Estimating the semantic similarity between short texts plays an increasingly prominent role in many fields related to text mining and natural language processing applications, especially with the large increase in the volume of textual data that is produced daily. Traditional approaches for calculating the degree of similarity between two texts, based on the words they share, do not perform well with short texts because two similar texts may be written in different terms by employing synonyms. As a result, short texts should be semantically compared. In this paper, a semantic similarity measurement method between texts is presented which combines knowledge-based and corpus-based semantic information to build a semantic network that repre

Background: The risk of antibiotics resistance (AR) increases due to excessive of antibiotics either by health care provider or by the patients.

Objective: The assessment of the self-medication Practice of over the counter drugs and other prescription drugs and its associated risk factor.

Subjects and Methods: Study design: A descriptive study was conducted from “20th December 2019 to 08th January 2021”. A pre validated and structured questionnaire in English and Urdu language was created to avoid language barrier including personal detail, reasons and source and knowledge about over the counter drugs and Antibiotics. Sample of the study was randomly selected.

Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. T

Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the wel

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the wel

The research aimed to study the job satisfaction of the staff of the Federal board of supreme Audit and its relation to the effectiveness of their performance, The questionnaire was adopted as a main tool in the collection of data and information from a random sample of (54) employees of the Federal board of supreme Audit. In light of this, the data were collected and analyzed and the hypotheses were tested using the statistical program (SPSS).

The researchers reached a number of conclusions, the most important of which were: (1) the respondents' response to the variables of job satisfaction and the effectiveness of the performance were medium; (2) there was a significant relationship between job satisfaction and performance effe

Background: Depression is a common mental disorder that presents with depressed mood;it can become chronic or recurrent and affect dental health .Thus this research aimed to assess the prevalence and severity of dental caries among students with different grade of depression in relation to physicochemical characteristics of stimulated whole saliva. Materials and methods: The total sample involved for depression status assessment is composed of 800 students for both gender aged 15 years old that were selected randomly , This was performed using children depression inventory (CDI) index that divided the students into four groups of depression(low or average grade, high average grade, elevated grade and very elevated grade). The diagnosis and