International Journal of Automation, Artificial Intelligence and Machine Learning

Multispecies Discrimination of Seals (Pinnipeds) using Hidden Markov Models (HMMs)

Marek B Trawicki — 2024-04-04

Hidden Markov Models (HMMs) were developed and implemented for the discrimination of 5 available Seals (Pinnipeds), namely the Bearded Seal (Erignathus barbatus), Harp Seal (Pagophilus groenlandicus), Leopard Seal (Hydrurga leptonyx), Ross Seal (Ommatophoca rossii), and Weddell Seal (Leptonychotes weddellii). The main objectives of the experiments were to study the impact of the frame size and step size and number of states for feature extraction and acoustic models on classification accuracy. Based on the experiments using Mel-Frequency Cepstral Coefficients (MFCCs) extracted from the vocalizations (15 ms frame size and 4 ms step size), HMMs containing 20 states with single underlying Gaussian Mixture Model (GMM) produced discrimination of 95.77%. From the results, the framework could be applied to analysis for other marine mammals for both classification and detection of vocalizations and species.

Application of Belief Theories for Railway Track Defect Detection

Alain Rivero — 2024-05-27

Faced with increasing traffic, railway infrastructures are encountering growing demands, particularly in high-traffic areas. In this context, rail and sleepers emerge as the components most susceptible to failure. To assist infrastructure managers (IM) in optimizing network maintenance, we have explored a novel method for detecting critical defects on the track. The objective is to develop a process for real-time analysis of railway infrastructure that is both frugal and efficient and can be installed on board commercial trains. This new infrastructure monitoring system integrates deep learning networks with a data fusion model based on belief theory. By modeling the decision-making process of a human operator, this processing chain has achieved detection rates exceeding 90% for the five primary defects: defective fasteners, broken fishplates and rails, surface defects, and missing nuts.

Nonlinear Curve Fitting to Measurement Points with WTLS Method Using Approximation of Linear Model

Jacek Puchalski — 2024-06-12

The paper presents an approximate method of fitting measurement points to parameterized arbitrary nonlinear curves described by complex equations, even implicit ones, the most commonly used method of least squares in general WTLS. An approximation of a linear model is used here, in which the laws of propagation of error and propagation of uncertainty are true, so that only the first derivative of the transforming function is relevant. The effectiveness of the method has been demonstrated in several numerical examples. The method was verified on several nonlinear functions using the iterative algorithm by Monte Carlo propagation of distribution and the classical method based on the Levenberg-Marquardt algorithm for nonlinear optimization.

Optimizing Stock Market Forecasts: The Role of AI and Hybrid Models in Predictive Analytics

Shivani Modi — 2024-06-28

Forecasting stock market movements is a challenging and significant task for both researchers and investors. Stock market movements are affected by local and global economic factors, as well as political developments. This field of research requires substantial knowledge of finance, statistics, and Artificial Intelligence to achieve reliable results. To understand stock market movements, we must interpret a significant amount of information from non-linear, volatile, and non-parametric raw data. To reduce the complexity of stock market forecasting, we need to extract key features from this raw data. To simplify the task of stock market forecasting for researchers and traders, we conducted a study on the Indian stock market and present a comprehensive summary report. This report includes an analysis of 50 research articles related to the Indian stock market, along with some highly cited articles pertaining to other international markets.