Graphical Interface Research Articles

Accurate automated segmentation of autophagic bodies in yeast vacuoles using cellpose 2.0.

Segmenting autophagic bodies in yeast TEM images is a key technique for measuring changes in autophagosome size and number in order to better understand macroautophagy/autophagy. Manual segmentation of these images can be very time consuming, particularly because hundreds of images are needed for accurate measurements. Here we describe a validated Cellpose 2.0 model that can segment these images with accuracy comparable to that of human experts. This model can be used for fully automated segmentation, eliminating the need for manual body outlining, or for model-assisted segmentation, which allows human oversight but is still five times as fast as the current manual method. The model is specific to segmentation of autophagic bodies in yeast TEM images, but researchers working in other systems can use a similar process to generate their own Cellpose 2.0 models to attempt automated segmentations. Our model and instructions for its use are presented here for the autophagy community.Abbreviations: AB, autophagic body; AvP, average precision; GUI, graphical user interface; IoU, intersection over union; MVB, multivesicular body; ROI, region of interest; TEM, transmission electron microscopy; WT,wild type.

Interactive and educational simulation tool for permanent magnet synchronous machines

Purpose This paper aims to present an interactive design and simulation tool for permanent magnet synchronous machines based on the finite-element-method. The tool is intended for education and research on electrical machines. Design/methodology/approach A coupling between the software MATLAB and finite element method magnetics is used. Several functionalities are included as modular scripts and represented in the form of a graphical user interface. Included are fully parametrized motor models, automatic winding generations and the evaluation of torque waveforms, core losses and speed-torque-diagrams. A survey was conducted to determine how the motivation of students concerning the covered topics is influenced by using the tool. Findings Due to its simplicity and the intuitive visualization of the results, the tool provides direct access to the topic of electrical machines without having to deal with separate scripts. The modular structure of the software allows simple extensions with new functions. Because students can directly contribute to the tool with their own work, their motivation for using and extending it increases. Originality/value The presented tool offers more functionalities compared to similar free software packages, e.g. the calculation of core losses and speed-torque diagrams. Also, it is designed in such a way that it can be easily understood and extended by students.

Robotic Process Automation Efficiency for Mobile App Testing: An Empirical Investigation

In today’s rapidly evolving software environment, the graphical user interface (GUI) plays a crucial role in providing intuitive, user-friendly interaction. However, traditional intrusive GUI testing methods often face challenges such as interrupting user workflows, requiring significant manual effort and insufficient test scenario coverage. Non-intrusive testing methods, such as Robotic Process Automation (RPA), offer a solution to validate GUI functionality without modifying the application’s code or affecting the user experience. RPA systems automate repetitive tasks by simulating user interactions, becoming valuable tools in GUI testing. However, challenges like limited computational resources, time constraints, or restricted exploration capabilities may limit the efficiency of individual RPA agents, thus restricting coverage and effectiveness. To address this issue, this study explores the performance of a single RPA agent versus an RPA cluster under different testing conditions, using three popular testing methods: Monkey, Stoat and Q-testing. Experimental results indicate that an RPA cluster outperforms a single RPA in GUI coverage and error detection, making a significant contribution to the field of non-intrusive GUI exploration testing. The findings of this study provide directions for future research to ensure the delivery of high-quality mobile applications.

RSR-YOLO: a real-time method for small target tomato detection based on improved YOLOv8 network

In tomato producing fields, automated large-area detection method is critical for fruit health monitoring and harvesting. However, due to the limited feature information included in tomatoes, large-area detection across long distances results in more missing or incorrect detections. To address this issue, this research proposes an improved YOLOv8 network, RSR-YOLO, for long-distance identification of tomato fruits. Firstly, this paper designs a partial group convolution (PgConv) and furthermore an innovative FasterNet (IFN) module for feature extraction, taking into account the impact of split operations on the computational complexity of the backbone network. The IFN module is lightweight and efficient, which improves the detection accuracy and real-time detection performance of the model. Secondly, this research combines the Gather and Distribute mechanism (GD) and redesigns the feature fusion module to implement the extraction and fusion of various levels of tomato features, given the critical significance that low-dimensional features play in small target recognition and localization. Finally, Repulsion Loss is used in this paper to examine the impact of fruit overlap and leaf occlusion on detection outcomes. RSR-YOLO achieves precision, recall, F1 score, and mean average precision (mAP@0.5) of 91.6%, 85.9%, 88.7%, and 90.7%, respectively, marking increases of 4.2%, 4%, 4.2%, and 3.6% compared to YOLOv8n. In addition, this paper designs a specialized Graphical User Interface (GUI) for the real-time detection task of tomatoes.

A novel method for developing contrast-detail curves from clinical patient images based on statistical low-contrast detectability.

This study aimed to develop a method to extract statistical low-contrast detectability (LCD) and develop contrast-detail (C-D) curves from clinical patient images.
Method: The LCD measurement and C-D curve development on the patient images were carried out in the region of air surrounding the patient as an alternative for a homogeneous region within the patient. A simple graphical user interface (GUI) was created to set the initial configuration for interest (ROI), ROI size, and minimum-detectable contrast (MDC). The process was started by segmenting with a threshold between -980 HU and -1024 HU to get an air mask. The mask was trimmed from the patient center coordinates to avoid distortion from the table scan. The mask was used to automatically place square ROIs with a predetermined size. The mean pixel values in Hounsfield units (HU) within each ROI was calculated. Next, the standard deviation (SD) from all the means was obtained. The for a particular target size was generated by multiplying SD by 3.29. A C-D curve was obtained by iterating this process for other ROI sizes. The method was applied to the homogeneous phantom to find the correlation of the parameters inside and outside of the phantom, and implemented on 30 patient images.
Results: Phantom images show a very strong correlation between LCDs obtained from outside and inside the phantom, with R2 of 0.97, 0.96, 0.92, 0.93, 0.80, and 0.88 for tube currents of 80, 100, 120, 140, 160, and 200 mA, respectively. This showed that the air region can act as a surrogate for a homogenous region in the phantom to obtain the LCD and C-D curve. 
Conclusion: The C-D curves obtained from outside the ACR phantom show a strong linear correlation with those from inside the phantom.

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

Book Recommendation System

In the digital age, the vast availability of books poses a significant challenge for readers to discover titles that match their interests and preferences. This report explores the use of machine learning for a Book Recommendation System (BRS) to help readers discover books suited to their interests in the digital age. It will utilize advanced algorithms and techniques, including collaborative filtering, content-based filtering, and hybrid models, to offer personalized recommendations based on users' reading history and behavior. The project aims to tackle challenges like data sparsity and the cold-start problem while ensuring scalability. Evaluation will be based on standard metrics like precision, recall, accuracy, and novelty, with a focus on ethical considerations such as fairness and user privacy. Ultimately, the goal is to develop a robust and user-centric system that enhances the book discovery process across digital platforms. A Graphical User Interface is also developed to discover information and display the recommended books. Keywords—Machine Learning, Book Recommendation System, K-Nearest Neighbor Algorithm

Real Estate Price Prediction using ANN

This report explores the use of machine learning to predict real estate prices, addressing challenges such as market dynamics and data complexity. Through extensive data analysis and feature engineering, the study implements decision trees, random forests, and neural networks to capture the intricate factors affecting property values. The performance of these models is rigorously evaluated, providing stakeholders with reliable forecasts and insights into key price determinants. This research enhances decision-making in the real estate sector by improving the accuracy and transparency of price predictions. A Graphical User Interface is also developed to enter required information and display the predicted price. Keywords—Machine Learning, Price Prediction, Artificial Neural Network

Virtual Mouse

Now a days the use of hand gesture recognition became popular due to the advance technology of artificial Intelligence (AI). In recent years, there has been a growing interest in developing alternative methods for human-computer interaction (HCI) that go beyond traditional input devices like keyboards and mouse. Hand gesture recognition has emerged as a promising approach, offering intuitive and natural ways for users to interact with digital interfaces. The proposed system utilizes computer vision techniques to detect and track hand gestures in real-time. By mapping specific gestures to mouse control commands, users can navigate and interact with graphical user interfaces (GUIs) without the need for physical input devices. In this paper A hand gesture- controlled virtual mouse system utilizes the AI and Machine learning algorithms to recognize the proper hand gestures and translate them into the mouse movements. The system we are designed that the people who have problems or difficulty using a traditional mouse or keyboard it will be appropriate for them. For this we are using camera that capture images of the user’s hand, which are processed by an AI and ML algorithm to recognize the gesture of hands being made. We trained the system by using a dataset of hand gestures. Keywords — Hand gesture recognition, Machine Learning, Artificial Intelligence, Virtual Mouse, Python, Media Pipe

The Design of Machine Learning-Based Computer-Aided System with LabVIEW For Abnormalities in Mammogram Images

Mammogram is the best way of breast cancer detection nowadays, as breast cancer is the most common form of cancer in the female gender and this form of cancer usually causes death. Many scientists, doctors, and engineers are working together to deal with such serious issues in human life. This paper, it is aimed to develop a new computer-aided system with a graphical coded language to detect abnormalities in mammogram images by using machine learning technics such as ANN and SVM. The developed algorithm has a graphical user interface (GUI) and all results are shown in there. The algorithm was created using three different stages. These are image processing and mass segmentation, feature selection and extraction, and classification. To test the accuracy of the system as the sensitivity, specificity, and accuracy, mammogram images with forty benign and forty malignant masses were used. The obtained results for measuring the sensitivity, specificity, and accuracy are 95%, 97.5%, and 96.25% for ANN and 97.5%, 97.5, and 97.5 for SVM, respectively. As can be said that the algorithm, user-friendly due to its user interface, can be preferred because it can detect many cancerous cells such as breast cancer with high accuracy.

Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists

Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists

Solar magnetic flux rope identification with GUITAR: GUI for Tracking and Analysing flux Ropes

Modelling the early evolution of magnetic flux ropes (MFRs) in the solar atmosphere is crucial for understanding their destabilization and eruption mechanism. Identifying the relevant magnetic field lines in simulation data, however, is not straightforward. In previous work an extraction and tracking method was developed to facilitate this task. Here, we present the corresponding graphical user interface (GUI), called GUITAR (GUI for Tracking and Analysing flux Ropes), with the aim to offer a variety of tools to the community for identifying and tracking MFRs. The starting point is a map of a selected proxy parameter for MFRs, e.g., a map of the twist-parameter Tw, current density, etc. We showcase how the GUITAR tools can be used to disentangle a multi-MFR system and facilitate in-depth analysis of their properties and evolution by applying them on a time-dependent data-driven magnetofrictional model (TMFM) simulation of solar active region AR12473. We show the MFR extraction using Tw maps, together with targeted use of mathematical morphology algorithms and discuss the evolution of the system.

MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis

In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called “MedKnee” is developed to assist physicians in the diagnosis process of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. To accomplish this task, 5000 knee X-ray images obtained from the Osteoarthritis Initiative public dataset (OAI) were divided into train, valid, and test datasets in a ratio of 7:1:2 with a balanced distribution across each KL grade. The pre-trained Xception model is used for transfer learning and then deployed in a Graphical User Interface (GUI) developed with Tkinter and Python. The suggested software was validated on an external public database, Medical Expert, and compared with a rheumatologist’s diagnosis on a local database, with the involvement of a radiologist for arbitration. The MedKnee achieved an accuracy of 95.36% when tested on Medical Expert-I and 94.94% on Medical Expert-II. In the local dataset, the developed tool and the rheumatologist agreed on 23 images out of 30 images (74%). The MedKnee’s satisfactory performance makes it an effective assistant for doctors in the assessment of knee osteoarthritis.

OPERATE–H2: A tool for optimizing underground hydrogen storage

Underground hydrogen storage (UHS) is crucial in facilitating the transition to clean energy by reducing reliance on fossil fuels and ensuring a stable supply of emission-free energy. However, safe and efficient storage conditions require careful planning. While reservoir simulations could determine optimal storage conditions, exploring all possible scenarios can be computationally prohibitive and time-consuming. Reduced-order models (ROMs) can efficiently simulate reservoir performance and analyze multiple strategies with reduced computational cost, providing valuable insights for UHS decision-making. This paper introduces an innovative integrated assessment framework with a user-friendly graphical user interface (GUI), designed to optimize UHS operations. The GUI features several advanced functionalities, including (1) temporal evolution for visualizing and analyzing UHS performance over withdrawal cycles, (2) local sensitivity analysis to determine the impact of individual parameters on UHS sites while keeping other inputs constants, (3) uncertainty quantification to identify the key factors for effective decision-making via tornado plots, (4) global sensitivity analysis to comprehensively quantify the interactions between geologic and operational parameters and their effect on UHS performance, and (5) sites screening feature to identify the most prominent potential sites for UHS installations. The GUI is built based on ROMs and the PyQt5 packages in Python. With user-friendly design and advanced features, Optimization, Evaluation, and Risk-Assessment Techniques for Hydrogen Energy tool (OPERATE–H2) enables users to evaluate different UHS scenarios quickly and effectively, helping to accelerate the transition to a cleaner and more sustainable energy future.

Graphics based business process harnessing tools, advancing digital maturity of business

PurposeThe advancement of digital maturity at a business level has proven to be challenging, with limited time, resources and skills in extracting and mapping business processes. In order to advance digitalization, the mapping of all business activities is essential. To close this gap, this research is aimed at enabling the operational level extraction and classification of business activities using a graphical methodology. This objective is accomplished through the development of a digital tool that leverages Software Development Life Cycle (SDLC) principles.Design/methodology/approachThis research is focused on facilitating rapid extraction of business activities with the adoption of a graphical methodology. Business activities are captured through a digital tool in a structured, interlinked and hierarchical manner.FindingsNotably, this work is novel as it introduces a new graphical tool to replace traditional tools in the domain of Business Process Mapping (BPM). This graphical tool is distinguished by a unique feature; that is a self-configuring database with the flexibility to capture all process options, providing innovative solutions to the challenges faced in digitalizing business processes.Originality/valueThis work is original in that a new graphical interface is developed to replace traditional tools in the space of BP data gathering. The unique challenge of an auto configuring database with flexibility to capture all process options is presented.

Comparative evaluation of the web-based contiguous cartogram generation tool go-cart.io.

Area cartograms are map-based data visualizations in which the area of each map region is proportional to the data value it represents. Long utilized in print media, area cartograms have also become increasingly popular online, often accompanying news articles and blog posts. Despite their popularity, there is a dearth of cartogram generation tools accessible to non-technical users unfamiliar with Geographic Information Systems software. Few tools support the generation of contiguous cartograms (i.e., area cartograms that faithfully represent the spatial adjacency of neighboring regions). We thus reviewed existing contiguous cartogram software and compared two web-based cartogram tools: fBlog and go-cart.io. We experimentally evaluated their usability through a user study comprising cartogram generation and analysis tasks. The System Usability Scale was adopted to quantify how participants perceived the usability of both tools. We also collected written feedback from participants to determine the main challenges faced while using the software. Participants generally rated go-cart.io as being more usable than fBlog. Compared to fBlog, go-cart.io offers a greater variety of built-in maps and allows importing data values by file upload. Still, our results suggest that even go-cart.io suffers from poor usability because the graphical user interface is complex and data can only be imported as a comma-separated-values file. We also propose changes to go-cart.io and make general recommendations for web-based cartogram tools to address these concerns.

MOUSE AND KEYBOARD MOTION SENSING USING HAND SIGNS

The project presents a way for Human- Computer Interaction (HCI) that employs a web camera or an integrated camera to control the movement of the indicator instead to the current methods, which typically involve manually entering buttons or altering the positions of a physical mouse. Instead, it can perform the functions that a physical computer mouse does, and it does it through camera and technology for computer vision to manage different mouse events. The Virtual Mouse color recognition application constantly acquires real-time image data and processes it using several filtering and conversion phases. The application utilizes an image processing technique to obtain the coordinates of the desired colors that have emerged within the frames holding a list of color combinations, each of them correlates to a different mouse function. The program will run the mouse function and transform it to an actual mouse function which will be able to applied on the user’s PC if the current color combination passes. The main aim of the project is to virtually interface with the system in virtually every manner imaginable. Keywords: Gesture Recognition, Human Computer Interactions, Graphical User Interface, Open-Source Computer Vision.

SMART CAMPUS BOT

A chat-bots aims to make a conversation between both human and machine.The machine has been embedded knowledge to identify the sentences and making a decision itself as a response to answer a question. Chat-bots will becompletely based on a text-based user interface, allowing the user to type commands and receive text as well as text to speech response.Chat-bots are usually stateful services, remembering previous commands in order to provide functionality. It can be utilized securely by an even largeraudience when chat-bots technology is integrated with popular web services. The college inquiry chat-bots will be built using artificial algorithms that analyze user's queries and understand user's message.The response principle is matching the input sentence from a user. The User can ask the question any college-related activities through the chat-bot without physically available to the college for inquiry. The System analyses the question and then answers to the user. With the help of artificial intelligence, the system answers the query asked by the students. The system replies using an effective Graphical User Interface as if a real person is talking to the user. The user just has to register himself to the system and has to login to the system. The chatbots consists of core and interface that is accessing in the Telegram by Ngrok. Natural language processing technologies are used for parsing, tokenizing, stemming and filtering the content of the complaint. Keywords— “Anaconda Prompt”,“College System,“Placement Information”, “Python”, “Rasa”, “Telegram User Interface”

Enhanced Accessibility for Visually Impaired & Blind Artists (EAVIBA) using Machine Learning

This research paper introduces EAVIBA, an innovative voice-driven interface aimed at empowering individuals with visual impairments and blindness in the domain of art and creativity. Rooted in principles of accessible design, EAVIBA integrates advanced features including voice commands, text-to-speech functionality, and a customizable graphical user interface, exemplifying a commitment to inclusive technology. The paper delves into the intricacies of EAVIBA's development, elucidating its architectural framework, seamless integration of assistive technologies, and adherence to recognized accessibility standards such as the Web Content Accessibility Guidelines (WCAG). Employing a comprehensive approach, the research paper outlines the iterative process of user testing, providing both qualitative and quantitative insights into critical aspects such as interface clarity, voice command recognition accuracy, and the efficacy of customization options. The results of these evaluations affirm EAVIBA's remarkable success in delivering an inclusive, accessible, and empowering environment for individuals with visual impairments to engage in artistic expression, thereby contributing significantly to the broader landscape of assistive technologies and accessible interfaces. Keywords: Visually impaired, blind artists, voice-driven interface, assistive technologies, voice recognition, machine learning.