Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

The aim of this paper is to investigate the effectiveness of combining mobile technologies and sensors to detect harmful particles in the air and address the problem of air pollution caused by traffic and exhaust emissions. The paper contains a systematic literature review of information technology research related to pollution detection in order to point out the main obstacles in the field and propose solutions. Furthermore, the paper presents the development of an Android smartphone-based real-time monitoring system that utilizes an external analog sensor board to acquire and evaluate physical measurements. The proposed system is calibrated for CO gas measurements, and it is compared with a commercial gas analyzer instrument. Results imply that the developed system is capable of detecting concentration levels in the air and that the accuracy is within the range of the industrial device’s accuracy.

The Internet of Things (IoT) encompasses a network of interconnected devices that collect, analyze, and exchange vast amounts of data. However, this connectivity creates opportunities for various types of cyberattacks, making IoT systems vulnerable and potentially leading to the compromise of sensitive information. Therefore, developing effective intrusion detection system (IDS) is one of the key challenges in IoT network security. The aim of this study is to develop a machine learning (ML) model for network traffic classification and attack detection in IoT environments. Through a comparative analysis of different algorithms, the study seeks to identify the model with the best performance, which could serve as a foundation for efficient IDS solutions tailored to the specific characteristics of IoT networks. The RT-IoT2022 dataset was used for experimental analysis, providing realistic framework for testing ML models, including k-nearest neighbors, Random Forest, XGBoost, multilayer perceptron, and various 1D convolutional neural network architectures. The study examines preprocessing techniques, focusing on dimensionality reduction (principal component analysis, variance inflation factor, Pearson’s test), outlier detection (interquartile range, Z -score, Isolation Forest), and transformation methods (Box–Cox, RobustScaler, Winsorization). Based on the results of the experiment, the most effective model and preprocessing technique were proposed.

Cardiovascular diseases are the main cause of death across the globe. Therefore, it is critical to detect them at an early stage, while an electrocardiogram (ECG) presents a widely used tool for making diagnosis. There is a large number of ECG device...

Water scarcity is fostering an urgent need to drive research into novel and synergistic water treatment approaches, with advanced oxidation processes (AOPs) emerging as a superior option for treating various contaminants. The spread of vinyl chloride (VC) through groundwater sources raises concerns for potable water production due to its toxic and carcinogenic properties. This study integrates ozone-based degradation experiments with data-driven modelling approaches to statistically characterize and predict VC removal under different water-matrix conditions. Ozonation alone enables partial removal of VC from two contaminated groundwater samples, while integration of O₃/H₂O₂ treatment further enhances the degradation efficacy (70–97%). Decreasing VC concentration below the parametric value of 0.5 µg/L requires application of the peroxone process or photodegradation by O₃/H₂O₂/UV for groundwater with higher levels of interfering compounds. Advanced machine learning models and ensemble methods were also tested to enhance predictive accuracy for target molecule degradation, considering water characteristics and treatment parameters as input features. An ensemble of Random Forest and Neural Network predictions yielded the best performance (R² = 0.99; Mean Squared Error = 10.8), demonstrating the effectiveness of ensemble approaches for complex chemical prediction tasks and highlighting areas for further refinement to improve interpretability and predictive consistency of AOP treatment outcomes. This study not only aligns with the current momentum in AI-assisted AOP research but also advances it by delivering a generalizable, reproducible, and interpretable ensemble model trained on experimentally diverse datasets.