IoT based smart water system frameworks may aid in reaching perfect water-asset use in the accuracy agricultural scene. The agricultural documented is changed from manual observing and control to Automation with the a...
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Preserving cultural history and heritage improves the sense of belongingness and cooperation, which leads to a healthy and prosperous economy. Museums housing historically famous artworks are one of the key contributi...
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Modern deep learning models have exhibited better performance than conventional machine learning methods across a range of text classification tasks, encompassing natural language inference, sentiment analysis, news c...
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The issue of certificate forgery is widespread in Southeast Asia, adversely affecting qualified individuals' access to employment opportunities. Job applicants are required to submit their academic records as evid...
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Integrating Artificial Intelligence (AI) into lung sound classification has markedly improved respiratory disease diagnosis by analyzing intricate patterns within audio data. This paper presents a two-phase approach f...
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Accurate diagnosis of COVID-19 using chest X-ray imaging is pivotal for large-scale screening and epidemic containment efforts. However, discerning COVID-19 from CT scan images poses formidable challenges due to the h...
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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
Deepfake technology has garnered considerable scholarly interest owing to its capacity to generate exceptionally lifelike yet artificially constructed media artifacts, posing serious threats to various aspects of soci...
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Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to retain and reason about temporal information remains limited, hindering their application in real-world scenarios where understanding the ...
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User preference is shifting away from desktops and laptop computers and towards smartphones. Using smartphones to expedite university teachers' attendance taking procedures would reduce lecture time and improve th...
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