Data Mining - Concepts and Techniques.

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

The Internet of Things (IoT) has become a reality. As the IoT is now becoming a far more common field, the demand for IoT technologies to manage the communication of devices with the rest of the world has increased. The IoT is connecting various individual devices called things and wireless sensor networks is also playing an important role. A thing can be defined as an embedded device based on a micro controller that can transmit and receive information. These devices are extremely low in power, memory, and resources. Therefore, the research community has recognized the importance of IoT device operating systems (OSs). An adequate OS with a kernel, networking, real-time capability, and more can make these devices flexible. This review provides a detailed comparison of the OSs designed for IoT devices on the basis of their architecture, scheduling methods, networking technologies, programming models, power and memory management methods, together with other features required for IoT applications. In addition, various applications, challenges, and case studies in the field of IoT research is discussed.

Internet of Things (IoT) Operating Systems Support, Networking Technologies, Applications, and Challenges: A Comparative Review

Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the detection of such visual responses associated with SARS-COV-2 infection. However, the limited availability of expert radiologists to interpret the CXR images and subtle appearance of disease radiographic responses remains the biggest bottlenecks in manual diagnosis. In this study, we present an automatic COVID screening (ACoS) system that uses radiomic texture descriptors extracted from CXR images to identify the normal, suspected, and nCOVID-19 infected patients. The proposed system uses two-phase classification approach (normal vs. abnormal and nCOVID-19 vs. pneumonia) using majority vote based classifier ensemble of five benchmark supervised classification algorithms. The training-testing and validation of the ACoS system are performed using 2088 (696 normal, 696 pneumonia and 696 nCOVID-19) and 258 (86 images of each category) CXR images, respectively. The obtained validation results for phase-I (accuracy (ACC) = 98.062%, area under curve (AUC) = 0.956) and phase-II (ACC = 91.329% and AUC = 0.831) show the promising performance of the proposed system. Further, the Friedman post-hoc multiple comparisons and z-test statistics reveals that the results of ACoS system are statistically significant. Finally, the obtained performance is compared with the existing state-of-the-art methods.

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

The Internet of Things (IoT) is the next technological leap that will introduce significant improvements to various aspects of the human environment, such as health, commerce, and transport. However, despite the fact that it may bring beneficial economic and social changes, the security and the privacy protection of objects and users remain a crucial challenge that has to be addressed. Specifically, now the security measures have to monitor and control the actions both of users and objects. However, the interconnected and independent nature of objects, as well as their constrained capabilities regarding the computing resources make impossible the applicability of the conventional security mechanisms. Moreover, the heterogeneity of various technologies which the IoT combines increases the complexity of the security processes, since each technology is characterized by different vulnerabilities. Furthermore, the tremendous amounts of data which is generated by the multiple interactions between the users and objects or among the objects make harder their management and the functionality of the access control systems. In this context, this paper intends to provide a comprehensive security analysis of the IoT, by examining and assessing the potential threats and countermeasures. More detailed, after studying and determining the security requirements in the context of the IoT, we implement a qualitative and quantitative risk analysis, investigating the security threats per layer. Subsequently, based on this process we identify the suitable countermeasures and their limitations, paying special attention to the IoT protocols. Finally, we provide research directions for future work.

Securing the Internet of Things: Challenges, threats and solutions

The chest radiograph is the globally accepted standard used for analysis of pulmonary diseases. This paper presents a method for automatic detection of pneumonia on segmented lungs using machine learning paradigm. The paper focuses on pixels in lungs segmented ROI (Region of Interest) that are more contributing toward pneumonia detection than the surrounding regions, thus the features of lungs segmented ROI confined area is extracted. The proposed method has been examined using five benchmarked classifiers named Multilayer Perceptron, Random forest, Sequential Minimal Optimization (SMO), Logistic Regression, and Classification via Regression. A dataset of a total of 412 chest X-ray images containing 206 normal and 206 pneumonic cases from the ChestX-ray14 dataset are used in experiments. The performance of the proposed method is compared with the traditional method using benchmarked classifiers. Experimental results demonstrate that the proposed method outperformed the existing method attaining a significantly higher accuracy of 95.63% with the Logistic Regression classifier and 95.39% with Multilayer Perceptron.

Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm

Machine learning techniques have been widely used for abnormality detection in medical images. Chest X-ray images (CXR) are among the non-invasive diagnostic tools used to detect various disease pathologies. The ambiguous anatomical structure of soft tissues is one of the major challenges for segregating normal and abnormal images. The main objective of this study is to mimic the expert radiologist’s interpretation procedure in computer-aided diagnosis (CAD) systems. We propose an automatic technique for detection of abnormal CXR images containing one or more pathologies like pleural effusion, infiltration, fibrosis, hila enlargement, dense consolidation, etc. due to tuberculosis (TB). The proposed abnormality detection technique is based on the hierarchical feature extraction scheme in which the features are used in two-level of hierarchy to categorize healthy and unhealthy groups. In level one the handcrafted geometrical features like shape, size, eccentricity, perimeter, etc. and in level 2 traditional first order statistical feature along with texture features like energy, entropy, contrast, correlation, etc. are extracted from segmented lung-fields. Further, a supervised classification approach is employed on the extracted features to detect normal and abnormal CXR images. The performance of the algorithm is validated on a total of 800 CXR images from two public datasets, namely the Montgomery set and Shenzhen set. The obtained results (accuracy = 95.60 ± 5.07% and area under curve (AUC) = 0.95 ± 0.06 for Montgomery collection, and accuracy = 99.40 ± 1.05% and AUC = 0.99 ± 0.01 for Shenzhen collection) shows the promising performance of the proposed technique for TB detection compared to the existing state of the art approaches. Further, the obtained results are statistically validated using Friedman post-hoc multiple comparison methods, which confirms the significance of the proposed method.

Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme

Analysis of Quantum Noise-Reducing Filters on Chest X-ray Images: A Review

The intense growth of Internet of Things (IoTs), its multidisciplinary nature and broadcasting communication pattern made it very challenging for research community/domain. Operating systems for IoTs plays vital role in this regard. Through this research contribution, the objective is to present an analytical study on the recent developments on operating systems specifically designed or fulfilled the needs of IoTs. Starting from study and advances in the field of IoTs with focus on existing operating systems specifically for IoTs. Finally the existing operating systems for IoTs are evaluated and compared on some set criteria and facts and findings are presented.

Tej Bahadur Chandra

Papers

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm

Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme

Analysis of Quantum Noise-Reducing Filters on Chest X-ray Images: A Review

Operating Systems for Internet of Things: A Comparative Study