Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

Adam Deep Learning with SOM for Human Sentiment Classification

Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing

Brain Tumor Categorization and Retrieval Using Deep Brain Incep Res Architecture Based Reinforcement Learning Network

The categorization and retrieval of brain tumors using Magnetic Resonance Imaging (MRI) is a difficult but necessary process for brain tumor diagnosis. In this study, a reinforcement learning agent is proposed that can interact with an environment that includes brain tumor images and retrieve and categorize the most comparable images to an unknown query image. This article proposes a unique fuzzy and Deep Learning (DL)-based Reinforcement Learning (RL) strategy for categorizing three types of brain tumors as well as no tumors. Deep Brain Incep Res Architecture 2.0 based Reinforcement Learning Network (DBIRA2.0-RLN), the proposed Convolutional Neural Network (CNN)-based technique, benefits from a novel architecture in which brain tumor descriptors are established using the inception block and effective skip-connection mapping arrangement. To improve the efficiency of DBIRA2.0-RLN, improved samples are created by training and testing the system with a fuzzy logic-based technique. To lower the dimension of the descriptor vector for improved image categorization and retrieval, the descriptor vector obtained from DBIRA2.0 is binary coded using Multilinear Principal Component Analysis. DBIRA2.0 produces and preserves brain tumors and no tumor descriptors in several layers, which are then used sequentially in numerous units to construct the final brain tumor categorization and retrieval. The proposed method’s output is tested using a dataset, and the accuracy rates obtained for meningioma tumor, glioma tumor, pituitary tumor, and no tumor are 97.1%, 98.7%, 94.3%, and 100% respectively, indicating that the proposed approach outperforms the other brain tumor categorization and retrieval approaches used in the literature

Pattern mining approaches used in social media data

Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data