An Improved Convolutional Neural Network Model for DNA Classification

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)classification.In any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature reduction.The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy.The two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for downsampling.They convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature vectors.However,there are parameters which directly affect how a CNN model is trained.In this paper,some issues concerned with the training of CNNs have been handled.The CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of epochs.Training and assessment of the performance of CNNs are carried out on 16S rRNA bacterial sequences.Simulation results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

Drone-based AI/IoT Framework for Monitoring,Tracking and Fighting Pandemics

Since World Health Organization(WHO)has declared the Coronavirus disease(COVID-19)a global pandemic,the world has changed.All life’s fields and daily habits have moved to adapt to this new situation.According to WHO,the probability of such virus pandemics in the future is high,and recommends preparing for worse situations.To this end,this work provides a framework for monitoring,tracking,and fighting COVID-19 and future pandemics.The proposed framework deploys unmanned aerial vehicles(UAVs),e.g.;quadcopter and drone,integrated with artificial intelligence(AI)and Internet of Things(IoT)to monitor and fight COVID-19.It consists of two main systems;AI/IoT for COVID-19 monitoring and drone-based IoT system for sterilizing.The two systems are integrated with the IoT paradigm and the developed algorithms are implemented on distributed fog units connected to the IoT network and controlled by software-defined networking(SDN).The proposed work is built based on a thermal camera mounted in a face-shield,or on a helmet that can be used by people during pandemics.The detected images,thermal images,are processed by the developed AI algorithm that is built based on the convolutional neural network(CNN).The drone system can be called,by the IoT system connected to the helmet,once infected cases are detected.The drone is used for sterilizing the area that contains multiple infected people.The proposed framework employs a single centralized SDN controller to control the network operations.The developed system is experimentally evaluated,and the results are introduced.Results indicate that the developed framework provides a novel,efficient scheme for monitoring and fighting COVID-19 and other future pandemics.