Deep convolutional neural networks (CNNs) are aimed at processing data that have a known network like topology. They widely used to recognise objects in images and diagnose patterns time series as well sensor classification. The aim of the paper is present theoretical practical aspects deep CNNs terms convolution operation, typical layers basic methods be for training learning. Some applications included signal image Finally, describes proposed block structure CNN classifying crucial...

Weed management technologies that can identify weeds and distinguish them from crops are in need of artificial intelligence solutions based on a computer vision approach, to enable the development precisely targeted autonomous robotic weed systems. A prerequisite such systems is create robust reliable object detection unambiguously food crops. One essential steps towards precision agriculture using annotated images train convolutional neural networks crops, which be later followed mechanical...

Differential privacy has recently gained prominence, especially in the context of private machine learning. While definition differential makes it possible to provably limit amount information leaked by an algorithm, practical implementations differentially algorithms often contain subtle vulnerabilities. Therefore, there is a need for effective methods that can audit before they are deployed real world. The article examines studies recommend guarantees It covers wide range topics on subject...

This research delineates a pivotal advancement in the domain of sustainable energy systems, with focused emphasis on integration renewable sources—predominantly wind and solar power—into hydrogen production paradigm. At core this scientific endeavor is formulation implementation deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance efficiency derived from sources. The study presents comprehensive evaluation efficacy fully connected...

Object detection based on deep learning can be widely used in all kinds of agricultural applications. In this paper, we present a neural network (DNN) model for quince and raspberry RGB images. The trained DNN is YOLOv5 architecture it has 7 berry classes related to the development stage. provides sufficiently good performance precision trade-off. It useful process phenotyping agriculture experts, where yield size parameters have estimated. Using our shown that possible achieve mean Average...

With long-term changes in temperature and weather patterns, ecologically adaptable fruit varieties are becoming increasingly important agriculture. For selection of candidate cultivars breeding or for yield predictions, set characteristics at different growth stages need to be described evaluated, which is largely done visually. This a time-consuming labor-intensive process that also requires sufficient expert knowledge. The annotated dataset Japanese quince - QuinceSet consists images...

In this technical note, we examine the capabilities of deep convolutional neural networks (DCNNs) for diagnosing osteoporosis through cone-beam computed tomography (CBCT) scans mandible. The evaluation was conducted using 188 patients’ mandibular CBCT images utilizing DCNN models built on ResNet-101 framework. We adopted a segmented three-phase method to assess osteoporosis. Stage 1 focused bone slice identification, 2 pinpointed coordinates cross-sectional views, and 3 bone’s thickness,...

The RaspberrySet dataset is a valuable resource for those working in the field of agriculture, particularly selection and breeding ecologically adaptable berry cultivars. This because long-term changes temperature weather patterns have made it increasingly important crops to be able adapt their environment. To assess suitability different cultivars or make yield predictions, necessary describe evaluate berries’ characteristics at various growth stages. process typically carried out visually,...

The paper proposes an efficient method for training a neural network to count moving objects in video, while another concurrently prepares labeled dataset the first one. detection, tracking, and counting of is crucial effective Intelligence Transportation Systems (ITS), which should reduce congestion recognize traffic offenders on highways urban areas. Creation data one essential prerequisites successful application supervised machine learning. In this paper, experimental results automatic...

Model understanding is critical in many domains, particularly those involved high-stakes decisions, e.g., medicine, criminal justice, and autonomous driving. Explainable AI (XAI) methods are essential for working with black-box models such as convolutional neural networks. This paper evaluates the traffic sign classifier of Deep Neural Network (DNN) from Programmable Systems Intelligence Automobiles (PRYSTINE) project explainability. The results explanations were further used CNN PRYSTINE...

Due to increase of computing power and innovative approaches an end-to-end reinforcement learning (RL) that feed data from high-dimensional sensory inputs, it is now plausible combine RL Deep perform Smart Building Energy Control (SBEC) systems. (DRL) revolutionizes existing Q-learning algorithm (DQL) profited by artificial neural networks. Neural Network (DNN) well trained calculate the Q-function. To create comprehensive SBEC system crucial choose appropriate mathematical background...

Extraction of meaningful information by using artificial neural networks, where the focus is upon developing new insights for sports performance and supporting decision making, crucial to gain success. The aim this article create a theoretical framework structurally connect multi-layer network domains through: (a) describing as complex socio-technical system; (b) identification pre-processing subsystem classification; (c) feature selection data-driven valued tolerance ratio method; (d)...

This paper introduces the application of artificial intelligence paradigm towards precision medicine in renal transplantation. The match optimal donor-recipient pair kidney transplantation Latvian Transplant Centre (LTC) has been constrained by lack prediction models and algorithms. Consequently, LTC seeks for practical intelligent computing solution to assist clinical setting decision-makers during their search match. Therefore, optimizing both donor recipient profiles, prioritizing...

Monitoring, detection, and control of traffic is a serious problem in many cities on roads around the world poses for effective safe management pedestrians with edge devices. Systems using computer vision approach must ensure safety citizens minimize risk collisions. This well suited multiple object detection by automatic video surveillance cameras roads, highways, pedestrian walkways. A new Annotated Virtual Detection Line (AVDL) dataset presented consisting 74,108 data files manually...

The paper deals with establishment, implementation and development of electronic prescription or e-Prescription in context e-Health solutions. It includes introduction a numerous innovative solutions, which are to be committed for data information flow, management functionality as well establishment new feasible communication forms between doctors, patients pharmacists. aim the study is describe some technical aspects system medical institutions, pharmacies; and, calculation total cost (TCI)...

Semantic segmentation based on the deep learning techniques can be used for non-invasive phenotyping of quinces. In this paper we present a neural network generating pixel wise mask from RGB and Hyperspectral images quinces using U-Net architecture. The generated will very useful experts involved in order to get dimension Also it future automatic plucking by robot. This also compares evaluation metrics model trained both HSI data. We were able achieve an accuracy 93.33% 70.225% data...

Deep Learning models are currently the cornerstone of artificial intelligence in medical imaging. The performance imaging is significantly influenced by amount and quality training data. Diffusion have recently attracted attention computer vision community as they enable photorealistic synthetic image-to-image translation. Previous attempts to use diffusion for super-resolution produced satisfactory high-resolution images from low-resolution inputs. However, drawback slow speed inference,...

Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They also often criticised as “black boxes” because their decision-making process is not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms structure observational data still a challenge. To further explore latent space achieve generic processing, we propose pipeline discovering directions of generative...

This study presents an innovative approach to fruit measurement using 3D imaging, focusing on Japanese quince (Chaenomeles japonica) cultivated in Latvia. The research consisted of two phases: manual measurements parameters (length and width) a calliper imaging algorithm based k-nearest neighbors (k-NN), the ingeniously designed “Imaginary Square” method, object projection analysis. Our results revealed discrepancies between data, highlighting challenges precision accuracy techniques....

Presently, there is a new paradigm for pharmacy practice defined and concept of the “seven-star pharmacist”given. The establishment system looking introduction development electronic prescription. aim study: to provide sound research individual pharmacies on bases discovering necessary financials Results Financial aspects have been evaluated by calculating payback time, ROI, NPV, IRR. On financial calculations, primary investment initial costs hardware software has estimated. Finally,...