Penetration testing is a technique that involves the identification and exploitation of security flaws by simulating real world attacks to find security loopholes. Manual pentesting is a tedious and time consuming pro...
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This study explores the transformative role of artificial intelligence (AI) in education and mental health services, emphasizing the use of chatbots designed to support mental well-being. Chatbots like Wysa, Woebot, a...
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Lung cancer, a leading cause of mortality globally, demands early and accurate detection to improve patient out-comes. Current diagnostic methods, primarily relying on CT scans, face challenges in lung cancer subtype ...
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The identification and segregation of liquid and solid waste for efficient recycling can be enhanced through the application of machine learning algorithms. Waste management plays a crucial role in minimizing environm...
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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
Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of *** diagnostic procedures for DR now include optical coher...
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Diabetic retinopathy(DR),a long-term complication of diabetes,is notoriously hard to detect in its early stages due to the fact that it only shows a subset of *** diagnostic procedures for DR now include optical coherence tomography and digital fundus *** digital fundus images alone could provide a reliable diagnosis,then eliminating the costly optical coherence tomography would be beneficial for all parties *** and their patients will find this *** deep convolutional neural networks(DCNNs),we provide a novel approach to this *** approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling *** order to collect more subtle information for categorization,two such DCNNs,each with a different number of layers,are *** establish these limits,we use DCNNs and features extracted from picture metadata to train a support vector machine *** our experiments,we used information from Kaggle’s open DR detection *** fed our model 34,124 training images,1,000 validation examples,and 53,572 test images to train and test *** of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and *** results show a higher identification rate(86.17%)than those found in the existing literature,indicating the suggested strategy may be *** have jointly developed an algorithm for machine learning and accompanying software,and we’ve named it deep *** of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our *** technology might be used for self-diagnosis,at-home care,and telemedicine.
Emotion Recognition is a gravitating concept in Speech Data Analysis due to its adaptability in identifying human emotion. Emotion is an inherent human trait exhibited through attitude, behaviors, words, speech, and g...
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To carry out vulnerability testing and find possible security issues, reverse TCP utilizing Metasploit is an effective and well-known penetration testing tool. This article gives a brief introduction to the method’s ...
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Sentiment analysis evaluates the software developer’s emotions during product development. Quality of the real time project can be assured by the detecting the amount of code smell present in the software code snippe...
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Crowdsourcing has become a potential strategy to treat a wide range of challenges faced by smart cities. Crowdsourcing allows cities to make use of the group’s wisdom and resources of their residents to improve urban...
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