A deep convolutional neural network approach using medical image classification.

The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to provide medical care to and recovery of infected people as well as protecting the uninfected population. In this paper, an automatic COVID-19 detection model using respiratory sound and medical image based on internet of health things (IoHT) is proposed. In this model, primarily to screen those people having suspected Coronavirus disease, the sound of coughing used to detect healthy people and those suffering from COVID-19, which finally obtained an accuracy of 94.999%. This approach not only expedites diagnosis and enhances accuracy but also facilitates swift screening in public places using simple equipment. Then, in the second step, in order to help radiologists to interpret medical images as best as possible, we use three pre-trained convolutional neural network models InceptionResNetV2, InceptionV3 and EfficientNetB4 and two data sets of chest radiology medical images, and CT Scan in a three-class classification. Utilizing transfer learning and pre-existing knowledge in these models leads to notable improvements in disease diagnosis and identification compared to traditional techniques. Finally, the best result obtained for CT-Scan images belonging to InceptionResNetV2 architecture with 99.414% accuracy and for radiology images related to InceptionV3 and EfficientNetB4 architectures with the accuracy is 96.943%. Therefore, the proposed model can help radiology specialists to confirm the initial assessments of the COVID-19 disease.

An improved equilibrium optimization algorithm for feature selection problem in network intrusion detection.

In this paper, an enhanced equilibrium optimization (EO) version named Levy-opposition-equilibrium optimization (LOEO) is proposed to select effective features in network intrusion detection systems (IDSs). The opposition-based learning (OBL) approach is applied by this algorithm to improve the diversity of the population. Also, the Levy flight method is utilized to escape local optima. Then, the binary rendition of the algorithm called BLOEO is employed to feature selection in IDSs. One of the main challenges in IDSs is the high-dimensional feature space, with many irrelevant or redundant features. The BLOEO algorithm is designed to intelligently select the most informative subset of features. The empirical findings on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets demonstrate the effectiveness of the BLOEO algorithm. This algorithm has an acceptable ability to effectively reduce the number of data features, maintaining a high intrusion detection accuracy of over 95%. Specifically, on the UNSW-NB15 dataset, BLOEO selected only 10.8 features on average, achieving an accuracy of 97.6% and a precision of 100%.