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Accepted papers
Mapping the Research Landscape of Depression Detection Using Machine Learning: Trends and Visual Insights
Ginto Chirayath, K. Premamalini, Jeena Joseph, Jobi Babu, F. Vincent Rajasekar
Accepted: 2026-03-27
Abstract
The application of Machine Learning (ML) in detecting depression has seen exponential growth with advancements in artificial intelligence, natural language processing, and biomedical signal analysis. The paper presents a systematic bibliometric study on research on detecting depression with a basis in ML from Scopus-indexed literature between 2008 and 2025. The database with 1,407 documents from 745 sources gives a 26.02% growth rate annually, which reflects growing academic interest in AI-based mental health diagnostics. The study utilizes CiteSpace, VOSviewer, and Biblioshiny in analyzing research trends, leading contributors, networks of collaboration, and thematic evolutions. Journal articles (708), followed by conference papers (644), are predominant in the field, which reflects its dynamic, multidisciplinary character. The research areas are primarily on deep learning, natural language processing, social media-based diagnosis, and detection methods with a basis in EEG. The leading authors, Li Y, and Zhang X, contribute from Chinese institutes predominantly. The leading collaborating nations are the United States, China, and India. Co-citation as well as coupling between bibliographics in research discloses a high level of incorporation between AI with clinical psychiatry, neurology, as well as digital-based healthcare interventions. The trend from traditional classification methods towards models with a basis in transformer models in form of BERT as well as GPT is observed. The thematic mapping discloses a new emergence in terms of mobile-based healthcare application as well as AI-based suicide prediction. The research highlights rapid development in AI in mental healthcare with far-reaching impacts on early detection, distant-based care, as well as ethics in AI development. The research in the future should be directed towards enhancing model interpretation, resolving data privacy issues, as well as improving AI-based mental healthcare in terms of global application.
Bernstein polynomials: a bibliometric data analysis since the year 1949 based on the Scopus database
Rushan Ziatdinov
Accepted: 2026-03-27
Abstract
It's hard to imagine human life in the digital and AI age without polynomials because they are everywhere but mostly invisible to ordinary people: in data trends, on computer screens, in the shapes around us, and in the very fabric of technology. One of these, the simple but elegant Bernstein polynomials, was discovered by a scientist from the Russian Empire, Sergei Bernstein, in 1912 and plays a central role in mathematical analysis, computational and applied mathematics, geometric modelling, com-puter-aided geometric design, computer graphics and other areas of science and engineering. They have been the subject of much research for over a hundred years. However, no work has carried out database-derived research analysis, such as biblio-metric, keyword or network analysis, or more generally, data analysis of manuscript data related to Bernstein polynomials ex-tracted from digital academic databases. This work, which appears to be the first-ever attempt at the bibliometric data analysis of Bernstein polynomials, aims to fill this gap and open researchers' eyes to potentially new or underexplored areas of mathe-matics and engineering where Bernstein polynomials may one day be used to make discoveries. The results may be helpful to academics researching Bernstein polynomials and looking for potential applications, collaborators, supervisors, funding or jour-nals to publish in.
Color and Material Matching Space Effect Optimization in Soft Decoration Design Based on Computer Image Processing Algorithm
Shan Dan, Wanchun Wang, Qinwei Xu
Accepted: 2025-12-13
Abstract
This study introduces an intelligent optimization method based on computer vision to solve the problem that color and material matching in soft decoration design is highly subjective and lacks quantitative standards. ConvNeXt (Convolutional Next Network) is used to extract the color-material features of the interior space, and a GMM (Gaussian Mixture Model) color database is constructed to quantify the coordination of different color distributions. The PSO (Particle Swarm Optimization) algorithm is used for multi-objective optimization to ensure the balance and visual beauty of color matching. The design scheme is realistically rendered in the Unity platform to simulate the visual effects in the actual environment, and CHI (Color Harmony Index), ECS (Emotional Color Similarity) and MCI (Material Conflict Index) are used to quantitatively evaluate the design effect. The results show that the proposed method is superior to traditional manual design and feature extraction combined with manual design in terms of color harmony, consistency of emotional expression and material adaptability. The average CHI of the proposed design method reaches 0.92, the average ECS is 0.93, and the average MCI is reduced to 0.12, which significantly optimizes the visual coordination and overall aesthetic effect of the soft decoration scheme. The design method in this paper can improve efficiency while ensuring the aesthetics of the design, and provides a feasible quantitative optimization strategy for intelligent soft decoration design.
On the issue of visualization of the almost periodic structure of a snow avalanche
B.A. Krynecky, A.V. Kalach, A.A. Paramonov
Accepted: 2025-12-04
Abstract
The article considers the specifics of interpreting the results of almost periodic analysis in the problem of structural segmentation of a snow avalanche, applicable to increasing the efficiency of forecast models and improving avalanche control measures. The preliminary stage of linearization of the avalanche structure is based on the polygonal approximation of the body area of the phenomenon, presenting the data in a rectangular format. The study of straightened data allows us to form a network of uniform rectangular areas of uniform data, for which the problem of displaying the markup on the original data arises. Such markup allows us to clearly separate the areas of laminar and turbulent avalanche descent, as well as to highlight the significant boundaries of the areas of onset, development and braking of the snow mass. The article provides mathematical and algorithmic sup-port necessary for reproducing longitudinal and transverse boundaries on the original data for sequences of polygons of arbitrary duration. The results can be applied in the development of visualization systems, warning and prevention of avalanche activity, thereby stimulating the strengthening of the effectiveness of measures to prevent damage from hazardous natural phenomena.
Visualization of lung vessels in optically cleared specimens using second harmonic generation
S.A. Portnov, S.S. Shalyapin, A.O. Bogorodskiy, V.I. Borshchevskiy, M.A. Shevchenko
Accepted: 2025-12-04
Abstract
Second harmonic generation (SHG) is a nonlinear optical process, and SHG microscopy is a key method for imaging in biomedical science. In the present study, we evaluated the usage of SHG for the imaging of the airways and vessels in the optically cleared mouse lung. SHG visualized F-actin and mainly collagen fibers bordering both airways and vessels. For distinguishing airways from vessels, we used additional staining of the airways with Alexa°Fluor°488-labeled streptavidin and developed the image processing technique for the separation of Alexa°Fluor°488 and SHG signals.
The approach described here allows a high-resolution imaging of anatomic structures of the lung of small animals and can be further used for drug and pathogen detection in experimental disease models.
Application of semantic segmentation cascade approach for visualization of optical coherence tomography data
V.V. Laptev, V.V. Danilov, E.A. Ovcharenko, K.Yu. Klyshnikov, I.S. Bessonov, N.V. Litvinyuk, N.A. Kochergin
Accepted: 2025-12-03
Abstract
A main goal in contemporary cardiology is to assess the risk of acute coronary syndrome (ACS) in individuals with ischemic heart disease in order to create preventative strategies and identify the best treatment plan. The objective of this research is to create a automated method for promptly identifying high-risk coronary lesions that are at risk of rupture (unstable plaques) in order to prevent ACS. We collected optical coherence tomography (OCT) volumes from 40 patients, with each OCT volume representing an RGB video of 704x704 pixels per frame, acquired over a certain depth. After filtering and manual annotation, 11,771 images were obtained to identify four types of objects: Lumen, Fibrous cap, Lipid core, and Vasa vasorum. To segment and quantitatively assess these features, we configured and evaluated the performance of nine deep learning models (U-Net, MA-Net, DeepLabV3++). The study presents two approaches for training the aforementioned models: 1) detecting all analyzed objects and 2) applying a cascade of neural network models to separately detect subsets of objects. The results demonstrate the superiority of the cascade approach for analyzing OCT images. The combined use of DeeplabV3+ and MA-Net models achieved the highest average Dice similarity coefficient (DSC) of 0.761.
Research of neural network image style transfer
T.M. Shamsutdinova
Accepted: 2025-12-03
Abstract
The purpose of this study is to consider theoretical and practical issues of using convolutional neural networks for image style transfer.
The objectives of the study are to analyze literary sources on the problem, to consider, test and compare some neural network models (with open source code) for the purpose of their further study.
In particular, four neural networks that implement style transfer by using the TensorFlow library were selected and studied. In some cases (model 2 and model 4), it was necessary to modify the Python code. The Colab environment was used for the experiments. Fragments of famous paintings, as well as photographs and stylized images of multi-colored geometric figures, were used as test images.
A conclusion is made about the quality of the studied models. It is concluded that despite the current achievements in the field of style transfer, researchers still face a number of challenges, including improving the quality of image generation and reducing computational costs.
Neural Network-Based Dynamic Grasping of Moving Objects with Robotic Manipulators
Yin Cao, A.A. Boguslavsky
Accepted: 2025-11-14
Abstract
We explore the use of the open‐source library Stable Baselines3 to implement reinforcement learning via deep neural networks for controlling a manipulator of grasping moving objects along a conveyor belt. Unlike static object grasping, this task requires accounting for dynamic factors, significantly complicating the process. We provide a detailed description of the physical-kinematic modeling of the manipulator in PyBullet .and the integration of both the manipulator’s and the moving objects’ parameters into the neural network for training. The results of this study demonstrate that the decision-making capabilities and autonomous behavior provided by reinforcement learning algorithms can be successfully applied to complex tasks, such as dynamic object grasping.
Swarm Intelligence: A Quantitative Analysis of Research Publications and Trends
Jeena Joseph, Binu Thomas, L.S. Masalsky, O.S. Logunova, Sabeen Govind
Accepted: 2025-11-05
Abstract
The research investigates the scholarly landscape of swarm intelligence by conducting a comprehensive bibliometric analysis of data sourced from the Scopus database. The search was performed with the help of keywords "swarm intelligence" on the date of 25 January 2024, and eventually, it was found that a total of 1374 articles with diversified sources up to 800 were identified and have been dated between 1996 to 2024. Related dimensions to be studied include annual scientific production, collaboration of publication and bibliographic coupling, leading authors, main sources and affiliations, and keyword co-occurrence. Visualizations used here include line graphs, bubble charts, network diagrams, and three-field plots to present key findings. One can observe from the results that the number of publications increases linearly, although a sharp increase is significantly noticed in 2024. Prolific and influential authors or sources in this area are identified. Moreover, keyword co-occurrence analysis brings out the central concepts or thematic areas cutting across the articles on research in swarm intelligence. A publication collaboration study, such as bibliographic coupling analysis, helps to unveil the international research linked network and the extent to which papers are interlinked. Overall, the understanding that this research has provided me is the tendency, dynamics, and change that has happened in the swarm intelligence research.
GSDMM Clustering Results Visualization Technique for Short Texts
B.N. Chigarev
Accepted: 2025-10-29
Abstract
The aim of the study is to propose a technique for visualizing the results of short text clustering using the Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM) algorithm, in order to facilitate the analysis of the results and the selection of the hyperparameters of this algorithm and dictionary. GSDMM is selected as the most popular short text clustering algorithm on GITHUB. The algorithm implemented by Ryan Walker on Rust was used. The program Scimago Graphica was used to create bar charts. 16486 bibliometric records on the topic “Visualization” exported from the Scopus database on November 12, 2024 served as the source of short texts. Only Author keywords are used as short texts in this paper. A technique for visualizing the results of short text clustering using the GSDMM algorithm is proposed, which is based on comparing the occurrence of keywords in a given cluster and in each of the other clusters. It is shown that the cluster topics obtained using the GSDMM algorithm can be compared with the results of author keyword clustering performed using the VOSviewer program. The obtained results can be interpreted as a certain stability of cluster themes obtained by essentially different methods. The author suggests to expand the study by creating a thematic dictionary of abbreviations, analyzing the influence of the dictionary on the clustering results of the GSDMM algorithm, and extending the method of visualizing the clustering results to other short texts such as titles and abstracts.
Enhanced Crack Width and Depth Measurement through Binary Image Processing and Geometric Analysis
Kavita Bodke, Sunil Bhirud, Keshav Kashinath Sangle
Accepted: 2025-10-07
Abstract
This paper presents a novel, image-based approach for automatically quantifying structural crack width and depth in concrete using binary image processing techniques. Concrete cracks are critical indicators of potential structural failure, and traditional manual inspection methods are often time-consuming, unsafe, and prone to inaccuracies. The proposed method automates crack detection by converting RGB images of concrete surfaces into binary images, isolating the cracks, and measuring their width using the Euclidean distance formula. The depth of the cracks is then estimated using trigonometric relationships based on the measured crack width and viewing angles (30°, 45°, and 60°). This lightweight, cost-effective approach provides a practical alternative to more complex machine learning-based detection methods, making it ideal for real-time infrastructure health monitoring. The results highlight the effectiveness of this technique in accurately measuring crack width and depth across multiple angles, providing critical data for infrastructure health monitoring.
Visual Display of Tropical Cyclone Structure Zone Hazard Assessment Based on Almost Periodic Analysis
A.A. Paramonov, A.V. Kalach
Accepted: 2025-09-24
Abstract
A visual hazard assessment of tropical cyclone structure zones based on almost periodic anal-ysis is proposed. We consider a visualization toolkit that allows us to customize and display both tropical cyclone zones whose radii are multiples of the found characteristic near-periods and shading of the interzonal space taking into account the degree of hazard relative to the cyclone center.
As the main tools we used the visualization modules of the matplotib library Circle and Wedge, which allow us to customize the identified structural zones according to their hazard de-gree. This development can be useful for emergency-rescue services as an operational diagnostic tool to support decision-making in emergencies caused by natural hazards.
Automated Diabetic Retinopathy Diagnosis and Classification Using Deep Learning with Capsule Network Layers and Stochastic Ensemble Approach
M.A Abini, S Sridevi Sathya Priya
Accepted: 2025-09-10
Abstract
Diabetic retinopathy (DR) remains one of the most common vision-related complications of diabetes and requires timely, accurate diagnosis to prevent severe outcomes. Conventional diagnostic approaches rely on the expertise of ophthalmologists, who manually examine retinal images for lesions—a process that can be time-consuming and prone to fatigue-related errors. To address these limitations, this work proposes a fully automated framework for DR detection and stage classification that leverages recent advances in deep learning. The study focuses on the five recognized stages of DR, ranging from the earliest form, non-proliferative diabetic retinopathy (NPDR), through to the advanced proliferative stage (PDR). The method integrates two powerful pre-trained convolutional neural networks, ResNetV2 and MobileNet, with capsule network layers to enhance feature representation. A stochastic ensemble strategy is applied to further strengthen the robustness of predictions. Experimental evaluation on the Kaggle APTOS 2019 dataset demonstrates a test accuracy of 99.81%, outperforming comparable methods in the literature. Performance was assessed using standard metrics such as precision, recall, F1-score, and the ROC curve. Beyond classification accuracy, the approach also offers improved interpretability through capsule-based visualization techniques and ensemble-driven lesion localization, enabling better identification of retinal abnormalities across different DR stages.
Using Sperm Imaging with Laser Interference Microscopy for Comprehensive Assessment of the Functional State of Cells during Cryopreservation and under the Action of Molecular Hydrogen
A.V. Deryugina, M.N. Ivaschenko, P.S. Ignatiev, V.B. Metelin
Accepted: 2025-04-29
Abstract
Significant advances have been made in sperm cryopreservation but the search for effective sperm cryopreservation technologies is a pressing issue in modern biology and medicine. The most effective cryopreservation leaves 50-60% of viable cells. The paper discusses the use of molecular hydrogen (H2) as a new approach to enhancing sperm protection during freezing and thawing. H2 is a universal antioxidant and limits damage to biomolecules. Visual registration of spermatozoa under the action of H2 was performed using modern microscopy techniques. Laser interference microscopy was used in the work. Laser interference microscopy records the cell surface architectonics depending on the modulation of the optical density of cellular structures. This visualization option provides information on the metabolic level and expands the possibilities for interpreting experimental results. Sample preparation, dyes, and fixatives are not used in interference visualization. The paper presents an analysis of phase images of spermatozoa during cryopreservation and using H2 as a cryoprotector. Verification of the method for analyzing phase characteristics of spermatozoa as an indicator of the metabolic state of cells was performed by analyzing clinical and laboratory parameters of spermatozoa. The phase height of spermatozoa during cryopreservation decreased, the intensity of energy processes decreased, and the oxidative potential of cells increased. A direct correlation was shown between the phase height of spermatozoa and the concentration of ATP, and an inverse correlation was found from the concentration of malondialdehyde (MDA). The use of H2 determined an increase in the phase height of spermatozoa, an increase in energy metabolism, and a decrease in cell oxidation. Changes in the metabolic activity of spermatozoa under the action of H2 were combined with an improvement in sperm fertility. Thus, phase interference microscopy allows for a qualitative and quantitative assessment of the physiological state of spermatozoa. It is an objective method of vital analysis of complex metabolic activity of cells. It can be used for express diagnostics of their functional state.
Pressure-Gradient Method for the Visualization of a Wave Attractor
Stepan Elistratov
Accepted: 2025-04-13
Abstract
A wave attractor, a phenomenon of self-focusing of internal/inertial waves on a closed trajectory, has recently been widely studied from different viewpoints. How-ever, there is a lack of investigations concerning its visualization. Peculiar set-ups lately studied show that conventional methods need some improvement.
Herewith, in gas dynamics, the Schlieren method, based on the density gradient, is widely used. Concerning incompressible flows, it is inapplicable; however, pressure can be considered instead density. In this work, a pressure gradient is used as a way to visualize an attractor.
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