The Stock value forecast is a significant issue to determine the future direction of the financial Markets. Many research works are carried out and design many techniques to predict stock price of Individual stocks. B...
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We introduce the AI-Generated Optimal Decision (AIGOD) algorithm and the Deep Diffusion Soft Actor-Critic (DDSAC) framework, marking a significant advancement in integrating Human Digital Twins (HDTs) with AI-Generate...
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作者:
Maheswari, M. UmaAloysius, A.Purusothaman, P.
Bharathidasan University Department of Computer Science Tiruchirappalli India
Department of Computer Science Tiruchirappalli India
The widespread adoption of electronic health record (EHR) systems in response to a diverse array of requirements for primary and secondary healthcare, there is now an abundance of clinical data that can be accessed wi...
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Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing ***,traditional optimization methods often overlook the energy imbalance caused by node loa...
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Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing ***,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network *** To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is *** K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search *** proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the *** The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering *** calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network *** This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network *** research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.
Recommendation systems often neglect global patterns that can be provided by clusters of similar items or even additional information such as text. Therefore, we study the impact of integrating clustering embeddings, ...
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Due to the exponential increase in data volume, the widespread use of intelligent information systems has created significant obstacles and issues. High dimensionality and the existence of noisy and extraneous data ar...
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Due to the exponential increase in data volume, the widespread use of intelligent information systems has created significant obstacles and issues. High dimensionality and the existence of noisy and extraneous data are a few of the difficulties. These difficulties incur high computing costs and have a considerable effect on the accuracy and efficiency of machine learning (ML) methods. A key idea used to increase classification accuracy and lower computational costs is feature selection (FS). Finding the ideal collection of features that can accurately determine class labels by removing unnecessary data is the fundamental goal of FS. However, finding an effective FS strategy is a difficult task that has given rise to a number of algorithms built using biological systems based soft computing approaches. In order to solve the difficulties faced during the FS process;this work provides a novel hybrid optimization approach that combines statistical and soft-computing intelligence. On the first dataset of diabetes disease, the suggested approach was initially tested. The approach was later tested on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset after yielding encouraging results on diabetes dataset. While finding the solution, typically, data cleaning happens at the pre-processing stage. Later on, in a series of trials, different FS methods were used separately and in hybridized fashion, such as fine-tuned statistical methods like lasso (L1 regularization) and chi-square, as well as binary Harmony search algorithm (HSA) which is based on soft computing algorithmic approach. The most efficient strategy was chosen based on the performance metric data. These FS methods pick informative features, which are then used as input for a variety of traditional ML classifiers. The chosen technique is shown along with the determined influential features and associated metric values. The success of the classifiers is then evaluated using performance metrics like accuracy, preci
While Federated Learning (FL) manages well data diversity in distrusted learning, it faces additional complexities in scenarios with "frugal labeling,"where the client nodes host a mix of partially or fully ...
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Nowadays online news websites are one of the quickest ways to get information. However, the credibility of news from these sources is sometimes questioned. One common problem with online news is the prevalence of clic...
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Process-based learning is crucial for the transmission of intangible cultural heritage, especially in complex arts like Chinese calligraphy, where mastering techniques cannot be achieved by merely observing the final ...
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This research paper presents the development of a weather forecasting model that incorporates real-time data through Application Programming Interfaces. This model utilises simple algorithms to analyse meteorological ...
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