This study explores the application of deep learning techniques, specifically Convolutional Neural Networks, for the classification of dog breeds from images. By employing varying input image resolutions, the research evaluates the impact of resolution on the accuracy and efficiency of the model. Five experiments were conducted using resolutions of 64, 128, 224, 256, and 512 pixels to assess model performance. The results indicate that an input resolution of 256x256 pixels yields the highest accuracy, achieving 94.74% with an optimal balance between detail and processing complexity. However, certain breeds, such as the American Foxhound and Anatolian Shepherd Dog, exhibited lower classification performance, highlighting the importance of considering breed-specific characteristics in model development. The findings emphasize the critical role of image resolution in training deep learning models and suggest that a 256x256 resolution offers the best trade-off between accuracy and computational efficiency for dog breed classification.

This paper presents the development and evaluation of a simulated inventory management system using NetLogo, with a focus on demonstrating the potential of TurtleBot 4 robotics to optimize stock control in warehouse environments. Faced with technical challenges in using a physical TurtleBot 4, the project shifted to a simulation approach, which allowed for a detailed exploration of how agent-based models can improve inventory management processes. Drawing on research into SLAM algorithms, real-world business practices, and inventory management systems, the simulation replicates key warehouse functions, including the receiving, storing, and dispatching of goods. The project’s design includes a graphical user interface (GUI) that simulates wireless data transfer, enabling real-time interaction with the system. The artefact was tested successfully in various scenarios, highlighting the potential of robotics to enhance efficiency and streamline operations. The artefact overall performed as intended, providing valuable insights into the future of robotic integration in inventory management.

Alzheimer's disease (AD) is neurodegenerative brain illness. It is classified as a degenerative illness since it worsens with time. A multitude of risk factors contribute to the development of Alzheimer's disease, such as demographic information, test scores, and genetics. The paper presents the comparison of machine learning algorithms to identify the highest accuracy level in classification of Late Onset Alzheimer's disease. Dataset from the Alzheimer's Disease Neuroimaging Initiative has been requested to train and test the machine learning models. The dataset included 539 normal controls and 411 Alzheimer's Disease individuals. A main dataset includes variables that are often used in clinical practice to develop the machine learning algorithms. Another dataset was created that exclusively included subjects aged 65 and up in order to assess the accuracy of algorithms used to diagnose late-onset Alzheimer's disease. According to the benchmarked findings, Linear Discriminant Analysis performed the most efficiently, achieving accuracy and an F1-score of 1.

The strategy of spatial development of the country for the period up to 2024, based on the infrastructure of a specific type of transport, provides for the connection of the territories of settlements with modern communications; phased reconstruction and modernization; personnel, technical and technological support for interaction and digital transformation of the Russian transport complex. The development of the Russian Arctic is a priority area, since it has a significant natural resource, socio-economic and transport potential, which must be maximized, taking into account all the features of this region. It is important to develop the northern territories in such a way that the transport infrastructure of the Arctic meets the requirements in the field of comfort and safety of the human environment. This can be achieved through the use of vehicles of increased environmental friendliness and energy efficiency. In the study we analyse the possibility of converting shift buses used to deliver personnel involved in the development of fields in the Arctic zone to gas motor fuel using simulation models.

Alzheimer's disease (AD) is a type of brain disorder that is regarded as a degenerative disease because the corresponding symptoms aggravate with the time progression. Single nucleotide polymorphisms (SNPs) have been identified as relevant biomarkers for this condition. This study aims to identify SNPs biomarkers associated with the AD in order to perform a reliable classification of AD. In contrast to existing related works, we utilize deep transfer learning with varying experimental analysis for reliable classification of AD. For this purpose, the convolutional neural networks (CNN) are firstly trained over the genome-wide association studies (GWAS) dataset requested from the AD neuroimaging initiative. We then employ the deep transfer learning for further training of our CNN (as base model) over a different AD GWAS dataset, to extract the final set of features. The extracted features are then fed into Support Vector Machine for classification of AD. Detailed experiments are performed using multiple datasets and varying experimental configurations. The statistical outcomes indicate an accuracy of 89% which is a significant improvement when benchmarked with existing related works.

Alzheimer's disease is one of the brain disorders. It's also characterized as a degenerative disease because it becomes worse over time. Apolipoprotein E (APOE) is a genetic risk factor for Alzheimer's disease that has been linked to the disease in several genome-wide association studies (GWAS). Single nucleotide polymorphisms are the most common type of genetic variation among individuals (SNPs). SNPs have been identified as important biomarkers for this condition. SNPs aid in the study and detection of the disease in its early stages. We focus on employing a stacked Machine Learning (ML) model to categories Alzheimer's patients in this paper. The model was tested on all AD genetic data from phase 1 of the neuroimaging project (ADNI-1). The results showed that the stacked model outperformed other machine learning methods with an overall accuracy of 93.7 percent. The findings suggest that stacking approaches are effective in detecting Alzheimer's disease.