Model Creation Process Research Articles

Design of a Human Muscle-Powered Flying Machine

This study explores the design and development of a human-powered aircraft (HPA), leveraging modern engineering techniques, materials science, and advanced CAD/CAM tools. The project addresses key aspects of aircraft design, including the geometry of wings and tail, control and power transmission mechanisms, propeller selection, and material identification to achieve ultra-lightweight construction. The 3DExperience platform facilitated comprehensive model creation, simulation, and production process development, while XFLR5 was employed for aerodynamic profile analysis using the vortex lattice and panel methods. JavaProp aided in evaluating propeller thrust and power requirements. Computational fluid dynamics (CFD) simulations using the SST k-ω turbulence model provided critical insights into flow behavior. The design was found to be theoretically capable of flight, although challenges arose in selecting appropriate software for aerodynamic analysis, leading to the use of XFLR5 for early-stage design and the more advanced 3DExperience platform for final evaluations. Although structural strength analyses were not performed due to the complexity of composite materials, future work in this area could enhance the precision of component selection and aircraft mass estimation.

Optimizing fraud detection in financial transactions with machine learning and imbalance mitigation

AbstractThe rapid advancement of the Internet and digital payments has transformed the landscape of financial transactions, leading to both technological progress and an alarming rise in cybercrime. This study addresses the critical issue of financial fraud detection in the era of digital payments, focusing on enhancing operational risk frameworks to mitigate the increasing threats. The objective is to improve the predictive performance of fraud detection systems using machine learning techniques. The methodology involves a comprehensive data preprocessing and model creation process, including one‐hot encoding, feature selection, sampling, standardization, and tokenization. Six machine learning models are employed for fraud detection, and their hyperparameters are optimized. Evaluation metrics such as accuracy, precision, recall, and F1‐score are used to assess model performance. Results reveal that XGBoost and Random Forest outperform other models, achieving a balance between false positives and false negatives. The study meets the requirements for fraud detection systems, ensuring accuracy, scalability, adaptability, and explainability. This paper provides valuable insights into the efficacy of machine learning models for financial fraud detection and emphasizes the importance of striking a balance between false positives and false negatives.

Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.

Shrimp and Fish Underwater Image Clasification Using Features Extraction and Machine Learning

Shrimp cultivation is one type of cultivation that has a significant impact on the social status of coastal communities. Shrimp farming traditionally faces several challenges, including water pollution, imbalances in temperature, feed, media, and costs. Monitoring the condition of shrimp in the cultivation environment is very necessary to determine the condition of shrimp in the water. Classification of shrimp and fish is the first step in monitoring the condition of shrimp underwater. This research proposes the development of a method for classifying shrimp and fish underwater using feature extraction and machine learning. The flow of this research is: (1) preparing data from ROI detection results, (2) extraction process of morphometric characteristics P and T, (3) calculating the value of morphometric characteristics P and T, (4) data breakdown for training data and testing data, (5) Model creation process, data training and data testing using SVM, RF, DT, and KNN, (6) Evaluation of classification results using a confusion matrix. From this research, it was found that the Random Forest method obtained the highest accuracy, namely 0.93. From this matrix, the values ​​obtained are True Positive = 349, False Positive = 28, True Negative = 223, False Negative = 0.

Potential for artificial intelligence in medicine and its application to male infertility.

The third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production. This review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility. In research on male infertility, I established an AI-based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non-obstructive azoospermia, both by no-code AI. AI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No-code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.

Process Query Language: Design, Implementation, and Evaluation

Organizations can benefit from the use of practices, techniques, and tools from the area of business process management. Through the focus on processes, they create process models that require management, including support for versioning, refactoring and querying. Querying thus far has primarily focused on structural properties of models rather than on exploiting behavioral properties capturing aspects of model execution. While the latter is more challenging, it is also more effective, especially when models are used for auditing or process automation. The focus of this paper is to overcome the challenges associated with behavioral querying of process models in order to unlock its benefits. The first challenge concerns determining decidability of the building blocks of the query language, which are the possible behavioral relations between process tasks. The second challenge concerns achieving acceptable performance of query evaluation. The evaluation of a query may require expensive checks in all process models, of which there may be thousands. In light of these challenges, this paper proposes a special-purpose programming language, namely Process Query Language (PQL) for behavioral querying of process model collections. The language relies on a set of behavioral predicates between process tasks, whose usefulness has been empirically evaluated with a pool of process model stakeholders. This study resulted in a selection of the predicates to be implemented in PQL, whose decidability has also been formally proven. The computational performance of the language has been extensively evaluated through a set of experiments against two large process model collections.

Development of a reduced-order virtual model of a diesel locomotive cooling module

The article describes the process of developing a virtual model of a reduced-order cooling module for subsequent integration into a 1D virtual model of a diesel locomotive. A description of the steps in the model creation process is provided, including data collection, selection of mathematical models, and verification and validation procedures. The results of the work show that the created virtual model provides a convenient and accurate tool for analyzing and optimizing the cooling system of a diesel locomotive, which expands the possibilities for optimizing the design and improving the system control algorithms, which in turn leads to increased reliability and efficiency of its operation. The study highlights the prospects for the application of reduced-order virtual models in the railway industry.

Computer Vision-Assisted Object Detection and Handling Framework for Robotic Arm Design Using YOLOV5

In recent years, there has been a surge in scientific research using computer vision and robots for precision agriculture. Productivity has increased significantly, and the need for human labor in agriculture has been dramatically reduced owing to technological and mechanical advancements. However, most current apple identification algorithms cannot distinguish between green and red apples on a diverse agricultural field, obscured by tree branches and other apples. A novel and practical target detection approach for robots, using the YOLOV5 framework is presented, in line with the need to recognize apples automatically. Robotic end effectors have been integrated into a Raspberry Pi 4B computer, where the YOLOV5 model has been trained, tested, and deployed. The image was taken with an 8-megapixel camera that uses the camera serial interface (CSI) protocol. To speed up the model creation process, researchers use a graphical processing computer to label and preprocess test images before utilizing them. Using YOLOV5, a computer vision system-assisted framework aids in the design of robotic arms capable of detecting and manipulating objects. The deployed model has performed very well on both red and green apples, with ROC values of 0.98 and 0.9488, respectively. The developed model has achieved a high F1 score with 91.43 for green apples and 89.95 for red apples. The experimental findings showed that robotics are at the forefront of technological advancement because of the rising need for productivity, eliminating monotonous work, and protecting the operator and the environment. The same discerning can be applied to agricultural robots, which have the potential to improve productivity, safety, and profit margins for farmers while reducing their impact on the environment. The system’s potential could be seen in an assortment of fields, including sophisticated object detection, nuanced manipulation, multi-robot collaboration, and field deployment.

IoT-based automatic deep learning model generation and the application on Empty-dish Recycling Robots

As a modern computer vision technology, deep learning (DL) revolutionizes our lives and reshapes our world with its high performance. Usually, to accomplish each task, researchers need to collect the dataset and modify the basic DL model to optimize the model. In addition, collecting the dataset is time-consuming, and building separate Artificial Intelligence (AI) models for each task is significantly ineffective Therefore, AI research faces challenges in realizing effective data collection and DL model generation. To solve these problems, we design an Internet of Things (IoT)-based automatic DL model generation strategy which also includes an IoT-based data collection method. In detail, we design an Android application that captures images and sends them to a cloud server to create a dataset. Additionally, we apply a two-level checking function to filter out anomalous data and ensure the dataset’s correctness. Next, the cloud server uses the dataset to train and generate a DL model automatically. We have prepared the state-of-the-art DL components in the cloud server and propose an automatic DL model creation process for model generation. We have applied the proposal to Empty-dish Recycling Robots for demonstration and evaluation purposes to easily understand the proposal and measure the performance. The experimental results show that the system successfully collects the dataset and automatically generates the DL model. Furthermore, the checking function deployed on Android devices requires only 0.84 MB and achieves 99.86% accuracy. During training, the time spent on each automatic DL model generation is evidently decreased by about 9.00% to 28.00%.

SIBI (Sistem Bahasa Isyarat Indonesia) berbasis Machine Learning dan Computer Vision untuk Membantu Komunikasi Tuna Rungu dan Tuna Wicara

The Indonesian Sign Language System (SIBI) is used to translate sign language into text or speech. SIBI helps improve communication between people using sign language and those who do not understand it. Unlike commonly used languages, SIBI sign language is less known to most people due to a lack of interest. To address this, an artificial intelligence-based application was developed, focusing on deep learning to recognize SIBI sign language hand movements in real-time. The model was created with 20 epochs, a batch size of 16, and a learning rate of 0.001. It consists of 13 layers, with the ReLU activation function used for the input layer, while the output layer uses Sigmoid. The ADAM optimizer was used to expedite the model creation process. The image dataset used had a size of 300x300 pixels. In the classification testing of the SIBI alphabet results in this study, it was tested using distance tests. The distance between the webcam and the SIBI language speaker was divided into two categories: 40 cm and 60 cm. For a 40-cm distance, an accuracy of 87.50% was obtained, and for a 60-cm distance, an accuracy of 79.17% was achieved. One limitation of this study is that two alphabets, J and Z, were not included in the dataset. This is because recognition of these two alphabets requires not only finger pattern recognition but also recognition of their gesture patterns.

Развитие CAE системы численного моделирования НДС SIGMA GT

The paper presents the directions and results of developing the Sigma GT software suite designed for numerical modeling of the stress-and-strain state of a rock mass using the finite element method. It describes the process of computational module optimization to ensure the possibility of refined simulation in subareas of the original model. The process of storing coarse grid matrices is upgraded, which optimizes the use of computational resources when calculating the stress-and-strain state of large-scale models. The trial-and-error procedure has been improved up to the system of hierarchical interrelation of different scale models, which allows to actualize the boundary conditions of individual block models when modifying the field model configuration. An extensible library of local models is presented, which makes it possible to quickly generate models of mining system elements with adequate boundary conditions. The expandable functionality of the software product is demonstrated, which allows to accelerate the process of finite element model creation. This tool allows the software user to generate local models of various configurations, minimizing time consumption and possible errors in preparation of the finite element mesh. Application and functionality of tools for post-processing of modeling results, both in 2D and 3D form, are described. Export of stress isolines allows saving the calculation data in the format used at the mining enterprise and combining them with the maps of mine working. 3D visualization of the calculated data contributes to a comprehensive assessment of the stress-and-strain state vector field and identification of the areas with critical stress and strain concentrations under a certain mining development option.

"From Vision to Reality: Virtual Reality's Impact on Baffle Planning in Congenital Heart Disease".

This study aims to evaluate the feasibility and utility of virtual reality (VR) for baffle planning in congenital heart disease (CHD), specifically by creating patient-specific 3D heart models and assessing a user-friendly VR interface. Patient-specific 3D heart models were created using high-resolution imaging data and a VR interface was developed for baffle planning. The process of model creation and the VR interface were assessed for their feasibility, usability, and clinical relevance. Collaborative and interactive planning within the VR space were also explored. The study findings demonstrate the feasibility and usefulness of VR in baffle planning for CHD. Patient-specific 3D heart models generated from imaging data provided valuable insights into complex spatial relationships. The developed VR interface allowed clinicians to interact with the models, simulate different baffle configurations, and assess their impact on blood flow. The VR space's collaborative and interactive planning enhanced the baffle planning process. This study highlights the potential of VR as a valuable tool in baffle planning for CHD. The findings demonstrate the feasibility of using patient-specific 3D heart models and a user-friendly VR interface to enhance surgical planning and patient outcomes. Further research and development in this field are warranted to harness the full benefits of VR technology in CHD surgical management.

Visual Brick model authoring tool for building metadata standardization

The Brick ontology is a unified semantic metadata standard for building assets and their relationships, serving as a key enabler for effective interoperability and automation of building systems and analytics. However, creating a Brick model, in other words, standard semantic metadata based on the Brick ontology for a building dataset, can be a complex task. This paper presents two case studies of the creation of Brick models for real-world residential and commercial building datasets, highlighting the challenges during the Brick model creation process. Additionally, the paper introduces VizBrick, an interactive authoring tool for creating semantic building metadata. VizBrick facilitates the creation of Brick models by providing an intuitive visual interface and interactive capabilities, such as keyword search, automatic mapping suggestions, and recommendations. The use of VizBrick is shown to significantly reduce the time and effort required during the Brick model creation process.

A multi-dimensional evolution modeling method for digital twin process model

Digital twin, visually monitor the dynamic evolution process of part cutting in a digital way, which has attracted widespread attention in smart manufacturing. Under this background, creating a high fidelity and evolvable virtual model has become the key to putting digital twin into practice in the manufacturing field. However, the 3D process model pays scant attention on the dynamically machining process, multi-dimensional evolutionary machining feature, and not even the coupling machining semantics. How to realize the evolution process model creation from the perspective of semantics dimension, time dimension, and feature dimension, an evolutionary concurrent modeling method is proposed based on the process knowledge and machining feature. Firstly, the geometric evolution process of typical features is analyzed, and the association mechanism between processing semantics and evolution features is constructed. Secondly, the mathematical model of the evolution feature is constructed based on the part processing information, and the creation method of processing semantics-driven evolutionary feature is proposed. Thirdly, the association mechanism of "machining semantics-process parameters-geometric features" is built, and the dynamic evolution process model which achieve synchronous evolution with the processing is created based on machining semantics. Finally, a case study is carried out with typical processing feature. The results show that the multi-dimensional evolution modeling of the digital twin process model can realize the synchronous visual monitoring and simulation of product processing. The correlation based on semantic features can improve the efficiency and quality of process model construction and provide solutions for dynamic analysis and decision-making of smart process design.

The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions

Artificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices.

Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture

As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citizens, not just those in technology careers. Previous research in AI education materials has largely focused on the introduction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models. Therefore, there is a need for enriching AI educational tools with more adaptable and flexible platforms for interested educators with any level of technical experience to utilize within their teaching material. As such, we propose the development of an open-source tool (Build-A-Bot) for students and teachers to not only create their own transformer-based chatbots based on their own course material but also learn the fundamentals of AI through the model creation process. The primary concern of this paper is the creation of an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. The model uses contexts given by their instructor, such as chapters of a textbook, to answer questions and is deployed on an interactive chatbot/voice agent. The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent, diverging from previous chatbot work where students and teachers use the bots as black-boxes with no abilities for customization or the bots lack AI capabilities, with the majority of dialogue scripts being rule-based. In addition, our tool is designed to make each step of this pipeline intuitive for students at a middle-school level. Further work primarily lies in providing our tool to schools and seeking student and teacher evaluations.

A STUDY ON CONVERGENCE MODELING OF CULTURAL ARTIFACT USING X-RAY COMPUTED TOMOGRAPHY AND THREE-DIMENSIONAL SCANNING TECHNOLOGIES

Abstract. Optical three-dimensional (3D) scanning technology is extensively used for digital recording and shape analysis of cultural heritage because it is simple to obtain precise surface shape and color information. In addition, X-ray computed tomography (X-ray CT) is used to record and analyze the internal shape of cultural heritage. However, while the 3D shape acquired by X-ray CT technology is excellent for visualizing the internal shape, expressing the high resolution of the surface shape is difficult. If the benefits of each technology are used to digitize the inside and outside of cultural heritage, the results of surface shape recording based on 3D scanning can be advanced, and various stereoscopic analysis can be performed. Therefore, an optimization modeling process that combines X-ray CT and optical surface scanning is required to record both internal and external shapes of cultural heritage. In this study, optimization modeling methodology was applied to cultural heritage by combining X-ray CT technology and optical 3D scanning technology. Based on the models obtained through both technologies, a mesh-based optimization convergence modeling methodology considering internal and external surface shapes and color is organized. Furthermore, because the X-ray CT data has a characteristic of a voxel base, it is determined that additional convergence methods, including internal voxel data, are required, and a voxel-based optimization model process is established concurrently. Following that, two convergence modeling methods were applied to each of the deer-shaped horn cup artifacts that had an interior shape. As a result, the 3D convergence model creation and mapping optimization process, which can reveal important internal and external information about artifacts such as surface color, fine shape, and internal structure, can be improved. This 3D convergence modeling methodology was able to supplement the two technologies while preserving the benefits of optical 3D scanning and X-ray CT technology. This is thought to contribute to the expansion of the digital recording process from a single technology to multiple technologies. The X-ray CT-based convergence modeling pipeline is expected to be an opportunity in promoting the revitalization of X-ray CT technology in the cultural heritage field because it can be used for a wide range of purposes such as shape analysis, preservation status evaluation, monitoring, restoration, and reproduction.

Towards the isogeometric aero-engine

Creating very detailed finite element models of aero-engines is a very time-consuming process especially if structured meshes have to be generated for thousands of parts. Isogeometric analysis offers the possibility of an accelerated model creation process while achieving higher accuracy by using the non-uniform rational B-spline functions used for the geometry description also as basis functions for the finite element analysis. In this case, no meshing is necessary anymore. Here, we demonstrate this process by applying a self-developed tool which creates a geometry description and a computational model of a part at the same time. By the help of this tool we build up a simplified mechanical aero-engine model, which is used for transient implicit computations simulating the acceleration process of the rotor. We discuss the results as well as the computational time of isogeometric models in comparison to classical finite element models.

A comprehensive survey of the urban building energy modeling (UBEM) process and approaches

Fossil fuels increase the emission values of greenhouse gases such as CO2 in the atmosphere and cause global warming and climate change. At the same time, fossil fuel reserves are facing depletion in the near future, and energy supply also has an important dimension such as national security and foreign dependency. All these show that turning to renewable energy sources and developing solutions and policies for energy saving has become a necessity both globally and locally. For such reasons, modeling of urban structures, which have a great contribution to energy consumption, and simulating the energy demand on an urban scale are of great importance for the effective use of energy. Research on this has shown that UBEM (Urban Building Energy Modeling) is an effective solution to these problems. However, UBEM contains different technical problems for implementation. Due to its versatility, various concepts related to this field lead to complexity. With this increasing complexity, there is a growing need to compile concepts from a holistic perspective. In this study, it is aimed to create a solution to these challenges. For this purpose, a comprehensive and up-to-date research of various modeling approaches and model creation process used in urban building energy modeling has been conducted. Studies on these approaches are summarized and a systematic review of the literature is made. At the same time, the study is in the nature of guiding and forming the general knowledge level with the basic concepts that should be known to those who will work on UBEM.

Evaluation of disease outbreak in terms of physico-chemical characteristics and heavy metal load of water in a fish farm with machine learning techniques

Diseases are quite common in fish farms because of changes in physico-chemical characteristics in the aquatic environment, and operational concerns, i.e., overstocking and feeding issues. In the present study, potential factors (water physico-chemical characteristics and heavy metal load) on the disease-causing state of the pathogenic bacteria Lactococcus garvieae and Vagococcus sp. were examined with machine learning techniques in a trout farm. Recording of physico-chemical characteristics of the water, fish sampling and bacteria identification were carried out at bimonthly intervals. A dataset was generated from the physico-chemical characteristics of the water and the occurrence of bacteria in the trout samples. The eXtreme Gradient Boosting (XGBoost) algorithm was used to determine the most important independent variables within the generated dataset. The most important seven features affecting bacteria occurrence were determined. The model creation process continued with these seven features. Three well-known machine learning techniques (Support Vector Machine, Logistic Regression and Naïve Bayes) were used to model the dataset. Consequently, all the three models have produced comparable results, and Support Vector Machine (93.3% accuracy) had the highest accuracy. Monitoring changes in the aquaculture environment and detecting situations causing significant losses through machine learning techniques have a great potential to support sustainable production.