This study presents a comprehensive optimization and comparative analysis of thermoelectric(TE)infrared(IR)detec-tors using Bi_(2)Te_(3) and Si *** theoretical modeling and numerical simulations,we explored the impact...
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This study presents a comprehensive optimization and comparative analysis of thermoelectric(TE)infrared(IR)detec-tors using Bi_(2)Te_(3) and Si *** theoretical modeling and numerical simulations,we explored the impact of TE mate-rial properties,device structure,and operating conditions on responsivity,detectivity,noise equivalent temperature difference(NETD),and noise equivalent power(NEP).Our study offers an optimally designed IR detector with responsivity and detectivity approaching 2×10^(5) V/W and 6×10^(9) cm∙Hz^(1/2)/W,*** enhancement is attributed to unique design features,includ-ing raised thermal collectors and long suspended thin thermoelectric wire sensing elements embedded in low thermal conductivity organic materials like ***,we demonstrate the compatibility of Bi_(2)Te_(3)-based detector fabrication pro-cesses with existing MEMS foundry processes,facilitating scalability and ***,for TE IR detectors,zT/κemerges as a critical parameter contrary to conventional TE material selection based solely on zT(where zT is the thermoelec-tric figure of merit andκis the thermal conductivity).
To sustain ultra-reliable and low latency communication for the fifth generation (5G) networks, the latency of data forwarding over the core network is conventionally ignored. To significantly reduce the latency, a ba...
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Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it ...
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Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.
Carbon credits are digital assets used in the fight against climate change. Understanding the complex interplay of factors that affect the carbon trading market is essential to determining the critical influences on c...
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Predicting health insurance premiums is a crucial task for both insurance companies and policyholders. This paper explores the use of regression approaches to predict health insurance premiums. The study uses a datase...
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Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed ***,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardia...
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Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed ***,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is *** addition,the diagnosis requires experienced medical experts and is ***,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning *** study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational *** dataset containing averaged signals with window size 10 is used as an *** competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the *** outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy *** findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.
Big data has emerged very fast, and this has brought both opportunities and problems that are related to the application of deep learning. This paper explores how deep learning can be implemented using big data and in...
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In the modern world, life expectancy turns out to be the most valuable factor in determining population's health. Today, it is more crucial to predict the future because life is more precarious due to a variety of...
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We consider random simple temporal graphs in which every edge of the complete graph Kn appears once within the time interval [0, 1] independently and uniformly at random. Our main result is a sharp threshold on the si...
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Pneumonia is an infection that is caused by bacteria, viruses, or fungi. It is a form of acute respiratory infection that affects the lungs and is a leading cause of death in developing countries. Pneumonia leads to o...
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