Detecting pneumonia using convolutions and dynamic capsule routing for chest X-ray images

An entity\u27s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models-Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)-detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley

Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases

With the increasing incidence of severe skin diseases, such as skin cancer, endoscopic medical imaging has become urgent for revealing the internal and hidden tissues under the skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most skin diseases have similar features, which make it challenging for dermatologists to diagnose patients accurately. Therefore, machine and deep learning techniques can have a critical role in diagnosing dermatoscopy images and in the accurate early detection of skin diseases. In this study, systems for the early detection of skin lesions were developed. The performance of the machine learning and deep learning was evaluated on two datasets (e.g., the International Skin Imaging Collaboration (ISIC 2018) and Pedro Hispano (PH2)). First, the proposed system was based on hybrid features that were extracted by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and wavelet transform (DWT). Such features were then integrated into a feature vector and classified using artificial neural network (ANN) and feedforward neural network (FFNN) classifiers. The FFNN and ANN classifiers achieved superior results compared to the other methods. Accuracy rates of 95.24% for diagnosing the ISIC 2018 dataset and 97.91% for diagnosing the PH2 dataset were achieved using the FFNN algorithm. Second, convolutional neural networks (CNNs) (e.g., ResNet-50 and AlexNet models) were applied to diagnose skin diseases using the transfer learning method. It was found that the ResNet-50 model fared better than AlexNet. Accuracy rates of 90% for diagnosing the ISIC 2018 dataset and 95.8% for the PH2 dataset were reached using the ResNet-50 model

Enterprise Architecture Best Practices in Large Corporations

Enterprise architecture (EA) is an integrated strategy, business, and information systems approach for analysis, governance, and information technology (IT) alignment. It is a comprehensive blueprint that requires the careful planning, documentation, and analysis of all the operations of an organization. Employing EA helps companies achieve strategic goals with the support of business activities and information systems. However, some large corporations avoid EA frameworks and methodologies owing to their implementation difficulties or the presence of conflicting frameworks and business needs. The goal of this paper is to increase large organizations’ awareness of enterprise architecture best practices (EABPs) and methods of EA framework implementation. Thus, this research has developed an EABP capability matrix to measure companies’ capacities to implement EABPs and provided lessons based on how 17 organizations implemented EABPs. Based on an analytical literature review, the developed matrix includes eight critical EABPs categorized under four themes: EA framework and methodology, strategic practices, business activities, and information systems. As practical and theoretical contributions: (1) This inclusive approach was not found in the EA literature as most past research focuses on only one of these themes. (2) The EA matrix can be used as a measurement matrix research methodology to measure the extent to which cases adopt EABPs, making it beneficial to EA researchers and practitioners. (3) EA practitioners can also use it to practically determine and rectify the weak points of EABPs, thus taking advantage of EA frameworks. The findings indicate that many large organizations implement EABPs as business-as-usual practices without EA frameworks and methodologies. However, those that adopt an EA framework use the open group architecture framework and rely heavily on enterprise resource planning in the implementation of EABPs

A Technology-Dependent Information Literacy Model within the Confines of a Limited Resources Environment

The purpose of this paper is to investigate information literacy as an increasingly evolving trend in computer education. A quantitative research design was implemented, and a longitudinal case study methodology was conducted to measure tendencies in information literacy skill development and to develop a practical information literacy model. It was found that both students and educators believe that the combination of information literacy with a learning management system is more effective in increasing information literacy and research skills where information resources are limited. Based on the quantitative study, a practical, technology-dependent information literacy model was developed and tested in a case study, resulting in fostering the information literacy skills of students who majored in information systems. These results are especially important in smaller universities with libraries having limited technology capabilities, located in developing countries

Deep and hybrid learning of MRI diagnosis for early detection of the progression stages in Alzheimer’s disease

Alzheimer’s, or so-called dementia, is one of the types of diseases that affects brain cells and causes memory loss, difficulty in thinking, and forgetfulness. Thus far, there is no effective treatment for AD, but treatment could be helpful in impeding the progression of the disease. Therefore, early AD diagnosis is effective in limiting the disease from progressing to advanced and dangerous stages. Physicians and radiologists face difficulties in diagnosing healthy nerve cells from soft tissue, and it requires substantial expertise and a long time to decipher the MRI images. Thus, artificial intelligence techniques can play a key role in diagnosing MRI images for early detection of AD. In this study, four proposed systems with different methodologies and materials for tracking the stages of AD development are presented. The first proposed system is to classify a data set using artificial neural networks (ANNs) and feed-forward neural networks (FFNN) based on the features extracted in a hybrid manner by using a combination of Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT), and Gray Level Co-occurrence Matrix (GLCM) algorithms. The second proposed system is to classify the data set using two deep learning models – ResNet-18 and AlexNet – that are pre-trained based on deep feature map extraction. The third proposed system is to diagnose the data set using a hybrid technology between ResNet-18 and AlexNet models to extract feature maps and machine learning (SVM) to classify feature maps. The fourth proposed system diagnoses the data set using ANN and FFNN algorithms based on the hybrid features of ResNet-18 and AlexNet deep learning models and traditional algorithms (LBP, DWT, and GLCM). All the proposed techniques achieved superior results in the diagnosis of MRI images for early detection of AD. The FFNN algorithms based on the hybrid features extracted by ResNet-18 with features extracted using traditional algorithms achieved an accuracy of 99.8%, precision of 99.9%, sensitivity of 99.75%, specificity of 100%, and AUC of 99.94%

An Empirical Investigation of Security Vulnerabilities within Web Applications

Building secure software is challenging, time-consuming, and expensive. Software vulnerability prediction models that identify vulnerable software components are usually used to focus security efforts, with the aim of helping to reduce the time and effort needed to secure software. Existing vulnerability prediction models use process or product metrics and machine learning techniques to identify vulnerable software components. Cross-project vulnerability prediction plays a significant role in appraising the most likely vulnerable software components, specifically for new or inactive projects. Little effort has been spent to deliver clear guidelines on how to choose the training data for project vulnerability prediction. In this work, we present an empirical study aiming at clarifying how useful cross-project prediction techniques are in predicting software vulnerabilities. Our study employs the classification provided by different machine learning techniques to improve the detection of vulnerable components. We have elaborately compared the prediction performance of five well-known classifiers. The study is conducted on a publicly available dataset of several PHP open-source web applications in the context of cross-project vulnerability prediction, which represents one of the main challenges in the vulnerability prediction field