Stress is the physiological response to mental, emotional, or physical stress, which varies between individuals. A survey by Ipsos Global showed that around 30% of respondents identified stress as a significant health...
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Time-series water level prediction during natural disasters,for example,typhoons and storms,is crucial for both flood control and *** data-driven models that harness deep learning(DL)techniques has emerged as an attra...
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Time-series water level prediction during natural disasters,for example,typhoons and storms,is crucial for both flood control and *** data-driven models that harness deep learning(DL)techniques has emerged as an attractive and effective approach to water level *** paper proposed an innovative data-driven methodology using DL network architectures of Gated Recurrent Unit(GRU),Long Short-Term Memory(LSTM),and Bidirectional Long-Short Term Memory(Bi-LSTM)to predict the water level at the Le Thuy station in the Kien Giang *** models were implemented and validated based on hourly rainfall and water level observations at meteo-hydrological *** combinations of input variables with different time leads and time lags were established to evaluate the forecast capability of three proposed models by using five metrics,that is,R2,MAE,RMSE,Max Error Value,and Max Error *** results revealed that the LSTM model outperformed the Bi-LSTM and GRU models,when water level and rainfall observations for one-time lag at three stations were used to predict the water level at the Le Thuy station with 1-h time lead,with the five metrics registering at 0.999;3.6 cm;2.6 cm;12.9 cm;and−1 h,respectively.
Safety of railway is the major problem worldwide. It has problems like cracks or any fault in the railway tracks. These problems can cause severe accidents if it is not detected regularly and early. In traditional fau...
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The ever-increasing importance of education has driven researchers and educators to seek innovative methods for enhancing student performance and understanding the factors that contribute to academic success. This pap...
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This paper proposes a face recognition system based on steerable pyramid transform (SPT) and local directional pattern (LDP) for e-health secured login in cloud domain. In an e-health login, patients periodically forg...
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Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylh...
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Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. Accurate flood detection plays a vital role in mitigating these impacts by allowing timely early warnings and strategic planning. Recent advancements in flood prediction research include the development of robust, accurate, and low-cost flood models designed for urban deployment. By applying and utilizing powerful deep learning models show promise in improving the accuracy of prediction and prevention. But those models faced significant issues related to scalability, data privacy concerns and limitations of cross-border data sharing including the inaccuracies in prediction models due to changing climate patterns. To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. This approach promotes data privacy and allows collaborative learning while working under cross-border data-sharing constraints, therefore improving the accuracy of prediction. The results showed that the best-performing FNN model achieved an R-squared value of 0.96, a Mean Absolute Error (MAE) value of 0.02, Percent bias (PBIAS) value of 0.4185 and lower Root Mean Square Error (RMSE) in the FL environment. Explainable AI techniques, such as SHAP, sheds light on the most significant role played by upstream rainfall and river dynamics, particularly from Cherrapunji and the Surma-Kushiyara river system, in d
This paper provides a detailed comparison of traditional networking architectures and Software Defined Networking (SDN) approaches, with a focus on bandwidth optimization and traffic management. Simulations were condu...
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Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input. In this paper we reveal additional fundamental advantages of stochastic methods over deterministic ones,...
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Database security has grown to a necessary level of importance today, characterized by the escalating demand for data storage. The surge in data storage requirements, propelled by technological advancements, has led t...
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