درخت حوزه‌های تخصصی

In the academic context, social networking sites (SNSs) have reshaped the way university students connect and communicate with each other, and the way they learn, thus influencing their identities and dimensions. This paper aims to investigate the impacts of SNSs use by students on their academic performance at the University of Taiz. A survey questionnaire was conducted to a total sample of 357 undergraduate students via personal administration and by an online platform to gather the initial information on their use of SNSs and the influence on their academic performance. The hypotheses of this study were studied and tested using descriptive statistics, regression model, T-test, and analysis of variance (ANOVA). The findings of H1 indicate that the impact of the use of SNSs on students’ academic performance was statistically significant Interaction with the teacher (II) Collaboration with coworkers (CC) Engagement (EN) and learning a cooperating (LC). Meanwhile, the results of H2 indicate no significant differences between the mean averages of the respondents’ answers for (purposes, the impact of the use of SNSs, and academic performance) due to gender, and age, respectively. Thus, using SNS as a learning tool has a great potential to improve students’ academic performance because it allows students to be more connected. Overall findings of this study indicate that the use of SNSs impact undergraduate students by factors are studied on academic Performance to some extent and suggested future strategies to enhance students’ awareness to manage their time, multitasking skills, and study activities to enhance their academic performance and achievements.

Security solutions of stereo images are always a major concern during the transmission and communication. In this manuscript, a simple yet efficient framework for encrypting stereo images is formulated using discrete cosine transform (DCT), generalized logistic map, Schur decomposition and magic square method. The framework initiated with the integration of DCT and generalized logistic map to unify both pair of images. This unified image is then encrypted using the Schur decomposition and magic square method. The various experiments and analysis have been done to explore the validity, proficiency and performance of the purport framework.

VANET (Vehicular Ad-hoc Network) is a developing technology, which is a combination of cellular technology, ad-hoc network & wireless LAN to improve the safety of vehicle as well as driver. VANET communication can be of two types, first one is broadcast and second one is unicast. Either communication may be broadcast or unicast both are sensitive to different types ofassaults, for example message forgery, (DOS) denial of service, Sybil assault, Greyhole, Blackhole & Wormhole assault. In this paper machine learning method is used to detect the wormhole assault in VANET’s multi-hop communication. We have created a scenario of VANET by using AODV routing protocol on NS-3.24.1 simulator, which utilizes the overall mobility traces generated by the simulator SUMO-0.32.0 to model the wormhole assault. The simulation is performed by using NS-3.24.1 simulator, and the statistics created by flow monitor are collected. The collected data is pre-processed and the k-NN & Random Forest algorithms are applied on this data, to make the model such type so that it can memorize the wormhole attack. The novelty of this research work is that with the help of proposed detection & prevention technique, vehicular ad-hoc network can be made free from wormhole assault by using ML approach. The performance of proposed machine learning models is compared with existing work. In this way it is clear that our proposed approach by using ML is powerful tool by which the wormhole assaults can be detected in VANETs. A scheme based on packet lease and cryptographic techniques is used to prevent the wormhole attack in VANET