scholarly journals information technology; image recognition; machine learning; plant identification from photo images; online-system; SURF; SIFT; FREAK; BOVW

2019 ◽  
Vol 23 (0) ◽  
Author(s):  
О. G. Baybuz ◽  
M. G. Sydorova ◽  
A. Ye. Polonska ◽  
Y. O. Rudakova
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Image Recognition ◽  
Plant Identification ◽  
Online System

The nexus between information technology and environmental pollution: Application of a new machine learning algorithm to OECD countries

2021 ◽  
Vol 72 ◽  
pp. 101256
Author(s):  
Cosimo Magazzino ◽  
Marco Mele ◽  
Giovanna Morelli ◽  
Nicolas Schneider
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Environmental Pollution ◽  
Learning Algorithm ◽  
Oecd Countries ◽  
Machine Learning Algorithm ◽  
New Machine

Water Droplet Erosion Life Prediction Method for Steam Turbine Blade Materials Based on Image Recognition and Machine Learning

2020 ◽  
Author(s):  
Zheyuan Zhang ◽  
Tianyuan Liu ◽  
Di Zhang ◽  
Yonghui Xie
Keyword(s):  
Machine Learning ◽  
Turbine Blade ◽  
Image Recognition ◽  
Water Droplet ◽  
Turbine Blades ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Feature Maps ◽  
Water Droplet Erosion ◽  
Data Processing Method

Abstract In this paper, a method for predicting remaining useful life (RUL) of turbine blade under water droplet erosion (WDE) based on image recognition and machine learning is presented. Using the experimental rig for testing the WDE characteristics of materials, the morphology pictures of specimen surface at different times in the process of WDE are collected. According to the data processing method of ASTM-G73 and the cumulative erosion-time curves, the WDE stages of materials is quantitatively divided and the WDE life coefficient (ζ) is defined. The life coefficient (ζ) could be used to calculate the RUL of turbine blades. One convolutional neural network model and three machine learning models are adopted to train and predict the image dataset. Then the training process and feature maps of the Resnet model are studied in detail. It is found that the highest prediction accuracy of the method proposed in this paper can be 0.949, which is considered acceptable to provide reference for turbine overhaul period and blade replacement time.

scholarly journals Combined learning, a good solution for Vietnamese universities

2020 ◽  
Vol 9 (1) ◽  
pp. 78-85
Author(s):  
Pham Thi Thu Hoa
Keyword(s):  
Higher Education ◽  
Information Technology ◽  
Online Learning ◽  
South Australia ◽  
Traditional Classroom ◽  
Learning System ◽  
Face To Face ◽  
Online System ◽  
Advantages And Disadvantages ◽  
Pros And Cons

Information technology has created tremendous chances in higher education across the globe. With the availability and flexibility of learning online, there are increasing competitions between online learning and traditional, face-to-face classroom. These two types of learning both have their pros and cons. From the advantages and disadvantages of online learning, universities have recently changed the way of their teaching through combining online learning and traditional classroom. Therefore, it is necessary to investigate and understand the advancements of the combination of the two types of learning. In this paper, we will introduce the advantages and disadvantages of online learning and the combined learning. We also share our experience on teaching at Flinders University, South Australia through Flinders learning online system. From this knowledge, we will suggest some solutions about building a combined learning system which is suitable for Vietnamese universities.

scholarly journals Research on the Application of Artificial Intelligence Machine Learning Technology in Improving the Accuracy of Engineering Image Processing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042007
Author(s):  
Xiaowen Liu ◽  
Juncheng Lei
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Recognition ◽  
Recognition Performance ◽  
Color Space ◽  
Gaussian Model ◽  
Image Feature ◽  
Layer By Layer ◽  
Image Information

Abstract Image recognition technology mainly includes image feature extraction and classification recognition. Feature extraction is the key link, which determines whether the recognition performance is good or bad. Deep learning builds a model by building a hierarchical model structure like the human brain, extracting features layer by layer from the data. Applying deep learning to image recognition can further improve the accuracy of image recognition. Based on the idea of clustering, this article establishes a multi-mix Gaussian model for engineering image information in RGB color space through offline learning and expectation-maximization algorithms, to obtain a multi-mix cluster representation of engineering image information. Then use the sparse Gaussian machine learning model on the YCrCb color space to quickly learn the distribution of engineering images online, and design an engineering image recognizer based on multi-color space information.

scholarly journals Implementation of One-Stop Services Policy (PTSP) in North Jakarta City, DKI Jakarta Province

2020 ◽  
Vol 10 (2) ◽  
pp. 381
Author(s):  
Mary Ismowati ◽  
Ahmad Hidayat Rahadian ◽  
Muhammad Ali Massyhury ◽  
Muhammad Rafi Suryadi
Keyword(s):  
Information Technology ◽  
Human Resources ◽  
Fully Integrated ◽  
Online System ◽  
The North ◽  
Doing Business ◽  
One Stop ◽  
Ease Of Doing Business ◽  
The One ◽  
Research Show

The aim of the study was to obtain a formulation of the implementation of the One-Stop Services Policy (PTSP) in the North Jakarta Administrative City, namely the implementation of Perda No. 12 of 2013, and the Implementation of Presidential Regulation No. 91 of 2017 concerning the acceleration of ease of doing business through the implementation of an information technology-based licensing system (OSS). The research method used a qualitative approach by conducting interviews with a number of informants to determine the conditions and phenomena of the implementation of PTSP policies in North Jakarta. The research involved two research members from students in the framework of thesis research. The results of the research show that implementers have understood their duties and functions, but in the smooth running of their duties, they are constrained by a lack of human resources both in quantity and quality, support for information technology equipment that is not up to date, including the availability of information technology personnel at the district and sub-district levels. Then the OSS policy according to PP No. 24/2018 has not been fully effective, because it has not been fully integrated with the existing licensing system in PM-PTSP DKI Jakarta, namely JakEVO. The main obstacle to policy implementation, namely in terms of human resources, employee status has not been transferred to functional positions so that it affects career ranks and motivation. In addition, there is no HR competency standard for services, for online system services are not implemented according to target. The solution to overcome the obstacles conceptually has not been done.

scholarly journals The Pros & Cons of Business Innovation at IT Organizations

2019 ◽  
Vol 7 (1) ◽  
pp. 82-85
Author(s):  
Geetha Swaminathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Technology ◽  
Cloud Computing ◽  
New Technologies ◽  
Global Market ◽  
Innovative Product ◽  
Business Innovation ◽  
It Organization ◽  
Pros And Cons

In the 21st Century, the buzzword is often used in all fields is “Innovation". It is no wonder using Innovation in day to the conversation as well as striving for innovation execution at organisations in Information Technology (IT) sectors. When we need to talk about innovation in IT sectors in the fast-moving technology IT organisations, they are in a position in increasing its capability in its innovative product and services. There is a lot of benefits out of business innovations that are being reaped in IT companies; there are apparent disadvantages are also the outcome of them. It is quite common, despite all benefits and drawbacks, they are in apposition to survive in the global market. That becomes a great challenge to all IT organisations. In IT organisations which consist of departments such as Development, Testing, Consulting, Networking, Infrastructure, Process and having common platforms and legacy languages, Apart from that they are in the way of invading new technologies such as Digital, Mobile, IoT, Artificial Intelligence, Machine learning Cloud computing. In all the fields, as mentioned above and area, they need to do innovation to sustain their business. This paper will provide elaborate results on Pros and Cons of Business Innovation in IT Organization.

scholarly journals John Senior and Éva Gyarmathy: AI and Developing Human Intelligence, Future Learning and Educational Innovation

2021 ◽  
Vol 28 (3) ◽  
pp. 442-446
Author(s):  
Valentin Kuleto ◽  
Milena Ilić
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Problem Solving ◽  
Computer Science ◽  
Image Recognition ◽  
Language Processing ◽  
Educational Innovation ◽  
Human Intelligence ◽  
Intelligent Machines ◽  
Future Learning

AI is a branch of computer science that emphasises the development of intelligent machines that think and work like humans. Examples of AI applications are speech recognition, natural language processing, image recognition etc. The term ML represents the application of AI to enable systems’ ability to learn and improve based on experience, without the explicit need for programming, using various problem-solving algorithms. For example, in machine learning, computers learn based on the data they process, not program instructions

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