Automatic Software Research Articles

Automatic characterization of Meibomian gland morphology

Aims/Purpose: To characterize the Meibomian gland (MG) morphology in healthy subjects with an automatic software.Methods: An automatic software was developed for image analysis in R code, consisting of 4 stages: (1) selection of the region of interest, (2) image preprocessing to obtain a binary and a skeleton image, (3) modelling of each MG to fit each MG to a piecewise linear model, and (4) quantification of the following MG features: tortuosity, MG shortening, number of MGs, deviation from a vertical line, and MG area. Meibography images of the upper eyelid were acquired using the Easy Tear View+®. A group of 156 healthy subjects were included, with a mean age and confidence interval (CI) of 43.88 (95% CI: 41.38–46.37) years, range 16–76 years. Reference intervals (RI) were calculated for each MG feature to be used as a diagnostic tool to classify a specific value as normal or non‐normal. Generalized Additive Models for Location, Scale and Shape were used to estimate the percentile curve for each morphologic feature of MG, considering the influence of age and sex.Results: Age had an influence on tortuosity, mean = 0.3 (95% CI: 0.3–0.31), RI: 0.19–0.4 <20 years, and 0.24–0.39 at 50–60 years. Age and sex had an influence in the MG shortening, mean = 63% (95% CI: 62–64%), mean RI: 58–74% <20 years; 59–84% >75 years; women RI: 56–71% <20 years; 57–82% >75 years. The rest of the variables were not affected by age or sex: number of MGs = 16.05 (95% CI: 15.28–16.82), RI: 8–27; vertical deviation = 0.37 (95% CI: 0.36–0.38), RI: 0.29–0.45, and MG area = 14% (95% CI: 14–15%), RI: 8.17–19.81%.Conclusions: Tortuosity variability decreases between 45 and 60 years of age. The MG shortening increases with age and is higher in men. These values could be useful to classify a meibography image as normal or non‐normal, helping to diagnose MG alterations in an objective way.

The Evaluation of Problems and Prospects of Admission Systems in GST (General, Science & Technology) Universities in Bangladesh

The study evaluates the Global Scholarship Test (GST) admission system in Bangladesh, focusing on its effectiveness in reducing costs, time of admission tests, and financial burden for students and guardians. The study used a structured questionnaire and closed-ended surveys to collect data from 400 respondents at Islamic University, Kushtia. The data reliability test was conducted from December 2023 to March 2024, with Cronbach’s alpha values of 0.80, 0.72, and 0.77. The study found that students and guardians agreed that the GST system reduced costs and time of admission tests, but faced issues such as time-consuming admission processes, coordination gaps, and financial difficulties. Reputable universities face financial anomalies, violation of statutes, rules, and ordinances, and ignored representatives of worthy resources. The GST authority should monitor management systems, reduce admission time, provide guidelines, and address long queuing systems. Automatic software could help address financial burdens for lower-scoring subjects.

Track Lab: extensible data acquisition software for fast pixel detectors, online analysis and automation

Fast, incremental evolution of physics instrumentation raises the question of efficient software abstraction and transferability of algorithms across similar technologies. This contribution aims to provide an answer by introducing Track Lab, a modern data acquisition program focusing on extensibility and high performance. Shipping with documented API and more than 20 standard modules, Track Lab allows complex analysis pipelines to be constructed from simple, reusable building blocks. Thanks to multi-threaded infrastructure, data can be clustered, filtered, aggregated and plotted concurrently in real-time. In addition, full hardware support for Timepix2, Timepix3 pixel detectors and embedded photomultiplier systems enables such analysis to be carried out online during data acquisition. Repetitive procedures can be automated with support for motorized stages and X-ray tubes. Freely distributed on 7 popular operating systems and 2 CPU architectures, Track Lab is a versatile tool for high energy physics research.

The impact of automatic history-taking software on data quality in the cardiology outpatient clinic: Retrospective observational study.

Healthcare delivery now mandates shorter visits despite the need for more data entry, under-mining patient-provider interaction. Furthermore, enhancing access to the outcomes of prior tests and imaging conducted on the patient, along with accurately documenting medication history, will significantly elevate the quality of healthcare service delivery. To enhance the efficiency of clinic visits, we have devised a patient-provider portal that systematically gathers symptom and clinical data from patients through a computer algorithm known as Automated Assessment of Cardiovascular Examination (AACE). We intended to assess the quality of computer-generated Electronic Health Records (EHRs) with those documented by physicians. We conducted a cross-sectional study employing a paired-sample design, focusing on individuals seeking assessment for active cardiovascular symptoms at outpatient adult cardiovascular clinics. Participants initially completed the AACE, and subsequently, in the first protocol, patients were subjected to routine care without providing the AACE forms to examining physicians. In the second protocol, the AACE form was presented to the physician before the examination, and participants were subjected to routine care. We assessed the impact of AACE forms generated through computerized history-taking method on the examination, considering various clinical outcomes and satisfaction surveys. We included non-randomized eligible patients who visited seven general cardiology outpatient clinics between September 18, 2023, and October 27, 2023. These clinics were staffed by the same physicians who were unaware of the content and details of the study. A total of 762 patients (394 patients in protocol 1 and 368 patients in protocol 2) were included in the study. The mean overall impression score for computer-generated EHRs was higher versus physician EHRs (4.2 vs. 2.6; p < .001). Our study demonstrated that EHRs created by physicians' exhibit inaccuracies or deficiencies in various pieces of information. In the second protocol, in which the AACE form was presented to the physician before the examination, it was determined that the examination time was shorter, the number of tests requested, and the number of new drugs prescribed were less. We observed that the patient-provider portal, systematically collecting symptom and clinical data from patients through a computer algorithm known as AACE, yielded records that were of higher quality, more comprehensive, better organized, and more relevant compared to those documented by physicians.

Plot Dynamic Adaptation and Character Interaction Design of Artificial Intelligence in Musical Theater Performing Arts

Abstract In recent years, artificial intelligence technology has received widespread attention in the field of artistic creation. This paper proposes the use of intelligent software to create a musical work that matches the emotions of the audience when they watch a musical performance so as to mobilize the development of the musical plot and to involve the virtual digital man in the performance of the musical to increase the interactivity of the characters. Both the music automatic generation software and the application of the virtual digital person need to match the rhythm of the performance. Accordingly, this paper proposes a multimodal emotion recognition model based on deep learning, which is applied to recognize the audience’s emotions in real time to create music that matches the plot development and reasonable role interaction. Through simulation experiments, the average recognition rate of multimodal based on decision layer fusion is 84.8%, which is slightly higher than the average recognition rate based on feature fusion and much higher than the average recognition rate of single-modal speech emotion (67.5%) and face expression (78.5%). 76% and 73% of the audience members liked the “music” and “character” elements of the musical performance with AI technology, and 62% of the audience members expressed their desire to watch it again.

Development of Auto Dense-breast Classification on Mammography Images Using Image Similarity

In Japan, radiologists perform qualitative visual classification to define four categories of mammary gland density. However, an objective estimation of mammary gland density is necessary. To address this, we developed an automatic classification software using image similarity. We prepared 741 cases of mediolateral oblique images (MLO) for evaluation, and they were diagnosed as normal among the mammography images taken at our hospital. Image matching was performed using the evaluation images and an image database for breast density determination. In this study, the image similarity used zero normalized cross-correlation (ZNCC) as an index. In addition, if the breast thickness is less than 30 mm and each breast density category ZNNC has the same value, the category is evaluated on the fat side. We compared the results of qualitative visual classification and automatic classification methods to assess consistency. The agreement with the subjective breast composition classification was 78.5%, and the weighted kappa coefficient was 0.98. One mismatched case was evaluated on the higher density side with the same ZNCC value between categories and a breast thickness greater than 30 mm. Image similarity provides an excellent estimation of quantification of breast density. This system could contribute to improving the efficiency of the mammography screening system.

Automatic unit test generator software by collecting run-time data

Automatic unit test generator software by collecting run-time data

An Intelligent Data Analysis System for Biological Macromolecule Crystallography

An Intelligent Data Analysis System for Biological Macromolecule Crystallography

Enhancing the Contouring Efficiency for Head and Neck Cancer Radiotherapy Using Atlas-based Auto-segmentation and Scripting.

Intensity-modulated radiation therapy can deliver a highly conformal dose to a target while minimizing the dose to the organs at risk (OARs). Delineating the contours of OARs is time-consuming, and various automatic contouring software programs have been employed to reduce the delineation time. However, some software operations are manual, and further reduction in time is possible. This study aimed to automate running atlas-based auto-segmentation (ABAS) and software operations using a scripting function, thereby reducing work time. Dice coefficient and Hausdorff distance were used to determine geometric accuracy. The manual delineation, automatic delineation, and modification times were measured. While modifying the contours, the degree of subjective correction was rated on a four-point scale. The model exhibited generally good geometric accuracy. However, some OARs, such as the chiasm, optic nerve, retina, lens, and brain require improvement. The average contour delineation time was reduced from 57 to 29 min (p<0.05). The subjective revision degree results indicated that all OARs required minor modifications; only the submandibular gland, thyroid, and esophagus were rated as modified from scratch. The ABAS model and scripted automation in head and neck cancer reduced the work time and software operations. The time can be further reduced by improving contour accuracy.

Identifying artifacts in EDS of fouled RO membranes through Raman and ICP-MS analysis

Fouled reverse osmosis (RO) membranes, primarily characterized by Scanning Electron Microscope and Energy Dispersive X-ray Spectrometer (SEM-EDS), face several limitations including spectral inaccuracies and misidentification of elements caused by automatic peak assessment software. This work addresses the latter by analyzing fouled polyamide membranes from community RO plants of Rajasthan treating brackish groundwater. Initially, EDS analysis revealed unexpected elements including Sc, Ti, V, and Cr, alongside anticipated foulants like Ca, Mg, Al, Si, Fe, and Mn. However, upon manual reassessment guided by spectral libraries and elemental likelihood, the peaks originally identified by the software as Sc, Ti, and V were reattributed to Ca and Ba, emphasizing the importance of careful peak assignment. Further, Raman spectroscopy of the same membranes detected calcite, dolomite, and molybdenum-bearing minerals. A peak initially assigned to Mo by the software was reinterpreted as interlayer carbonate, supported by literature on hydrotalcite structures. Subsequent ICP-MS analysis of the deposited salts showed that both Sc and Mo were below the detectable threshold, validating the reassessed findings. This study underscores the challenges of accurately identifying the inorganic scales to develop the right descaling approach by emphasizing the importance of integrating multiple analytical methods for comprehensive analysis and effective membrane cleaning solutions.

Feasibility of Adapting Various Tumor-to-normal Bone Ratio Images on an Automatic Quantification Package for Phantom-based Image Quality Assessment in Bone SPECT

We investigated the impact of the tumor-to-normal bone ratio (TNR) on the concordance rate between a detectability score classified by software (DSsoft) using an automatic quantification package for bone SPECT (Hone Graph) and a detectability score classified by visual assessment (DSvisual), and considered the feasibility of applying this software to various TNR images. 99mTc solution was filled into a SIM2 bone phantom to achieve TNRs of 4, 6, and 8, performed by dynamic SPECT acquisitions performed for 12 minutes; reconstructions were performed using ordered subset expectation maximization at timepoints ranging from 4 to 12 minutes. This yielded a total of 384 lesions (96 SPECT images). We investigated the weighted kappa (κw) coefficient between DSsoft and DSvisual at various TNRs and evaluated the change in analysis accuracy before and after applying newly created analysis parameters. DSs were defined on a 4-point scale (4: excellent, 3: adequate, 2: average, 1: poor), and visual evaluations were conducted by three board-certified nuclear medicine technologists. The κw coefficients between DSsoft and DSvisual were 0.75, 0.97, and 0.93 for TNRs 4, 6, and 8, respectively, with each κw coefficient being significant (p<0.01). In the TNR 4 image group, κw coefficients significantly increased with the implementation of new parameters proposed in this study. We concluded that the software's automatic analysis would be closer to a visual assessment within the TNR range of 4-8 and that applying new parameters derived from this study to images with TNR 4 further improves the software's automatic analysis accuracy of DSsoft. We suggest that software will be a useful tool for optimizing bone SPECT imaging techniques.

MAARS Software for Automatic and Quantitative Analysis of Mitotic Progression.

In this chapter, we describe a software called MAARS (Mitotic Analysis And Recording System) that enables automatic and quantitative analysis of mitotic progression on an open-source platform. This computer-assisted analysis of cell division allows the unbiased acquisition of multiple parameters such as cell shape or size, metaphase or anaphase delays, as well as various mitotic abnormalities. This chapter describes the power of such an expert system to highlight the complexity of the mechanisms required to prevent mitotic chromosome segregation errors, leading to aneuploidy.

Развитие современных программных продуктов автоматизации бухгалтерского и управленческого учета в России

The article provides a comparative analysis of the list of accounting and management accounting automation software products on a paid and free basis.

Analysis of Major Lobe Volume and Asymmetry of the Brain by Gender: A vol2Brain Study

Objective: In recent years, with the use of three-dimensional (3D) software tools, volumetric measurements of brain morphometry have gained importance. In this study, we aim to investigate the ratio of the volumes of the major structures of the brain (frontal, parietal, temporal, and occipital lobes) to the volume of the intracranial cavity (ICC) and the change of asymmetry according to gender, using web-based vol2Brain, which is one of the current and automatic software tools. Materials and Methods: 3D-T1-weighted magnetic resonance images of 80 healthy individuals (43 females and 37 males) of both genders were included in our study. The volumes of major brain lobes were calculated with the vol2Brain pipeline software tool and their ratio to ICC was compared by gender. Results: Males ICC volume, frontal, parietal, temporal, and occipital lobe total volume values were statistically higher than females (P &lt; 0.05). Similarly, the right and left volume values of male were higher than those of female in all measurements. However, when the frontal, parietal, temporal, and occipital lobe volumes of male and female were proportion to the ICC volume, there was no statistically significant difference (P &gt; 0.05). There was no statistically significant difference in the asymmetry results of these cortical structures examined in both genders (P &gt; 0.05). Conclusions: Although the male brain is known to be large, no difference was found between the male and female brains as a result of the ratio. Considering the asymmetry values, frontal, parietal, occipital, and temporal lobe volume asymmetry values did not differ statistically in both genders.

Research on Artificial Intelligence-Assisted Software Test Automation Methods

Abstract Software testing faces problems such as low automation and difficult reuse of testing methods. The purpose of this paper is to explore software testing automation methods with the aid of artificial intelligence. In this paper, based on the BS algorithm, the RF algorithm is constructed by Bagging integration, the RF algorithm is optimized by reducing the generalization error through the residual function, and the random forest model for software automation detection is constructed. After that, the model is examined and analyzed using automated detection of malicious software samples as a case study. The experimental results show that the accuracy of the Random Forest algorithm after feature selection reaches 98.9%, its prediction time is the least (7 seconds), and the Random Forest algorithm for training is the best. Z software’s optimized RF algorithm model has an accuracy of between 86% and 99.3% when detecting seven malicious types of samples. This paper’s proposed random forest algorithm model based on artificial intelligence assistance is well-suited for automated software testing, and the detection method is feasible.

Evolution in geosynthetics starts with utilizing data technology

This paper focuses on the automation of the geosynthetics installation QA/QC process by way of software integration in the field data entry and office data management. Technology is essential to the advancement of geosynthetic installations, especially data application. In its most basic form, data application is a means of gathering, storing, and utilizing information more easily than its predecessor(s). If implemented appropriately, data application technology improves process efficiency. Users and downstream stakeholders of process improvements can then capitalize by delivering quicker, higher-quality, more economical products. The geosynthetics industry is lagging in the area of data application. From a feasibility perspective and assessing practical implementation opportunities, many workflows widely used for tracking geosynthetic installations and its associated means and methods are antiquated and, furthermore, lack reliable authenticity verification. The current accountability requirements require a robust catalog of information reporting to substantiate this important environmental construction practice. The combination of room to evolve in practice and existing data requirements make the geosynthetics industry an ideal suitor for this technology integration. While advancements in materials manufacturing, welding equipment, and post-installation leak surveys already contribute to progress, there is a lack of universally accepted information infrastructure to aggregate all facets of an installation. Software automation creates a cyclical geosynthetics ecosystem that benefits the industry as a whole.

Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

This article outlines the design and development process of an automatic product categorization software intended for use in e-commerce and online marketplace platforms. The project aims to tackle the urgent issue of effectively classifying a wide range of products within digital markets. By utilizing artificial intelligence (AI) and machine learning methodologies, the software effectively analyzes product descriptions, enabling users to seamlessly incorporate products through automated categorization. The significance of the project is rooted in its ability to guarantee the accuracy and consistency of category hierarchies, automate the process of categorizing, and improve the overall user experience. The main goals involve the process of project planning, data collecting, and preparation. Machine learning models have been built and subsequently incorporated to facilitate the study of product descriptions. Through strict evaluation and optimization processes, a high level of accuracy and efficiency is achieved, resulting in several anticipated benefits. These benefits cover automated product categorization, enhanced user experience, and the potential for online platforms to gain a competitive edge. The key elements of innovation involve AI-driven textual analysis, learning methodologies grounded in data, and the ability to adapt to diverse industry contexts. Precautions and backup strategies are implemented to tackle technical issues, including the selection of machine learning libraries and algorithms, ensuring data quality, and integrating with various platforms. The success criteria encompass the objective of achieving a minimum prediction accuracy rate of 90%, optimizing business efficiency, enhancing user pleasure, and ensuring smooth system functioning. This project is a significant contribution to the field of product categorization inside the digital marketplace, as it provides automation, accuracy, and efficiency, ultimately resulting in an enhanced user experience.

The importance of patient characteristics, operators, and image quality for the accuracy of heart failure diagnosis by general practitioners using handheld ultrasound devices.

To evaluate whether the characteristics of patients, operators, and image quality could explain the accuracy of heart failure (HF) diagnostics by general practitioners (GPs) using handheld ultrasound devices (HUDs) with automatic decision-support software and telemedical support. Patients referred to an outpatient cardiac clinic due to symptoms indicating HF were examined by one of five GPs after dedicated training. In total, 166 patients were included [median (inter-quartile range) age 73 (63-78) years; mean ± standard deviation ejection fraction 53 ± 10%]. The GPs considered whether the patients had HF in four diagnostic steps: (i) clinical examination, (ii) adding focused cardiac HUD examination, (iii) adding automatic decision-support software measuring mitral annular plane systolic excursion (autoMAPSE) and ejection fraction (autoEF), and (iv) adding telemedical support. Overall, the characteristics of patients, operators, and image quality explained little of the diagnostic accuracy. Except for atrial fibrillation [lower accuracy for HUD alone and after adding autoEF (P < 0.05)], no patient characteristics influenced the accuracy. Some differences between operators were found after adding autoMAPSE (P < 0.05). Acquisition errors of the four-chamber view and a poor visualization of the mitral plane were associated with reduced accuracy after telemedical support (P < 0.05). The characteristics of patients, operators, and image quality explained just minor parts of the modest accuracy of GPs' HF diagnostics using HUDs with and without decision-support software. Atrial fibrillation and not well-standardized recordings challenged the diagnostic accuracy. However, the accuracy was only modest in well-recorded images, indicating a need for refinement of the technology.

Development of a simulation model of the activities of a transport and forwarding enterprise in the organization of international road cargo transportation

The object of this study is the process of planning the work of a transport and forwarding company when serving different categories of customers who need organizational support for the delivery of goods by road transport in international traffic. The solved problem is due to the need to devise recommendations for organizing the work of forwarders when interacting with customers of transport and forwarding services that export or import different categories of cargo. A simulation model of the transport and forwarding company’s work in customer service was developed and implemented in the GPSS World simulation automation package. The model provides for the optimization of organizational and management processes in cooperation with customers of transport and forwarding services. When developing the model, the duration of the types of work in which freight forwarders are involved in the export and import of goods by road transport was taken into account. The model also predicts the probability of errors in the forwarder’s work and the duration of their elimination. The application of the developed model in practice will enable the owners of transport and forwarding companies to plan the full-time number of forwarders and the duration of service provision under different conditions of interaction with customers. The simulation results reflect the performance indicators of the enterprise in serving different categories of customers. This will make it possible to optimize the work of the enterprise by planning personnel support. At the same time, the duration of transport and forwarding services will be reduced by 10–15 %, the capacity of the enterprise will increase by 6–11 %, and the reliability of service provision will increase by 8–14 %

Automatic sign language translation system using neural network technologies and 3D animation

Implementation of automatic sign language translation software in the process of social inclusion of people with hearing impairment is an important task. Social inclusion for people with hearing disabilities is an acute problem that must be solved in the context of the development of IT technologies and legislative initiatives that ensure the rights of people with disabilities and their equal opportunities. This substantiates the relevance of the research of assistive technologies, in the context of software tools, such as the process of social inclusion of people with severe hearing impairment in society. The subject of research is methods of automated sign language translation using intelligent technologies. The purpose of the work is the development and research of sign language automation methods to improve the quality of life of people with hearing impairments in accordance with the "Goals of Sustainable Development of Ukraine" (in the "Reduction of Inequality" part). The main tasks of the research are the development and testing of methods of converting sign language into text, converting text into sign language, as well as automating translation from one sign language to another sign language using modern intelligent technologies. Neural network modeling and 3D animation methods were used to solve these problems. The following results were obtained in the work: the main problems and tasks of social inclusion for people with hearing impairments were identified; a comparative analysis of modern methods and software platforms of automatic sign language translation was carried out; a system combining the SL-to-Text method is proposed and investigated; the Text-to-SL method using 3D animation to generate sign language concepts; the method of generating a 3D-animated gesture from video recordings; method of implementing the Sign Language1 to Sign Language2 technology. For gesture recognition, a convolutional neural network model is used, which is trained using imported and system-generated datasets of video gestures. The trained model has a high recognition accuracy (98.52%). The creation of a 3D model for displaying the gesture on the screen and its processing took place in the Unity 3D environment. The structure of the project, executive and auxiliary files used to build 3D animation for the generation of sign language concepts includes: event handler files; display results according to which they carry information about the position of the tracked points of the body; files that store the characteristics of materials that have been added to certain body mapping points. Conclusions: the proposed methods of automated translation have practical significance, which is confirmed by the demo versions of the software applications "Sign Language to Text" and "Text to Sign Language". A promising direction for continuing research on the topic of the work is the improvement of SL1-to-SL2 methods, the creation of open datasets of video gestures, the joining of scientists and developers to fill dictionaries with concepts of various sign languages.