these intrusion detection systems use signature-based classification to detect predefined attacks and monitor the network's overall traffic. These intrusion detection systems often fail when an unseen attack occurs, which does not match with predefined attack signatures, leaving the system hopeless and vulnerable. In addition, as new attacks emerge, we need to update the database of attack signatures, which contains the attack information. This raises concerns because it is almost impossible to define every attack in the database and make the process costly also. Recently, research in conjunction with artificial intelligence and network security has evolved. As a result, it created many possibilities to enable machine learning approaches to detect the new attacks in network traffic. Machine learning has already shown successful results in the domain of recommendation systems, speech recognition, and medical systems. So, in this paper, we utilize machine learning approaches to detect attacks and classify them. This paper uses the CSE-CIC-IDS dataset, which contains normal and malicious attacks samples. Multiple steps are performed to train the network traffic classifier. Finally, the model is deployed for testing on sample data.

Particle swarm optimization neural network for research on artificial intelligence college English classroom teaching framework

2020 ◽  
pp. 1-13
Author(s):  
Dong Juan ◽  
Yu Hong Wei
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Personalized Learning ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
College English ◽  
Learning Path ◽  
English Classroom

This paper based on the algorithm of particle swarm optimization neural network, the university English classroom training framework with artificial intelligence is researched and designed, and a personalized learning path based on an improved binary particle swarm algorithm based on the non-linear increase of inertial weights and the exploration of unknown space is proposed. The recommendation method improves the algorithm’s convergence speed and convergence accuracy. It is easy to jump out of the local optimum through the improvement of the algorithm, thereby solving the problem of low recommendation accuracy of the personalized learning path and improving the recommendation efficiency. To verify the recommended effect of the model and algorithm, this paper designs a simulation experiment and a learning platform that take the college English course as an example to verify the running performance and practical application effect of the proposed method. The above experiments show that the proposed method can improve the matching degree of the personalized learning path and the needs of learners, and improve the accuracy of application in personalized learning path recommendation.

Robot Teaching, Pedagogy, and Policy

2020 ◽  
pp. 787-803
Author(s):  
Elana Zeide
Keyword(s):  
Artificial Intelligence ◽  
Analytical Methods ◽  
Personalized Learning ◽  
Educational Process ◽  
Learning Systems ◽  
Policy Decisions ◽  
Public Discussion ◽  
Teaching Pedagogy ◽  
Do So

This chapter looks at the use of artificial intelligence (AI) in education, which immediately conjures the fantasy of robot teachers, as well as fears that robot teachers will replace their human counterparts. However, AI tools impact much more than instructional choices. Personalized learning systems take on a whole host of other educational roles as well, fundamentally reconfiguring education in the process. They not only perform the functions of robot teachers but also make pedagogical and policy decisions typically left to teachers and policymakers. Their design, affordances, analytical methods, and visualization dashboards construct a technological, computational, and statistical infrastructure that literally codifies what students learn, how they are assessed, and what standards they must meet. However, school procurement and implementation of these systems are rarely part of public discussion. If they are to remain relevant to the educational process itself, as opposed to just its packaging and context, schools and their stakeholders must be more proactive in demanding information from technology providers and setting internal protocols to ensure effective and consistent implementation. Those who choose to outsource instructional functions should do so with sufficient transparency mechanisms in place to ensure professional oversight guided by well-informed debate.

scholarly journals Time Saving Students’ Formative Assessment: Algorithm to Balance Number of Tasks and Result Reliability

2021 ◽  
Vol 11 (13) ◽  
pp. 6048
Author(s):  
Jaroslav Melesko ◽  
Simona Ramanauskaite
Keyword(s):  
Formative Assessment ◽  
Item Response ◽  
Item Difficulty ◽  
Classical Test Theory ◽  
Personalized Learning ◽  
Test Theory ◽  
Time Saving ◽  
Learning Path ◽  
Crucial Component

Feedback is a crucial component of effective, personalized learning, and is usually provided through formative assessment. Introducing formative assessment into a classroom can be challenging because of test creation complexity and the need to provide time for assessment. The newly proposed formative assessment algorithm uses multivariate Elo rating and multi-armed bandit approaches to solve these challenges. In the case study involving 106 students of the Cloud Computing course, the algorithm shows double learning path recommendation precision compared to classical test theory based assessment methods. The algorithm usage approaches item response theory benchmark precision with greatly reduced quiz length without the need for item difficulty calibration.

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