Matching Technique for Resource Discovery using PageRank

Nowadays, anybody can search and get the information which he/she needs by using the internet, World Wide Web (WWW) which consists of a lot software programs called (Search Engine). These programs such as (Yahoo, Google, Msn…etc) help users in finding his/her information. The main idea on most of Search Engines is to survey the web sites, analyze and build their databases. It also works as a check to the input (user keyword) on his/her databases, and then finds the best sites and ranking it. Finally it returns the ranking sites to the user. With several Search Engines, each one of the following has special techniques to implement its idea. For example, Google Search Engine uses PageRank technique to rank its results, and so far this technique differentiates Google as the best Search Engine. This work supposes to implement PageRank technique into Grid Computing environment, where in this environment there are a lot of resources, users with jobs want to achieve their jobs, and schedules which match between resources and users job. In this work, we will apply PageRank inside PBS scheduler to match users with resources, where PageRank technique works on regard of both the state of users and resources

Gender Prediction from Retinal Fundus Using Deep Learning

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers

Predicting Whether Student will continue to Attend College or not using Deep Learning

According to the literature review, there is much room for improvement of college student retention. The aim of this research is to evaluate the possibility of using deep and machine learning algorithms to predict whether students continue to attend college or will stop attending college. In this research a feature assessment is done on the dataset available from Kaggle depository. The performance of 20 learning supervised machine learning algorithms and one deep learning algorithm is evaluated. The algorithms are trained using 11 features from 1000 records of previous student registrations that have been enrolled in college. The best performing classifier after tuning the parameters was NuSVC. It achieved Accuracy (91.00%), Precision (91.00%), Recall (91.00%), F1-score (91.00%), and time required for training and testing (0.04 second). Additionally, the proposed DL algorithm scored: Accuracy (93.00%), Precision (93.00%), Recall (93.00%), F1-score (93.00%), time required for training and testing (0.66 second) for predicting whether student will continue to attend college or not