This study presents the formalization of mathematical models of hardware-optical distortions in digital images captured during aerial photography from onboard systems of Unmanned Aerial Vehicles (UAVs). These distortions significantly affect the accuracy and reliability of automated object detection and classification algorithms in complex outdoor environments.
Stock price values are known for their volatility due to multiple factors making their predictability a difficult task. As social media posts and news can be considered as one of the major factors in price change, we aim in this paper to predict the next-day stock price of 4 different companies, using both social media and financial datasets that range from September 30, 2021, to September 30, 2022, as inputs. The datasets go through a preprocessing pipeline that includes sentiment analysis methods, where tweets are classified by employing TextBlob and finetuned RoBER
The increasing prevalence of automated social media accounts, or Social Media Bots (SMBs), presents significant challenges in maintaining authentic online discourse and preventing disinformation campaigns on social platforms. This research introduces a novel multiclass classification framework for detecting and categorizing SMBs, leveraging fine-tuned transformer-based models. In this study, we conducted a comprehensive comparative analysis of various transformer variants, including BERT, DistilBERT, RoBERTa, DeBERTa, XLNet, and ALBERT, to evaluate their efficacy in r
This study developed a real-time fish classification and counting system for six types of fish using the YOLOv5 machine learning model with high accuracy. The system achieved an F1-score of 0.87 and a precision confidence curve with an all-classes value of 1.00 at a confidence level of 0.920, demonstrating the model's reliability in object detection and classification. Real-time testing showed that the system could operate quickly and accurately under various environmental conditions with an average inference speed of 30 FPS. However, several challenges remain, such
The application of deep learning models for forecasting solar energy generation is considered. An analysis and comparison of the efficiency of recurrent (LSTM, GRU), convolutional (CNN), and temporal convolutional networks (TCN) for forecasting time series of solar energy generation were conducted. The possibility of improving forecasting accuracy by constructing a hybrid model combining ARIMA and CNN was explored. The results of experiments for different EU countries are presented, and a comparison of models in terms of forecasting accuracy and computational efficiency is performed as w
The paper uses modern artificial intelligence methods to investigate models and methods for determining grape disease. The existing methodologies for classification and recognition by images of grape diseases using neural networks are analyzed. Several problems for improving recognition results are highlighted.
The problem of high-performance and efficient order scheduling is a common combinatorial optimization problem in various industrial contexts. Creation of a model capable of generating schedules balanced in terms of quality and computational time poses a significant challenge due to the large action space. This study proposes a high-performant environment and a reinforcement learning model for allocating orders to resources using a mechanism of invalid action masking.
In this paper, we establish the existence of a class of discrete nonlinear systems involving the anisotropic $\vec{p}(\cdot)$-Laplacian operator using an optimization based approach. We then simulate the solutions by implementing a deep learning model. The numerical results demonstrate that the proposed method is stable and robust compared to conventional approaches such as the Newton–Krylov method.
The paper investigates the problem of predicting changes in user states (including churn) based on session data using deep neural networks. The paper considers the use of long short-term memory models and convolutional neural networks, as well as the use of byte pair coding for data pre-processing. The functionality of the developed information system for forecasting changes in the state of users and interpreting forecasting models, which combines methods of data analysis, building forecasting models and explaining the results, is analysed.
The rapid expansion of the Internet of Things (IoT) has resulted in a substantial increase of diverse data from distributed devices. This extensive data stream makes it increasingly important to implement robust and efficient real-time anomaly detection techniques that can promptly alert about issues before they could escalate into critical system failures.