User Interface Research Articles

Delta GUI change detection using inferred models

Recent software development methodologies emphasize iterative and incremental evolution to align with stakeholders’ needs. This perpetual and rapid software evolution demands ongoing research into verification practices and technologies that ensure swift responsiveness and effective management of software delta increments. Strategies such as code review have been widely adopted for development and verification, ensuring readability and consistency in the delta increments of software projects. However, the integration of techniques to detect and visually report delta changes within the Graphical User Interface (GUI) software applications remains an underutilized process. In this paper, we set out to achieve two objectives. First, we aim to conduct a comprehensive review of existing studies concerning GUI change detection in desktop, web, and mobile applications to recognize common practices. Second, we introduce a novel change detection tool capable of highlighting delta GUI changes for this diverse range of applications. To accomplish our first objective, we performed a systematic mapping of the literature using the Scopus database. To address the second objective, we designed and developed a GUI change detection tool. This tool simultaneously transits and compares state models inferred by a scriptless testing tool, enabling the detection and highlighting of GUI changes to detect the widgets or functionalities that have been added, removed, or modified. Our study reveals the existence of a multitude of techniques for change detection in specific GUI systems with different objectives. However, there is no widely adopted technique suitable for the diverse range of existing desktop, web, and mobile applications. Our tool and findings demonstrate the effectiveness of using inferred state models to highlight between 8 and 20 GUI changes in software delta increments containing a large number of changes over months and between 4 and 6 GUI changes in delta increments of small iterations performed over multiple weeks. Moreover, some of these changes were recognized by the software developers as GUI failures that required a fix. Finally, we expose the motivation for using this technique to help developers and testers analyze GUI changes to validate delta increments and detect potential GUI failures, thereby fostering knowledge dissemination and paving the way to standard practices.

Data-driven farming: implementing internet of things for agricultural efficiency

<p>Integrating internet of things (IoT) technology into agriculture has become essential to address challenges such as low crop productivity, which is often due to insufficient soil nutrients and suboptimal environmental conditions. This paper discusses the design and implementation of an IoT-based system for agriculture that aims to automate key parameters, facilitate real-time monitoring, and promote sustainable practices. Equipped with a graphical user interface (GUI), the system focuses on improving irrigation, regulating temperatures, and correcting soil nutrient deficiencies to improve crop productivity. Our research includes the use of humidity sensors to monitor soil moisture and temperature sensors. Soil nutrient levels, especially nitrogen, phosphorus, and potassium (NPK), were also assessed. We conducted experiments on three radish varieties using this IoT system and compared the results with traditional farming methods. The germination rate was impressive, reaching 98% within the first four days, while in a traditional greenhouse, it did not exceed 50%. As for plant height and leaf area, the smart greenhouse was better. These results were promising and demonstrated the potential of IoT in enhancing agricultural productivity. These results highlight the significant impact of IoT technology in enhancing agricultural productivity and its potential for broader application in this sector.</p>

Comparative study of statistical computational approaches to investigate the degraded compressive strength of concrete under the freeze-thaw effect

The primary cause of frost damage and early failure in concrete structures is the repeated freezing and thaw cycles (FTC). The evolution of internal fractures and scaling at the surface of concrete can be used to assess the impact of frost damage. Surface scaling mechanisms and interior frost damage are contingent upon numerous environmental factors, including the rate of freezing, low temperature, and duration of the freezing point. However, evaluating the amount of strength loss of concrete material is a challenging factor to consider. This work explores the application of predictive modelling tools, including random forest (RF), multilayer perceptron (MLP), decision tree (DT), and bagging, to evaluate the degraded compressive strength (D-CS) of concrete. The models use five input variables: initial compressive strength (I-CS), water-to-cement ratio (W/C), FTC, minimum temperature (Tmin), and maximum temperature (Tmax), with D-CS as the output. Model performance was compared using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) metrics, with the bagging model achieving the highest predictive performance at 92 %. A 10-fold cross-validation approach validated the model's accuracy. The influence of each variable was assessed using SHapley Additive exPlanations (SHAP) analysis. Importantly, a user-friendly Graphical User Interface (GUI) was developed based on the models, making it easy for researchers and professionals to make predictions and thereby increasing the practicality and accessibility of this research. This study aids the research community in selecting appropriate models to forecast the strength of various concrete types, thereby enhancing the practical application of this research.

Designing a Low-Cost Force Plate for Postural Assessment

The purpose of this study was to develop a pair of single-pedestal types with a low-cost and user-friendly force plate for assessing ground reaction force (GRF), center of pressure (COP), and plantar pressure distribution (PPD). These parameters are crucial for assessing human movement and maintaining body balance. The load cells were tested with standard weights of 5, 10, 15, and 20 kg. Using linear regression, the analysis showed a strong linear relationship between the load cell output and the standard weight, with an R-squared value of 0.99. Furthermore, the error margin was determined to be less than +/- 5%. In the initial equipment assessment, a healthy female was evaluated the posture under both eye-open and eye-closed conditions through three sets of measurements. The results showed about a two-fold change in the COP parameter, possibly due to swaying when the eyes were closed. Additionally, the PPD parameter revealed varying foot weight distribution levels, even though the GRF values were almost the same. The equipment's graphic user interface (GUI) provides real-time graphical representations for GRF, COP, and PPD, aiding in training effectiveness for enhancing postural assessment under professional guidance. In future studies, we plan to use the developed equipment to observe its effectiveness by measuring a larger and more diverse population.

Web apps for profile fitting and power balance analysis at Wendelstein 7-X.

Two novel web apps for W7-X are introduced: Profile Cooker and Power House. They are designed to streamline the workflow of profile fitting and power balance analysis while offering a graphical user interface that works in any common browser. This allows us to compile a comprehensive database of experimental power balance results. All fitting functions available in Profile Cooker are presented and compared on the basis of example profiles. The power balance equation assumed in Power House is established and its individual terms are discussed. The main focus of the power balance analysis is on the turbulent transport coefficients. A model for quick calculation of neutral beam power deposition based on experimental profiles is presented. Neoclassical root transition poses an issue for power balance analysis due to the uncertainty of the radial electric field. A global, neoclassical simulation with the code EUTERPE is performed for a set of experimental profiles to gain an understanding of the neoclassical root transition.

Accurate Raycasting Selection With Rotation Gesture Using a 6-DOF Tracking Device.

A six degrees of freedom (6-DOF) controller is a commonly used input device in three-dimensional user interface (3DUI) applications. However, for the fundamental task of target selection in 3D space, the selection accuracy decreases owing to the Heisenberg effect during the manipulation of the 6-DOF controller. Based on the pointing action of a 6-DOF device, we establish the mathematical model for raycasting and analyze the possibility of using a rotation gesture to select a target. This study proposes a target selection method using the axial rotation of the user's wrist, which reduces the negative impact of a discrete input for triggering the selection on accuracy. The detection model can identify the start time of the user's rotation action. The custom designed control display gain (CD gain) function can maintain the ray's stability during the rotation gesture. This method was verified using a 6-DOF pen and Vive controller in three experiments. The results show that the accuracy of the proposed rotation gesture-based raycasting target selection method is superior to that of the traditional button-pressing method, and it can be integrated into existing tracking systems for further application.

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

ABSTRACT 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.

Formative and validation human factors studies of a new disposable prefilled injection device for subcutaneous delivery of acthar gel (repository corticotropin injection)

ABSTRACT Background The administration of repository corticotropin injection (Acthar Gel) via a single-dose prefilled injector (SelfJect) is intended to provide a simple, ergonomic alternative to traditional injection. Iterative human factors (HF) studies were conducted to identify potential use deviations and ensure appropriate device use. Research design and methods This article presents seven formative studies, a validation study (with prior pilot validation studies), and a supplemental validation study with participants including lay users, patients, caregivers, and healthcare providers. Participant interactions with SelfJect and the user interface were assessed. Use deviations, user preferences, and participants’ ability to successfully complete tasks were evaluated to generate modifications to the device and user interface. Results In the validation study, 91% of participants successfully administered their first injection. Use errors were rare with simulated-use (6.9%) and knowledge-based (1.6%) testing. Use deviations were commonly attributed to experimental artifact or information oversight, and device warming had the most use errors (49% of participants), even with extensive testing and adjustments to the user interface. Conclusions SelfJect was able to be used in a safe and effective manner by the intended users. Iterative HF studies informed the mitigation of use-related risks to reduce the occurrence of use deviations during simulated use.

Developing crossplatform software applications to enhance compatibility across devices and systems

In an increasingly interconnected world, the need for software applications that function seamlessly across diverse devices and operating systems is paramount. Developing crossplatform software applications addresses this need by providing a unified user experience and operational efficiency regardless of the hardware or system being used. This approach eliminates the need for multiple versions of the same application, streamlining development and reducing costs while improving accessibility and consistency. Crossplatform development involves creating software that is compatible with various operating systems such as Windows, macOS, iOS, and Android, as well as different device types including desktops, tablets, and smartphones. Key methodologies in this domain include the use of frameworks and tools such as React Native, Flutter, and Xamarin, which allow developers to write code once and deploy it across multiple platforms. These frameworks offer a range of features to enhance user interfaces, manage system resources efficiently, and ensure robust performance across devices. The benefits of crossplatform applications are manifold. They provide a consistent user experience, as the same application behaves similarly across different devices, enhancing usability and customer satisfaction. Additionally, they simplify maintenance and updates, as changes need only be implemented once rather than across multiple codebases. This approach also accelerates timetomarket by leveraging shared codebases, thereby enabling faster development cycles and quicker deployment. However, developing crossplatform applications also presents challenges. Ensuring consistent performance and functionality across diverse systems can be complex, requiring careful design and testing. Developers must also navigate varying hardware capabilities and user interface guidelines for different platforms. Despite these challenges, advances in development frameworks and tools continue to improve the efficiency and effectiveness of crossplatform solutions. In conclusion, crossplatform software development represents a strategic approach to enhancing compatibility and accessibility across devices and systems. By leveraging modern frameworks and tools, organizations can deliver cohesive, highquality applications that meet the needs of a diverse user base while optimizing development resources and costs. Keywords: : Developing, CrossPlatform, Software Applications, Compatibility, Devices.

The Design of the Nusantara Quest Game on Mobile Platforms for the Cultural Arts Subject for Grade X Students

This research aims to design a mobile-based game called Nusantara Quest to enhance student engagement and learning outcomes in Arts Education for 10th-grade students. The educational game is developed using the Unity platform and follows the ADDIE model (Analyze, Design, Development, Implementation, Evaluation). The analysis phase involves studying student characteristics and material needs. During the design phase, game flow, user interface, and educational content are created. The development phase includes game creation and validation by media and content experts. Implementation involves trials with students from SMK Negeri 5 Sidrap, and the evaluation phase assesses the effectiveness of the game in improving student interest and understanding of Arts Education material. The research findings indicate that the quiz-based educational game is valid and effective as an interactive learning media S

Development of a PXIe-based data acquisition and control system for hydrogen pellet injection system

A data acquisition and control (DAC) system has been developed for the solid hydrogen pellet injection system. This injector is a gas gun type injector, where solid hydrogen pellet ice is formed using a closed cycle cryocooler, and high-pressure helium gas or a pneumatic punch is used to dislodge the pellet. The DAC system is based on National Instrument's embedded controller NI PXIe-8133, along with associated I/O DAC cards and the LabVIEW application. A graphical user interface (GUI) developed using LabVIEW software allows users to remotely control the pellet formation and injection process. The data is stored locally on a desktop computer or in cloud storage for further analysis. The images of the injected pellet were obtained by using a Phantom V-1210 high-speed camera at 100-Killo FPS, which reveals the size and speed of the pellet. The developed DAC system provides the flexibility needed to operate the injector remotely during the plasma discharge in a tokamak environment. The injector setup system has been successfully tested in a test bench operation, and it will be integrated with the ADITYA-U tokamak.

Machine learning-assisted optimization of food-grade spirulina cultivation in seawater-based media: From laboratory to large-scale production

The shortage of food and freshwater sources threatens human health and environmental sustainability. Spirulina grown in seawater-based media as a healthy food is promising and environmentally friendly. This study used three machine learning techniques to identify important cultivation parameters and their hidden interrelationships and optimize the biomass yield of Spirulina grown in seawater-based media. Through optimization of hyperparameters and features, eXtreme Gradient Boosting, along with the recursive feature elimination (RFE) model demonstrated optimal performance and identified 28 important features. Among them, illumination intensity and initial pH value were critical determinants of biomass, which impacted other features. Specifically, high initial pH values (>9.0) mainly increased biomass but also increased nutrient sedimentation and ammonia (NH3) losses. Both batch and continuous additions could decrease nutrient losses by increasing their availability in the seawater-based media. When illumination intensity exceeded 200 μmol photons/m2/s, it amplified the growth of Spirulina by mitigating the light attenuation caused by a high initial inoculum level and counteracted the negative effect of low temperature (<25 °C). In large-scale cultivation, production efficiency would be reduced if illumination was not maintained at a high level. High salinity and sodium bicarbonate (NaHCO3) addition promoted carbohydrate accumulation, but suitable dilution could keep the required protein content in Spirulina with relatively low media and production costs. These findings reveal the interactive influence of cultivation parameters on biomass yield and help us determine the optimal cultivation conditions for large-scale cultivation of Spirulina-based seawater system based on a developed graphical user interface website.

Network security enhancement with advanced techniques in network scanning.

This article details the development and evaluation of an advanced network scanning framework that uses Scapy for packet manipulation and Tkinter for the GUI. The framework aims to improve detection accuracy, resource efficiency, and user interface usability, addressing the shortcomings of traditional methods. Key components include a Network Scanner, Packet Handler, Security Measures, and a GUI, all designed for comprehensive network security. Tested in a controlled environment, the framework achieved a 100% detection rate, surpassing traditional methods, and demonstrated resource efficiency with a 20% reduction in CPU usage and a 60 MB decrease in memory usage. The analysis showed network diversity and low utilization, suggesting capacity for future expansion. The framework's GUI effectively displayed scan results, enhancing usability. The novelty of this research lies in its integration of optimized ARP packet handling with a user-friendly interface, ensuring real-time threat detection and scalability. The study contributes to the advancement of network scanning techniques and proposes future enhancements like machine learning integration for better threat detection.

PENGEMBANGAN APLIKASI LAYANAN BAGIAN UMUM (ALBUM 2.0) DALAM PENGELOLAAN BARANG MILIK NEGARA PADA DIREKTORAT JENDERAL HAK ASASI MANUSIA

This research aims to design the development of an information management system for State-Owned Goods (Barang Milik Negara) within government agencies as an effort to optimize internal supervision functions over the implementation of information systems and digitize the management process of State-Owned Goods. This research focuses on the Secretariat of the General Affairs Division of the Directorate General of Human Rights as the research object because the information system implemented by the General Affairs Division, namely the General Affairs Service Application (ALBUM), has problems based on the problem analysis and needs assessment conducted. This study uses Research and Development (R&amp;D) design with the ADDIE model. The development phase is limited to the Design stage, involving the creation of Data Flow Diagrams (DFD) and User Interface designs using CANVA and FIGMA as tools to assist in the design development process. Based on the analysis results, there is a need for information system development in the General Affairs Service Application (ALBUM) in the aspect of internal service. The internal services that can be developed include the Inventory Items feature by adding a display for additional requested items, and the second development is the addition of a loan feature for State-Owned Goods. The design development aligns with the problem analysis and the needs derived from the information obtained. It is expected that the development carried out will optimize the internal supervision function over the implementation of the General Affairs Service Application (ALBUM) information system and in every aspect of the State-Owned Goods management process

CELL: a Python package for cluster expansion with a focus on complex alloys

We present the Python package CELL, which provides a modular approach to the cluster expansion (CE) method. CELL can treat a wide variety of substitutional systems, including one-, two-, and three-dimensional alloys, in a general multi-component and multi-sublattice framework. It is capable of dealing with complex materials comprising several atoms in their parent lattice. CELL uses state-of-the-art techniques for the construction of training data sets, model selection, and finite-temperature simulations. The user interface consists of well-documented Python classes and modules (http://sol.physik.hu-berlin.de/cell/). CELL also provides visualization utilities and can be interfaced with virtually any ab initio package, total-energy codes based on interatomic potentials, and more. The usage and capabilities of CELL are illustrated by a number of examples, comprising a Cu-Pt surface alloy with oxygen adsorption, featuring two coupled binary sublattices, and the thermodynamic analysis of its order-disorder transition; the demixing transition and lattice-constant bowing of the Si-Ge alloy; and an iterative CE approach for a complex clathrate compound with a parent lattice consisting of 54 atoms.

Creating anatomically-derived, standardized, customizable, and three-dimensional printable head caps for functional neuroimaging.

Consistent and accurate probe placement is a crucial step towards enhancing the reproducibility of longitudinal and group-based functional neuroimaging studies. While the selection of headgear is central to these efforts, there does not currently exist a standardized design that can accommodate diverse probe configurations and experimental procedures. We aim to provide the community with an open-source software pipeline for conveniently creating low-cost, 3-D printable neuroimaging head caps with anatomically significant landmarks integrated into the structure of the cap. We utilize our advanced 3-D head mesh generation toolbox and 10-20 head landmark calculations to quickly convert a subject's anatomical scan or an atlas into a 3-D printable head cap model. The 3-D modeling environment of the open-source Blender platform permits advanced mesh processing features to customize the cap. The design process is streamlined into a Blender add-on named "NeuroCaptain". Using the intuitive user interface, we create various head cap models using brain atlases, and share those with the community. The resulting mesh-based head cap designs are readily 3-D printable using off-the-shelf printers and filaments while accurately preserving the head topology and landmarks. The methods developed in this work result in a widely accessible tool for community members to design, customize and fabricate caps that incorporate anatomically derived landmarks. This not only permits personalized head cap designs to achieve improved accuracy, but also offers an open platform for the community to propose standardizable head caps to facilitate multi-centered data collection and sharing.

RENTALIN APPS: Development of a Motorbike Rental Application Using Design Thinking for a Digital Rental Solution

The motorbike rental industry in Indonesia is characterized by significant challenges, including security risks, inefficient operations, and difficulty for customers in finding reliable rental services. This study aims to develop a comprehensive digital solution through the RENTALIN application, which addresses these challenges using a Design Thinking approach. The objective of this research was to create an application that enhances both the operational efficiency for rental owners and the user experience for customers. Data was collected from 13 motorbike rental owners, reported issues such as theft, fraud, and difficulties in tracking motorbikes. Additionally, surveyed customers indicated challenges in finding trustworthy rental services. Based on these findings, the RENTALIN application was designed with features such as GPS-based motorbike tracking, secure data verification systems, and an intuitive user interface. These solutions aim to improve security, expand market reach, and simplify the rental process. Testing of the prototype showed that the application effectively increases the efficiency and safety of the rental process, providing a more seamless experience for both rental owners and customers. RENTALIN demonstrates significant potential to become a leading platform in the Indonesian motorbike rental industry, offering targeted solutions to the sector’s critical issues.

QRGB+: Advanced QR Code Generator with RGB Color Method in Python to Expand Data Capacity

The present work entitled QRGB, consists of the development of an application in Python for the generation of QR codes using the additive color generation (RGB) method. This innovative method allows increasing the density of information stored in QR codes by using three color layers (red, green and blue), each representing a different set of data. QRGB offers an efficient and secure solution for storing and transmitting large amounts of information in limited spaces, significantly improving the capabilities of traditional black and white QR codes. By using three layers of colors, QRGB codes can store up to three times more information in the same space. This technique not only increases storage capacity but also improves information security, making it difficult to forge or manipulate the code. The overlay of multiple data layers allows redundancy to be implemented, increasing the robustness of the code against damage or reading errors. QRGBs are especially useful in applications that require the transmission of large amounts of data in limited spaces, such as in the packaging industry, digital business cards, and interactive advertising. Additionally, they have great potential in areas such as document and banknote security, where the authenticity and integrity of information are crucial. These points provide a solid foundation for understanding the innovation and advantages of colored QR codes (QRGB) compared to traditional QR codes, highlighting their applicability and potential in various sectors. This article presents a novel method for encoding and decoding information using a QRGB code, which involves the generation of three independent QR codes and their superimposition according to the additive color system (RGB). The research highlights the challenges encountered during the encoding and decoding processes due to the lack of specific libraries in Python, which required the creation of a custom solution using open source tools. The implementation takes advantage of Python and its libraries: qrcode[pil] to generate QR codes with the Pillow dependency for image manipulation, Pillow to open, manipulate and save different image formats, and opencv-python to perform tasks such as image processing and object detection. Despite facing issues with color mixing and accurate information retrieval, the proposed method demonstrates a significant increase in data density within a single QR code. Future work will focus on optimizing the algorithm and exploring potential applications in data security and high-density information storage. This Python script is designed to generate and decode QR codes with a logo overlay using a graphical user interface (GUI) built with Tkinter. The script combines several functionalities, such as creating QR codes, overlaying a logo, combining QR images of different colors, and manually decoding combined QR codes.

Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste

Abstract Plastic waste (PW) poses a significant threat as a hazardous material, while the production of cement raises environmental concerns. It is imperative to urgently address and reduce both PW and cement usage in concrete products. Recently, several experimental studies have been performed to incorporate PW into paver blocks (PBs) as a substitute for cement. However, the experimental testing is not enough to optimize the use of waste plastic in pavers due to resource and time limitations. This study proposes an innovative approach, integrating experimental testing with machine learning to optimize PW ratios in PBs efficiently. Initially, experimental investigations are performed to examine the compressive strength (CS) of plastic sand paver blocks (PSPBs). Varied mix proportions of plastic and sand with different sizes of sand are employed. Moreover, to enhance the CS and meet the minimum requirements of ASTM C902-15 for light traffic, basalt fibers, a sustainable industrial material, are also utilized in the manufacturing process of environmentally friendly PSPB. The highest CS of 17.26 MPa is achieved by using the finest-size sand particles with a plastic-to-sand ratio of 30:70. Additionally, the inclusion of 0.5% basalt fiber, measuring 4 mm in length, yields further enhancement in outcome by significantly improving CS by 25.4% (21.65 MPa). Following that, an extensive experimental record is established, and multi-expression programming (MEP) is used to forecast the CS of PSPB. The model’s projected results are confirmed by using various statistical procedures and external validation methods. Furthermore, comprehensive parametric and sensitivity studies are conducted to assess the effectiveness of the MEP-based proposed models. The sensitivity analysis demonstrates that the size of the sand particles and the fiber content are the primary factors contributing to more than 50% of the CS in PSPB. The parametric analysis confirmed the model’s accuracy by demonstrating a comparable pattern to the experimental results. Furthermore, the results indicate that the proposed MEP-based formulation exhibits high precision with an R 2 of 0.89 and possesses a strong ability to predict. The study also provides a graphical user interface to increase the significance of ML in the practical application of handling waste management. The main aim of this research is to enhance the reuse of PW to promote sustainability and economic benefits, particularly in producing green environments with integration of machine learning and experimental investigations.

Prediction of Ultra-High-Performance Concrete (UHPC) Properties Using Gene Expression Programming (GEP)

In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m3). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m3. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R2, i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model’s capabilities within the domain of civil engineering.