The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the...
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The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance,...
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The number of Internet of Things (IoT) devices has increased rapidly in recent years, but lack effective methods to integrate their computational power. In this article, we propose NC-Load, which couples IoT devices i...
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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.
Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-ba...
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Optical microring modulator plays a critical role in optical communication and computation due to its ultra-compact footprint and low-driving voltage;however, the linearity of microring resonator is constrained by its...
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Parkinson’s Disease (PD) is a neurodegenerative disorder that requires correct diagnosis and continuous monitoring of the disease severity. The state-of-the-art methods tend to be unimodal or lack robustness in gener...
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Parkinson’s Disease (PD) is a neurodegenerative disorder that requires correct diagnosis and continuous monitoring of the disease severity. The state-of-the-art methods tend to be unimodal or lack robustness in generalizing between modalities, and hence cannot be applied clinically in diverse populations. A comprehensive approach is a multi-modal framework that overcomes these limitations by integration of brain Magnetic Resonance Imaging (MRI) data, gait analysis, and speech signals for enhanced classification and severity estimation of PD. A Hierarchical Attention-based Multi-modal Fusion (HAMF) model is developed in this paper to employ hierarchical attention mechanism at feature and decision levels to help the model learn representations at various levels. This leads to richer feature extraction, besides fusing different data modalities with accurate integration. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods are used in optimizing the model, by which the convergence speed raised by 15–20 %. An accuracy of 94.2 % was achieved, thus improving by 4–5 %, compared to the existing methodologies. Temporal Convolutional Network (TCN) which can capture long-range temporal dependencies, was used in the longitudinal severity estimation task, achieving a Mean Squared Error (MSE) of 0.12 in disease progression forecasting. Beyond this, Domain-Adversarial Neural Network (DANN) enables improved cross-domain generalization and maintains a consistent classification accuracy of 90-93% on diversified datasets. Finally, SHapley Additive exPlanations - Class Activation Maps (SHAP-CAM) further enhanced the model explainability. During the conduct of this work, 85% of all cases provided clinically interpretable insights that allowed clinicians to conduct personalized treatment planning in a more robust and interpretable way. This work substantially extends current multi-modal diagnosis and analysis of PD progression by offering a robust and interpretable tool to
Task scheduling in distributed cloud and fog computing applications must be efficient to optimize resource utilization, minimize latency, and comply with strict service level agreements. The dynamic and heterogeneous ...
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Few-Shot Action Recognition (FSAR) aims to recognize novel class action with limited annotated training data from the same class. Most FSAR methods subconsciously follow the few-shot image classification solutions by ...
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The design simulation and manufacturing of an x-band frequency uneven amplitude 90° hybrid coupler are described in this paper. This hybrid coupler is used to create a feeder network with eight output ports opera...
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