Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data

Machine learning technologies have been growing rapidly in recent years. Researchers have come up with several data processing architectures, enabling machines to consume, interpret, and produce understandable output from real-world data to improve the quality of our lives. The NeuCube architecture is a data processing architecture for spatio/spectro-temporal data which consists of four main modules: a spike encoding module, a recurrent SNN reservoir, an output module, and an optimization module. Despite it has been utilised on many various applications, most improvement of the architecture focuses on user experience rather than improving the result accuracy. Upon exploration of the architecture, the weighted k-nearest neighbours algorithm used for the classification module is found to be prone to misclassification as it relies solely on the majority voting rule to determine the class for new data vector. Additionally, it does not consider the class-specific fuzzy weight information during the classification process. Therefore, a data modelling mechanism which implements PIfwkNN classifier algorithm for improving the overall classification accuracy of the NeuCube architecture has been proposed. The proposed data modelling applies an additional class-specific fuzzy weight information to new data vectors during the classification process. In this research, the optimal parameters set for experiments has also been identified. The approach has been validated by using the Kuala Krai Rainfall Dataset, Dow Jones Index Data Set, and Gold Price and Performance Dataset for the 3-days earlier and 1-day earlier event prediction. From the experiments, the improved personalised data modelling using PIfwkNN classifier has shown a significant increase in terms of overall classification accuracy as compared to the conventional MLP, fkNN, and NeuCube with wkNN classifier

Comparative analysis of spatio/spectro-temporal data modelling techniques

A fundamental challenge in spatio/spectro-temporal data (SSTD) is to learn the pattern and extract meaningful information that lies within the data. The close interrelationship between the space and temporal components of SSTD directly increases the complexity and challenges in modelling the data [1]. Other challenges include the dynamic pattern of spatial components features and inconsistency in the number of samples and feature-length used in the training and sampling datasets [2]. Data pre-processing method such as removal of irregular-feature data structure, however, may cause data loss which will lead to the final result become error prone. Despite the difficulties to process information from SSTD, several works on predictive modelling have been published, including applications on brain data processing [3], stroke data [4-5], forecasting of weather-driven damage in electrical distribution system [6], and ecological or environmental event prediction [7]. According to [8], environmental events often occur in a predictable temporal structure. Hence, the ability to exploit spiking neural network (SNN) by incorporating SSTD modelling techniques may be able to aid the process of discovering the hidden pattern and relationship between the two components of STTD; time and space. Recent work in [5], stated that most events occurring in nature form SSTD which requires measuring spatial or/and spectral components over time. Therefore, this paper presents the comparative analysis between various techniques used to process information from SSTD. Section 2 overviews two different inference-based techniques for SSTD modelling which includes global modelling, local modelling, and personalized modelling; and data modelling for SSTD classifier including, support vector machines (SVM), Evolving Classification Function (ECF), k-Nearest Neighbor (kNN), weighted k-Nearest Neighbor (wkNN), and weighted-weighted k-Nearest Neighbor (wwkNN). Section 3 presents the results of the assessment both SSTD inference-based modelling techniques and data training algorithms, while Section 4 concludes the analysis and ideas for future works

Empowering Self-Management through M-Health Applications

The advancement in mobile technology has led towards a new frontier of medical intervention that never been thought possible before. Through the development of MedsBox Reminder (MBR) application for Android as a pilot project of M-Health, health care information system for patient selfmanagement is made possible. The application acts as an assistant to remind users for their timely medicine intake by notifying them through their mobile phone. MedsBox Reminder application aims to facilitate in the self-management of patient's health where they can monitor and schedule their own medicine intake more efficiently. Development of the application is performed using Android Studio 1.4, Android SDK, MySQL database, SQLite, Java language and Netbeans IDE 8.1. Object-Oriented System Development (OOSD) methodology has been adapted to facilitate the development of the application

Empowering Self-Management through M-Health Applications

The advancement in mobile technology has led towards a new frontier of medical intervention that never been thought possible before. Through the development of MedsBox Reminder (MBR) application for Android as a pilot project of M-Health, health care information system for patient selfmanagement is made possible. The application acts as an assistant to remind users for their timely medicine intake by notifying them through their mobile phone. MedsBox Reminder application aims to facilitate in the self-management of patient's health where they can monitor and schedule their own medicine intake more efficiently. Development of the application is performed using Android Studio 1.4, Android SDK, MySQL database, SQLite, Java language and Netbeans IDE 8.1. Object-Oriented System Development (OOSD) methodology has been adapted to facilitate the development of the application

Cakelicious: Web App for Designing a Customised Wedding Cakes

In the fast-paced changing world, the Internet keeps people connected to each other. Online shopping has changed the way people buy things, and so does how people book flight tickets and movie passes. Cakelicious Web App is another interesting story of how we revolutionize the way people book wedding cakes the way they love it. The system is designed to replace the current manual booking methods used by Dr. Munie’s Kitchen for managing cakes order, thus is more efficient and effective, as well as meets the user requirements. Prototyping methodology approach has been used to develop and test the system in a systematic manner, which includes the development phases of planning, design, and testing and implementation. This system is developed using the PHP programming language, MySQL database, and runs on an Apache web server

iBid: A Competitive Bidding Environment for Multiscale Tailor

Nowadays, various online auction web services are available, allowing people to bid on items to be purchased at a competitive price. The same approach is applicable to allow people to bid on projects on Freelancer website. Here, we present an environment for customers to publish a project online, whereby marketers are able to bid on projects, called the iBid system. The iBid system demonstrates an application of bidding system which is capable of assisting customers find local tailors according to three criteria namely location, type of sewing and cost. Reversed auction mechanism is used where the customer will control the business. The prototyping methodology approach has been used to develop the system running on a PHP server and a MySQL database

Event Detection and Information Extraction Strategies from Text: A Preliminary Study Using GENIA Corpus

In the world we live today, data is the new oil. Data can reveal hidden knowledge that gives us an advantage over our competitors. However, data that are present in an unstructured form such as text documents are difficult to be processed by conventional machine learning algorithms. Therefore, in this study, we attempted to perform information extraction from textual data using current and state-of-the-art models to understand their working mechanisms. To perform this study, we have chosen the GENIA corpus for evaluating the performance of each model. These selected event extraction models are evaluated based on specific measures which are precision, recall, and F-1 measure. The result of our study shows that the DeepEventMine model has scored the highest for trigger detection with a precision of 79.17%, recall at 82.93%, and F-1 measure at 81.01%. Similarly, for event detection, the DeepEventMine model has scored highest among other models with a precision of 65.24%, recall at 55.93%, and F-1 measure at 60.23% based on the selected corpus

Predictive Analytics for Oil and Gas Asset Maintenance Using XGBoost Algorithm

One of the most important aspects of the oil and gas industry is asset management at their respective platforms. Without proper asset management, it will lead to various unexpected scenarios including an increase in plant deterioration, increased chances of accidents and injuries, and breakdown of assets at unexpected times which will lead to poor and hurried maintenance. Given the significant economic contribution of the oil and gas sector to oil-producing countries like Malaysia, accurate asset maintenance prediction is essential to ensure that the oil and gas platform can manage its operations profitably. This research identifies the parameters affecting the asset failure on oil and platform that will be interpreted using the XGBoost gradient boosting model from machine learning libraries. The model is used to predict the asset's lifetime based on readings collected from the sensors of each machine. From result, our prediction method using XGBoost for asset maintenance has presented a 6.43% increase in classification accuracy as compared to the Random Forest algorithm