https://researchlakejournals.com/index.php/AAIML/issue/feed International Journal of Automation, Artificial Intelligence and Machine Learning 2024-07-04T03:50:52-05:00 Anke Berger editor.ijaaiml@researchlakejournals.com Open Journal Systems <p>The <strong>International Journal of Automation, Artificial Intelligence and Machine Learning</strong> (IJAAIML) provides a forum for researchers and practitioners from the fields of artificial intelligence landscape – tools, techniques and platforms, machine learning algorithms – supervised, unsupervised, ensemble&nbsp; techniques, application of neural networks &nbsp;and deep neural networks, application of deep learning to computer vision and natural language processing, robotic automation and newly developed cross-disciplinary studies that exhibit intelligent autonomous behavioral characteristics. The journal delivers the cutting-edge research on techniques, technologies and algorithms and promotes development in emerging disciplines that support robotic automation, artificial intelligence and machine learning. The journal offers the access to opportunities to exploration and implementation of systems from the basis of concepts, theories, patterns and procedures that exhibit properties, phenomena, or abilities of any man or machine that have substantive impact to real world problems.</p> <p>This journal recognizes that artificial intelligence (AI) has come a long way from fuzzy logic to feedback mechanism in intelligent systems to John McCarthy’s tireless efforts that promotes continued research on this subject. This journal positions to seize the moment of change and innovation in strategic transformation that the subject topics impact how human live, interact, study and work. This journal opens to policy making and standards formation from government and regulatory agency, business organization and industry associations, for instance, from the perspective how they deploy and integrate research and development in subject advancements into business process, corporate governance, and large-scale compliance and regulatory initiatives. Proof of concepts and pilots of innovation can be incorporated as case studies, product demo or service offerings in any significant stage of the maturity or lifecycle. The journal encourages the researchers and practitioners to take part and join the community today, rather than wait for perfection of their product or services.&nbsp;&nbsp;</p> <p>Topics of interest include but are not limited to, the following</p> <ul> <li class="show">Intelligent automation</li> <li class="show">Machine learning techniques</li> <li class="show">Deep learning techniques</li> <li class="show">Expert systems</li> <li class="show">Big data and data mining</li> <li class="show">Fuzzy pattern recognition</li> <li class="show">Fuzzy system applications in robotics</li> <li class="show">Fuzzy neural systems, neuro-fuzzy systems</li> <li class="show">Adaptive autonomous robots</li> <li class="show">Natural language processing</li> <li class="show">Neural networks</li> <li class="show">Parallel processing</li> <li class="show">Pattern recognition</li> <li class="show">Computational intelligence</li> <li class="show">Computational linguistics</li> <li class="show">Computer vision</li> <li class="show">Human machine interaction</li> <li class="show">Neuro-computing</li> <li class="show">Soft computing and fuzzy inference</li> <li class="show">Speech analysis and recognition</li> <li class="show">Stochastic optimization</li> </ul> https://researchlakejournals.com/index.php/AAIML/article/view/308 Multispecies Discrimination of Seals (Pinnipeds) using Hidden Markov Models (HMMs) 2024-06-28T23:00:10-05:00 Marek B Trawicki marek.trawicki@marquette.edu <p>Hidden Markov Models (HMMs) were developed and implemented for the discrimination of 5 available Seals (Pinnipeds), namely the Bearded Seal (<em>Erignathus barbatus</em>), Harp Seal (<em>Pagophilus groenlandicus</em>), Leopard Seal (<em>Hydrurga leptonyx</em>), Ross Seal (<em>Ommatophoca rossii</em>), and Weddell Seal (<em>Leptonychotes weddellii</em>). 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.</p> 2024-04-04T00:00:00-05:00 Copyright (c) 2024 Marek B Trawicki https://researchlakejournals.com/index.php/AAIML/article/view/324 Application of Belief Theories for Railway Track Defect Detection 2024-06-28T23:00:11-05:00 Alain Rivero alain.rivero@reseau.sncf.fr Sasa Radosavljevic sasa.radosavljevic@reseau.sncf.fr Philippe Vanheeghe Philippe.vanheeghe@centralelille.fr <p>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.</p> 2024-05-27T00:00:00-05:00 Copyright (c) 2024 Alain Rivero, Sasa Radosavlievic, Philippe Vanheeghe https://researchlakejournals.com/index.php/AAIML/article/view/326 Nonlinear Curve Fitting to Measurement Points with WTLS Method Using Approximation of Linear Model 2024-06-28T23:00:11-05:00 Jacek Puchalski jacek.puchalski@gum.gov.pl <p>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.</p> 2024-06-12T00:00:00-05:00 Copyright (c) 2024 Jacek Puchalski https://researchlakejournals.com/index.php/AAIML/article/view/329 Optimizing Stock Market Forecasts: The Role of AI and Hybrid Models in Predictive Analytics 2024-07-04T03:50:52-05:00 Shivani Modi shivani@konko.ai Ved Prakash Upadhyay Iamved05@gmail.com <p>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.</p> 2024-06-28T03:58:38-05:00 Copyright (c) 2024 Shivani Modi, Ved Prakash Upadhyay