The blockchain technology is a relatively new approach in the field of information technologies. As one of its first implementations, bitcoin as a cryptocurrency has gained a lot of attention. Together with Ethereum, blockchain implementation with focus on smart contracts, they represent the very core of modern cryptocurrency development. This paper is meant to give a brief introduction to these topics.

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

This paper describes an artificial neural network for classification of asteroids into families. The data used for artificial neural network training and testing were obtained by the Hierarchical Clustering Method (HCM). We have shown that an artificial neural networks can be used as a validation method for the HCM on families with a large number of members.

This paper shows an Android-based system for identification of objects based on reading of QR codes. The system is developed to facilitate identification of various items that exist in already created inventory. The designed system is composed of a database, Web service for intermediary access to the database via Web, and the client Android application, that can be run on mobile phones or tablet computers. It is shown how this system can be used for cataloguing the computer equipment, but the usage of the system is not limited solely to this.

We present a deep learning model for the recognition of speech commands in the English language. The dataset is based on the Google Speech Commands Dataset by Warden P., version 0.01, and it consists of ten distinct commands (“left”, “right”, “go”, “stop”, “up”, “down”, “on”, “off”, “yes”, and “no”) along with additional “silence” and “unknown” classes. The dataset is split in a speaker-independent manner, with 70% of speakers assigned to the training set and 15% to the test set and validation set. All audio clips are sampled at 16 kHz, with a total of 46 146 clips. Audio files are converted into Mel spectrogram representations, which are then used as input to a deep learning model composed of a four-layer convolutional neural network followed by two fully connected layers. The model employs Rectified Linear Unit (ReLU) activation, the Adam optimizer, and dropout regularization to improve generalization. The achieved testing accuracy is 96.05%. Micro- and macro-averaged precision, recall, and F1-score of 95% are reported to reflect class-wise performance, and a confusion matrix is also provided. The proposed model has been deployed on a Raspberry Pi 5 as a Fog computing device for real-time speech recognition applications.

When it comes to minimalist design and dark mode, these are two very current topics in today’s design, development, and implementation of web and mobile applications. Often heard is the phrase „Less is more,“ which will be explained in detail through this paper, both from a theoretical perspective and with practical examples. Minimalist design has numerous advantages from various perspectives, but it also presents a significant challenge for designers because it requires highlighting everything important in the right way without excessive decoration and too many details. On the other hand, dark mode also presents a challenge, but from a technical standpoint, it has many advantages, as well as from the perspective of user experience. In comparison to lighter themes, it facilitates viewing the screen during the night and contributes to energy savings. In summary, today, minimalism and dark mode, in combination, when creating design software, improve the user interface (UI) and user experience, and enhance application performance in various fields.