Data Visualization in Big Data Analysis: Applications and Future Trends

The advent of the big data era has made data visualization a crucial tool for enhancing the efficiency and insights of data analysis. This theoretical research delves into the current applications and potential future trends of data visualization in big data analysis. The article first systematically reviews the theoretical foundations and technological evolution of data visualization, and thoroughly analyzes the challenges faced by visualization in the big data environment, such as massive data processing, real-time visualization requirements, and multi-dimensional data display. Through extensive literature research, it explores innovative application cases and theoretical models of data visualization in multiple fields including business intelligence, scientific research, and public decision-making. The study reveals that interactive visualization, real-time visualization, and immersive visualization technologies may become the main directions for future development and analyzes the potential of these technologies in enhancing user experience and data comprehension. The paper also delves into the theoretical potential of artificial intelligence technology in enhancing data visualization capabilities, such as automated chart generation, intelligent recommendation of visualization schemes, and adaptive visualization interfaces. The research also focuses on the role of data visualization in promoting interdisciplinary collaboration and data democratization. Finally, the paper proposes theoretical suggestions for promoting data visualization technology innovation and application popularization, including strengthening visualization literacy education, developing standardized visualization frameworks, and promoting open-source sharing of visualization tools. This study provides a comprehensive theoretical perspective for understanding the importance of data visualization in the big data era and its future development directions.

A Discussion of Artificial Intelligence in Visual Art Education

Since ChatGPT emerged on November 30, 2022, Artificial Intelligence (AI) has been increasingly discussed as a radical force that will change our world. People have become used to AI in which such ubiquitous technologies as Siri, Google, and Netflix deploy AI algorithms to answer questions, impart information, and provide recommendations. However, many individuals including originators and backers of AI have recently expressed grave concerns. In this paper, the authors will assess what is occurring with AI in Visual Arts Education, outline positives and negatives, and provide recommendations addressed specifically for teachers working in the field regarding emerging AI usage from kindergarten to grade twelve levels as well as in higher education.

Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People

The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing(NLP)and computer vision(CV).It can be driven by applications like image retrieval or indexing,virtual assistants,image understanding,and support of visually impaired people(VIP).Though the VIP uses other senses,touch and hearing,for recognizing objects and events,the quality of life of those persons is lower than the standard level.Automatic Image captioning generates captions that will be read loudly to the VIP,thereby realizing matters happening around them.This article introduces a Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System(RDOAI-ICS)for Visually Impaired People.The presented RDOAI-ICS technique aids in generating image captions for VIPs.The presented RDOAIICS technique utilizes a neural architectural search network(NASNet)model to produce image representations.Besides,the RDOAI-ICS technique uses the radial basis function neural network(RBFNN)method to generate a textual description.To enhance the performance of the RDOAI-ICS method,the parameter optimization process takes place using the RDO algorithm for NasNet and the butterfly optimization algorithm(BOA)for the RBFNN model,showing the novelty of the work.The experimental evaluation of the RDOAI-ICS method can be tested using a benchmark dataset.The outcomes show the enhancements of the RDOAI-ICS method over other recent Image captioning approaches.

Effective method for tracking multiple objects in real-time visual surveillance systems

An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal ＂visual＂ object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.

Intelligent Video Surveillance System for Elderly People Living Alone Based on ODVS

Intelligent video surveillance for elderly people living alone using Omni-directional Vision Sensor (ODVS) is an important application in the field of intelligent video surveillance. In this paper, an ODVS is utilized to provide a 360° panoramic image for obtaining the real-time situation for the elderly at home. Some algorithms such as motion object detection, motion object tracking, posture detection, behavior analysis are used to implement elderly monitoring. For motion detection and object tracking, a method based on MHoEI(Motion History or Energy Images) is proposed to obtain the trajectory and the minimum bounding rectangle information for the elderly. The posture of the elderly is judged by the aspect ratio of the minimum bounding rectangle. And there are the different aspect ratios in accordance with the different distance between the object and ODVS. In order to obtain activity rhythm and detect variously behavioral abnormality for the elderly, a detection method is proposed using time, space, environment, posture and action to describe, analyze and judge the various behaviors of the elderly in the paper. In addition, the relationship between the panoramic image coordinates and the ground positions is acquired by using ODVS calibration. The experiment result shows that the above algorithm can meet elderly surveillance demand and has a higher recognizable rate.

Frequent Trajectory Patterns Mining for Intelligent Visual Surveillance System

A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated.In addition,a fuzzy c-means(FCM)based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories.The algorithm can be further used to describe activities and identify anomalies.The experiments on two real scenes show that the algorithm is effective.

Semi-automatic Video Annotation Tool to Generate Ground Truth for Intelligent Video Surveillance Systems

Generating ground truth data for developing object detection algorithms of intelligent surveillance systems is a considerably important yet time-consuming task; therefore, a user-friendly tool to annotate videos efficiently and accurately is required. In this paper, the development of a semi-automatic video annotation tool is described. For efficiency, the developed tool can automatically generate the initial annotation data for the input videos utilizing automatic object detection modules, which are developed independently and registered in the tool. To guarantee the accuracy of the ground truth data, the system also has several user-friendly functions to help users check and edit the initial annotation data generated by the automatic object detection modules. According to the experiment＇s results, employing the developed annotation tool is considerably beneficial for reducing annotation time; when compared to manual annotation schemes, using the tool resulted in an annotation time reduction of up to 2.3 times.

Artificial intelligence-assisted colonoscopy:A review of current state of practice and research

Colonoscopy is an effective screening procedure in colorectal cancer prevention programs;however,colonoscopy practice can vary in terms of lesion detection,classification,and removal.Artificial intelligence(AI)-assisted decision support systems for endoscopy is an area of rapid research and development.The systems promise improved detection,classification,screening,and surveillance for colorectal polyps and cancer.Several recently developed applications for AIassisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas.However,their value for real-time application in clinical practice has yet to be determined owing to limitations in the design,validation,and testing of AI models under real-life clinical conditions.Despite these current limitations,ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination,including polypectomy procedures,are at the concept stage.However,further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice,to navigate the approval process from regulatory organizations and societies,and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety.This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.

VIS+AI: integrating visualization with artificial intelligence for efficient data analysis

Visualization and artificial intelligence(AI)are well-applied approaches to data analysis.On one hand,visualization can facilitate humans in data understanding through intuitive visual representation and interactive exploration.On the other hand,AI is able to learn from data and implement bulky tasks for humans.In complex data analysis scenarios,like epidemic traceability and city planning,humans need to understand large-scale data and make decisions,which requires complementing the strengths of both visualization and AI.Existing studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as VIS4AI.However,how can AI and visualization complement each other and be integrated into data analysis processes are still missing.In this paper,we define three integration levels of visualization and AI.The highest integration level is described as the framework of VIS+AI,which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces.We also summarize future directions of VIS+AI to inspire related studies.

Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Several studies have shown a significant adenoma miss rate up to 35%during screening colonoscopy,especially in patients with diminutive adenomas.The use of artificial intelligence(AI)in colonoscopy has been gaining popularity by helping endoscopists in polyp detection,with the aim to increase their adenoma detection rate(ADR)and polyp detection rate(PDR)in order to reduce the incidence of interval cancers.The efficacy of deep convolutional neural network(DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos.Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR.In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.

Advanced imaging and artificial intelligence for Barrett's esophagus:What we should and soon will do

Barrett’s esophagus(BE)is a well-established risk factor for esophageal adenocarcinoma.It is recommended that patients have regular endoscopic surveillance,with the ultimate goal of detecting early-stage neoplastic lesions before they can progress to invasive carcinoma.Detection of both dysplasia or early adenocarcinoma permits curative endoscopic treatments,and with this aim,thorough endoscopic assessment is crucial and improves outcomes.The burden of missed neoplasia in BE is still far from being negligible,likely due to inappropriate endoscopic surveillance.Over the last two decades,advanced imaging techniques,moving from traditional dye-spray chromoendoscopy to more practical virtual chromoendoscopy technologies,have been introduced with the aim to enhance neoplasia detection in BE.As witnessed in other fields,artificial intelligence(AI)has revolutionized the field of diagnostic endoscopy and is set to cover a pivotal role in BE as well.The aim of this commentary is to comprehensively summarize present evidence,recent research advances,and future perspectives regarding advanced imaging technology and AI in BE;the combination of computer-aided diagnosis to a widespread adoption of advanced imaging technologies is eagerly awaited.It will also provide a useful step-by-step approach for performing high-quality endoscopy in BE,in order to increase the diagnostic yield of endoscopy in clinical practice.

Innovative applications of artificial intelligence in zoonotic disease management

Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.

Artificial intelligence in dentistry:Harnessing big data to predict oral cancer survival

BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.

Foundation models meet visualizations: Challenges and opportunities

Recent studies have indicated that foundation models, such as BERT and GPT, excel atadapting to various downstream tasks. This adaptability has made them a dominant force in buildingartificial intelligence (AI) systems. Moreover, a newresearch paradigm has emerged as visualizationtechniques are incorporated into these models. Thisstudy divides these intersections into two researchareas: visualization for foundation model (VIS4FM)and foundation model for visualization (FM4VIS).In terms of VIS4FM, we explore the primary roleof visualizations in understanding, refining, and evaluating these intricate foundation models. VIS4FMaddresses the pressing need for transparency, explainability, fairness, and robustness. Conversely, in termsof FM4VIS, we highlight how foundation models canbe used to advance the visualization field itself. Theintersection of foundation models with visualizations ispromising but also introduces a set of challenges. Byhighlighting these challenges and promising opportunities, this study aims to provide a starting point forthe continued exploration of this research avenue.

Spatiotemporal visualization for the global COVID-19 surveillance by balloon chart

Background:Considering the widespread of coronavirus disease 2019(COVID-19)pandemic in the world,it is important to understand the spatiotemporal development of the pandemic.In this study,we aimed to visualize time-associated alterations of COVID-19 in the context of continents and countries.

Building a pathway to One Health surveillance and response in Asian countries

To detect and respond to emerging diseases more effectively,an integrated surveillance strategy needs to be applied to both human and animal health.Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat.It is not realistic to either invest only in efforts to detect emerging diseases,or to rely solely on event-based surveillance.A comprehensive strategy is needed,concurrently investigating and managing endemic zoonoses,studying evolving diseases which change their character and importance due to influences such as demographic and climatic change,and enhancing understanding of factors which are likely to influence the emergence of new pathogens.This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect.As yet there is no fully formed blueprint that can be applied in Asian countries.Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.

Intelligent 3D garment system of the human body based on deep spiking neural network

Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

Video-Based Crowd Density Estimation and Prediction System for Wide-Area Surveillance

Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.

A framework of force of information influence and application for C4KISR system

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.