In this paper, a Modified Weighted Low Energy Adaptive Clustering Hierarchy (MW-LEACH) protocol is implemented to improve the Quality of Service (QoS) in Wireless Sensor Network (WSN) with mobile sink node. The Quality of Service is measured in terms of Throughput Ratio (TR), Packet Loss Ratio (PLR) and Energy Consumption (EC). The protocol is implemented based on Python simulation. Simulation Results showed that the proposed protocol provides better Quality of Service in comparison with Weighted Low Energy Cluster Hierarchy (W-LEACH) protocol by 63%.

      Finding similarities in texts is important in many areas such as information retrieval, automated article scoring, and short answer categorization. Evaluating short answers is not an easy task due to differences in natural language. Methods for calculating the similarity between texts depend on semantic or grammatical aspects. This paper discusses a method for evaluating short answers using semantic networks to represent the typical (correct) answer and students' answers. The semantic network of nodes and relationships represents the text (answers). Moreover, grammatical aspects are found  by measuring the similarity of parts of speech between the answers. In addition, finding hierarchical relationships between nodes in netwo

    Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and

      Many neuroscience applications, including understanding the evolution of the brain, rely on neural cell instance segmentation, which seeks to integrate the identification and segmentation of neuronal cells in microscopic imagery. However, the task is complicated by cell adhesion, deformation, vague cell outlines, low-contrast cell protrusion structures, and background imperfections. On the other hand, existing segmentation approaches frequently produce inaccurate findings. As a result, an effective strategy for using the residual network with attention to segment cells is suggested in this paper. The segmentation mask of neural cells may be accurately predicted. This method is built on U-net, with EfficientNet serving as the e

In this paper an authentication based finger print biometric system is proposed with personal identity information of name and birthday. A generation of National Identification Number (NIDN) is proposed in merging of finger print features and the personal identity information to generate the Quick Response code (QR) image that used in access system. In this paper two approaches are dependent, traditional authentication and strong identification with QR and NIDN information. The system shows accuracy of 96.153% with threshold value of 50. The accuracy reaches to 100% when the threshold value goes under 50.

Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

     is at an all-time high in the modern period, and the majority of the population uses the Internet for all types of communication. It is great to be able to improvise like this. As a result of this trend, hackers have become increasingly focused on attacking the system/network in numerous ways. When a hacker commits a digital crime, it is examined in a reactive manner, which aids in the identification of the perpetrators. However, in the modern period, it is not expected to wait for an attack to occur. The user anticipates being able to predict a cyberattack before it causes damage to the system. This can be accomplished with the assistance of the proactive forensic framework presented in this study. The proposed system combines

Industrial wastewater containing nickel, lead, and copper can be produced by many industries. The reverse osmosis (RO) membrane technologies are very efficient for the treatment of industrial wastewater containing nickel, lead, and copper ions to reduce water consumption and preserving the environment. Synthetic industrial wastewater samples containing Ni(II), Pb(II), and Cu(II) ions at various concentrations (50 to 200 ppm), pressures (1 to 4 bar), temperatures (10 to 40 ^oC), pH (2 to 5.5), and flow rates (10 to 40 L/hr), were prepared and subjected to treatment by RO system in the laboratory. The results showed that high removal efficiency of the heavy metals could be achieved by RO process (98.5%, 97.5% and 96% for Ni(II),