Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

    The current research aims to identify the Impact of  strategy of modeling in the of deductive thinking and the attitvde towards mathematics among students in the high school stage

 through check the following hypotheses:       

1.There is no difference statistically significant at the level (0.05) between the scores  mean of the experimental group students who have studied according to the modeling strategy and scores of control group students who have studied according to ordinary method in deductive thinking. 

2.

The research aims to find out the effect of Cognitive Acceleration strategy and random excitement strategy in achievement of geography material and developing the reflective thinking for students of literary fifth class . 

  the researcher depended a partial control experimental design with the three groups(the competence groups of the pre & post tests), The sample is deliberately selected  from first AL-Rusafah Directorate General of Education in Baghdad.  AL.fardoos Interme

The study aims at investigating the effectiveness of the Virtual Library Technology, in developing the achievement of the English Language Skills in the Center of Development and Continuous Education, in comparison with the individual learning via personal computer to investigate the students' attitude towards the use of both approaches. The population of the study includes the participants in the English Language course arranged in the Center. The sample includes 60 students who were randomly chosen from the whole population (participants in English Courses for the year 2009-2010). The sample is randomly chosen and divided into two experimental groups. The first group has learned through classroom technology; while the other group has l

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is

     In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather t

The study aims to identify the degree of Tabuk University practices to raise the intellectual awareness of students through scientific research, as well as to identify the degree of Tabuk University practicing to raise the intellectual awareness through the educational process (faculty member – activities). The study also seeks to identify the degree of Tabuk University practicing to raise the intellectual awareness of students through community service and university media. The study is descriptive in nature that employed the questionnaire as a tool in collecting data. Total of (540) students were chosen randomly from different colleges at universities of Tabuk to form the study sample. The results showed that faculty member has pract