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DNA sequence classification is considered a significant challenge for biological researchers to scientifically analyze the enormous volumes of biological data and discover different biological features. In genomic research, classifying DNA sequences may help learn and discover the new functions of a protein. Insulin is an example of a protein that the human body produces to regulate glucose levels. Any mutations in the insulin gene sequence would result in diabetes mellitus. Diabetes is one of the widely spread chronic diseases, leading to severe effects in the longer term if diagnosis and treatment are not appropriately taken. In this research, the authors propose a hybrid deep learning model based on Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to classify the DNA sequence for the insulin gene to predict type 2-diabetes based on gene sequence mutation. To evaluate the proposed models, we used several performance indexes such as accuracy, precision, sensitivity, recall, and F1 score. The experiments shown in the paper reveal that the proposed model accomplished the best results. The overall accuracy of the learning process is recorded as 99% for the proposed hybrid LSTM-CNN model while it is recorded as 97.5%, and 95% for CNN, and LSTM, respectively. [ABSTRACT FROM AUTHOR]

Day after day, the importance of relying on nature in many fields such as food, medical, pharmaceutical industries, and others is increasing. Essential oils (EOs) are considered as one of the most significant natural products for use as antimicrobials, antioxidants, antitumorals, and anti-inflammatories. Optimizing the usage of EOs is a big challenge faced by the scientific researchers because of the complexity of chemical composition of every EO, in addition to the difficulties to determine the best in inhibiting the bacterial activity. The goal of this article is to present a new computational tool based on two methodologies: reduction by using rough sets and optimization with particle swarm optimization. The developed tool dubbed as Essential Oil Reduction and Optimization Tool is applied on 24 types of EOs that have been tested toward 17 different species of bacteria. [ABSTRACT FROM AUTHOR]