Task scheduling for multiple humanoid robot manipulators in industrial and collaborative settings remains a significant challenge due to the complexity of coordination, resource sharing, and real-time decision-making. In this study, we propose a framework for modeling task scheduling for multiple humanoid robot manipulators by using the symbolic discrete controller synthesis technique. We encode the task scheduling problem as discrete events using parallel synchronous dataflow equations and apply our synthesis algorithms to manage the task scheduling of multiple humanoid robots via the resulting controller. The control objectives encompass the fundamental behaviors of the system, strict rules, and mutual exclusions over shared resources, categorized as the safety property, whereas the optimization objectives are directed toward maximizing the throughput of robot-processed products with optimal efficiency. The humanoid robots considered in this study consist of two pairs of six-degree-of-freedom (6-DOF) robot manipulators, and the inverse kinematics problem of the 6-DOF arms is addressed using metaheuristic approaches inspired by biomimetic principles. Our approach is experimentally validated, and the results demonstrate high accuracy and performance compared to other approaches reported in the literature. Our approach achieved an average efficiency improvement of 40% in 70-robot systems, 20% in 30-robot systems, and 10% in 10-robot systems in terms of production throughput compared to systems without a controller.

Lifelong music education is critical need for all with a particular focus on adult learners and seniors. One of the difficulties in music education is identifying chords accurately. This is a preliminary study to develop a chord identification application using Artificial Intelligence (AI) technologies. I seek to answer the key research question of how audio processing algorithms and deep learning models can be used to provide real-time, accurate and user-friendly chord recognition that meets the diverse needs of adult learners and senior citizens. Our overall goal is to create an application that not only assists with chord identification, but also fosters a lifelong love of music and learning. My methodology is based on the principles of adult and senior education initiatives and includes the following key steps: using ready-made datasets for audio processing and feature extraction, transforming waveforms into mel spectrograms, and preparing and extending the datasets where necessary. I then train and optimise deep learning models, such as various convolutional neural network (CNN) architectures, to achieve high accuracy in chord recognition. By using advanced technologies and adhering to the principles of lifelong learning, our research aims to enhance the musical journey of individuals throughout their lives, contributing to both personal enrichment and cognitive well-being.

This research aims at artificial intelligence-based text classification and question answering solutions in disaster literacy education and underlines the potential of artificial intelligence applications in disaster literacy education. Text classification and question answering applications emerge as effective tools in gaining disaster preparedness skills. Text classification guides students by identifying the topics covered in the educational material and helps focused learning. Question-answer applications enhance the learning experience by automatically extracting answers from texts. In the study, the Python programming language was used to demonstrate the AI applications. However, it emphasises the need to address larger datasets, more complex models and broader application areas in real-world scenarios. In particular, factors such as the use of pre-trained models, data cleaning and label consistency significantly affect the success of artificial intelligence applications. Therefore, careful consideration and optimisation of these factors can contribute to the improvement of application performance. The research results provide important recommendations for researchers, educators and practitioners aiming to develop AI-based disaster literacy education. However, the study underlines the need for further expansion in this area and advocates for more comprehensive research involving diverse datasets, various application contexts and more complex scenarios.

The SETSCI Conference Proceedings is an Open Access publication created for the storage and indexing of proceedings | The COVID-19 pandemic has necessitated the development of rapid, accurate, and automated diagnostic tools to assist healthcare professionals in patient screening and management. This comprehensive study presents a detailed comparative analysis of ten state-of-the-art Convolutional Neural Network (CNN) architectures for COVID-19 detection using chest X-ray radiography images. The research systematically evaluates DenseNet, ConvNeXTiny, EfficientNetB0, ResNet50, VGG19, VGG16, MobileNet, GoogleNet, AlexNet, and LeNet models using the COVID-19 Radiography Database. Each model was rigorously trained and validated using standardized protocols to ensure fair comparison. Comprehensive performance metrics including validation accuracy, test accuracy, loss functions, convergence analysis, and overfitting assessment were employed. The experimental results demonstrate that modern architectures, particularly DenseNet and ConvNeXTiny, achieved superior performance with 97% validation accuracy and 96% test accuracy, exhibiting excellent generalization capabilities with minimal overfitting (1.00% accuracy difference). The study also analyzes computational efficiency, training dynamics, and practical deployment considerations. These findings provide crucial insights for developing robust computer-aided diagnosis systems for COVID-19 detection and establish benchmarks for future research in medical image classification.

Phylogenetic trees describe evolution but due to hybridization events, recombination events or lateral gene transfer, it can be represented as a phylogenetic network. In phylogenetic networks, some of the branches of tree combine and create a reticulation node. Level of a network is decided to look at how many nodes in a connected component in a network. In this research, Int this paper, the structure of directed and undirected level-2 networks and how the can be decomposed into level-2 generators is studied..