Pattern matching algorithms are usually used as detecting process in intrusion detection system. The efficiency of these algorithms is affected by the performance of the intrusion detection system which reflects the requirement of a new investigation in this field. Four matching algorithms and a combined of two algorithms, for intrusion detection system based on new DNA encoding, are applied for evaluation of their achievements. These algorithms are Brute-force algorithm, Boyer-Moore algorithm, Horspool algorithm, Knuth-Morris-Pratt algorithm, and the combined of Boyer-Moore algorithm and Knuth–Morris– Pratt algorithm. The performance of the proposed approach is calculated based on the executed time, where these algorithms are applied o

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

This study offers the elastic response of the variable thickness functionally graded (FG) by single walled carbon nanotubes reinforced composite (CNTRC) moderately thick cylindrical panels under rotating and transverse mechanical loadings. It’s considered that, three kinds of distributions of carbon nanotubes which are uniaxial aligned in the longitudinal direction and two functionally graded in the transverse direction of the cylindrical panels. Depending on first order shear deformation theory (FSDT), the governing equations can be derived. The partial differential equations are solved by utilizing the technique of finite element method (FEM) with a program has been built by using FORTRAN 95. The results are calculat

        Urban land price is the primary indicator of land development in urban areas. Land prices in holly cities have rapidly increased due to tourism and religious activities. Public agencies are usually facing challenges in managing land prices in religious areas. Therefore, they require developed models or tools to understand land prices within religious cities. Predicting land prices can efficiently retain future management and develop urban lands within religious cities. This study proposed a new methodology to predict urban land prices within holy cities. The methodology is based on two models, Linear Regression (LR) and Support Vector Regression (SVR), and nine variables (land price, land area,

Introduction: The present study was performed to evaluate the influence of a 1064 nm fiber laser on shear bond strength (SBS) at the interface of titanium and resin cement. Methods: Forty titanium discs of 6 mm × 3 mm (diameter and thickness respectively) were categorized into four groups (n=10): control group without any surface treatment and three groups treated with a fiber laser with 81 ns pulse duration, 30 kHz frequency, 10000 mm/s scanning speed, 0.05 mm spot size, and different average power values (3, 5 and 7 W) depending on the tested group. Titanium disc characterization was performed by the scanning electron microscope (SEM) and surface roughness tester. Phase analysis was achieved using an X-ray diffractometer (XRD). F

In this work, a novel design for the NiO/TiO2 heterojunction solar cells is presented. Highly-pure nanopowders prepared by dc reactive magnetron sputtering technique were used to form the heterojunctions. The electrical characteristics of the proposed design were compared to those of a conventional thin film heterojunction design prepared by the same technique. A higher efficiency of 300% was achieved by the proposed design. This attempt can be considered as the first to fabricate solar cells from highly-pure nanopowders of two different semiconductors.

Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This

Patients are very concerned about the lengthy nature of orthodontic treatment. It is necessary to find a non-invasive way to quicken physiologic tooth movement. This study's objective was to assess the effectiveness of low-intensity laser therapy in shortening the time and discomfort of orthodontic treatment. Experimental work: Using a split-mouth study to compare tooth movement with conventional treatment and laser-accelerated orthodontic tooth movement. A patient presenting with a class II division I malocclusion characterized by the misalignment of the upper and lower teeth as classified by Angle’s molar classification system was indicated to undergo fixed orthodontic appliance orthodontic treatment. The treatment plan involved bila

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve