The conventional way of respiratory coronavirus disease 2019 (COVID-19) diagnosis is reverse transcription polymerase chain reaction (RT-PCR), which is less sensitive during early stages; especially if the patient is asymptomatic, which may further cause more severe pneumonia. In this context, several deep learning models have been proposed to identify pulmonary infections using publicly available chest X-ray (CXR) image datasets for early diagnosis, better treatment and quick cure. In these datasets, the presence of less number of COVID-19 positive samples compared to other classes (normal, pneumonia and Tuberculosis) raises the challenge for unbiased learning of deep learning models. This learning problem can be considered as one-class classification problem where the target class samples are present and other classes are absent or ill-defined. All deep learning models opted class balancing techniques to solve this issue; which however should be avoided in any medical diagnosis process. Moreover, the deep learning models are also data hungry and need massive computation resources. Therefore, for quicker diagnosis, this research proposes a novel pinball loss function based one-class support vector machine (PB-OCSVM), that can work in presence of limited COVID-19 positive CXR samples (target class or class-of-interest (CoI) samples) with objectives to maximize the learning efficiency and to minimize the false predictions. The performance of the proposed model is compared with conventional OCSVM and recent deep learning models, and the experimental results prove that the proposed model outperformed state-of-the-art methods. To validate the robustness of the proposed model, experiments are also performed with noisy CXR images and UCI benchmark datasets.

Mankind has been looking up at the stars for centuries, wondering what lies in deep space, and if other civilizations like ours exist. Following this, with the significant advancement in the field of cosmology and space missions, there has been an exponential increase in the astronomical data collected by space telescopes to explore the possibility of harboring extraterrestrial life. And there is still no consensus on whether a planet is habitable, potentially habitable or inhabitable. The only habitable planet is Earth, therefore, the hypothesized exoplanets are categorized using Earth as a reference, also known as the “Earth Habitability Index” (EHI). In this regard, a number of additional metrics have been developed to categorize an exoplanet's habitability score, such as the Cobb-Douglas Habitability Score, which is a metric based on the Cobb-Douglas habitability production function (CD-HPF). Many classification-based algorithms have already been developed, but they have limitations, such as the possibility of misleading accuracy scores when applied to highly unbalanced datasets. Recently, some work has also been proposed in anomaly detection using memetic algorithms, which belong to the class of metaheuristic algorithms, but the number of feature sets used is significantly less compared to the ones impacting the habitability score of an exoplanet. In this present research, we are proposing a novel variational auto-encoder algorithm that works on a probability distribution function belonging to the class of anomaly detection and will work on a greater number of features in a significantly larger dataset. The proposed algorithm follows an unsupervised learning approach to detect anomalies in order to determine potentially habitable exoplanets. To validate the approach, the obtained results will be cross-matched with the dataset provided by the Planetary Habitability Laboratory's habitable exoplanet catalog (PHL-HEC).

Data intensive applications are one of the most critical real-time applications which are desired in most of the new-normal practices such as recommendation systems, social media analytics systems, fake news detection systems, etc. However, to deploy such solutions for real-time usage, software testing and quality assurance plays a vital role to understand the application behavior. The characteristic 4 Vs of big data adds complexities or challenges that need to be addressed for real-time applications or development. Testing of big data applications can be made efficient by designing and executing test plans; approach and strategy for all V’s of big data. This tutorial comprehensively covers both the theoretical and practical aspects of testing data-intensive applications. The tutorial discusses testing data-intensive applications built on top of modern big data frameworks such as Hadoop, Spark, Flink, NoSQL, Hive, Zookeeper, Elastic Search, Flume, and Kafka. The hands-on with MapReduce unit testing, Spark streaming testing, Kafka unit testing and related testing libraries has been covered with simple integration of testing examples and test case driven developments.

Adversarial attacks against deep learning models have gained significant attention and recent works have pro-posed explanations for the existence of adversarial examples and techniques to defend the models against these attacks. Attention in computer vision has been used to incorporate focused learning of important features and has led to improved accuracy. Recently, models with attention mechanisms have been proposed to enhance adversarial robustness. Following this context, this work aims at a general understanding of the impact of attention on adversarial robustness. This work presents a comparative study of adversarial robustness of non-attention and attention based image classification models trained on CIFAR-10, CIFAR-100 and Fashion MNIST datasets under the popular white box and black box attacks. The experimental results show that the robustness of attention based models may be dependent on the datasets used i.e. the number of classes involved in the classification. In contrast to the datasets with less number of classes, attention based models are observed to show better robustness towards classification.

Autonomous detection and classification of objects are admired area of research in many industrial applications. Though, humans can distinguish objects with high multi-granular similarities very easily; but for the machines, it is a very challenging task. The convolution neural networks (CNN) have illustrated efficient performance in multi-level representations of objects for classification. Conventionally, the existing deep learning models utilize the transformed features generated by the rearmost layer for training and testing. However, it is evident that this does not work well with multi-granular data, especially, in presence of deceptive similar classes (almost similar but different classes). The objective of the present research is to address the challenge of classification of deceptively similar multi-granular objects with an ensemble approach that utilizes activations from multiple layers of CNN (deep features). These multi-layer activations are further utilized to build multiple deep decision trees (known as Random forest) for classification of objects with similar appearance. The Fruits-360 dataset is utilized for evaluation of the proposed approach. With extensive trials it was observed that the proposed model outperformed over the conventional deep learning approaches.

Existing dimensionality reduction (DR) techniques such as principal component analysis (PCA) and its variants are not suitable for target class mining due to the negligence of unique statistical properties of class-of-interest (CoI) samples. Conventionally, these approaches utilize higher or lower eigenvalued principal components (PCs) for data transformation; but the higher eigenvalued PCs may split the target class, whereas lower eigenvalued PCs do not contribute significant information and wrong selection of PCs leads to performance degradation. Considering these facts, the present research offers a novel target class-guided feature extraction method. In this approach, initially, the eigendecomposition is performed on variance–covariance matrix of only the target class samples, where the higher- and lower-valued eigenvectors are rejected via statistical analysis, and the selected eigenvectors are utilized to extract the most promising feature subspace. The extracted feature-subset gives a more tighter description of the CoI with enhanced associativity among target class samples and ensures the strong separation from nontarget class samples. One-class support vector machine (OCSVM) is evaluated to validate the performance of learned features. To obtain optimized values of hyperparameters of OCSVM a novel [Formula: see text]-ary search-based autonomous method is also proposed. Exhaustive experiments with a wide variety of datasets are performed in feature-space (original and reduced) and eigenspace (obtained from original and reduced features) to validate the performance of the proposed approach in terms of accuracy, precision, specificity and sensitivity.