AIDS, which is caused by the most widespread HIV-1 virus, attacks the immune system of the human body, and despite the incredible endeavors for finding proficient medication strategies, the continuing spread of AIDS and claiming subsequent infections has not yet been decreased. Consequently, the discovery of innovative medicinal methodologies is highly in demand. Some available therapies, based on peptides, proclaim the treatment for several deadly diseases such as AIDS and cancer. Since many experimental types of research are restricted by the analysis period and expenses, computational

Recommender systems are receiving great attention these days, as various researchers and major companies are conducting continuous research in this field. Companies like Google and Amazon have provided different effective models for video recommendation systems, but the educational field is poorly studied as other researchers explained. Different researchers proposed various approaches showing the challenges related to recommender systems and have proposed various effective recommender systems. This paper aims to propose a hybrid recommender framework that can recommend educational courses'

IPv4 address exhaustion pushed IETF to create IPv6, the improved substitute of IPv4. The Internet complexity and its enormous size prolong the transition from IPv4 to IPv6 process. This means that both versions will necessarily co-exist. Meanwhile, tunneling appears as a solution trend. The tunneling is a transition technique that is considered temporary till all ISPs would support IPv6. At this paper, we compare the routing convergence of two tunnel types, 6to4 and Manually Configured versus the conventional IPv4 and IPv6 protocols. We analyze the network resources consumed during cold start

Nowadays, most service providers adopt Cloud Computing technology. Moving to Cloud creates new risks and challenges. The Cloud era is to outsource our services to Cloud Service Provider (CSP). However, we have to develop a strong governance framework to review the service level, to manage risk effectively and to certify that our critical information is secure. In this paper, we develop an innovative governance model. It is based on the theoretical Guo, Z., Song, M. and Song, J governance model for Cloud computing. We distribute Cloud Control Matrix (CCM) on the Guo's model categories. This

This paper presents improved automatic moving target detection and tracking framework that is suitable for UAV imagery. The framework is comprised of motion compensation phase to detect moving targets from a moving camera, target state estimation with Kalman filter, and overlap-rate-based data association. Finally, P-N learning is used to maintain target appearance by utilizing novel structural constraints to select positive and negative samples, where data association decisions are used as positive (P) constraints. After learning target appearance, a cascaded classifier is employed to detect

The recommendation system, also known as recommender system or recommendation engine/platform, is considered as an interdisciplinary field. It uses the techniques of more than one field. Recommender system inherits approaches from all of machine learning, data mining, information retrieval, information filtering and human-computer interaction. In this paper, we propose our value-added architecture of the hybrid information filtering engine for job recommender system (HIFE-JRS). We discuss our developed system’s components to filter the most relevant information and produce the most

Cloud computing is becoming more and more popular. It is increasing in popularity with companies as it enables them to share various resources in a cost effective way. Cloud computing has lots of advantages, however some issues need to be handled before organizations and individuals have the confidence to rely on it. Security and privacy are at the forefront of these important issues. In this paper, the evolution of cloud computing along with its deployment and delivery models are highlighted. Also, the difference between cloud computing and other deployment models are discussed. We present

Both Big Data and Software-Defined Network have a significant impact in both academic and practical aspects. These two areas have been addressed separately, but both did not contribute to the same subset area of contribution. However, Big Data can greatly facilitate, improve, and have a great impact on Software Defined Network, and vice versa. In this paper, we show how SDN helps Big Data solve several issues regarding Big Data applications, including data processing in the data centers, data delivery and traffic monitoring. For Big Data, we also show how it can help SDN as well, including

At the time of writing, the COVID-19 pandemic is one of the lead causes of death worldwide and has caused significant changes to everyone's lives. While a vaccine is still unavailable, early screenings and detection of the disease can significantly help in managing the healthcare system's capacity as well as allow radiologists and clinicians better assign their priorities. With deep learning's rapid advancements over the last few years, its application in solving this issue is only natural. This paper aims to outline the works of a few major developments in the field of using deep learning to

The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods’accuracy and performance. There are existing techniques that use k-nearest neighbors algorithm for imputing the missing values but determining the appropriate k value can be a challenging task. There are other existing imputation techniques that are based on hard clustering