This study aims to enhance mutual fund allocation efficiency by integrating advanced Long Short-Term Memory (LSTM) networks with Bayesian optimization techniques, focusing on Top Tech Companies Stock Price dataset wit...
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ISBN:
(数字)9798350369083
ISBN:
(纸本)9798350369090
This study aims to enhance mutual fund allocation efficiency by integrating advanced Long Short-Term Memory (LSTM) networks with Bayesian optimization techniques, focusing on Top Tech Companies Stock Price dataset within the Industrials, Health Care, Information technology, and Financials sectors. The primary goal is to develop a predictive model that optimizes mutual fund portfolios by accurately forecasting stock performance in these key sectors. The dataset includes historical stock prices and financial metrics for Tech Companies Stock Price in the specified sectors. data preprocessing involves cleaning, normalization, and feature extraction to ensure high-quality inputs for the LSTM model. Based on historical patterns, Long Short-Term Memory networks are used to forecast future stock values because of its famed capacity to detect temporal relationships in time series data. By optimizing the hyperparameters of the LSTM model, Bayesian optimization has the capacity to improve the model's resilience and prediction performance. By methodically examining the hyperparameter space to find the ideal configuration, this optimization methodology enhances model performance without requiring a lot of manual adjustment. The optimized LSTM model is applied to simulate mutual fund allocation strategies, aiming to maximize returns while managing risk. The performance evaluation of the proposed LSTM-Bayesian optimization framework highlights its significant advantages over traditional allocation methods, basic LSTM models, and linearregression models. By analysing metrics such as MAE, rMSE, MAPE, cumulative return, Sharpe ratio, Sortino ratio, and maximum drawdown, the framework achieves superior accuracy, risk-adjustedreturns, androbustness in mutual fund allocation are evident. The results demonstrate that the proposed approach not only enhances predictive accuracy but also effectively manages risk, leading to better investment outcomes.
Magnetic resonance Imaging (MrI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandw...
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A novel method for monitoring and controlling humidity levels in smart hospital settings by means of the Internet of Things (IoT). To maximize patient comfort and infection control measures, IoT device connection allo...
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ISBN:
(数字)9798350376517
ISBN:
(纸本)9798350376524
A novel method for monitoring and controlling humidity levels in smart hospital settings by means of the Internet of Things (IoT). To maximize patient comfort and infection control measures, IoT device connection allows forreal-time humidity level monitoring and modification. Improving the system's responsiveness to humidity data is possible with the use of the k-Nearest Neighbors (KNN) algorithm. Machine learning (ML) method, the system can forecast the ideal relative humidity from past data and present weather. A major factor in preventing the spread of infectious diseases in healthcare facilities is keeping the relative humidity at an optimal level. The general health and success of the healing process depend on the patient's level of comfort. The proposed IoT system for smart hospital humidity management considers medical and patient needs. The system's capacity to dynamically adjust humidity levels based on data improves infection control and patient satisfaction.
Humans acquire and accumulate knowledge through language usage and eagerly exchange their knowledge for advancement. Although geographical barriers had previously limited communication, the emergence of information te...
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In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-enddeep neural netwo...
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In contemporary medicine, biomedical image interpretation is essential fordisease diagnosis and the selection of appropriate treatments. However, manually scrutinizing these images is time-consuming and may lead to e...
In contemporary medicine, biomedical image interpretation is essential fordisease diagnosis and the selection of appropriate treatments. However, manually scrutinizing these images is time-consuming and may lead to erroneous conclusions. The proposed work offers a novel approach to resolving these issues by utilizing a mixed machine-learning technique to improve the interpretation of biological images. The proposed system accurately evaluates physical images using machine learning techniques, including convolutional neural networks anddecision trees. The algorithm aims to integrate the most beneficial aspects of multiple image analysis methods to enhance their overall performance. Using a carefully selecteddataset, demonstrate the algorithm's precision androbustness compared to other approaches. The findings imply that the algorithm could substantially alter the interpretation of biological images in clinical and academic settings. This discovery has far-reaching implications, paving the way for improveddiagnostic precision and further study of the human body. This hybrid method is a promising step toward automating image processing and paves the way for new research and implementation opportunities in healthcare technology.
Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams. Compared with Visual Question Answering(VQA), TQA contains a large number of unco...
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In cardiac Magnetic resonance Imaging (MrI) analysis, T2 mapping is a tissue quantification technology to measure water and inflammation levels and has been recognized as an important versatile index for myocardial pa...
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Multilingual Language Models (MLLMs) such as mBErT, XLM, XLM-r, etc. have emerged as a viable option for bringing the power of pretraining to a large number of languages. Given their success in zero-shot transfer lear...
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Multilingual Language Models (MLLMs) such as mBErT, XLM, XLM-r, etc. have emerged as a viable option for bringing the power of pretraining to a large number of languages. Given their success in zero-shot transfer learning, there has emerged a large body of work in (i) building bigger MLLMs covering a large number of languages (ii) creating exhaustive benchmarks covering a wider variety of tasks and languages for evaluating MLLMs (iii) analysing the performance of MLLMs on monolingual, zero-shot cross-lingual and bilingual tasks (iv) understanding the universal language patterns (if any) learnt by MLLMs and (v) augmenting the (often) limited capacity of MLLMs to improve their performance on seen or even unseen languages. In this survey, we review the existing literature covering the above broad areas of research pertaining to MLLMs. Based on our survey, we recommend some promising directions of future research.
Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs ...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like random Forests (rF), Support Vector Machines (SVM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemble learning techniques, feature engineering, andreal-time monitoring enable adaptability in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregulardatarequests is proved by anomaly detection rates utilizing One-Class SVMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises.
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