This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper,

Software Defined Network (SDN) is a new technology that separate the ‎control plane from the data plane. SDN provides a choice in automation and ‎programmability faster than traditional network. It supports the ‎Quality of Service (QoS) for video surveillance application. One of most ‎significant issues in video surveillance is how to find the best path for routing the packets ‎between the source (IP cameras) and destination (monitoring center). The ‎video surveillance system requires fast transmission and reliable delivery ‎and high QoS. To improve the QoS and to achieve the optimal path, the ‎SDN architecture is used in this paper. In addition, different routing algorithms are ‎used with different steps. First, we eva

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

Background: The synthesis and characterization of novel liquid crystalline compounds have garnered signi|cant attention due to their potential applications in biomedical sciences, including drug delivery systems, biosensing, and diagnostic tools. This study focuses on synthesizing and characterizing new thiazolothiadiazole-based liquid crystals and evaluating their mesophase properties. Methods: A series of novel compounds containing 5H-thiazolo[4,3−b][1,3,4] thiadiazole units were synthesized via multi-step chemical reactions. The synthesis involved the reaction of chloroethyl acetate with 4−hydroxybenzaldehyde to yield an aldehyde intermediate, followed by subsequent transformations using hydrazine hydrate, ethylacetoacetate, and 1,2

The compound 2,2'-(((1H-benzo(d)imidazol-2-yl)methyl)azanediyl)bis(ethan-1-ol) was reacted with benzyl bromide to afford compound (1) which used as row material to prepare a series of compounds through condensation reaction, the starting compound were reacted with tosyl chloride to protect the OH group  to afford compound 2, then reacted benzyl bromide to produce compound (2), then the compound (2) treated with three compounds ( 2-mercaptobenzthiazole, 2-mercaptobenimidazol and 2-chloromethyl benzimidazole) to form compounds 3a,b, 4a,b and 5a,b respectively. In the another step the click reaction of compound 2,2'-(((1H-benzo(d)imidazol-2-yl)methyl)azanediyl)bis(ethan-1-ol) with Propargyl bromide produce compound  6  which reacted