This study suggests an optimized machine learning model to enhance the diagnosis of aortic stenosis (AS), a critical cardiovascular disease involving the narrowing of the aortic valve. Timely and accurate diagnosis of AS is needed to decrease morbidity and mortality rates, particularly in the elderly. Conventional diagnostic techniques like echocardiography are accurate but are suffering from inter-observer variation and are expensive. Relying on the strength of machine learning (ML), this paper explores the application of three widely used classification models’ Random Forest (RF), Support Vector Machine (SVM), and k-nearest Neighbors (KNN) to develop a robust, self-sustaining diagnostic tool. We used Bayesian optimization to optimize the hyperparameters of each model on a dense grid of 39 features in 60 patients. These are demographic information, clinical history, and spectral features of heart sound recordings. The data were preprocessed and balanced using SMOTE to facilitate stable training of the models. The findings reveal that the optimal KNN model performed better compared to others regarding accuracy, sensitivity, specificity, and F1 score. These results underscore the promise of optimal ML models to aid clinicians with precise and efficient AS diagnosis and create opportunities for future application in clinical practice.

This research aims to develop practical insights for policy and financial support systems to enhance the success of startup companies with innovation-driven growth. A Venn diagram helps visualize the pattern of companies that have achieved global success in business. It was found that international companies, such as HONDA, SONY, and UNIQLO, reveal common success pathways marked by innovation in their company histories. As a first step, business growth was driven by product innovation. Then, the first wave of internationalization contributed to expanding their market innovation. For further development, the company need several years for organizational innovation. Continuous business growth is shown after all these steps. The empirical study of Japan Advanced Chemicals (JAC)as a growing company in the semiconductor industry with 20 years of innovation was conducted by the authors. A typical pattern was observed in the Venn diagram among JAC and developed global companies. JAC’s sales have been growing in a pattern similar to that of globally developed companies. The pattern of JAC is not typical among competitors in the industry. It was, therefore, concluded that JAC is a potential candidate company for further growth. This study suggests that startup companies exhibiting this typical pattern have a higher potential for success due to innovation within their industry. This research explores the potential to enhance the five-year survival rate of startup companies. Practically, the insights from this study can guide policymakers in designing the most efficient scheme to support the growth of startup companies.

This article describes a new approach attempting to understand the interconnection of Mega Sport Events (MSEs) and any nation’s innovation ecosystem, measured by the trajectory of patent applications. Through advanced machine learning models, over two decades (1985-2022) of data from six host countries are analysed to uncover how investment expenditures interact with economic conditions and environmental, social, and governance (ESG) factors. There is a reality that seems to be dualistic: while strategically targeted operational expenditures stimulate innovation capabilities, there is excessive expenditure that stifles the output. Even more interestingly, the demographic structure is found to be a strong driver of innovation, rejecting dominant theorisations regarding the benefits of MSEs. Differentiating the impact of sustainability strategies and social integration policies is important. These conclusions provide vital insights to public management on the controversial spending issues regarding event bidding and resource distribution. This analysis demonstrates the apparent contradiction between the sustainability of investment and the innovation yield, fundamentally changing how countries can leverage the true legacy of sporting events, which encompass economic development.

This paper delivers an up-to-date bibliometric review and analyzes the evolving landscape of artificial intelligence (AI) and neuromarketing integration. It systematically analyzes a comprehensive set of scholarly articles, publications, and research outputs to unveil trends and patterns in the intersection of AI and neuromarketing. The review highlights the evolution of methodologies, tools, and applications resulting from integrating AI and neuromarketing. It shows a significant and constant increase in scholarly interest in the intersection of AI and neuromarketing over the past decades. The study also emphasizes the multifaceted approach of AI and Neuromarketing research, encompassing fields such as computer science, decision science, engineering, psychology, and medicine. the research is categorized into five clusters: (1) the neuroscience and brain structure; (2) methodological approaches and neuroscience tools for diagnostics and imaging; (3) signal processing and analysis; (4) machine learning and neural networks; and (5) applications in cognition and perception. The insights derived from the bibliometric analysis contribute to a deeper understanding of the current state of research and provide a roadmap for future investigations, guiding scholars, practitioners, and policymakers in navigating the dynamic landscape of AI-driven advancements in neuromarketing.

This study investigates the significant, yet understudied, consequences of hosting mega sporting events (MSEs) on the digital evolution of the host country, an essential aspect of urban and national development. Employing neural boosting and support vector machine (SVM) models, we examined the intricate connection between the hosting of MSEs and the advancement of digital infrastructure and capabilities. The methodology employs data from 89 countries over five years, focusing on their digital evolution scores relative to hosting major sports events. Our findings demonstrate a positive correlation between hosting MSEs and enhanced evolution scores, suggesting that the demands and opportunities presented by these events promote substantial improvements in a country’s digital landscape. The main contribution of this study lies in its innovative methodology and the empirical evidence it provides, which emphasizes the role of MSEs as accelerators of digital progress. Practically, the insights from this study can guide policymakers, event organizers, and business leaders in capitalizing on the opportunities to strengthen digital transformation initiatives. Strategic planning and investing in digital technologies lead to these events and related businesses or industries to enhance their global digital competitiveness.

Japan is one of the world's leading super-aging societies, with the highest average life expectancy in the world. 30.3% of the population will be 65 years old or older by 2025, and 13.0% will be 75 years old or older. In addition, the number of heart failure patients is increasing yearly. The number of heart failure patients is increasing by about 10,000 each year and is estimated to reach 1.2 million by 2020 and 1.3 million by 2030. The reason for the rapid increase in the number of heart failure patients in Japan is the aging of the population. Therefore, machine learning to predict atrial fibrillation is employed in this paper. We conducted a trial using risk assessment of cerebral infarction and other factors. As a result of the analysis, we extracted highly influential evaluation indices for each characteristic of atrial fibrillation.

Innovation as an R&D project is significant for most companies. The farm's innovation capability has become one of the critical engines of competitive advantage for sustainable growth. This article aims to indicate the relative Innovation Capability of the enterprise from the research practitioner's perspective by the interdisciplinary statistical approach to understanding innovation management research. Using statistical methods, we could distinguish the present R&D status position. By grasping the R&D project state, issues and points for improvement can be clarified, and it becomes possible to guide the R & D project in the appropriate direction. The multidisciplinary approach offers that significantly contributes to further innovation management understanding.

The primary role of a teacher from a technical high school in Japan is to teach the students the specialized knowledge and skills for the target industry such as manufacturing. At the same time, it is nothing to say to enhance the humanity of the students. The society of teachers is aging same as the aging and the birth rate decline society as the whole environment of Japan. From those backgrounds, teachers in the technical high school with educational capability who can teach students appropriately are required. One of the methods to improve the educational capability of teachers more than ever, we propose a method to quantify the educational capability in this paper. The educational capability of each teacher can be confirmed objectively at the same time by our proposed method. From the result of analyses, the educational capability of teachers is obtained from the results of the questionnaire. Based on our proposed method, the optimal composition of teachers by each subject is generated. This is research in progress.

Lifestyle disease is a general term from habits related to personal activities such as stress, overeating, and lack of exercise. Typical examples are hypertension, hyperlipidemia, diabetes, obesity, and cancer. In Japan, lifestyle diseases account for about 30% of annual medical expenses and about 60% of annual deaths. Therefore, it is necessary to reflect lifestyle habits in order to prevent lifestyle diseases accurately. Diabetes mellitus has no symptom in hyperglycemia itself, and symptoms appear only after the vascular disorder that progresses as hyperglycemia persists. In the 2016 National Health and Nutrition Survey conducted by the Ministry of Health, Labour and Welfare, the total number of persons who are strongly suspected of having diabetes and persons who cannot deny the possibility of diabetes is estimated to be 20 million. Since the health condition of individuals is regularly examined at each stage in Japan, this paper employs the results of the regular check-up for predicting future lifestyle diseases by machine learning. From the results of analytics, the characteristics of the potential person were extracted.

This paper attempts to understand the R&D project current state by the visualization of Physics model simulation. These kinds of R&D project study have done by either quantitatively or qualitatively approach. However, the integration of the two types of approaches has not enough studied. The state of the R&D project is considered to be the sum of correlation interaction due to various factors. The physical Ising model applied as the 6-dimensional model was implemented into the research development to analyse the interaction of R&D project. This model consists of two parts: quantitative and qualitative. Although it was shown that the project could be analyzed and visualized by combining the quantitative part and the qualitative part, sufficient discussion was not made on the details of the qualitative part. In this research, we focused on the qualitative part and analyzed qualitative analysis by text mining and found adequate for its contents. As a result, we found that it is likely to be able to add new findings based on quantitative data analysis by text mining to the conventional evaluation method. This paper attempts to understand the R&D project current state by the visualization of Physics model simulation. These kinds of R&D project study have done by either quantitatively or qualitatively approach. However, the integration of the two types of approaches has not enough studied. The state of the R&D project is considered to be the sum of correlation interaction due to various factors. The physical Ising model applied as the 6-dimensional model was implemented into the research development to analyse the interaction of R&D project. This model consists of two parts: quantitative and qualitative. Although it was shown that the project could be analyzed and visualized by combining the quantitative part and the qualitative part, sufficient discussion was not made on the details of the qualitative part. In this research, we focused on the qualitative part and analyzed qualitative analysis by text mining and found adequate for its contents. As a result, we found that it is likely to be able to add new findings based on quantitative data analysis by text mining to the conventional evaluation method.

This paper presents investigating the fraud transaction detection in the mail order industry. These kinds of detection made intensively but the outcome of the research was not shared among the industry. As the B2C industry expands their market size, the fraud transactions increase in number. As a matter of course, this phenomenon is not only continuing but cleverly. One of the conclusive factors for this phenomenon is payment method. That is, the deferred payment method is primarily employed in Japan. The conventional primary indicator for the fraud detection is the ordered time-based information. They are the shipping address, the recipient name, and the payment method. Since conventional detecting method for the fraud depends on some heuristic knowledge, their market size enlargement makes hard to detect fraud transaction. For this background, this paper is presented investigating for comparing algorithms with the actual transaction data gathered from the mail-order industry in Japan. The comparison of weaker learner algorithms is made. The analytical results suggest Random forest is more accurate than XGBoost not only AUC score but parameter tuning costs. This result will make it use for the decision support knowledge for screening customer at the order received phase in the mail order industry.

This paper presents investigating fraud transaction detection in the mail order industry. These kinds of detection have done intensively, but the outcome of the research has not shared among the mail-order industry. As the B2C market such as the Amazon type business expands their market volume exponentially, the fraud transactions increase in number. As a matter of course, this phenomenon is not only continuing but clever. One of the conclusive factor for this phenomenon is the payment method. That is, the deferred payment method. The conventional primary indicator for the fraud detection is the ordered time based information. They are shipping address, recipient name, and the payment method. This kind of information makes use of the prediction in common. Conventional detecting method for the fraud depends on the human working experiences so far. From such kind of information, the mail-order company predicts the potential fraud customer with their working experience parameters. As the number of order transaction becomes large, fraud detection becomes difficult. The mail order industry needs something clever detection method. From these backgrounds, we observe the transaction data with the customer attribute information gathered from a mail order company in Japan and characterized the customer with a machine learning method. From the results of the intensive research, potential fraudulent transactions are identified. Intensive research revealed that the classification of the deliberate customer and the careless customer with machine learning.

This paper presents investigating the customer payment intention prediction in the mail order industry. As the B2C market expands their market volume, the fraud transactions increase in number. The primary indicator for the detection are the shipping address, the recipient name, and the payment method. These information usually make use of the prediction in the Japanese mail order industry. Conventional detecting method for the fraud depends on the human working experiences so far. As the number of transaction becomes large, fraud detection becomes difficult. The mail order industry needs something new method for the detection. The result of the Google Flu Trends shows, accurate prediction needs the heuristics knowledge. For these backgrounds, we observe the transaction data with the customer attribute information gathered from a mail order company in Japan and characterized the customer with machine learning method. From the results of the intensive research, potential fraudulent transactions are identified. Intensive research revealed that the classification of the deliberate customer and the careless customer with machine learning. This result will make use of the customer screening at the time of order received.

In Japan, general public those who are not medical experts usually buy OTC medicine at a pharmacy, depending on their illness condition. In this case, it is difficult for them to consider how much the OTC medicine is effective for their symptom. The components of OTC medicine have been used as ethical medicines for a long period of time. This is because the efficacy and safety of ethical medicine have been confirmed before being employed as OTC medicine. The information of those confirmed medicines is described in package inserts, which is aimed for medical professionals. Therefore, it is difficult for general public to understand what the package insert describes in terms of medical effects. In this study, from the information which appears in the package inserts of prescription medicines, a method for estimating the effect of OTC medicine is investigated. Also, a method of estimating the effects of medicines without directly compared data is proposed, only by using the information of package inserts of ethical medicines.

This paper presents investigating the customer characteristics of reminder effects in the mail order industry, especially the bad debt customers. These kinds of investigations have not made intensively, performed only such as the shipping address, the recipient name, and the payment method so far and the conventional method for predicting such knowledge depends on the employee's working experiences. For these backgrounds, we observe the transaction data with the bad debt customer information gathered from a mail order company in Japan and characterized the customer with machine learning method. From the results of the analysis, potential fraudulent transactions are identified. Intensive research revealed that the classification of the deliberate customer and the careless customer with machine learning. This result will make use of the revenue expansion with the improvement of the bad debt collections.

Online hotel reservation is widely used for planning travel today. Users of reservation usually refer to the hotel reviews before making their reservations. Numerical evaluation and an impression comment exist in the hotel review. In the case of popular hotels, the number of reviews can become very high, and all numerical evaluations can be fixed at high scores. Therefore, understanding the characteristics of the accommodations is difficult. Numerical evaluation criteria and impression comments are unrelated items. In this study, we analysed the features of the hotel associated with hotel review impression comments and numerical evaluations. We have presented a feature extraction approach for numerical evaluation criteria that are difficult to understand solely from numerical evaluation scores using characteristic expressions of impression comments found in online hotel reviews. And after extracting characteristic expressions from impression comments with text mining, we classified such expressions as positive or negative assessments of the numerical evaluation criteria. As a result, important evaluation criteria can be identified by analysing impression comments from reviews in which numerical evaluation items are scored equally. In addition, by analysing the characteristic expressions extracted from impression comments from reviews in which numerical evaluation of only one item differed, we can identify the reasons for the different scores.

This paper presents the evaluation method of the effect range from the package insert of medicine with text mining. Most of the people who take the over the counter medicines cannot understand the medicinal effects. This is because they have little knowledge of the medicine. The ingredients of the over the counter medicines are made from prescription products. The prescription product shows various laboratory findings to obtain authorization from the Ministry of Health, Labour and Welfare. The medical information is described in the package insert of the medicine, and anyone is available. It is possible to evaluate the effect of the ingredient in the over the counter medicines, if we analyse the medical information described in this package insert of the medicine with text mining method. This paper, we focus on both the antipyretic and the antitussive among lots of the over the counter medicines and make intensive research with them. However, variability is observed in the results of the analysis. We analyse the effect of magnification of each experimental configuration. Since the size of this fluctuation range comes from the experimental configuration. Therefore, we summarize the medication type and the experimental data of the medicine in each experimental configuration. As a result, we are succeeding in the effective range of medicine with text mining, if we extract the experimental configuration and analysis above summarization.

This paper presents investigating the customer characteristics of payment method change in the mail order industry. This time we are focusing on the transactional activity of bad debt customers. These kinds of investigations have not made intensively, such as the shipping address, the recipient name, and the payment method so far and the conventional method for predicting such knowledge depends on the employees’ working experiences. For these backgrounds, we observed the transaction data with the bad debt customer information gathered from a mail order company and characterized the customer with machine learning. From the results of the analysis, we are succeeded in characterizing the potential customers. Intensive research revealed that the characteristics of customers who make fraud transactions. This result will make use of the revenue expansion with the improvement of the bad debt collections in the target industry.

Strategic behavior of companies in the industry is composed of two parts, homogenization and differentiation. Strategic homogeneous behavior of the company builds the stabilized industry especially. Although at the present time the simulation is still in an early concept stage, we created it by multi-agent simulation (MAS). Basic concept of the homogeneous behavior simulations is reported in order to obtain feedback from a variety of perspectives. Then, in this paper the attempt of the MAS suggests that how to mix homogeneous behavior and differentiation behavior for the competitor's strategic behavior in industry.

This paper provides the board meeting proceedings analysis with text mining. Corporate management is executed based upon the decision made by the board meeting in general. However, the research which considered the contents of proceedings left behind the minutes of the company, and the relation of corporate profit with text mining is not enough so far. Therefore, we make an intensive research for the board meeting proceedings analysis with text mining to capture the change of the board meeting contents. As a result, the shift in the proceedings contents from short-term, store-based measures to long-term, corporate based planning coincides with the improvement of corporate performance.

This paper presents investigating the customercharacteristics from the bad debt list ofa mail order corporation. So far, suchinvestigations have not made intensively, especiallyprivate defaultrisks and conventional method for predicting such risks depend on the employee's working experiences. For these reason, at first,we observedtheactual bad debt list from amail order corporationand analyzedsales data. From the results of the observation, we makeuse of the machine learningmethod to characterizethe potential bad debt customers. Intensive research hasrevealed that the characteristicsof customers, who might fall into the bad debt list, popularitems and so on. This method willmake use forthe revenue expansion;improvement of collectionof the bad debts.

A function test for software is one of the Black Box testing methods. As a model for a function test, Decision table is useful to describe logical relationship between operating conditions exhaustively for a single function. However, in case that there are relationships between functions, Decision table cannot describe them. As a result, an insufficient or an excessive testing may be performed. In this paper we expand Decision table model to describe the relationships and propose a design method for function combination testing by using the expanded model. Our method makes test cases which achieve exhaustive combination for functions which has relationships each other. On the other hand, it does not make combination for functions which has no relationship. As a result, we can achieve both exhaustive and efficient function testing.

In this paper, we explore the problem to efficiently design a series of similar plants. The target plants consist of complex mechano-electronical systems. It will take much time to design them from the initial phases. The problem requires solving complex nonlinear differential equations with multiple objectives. We apply a genetic algorithm with tabu lists, which is able to solve multi-modal and/or multi-objective problems. The paper presents the techniques we have applied to, especially focuses on the landscape search among feasible good solutions. The results are summarized as follows: (1) The GA method equipped with tabu search, minimal gap generation (MGG), and ordinary two-point crossover work well to obtain multiple solutions of the task, and (2) Classification of candidate solutions and neighborhood parameter search enable us to estimate the quality of solutions. The intensive experiments have suggested that the proposed method is effective for the tasks.

This paper proposes a method to extract and manage customer preference information used to create My-page, which is a customer service of Internet Service Providers. To provide useful information for customers on My-page, it is essential to accurately grasp the transitions of customer preferences as well as market trends. Customer preference information has conventionally been managed with two-dimensional vectors with customer and preference category axes. In this paper, we propose a method that manages customer preference information with three-dimensional vectors with customer, preference category, and time axes, which enables us to accurately grasp the transitions of customer preferences and market trends. The information volume in three-dimensional vectors is enormous compared with that in two-dimensional vectors. The proposed method addresses this problem by storing only positions and values of the points where information changes over time. This reduces the storage for two-dimensional vectors information per unit time to less than 5% of the original volume. Some kind of knowledge dictionary is required to extract customer preference information. Our proposed method dynamically updates the knowledge dictionary to accomplish a distributed system that performs information extraction at each access point. This enables us to reduce 60% of the CPU usage on the marketing server that is used for managing customer preference information.