Effective medical diagnosis is dramatically expensive, especially in third-world countries. One of the common diseases is pneumonia, and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia, the medical diagnosis of these diseases is a significant challenge. Hence, transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks. Unfortunately, experimentation and utilization of different models of transfer learning do not achieve satisfactory results. In this study, we suggest the implementation of an automatic detection model, namely CADTra, to efficiently diagnose pneumonia-related diseases. This model is based on classification, denoising autoencoder, and transfer learning. Firstly, pre-processing is employed to prepare the medical images. It depends on an autoencoder denoising (AD) algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features, in order to improve the diagnosis process. Then, classification is performed using a transfer learning model and a four-layer convolution neural network (FCNN) to detect pneumonia. The proposed model supports binary classification of chest computed tomography (CT) images and multi-class classification of chest X-ray images. Finally, a comparative study is introduced for the classification performance with and without the denoising process. The proposed model achieves precisions of 98% and 99% for binary classification and multi-class classification, respectively, with the different ratios for training and testing. To demonstrate the efficiency and superiority of the proposed CADTra model, it is compared with some recent state-of-the-art CNN models. The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases

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The Internet offers humanity many distinctive and indispensable services, whether for individuals or for institutions and companies. This great role has attracted the Internet attackers to develop their mechanisms to capture and obtain the data by illegal methods. This growth in the number of cyber‐attacks made scientists in a real challenge, to find advanced methods to face this danger. Due to the shortcomings of traditional data security means such as firewalls, encryption, and so forth, the motivation became to develop alternative systems to detect smart attacks. Intrusion detection systems (IDSs) have made remarkable progress in cyber‐security. They monitor the traffic in real time and continuously to detect the network attacks, giving alerts to the network administrator. In this article, two IDSs are introduced based on principles of transfer learning (TL) with convolutional neural networks. Our systems are built using the visual geometry group (VGG19) and residual network with 152 layers (ResNet152). UNSW‐NB15 intrusion detection dataset is used to evaluate the models. The proposals achieve high levels of precision, recall, and F1_score as 99%, 99%, and 99%, respectively. These achievements prove the efficiency of the proposed models in capturing cyber‐attacks with low alert rates.

This study proposes a convolutional neural network (CNN)-based identity recognition scheme using electrocardiogram (ECG) at different water temperatures (WTs) during bathing, aiming to explore the impact of ECG length on the recognition rate. ECG data was collected using non-contact electrodes at five different WTs during bathing. Ten young student subjects (seven men and three women) participated in data collection. Three ECG recordings were collected at each preset bathtub WT for each subject. Each recording is 18 min long, with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. The R peaks were detected based on the processed ECG (baseline wandering eliminated, 50-Hz hum removed, ECG smoothing and ECG normalization) and the QRS complex waves were segmented. These segmented waves were then transformed into binary images, which served as the datasets. For each subject, the training, validation, and test data were taken from the first, second, and third ECG recordings, respectively. The number of training and validation images was 84297 and 83734, respectively. In the test stage, the preliminary classification results were obtained using the trained CNN model, and the finer classification results were determined using the majority vote method based on the preliminary results. The validation rate was 98.71%. The recognition rates were 95.00% and 98.00% when the number of test heartbeats was 7 and 17, respectively, for each subject.