Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (ai) and natural langu...
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Breast cancer remains a significant health concern for women, and accurate prediction of recurrence is vital for effective treatment and improved outcomes. This research investigates the application of advanced ensemb...
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ISBN:
(数字)9798331540364
ISBN:
(纸本)9798331540371
Breast cancer remains a significant health concern for women, and accurate prediction of recurrence is vital for effective treatment and improved outcomes. This research investigates the application of advanced ensemble learning techniques to enhance the reliability of breast cancer recurrence predictions. By combining Deep Neural Networks (DNN) and Artificial Neural Networks (ANN) with traditional machinelearning methods, the proposed approach demonstrates significant improvements in accuracy, precision, sensitivity, specificity, F1-scores, and areas under the curve (AVCs). The study utilizes two breast cancer relapse datasets, UMCIO and WPBC, to evaluate the performance of the ensemble learning models. The results highlight the effectiveness of the proposed approach in accurately predicting breast cancer recurrence. This research contributes to the advancement of breast cancer diagnosis and treatment by providing a valuable tool for clinicians to assess the risk of recurrence and tailor treatment plans accordingly.
Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle the...
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Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (ai) and natural langu...
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ISBN:
(数字)9798350354171
ISBN:
(纸本)9798350354188
Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (ai) and natural language processing (NLP) has significantly advanced stock market forecasting. ai’s ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where ai has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements.
Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (ai) and natural langu...
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The convergence of Big Information Analytics (BDA) and the Internet of elements (IoT) which is defined by the use of Information-driven and Simplified Answers is changing patient care and the way healthcare is deliver...
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ISBN:
(数字)9798331566685
ISBN:
(纸本)9798331566692
The convergence of Big Information Analytics (BDA) and the Internet of elements (IoT) which is defined by the use of Information-driven and Simplified Answers is changing patient care and the way healthcare is delivered. healthcare systems get better diligent outcomes Construct the world of customized discourse plans and further break decision-making away exploitation real-time information from connected devices. The present situation and potential Uses of these technologies in the future are examined within the purview of the research. the contributions that these technologies render to prophetic analytics far Watching and effective Productivity are apt particular condition. The advancement of proactive patient management has been fueled by the capacity of healthcare professionals to convert vast volumes of Information into pertinent Understandings via the use of advanced analytics and the Internet of elements. notwithstanding Problems care cybersecurity interoperability and information secrecy restrictions have work resolute inch rate to full see the call given away free healthcare systems.
Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle the...
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ISBN:
(数字)9798331505745
ISBN:
(纸本)9798331505752
Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.
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