Social media is a platform where people express their opinions through user-generated text. Investigating opinion changes of people based on the influence of legitimate users and bots over time is crucial. The influen...
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In the contemporary timeframe, 3D human modeling has become increasingly important due to broad spectrum in leveraging utilities. It can create realistic representations of human anatomy, which is useful in health car...
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The spread of a life-threatening disease such as cancer presents a significant threat to the entire global population. The disease in question caused 10 million deaths worldwide in 2020, which has motivated us to expl...
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Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswil...
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Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential ***, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error *** extensive experimental analysis was performed on the benchmark *** evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.
Glioblastoma is an aggressive type of brain cancer with a high mortality rate. Early and accurate glioblastoma detection is crucial for timely and effective treatment. Hyperspectral Imaging (HSI) has emerged as a prom...
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In this work, a patient-centric paradigm utilizing IPFS (InterPlanetary File System) storage, blockchain technology, and Zero-Knowledge Proofs (ZKPs) is proposed for handling healthcare data. Traditional healthcare da...
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Facial expression recognition is becoming a core part of human-computer interaction and emotion detecting systems. However, the real-time scenarios such as the widespread use of face masks presents a big challenge to ...
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This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large...
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The accurate and timely diagnosis of intracranial tumors is crucial for effective treatment and management. In recent years, magnetic resonance imaging (MRI) has emerged as a valuable tool for detecting and diagnosing...
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In recent times, the spotlight has been on understanding and forecasting water quality, owing to the variety of pollutants that pose potential harm. This research aims to advance strategies for managing and minimizing...
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