User Interface Research Articles

WebCM: A Web-Based Platform for Multiuser Individual-Based Modeling of Multicellular Microbial Populations and Communities.

WebCM is a web platform that enables users to create, edit, run, and view individual-based simulations of multicellular microbial populations and communities on a remote compute server. WebCM builds upon the simulation software CellModeller in the back end and provides users with a web-browser-based modeling interface including model editing, execution, and playback. Multiple users can run and manage multiple simulations simultaneously, sharing the host hardware. Since it is based on CellModeller, it can utilize both GPU and CPU parallelization. The user interface provides real-time interactive 3D graphical representations for inspection of simulations at all time points, and the results can be downloaded for detailed offline analysis. It can be run on cloud computing services or on a local server, allowing collaboration within and between laboratories.

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

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.

Test Coverage in Microservice Systems: An Automated Approach to E2E and API Test Coverage Metrics

Test coverage is a critical aspect of the software development process, aiming for overall confidence in the product. When considering cloud-native systems, testing becomes complex, as it becomes necessary to deal with multiple distributed microservices that are developed by different teams and may change quite rapidly. In such a dynamic environment, it is important to track test coverage. This is especially relevant for end-to-end (E2E) and API testing, as these might be developed by teams distinct from microservice developers. Moreover, indirection exists in E2E, where the testers may see the user interface but not know how comprehensive the test suits are. To ensure confidence in health checks in the system, mechanisms and instruments are needed to indicate the test coverage level. Unfortunately, there is a lack of such mechanisms for cloud-native systems. This manuscript introduces test coverage metrics for evaluating the extent of E2E and API test suite coverage for microservice endpoints. It elaborates on automating the calculation of these metrics with access to microservice codebases and system testing traces, delves into the process, and offers feedback with a visual perspective, emphasizing test coverage across microservices. To demonstrate the viability of the proposed approach, we implement a proof-of-concept tool and perform a case study on a well-established system benchmark assessing existing E2E and API test suites with regard to test coverage using the proposed endpoint metrics. The results of endpoint coverage reflect the diverse perspectives of both testing approaches. API testing achieved 91.98% coverage in the benchmark, whereas E2E testing achieved 45.42%. Combining both coverage results yielded a slight increase to approximately 92.36%, attributed to a few endpoints tested exclusively through one testing approach, not covered by the other.

Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation.

Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D's functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

АВТОМАТИЗАЦІЯ ПРОЦЕСІВ КЕРУВАННЯ МІКРОКЛІМАТОМ ЖИТЛОВИХ ПРИМІЩЕНЬ

This paper is devoted to the research of the important issue of increasing the efficiency and optimization of microclimate management processes in residential premises. The purpose of this study is to create an automated system for accurate setting and control of the main parameters of the microclimate in the room, which will make it possible to increase comfort and energy efficiency. To achieve the task, the Arduino platform is used, which is an open hardware and software system with a wide range of opportunities for developing embedded systems. The study includes an analysis of the principles of creating a programmable timer using the Atmel AVR microcontroller, covering the selection of necessary hardware and modules, software development and user interface. The main functions of the programmable timer provide the ability to set a satisfactory microclimate according to the individual needs of the user and adjust parameters such as temperature, atmospheric pressure and humidity. The research results confirm that the developed programmable timer based on the Arduino platform provides effective control of the microclimate in the living room. Implementation of such a system will reduce heating and air conditioning costs, increase indoor comfort, and reduce overall energy consumption.

StoryForge: AI-Powered Narrative with Dynamic Imagery and Voice

StoryForge is an AI-powered narrative generation system that utilizes a combination of OpenAI’s GPT-3.5 Turbo model [1], Hugging Face’s Diffusion-based text-to-image gen- erative model Stable Diffusion XL (SDXL) [4], and Hugging Face’s Text-to-Speech Bark model [10] to create dynamic and engaging stories tailored to user-specified constraints. Users can input parameters such as genre, number of characters, character details, key events, mood, tone, point of view, and reader age to guide the narrative generation process. The system leverages the power of GPT-3.5 Turbo to generate the story text [1], SDXL to generate images for key events [4], and Bark to synthesize speech for the narrative [10], providing a comprehensive and immersive storytelling experience. A Streamlit-based user interface enables seamless interaction with the system, allowing users to input constraints, view generated text and images, and listen to the synthesized audio. It transforms user inputs into a multisen- sory narrative experience, marking a significant stride in the intersection of artificial intelligence and imaginative storytelling. StoryForge demonstrates the potential of AI to revolutionize storytelling, empowering users to create personalized and cap- tivating narratives with ease and embark on a journey where words, images, and voices converge to create truly one-of-a-kind tales. Index Terms—StoryForge, GPT-3.5 Turbo, Bark, Stable Dif- fusion XL, Streamlit, Hugging Face, Generative Model, Story- telling, Multisensory

Enhanced Autonomous Speed Control System for Integrated Cars

The Automated Vehicle Speed Control System (AVSCS) is an embedded framework that utilizes specialized hardware and software components to automatically regulate a vehicle's speed. This system is designed to be implemented in a variety of vehicles, including cars, trucks, and autonomous vehicles, to enhance safe and efficient driving. The AVSCS comprises several essential elements: sensors, a processor or microcontroller, a control calculation algorithm, and a user interface. The sensors gather real-time data about the vehicle's current speed, weather conditions, and surrounding road environment, including wheel speed, GPS, radar, LIDAR, and cameras. The microcontroller or processor processes the sensor data and employs control algorithms, such as PID (Proportional-Integral-Derivative) controllers or advanced Model Predictive Control (MPC) techniques, to calculate the optimal vehicle speed. This information is then used to adjust the throttle or braking mechanisms accordingly. Additionally, the user interface allows drivers to customize the system or set their desired speed preferences, providing a seamless and personalized driving experience. The integration of the AVSCS into various vehicle types can significantly increase security, streamline traffic,, and reduce energy consumption, making it a crucial component of the connected and autonomous vehicle ecosystem. Keywords: vehicle speed regulator, GPS, radar, LIDAR, and cameras

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

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.

Wavoice: An mmWave-Assisted Noise-Resistant Speech Recognition System

As automatic speech recognition evolves, deployment of the voice user interface (VUI) has boomingly expanded. Especially since the COVID-19 pandemic, the VUI has gained more attention in online communication owing to its non-contact property. However, the VUI struggles to be applied in public scenes due to the degradation of received audio signals caused by various ambient noises. In this article, we propose Wavoice , the first noise-resistant multi-modal speech recognition system that fuses two distinct voices sensing modalities (i.e., millimeter-wave signals and audio signals from a microphone) together. One key contribution is to model the inherent correlation between millimeter-wave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Additionally, we elaborate on two novel modules for multi-modal fusion embedded into the neural network, leading to accurate speech recognition. Extensive experiments prove the effectiveness of Wavoice under adverse conditions—that is, the character recognition error rate below 1% in a range of 7 m. In terms of robustness and accuracy, Wavoice considerably outperforms existing audio-only speech recognition methods with lower character error and word error rates.

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

Building a Banking Management System with Full Stack Java

The system encompasses a comprehensive range of functionalities crucial for efficient banking operations, including account management, transaction processing, customer relationship management, and security features. Leveraging Java's versatility, the system employs Spring Boot for backend development, Angular for frontend user interfaces, and Hibernate for database interaction, ensuring scalability, flexibility, and maintainability. Key aspects addressed in this paper include architectural design, database modeling, user interface development, security measures, and integration of essential banking features. Through a rigorous development process and adherence to industry standards, the resulting banking management system demonstrates reliability, performance, and adaptability to evolving banking needs. This paper contributes a valuable resource for developers and researchers interested in building modern, robust banking solutions using full stack Java technologies.

Prediction of Submergence of Islands Due to Glacier Melting & Sea Level Rise using Deep Learning

Prediction of Submergence of Island Due to Glacier Melting & Sea Level Rise will have significant contribution in the field of Environmental Engineering. This research paper present comprehensive overview of development and implementation this project, which designed to predict the submergence of island especially Lakshadweep Islands which is union territory of India. Capitalizing technologies like LSTM for forecasting sea level rise over period of time and Linear Regression for forecasting glacier melting and python for model training. To enhance the user experience also making it easy to understand the prediction we integrated JavaScript via python ELL framework to show charts and outputs. This paper discusses about the technical innovations, methodology and result of the project. It also spotlights its potential to help people living in islands which are submerging to evacuate them before this threat. This project aims to develop system to migrate people living on island and saving thousands of lives. Key Words: Submergence, LSTM, JavaScript, EEL, Model Training, Linear Regression, User Interface

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.

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.

Luggage Management System in order to Reduce Carbon Emissions and Ensure Sustainability

Air transport is a vital enabler of growth in the economy. It contributes to the economy by supporting trade and tourism. However, air transportation also affects our environment by increasing carbon emissions. Additionally, the use of private vehicles or taxis to access the airport also contributes to carbon emissions. With this study, considering that transportation to the airport has different difficulties in big cities, a mobile system will be developed that offers processes such as airport access, check-in, and luggage delivery in a single module, facilitates the person's transportation process to the airport and aims to protect the environment by directing them to public transportation. The main goal to be achieved with the luggage management software is to direct people's individual transportation to the airport to public transportation and thus, reduce carbon emissions. It is also aimed to automate the check-in process of luggage given to the plane and to reduce the time spent at the airport. Since the main aim of this study is to improve airport luggage handling, a business process improvement (BPM) approach will be adopted. Business process analysis and improvement will be used to identify, analyze, and optimize existing luggage processes. Additionally, user-centered design principles will be applied to add many user interface features to enhance user experience. The systems offering the same service are limited and there is a clear market in this field. The process will be digitized and streamlined. Time lost at the airport is going to be minimized and flight access is going to be facilitated.

INSURANCE AMOUNT PREDICTION BASED ON ACCIDENTAL CAR DAMAGE LEVEL USING CNN

There is a paradigm shift in the automotive industry with the advancement of technology, especially in the area of ​​insurance claims handling. In this study, we propose a new method for predicting insurance claims based on car damage levels, utilizing advanced artificial intelligence algorithms for image analysis, particularly employing Convolutional Neural Networks (CNN) for image processing and classification. Our strategy is to provide car damage estimates through picture analysis, making insurance claims in the automobile sector simpler. In order to facilitate user interaction with Flask and increase the predictability of our models, we have also implemented enhancements to the user interface. By combining image analysis with a user-friendly interface, our method offers a practical solution for insurance companies to expedite claim processing, reduce fraudulent activities, and ensure fair settlements for policyholders. This research contributes to the ongoing transformation of insurance claim processing within the automotive industry, highlighting the potential of merging image analysis methodologies with user interfaces for enhanced efficiency and usability. Keywords: Insurance claim prediction, Convolutional Neural Networks (CNNs), Image analysis, User interface, Flask, Automotive industry.