deep learning

Towards Urban Public Safety: A Literature Review of Deep Learning Approaches for CCTV-based Violence Detection

Violence detection in video surveillance systems is a critical challenge for ensuring public safety in smart cities.  This study presents a comprehensive analysis of deep learning architectures and transfer learning techniques for violence detection, evaluating their performance across benchmark datasets, including Hockey Fight, Movies, and UCF-Crime.  ResNet50, MobileNetV2, ConvLSTM, DenseNet121, and Xception models are compared in terms of accuracy, computational efficiency, and real-world applicability.  Results highlight ResNet50 as the most effective model, achievi

Radiomics and Transformer-Based Deep Learning for Non-Invasive Prediction of PD-L1 Expression in Non-Small Cell Lung Cancer: A Paradigm Shift in Precision Oncology

Traditional biomarker testing for Programmed Death-Ligand 1 (PD-L1) in Non-Small Cell Lung Cancer (NSCLC) remains invasive and costly.  This study proposes a non-invasive alternative by integrating radiomic features extracted from CT scans with advanced deep learning architectures.  We evaluated Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).  Our results demonstrate that Transformer-based models significantly outperform conventional appro

Advanced Approaches for Vulnerability Detection in Solidity-Based Smart Contracts: A Comparative Review

With the advancement of blockchain technology, Solidity-based smart contracts have become essential for automating and securing digital transactions across various sectors, from finance to supply chain management.  These contracts enable decentralized exchanges without intermediaries, enhancing transparency.  However, their immutable nature poses security challenges: any flaw in the code becomes permanent, exposing contracts to attacks and leading to financial and reputational losses.  This paper provides a comparative analysis of recent machine learning (ML) and deep l

Mathematical Modeling of Hardware-Optical Distortions in Aerial Image Data

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.

Sentiment-driven approach to refine stock price prediction

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

Advanced text-based transformer architecture for malicious social bots detection

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

Development of a deep learning-based system in Python 3.9 with YOLOv5: A case study on real-time fish counting based on classification

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

Forecasting solar energy generation using deep learning models

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

IDENTIFYING GRAPE DISEASES BY IMAGES USING ARTIFICIAL INTELLIGENCE METHODS

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.

ACTION-MASKED REINFORCEMENT LEARNING TECHNOLOGY FOR ORDER SCHEDULING

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.