DSpace collection: 專書之單篇

「『世界の終りとハードボイルド・ワンダーランド』における「ウエイ・オブ・ライフ」(way of life)―メカニズムとしての「思念」と『般若心経』」

title: 「『世界の終りとハードボイルド・ワンダーランド』における「ウエイ・オブ・ライフ」(way of life)―メカニズムとしての「思念」と『般若心経』」

「台湾における日本語教育のAI・DX技術の利活用―ChatGPTによるインプットからメタバースによる創造的アウトプットへ―」

title: 「台湾における日本語教育のAI・DX技術の利活用―ChatGPTによるインプットからメタバースによる創造的アウトプットへ―」

「『街とその不確かな壁』における霊魂子易辰也の人間「擬態」―連続する死と生―」

title: 「『街とその不確かな壁』における霊魂子易辰也の人間「擬態」―連続する死と生―」

Text Mining Techniques and Natural Language Processing

title: Text Mining Techniques and Natural Language Processing abstract: Text mining is an important branch of data mining that is used to analyze the text data. Text data is any type of data like structured data, unstructured data, and semi-structured data. All types of data are collected from different sources such as multimedia applications, mobile apps, digital systems, etc. These data are beneficial to get a good insight, meaningful results. We use data mining techniques like Support Vector Machine (SVM), Random Forest (RF), Multilayer Perception (MLP), Naive Bayes (NB), etc. to analyze the hidden relationship between data. We use three kinds of data types in text mining.

Towards Applicability of Machine Learning in Business Analytics for Sales Prediction

title: Towards Applicability of Machine Learning in Business Analytics for Sales Prediction abstract: In the field of data processing and analytics, machine learning has emerged as a topic of significant interest in the recent years. When it comes to decision-making, less structured retailers rely on their gut feeling, whereas more structured retailers make use of business intelligence. Predicting future retail sales is a common practise in the industry of organised retail, where it is tremendously helpful for making timely and strategic decisions in the face of intense competition. Predictions about future sales are often arrived at by doing an analysis of previous data, making use of a variety of statistical and mathematical methodologies, etc. This study is an endeavour to communicate with retail business owners, managers, and policymakers the accuracy of data derived from retail sales forecasting using machine learning algorithms. In this study, we apply the machine learning techniques known as Artificial Neural Network and Random Forest to a dataset that is often used for training purposes.

Optimized Support Vector Machine for Early and Accurate Heart Disease Detection

title: Optimized Support Vector Machine for Early and Accurate Heart Disease Detection abstract: Many academics use data mining to predict diseases. Some approaches can predict one sickness, while others can predict several. Sickness prediction may be improved. This article provides an overview of the numerous data categorization methods available today. Algorithms represent most commonly. Classifying data involves a lot of computation. To create a disease-fighting plan that works, enormous amounts of data must be analysed. Early diagnosis, severity assessment, and prognosis are frequent. Doing so may postpone disease development, improve quality of life, and lower medical costs. This approach uses machine learning. This article classifies and predicts cardiovascular disease data using machine learning. SVM, ANN, and RF classify heart disease data. Accuracy-wise, SVM is better for heart disease classification and detection.