scholarly journals An Analysis of the Effectiveness of Machine Learning Theory in the Evaluation of Education and Teaching

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bo Wu ◽  
Changlong Zheng
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Theory ◽  
Teaching Quality ◽  
Evaluation Methods ◽  
Colleges And Universities ◽  
Teaching Evaluation ◽  
Learning Ability ◽  
Paper Machine ◽  
Human Beings

Artificial intelligence was first proposed in the 1950s, when it was only a forward-looking concept. If machines can have the same learning ability as human beings and the computing power of computers themselves, this concept has been placed high hopes. Until about 2010, with the explosion of data volume and the improvement of computer performance, machine learning has become a leader in breaking through the bottleneck of artificial intelligence. Research on machine learning in education and teaching has attracted much attention. From the above research status, we can see that in the current period of the vigorous development of machine learning, many applications are still not perfect and ordinary education and teaching evaluation is difficult to meet people’s requirements, so how to gradually improve its effectiveness is a significant goal with research significance and practical interests. However, in the environment of colleges and universities, prediction information and evaluation methods have important application value and development space in education and teaching. In this context, according to the theory of machine science, the effectiveness of several conventional prediction and evaluation methods is analyzed. In this paper, machine learning theory is used to study college students’ performance prediction and credit evaluation, as well as teaching quality evaluation and comprehensive ability evaluation in colleges and universities. Questionnaire survey is used to investigate and analyze the results. The effectiveness of machine theory in teaching is analyzed. It is found that machine learning has great advantages in education and teaching evaluation. It builds models in complex computing environment and is not affected by human factors; the effectiveness of prediction and evaluation is significant.

scholarly journals Urban living environment assessment index system based on psychological security

2019 ◽  
Author(s):  
Xia Huiyi ◽  
◽  
Nankai Xia ◽  
Liu Liu ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Collection ◽  
Urban Environment ◽  
Evaluation System ◽  
Living Environment ◽  
Mobile Internet ◽  
Practical Significance ◽  
Human Beings ◽  
Urban Living

With the development of urbanization and the continuous development, construction and renewal of the city, the living environment of human beings has also undergone tremendous changes, such as residential community environment and service facilities, urban roads and street spaces, and urban public service formats. And the layout of the facilities, etc., and these are the real needs of people in urban life, but the characteristics of these needs or their problems will inevitably have a certain impact on the user's psychological feelings, thus affecting people's use needs. Then, studying the ways in which urban residents perceive changes in the living environment and how they perceive changes in psychology and emotions will have practical significance and can effectively assist urban management and builders to optimize the living environment of residents. This is also the long-term. One of the topics of greatest interest to urban researchers since then. In the theory of demand hierarchy proposed by American psychologist Abraham Maslow, safety is the basic requirement second only to physiological needs. So safety, especially psychological security, has become one of the basic needs of people in the urban environment. People's perception of the psychological security of the urban environment is also one of the most important indicators in urban environmental assessment. In the past, due to the influence of technical means, the study of urban environmental psychological security often relied on the limited investigation of a small number of respondents. Low-density data is difficult to measure the perceptual results of universality. With the leaping development of the mobile Internet, Internet image data has grown geometrically over time. And with the development of artificial intelligence technology in recent years, image recognition and perception analysis based on machine learning has become possible. The maturity of these technical conditions provides a basis for the study of the urban renewal index evaluation system based on psychological security. In addition to the existing urban visual street furniture data obtained through urban big data collection combined with artificial intelligence image analysis, this paper also proposes a large number of urban living environment psychological assessment data collection strategies. These data are derived from crowdsourcing, and the collection method is limited by the development of cost and technology. At present, the psychological security preference of a large number of users on urban street images is collected by forced selection method, and then obtained by statistical data fitting to obtain urban environmental psychology. Security sense training set. In the future, when the conditions are mature, the brainwave feedback data in the virtual reality scene can be used to carry out the machine learning of psychological security, so as to improve the accuracy of the psychological security data.

scholarly journals Machine Learning and Artificial Intelligence in Online Fake Transaction Alerting

2015 ◽  
Vol 3 (2) ◽  
pp. 115-126 ◽  
Author(s):  
Naresh Babu Bynagari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fraud Detection ◽  
Learning Ability ◽  
The Core ◽  
The Past ◽  
Music Selection ◽  
Intelligent Analysis ◽  
Set Up ◽  
Detection Mechanisms

Artificial Intelligence (AI) is one of the most promising and intriguing innovations of modernity. Its potential is virtually unlimited, from smart music selection in personal gadgets to intelligent analysis of big data and real-time fraud detection and aversion. At the core of the AI philosophy lies an assumption that once a computer system is provided with enough data, it can learn based on that input. The more data is provided, the more sophisticated its learning ability becomes. This feature has acquired the name "machine learning" (ML). The opportunities explored with ML are plentiful today, and one of them is an ability to set up an evolving security system learning from the past cyber-fraud experiences and developing more rigorous fraud detection mechanisms. Read on to learn more about ML, the types and magnitude of fraud evidenced in modern banking, e-commerce, and healthcare, and how ML has become an innovative, timely, and efficient fraud prevention technology.

Artificial intelligence in the workplace – A double-edged sword

2020 ◽  
Vol 37 (5) ◽  
pp. 253-265
Author(s):  
Uta Wilkens
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Organizational Learning ◽  
Learning Theory ◽  
Practical Implication ◽  
Job Design ◽  
Macro Level ◽  
Learning Approaches ◽  
Content Type ◽  
Meso Level

PurposeThe aim of this paper is to outline how artificial intelligence (AI) can augment learning process in the workplace and where there are limitations.Design/methodology/approachThe paper is a theoretical-based outline with reference to individual and organizational learning theory, which are related to machine learning methods as they are currently in use in the workplace. Based on these theoretical insights, the paper presents a qualitative evaluation of the augmentation potential of AI to assist individual and organizational learning in the workplace.FindingsThe core outcome is that there is an augmentation potential of AI to enhance individual learning and development in the workplace, which however should not be overestimated. AI has a complementarity to individual intelligence, which can lead to an advancement, especially in quality, accuracy and precision. Moreover, AI has a potential to support individual competence development and organizational learning processes. However, a further outcome is that AI in the workplace is a double-edged sword, as it easily shows reinforcement effects in individual and organizational learning, which have a backside of unintended effects.Research limitations/implicationsThe conceptual outline makes use of examples for illustrating phenomenon but needs further empirical analysis. The research focus on the meso level of the workplace does not fully refer to macro level outcomes.Practical implicationsThe practical implication is that it is a matter of socio-technical job design to integrate AI in the workplace in a valuable manner. There is a need to keep the human-in-the-loop and to complement AI-based learning approaches with non-AI counterparts to reach augmentation.Originality/valueThe paper faces workplace learning from an interdisciplinary perspective and bridges insights from learning theory with methods from the machine learning community. It directs the social science discourse on AI, which is often on macro level to the meso level of the workplace and related issues for job design and therefore provides a complementary perspective.

Artificial intelligence with fuzzy logic system for learning management evaluation in higher educational systems

2020 ◽  
pp. 1-11
Author(s):  
ZhiYuan Lv ◽  
Hengyun Shen
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Model ◽  
Fuzzy Logic ◽  
Evaluation System ◽  
Learning Experience ◽  
Training Model ◽  
Colleges And Universities ◽  
Teaching Evaluation ◽  
Higher Learning ◽  
The Mathematical Model

In this paper, the mathematical model and algorithm based on knowledge forgetting curve are studied. Through the analysis of the current mathematical modeling and application of “knowledge forgetting curve”, the artificial intelligence method of fuzzy mathematics knowledge and differential modeling is adopted. This paper puts forward the mathematical model and algorithm design of the new “knowledge forgetting curve”, which aims to improve the intelligence of the software and bring a new learning experience for the teaching evaluation of the education system in colleges and universities. The fuzzy logic theory is applied to the teaching evaluation system of higher learning pedagogy, according to pedagogy and other related theories, combined with the current teaching evaluation indicators of colleges and universities, the teaching evaluation indicators of higher learning education are set according to certain requirements. The sample wood data is divided into two parts by using the fuzzy logic principle, and the training model is obtained by training the sample data in the evaluation system, and the training model is used to intelligently evaluate and analyze the prediction data.

scholarly journals An Immersive Context Teaching Method for College English Based on Artificial Intelligence and Machine Learning in Virtual Reality Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Li Ma
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Reality ◽  
Teaching Method ◽  
Communicative Competence ◽  
Language Teaching ◽  
Teaching Experiment ◽  
Learning Ability ◽  
Virtual Reality Technology ◽  
College English

With the development of language research and language teaching, people realize that grammatical competence is an important part of communicative competence. In foreign language teaching, grammar teaching is not only necessary but also the main way to achieve the goal of communicative competence. This article mainly studies the virtual reality technology college English immersive context teaching method based on artificial intelligence and machine learning. The purpose is to improve students’ English learning ability. Through the comparative teaching experiment of two classes of freshmen in a university, the experimental class conducted VR technology-based immersive virtual context teaching from the perspective of constructivism, while the control class adopted common multimedia equipment and traditional teaching methods. In the classroom, teachers occupy most of the time, students only passively receive a lot of information from teachers, they have little chance to participate in the exchange of information and express ideas in the target language, and most of the time they are “immersed” in the Chinese environment. The overall English level was also better than that of the control class, with an average score of 2.8 points higher. This shows that college English immersive context teaching combining constructivism theory and VR technology can indeed improve students’ English level.

scholarly journals Artificial Intelligence: A New Tool in Oncologist's Armamentarium

2021 ◽  
Author(s):  
Vineet Talwar ◽  
Kundan Singh Chufal ◽  
Srujana Joga
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Organs At Risk ◽  
Elderly Care ◽  
External Validation ◽  
Data Access ◽  
Human Beings ◽  
Area Of Interest ◽  
Segmentation Image

AbstractArtificial intelligence (AI) has become an essential tool in human life because of its pivotal role in communications, transportation, media, and social networking. Inspired by the complex neuronal network and its functions in human beings, AI, using computer-based algorithms and training, had been explored since the 1950s. To tackle the enormous amount of patients' clinical data, imaging, histopathological data, and the increasing pace of research on new treatments and clinical trials, and ever-changing guidelines for treatment with the advent of novel drugs and evidence, AI is the need of the hour. There are numerous publications and active work on AI's role in the field of oncology. In this review, we discuss the fundamental terminology of AI, its applications in oncology on the whole, and its limitations. There is an inter-relationship between AI, machine learning and, deep learning. The virtual branch of AI deals with machine learning. While the physical branch of AI deals with the delivery of different forms of treatment—surgery, targeted drug delivery, and elderly care. The applications of AI in oncology include cancer screening, diagnosis (clinical, imaging, and histopathological), radiation therapy (image acquisition, tumor and organs at risk segmentation, image registration, planning, and delivery), prediction of treatment outcomes and toxicities, prediction of cancer cell sensitivity to therapeutics and clinical decision-making. A specific area of interest is in the development of effective drug combinations tailored to every patient and tumor with the help of AI. Radiomics, the new kid on the block, deals with the planning and administration of radiotherapy. As with any new invention, AI has its fallacies. The limitations include lack of external validation and proof of generalizability, difficulty in data access for rare diseases, ethical and legal issues, no precise logic behind the prediction, and last but not the least, lack of education and expertise among medical professionals. A collaboration between departments of clinical oncology, bioinformatics, and data sciences can help overcome these problems in the near future.

Applications of Machine Learning in Cyber Security Domain

2020 ◽  
pp. 64-82 ◽  
Author(s):  
Sailesh Suryanarayan Iyer ◽  
Sridaran Rajagopal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Security ◽  
Traditional Society ◽  
Future Trends ◽  
Human Beings ◽  
Online Transactions ◽  
Network Intrusion ◽  
The World ◽  
Applications Of Machine Learning

Knowledge revolution is transforming the globe from traditional society to a technology-driven society. Online transactions have compounded, exposing the world to a new demon called cybercrime. Human beings are being replaced by devices and robots, leading to artificial intelligence. Robotics, image processing, machine vision, and machine learning are changing the lifestyle of citizens. Machine learning contains algorithms which are capable of learning from historical occurrences. This chapter discusses the concept of machine learning, cyber security, cybercrime, and applications of machine learning in cyber security domain. Malware detection and network intrusion are a few areas where machine learning and deep learning can be applied. The authors have also elaborated on the research advancements and challenges in machine learning related to cyber security. The last section of this chapter lists the future trends and directions in machine learning and cyber security.

A Short Introduction to the Ethics of Artificial Intelligence

2020 ◽  
pp. 1-42
Author(s):  
S. Matthew Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intelligent Systems ◽  
Ethical Issues ◽  
Black Box ◽  
Human Beings ◽  
Key Issues ◽  
Long Term Impact ◽  
Bad Data

This introduction outlines in section I.1 some of the key issues in the study of the ethics of artificial intelligence (AI) and proposes ways to take these discussions further. Section I.2 discusses key concepts in AI, machine learning, and deep learning. Section I.3 considers ethical issues that arise because current machine learning is data hungry; is vulnerable to bad data and bad algorithms; is a black box that has problems with interpretability, explainability, and trust; and lacks a moral sense. Section I.4 discusses ethical issues that arise because current machine learning systems may be working too well and human beings can be vulnerable in the presence of these intelligent systems. Section I.5 examines ethical issues arising out of the long-term impact of superintelligence such as how the values of a superintelligent AI can be aligned with human values. Section I.6 presents an overview of the essays in this volume.

scholarly journals Ranking of Teaching Evaluation Index System of Public Physical Education Curriculum Based on Management by Objectives Using Analytic Hierarchy Process

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Chenyang Zhao ◽  
A. Gudamu
Keyword(s):  
Physical Education ◽  
Index System ◽  
Evaluation System ◽  
Formal Method ◽  
Teaching Quality ◽  
Colleges And Universities ◽  
Teaching Evaluation ◽  
Education Curriculum ◽  
Management By Objectives ◽  
Starting Point

Teaching evaluation is an important means to ensure the teaching quality of public physical education in colleges and universities. At present, physical education in colleges and universities is constantly changing, and the evaluation system of public physical education has also changed greatly. Based on the perspective of management by objectives, this paper makes a comprehensive evaluation of physical education teaching from two aspects: teachers’ teaching preparation and practical teaching. It takes the cultivation of students’ sports habits and lifelong sports consciousness as the ultimate goal, establishes 22 indexes to form an index system, and follows the requirements of the new curriculum teaching reform for the development of physical education. In order to continuously improve the level of physical education teaching as the guiding concept, the indexes are refined and decomposed. To determine the weight score of the three-level indexes and the index weight ranking, AHP is used, which is a formal method used to derive ranking from pairwise comparison technique, in accordance with a certain logical relationship, and provide reference for the evaluation of public physical education teaching in colleges and universities. In order to achieve the multiple objectives of public physical education curriculum as the starting point, attention should paid to the teaching focus of public physical education curriculum, and a comprehensive reform strategy should be put forward.

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