The application of the test case prioritization method is a key part of system testing intended to think it through and sort out the issues early in the development stage. Traditional prioritization techniques frequently fail to take into account the complexities of big-scale test suites, growing systems and time constraints, therefore cannot fully fix this problem. The proposed study here will deal with a meta-heuristic hybrid method that focuses on addressing the challenges of the modern time. The strategy utilizes genetic algorithms alongside a black hole as a means to create a smooth tradeoff between exploring numerous possibilities and exploiting the best one. The proposed hybrid algorithm of genetic black hole (HGBH) uses the

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 paper presents a method of designing and constructing a system capable of acquiring
the third dimension and reconstructs a 3D shape for an object from multi images of that object using
the principle of active optical triangulation. The system consists of an illumination source, a photo
detector, a movement mechanism and a PC, which is working as a controlling unit for the hard ware
components and as an image processing unit for the object multi view raw images which must be
processed to extract the third dimension. The result showed that the optical triangulation method
provides a rapid mean for obtaining accurate and quantitative distance measurements. The final
result's analysis refers to the necessity of usin

thin films of se:2.5% as were deposited on a glass substates by thermal coevaporation techniqi=ue under high vacuum at different thikness

In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

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

The effect of molecules intersystem crossing (Kisc) on characteristics
(energy and duration) of a Passive Q- switched Laser Pulse has been
studied by mathematical description (rate equations model) for
temporal performance of which was used as a saturable absorber
material (passive switch) with laser. The study shows that the energy
and duration pulse are decreasing while the molecules intersystem
crossing into saturable absorber energy levels is increasing.

A numerical study of the double-diffusive laminar natural convection in a right triangular solar collector has been investigated in present work. The base (absorber) and glass cover of the collector are isothermal and isoconcentration surfaces, while the vertical wall is considered adiabatic and impermeable. Both aiding and opposing buoyancy forces have been studied. Governing equations in vorticity-stream function form are discretized via finite-difference method and are solved numerically by iterative successive under relaxation (SUR) technique. Computer code for MATLAB software has been developed and written to solve mathematical model. Results in the form of streamlines, isotherms, isoconcentration, average Nusselt, and average Sherw