Motivation — The Importance of Analytics in Retail Retail industry faces a difficult situations as retailers would like to understand the products that are very similar to each other in order to evaluate which product is better off not promoting from the similar products in the same week to increase sales activity and profit margins. Retailers need to consider various aspects of individual store management in geographically distributed areas to allow continuous flow of inventory at […]
Feature Extraction by Distributed and Parallel means for industrial big data applications Motivation — Why Feature extraction is necessary? Feature extraction remains one of the most preliminary steps in machine learning algorithms to identify strong and weak relevant attributes. While many feature extraction algorithms are used during Feature Engineering for standard classification and regression problems, the problem turns increasing difficult for time series classification and regression problems where each label or regression target is associated […]
SecureSVM, Boosting, Bagging, Clustering, LSTM, CNN, GAN Introduction In continuation to my previous blogs, “Traditional vs Deep Learning in Retail Industry” and “Deep Learning Vs Deep Reinforcement Learning Algorithms in Retail Industry” this blog highlights on different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. This blog is structured as follows: Overview of introduction of blockchain and its predominant in retail industry. Different traditional algorithms(SecureSVM, Bagging, Boosting,Random Forest, Clustering-K-Means, Agglomerative) vs deep learning algorithms […]
Transfer Learning in various IoT applications using sensor fusion technology Motivation Deep learning has been known to learn non-linear data representations for large and complex datasets. It has been successful in exploring hidden features and explaining sources of data variations. As deep learning mechanisms evolved to solve multiple AI problems starting from time series, computer vision and Natural Language Processing (NLP), it became an useful AI technique to co-relate problems in similar domains by applying […]
Dimensionality Reduction to aid medical investigation In machine learning and data science problems, the main objective remains to find the most relevant features that plays a dominant role in determining and influencing the output results. In most data science problems, the dataset is overfilled with numerous features that results in overfitting and adds to huge training costs (both at cloud and device) and makes the process considerably slow. Successful research investigations has helped scientists, researchers […]
