Brain tumor identification and categorization serve a precarious role in early diagnosis and treatment planning. This study suggests a new deep learning (DL)-based structure for the automatic detection and categorizat...
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As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, c...
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Decreasing arable acreage and a growing world population are pushing for new farming systems. Achieving high crop production requires maintaining a balanced nutrient level in the soil. This study offers a machine lear...
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The Smart Parking System is an innovative solution designed to tackle the challenges of parking in densely populated urban areas. By integrating advanced technologies such as RFID (Radio Frequency Identification) sens...
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The study of credit risk is a major concern for financial companies looking to make wise lending decisions and limit potential losses. In this study, the use of R, a potent open-source programming language, is examine...
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ISBN:
(纸本)9789819745395
The study of credit risk is a major concern for financial companies looking to make wise lending decisions and limit potential losses. In this study, the use of R, a potent open-source programming language, is examined in the context of credit risk analysis. This study offers a thorough framework to improve the accuracy and efficiency of credit risk assessment by utilizing the flexible data manipulation, statistical analysis, and machine learning capabilities of R. The importance of credit risk analysis in financial institutions is discussed in the paper’s opening section, along with some of the difficulties it faces. The rich libraries, data processing capability, and data visualization features of R highlight how well-suited it is for this purpose. Data quality and consistency are stressed in the technique portion since it encompasses data collection, preprocessing, and feature engineering. To predict credit risk, a variety of statistical methods and machine learning models are used, which offers details on their benefits and interpretability. The research study also looks at model validation and evaluation, which ensures the stability and dependability of the credit risk models. Model accuracy, precision, and recall are evaluated using methods including exploratory data analysis (EDA), ROC analysis, and model performance indicators. This study concludes by highlighting how the use of R in credit risk analysis might enable financial institutions to make more knowledgeable lending decisions, lowering financial risks and promoting the stability of the financial sector. The purpose of the article is to offer a thorough methodology that financial institutions can use when performing credit risk analysis. This entails data gathering, preprocessing, feature engineering, statistical analysis, choosing a machine learning model, and model assessment. The goal of the paper is to provide practitioners with a detailed manual for implementing R-based data-driven credit risk an
During disasters, such as natural catastrophes the immediate survival of victims is at stake. There have been instances where victims who were genuinely in dire need of assistance were not provided with it when they s...
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The Class Imbalance Problem (CIP) is a critical challenge in machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority classes are underrepresented but crucial. This ...
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The widespread adoption of 5G networks has revolutionized communication, providing great connectivity and services across various verticals. Specifically, on the 5G Core (5GC) this has been made possible by leveraging...
Micro-expressions (MEs) are fleeting involuntary facial movements, which occur frequently when people attempt to conceal their emotions. Since human eyesight cannot detect fleeting and slight changes in facial express...
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As per the Anti-Phishing Working Group's (APWG) report, there were approximately 4.7 million phishing attacks in the year 2022. A significant portion of these phishing attacks were carried out by alluring unsuspec...
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