In this paper,we report a simulation study on the performance enhancement of Praseodymium doped silica fiber amplifiers(PDFAs)in O-band(1270-1350 nm)in terms of small signal gain,power conversion efficiency(PCE),and o...
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In this paper,we report a simulation study on the performance enhancement of Praseodymium doped silica fiber amplifiers(PDFAs)in O-band(1270-1350 nm)in terms of small signal gain,power conversion efficiency(PCE),and output optical power by employing bidirectional *** PDFA performance is examined by optimizing the length of Praseodymium doped silica fiber(PDF),its mode-field diameter(MFD)and the concentration of Pr^(3+).A small-signal peak gain of 56.4 dB,power conversion efficiency(PCE)of 47%,and output optical power of around 1.6 W(32 dBm)is observed at optimized parameters for input signal wavelength of 1310 *** noise figure(NF)of 4.1 dB is observed at input signal wavelength of 1310 ***,the effect of varying the pump wavelength and pump power on output optical power of the amplifier and amplified spontaneous emission(ASE)noise is also investigated,***,the impact of ion-ion interaction(up-conversion effect)on small-signal gain of the amplifier is also studied by considering different values of up-conversion coefficient.
Efficient cloud resource management is vital, as it ensures the accurate selection and allocation of resources to diverse workloads or applications. This entails real-time balancing of workload performance, compliance...
Efficient cloud resource management is vital, as it ensures the accurate selection and allocation of resources to diverse workloads or applications. This entails real-time balancing of workload performance, compliance, and cost to achieve optimization. Since there are many cloud users involved in scheduling tasks, or cloudlets, the scheduling process becomes complex. It involves selecting appropriate data centers, servers (hosts), and virtual machines (VMs). There are several bioinspired algorithms that are effective and popular for solving the NP-complete problem of cloudlet scheduling. By using these algorithms, cloudlets can be efficiently allocated to achieve faster execution times, better resource utilization, and a shorter waiting time. This study presents a hybrid technique that combines the strengths of both ant colony optimization and locust-inspired algorithm (HACO-LA), which have been shown to outperform other bio-inspired algorithms in cloud computing environments. Implementing this technique will lead to a decrease in the average response time, while also increasing the utilization of VMs and servers. The method was evaluated using the Cloud Sim toolbox with authentic data. It was compared to the preexisting Artificial Bee Colony Optimization (ABC) algorithm and Particle Swarm Optimization (PSO) algorithm. The findings demonstrated that HACO-LA surpassed both methods, leading to notable enhancements in server utilization, increased reliability, and decreased average response time.
Conventional power systems have been evolving towards prospective smart grids in recent decades as market needs have changed and independent innovations, such as the 'Internet of Things' (IoT), have developed....
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This paper presents an overview of on-board conductive charger topologies for electric vehicles (EVs). Battery packs in electric and plug-in hybrid electric vehicles (EVs/PHEVs) need frequent energy refills to fulfil ...
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Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various computer-Aided Diagnostic(CAD)methods have been developed to enhance the ...
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Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.
To fortify security mechanisms in software systems for the Internet of Things (IoT), this article presents a framework for AI-Enhanced Virtual Twin Modelling. In order to track and examine the actions of IoT devices i...
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ISBN:
(数字)9798350352931
ISBN:
(纸本)9798350352948
To fortify security mechanisms in software systems for the Internet of Things (IoT), this article presents a framework for AI-Enhanced Virtual Twin Modelling. In order to track and examine the actions of IoT devices in real-time, the suggested method makes use of virtual twin technology that is combined with machine learning techniques. Automated response generation, continuous threat assessment, and anomaly detection are made possible by creating a digital clone of the actual IoT network using the virtual twin paradigm. By combining deep learning models like CNNs and LSTMs, it becomes easier to forecast possible security risks in network traffic data by seeing intricate spatial and temporal patterns. When tested in a virtual Internet of Things (IoT) setting, the suggested framework outperforms conventional rule-based approaches by a wide margin, reducing reaction time by 35% while achieving an accuracy of $\mathbf{9 6 . 2 \%}$ in threat identification.
Traditional rehabilitation methods often focus on a single impairment, leading to challenges such as low motivation and inadequate responses to the diverse needs of patients, time taken for appointments, and lack of p...
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ISBN:
(数字)9798350377972
ISBN:
(纸本)9798350377989
Traditional rehabilitation methods often focus on a single impairment, leading to challenges such as low motivation and inadequate responses to the diverse needs of patients, time taken for appointments, and lack of personal monitoring of health status. To address these issues, The model employs meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, targeting multiple impairments while tailoring interventions to individual patient requirements. A hybrid model proposed would integrate cognitive and machine learning methodologies to enhance personalized rehabilitation. By selectively employing meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, the proposed system aims to bridge existing gaps in rehabilitation practices. The hybrid system is designed to efficiently manage patient data, reduce the complexity of rehabilitation tasks, and optimize computational resources. The use of stacking as an ensemble method further mitigates biases and uncertainties associated with individual models, thereby improving the reliability of the rehabilitation framework. Preliminary outcomes indicate that this hybrid approach achieves superior accuracy and efficiency compared to traditional methods, ultimately enhancing adaptability to the varied needs and preferences of patients.
This study is designed to develop Artificial Intelligence(AI)based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays(CXRs).The frontline physicians and radiologists suf...
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This study is designed to develop Artificial Intelligence(AI)based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays(CXRs).The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of *** this study,AI-based analysis tools were developed that can precisely classify COVID-19 lung *** available datasets of COVID-19(N=1525),non-COVID-19 normal(N=1525),viral pneumonia(N=1342)and bacterial pneumonia(N=2521)from the Italian Society of Medical and Interventional Radiology(SIRM),Radiopaedia,The Cancer Imaging Archive(TCIA)and Kaggle repositories were taken.A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was ***,the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning(ML)algorithms,which twice trained the learning *** ResNet101 with optimized parameters yielded improved performance to default *** extracted features from ResNet101 are fed to the k-nearest neighbor(KNN)and support vector machine(SVM)yielded the highest 3-class classification performance of 99.86%and 99.46%,*** results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of *** proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.
Underwater acoustic signal is one of its most vital features in oceanography for predicting the local variants, helps to process the signal, and also assists to discover non-stationary signals. wavelet neural net...
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In this study, a tubular solar air heater with swirl-flow and find absorber supported by radial and longitudinal fins (SAH) was used to improve thermohydraulic performance. The suggested SAH has been compared to the p...
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