Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

In this research ,Undoped Nio and 1%Li doped Nio thin films were deposited utilizing chemical spray pyrolysis on the glass substrates heated (450C). The effects of non-thermal plasma on the structural and optical properties were studied. XRD measurement shows that Nio and Nio:1%Li films were found to be polycrystalline and have cubic structure with a preferred orientation (111). Decreased crystal size after exposure especially at (7) sec. AFM data indicate that the surface roughness average and (RMS) values of the prepared doped films are increasing after exposure to plasma, the transmittance increases after doped samples exposure to plasma, it was found that the energy gap value decreased when doped samples exposure to plasma, also, thickn

Despite their potential as a sustainable energy technology, the operation of proton exchange membrane fuel cells (PEMFCs) in sub-freezing conditions remains a critical challenge due to the risk of ice formation and performance degradation. This study introduces a new passive thermal management technique using strategically arranged multi-layer phase change materials (PCMs) to address this challenge. A numerical model was developed to evaluate the thermal behavior across various PCM configurations, incorporating one, two, and three layers arranged both in parallel and series with distinct melting points ranging from 55 to 65 ◦C. The results show that multi-layer PCM configurations provide significant improvements over the single-layer base

This work describes the weathering effects (UV-Irradiation, and Rain) on the thermal conductivity of PS, PMMA, PS/PMMA blend for packaging application. The samples were prepared by cast method at different ratios (10, 30, 50, 70, and 90 %wt). It was seen that the thermal conductivity of PMMA (0.145 W/m.K), and for PS(0.095 W/m.K), which increases by PS ratio increase up to 50% PS/PMMA blend then decreased that was attributed to increase in miscibility of the blend involved. By UV-weathering, it was seen that thermal conductivity for PMMA increased with UV-weathering up to (30hr) then decreased, that was attributed to rigidity and defect formation, respectively. For 30%PS/PMMA, there results showed unsystematic decrease in thermal conduct

Dynamic Thermal Management (DTM) emerged as a solution to address the reliability challenges with thermal hotspots and unbalanced temperatures. DTM efficiency is highly affected by the accuracy of the temperature information presented to the DTM manager. This work aims to investigate the effect of inaccuracy caused by the deep sub-micron (DSM) noise during the transmission of temperature information to the manager on DTM efficiency. A simulation framework has been developed and results show up to 38% DTM performance degradation and 18% unattended cycles in emergency temperature under DSM noise. The finding highlights the importance of further research in providing reliable on-chip data transmission in DTM application.