The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

In Computer-based applications, there is a need for simple, low-cost devices for user authentication. Biometric authentication methods namely keystroke dynamics are being increasingly used to strengthen the commonly knowledge based method (example a password) effectively and cheaply for many types of applications. Due to the semi-independent nature of the typing behavior it is difficult to masquerade, making it useful as a biometric. In this paper, C4.5 approach is used to classify user as authenticated user or impostor by combining unigraph features (namely Dwell time (DT) and flight time (FT)) and digraph features (namely Up-Up Time (UUT) and Down-Down Time (DDT)). The results show that DT enhances the performance of digraph features by i

This paper was conducted to identifying the body growth averages for the infants of the age (3-6) months and their relation with brest (natural ) or artificial feeding The results showed that the higher percentage was for the infants with the natural feeding in comparison with those of the artificial or mixed feeding. Also there was a clear increase in the average of the body growth for those with the natural feeding and such results were closer to the standard criterion. While the averages of body growth for those with the artificial or mixed feeding were low. In addition, it was clear that the averages of body growth of the i

Cu (In, Ga) Se2 (CIGS) nano ink were synthesized from molecular precursors of CuCl, In Cl3, GaCl3 and Se metal heated to 240 °C for 1 hour in N2-atmosphere to form CIGS nanocrystal ink, Thin films were deposited onto Au/soda-lime glass (SLG) substrates. This work focused on CIGS nanocrystals, including their synthesis and application as the active light absorber layer in photovoltaic devices (PVs). This approach, using spin-coating deposition of the CIGS light absorber layers (75 mg/ml and 150 nm thickness), without high temperature selenization, has enabled up to 1.398 % power conversion efficiency under AM 1.5 solar illumination. X-ray diffraction (XRD) studies show that the structural formation of CIGS chalcopyrite structure. The mo

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv