• Journal of Service Research and Studies
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• v.12 no.2
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• pp.94-105
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• 2022

From the perspective of a post-industrial society, the main purpose of this study is to clarify the theoretical principles that distinguish service design from product service system (PSS) and to propose a new integrated design strategy. Service design is a user-centered design technique for improving or innovating existing services and solving problems in a continuous exploration and iteration process. On the other hand, PSS is a cutting-edge business approach for organizations looking to boost their competitiveness by providing integrated product and service capabilities to clients while also enhancing system operations. This paper discusses the concepts of service design and PSS, the present state of research, and design methodologies using a literature search. The distinctions between service design and PSS are examined and summarized during the design process. The ultimate content proposed in this study is to incorporate user experience into the PSS design process and propose a systematic process to meet users' needs to effectively implement the service design concept.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.19-24
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• 2023

Recently, with the recent rapid development of memory technology, various types of memory are developed and are used to improve processing speed in data management systems. In particular, NAND flash memory is used as a main media for storing data in memory-based storage devices because it has a nonvolatile characteristic that it can maintain data even at the power off state. However, since the recently studied memory-based storage device consists of various types of memory such as MRAM and PRAM as well as NAND flash memory, research on memory management technology is needed to improve data processing performance and efficiency of media in a storage system composed of different types of memories. In this paper, we propose a memory mapping scheme thought technique for efficiently managing data in the storage device composed of various memories for data management. The proposed idea is a method of managing different memories using a single mapping table. This method can unify the address scheme of data and reduce the search cost of data stored in different memories for data tiering.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.732-737
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• 2009

Due to various project delivery methods and the complexity of construction projects in the construction industry, developing the framework of construction management for critical, highly complex projects in the construction industry has become problematic. Currently, a set of construction manuals play a pivotal role in planning and managing construction projects as subcontractors try to complete their scope of work according to the instructions of a general contractor. It is challenging for general contractors to write a construction management procedure manual to cover various types of project delivery methods and construction projects. In construction, the construction procedure manuals describe specific actions to be taken through the project. In reality a few contactors own such manuals and their construction schedules include more construction operation activities. Thus, it is hard to estimate the workload and productivity of construction managers because the manual and the schedule do not present the amount of management efforts required to complete a project. This paper proposes a framework to present construction management tasks according to project delivery methods which can be applied to various construction projects. Actions for management tasks were mapped and were integrated with construction activities throughout the project life cycle. The framework can then be used to give specific instructions to project participants, collect management actions, and replicate management actions throughout the project life cycle. The framework can also be can used to visualize complete construction project to analyze and manage construction management activities in each phase of a project in order to enhance productivity and efficiency. The studies of existing construction manuals were carried out to identify construction managers' responsibilities. An artificial intelligence program, CLIPS (C-Language Integrated Production System) was used to search for appropriate actions for impending tasks from a set of predefined actions to be performed for a given situation. The framework would significantly help construction managers to understand interrelations among management tasks or actions within a project. Furthermore, the framework can be embedded into Building Information Modeling (BIM) or Facility Management Systems (FMS) so that designers and constructors would execute constructability review before construction begins.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2188-2208
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• 2023

Quality of Service (QoS) is a critical feature of Wireless Sensor Networks (WSNs) with routing algorithms. Data packets are moved between cluster heads with QoS using a number of energy-efficient routing techniques. However, sustaining high scalability while increasing the life of a WSN's networks scenario remains a challenging task. Thus, this research aims to develop an energy-balancing component that ensures equal energy consumption for all network sensors while offering flexible routing without congestion, even at peak hours. This research work proposes a Gravitational Blackhole Search Optimised splay tree routing framework. Based on the splay tree topology, the routing procedure is carried out by the suggested method using three distinct steps. Initially, the proposed GBSO decides the optimal route at initiation phases by choosing the root node with optimum energy in the splay tree. In the selection stage, the steps for energy update and trust update are completed by evaluating a novel reliance function utilising the Parent Reliance (PR) and Grand Parent Reliance (GPR). Finally, in the routing phase, using the fitness measure and the minimal distance, the GBSO algorithm determines the best route for data broadcast. The model results demonstrated the efficacy of the suggested technique with 99.52% packet delivery ratio, a minimum delay of 0.19 s, and a network lifetime of 1750 rounds with 200 nodes. Also, the comparative analysis ensured that the suggested algorithm surpasses the effectiveness of the existing algorithm in all aspects and guaranteed end-to-end delivery of packets.

• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.3
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• pp.302-318
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• 2023

Purpose: This study aimed to identify the variables related to the organizational silence of Korean hospital nurses and to examine the effect sizes of correlations between the related variables and sub-types of organizational silence. Methods: Relevant studies were searched through a systematic search in six Korean electronic databases (RISS, ScienceON, KCI, DBpia, e-Article, and KISS) using June 2022 as the end date. Thirteen studies were identified through a systematic review and eight of them were meta-analyzed. The correlation effect size r (ESr) for each related variable was calculated. Results: Twenty-two related variables were identified from the systematic review. Of them, organizational culture was the most frequently examined. Seven variables (three organizational, two leader-member exchange, and two consequences of organizational silence) were found eligible for the meta-analysis. The intention of turnover (ESr=.39; 95% confidence interval, 95% CI=.32 to .45) and leader-member exchange ("manager's leaderships" ESr=-.33, 95% CI= -.43 to -.21; "manager's inclination to reject negative feedback" ESr=.32, 95% CI=.23 to .39) had larger correlation effect sizes than the other variables that related to organizational silence, in particular, acquiescent silence, which had the largest correlation effect size among the three sub-types of organizational silence. Conclusion: These findings show that the intention of turnover and leader-member exchanges were the main factors that related to the organizational silence. This indicates that it is necessary to develop management and education programs, as well as communication systems that focus on reducing and managing organizational silence, especially acquiescent silence.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.473-480
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• 2023

This study evaluated a novel method of defining the automatic flight path of unmanned aerial vehicles (UAVs) for search and rescue missions in a VR environment. The developed VR content reserves miniature digital twins of a building in the fire and a steep mountain terrain site. The users drow the UAV's scanning path using hand gestures on the surface of digital twins, and then the UAV make an automatic flight along the defined path. According to human-in-the-loop simulation tests comparing the novel method with a conventional manual flight task with 19 participants, the novel method did not improve the mission performance but participants felt a lower mental workload. The designer may need to consider the automation support on the vulnerable points of the SAR mission environment while maintaining experts' mapping capability.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.13-21
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• 2023

In the recent era of the fourth industrial revolution, many industries aim to maximize the efficiency of products and services by introducing cutting-edge technologies such as artificial intelligence and big data. In this situation, organizational culture is changing a lot due to the influx of the MZ generation with strong individualistic tendencies and the decreased face-to-face communication between members. However, active communication with colleagues is still essential to maximize performance, and the margins created by simplifying work processes and automating processes must be used for creating work performance. This requires cooperation and commitment through the job immersion of members who have an active attitude. This study analyzed how the organization's autonomous work environment and trust among members, which are creative work performance conditions, affect job immersion using raw data from the Occupational Safety and Health Research Institute. As a result, it was found that both the organization's autonomous working environment and trust among members significantly effected the members' job immersion. in order to achieve productivity and value improvement in companies, efforts are needed to increase workers' job immersion by building an autonomous working environment and trust among members. The results of this study are expected to contribute significantly to the search for ways to increase workers' job commitment to improve organizational productivity.

• Imaging Science in Dentistry
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• v.53 no.2
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• pp.101-108
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• 2023

Purpose: The aim of this study was to conduct a scoping review and meta-analysis to provide overall estimates of the recall and precision of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography (CBCT) scans. Materials and Methods: A literature search was done in Embase, PubMed, and Scopus through October 31, 2022 to identify studies that reported the recall and precision values of artificial intelligence systems using oral and maxillofacial CBCT images for the automatic detection or segmentation of anatomical landmarks or pathological lesions. Recall (sensitivity) indicates the percentage of certain structures that are correctly detected. Precision (positive predictive value) indicates the percentage of accurately identified structures out of all detected structures. The performance values were extracted and pooled, and the estimates were presented with 95% confidence intervals(CIs). Results: In total, 12 eligible studies were finally included. The overall pooled recall for artificial intelligence was 0.91 (95% CI: 0.87-0.94). In a subgroup analysis, the pooled recall was 0.88 (95% CI: 0.77-0.94) for detection and 0.92 (95% CI: 0.87-0.96) for segmentation. The overall pooled precision for artificial intelligence was 0.93 (95% CI: 0.88-0.95). A subgroup analysis showed that the pooled precision value was 0.90 (95% CI: 0.77-0.96) for detection and 0.94 (95% CI: 0.89-0.97) for segmentation. Conclusion: Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.