This study focuses on the optimization of antireflection coatings (ARCs) to enhance the performance of silicon heterojunction (SHJ) solar cells. SHJ solar cells face a significant challenge in achieving their theoreti...
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The Internet of Things (IoT) has transformed how smart cities operate, significantly improving their inhabitants’ efficiency and overall quality of life. However, the massive volume of sensitive data generated by IoT...
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ISBN:
(数字)9798331517786
ISBN:
(纸本)9798331517793
The Internet of Things (IoT) has transformed how smart cities operate, significantly improving their inhabitants’ efficiency and overall quality of life. However, the massive volume of sensitive data generated by IoT devices presents challenges, including privacy concerns and communication overheads. Traditional centralized data processing can compromise privacy and require significant communication, leading to scalability problems and power drains. Federated Learning (FL) offers a solution by processing data locally and transmitting only the model parameters. Vanilla FL faces challenges in IoT environments due to latency, bandwidth constraints, and power drain. Hierarchical FL (HFL) effectively addresses these issues by hierarchically leveraging the processing capabilities of both cloud and edge servers to optimize resource utilization and efficiently minimize latency. This paper evaluates HFL using IoT-derived datasets, develops and implements the HFL framework for resource-constrained IoT systems, and conducts the first known HFL tests on relevant datasets to demonstrate its performance.
This paper introduces an advanced spectrum sensing (SS) method for next–generation wireless networks that utilize dual–path architecture called DPSegNet. This innovative model is designed to precisely segment 5G new...
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In recent years, US Emergency Medical Services (EMS) have faced a massive shortage of EMS workers. The sudden outbreak of the pandemic has further exacerbated this issue by limiting in-person training. Additionally, c...
In recent years, US Emergency Medical Services (EMS) have faced a massive shortage of EMS workers. The sudden outbreak of the pandemic has further exacerbated this issue by limiting in-person training. Additionally, current training modalities for first responders are costly and time-consuming, further limiting training opportunities. To overcome these challenges, this paper compares the efficacy of augmented reality (AR), an emerging training modality, and video-based training to address many of these issues without compromising the quality of the training with reduced instructor interaction. We examined performance, subjective, and physiological data to better understand workload, user engagement, and cognitive load distribution of 51 participants during training. The statistical analysis of physiological data and subjective responses indicate that performance during AR and video-based training and retention phases depended on gender perception of workload and cognitive load (intrinsic, germane, extraneous). However, user engagement was higher in AR-based training for both genders during training.
Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer...
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Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.
Reverse logistics is an important aspect of supply chain management, as it involves the return and disposal of products, materials, and equipment. To make strategic and cost-effective decisions about reverse logistics...
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The literature on master production scheduling for product mix problems under the Theory of Constraints (TOC) was considered by many previous studies. Most studies assume a static resources availability. In this study...
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作者：
Ali, MohammadKhalid, Muhammad
K.A.CARE-ERIC Dhahran31261 Saudi Arabia Kfupm
Electrical Engineering Department Dhahran31261 Saudi Arabia Kfupm
Interdisciplinary Research Center for Renewable Energy and Power Systems Dhahran31261 Saudi Arabia
The transcendental equations can be solved by employing analytical or metaheuristic approaches. The metaheuristic approaches are easier to understand and when implemented result in solutions with proximity to the exac...
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Canine intervertebral disc degeneration (IVDD) is a progressive spinal disorder that compromises mobility, induces chronic pain, and reduces quality of life. Existing treatments provide only symptomatic relief and do ...
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