Shoulder Implant Manufacturer Detection by Using Deep Learning: Proposed Channel Selection Layer

Total Shoulder Arthroplasty (TSA) is the process of replacing the damaged ball and socket joint in the shoulder with a prosthesis made with polyethylene and metal components. After this procedure, intervention may be required as a result of damage to the prosthesis, except for the need for an examination regarding the prosthesis at certain periods. If the patient does not have information about the model and manufacturer of the prosthesis, the treatment process is delayed. Artificial intelligence-assisted systems can speed up the treatment process by classifying the manufacturer and model of the prosthesis. In this study, artificial intelligence methods were applied to classify shoulder implants using X-Ray images. The model and manufacturer of the prosthesis is detected by using the proposed deep learning method. Besides, the most commonly used machine learning classifiers were applied for the same problem to compare the results and show the effectiveness of the proposed method. In addition, the accuracy and precision analysis and measurements of the processing times for the applied methods were performed to reveal the performance, accuracy, and efficiency of the study. In order to measure the performance of the proposed method, it was compared with studies on the same problem in the literature. As a result of the comparison, it was found that the proposed method, with an accuracy rate of 97.2%, performed better than the other studies. In the study, the implant manufacturer and model are classified in order to carry out the implant surgery process in the best way with the proposed deep learning model. With the success of the proposed system, the applicability of this model in similar prosthesis classifications has been shown. Differently from the studies in the literature, the channel selection formula is presented in the proposed deep learning method recommended for selecting the most distinctive feature filters.

Mining peptides against SARS-CoV-2 entry from constructed RBMs-hACE2 isomer libraries using machine learning and molecular docking

Cellular entry of SARS-CoV-2 initiates from the protein-protein interactions (PPIs) between viral surface protein S and human angiotensin converting enzyme 2 (hACE2). Peptide-based drugs have the advantage of small molecule compounds to block such viral-host PPIs. Thus the viral targetregions on hACE2 have been believed as promising templates for designing specific inhibitory peptides against SARS-CoV-2 infection. However, starting from a few potential templates, in silico design and prediction between binding affinity and bioactivities in vivo are very challenging, herein a novel design strategy was implemented by mining constructed template isomer libraries using feature filters, supervised classifier and peptide protein docking.Applying these methods and the isomer libraries, 4 peptides were identified from 12 millions candidates owing to their distinct stability, interaction activity, inhibitory specificity, binding affinity, transmembrane potentials and effective conformation. These results have supplied a panel of specific anti-COVID19 leads for further drug development, supporting a new feasible antiviral strategy for targeting both intracellular and extracellular SARS-CoV-2 S proteins simultaneously. The methods have provided a useful tool for mining antiviral-peptides against viral diseases.

Intelligent fault diagnosis of rotating machinery based on a novel lightweight convolutional neural network

For most existing fault diagnosis methods, feature extraction is always based on a complex artificial design and the complete feature extraction from an original signal. With the gradual complication of modern industrial machinery and equipment, it has become more difficult for traditional feature extractors to achieve the desired results. Deep convolutional neural networks (DCNNs) have been developed as effective techniques for fault classification but require large-scale high-intensity computing and prohibitive hardware resource requirements. This paper proposes a lightweight CNN that can be easily used for the fault diagnosis of rotating machinery by adjusting the network structure and optimizing the network. First, the raw vibration acceleration signal is transformed into a two-dimensional gray image. Second, two mature and commonly used modules named LeNet and NIN are combined to form a new model with a simple structure. Then, through parameter adjustment and optimization, an improved and optimized CNN with a lightweight structure and fewer parameters is constructed. The experimental verification has shown that this method has high accuracy and stability in fault diagnosis. Finally, the application of this new network for the fault diagnosis of rolling bearings with different damage levels but similar fault types shows high diagnostic accuracy and good generalization ability. In addition, we attempt to explain how the feature filters of a CNN work by visualizing the convolutional layer of the network.

Risk Assessment for Cardiac Arrest : A Deep Learning Approach With Channel Selection

Background: Risk of developing cardiovascular diseases, in the world, is increasing day by day. Accordingly, the number of deaths due to heart attacks is quite remarkable. Early risk assessment and diagnosis of heart disease are vital to prevent heart attacks by providing effective treatment planning and evaluation of outcomes. When a patient with high risk of heart attack is not treated correctly, chances of survival may reduce dramatically. For this reason, artificial intelligence-assisted systems can support the decision of doctors and it can anticipate risk without fatal consequences.Methods: In this study, individuals who has heart attack risks are predicted by using a proposed CNNs method. A set of medical data from patients with heart attacks and healthy individuals are provided from the UCI database. Reinforced deep learning and ANFIS architectures are also applied to the same problem in order to compare the results and put forth the efficiency of proposed method. In addition, ROC analysis and measurements of processing times for the applied methods were performed to reveal the performance, accuracy and efficiency of the study.Results: The proposed CNNs method and other methods are tested and evaluated. The accuracy performance of the methods were 94.34% for the proposed CNNs method, 91.58% for the ANFIS, and 92.66% for the deep multilayer neural network. Highest accuracy has been obtained by using the proposed CNNs method, which is 94.34%. The reasons why the proposed CNNs method is better than other methods is the use of channel selection layer, the number of convolution and pooling layers, the filter size used in these layers, and the functions used in the loss and activation layers.Conclusions: In the study, the channel selection formula is introduced in the proposed CNNs model to select the most discriminatory feature filters. Besides, the applicability of proposed CNNs method with images obtained from numerical data has been demonstrated. With the early prediction system proposed, it is now possible to take precautionary measures against possible cardiac arrest. In this study; a new method based on CNNs is proposed for early detection of possible heart attack, which is a great risk for human life. Different from studies in the literature, the channel selection formula is presented in the proposed CNNs method to select the most selective feature filters. Besides differently, it was used in the proposed CNNs method by converting all numerical data from dataset into 2D images. Afterwards, to show whether this the proposed method is applicable or not, the dataset which is numerical form was applied to other methods and compared.

UNBIASEDNESS OF FEATURE SELECTION BY HYBRID FILTERING

In this article we examine characteristics of feature selection algorithms by introducing their aspects important in practice. We will focus on the unbiasedness, analyse it and investigate a robust hybrid method of feature selection, being a composition of several feature filters, that could ensure unbiased results of selection. Using parallel multi-measures and voting, we reduce the risk of selecting non-optimal features, a common situation when we select attributes using single evaluation based on one evaluation criterion. To test this method we selected a personal bankruptcy dataset, containing various types of attributes and one of the popular machine learning benchmarks. By the performed experiments we will demonstrate that an approach of multi-evaluation used for features filtering may lead to the creation of effective and fast methods of features selection with an unbiased outcome.

Self-Organized Formation of Various Invariant-Feature Filters in the Adaptive-Subspace SOM

The adaptive-subspace self-organizing map (ASSOM) is a modular neural network architecture, the modules of which learn to identify input patterns subject to some simple transformations. The learning process is unsupervised, competitive, and related to that of the traditional SOM (self-organizing map). Each neural module becomes adaptively specific to some restricted class of transformations, and modules close to each other in the network become tuned to similar features in an orderly fashion. If different transformations exist in the input signals, different subsets of ASSOM units become tuned to these transformation classes.