This research study introduces a novel approach for the classification of ocular diseases that employs the EfficientNetB3 architecture. The backbone of our model is EfficientNetB3, which has a total of 42 layers and a...
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This research demonstrates a novel approach for evaluating teacher performance by conducting aspect-based sentiment analysis (ABSA) on student feedback. A large dataset of over 2 million student comments about teacher...
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The emergence of the COVID-19 epidemic has emphasized the continuous need for innovative and discreet methods to monitor and evaluate the progression of the disease. Wearable technology, equipped with an array of sens...
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With the technological developments past decade, there is an increase seen in the usage of social media applications as more people have come over on such platforms to show their thoughts. We look for the problems fac...
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Trust is essential in the constantly developing environment of online social networks. The Nexus Terroism Impact and Intro Trust facilitate the communication, decision-making, and maintenance of digital communities am...
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Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die where...
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This study aims to design and implement a financial risk control system based on machine learning algorithms and big data technologies to improve the risk management capability of financial institutions. The system is...
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ISBN:
(数字)9781510682818
ISBN:
(纸本)9781510682801
This study aims to design and implement a financial risk control system based on machine learning algorithms and big data technologies to improve the risk management capability of financial institutions. The system is designed using key steps such as data collection and collation, feature engineering, model selection and training, risk assessment and prediction, risk reporting and monitoring, and automated decision making and *** the data collection and collation phase, the system collects a large amount of financial market data, transaction data and customer data, and ensures the accuracy and consistency of the data through data cleansing and collation. Next, the system performs feature engineering, using machine learning algorithms to extract and transform features from the data to better express the characteristics and relationships of the data. This includes statistical features, time series features, technical indicators, etc. The system then selects machine learning models suitable for financial risk control, such as decision trees, random forests, support vector machines, etc., and uses historical data to train and optimise the models. The trained models are used to perform risk assessment and prediction on current data, such as assessing the risk of investment varieties such as stocks and bonds, predicting market volatility, and detecting abnormal transactions. The system also generates detailed risk reports and monitoring indicators to help financial institutions identify and respond to potential risk events in a timely manner. This includes the calculation of risk indicators and the generation of risk alerts, etc. Finally, the system can automatically trigger risk management strategies and trading decisions based on risk assessment results to reduce risk and improve investment returns. The design and implementation can effectively improve the risk management capability of financial institutions and promote the stable development of the financial mar
Business Process Modeling (BPM) is a skill considered fundamental for computer engineers, with Business Process Modeling Notation (BPMN) being one of the most commonly used notations for this discipline. BPMN modeling...
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ISBN:
(纸本)9798400700446
Business Process Modeling (BPM) is a skill considered fundamental for computer engineers, with Business Process Modeling Notation (BPMN) being one of the most commonly used notations for this discipline. BPMN modeling is present in different curricula in specific Master's Degree courses related to software engineering, but, in practice, students often underperform on BPMN modeling exercises due to difficulties in learning good modeling practices. In recent years, more and more fields of computer science have employed gamification (the usage of game elements in non-recreational contexts to gain benefits in terms of interest, participation, motivation, and enjoyment) with positive results during both development and teaching processes. Thus, we have developed a platform for BPMN modeling that employs gamification mechanics to facilitate learning good modeling practices with mechanisms such as rewarding good modeling solutions and penalizing less correct ones, with a dedicated feedback mechanism that maps correctly modeled elements to the corresponding concept. A preliminary laboratory experiment has been conducted with students of an Information Systems course to evaluate how students receive the mechanics and if there may be benefits in using a gamified environment for teaching process modeling throughout an entire course.
Investigating sophisticated cellular and intercellular behaviors in animals is crucial to biological research, which calls for an intravital high-precision recording at ultrahigh spatiotemporal resolution. Light-field...
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Emotional analysis plays an important role in improving AI's ability to learn and respond to human emotions. Face emotion recognition is expeditiously expanding its field of research with important applications ac...
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