In this paper, the continuous time Markov model for the L (cons/2-4:F) system is described. Each component of the system has three states (good state, degraded state and failed state). The failure and repair times fol...
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In recent years,biometric sensors are applicable for identifying impor-tant individual information and accessing the control using various identifiers by including the characteristics like afingerprint,palm print,iris r...
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In recent years,biometric sensors are applicable for identifying impor-tant individual information and accessing the control using various identifiers by including the characteristics like afingerprint,palm print,iris recognition,and so ***,the precise identification of human features is still physically chal-lenging in humans during their lifetime resulting in a variance in their appearance or *** response to these challenges,a novel Multimodal Biometric Feature Extraction(MBFE)model is proposed to extract the features from the noisy sen-sor data using a modified Ranking-based Deep Convolution Neural Network(RDCNN).The proposed MBFE model enables the feature extraction from differ-ent biometric images that includes iris,palm print,and lip,where the images are preprocessed initially for further *** extracted features are validated after optimal extraction by the RDCNN by splitting the datasets to train the fea-ture extraction model and then testing the model with different sets of input *** simulation is performed in matlab to test the efficacy of the modal over multi-modal datasets and the simulation result shows that the proposed meth-od achieves increased accuracy,precision,recall,and F1 score than the existing deep learning feature extraction *** performance improvement of the MBFE Algorithm technique in terms of accuracy,precision,recall,and F1 score is attained by 0.126%,0.152%,0.184%,and 0.38%with existing Back Propaga-tion Neural Network(BPNN),Human Identification Using Wavelet Transform(HIUWT),Segmentation Methodology for Non-cooperative Recognition(SMNR),Daugman Iris Localization Algorithm(DILA)feature extraction techni-ques respectively.
Introduction: Photonic devices play a pivotal role in the realm of high-speed data communication due to their inherent capability to expedite the transfer of information. Historically, research efforts in this domain ...
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Introduction: Photonic devices play a pivotal role in the realm of high-speed data communication due to their inherent capability to expedite the transfer of information. Historically, research efforts in this domain have predominantly concentrated on investigating the fundamental mode propagation within photonic waveguides. Methods: This study diverges from the conventional approach by delving into the untapped potential of higher-order modes in addition to the fundamental mode of propagation. The exploration of these higher-order modes opens up new possibilities for optimizing and enhancing the performance of photonic devices in high-speed data communication scenarios. As a distinctive aspect of this study, various coating materials were scrutinized for their impact on both fundamental and higher-order mode propagation. The materials under examination included AlN (aluminum nitride), Germanium, and Silicon. These materials were chosen based on their unique optical properties and suitability for influencing different modes of light propagation. The findings from the study reveal that applying a coating of germanium demonstrates advantageous characteristics, particularly in terms of reduced signal loss, even when considering higher-order modes of propagation within photonic devices. Results: In this context, the results indicate that germanium-coated waveguides exhibit notably low propagation losses, with measurements as minimal as 0.25 dB/cm. This low level of loss is particularly noteworthy, especially when the waveguide has a width of 550 nm and is coated with a thickness of 50 nm. The dimensions and coating specifications play a crucial role in determining the efficiency of light transmission within the waveguide. Conclusion: The fact that the propagation loss is substantially low under these conditions suggests that the germanium-coated waveguide, even when considering higher-order modes of light propagation, can effectively maintain the integrity of the optica
Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of r...
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Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this doma
Upper limb movements are essential in everyday life;hand paralysis caused by post-stroke affects the regular lives of *** regain hand mobility and motor function,we must perform repetitive ***,rehabilitation devices a...
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Upper limb movements are essential in everyday life;hand paralysis caused by post-stroke affects the regular lives of *** regain hand mobility and motor function,we must perform repetitive ***,rehabilitation devices are essential to help post-stroke victims exercise and recover from paralysis more *** proposed research developed a 3D-printed portable rehabilitative device for elbow and shoulder *** device assists patients with hand impairments in performing repetitive elbow flexion and internal/external rotation of the *** post-stroke patients(each group of 10)were randomly assigned to the traditional training group or rehabilitation device training *** study compared the outcomes of the two groups before and after eight weeks of *** the therapy,the patient's performance was evaluated using standard clinical tests such as the Fugl-Meyer Assessment(FMA)and Active Range of Motion(AROM)of the elbow and *** results of the RDT(Rehabilitation Device Training)group were greater than those of the TT(Traditional Training)group,and the p values(p<0.05)show a significant *** proposed device is a simple,low-cost,portable structure that improves muscular strength and mobility in the shoulder and elbow.
The Problem of Photovoltaic(PV)defects detection and classification has been well *** techniques exist in identifying the defects and localizing them in PV panels that use various features,but suffer to achieve higher...
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The Problem of Photovoltaic(PV)defects detection and classification has been well *** techniques exist in identifying the defects and localizing them in PV panels that use various features,but suffer to achieve higher *** efficient Real-Time Multi Variant Deep learning Model(RMVDM)is presented in this article to handle this *** method considers different defects like a spotlight,crack,dust,and micro-cracks to detect the defects as well as loca-lizes the *** image data set given has been preprocessed by applying the Region-Based Histogram Approximation(RHA)*** preprocessed images are applied with Gray Scale Quantization Algorithm(GSQA)to extract the *** features are trained with a Multi Variant Deep learning model where the model trained with a number of layers belongs to different classes of *** class neuron has been designed to measure Defect Class Support(DCS).At the test phase,the input image has been applied with different operations,and the features extracted passed through the model *** output layer returns a number of DCS values using which the method identifies the class of defect and localizes the defect in the ***,the method uses the Higher-Order Texture Localization(HOTL)technique in localizing the *** pro-posed model produces efficient results with around 97%in defect detection and localization with higher accuracy and less time complexity.
With the development of information technology and cloud computing,data sharing has become an important part of scientific *** traditional data sharing,data is stored on a third-party storage platform,which causes the...
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With the development of information technology and cloud computing,data sharing has become an important part of scientific *** traditional data sharing,data is stored on a third-party storage platform,which causes the owner to lose control of the *** a result,there are issues of intentional data leakage and tampering by third parties,and the private information contained in the data may lead to more significant ***,data is frequently maintained on multiple storage platforms,posing significant hurdles in terms of enlisting multiple parties to engage in data sharing while maintaining *** this work,we propose a new architecture for applying blockchains to data sharing and achieve efficient and reliable data sharing among heterogeneous *** design a new data sharing transaction mechanism based on the system architecture to protect the security of the raw data and the processing *** also design and implement a hybrid concurrency control protocol to overcome issues caused by the large differences in blockchain performance in our system and to improve the success rate of data sharing *** took Ethereum and Hyperledger Fabric as examples to conduct crossblockchain data sharing *** results show that our system achieves data sharing across heterogeneous blockchains with reasonable performance and has high scalability.
This new method merging various disciplines into each stage, introduces a novel integration of state-of-the-art neural networks (MobileNet, ResNet, and VGG19) with an adaptive nature based on the Manta Ray scheme with...
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The advent of technologies like Deep Learning has revolutionized human interaction, transcending language and disability barriers. Sign Language Recognition (SLR) systems have emerged as vital tools, facilitating seam...
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Ophthalmic diagnostics play a critical role in the early detection and management of various ocular diseases. Among the advanced imaging modalities employed in ophthalmology, Optical Coherence Tomography (OCT) has eme...
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