Data Mining - Concepts and Techniques.

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

The massive growth in the scale of data has been observed in recent years being a key factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety of data that require a new high-performance processing. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. The presence of data preprocessing methods for data mining in big data is reviewed in this paper. The definition, characteristics, and categorization of data preprocessing approaches in big data are introduced. The connection between big data and data preprocessing throughout all families of methods and big data technologies are also examined, including a review of the state-of-the-art. In addition, research challenges are discussed, with focus on developments on different big data framework, such as Hadoop, Spark and Flink and the encouragement in devoting substantial research efforts in some families of data preprocessing methods and applications on new big data learning paradigms.

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Big data preprocessing: methods and prospects

The main reason behind failure of lots of software is poor quality thus estimation software quality become an important task in software industry. An early estimation towards pre-released software quality plays an important role in increasing probability of project success. By analyzing the metric data we can forecast the quality of the object oriented system. Software metrics are necessary to identify where the resources are needed; they are a crucial source of information for decision making. This paper represents proposed model for estimation quality of software product.

A hierarchical model for object-oriented design quality assessment

How different cultures react and respond given a crisis is predominant in a society’s norms and political will to combat the situation. Often, the decisions made are necessitated by events, social pressure, or the need of the hour, which may not represent the nation’s will. While some are pleased with it, others might show resentment. Coronavirus (COVID-19) brought a mix of similar emotions from the nations towards the decisions taken by their respective governments. Social media was bombarded with posts containing both positive and negative sentiments on the COVID-19, pandemic, lockdown, and hashtags past couple of months. Despite geographically close, many neighboring countries reacted differently to one another. For instance, Denmark and Sweden, which share many similarities, stood poles apart on the decision taken by their respective governments. Yet, their nation’s support was mostly unanimous, unlike the South Asian neighboring countries where people showed a lot of anxiety and resentment. The purpose of this study is to analyze reaction of citizens from different cultures to the novel Coronavirus and people’s sentiment about subsequent actions taken by different countries. Deep long short-term memory (LSTM) models used for estimating the sentiment polarity and emotions from extracted tweets have been trained to achieve state-of-the-art accuracy on the sentiment140 dataset. The use of emoticons showed a unique and novel way of validating the supervised deep learning models on tweets extracted from Twitter.

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Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets

Real-Time Sentiment Analysis of Twitter Streaming data for Stock Prediction

Feature selection and classification of microarray data using Hadoop frameworkMapReduce based statistical tests are proposed for feature selection (FS)MapReduce based PSVM (mrPSVM) classifier is proposed for classification of microarrayComparative analysis of results is performed with FS methods in permutation with mrPSVM Microarray-based gene expression profiling has emerged as an efficient technique for classification, diagnosis, prognosis, and treatment of cancer disease The nature of this disease changes frequently, this generates a huge volume of data The data retrieved from microarray covers its varieties (veracity) of nature, and changes observed as time changes (velocity) Therefore, the analysis of microarray dataset in a very short period is essential The major drawback of microarray data is the 'curse of dimensionality problem', this hinders the useful information of dataset and leads to computational instability Therefore, selecting relevant genes is an imperative in microarray data analysis Most of the existing schemes employ a two phase process: feature selection/extraction followed by classification In this paper, various statistical methods (tests) based on MapReduce are proposed to select the relevant features After feature selection, MapReduce based proximal support vector machine (mrPSVM) classifier is also proposed to classify the microarray data These algorithms are successfully implemented on Hadoop framework A comparative analysis is done on these feature selection methodologies using microarray datasets of various dimensions Experimental results show that the ensemble of mrPSVM classifier and various feature selection methods produces a better accuracy rate on the benchmark dataset

Classification of microarray using MapReduce based proximal support vector machine classifier

Deep learning has brought significant developments in image understanding tasks such as object detection, image classification, and image segmentation. But the success of image recognition largely relies on supervised learning that requires huge number of human-annotated labels. To avoid costly collection of labeled data and the domains where very few standard pre-trained models exist, self-supervised learning comes to our rescue. Self-supervised learning is a form of unsupervised learning that allows the network to learn rich visual features that help in performing downstream computer vision tasks such as image classification, object detection, and image segmentation. This paper provides a thorough review of self-supervised learning which has the potential to revolutionize the computer vision field using unlabeled data. First, the motivation of self-supervised learning is discussed, and other annotation efficient learning schemes. Then, the general pipeline for supervised learning and self-supervised learning is illustrated. Next, various handcrafted pretext tasks are explained that enable learning of visual features using unlabeled image dataset. The paper also highlights the recent breakthroughs in self-supervised learning using contrastive learning and clustering methods that are outperforming supervised learning. Finally, we have performance comparisons of self-supervised techniques on evaluation tasks such as image classification and detection. In the end, the paper is concluded with practical considerations and open challenges of image recognition tasks in self-supervised learning regime. From the onset of the review paper, the core focus is on visual feature learning from images using the self-supervised approaches.

Mukesh Kumar

Papers

Real-Time Sentiment Analysis of Twitter Streaming data for Stock Prediction

Classification of microarray using MapReduce based proximal support vector machine classifier

Review on self-supervised image recognition using deep neural networks

Feature Selection and Classification of Microarray Data using MapReduce based ANOVA and K-Nearest Neighbor

Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier