• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.125-139
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• 2017

Recently, interest in improving the quality of EMS(emergency medical services) has been increasing. Much effort is being made to innovate the EMS process. The rapid progress of ICT technology has accelerated the automation or intelligence of EMS processes. This study suggests an emergency room management method based on real-time data considering resource utilization optimization, minimization of human error and enhancement of predictability of medical care. Emergency room operation indices - Emergency care index, Short stay index, Human error inducing index, Waiting patience index - are developed. And emergency room operation rules based on these indices are presented. Simulation was performed on a virtual emergency room to verify the effectiveness of the proposed operating rule. Simulation results showed excellent performance in terms of length of stay.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.237-244
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• 2008

In this paper, an infra-structure using the ontology based on the pulse information is proposed for the context-aware service of medical information system in ubiquitous computing environment. An diagnosis service inference system that represents the pulse data which was generated by the pulse-diagnosis with wearable signal, temperature, humidity, time, and other factors as ontology with artificial intelligence methods and describes the service scenario based on the ontology is designed and implemented.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.321-331
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• 2003

Experimental software datasets describing software projects in terms of their complexity and development time have been the subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such approaches as neural networks, fuzzy, and fuzzy neural network models. In this study, we introduce the concept of the Rule-based fuzzy polynomial neural networks (RFPNN) as a hybrid modeling architecture and discuss its comprehensive design methodology. The development of the RFPNN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the RFPNN results from a synergistic usage of RFNN and PNN. RFNN contribute to the formation of the premise part of the rule-based structure of the RFPNN. The consequence part of the RFPNN is designed using PNN. We discuss two kinds of RFPNN architectures and propose a comprehensive learning algorithm. In particular, it is shown that this network exhibits a dynamic structure. The experimental results include well-known software data such as the NASA dataset concerning software cost estimation and the one describing software modules of the Medical Imaging System (MIS).

• Korean Journal of Artificial Intelligence
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• v.5 no.2
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• pp.15-25
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• 2017

Recently, technologies are being developed to recognize and authenticate users using bioinformatics to solve information security issues. Biometric information includes face, fingerprint, iris, voice, and vein. Among them, face recognition technology occupies a large part. Face recognition technology is applied in various fields. For example, it can be used for identity verification, such as a personal identification card, passport, credit card, security system, and personnel data. In addition, it can be used for security, including crime suspect search, unsafe zone monitoring, vehicle tracking crime.In this thesis, we conducted a study to recognize faces by detecting the areas of the face through a computer webcam. The purpose of this study was to contribute to the improvement in the accuracy of Recognition of Face Based on CNN Algorithms. For this purpose, We used data files provided by github to build a face recognition model. We also created data using CNN algorithms, which are widely used for image recognition. Various photos were learned by CNN algorithm. The study found that the accuracy of face recognition based on CNN algorithms was 77%. Based on the results of the study, We carried out recognition of the face according to the distance. Research findings may be useful if face recognition is required in a variety of situations. Research based on this study is also expected to improve the accuracy of face recognition.

• Korean Journal of Artificial Intelligence
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• v.4 no.1
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• pp.8-10
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• 2016

These days, citizens have a lot of ways to get access to IT. In the past, they tended to neglect IT that was thought to be difficult. But, currently, everyone can manufacture and get access not only software but also hardware when he has an idea. Arduino is used. Rinnai had recently released new product named Smart Sensor Range. Safe consumer who gave priority to the safety made new trend gave attention to fire prevention and smart sensor range. The ones who buy gas range prefer safety to economic advantage and/or fire power. The safety system does not always prevent fire accident. This study makes design and produces alarm that perceives temperature of pot when boiling. Not only temperature sensor but also alarm sensor was used to make alarm of boiling water and to give convenient living life. The arduino can be used at practical life to make products for various kinds of people. The invention can give convenience to housewives at kitchen, children and many persons making use of gas range. Another function can be added to develop. This arduino can develop a lot of products by using the study and other designs.

• The Korean Journal of Food & Health Convergence
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• v.7 no.5
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• pp.1-7
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• 2021

The aging of the population has a fundamental impact on the national economy, including decline in productive population, atrophy of available funds, slowdown of technological innovation, slowdown of economic growth, and decrease in vitality of society as a whole. Increase of elderly population would lead to increase in elderly welfare consumers, which would also lead to increase the demand for elderly welfare services. However, due to the continuation of the low birth rate, there is a great shortage of human resources who can handle this. In such a situation, the main goal of the elderly welfare system in the future should aim to actively try to design effective policies, prepare systems, and implement services for the problems of the aged society, and to find ways to expand the finances, manpower, methods, and facilities necessary for the welfare of the elderly. Elderly welfare services in Korea have been changed and developed in accordance with socioeconomic changes such as industrialization and urbanization. This study examines the changes in elderly welfare services in Korea by the flow of times and presents a method which utilizes artificial intelligence and Internet of Things in services for the elderly welfare consumers to improve both quality and efficiency.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.