Data Visualization Techniques Research Articles

Shifting Sands: Examining and Mapping the Population Structure of Greece Through the Last Three Censuses

This paper aims to facilitate a more nuanced understanding of regional disparities in the population age structure at a local scale by applying a recent method for visualizing these disparities. Utilizing data from the three most recent population censuses in Greece, this method applies advanced data visualization techniques to map age distributions, highlighting significant variations in aging patterns across municipalities, towns, and districts. Traditional demographic analysis often overlooks local heterogeneities, leading to broad policies that often fail to address the unique needs of specific regions. Detailed maps are created by integrating geographic data with census data (using R and GIS), enabling policymakers to pinpoint areas with specific demographic challenges and opportunities. This localized approach reveals critical insights, such as regions experiencing rapid population aging, areas with younger population profiles, and zones undergoing demographic transitions. The visualization tool significantly improves the formulation of targeted strategies, enhancing the effectiveness of policies related to healthcare, workforce planning, and social services distribution. Through case studies and comparative analysis, we demonstrate the practical applications and advantages of this method in shaping public policy and strategic planning. This paper contributes to the field of geodemography by introducing and demonstrating a visualization method that enhances the accuracy of demographic analysis, providing policy makers with useful information to better address local demographic challenges and tailor strategies to specific regional needs.

Research on the Application of Computer Technology in Biostatistics

This paper explores the application of computer technology in biostatistics, focusing on the technological advancements and their impact on data management, data processing and analysis, and data visualization. With the development of computer technology, data storage and management methods have significantly improved, particularly with the introduction of database systems and big data technologies, which have greatly enhanced the efficiency and accuracy of handling biostatistical data. Additionally, the application of machine learning and artificial intelligence has made biostatistical data analysis more intelligent and automated, providing new tools for identifying and predicting complex data patterns. Furthermore, advanced data visualization techniques have enhanced the interpretability of statistical results through clear graphical presentations, promoting the application of biostatistics. Through the analysis of specific case studies, this paper demonstrates the practical effects and challenges of computer technology in improving biostatistical research capabilities. Finally, the paper anticipates the future impact of emerging computing technologies, such as quantum computing, on biostatistics and suggests future research directions and recommendations.

A framework for visual comparison of scalar fields with uncertainty

AbstractScientists working with uncertain data, such as climate simulations, medical images, or ensembles of physical simulations, regularly confront the problem of comparing observations, e.g., to identify similarities, differences, or patterns. Current approaches in comparative visualization of uncertain scalar fields mainly rely on juxtaposition of both data and uncertainties, where each is represented using, e.g., color mapping or volume rendering. While interpretation of uncertain scalar data from visual encodings is already cognitively challenging, comparison of uncertain fields without explicit visualization support adds a further layer of complexity. In this paper, we present a theoretical framework to devise and describe a class of techniques that directly visualize differences between two or more uncertain scalar fields in a single image. We model each such technique as a combination of one or more interpolation stages, with the application of distance measures on random variables to the resulting distributions, and an appropriate visual encoding. Our framework captures existing methods and lends itself well to formulating new comparative visualization techniques for uncertain data for different visualization scenarios. Furthermore, by modeling uncertain scalar field differences as random variables themselves, we enable additional opportunities for comparison. We demonstrate the usefulness of our framework and its properties by applying it to effective comparative visualization techniques for several synthetic and real-world data sets.

Data Visualization of Bike Store Sales in Europe

This paper presents a comprehensive analysis of bike sales and customer demographics in Europe based on a dataset obtained from Kaggle.com. The dataset covers the period from 2011 to 2016 and includes information on customer age, gender, product categories, order quantities, unit costs, unit prices, profit, and revenue. The analysis utilizes data visualization techniques to identify key trends, including the top-selling products, customer purchasing power across different age groups and customer genders, and product profit margins. The findings suggest opportunities for retailers to target specific customer segments and product categories to maximize profits. Additionally, the report highlights the growing popularity of cycling as a sport and a healthy lifestyle choice, indicating a potential for increased demand in the bike retail market. Overall, the insights provided in this report aim to assist retailer chains in developing effective strategies to capitalize on the opportunities presented by the European bike sales market.

Development of Prediction Models to Detect the Presence of MGMT Promoter Methylation for Prognosis of Brain Tumor

Methylation of the MGMT promoter is a molecular marker of predictive relevance in brain tumors. Methylation of the MGMT promoter is related to an improved prognosis and may influence therapy decisions. According to the most recent Malaysian National Cancer Registry Report (MNCRR) for 2012–2016, there were 2097 cases of brain tumors in Malaysia overall, with 1117 cases involving men and 908 cases involving women. This research aims to create prediction models for detecting MGMT promoter methylation in brain tumors. So, a dataset from the BraTS (Brain Tumor Segmentation) challenge, which included MRI scans and clinical data for patients with brain tumors, was utilized and explored using Exploratory Data Analysis (EDA) and data visualization techniques. The dataset has 306 methylation cases identified as “1” and 276 unmethylated cases identified as “0”. The average number of scans of train data for modality per patient is between 127 and 171, which provides a wealth of information for pattern learning while the average number of scans of test data for modality per patient is between 124 and 165. In both sets, FLAIR has the least number of files while T2w has the highest number of files among them. Two models of deep learning approaches, ResNet50 and EfficientNetV2, were used to construct and form the prediction models. Various criteria were employed to evaluate the performance of the models. The T2w modality consistently achieved the highest validation accuracy, precision, recall, and F1-score for both the ResNet50 and EfficientNetV2 models. Specifically, the T2w modality achieved a validation accuracy of 0.9453 and a validation loss of 0.2417 for ResNet50. Furthermore, the brain tumor diagnosis interface utilized DICOM images from MRI scans to identify MGMTp methylation status, aiding in therapy effectiveness prediction. Pre-trained ResNet50 model on T2w images was used for classification. The interface displayed the original MRI image, predicted state, and treatment effectiveness indication, while promptly notifying users of invalid or inadequate data for accurate analysis.

Enhancing quantitative imaging to study DNA damage response: A guide to automated liquid handling and imaging

Laboratory automation and quantitative high-content imaging are pivotal in advancing diverse scientific fields. These innovative techniques alleviate the burden of manual labour, facilitating large-scale experiments characterized by exceptional reproducibility. Nonetheless, the seamless integration of such systems continues to pose a constant challenge in many laboratories. Here, we present a meticulously designed workflow that automates the immunofluorescence staining process, coupled with quantitative high-content imaging to study DNA damage signalling as an example. This is achieved by using an automatic liquid handling system for sample preparation. Additionally, we also offer practical recommendations aimed at ensuring the reproducibility and scalability of experimental outcomes. We illustrate the high level of efficiency and reproducibility achieved through the implementation of the liquid handling system but also addresses the associated challenges. Furthermore, we extend the discussion into critical aspects such as microscope selection, optimal objective choices, and considerations for high-content image acquisition. Our study streamlines the image analysis process, offering valuable recommendations for efficient computing resources and the integration of cutting-edge deep learning techniques. Emphasizing the paramount importance of robust data management systems aligned with the FAIR data principles, we provide practical insights into suitable storage options and effective data visualization techniques. Together, our work serves as a comprehensive guide for life science laboratories seeking to elevate their high-content quantitative imaging capabilities through the seamless integration of advanced laboratory automation.

AI 도구 활용에 관한 초등교사의 인식 분석

Objectives The purpose of this study is to understand the level of awareness of AI tools among elementary school teachers, their perceptions of the use of AI tools at different stages of lesson design, and their considerations when introducing AI tools into the classroom. Methods For this purpose, a survey was conducted among 130 teachers working in elementary schools. To analyze the survey results, the frequency, mean value, and standard deviation of each question were analyzed, and independent sample t-test and one-way analysis of variance were conducted to check the significance of differences by respondents' characteristics. In addition, a data visualization technique called ‘word cloud’ was used for open-ended descriptive questions, and a keyword-centered analysis method was used to summarize the main questions and categorize and code key words for analysis. Results The main findings of the study are as follows First, According to our survey, elementary school teachers use Entry, ChatGPT, and Autodraw the most among the AI tools they use in the classroom. Second, When analyzing elementary school teachers' perceptions of using AI tools in lesson design, we found that they expect the most help from AI tools in the assessment phase (M=4.07) and the most help in automating assignment and test grading (M=4.35). Third, elementary teachers were more likely to be positive about the need to utilize AI tools, but less likely to be optimistic about their readiness. Fourth, elementary school teachers cited difficulty in preparing education for AI tools (M=4.02) as a reason for the difficulty in utilizing AI tools, and the configuration of facilities and educational environment for utilizing AI tools (M=4.25) as an enabling factor for utilizing AI tools. Fifth, according to the word cloud analysis, diverse teaching and learning was cited as an expectation for the introduction of AI tools, over-reliance was cited as a concern, and continuous and gradual teacher training was cited as a way to increase the use of AI tools. Conclusions This study has several implications for the use of AI tools in education. First, teacher education is needed to ensure that elementary school teachers have expertise in AI tools and can actively utilize them. Second, AI-based educational platforms that can be used across the board in lesson design should be developed and utilized in the classroom. Third, we need to expand the infrastructure to support the use of AI tools in schools. Fourth, administrative and technical support is needed to ensure that the use of AI tools does not lead to additional work for teachers.

Prediction of Heart Disease Risk among Patients in Federal Medical Centre, Abeokuta Using Naïve Bayes

Heart disease is a silent killer, which can cause sudden death of individuals without obvious symptoms. The risk of heart disease is a sign in human beings that must not be neglected. Therefore, this study aimed to predict heart disease among patients in the Federal Medical Hospital Centre (FMC), Abeokuta. Descriptive statistics and data visualization techniques were used to gain insights into the distribution and relationships among the variables. Subsequently, a Naive Bayes classifier model was built using 80% of the data for training and 20% for testing. In addition, a Decision Tree Algorithm (DT) model was used to compare the performance of the Naive Bayes model. The performances of the two models were evaluated using accuracy, sensitivity, ROC-AUC, specificity, precision, and the F1-score. The Naive Bayes model achieved an overall accuracy of 83.61%, precision of 89.29%, recall of 78.12%, F1-Score of 83.33%, ROC-AUC of 90%, sensitivity of 78.12%, and specificity of 89.66. On the other hand, they were compared with the Decision Tree (DT) model which achieved an overall accuracy of 75.41%, precision of 77.42%, recall of 75%, F1-Score of 76.19%, ROC-AUC of 84.54%, sensitivity of 75%, and specificity of 75.86%. Similarly, the confusion matrix for both analyses gave the correct classification of 25 and 22 cases of patients who have heart disease while their wrong classification was 7 and 10 cases of patients who have no heart disease respectively. Furthermore, the importance of features carried out on both showed that the most significant features are the maximum heart rate achieved, fasting blood sugar, resting blood pressure, and chest pain respectively for Naive Bayes and Decision Tree. The findings of the analysis showed that the Naive Bayes model outperformed the Decision Tree in every aspect of the analyses in predicting the risk of heart disease based on the data used and, it suggested that medical health insurance should consider incorporating predictive modelling techniques like Naive Bayes into their risk assessment algorithms, which can be of great use in the medical line.

Biomarkers and Data Visualization of Insulin Resistance and Metabolic Syndrome: An Applicable Approach.

Type 2 diabetes, prediabetes, and insulin resistance (IR) are widespread yet often undetected in their early stages, contributing to a silent epidemic. Metabolic Syndrome (MetS) is also highly prevalent, increasing the chronic disease burden. Annual check-ups are inadequate for early detection due to conventional result formats that lack specific markers and comprehensive visualization. The aim of this study was to evaluate low-budget biochemical and hematological parameters, with data visualization, for identifying IR and MetS in a community-based laboratory. In a cross-sectional study with 1870 participants in Patras, Greece, blood samples were analyzed for key cardiovascular and inflammatory markers. IR diagnostic markers (TyG-Index, TyG-BMI, Triglycerides/HDL ratio, NLR) were compared with HOMA-IR. Innovative data visualization techniques were used to present metabolic profiles. Notable differences in parameters of cardiovascular risk and inflammation were observed between normal-weight and obese people, highlighting BMI as a significant risk factor. Also, the inflammation marker NHR (Neutrophils to HDL-Cholesterol Ratio) Index was successful at distinguishing the obese individuals and those with MetS from normal individuals. Additionally, a new diagnostic index of IR, combining BMI (Body Mass Index) and NHR Index, demonstrated better performance than other well-known indices. Lastly, data visualization significantly helped individuals understand their metabolic health patterns more clearly. BMI and NHR Index could play an essential role in assessing metabolic health patterns. Integrating specific markers and data visualization in routine check-ups enhances the early detection of IR and MetS, aiding in better patient awareness and adherence.

OPTIMIZATION OF PHOTOVOLTAIC SYSTEM SAFETY THROUGH DATA ANALYSIS: DEVELOPMENT AND IMPLEMENTATION OF AN INTERACTIVE DASHBOARD FOR EVENT MONITORING AND PREVENTIVE STRATEGY FORMULATION

The development of the photovoltaic (PV) sector in Poland, as a crucial component of the renewableenergy transition strategy, faces a challenge related to limited user awareness of operational risks.Our study addresses this gap by presenting the development and implementation of an interactivedashboard for the National Fire Department’s Decision Support System (SWD PSP), aimed atoptimizing the safety of PV installations through data analysis and the formulation of preventivestrategies. Utilizing data from fire protection units, this tool enables the monitoring of incidentsand identification of potential threats, while simultaneously increasing societal awareness. Thedashboard, leveraging advanced data visualization techniques, provides easy navigation anddynamic presentation of statistics, facilitating quick responses to changing data and potentialhazards. The results of our study highlight the significance of such tools in enhancing the safety ofusing PV installations, which can contribute to the further development of the renewable energysector, ensuring its safety and efficiency.

A Data Pipeline for Enhancing Quality of MAUDE-Based Studies.

The increasing use of MAUDE reports in patient safety research highlights the importance of understanding the processing and dissemination of open-access MAUDE data. However, the absence of a structured data pipeline undermines the reproducibility and transparency of studies relying on MAUDE data. In response, we conducted a comprehensive analysis of a recent study on endoscopic clips, assessing methodologies and results. We advocate for implementing an extract, transform, and load (ETL) pipeline, utilizing openFDA and integrating keyword search strategies and data visualization techniques. This approach aims to enhance the quality of MAUDE-based studies, ensuring their reproducibility and transparency. Moreover, ETL serves as a cornerstone in data engineering, enabling real-time data management and quality assurance, thus promoting the sustainability and collaboration of MAUDE-based patient safety research.

The Autoimmune Library: An Arts-Based Approach to Women of Color’s (Counter)Narratives of Chronic Illness

ABSTRACT Guided by critical race theorizing (CRT) and arts-based methodologies (i.e., metaphor and pictorial narrative mapping [PNM]), the present study analyzed the healthcare (counter)narratives of 150 United States (U.S.) women of color (i.e., Black/African American, Hispanic/Latina, Native American/Alaska Native, and Multiracial) who have autoimmune disease. Sensitized by the metaphor of a library book, participants were asked to story their healthcare journey through identification of a title, chapters, genre, and book cover description (i.e., [Illness] Storybook Survey). Using critical thematic analysis, we first identified dominant and counter-narratives present in participant storybooks, categorized by literary supra-themes of characters (i.e., dominant narrative of me versus my body, counter-narratives of me versus the system and illness as lineal), plot (i.e., dominant narrative of a hero’s journey, counter-narrative of chaos), and tone (i.e., dominant narrative of inspiration, counter-narratives of tangible self-help and humor). Next, guided by PNM, a data visualization technique, each author illustrated one counter-narrative within each literary supra-theme. Our analysis interrogates how participants’ stories both reify and resist ideological and structural power at the intersections of their racial, gender, and illness identities. Collectively, this study offers unique contributions to critical, intersectional, and arts-based approaches to communication research, and forwards new methods for studying health narratives historically located in the margins.

Identification of response regulation governing ozone formation based on influential factors using a random forest approach

The pursuit of enhanced scientific, refined, and precise ozone and air quality control continues to pose significant challenges. Using data visualization techniques and random forest (RF) algorithms, the temporal distribution of atmospheric pollutants and the interrelationship between O3 concentration and its influential factors were investigated with one-year monitoring data in Deqing county in 2021. The local atmospheric conditions predominantly belonged to NOx-sensitive and transition zone. Extremely high O3 concentration were primarily observed when temperatures (T) exceeded 30 °C, with relative humidity (RH) ranging between 30 and 60 %. NO2, RH and T were identified as the top 3 important factors, and O3 concentration have stronger linearly relationship to RH and T, while stronger nonlinearly relationship to NO2. By employing an optimized RF model, controlling consistent mild and high reaction atmospheric conditions, the O3 concentration response to the change of individual influencing factors was acquired. The O3 concentration increased and then decreased in response to the increasing NO2 concentration, displaying a characteristic inflection point at 10 μg m−3. More reactive radicals produced at higher VOCs concentration and continuing NOx cycle at lower NO2 concentration, resulting in the acceleration in the direction of producing more O3. Therefore, the significant different O3 response to variation of VOCs and NOx concentration between mild and high reaction atmospheric conditions, as well as the existing of oxidant elevation should be considered in local air quality control. This study demonstrates the efficacy of ML methods in simulating nonlinear response of O3, supports the understanding of local O3 formation and quick guidance for precise local O3 pollution control and the related strategies.

CUSTOMER SEGMENTATION USING RFM ANALYSIS

This research presents a comprehensive approach to customer segmentation using Recency, Frequency, and Monetary (RFM) analysis, combining statistical insights, data visualization, and machine learning techniques. The study utilizes a real-world dataset obtained from a retail environment, aiming to categorize customers based on their recent purchasing behavior, visit frequency, and monetary contributions to the store. The code begins with data preparation and exploration, ensuring data integrity by addressing issues such as negative quantities and missing customer identifiers. Following this, the Recency, Frequency, and Monetary metrics are computed, providing a holistic view of customer engagement and spending patterns. Visualizations, including violin plots, histograms, and box plots, are employed to intuitively convey the distribution of these metrics. The research then delves into the quantile-based segmentation of customers, allowing for a more granular classification. Quantiles are calculated to divide customers into four segments for each RFM metric. The resulting quantile labels are applied to the dataset, enabling the creation of a compound RFM quantile that combines recency, frequency, and monetary information. This combined quantile facilitates the definition of distinct customer segments. To further enhance the interpretability of customer segments, the study introduces a set of rules for labeling customers based on their RFM quantiles. These rules yield segments such as "Best Customer," "Loyal Customer," "Big Spender," "Dead Beats," and "Lost Customer." The resulting customer segmentation is presented visually through histograms and a pie chart, providing a clear and concise representation of the distribution of customers across different segments. Moreover, the research integrates machine learning models, including XGBClassifier, and CatBoostClassifier, to explore the potential of automating the segmentation process and predicting customer segments based on historical data. However, the machine learning aspect is introduced with commented-out sections, leaving room for further exploration and experimentation. In conclusion, this research contributes a comprehensive and detailed code implementation for RFM-based customer segmentation. The integration of visualization techniques aids in the interpretation of customer behavior, while the inclusion of machine learning models opens avenues for predictive analytics in customer segmentation. The presented approach provides valuable insights for businesses seeking to tailor marketing and customer relationship strategies based on individualized customer segments. KEYWORDS— Customer Segmentation, Frequency, Monetary Value, Recency, RFM Analysis.

Research on Emotion Analysis Based on a Topic Emotion Fusion Model: The Case of the "Post-holiday Symptoms of Pharyngitis" Event on the Weibo Platform

[Objective] From early 2023 to 2024, multiple public health incidents related to "post-holiday pharyngitis" erupted in china, impacting public health and education among other areas. This study analyzes the data dissemination trends and the emotional development of these incidents. The insights are aimed at enhancing the government and disease control agencies' mechanisms for guiding and responding to such public sentiment incidents, thereby improving preparedness and response capabilities for potential future public health events. [Methods] The methodology employed involves using three instances of "post-holiday pharyngitis" events on the Weibo platform as data samples. The study applies the LDA model integrated with TD-IDF weighted Word2vec algorithm for thematic organization across different periods. The DUTIR sentiment dictionary is used to analyze public sentiment, and data visualization techniques are employed to present the thematic-emotional evolution within the incidents. [Results] The results suggest that the proposed thematic-emotional fusion model effectively extracts and analyzes the dissemination trends and emotional evolution of the event, providing actionable suggestions for government and public health agencies in managing such public sentiment incidents.

NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics

Stroke is a severe illness, that requires early stroke detection and intervention, as this would help prevent the worsening of the condition. The research is done to solve stroke prediction problem, which may be divided into a number of sub-problems such as an individual's predisposition to develop stroke. To attain this objective, a multiturn dataset consisting of various health features, such as age, gender, hypertension, and glucose levels, takes a central role. A multiple approach was put forward concentrating on integrating the machine learning techniques, such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine (SV), together to develop an ensemble machine called Neuro-Health Guardian. The hypothesis "Neuro-Health Guardian Model" integrates these algorithms into one, purported to make stroke prediction more accurate. The topic dives into each instance of preparation of data for analysis, data visualization techniques, selection of the right model, training, testing, ensembling, evaluation, and prediction. The models are validated with error rate accounted from their accuracy, precision, recall, F1 score, and finally confusion matrices for a look. The study's result is showing that the ensemble model that combines the multiple algorithms has the edge over them and this is evidently by the fact that it can predict stroke rises. Additionally, accuracy, precision, recall, and F1 scores are measured in all models and the comparison is done to provide a clear comparison of the models' performance. In short, the article presented the formation of the ongoing stroke prediction that revealed the ensemble model as a good anticipation. Precise stroke predisposition forecasting can assist in early intervention thereby preventing stroke-related deaths, and limiting disability burden by stroke. The conclusions that have come out of this study offer a great action item for the development of predictive models related to stroke prevention and treatment.

Tasks and Visualizations Used for Data Profiling: A Survey and Interview Study.

The use of good-quality data to inform decision making is entirely dependent on robust processes to ensure it is fit for purpose. Such processes vary between organisations, and between those tasked with designing and following them. In this article we report on a survey of 53 data analysts from many industry sectors, 24 of whom also participated in in-depth interviews, about computational and visual methods for characterizing data and investigating data quality. The paper makes contributions in two key areas. The first is to data science fundamentals, because our lists of data profiling tasks and visualization techniques are more comprehensive than those published elsewhere. The second concerns the application question "what does good profiling look like to those who routinely perform it?", which we answer by highlighting the diversity of profiling tasks, unusual practice and exemplars of visualization, and recommendations about formalizing processes and creating rulebooks.

Real-time collection of the functional parameters for a passive house

This paper explores the concept of a real-time collection of functional parameters for passive houses and its significance in achieving energy efficiency and occupant comfort. In this case, both the specific parameters of the HVAC system and the electrical parameters will be analyzed with high accuracy. In addition, the microgrid power system application includes wind power, solar power and batteries connected online to the grid to help ensure continuous power supply for the house. The implemented solution aims at various technologies, sensors, and smart meters used for collecting meaningful information in passive houses. It also discusses the role of data analytics and visualization techniques with the purpose of providing a user-friendly interface. For straightforward analysis, all collected information will be stored both in the cloud and on a personal server.

The educational resource management based on image data visualization and deep learning

In order to address issues such as inaccurate education resource positioning and inefficient resource utilization, this study optimizes the Educational Resource Management System (ERMS) by combining image data visualization techniques with convolutional neural networks (CNNs) technology in deep learning. Firstly, the crucial role of ERMS in education and teaching is analyzed. Secondly, the application of image data visualization techniques and CNNs in the system is explained, along with the associated challenges. Finally, by optimizing the CNNs model and system architecture and validating with experimental data, the rationality of the proposed model is confirmed. Experimental results indicate a significant improvement in various performance metrics compared to traditional models. The recognition accuracy on the Mnist dataset reaches 98.1 %, and notably, on the cifar-10 dataset, the optimized model achieves an accuracy close to 98.3 % with improved runtime reduced to only 640.4 s. Additionally, through systematic simulation experiments, the designed system is shown to fully meet the earlier requirements for system functionality, validating the feasibility and rationality of the model and system in this study. Therefore, this study holds high practical value for optimizing ERMS and provides meaningful insights into image data visualization techniques and CNNs optimization.

Mapping the current trends and hotspots of adult hippocampal neurogenesis from 2004-2023: a bibliometric analysis.

We utilized bibliometric and data visualization techniques to discern the primary research domains and emerging frontiers in the field of adult hippocampal neurogenesis (AHN). We systematically searched the Web of Science database for AHN-related articles published between 2004 and 2023. The retrieved articles were filtered based on publication types (articles and reviews) and language (English). We employed CiteSpace, VOSviewer, and the online bibliometric platform (bibliometric.com) to visualize and analyze the collected data. In total, 1,590 AHN-related publications were discovered, exhibiting a steady increase in yearly publications over time. The United States emerged as the leading contributor in AHN research in terms of both publication quantity and national influence. Among all research institutions in the field of AHN, the University of California System exhibited the highest impact. Kempermann, Gerd was the most active author. The publications of the top three active authors primarily focused on the functions of AHN, and reversing hippocampal damage and cognitive impairment by improving AHN. An analysis of reference co-citation clustering revealed 8 distinct research clusters, and the notable ones included "adult hippocampal neurogenesis," "neurogenesis," "hippocampus," "dentate gyrus," "neural stem cell," and "depression." Additionally, a burst keyword detection indicated that 'anxiety' is a current research hotspot in the field of AHN. This in-depth bibliographic assessment of AHN offers a deeper insight into the present research hotspots in the field. The association between AHN and cognitive diseases, such as Alzheimer's disease (AD) and anxiety, has emerged as a prominent research hotspot.