The advent of the Internet of Things (IoT) has transformed the way devices communicate, with an ever-increasing need for seamless interoperability and energy-efficient communication. This paper presents a unified omni...
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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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Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucia...
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Robotic surveillance system integrating metal detection and gas sensing technologies to enhance land mine and explosive detection in military operations. The mobile robotic platform autonomously navigates and maps ter...
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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
Satellite networks comprise of large number of satellite constellations. It makes the management of keys used for different security mechanism a very important issue to be studied. Key management is set of procedure t...
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The Decentralized technology 'Blockchain' has some point Centralization in terms of Remote Procedure Call Mechanism. Relying on centralized RPC (Remote Procedure Call) is risky and running a node can be expens...
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This research paper conducts an exploration of prevalent machine learning classification algorithms, emphasizing the refinement of Random Forest models to optimize performance. The initial phase involves a comparative...
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Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The signific...
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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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