• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• The Journal of Korean Association of Computer Education
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• v.23 no.1
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• pp.37-44
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• 2020

As the importance of artificial intelligence(AI) education is emphasized recently, policies and researches are being promoted to develop the AI curriculum or courses for K-12 students in worldwide. In this study, researcher analysed a synthesis of contents and standards on AI education curriculum to present implications for AI education in the elementary and secondary schools. As a result, Korea and the United States are proposing national curriculum standards to provide the basis for AI curriculum establishment in school sites and to provide guidelines for various related policies such as teacher training programs. The EU's AI education is characterized by its curriculum and online courses to ensure that all citizens of the EU have AI literacy, rather than designating students or subjects at specific school levels. In terms of educational contents and levels, Korea, United States, and EU's curriculum or standards includes basics and applications related to machine learning and neural network based on the fundamental concepts and principles of artificial intelligence.

• Journal of Korean Society for Quality Management
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• v.51 no.2
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• pp.283-296
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• 2023

Purpose: The purpose of this study is to actually implement and verify whether welding defects can be detected in real time by utilizing deep learning AI solutions in the welding process of electric vehicle hairpin winding motors. Methods: AI's function and technological elements using synthetic neural network were applied to existing electric vehicle hairpin winding motor laser welding process by making special hardware for detecting electric vehicle hairpin motor laser welding defect. Results: As a result of the test applied to the welding process of the electric vehicle hairpin winding motor, it was confirmed that defects in the welding part were detected in real time. The accuracy of detection of welds was achieved at 0.99 based on mAP@95, and the accuracy of detection of defective parts was 1.18 based on FB-Score 1.5, which fell short of the target, so it will be supplemented by introducing additional lighting and camera settings and enhancement techniques in the future. Conclusion: This study is significant in that it improves the welding quality of hairpin winding motors of electric vehicles by applying domestic artificial intelligence solutions to laser welding operations of hairpin winding motors of electric vehicles. Defects of a manufacturing line can be corrected immediately through automatic welding inspection after laser welding of an electric vehicle hairpin winding motor, thus reducing waste throughput caused by welding failure in the final stage, reducing input costs and increasing product production.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.501-511
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• 2022

In 2021, ransomware attacks became popular, and the number is rapidly increasing every year. Since PowerShell is used as the primary ransomware technique, the need for PowerShell-based malware detection is ever increasing. However, the existing detection techniques have limits in that they cannot detect obfuscated scripts or require a long processing time for deobfuscation. This paper proposes a simple and fast deobfuscation method and a deep learning-based classification model that can detect PowerShell-based malware. Our technique is composed of Word2Vec and a convolutional neural network to learn the meaning of a script extracting important features. We tested the proposed model using 1400 malicious codes and 8600 normal scripts provided by the AI-based PowerShell malicious script detection track of the 2021 Cybersecurity AI/Big Data Utilization Contest. Our method achieved 5.04 times faster deobfuscation than the existing methods with a perfect success rate and high detection performance with FPR of 0.01 and TPR of 0.965.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.225-234
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• 2023

In this era of information and communication technology (ICT), tremendous improvements have been witnessed in our daily lives. The impact of these technologies is subjective and negative or positive. For instance, ICT has brought a lot of ease and versatility in our lifestyles, on the other hand, its excessive use brings around issues related to physical and mental health etc. In this study, we are bridging these both aspects by proposing the idea of AI based mental healthcare (AIMS). In this regard, we aim to provide a platform where the patient can register to the system and take consultancy by providing their assessment by means of a chatbot. The chatbot will send the gathered information to the machine learning block. The machine learning model is already trained and predicts whether the patient needs a treatment by classifying him/her based on the assessment. This information is provided to the mental health practitioner (doctor, psychologist, psychiatrist, or therapist) as clinical decision support. Eventually, the practitioner will provide his/her suggestions to the patient via the proposed system. Additionally, the proposed system prioritizes care, support, privacy, and patient autonomy, all while using a friendly chatbot interface. By using technology like natural language processing and machine learning, the system can predict a patient's condition and recommend the right professional for further help, including in-person appointments if necessary. This not only raises awareness about mental health but also makes it easier for patients to start therapy.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.132-138
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• 2022

Due to the impacts of the current pandemic COVID-19 and the continuation of studying online. There is an urgent need for an effective and efficient education platform to help with the continuity of studying online. Therefore, the question bank system (QB) is introduced. The QB system is designed as a website to create a single platform used by faculty members in universities to generate questions and store them in a bank of questions. In addition to allowing them to add two types of questions, to help the lecturer create exams and present the results of the students to them. For the implementation, two languages were combined which are PHP and Python to generate questions by using Artificial Intelligence (AI). These questions are stored in a single database, and then these questions could be viewed and included in exams smoothly and without complexity. This paper aims to help the faculty members to reduce time and efforts by using the Question Bank System by using AI and Natural Language Processing (NLP) to extract and generate questions from given text. In addition to the tools used to create this function such as NLTK and TextBlob.

• Journal of Sensor Science and Technology
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• v.29 no.1
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• pp.19-26
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• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.155-161
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• 2020

In general, optimization problems are difficult to solve simply. The reason is that the given problem is solved as soon as it is simple, but the more complex it is, the very large number of cases. This study is about the optimization of AI neural network. What we are dealing with here is the relief method for constructing AI network. The main topics deal with non-deterministic issues such as the stability and unstability of the overall network state, cost down and energy down. For this one, we discuss associative memory models, that is, a method in which local minimum memory information does not select fake information. The simulated annealing, this is a method of estimating the direction with the lowest possible value and combining it with the previous one to modify it to a lower value. And nonlinear planning problems, it is a method of checking and correcting the input / output by applying the appropriate gradient descent method to minimize the very large number of objective functions. This research suggests a useful approach to relief method as a theoretical approach to solving optimization problems. Therefore, this research will be a good proposal to apply efficiently when constructing a new AI neural network.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.981-993
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• 2018

Smart healthcare is convergence of ICT and healthcare services, and interdisciplinary research has been actively conducted in various fields. The objective of this study is to investigate trends of smart healthcare research using topic modeling and ego network analysis. Text analysis, frequency analysis, topic modeling, word cloud, and ego network analysis were conducted for the abstracts of 2,690 articles in Scopus from 2001 to April 2018. Topic Modeling analysis resulted in eight topics, Topics included "AI in healthcare", "Smart hospital", "Healthcare platform", "Blockchain in healthcare", "Smart health data", "Mobile healthcare", " Wellness care", "Cognitive healthcare". In order to examine the topic modeling results core deeply, we analyzed word cloud and ego network analysis for eight topics. This study aims to identify trends in smart healthcare research and suggest implications for establishing future research direction.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.262-265
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• 2018

Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.