A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m

Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classif

This study focused on the role and importance of alkaloid compounds in Punica granatum peels which is one of many wide distribution medicinal fruits. Two kinds of pathogenic fungi were isolated from patients in Baghdad to be tested, also a type of extracts was prepared, alkaloids were isolated and partially purified and detected by two ways, a classic depended technique also used for determine these alkaloids, results showed an observed differences among extracts or treatments towards the fungi samples. So this study was one of the scientific applications to find natural alternative compounds that inhibit the growth of several pathogenic organisms that cause dangers and harms for human health.

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

In this paper, the first integrals of Darboux type of the generalized Sprott ET9 chaotic system will be studied. This study showed that the system has no polynomial, rational, analytic and Darboux first integrals for any value of . All the Darboux polynomials for this system were derived together with its exponential factors. Using the weight homogenous polynomials helped us prove the process.

Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model wa

An indoor spraying robot is built in this research to solve numerous challenges associated with manual spraying. The mechanical, hardware and essential technologies used are all detailed and designed. The proposed spraying robot's conceptual design is split into two parts: hardware and software. The mechanical design, manufacturing, electrical, and electronics systems are described in the hardware part, while the control of the robot is described in the software section. This robot's kinematic and dynamic models were developed using three links that move in the x, y, and z directions. The robot was then designed using SolidWorks software to compute each connection's deflection and maximum stresses. The characteristics of the stepper moto

            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the

        The purified frog skin peptides were tested on leukemic patients lymphocytes, which revealed effects of cytotoxicity. Four frogs (Rana ridibunda) were stimulated by single intra-peritoneal injection of norepinephrine-HCl . Five different peptides;1(18) A, 2(19) L, 3(20) I,4(21) E and 5(22) Y were isolated and quantified. The peptide 3(20)I had 5.87% of hemolysis, while healthy human lymphocytes cytotoxic activity was for 2(19)L with inhibition( -10.4%).All peptides were subjected to polyacrylamide gel electrophoresis. The results revealed peptides 1(18)A, 2(19)L, 3(20)I which appeared as low as 10 KDa  marker. Theoretically, the whole polypeptide had a molecular weight 7488.61 Dalt

The paper aims to propose Teaching Learning based Optimization (TLBO) algorithm to solve 3-D packing problem in containers. The objective which can be presented in a mathematical model is optimizing the space usage in a container. Besides the interaction effect between students and teacher, this algorithm also observes the learning process between students in the classroom which does not need any control parameters. Thus, TLBO provides the teachers phase and students phase as its main updating process to find the best solution. More precisely, to validate the algorithm effectiveness, it was implemented in three sample cases. There was small data which had 5 size-types of items with 12 units, medium data which had 10 size-types of items w