HighTech and Innovation Journal
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Identification of Knowledge Management Barriers in Scientific R&D Projects in Czech Academic Environment
Full text linkThe primary aim of the presented paper is the identification of barriers to knowledge sharing by scientific R&D project team members in a Czech academic environment. In order to fulfill this aim, secondary analysis was used to process the literature search in the context of the problem being solved, and qualitative and quantitative research in the form of a structured interview and a questionnaire survey were used to identify the barriers to knowledge sharing by scientific R&D project team members. The essential output of the presented paper is the identification of barriers to knowledge sharing by members of the scientific R&D project team in Czech public and private universities and the discussion of their causes and impacts in scientific R&D projects. With regard to the aim of the presented paper and the formulated research questions, our effort is that the presented discussion outputs are not only academic considerations but scientific analyses and outputs capable of concrete life. Above all, we propose an innovation of Riege's model of barriers to knowledge sharing, as it was chosen as a comparative basis for our research; however, it was found that it is not applicable in the current conditions of scientific R&D project management in the Czech academic environment. Doi: 10.28991/HIJ-2023-04-01-02 Full Text: PD
Navigating the Convergence of Artificial Intelligence and Space Law: Challenges and Opportunities
Full text linkThe space industry is one of the most technologically advanced industries that aims for scientific explorations that benefit humanity on multiple fronts. Further, Artificial Intelligence (AI) technologies comprise game-changing tools that could be utilized to facilitate space exploration aims. The emergence of AI in the space industry would evolve how both industries look. Since many challenges in the current space industry could be addressed by implementing artificial intelligence, space objects will create "Intelligent Space Objects". Different studies were conducted to explore the implementation of AI technologies in space activities and their legal implications. The scope of this paper goes beyond the existing work. It will investigate the main AI applications in space and then explore their legal challenges, including issues related to regulations, liability, and policy questions. Accordingly, it will discuss the need for developing a novel legal framework to address these challenges, creating a strategic opportunity for international collaboration between states and organizations that will contribute to advancing space law. This study will review, evaluate, and analyze the current situation and recommend ways to establish a novel international space organization. Doi: 10.28991/HIJ-2023-04-01-04 Full Text: PD
Development and Algorithmization of a Method for Analyzing the Degree of Uniqueness of Personal Medical Data
Full text linkThe purpose of this investigation is to develop a method for quantitative assessment of the uniqueness of personal medical data (PMD) to improve their protection in medical information systems (MIS). The relevance of the goal is due to the fact that impersonal PMD can form unique combinations that are potentially of interest to intruders and threaten to reveal the patient's identity and medical confidentiality. Existing approaches were analyzed, and a new method for quantifying the degree of uniqueness of PMD was proposed. A weakness in existing approaches is the assumption that an attacker will use exact matching to identify people. The novelty of the method proposed in this paper lies in the fact that it is not limited to this hypothesis, although it has its limitations: it is not applicable to small samples. The developed method for determining the PMD uniqueness coefficient is based on the assumption of a multidimensional distribution of features, characterized by a covariance matrix, and a normal distribution, which provides the most reliable reflection of the existing relationships between features when analyzing large data samples. The results obtained in computational experiments show that efficiency is no worse than that of focus groups of specialized experts. Doi: 10.28991/HIJ-2023-04-01-09 Full Text: PD
Utilization of the Weighted Product-Based CIPP Evaluation Model in Determining the Best Online Platform
Full text linkSince the COVID-19 pandemic, there have been many free online platforms that can be used to support the online learning process at health colleges in Bali. However, it is difficult to determine the best online platform from the various choices of free online platforms that are scattered on the internet. Therefore, it needs innovations that contribute to helping solve these problems. One model as an innovation that can be used and contributes to solving problems is the Weighted Product-based CIPP evaluation model. The model needs to be measured for the quality of its calculations to ensure success in determining the best online platform. Therefore, this research aimed to show the quality of the Weighted Product method calculation integrated with the CIPP (Context-Input-Process-Product) model in determining the best platform used in health colleges during the COVID-19 pandemic. The instrument used to assess the quality of that calculation was a questionnaire consisting of eight questions. The subjects involved in the assessment were 20 experts. The research was carried out at several health colleges in Bali. The analytical technique used in analyzing the research data was descriptive-quantitative. The analysis was carried out by comparing the quality percentage of the calculation simulation with the quality standard based on an eleven-point scale. The study results showed the quality percentage of calculation simulation was 87.250%, so it was included in the very good category. This research has a significant impact on the progress of the educational evaluation field through research findings in the form of the appearance of the combination of the Weighted Product method with the CIPP evaluation model. The novelty of this research is the combination of the Weighted Product method and the CIPPmodel, which makes it easier for educational evaluators to determine the best online platform that supports online learning during the COVID-19 pandemic and even post-COVID-19. Doi: 10.28991/HIJ-2023-04-01-015 Full Text: PD
Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer
Full text linkOne of the best-known and frequently used areas of Deep Learning in image processing is the Convolutional Neural Network (CNN), which has architectural designs such as Inceptionv3, DenseNet201, Resnet50, and MobileNet used in image classification and pattern recognition. Furthermore, the CNN extracts feature from the image according to the designed architecture and performs classification through the fully connected layer, which executes the Machine Learning (ML) algorithm tasks. Examples of ML that are commonly used include Naive Bayes (NB), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Decision Tree (DT). This research was conducted based on an AI model development background and the need for a system to diagnose COVID-19 quickly and accurately. The aim was to classify the aforementioned CNN models with ML algorithms and compare the models' accuracy before and after Bayesian optimization using CXR lung images with a total of 2000 data. Consequently, the CNN extracted 80% of the training data and 20% for testing, which was assigned to four different ML models for classification with the use of Bayesian optimization to ensure the best accuracy. It was observed that the best model classification was generated by the MobileNetV2-SVM structure with an accuracy of 93%. Therefore, the accuracy obtained using the SVM algorithm is higher than the other three ML algorithms. Doi: 10.28991/HIJ-2023-04-03-05 Full Text: PD
The Prediction of Douyin Live Sales based on Neural Network Algorithms
Full text linkThis paper aims to optimize neural network algorithms in order to improve their predictive performance and meet the demand for Douyin live sales forecasting. The Douyin live data of Florasis between January 2022 and June 2022 were collected from Huitun data and preprocessed. Using the Pearson correlation coefficient, six factors that are highly correlated with live sales were selected for subsequent prediction. This paper briefly introduces the back-propagation neural network (BPNN) algorithm and analyzes its parameter optimization methods, including particle swarm optimization (PSO), the artificial bee colony (ABC) algorithm, and the beetle antler search (BAS) algorithm. Then, an improved beetle antennae search (IBAS) algorithm was developed by introducing inertia weight and used to construct an IBAS-BPNN model for predicting sales volume in Douyin live streaming. The results showed that compared with the BPNN, PSO-BPNN, and ABC-BPNN algorithms, the IBAS-BPNN algorithm had better prediction performance, with a root-mean-square error of 335.6694, a mean absolute percentage error of 0.0532%, an equilibrium coefficient of 0.9889, and a shorter training time of 90.07 s. The experimental results demonstrate the reliability of the IBAS-BPNN algorithm for predicting sales volume in Douyin live streaming, providing new insights into parameter optimization of BPNN and offering references for further research on BPNN parameter optimization. It also provides an effective method with both timeliness and high accuracy for predicting sales volume in Douyin live streaming in practical applications. Doi: 10.28991/HIJ-2023-04-02-09 Full Text: PD
Evolutionary Algorithm-Based Energy-Aware Path Planning with a Quadrotor for Warehouse Inventory Management
Full text linkQuadrotors have been vital for automating warehouse processes. However, a significant gap in recent studies is that they use a single quadrotor with limited battery life, considering that their objective involves navigation in a large-scale environment such as a warehouse. Using an energy consumption model to enable more efficient navigation can be explored. Conventional data-driven energy models and path planning algorithms are insufficient for describing the various motions that a quadrotor can perform in warehouse operations, such as changes in yaw. This study aims to design a novel exhaustive data-driven energy consumption model and evolutionary algorithm-based path planning algorithm to consider various quadrotor movements involved in warehouse operations. The quadrotor is tasked with performing a set of movements to each be represented as a power equation in terms of their velocity. The obtained equations were subsequently used as the primary optimization objective for the path planning algorithm, which included yaw angle objectives and constraints. A set of experiments was performed with Crazyflie quadrotors to verify the model and the algorithm. The results showcased the accuracy of the energy consumption model, which was kept at a maximum difference of 0.6%. The designed path planning algorithm obtained greater energy efficiency in the generated paths compared to other state-of-the-art evolutionary algorithms with similar objectives and constraints. Doi: 10.28991/HIJ-2023-04-04-012 Full Text: PD
Forming the Architecture of a Multi-Layered Model of Physical Data Storage for Complex Telemedicine Systems
Full text linkThe relevance of this research is determined by the need to study the issues of improving data storage technologies for complex telemedicine systems. The objective is to create a multi-layered data storage model for complex telemedicine systems to ensure the most complete use of their capacity and the timely expansion of existing storage. The research is conducted on the basis of an analysis of existing opportunities and problems in the field of data storage technologies. An analysis of the main features of the development of data storage technologies revealed that the existing models have no detailed description of the recording and physical storage of data bits, which is necessary for describing the storage process. Different architectures are reviewed, and their strengths and weaknesses are discussed. Within the framework of a demonstration experiment using the Kohonen neural network apparatus as a tool for solving the problem of placing objects in accordance with the required parameters, it is shown that the proposed storage system resource management model is operable and allows solving the problem of rational use of physical resources. As a result, a multilevel model of data storage is proposed, which combines the levels of storage process organization and technology. The distinguishing feature of this method is the comparison of storage organization levels, data media, and characteristics of physical storage and stored files. Doi: 10.28991/HIJ-2023-04-04-09 Full Text: PD
Descriptive Statistical Analysis of the Coach-Player Relationship with CART-Q and SCI
Full text linkProviding maximum performance in a long-term competitive load is associated with a quality relationship between the player and his coach. A coach plays one of the most important roles in an athlete's sports career and has the potential to positively or negatively impact the mental health of athletes. The aim of the presented paper is to map the bond between the quality of the relationship between the player and the coach and the sports self-confidence of elite junior tennis players. The research sample consisted of 236 elite junior tennis players competing at the national and international levels. The average age was 17.2 years. Data collection was carried out using the questionnaire methods of the Coach-Athlete Relationship Questionnaire (CART-Q) and the Sport Confidence Inventory (SCI). The results found significant differences in the perception of the quality of the coaching relationship between Czech and foreign athletes. Gender differences were also found among Czech athletes. A significant relationship was found between the quality of the player-coach relationship and sports self-confidence. The results point to the connections between performance, mental well-being, and the quality of the relationship between the player and the coach and can be the basis for further studies and motivate coaches to think about whether there is a need to modify the ways of training and dealing with their athletes. Doi: 10.28991/HIJ-2023-04-02-01 Full Text: PD
The Importance of Good Governance in the Government Organization
Full text linkThe intention of this research was to evaluate the influence of the implementation of good governance on the performance of one of the government organizations. The method used was a quantitative-descriptive with causality approach, which was then analyzed by PLS-SEM. The amount of the research sample was 303 of state civil apparatus at the Land Transportation Management Center throughout 38 provinces in Indonesia. The research findings explained that transparency has an impact on the increasing accountability and responsibility of government organizations but has no power to boost their performance. Accountability has an influence on increasing responsibility and the performance of an organization. Responsibility is also found to be effective in rebuilding the performance of government organizations. This research brings the latest issue to the surface. By linking all indicators of good governance factors, namely transparency-accountability, transparency-responsibility, and accountability-responsibility, which is rarely done in previous research that focused on the connection between good governance-performance. In addition to that, this research was also conducted at the Land Transportation Management Center throughout 38 provinces, so the level of accuracy would be high and had a huge impact on related government organizations in Indonesia. Doi: 10.28991/HIJ-2023-04-01-06 Full Text: PD