One of the challenges of treating lung tumors in radiation therapy is the patient's respiratory movements during the treatment, which lead to tumor motion. The goal of respiratory motion prediction is to predict t...
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You may be willing to lose pounds for peculiar reasons or to improve your health. It can diminish your likelihood of certain conditions, such as heart strokes and diabetes. It can put down your blood pressure and over...
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Gastrointestinal diseases are increasing at a fast rate. Some of these lead to colorectal cancer. The presence of polyps in the large intestine may lead to colorectal cancer in later stages. Early detection and predic...
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The emergence of COVID-19 has underscored the urgency of accurate medical diagnosis, particularly in the context of chest X-ray image classification for various lung conditions, including COVID-19, normal cases, viral...
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Heart disease, lung disease, respiratory disease, etc. are currently the top killers. The majority of liver problems are difficult to detect early on. One of these is fatty liver disease, a common disorder brought on ...
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Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accur...
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India is rapidly moving towards the digitization of money in all aspects. Cryptocurrency has grown widely in India and around the world among investors for financial activities like buying, selling, and trading. Accor...
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Moving around in their surroundings is challenging for elderly people and people with visual impairments. The majority of functional sticks in use today are not intelligent and lack an innate ability to recognize obst...
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Source camera identification is a critical task in digital image forensics that helps verify the authenticity of images by identifying the camera sensor used to capture them. In this paper, we propose an enhanced meth...
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Companies in the complex financial world face several risks that require careful investigation and management. This critical review assesses various frameworks for large-scale financial risk analysis in organizations....
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ISBN:
(纸本)9798331523923
Companies in the complex financial world face several risks that require careful investigation and management. This critical review assesses various frameworks for large-scale financial risk analysis in organizations. Globalization, technological advancements, regulatory changes, and financial market interconnectedness pose significant challenges to traditional risk management methods. This report identifies and analyzes key frameworks, including quantitative models, qualitative assessments, scenario analysis, stress testing, and Monte Carlo simulations, evaluating their adaptability and usefulness across different organizational settings based on industry categorization, scale, complexity, and regulatory framework. This study highlights the strengths and weaknesses of each framework, with a particular focus on their applicability to market, credit, liquidity, operational, and strategic risks. Furthermore, it examines the incorporation of emerging risk factors such as climate change, geopolitical instability, cyber risks, and socio-economic developments into these frameworks. Advanced methodologies, including machine learning algorithms, artificial intelligence, and big data analytics, have shown potential in enhancing the precision and reliability of risk analysis, enabling firms to detect, quantify, and mitigate large-scale hazards effectively. The impact of regulatory frameworks such as Basel III, Solvency II, Dodd-Frank Act, and IFRS on financial risk analysis is also discussed, emphasizing the need for alignment with evolving compliance requirements. The results demonstrate the superiority of advanced models, such as Long Short-Term Memory (LSTM) networks, which achieved the highest accuracy (94%) and F1 Score (0.91), showcasing their effectiveness in handling sequential and temporal data. Random Forest models also performed robustly, with an accuracy of 92% and an F1 Score of 0.90, highlighting their capability for feature importance ranking. The Support Vecto
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