Knee joint segmentation and classification are critical tasks in medical imaging, having applications in diagnosis, treatment planning, and surgical navigation. The intricate architecture of the knee joint and the var...
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The intricate neurological condition known as epilepsy, which is common across the world, presents consid-erable difficulties in accurately identifying and differentiating between non-epileptic and epileptic activity ...
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Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity co...
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Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.
Online social networks have become essential platforms for individuals to share their thoughts and opinions with friends, family, and the broader community on various topics. Through text, picture, audio, and video co...
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The proposed study predicts Caffeine inflorescence diseases employing strong ML, and forecasting technologies. This research increases prediction abilities, evaluates cutting-edge technologies, and studies unique trai...
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This study introduces an innovative approach to enhance seismic signal quality through the integration of Denoising Convolutional Neural Networks (DCNN) and Kalman Filtering (KF). Seismic data often suffers from perva...
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The power transformer is a crucial asset and a fundamental component of the power grid. Assets undergo aging due to the stresses present in insulation materials. Partial discharges (PDs) are the most common fault sour...
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Water management in regions with high concentration of population and industry has posed serious problems for maintaining water quality. Conventional approaches for water quality monitoring using grab samples with lab...
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This paper investigates two neurodegenerative disorders in adults - Alzheimer's (AD) and Parkinson's (PD). Most works in the research explored these diseases independently - AD vs. healthy subjects and PD vs. ...
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In the era of multimedia technology digital images are essential and keeping them safe from unauthorised access is crucial. To address this issue, the proposed research explores the intersection of image steganography...
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