This paper compares between several parameter estimation methods (Classical Gramm-Schmidt (CGS), Modified Gramm-Schmidt (MGS), Householder Transform (HT), and Givens Rotation (GR)) for Nonlinear Autoregressive with Exogenous Inputs (NARX) system identification of a DC motor model using Binary Particle Swarm Optimization (BPSO) by (Kennedy and Eberhart, 1997) as the structure selection method. First, we describe the application of BPSO for model structure selection, by representing its particles' solutions as probabilities of change in a binary string. The binary string was then used to select a subset of regressor columns from the regressor matrix. The parameters (linear least squares solution) were then estimated using CGS, MGS, GT and GR. One-Step Ahead (OSA) and correlations tests performed on the DC motor dataset show that: 1) The BPSO-based selection method has the potential to become an effective method to determine parsimonious NARX model structure, and 2) The CGS, HT and GR algorithms were the best choices for parameter estimation of the model.

This project presents MySQL database for storage of fingerprint data. The application of the database is for future process, fingerprint matching. In this project, Matlab R2009 is the software used for fingerprint image enhancement and minutia extraction. MySQL is a relational database management system (RDBMS) that runs as a server providing multi-user access to a number of databases. XAMPP Control Panel V2.5 software is also used for free and open source cross-platform web server package, consisting mainly of the Apache HTTP Server, MySQL database, and interpreters for scripts written in the PHP and Perl programming languages. It is found that this project shows an efficient and practical data storage of fingerprint.

Water quality is one of the most highly debated issues worldwide at the moment. Inadequate water supplies affect human health, hinder food production, and degrade the environment. Using contemporary technology to analyze pollution statistics can help solve pollution issues. One option is to take advantage of advancements in intelligent data processing to conduct hydrological parameter analysis. To perform conclusive water quality studies, a lot of data is necessary. Unfilled data (information gaps) in the long-term hydrological data set may be due to equipment faults, collection schedule delays, or the data collection officer’s absence. The lack of hydrological data skews its interpretation. Therefore, interpolation is used to recreate and fill missing hydrological data. From 2012 to 2017, the Klang River’s biochemical oxygen demand (BOD) in Selangor, Malaysia, was sampled. This study examined three methods of interpolation for their effectiveness using the MATLAB software: piecewise cubic hermite interpolating polynomial (PCHIP), cubic Spline data interpolation (Spline), and modified Akima partitioned cubic hermite interpolation (Makima). The accuracy is assessed using root mean square error (RMSE). All interpolation algorithms offer excellent results with low RMSE. However, PCHIP delivers the best match between interpolated and original data.

Poverty, an intricate global challenge influenced by economic, political, and social elements, is characterized by a deficiency in crucial resources, necessitating collective efforts towards its mitigation as embodied in the United Nations' Sustainable Development Goals. The Gini coefficient is a statistical instrument used by nations to measure income inequality, economic status, and social disparity, as escalated income inequality often parallels high poverty rates. Despite its standard annual computation, impeded by logistical hurdles and the gradual transformation of income inequality, we suggest that short-term forecasting of the Gini coefficient could offer instantaneous comprehension of shifts in income inequality during swift transitions, such as variances due to seasonal employment patterns in the expanding gig economy. System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. In this study, we introduce a NAR Multi-Layer Perceptron (MLP) approach for brief term estimation of the Gini coefficient. Several parameters were tested to discover the optimal model for Malaysia's Gini coefficient within 1987–2015, namely the output lag space, hidden units, and initial random seeds. The One-Step-Ahead (OSA), residual correlation, and residual histograms were used to test the validity of the model. The results demonstrate the model's efficacy over a 28-year period with superior model fit (MSE: 1.14 × 10⁻⁷) and uncorrelated residuals, thereby substantiating the model's validity and usefulness for predicting short-term variations in much smaller time steps compared to traditional manual approaches.

Current smartphones are equipped with various sensors, which can be used for research and data collection purposes. This papers presents an approach to use the gyroscope sensor present in many smartphones to perform gesture recognition. Two phones were strapped onto the subject body. Gyroscope readings were obtained during several gestures. The gyroscope readings were sent to MATLAB using the SensorUDP application installed on the phone. A total of 125 readings of 4 gestures were collected from 4 subjects and fed to a Multi-Layer Perceptron (MLP) classifier. Tests were performed to determine the optimal threshold and number of hidden units, respectively. The best classifier produced 99.69% accuracy.

This paper focuses on a system identification review for distillation column. A recent developments and approaches explored by reseachers demonstrated the improvement and efficiency of linear and nonlinear modeling. Numerous investigation on the modeling of distillation column including essential oil extraction application have attracted much attention. Much research in recent years has focused on both linear and nonlinear modeling for distillation column. Although both models improves performance of the system, a comparative assessment revealed greater achievement of the nonlinear model for distillation column compared to the linear model. The significance of various input signals and the performance have been highlighted for the nonlinear system.