Internet intrusion detection systems (IDS) use machine learning models, which one needs to train using public datasets. The training process requires a training set, which is a majority part of such a dataset, while validation is performed on its second part - the validation set. Finally, to evaluate the quality of the output model, one utilizes the test set, which is the third part. The...
The paper explores different solutions for implementing self-learning artificial intelligence (AI) competitive bots for the game Blood Bowl. The winners of the most of the previous competitions were scripted bots but in recent years bots based on machine learning started to outspace their competition. Blood Bowl is a two-player, turn-based, asymmetric board game that combines elements of...
Procedural plot generation is a topic widely researched in the context of video games. This paper discusses parts of the existing research using Role Playing Games as a target for plot generation. Analysis of table-, atom-, and Large Language Model-based approaches to plot generation for Role Playing Games indicates that more is needed. This paper proposes the solution to this problem using a...
Accurate diagnosis and prognosis are crucial for the effective treatment of breast cancer. To improve diagnostic accuracy and reduce human error, this study presents a novel approach using deep neural networks (DNNs) to detect cancerous lesions in microscopic specimens from the breast. In this paper, I examine the performance of convolutional neural network (CNN) model as well as CNN-based...
Abstract:
In software development, clean code and clean architecture are crucial aspects that
ensure separation between business logic, application logic, and framework-related
code. However, in the dynamic world of web development, these approaches are not
commonly utilized due to the lack of standardization among frontend frameworks and
libraries. This often leads to complications in...
Advancements in UNet architectures have been pivotal in medical image segmentation, particularly for blood vessel segmentation, which is crucial for medical diagnosis and treatment. This article presents a comprehensive comparative study of various UNet models, examining their effectiveness in blood vessel identification within medical imaging. We explore the evolution of these models from the...
This scientific article describes a project focused on creating a convolutional neural network designed to assist physicians in diagnosing diabetic retinopathy based on Optical Coherence Tomography (OCT) images. The project involves an in-depth analysis of existing research on the classification of this disease using fundus images. Leveraging a diverse dataset of OCT images, encompassing both...
Contemporary medical diagnostics increasingly utilise advanced information technologies to analyse microscopic specimens, crucial in detecting and diagnosing cancerous changes. In our work, we focused on the analysis of microscopic specimens from the colon, which is extremely important in detecting early stages of cancer. We used Python to process and analyse these images, offering extensive...
In this comprehensive exploration, the author delves
into a visionary initiative aimed at fundamentally transforming
language learning through the implementation of a groundbreaking Conversational AI system. The research underscores the
pivotal role of innovative features, particularly the successful
integration and testing of TextToSpeech and real-time translations, which have emerged as...
Finite-Difference Time-Domain method of electromagnetic field computation often requires significant amount of time and memory resources, which emphasizes the necessity for the development of high-performance programs. In this paper, we present enhancements to the performance of an electromagnetic field solver using the FDTD method on an asymmetric grid. The improvement in efficiency was...
The research focused on classic image captioning based on a coder-decoder structure, where the coder encodes the image features. At the same time, the decoder produces a caption – a phrase describing the image content. We investigated the decoder part by testing multiple convolutional-neural-network-based backbones – feature extractors. This investigation aimed to find the optimal encoder,...
Quantum computers promise to revolutionize several fields, including cryptography. In recent years, researchers have made significant progress in developing quantum algorithms that can solve computational problems much faster than classical computers. These advances have led to concerns about the security of traditional cryptography algorithms, as they may be vulnerable to quantum attacks. In...
This paper presents a novel approach, Analyze-Select-Match (ASM), for local email categorization using small language models. The objective is to enable users to organize emails on their local machines by categorizing them into user-defined labels with flexibility in both quantity and quality. A dataset of email samples was curated, and five models, sized for widespread graphics card...
