Classifying Drug Resistance in the NCI60 Cancer Cell Lines Based on the mRNA Expression Levels of the 48 ABC Transporters

Poster for the 2014 MCBIOS Conference. This poster discusses classifying drug resistance in the NCI60 cancer cell lines based on the mRNA expression levels of the 48 ABC transporters

Measuring the impact of suspending Umrah, a global mass gathering in Saudi Arabia on the COVID‑19 pandemic

This article uses a stratified SEIR epidemic model to evaluate the impact of Umrah, a global Muslim pilgrimage to Mecca, on the spread of the COVID-19 pandemic during the month of Ramadan, the peak of the Umrah season. The analyses provide insights into the effects of global mass gatherings on the progression of the COVID-19 pandemic locally and globally

Scramble-free synthesis of unhindered trans-A2B2-mesoaryl porphyrins via bromophenyl dipyrromethanes

Trans-disubstituted porphyrins are highly valuable intermediates across diverse fields, but they pose a significant synthesis challenge in some cases due to scrambling and formation of complex mixtures. Conditions that minimize scrambling also lower yields, but steric hindrance around the meso-aryl substituent can effectively suppress scrambling altogether. Here we report a straightforward approach to valuable trans-A2B2 porphyrin intermediates that are otherwise very difficult to obtain, through use of removable blocking bromide substituents

UNT Graduate Exhibition

Poster for the 2014 UNT Graduate Exhibition in the Computer Science and Information Technology category. This poster discusses data mining techniques for predicting breast cancer survivability among women in the United States

An Interactive Model for Vector Borne Diseases: A Simulation for Zika in French Polynesia

Post presented at Contagion 2016, a satellite meeting at the 2016 Conference on Complex Systems. This poster presents a stochastic agent-based model that simulates the transmission of ZIKA via mosquitoes in 11 islands in the French Polynesia

Hybrid arithmetic optimization algorithm with deep learning model for secure Unmanned Aerial Vehicle networks

Securing Unmanned Aerial Vehicle (UAV) systems is vital to safeguard the processes involved in operating the drones. This involves the execution of robust communication encryption processes to defend the data exchanged between the UAVs and ground control stations. Intrusion detection, powered by Deep Learning (DL) techniques such as Convolutional Neural Networks (CNN), allows the classification and identification of potential attacks or illegal objects in the operational region of the drone, thus distinguishing them from the routine basics. The current research work offers a new Hybrid Arithmetic Optimizer Algorithm with DL method for Secure Unmanned Aerial Vehicle Network (HAOADL-UAVN) model. The purpose of the proposed HAOADL-UAVN technique is to secure the communication that occurs in UAV networks via threat detection. At the primary level, the network data is normalized through min-max normalization approach in order to scale the input dataset into a useful format. The HAOA is used to select a set of optimal features. Next, the security is attained via Deep Belief Network Autoencoder (DBN-AE)-based threat detection. At last, the hyperparameter choice of the DBN-AE method is implemented using the Seagull Optimization Algorithm (SOA). A huge array of simulations was conducted using the benchmark datasets to demonstrate the improved performance of the proposed HAOADL-UAVN algorithm. The comprehensive results underline the supremacy of the HAOADL-UAVN methodology under distinct evaluation metrics