Development Of Learning Applications Research Articles

Automated Detection of Neurodevelopmental Disorders Using Face-to-Face Mobile Technology Among Typically Developing Greek Children: Randomized Controlled Trial.

Neurodevelopmental disorders (NDs) are characterized by heterogeneity, complexity, and interactions among multiple domains with long-lasting effects in adulthood. Early and accurate identification of children at risk for NDs is crucial for timely intervention, yet many cases remain undiagnosed, leading to missed opportunities for effective interventions. Digital tools can help clinicians assist and identify NDs. The concept of using serious games to enhance health care has gained attention among a growing group of scientists, entrepreneurs, and clinicians. This study aims to explore the core principles of automated mobile detection of NDs in typically developing Greek children, using a serious game developed within the SmartSpeech project, designed to evaluate multiple developmental domains through principal component analysis (PCA). A total of 229 typically developing children aged 4 to 12 years participated in the study. The recruitment process involved open calls through public and private health and educational institutions across Greece. Parents were thoroughly informed about the study's objectives and procedures, and written consent was obtained. Children engaged under the clinician's face-to-face supervision with the serious game "Apsou," which assesses 18 developmental domains, including speech, language, psychomotor, cognitive, psychoemotional, and hearing abilities. Data from the children's interactions were analyzed using PCA to identify key components and underlying principles of ND detection. A sample of 229 typically developing preschoolers and early school-aged children played the Apsou mobile serious game for automated detection of NDs. Performing a PCA, the findings identified 5 main components accounting for about 80% of the data variability that potentially have significant prognostic implications for a safe diagnosis of NDs. Varimax rotation explained 61.44% of the total variance. The results underscore key theoretical principles crucial for the automated detection of NDs. These principles encompass communication skills, speech and language development, vocal processing, cognitive skills and sensory functions, and visual-spatial skills. These components align with the theoretical principles of child development and provide a robust framework for automated ND detection. The study highlights the feasibility and effectiveness of using serious games for early ND detection in children. The identified principal components offer valuable insights into critical developmental domains, paving the way for the development of advanced machine learning applications to support highly accurate predictions and classifications for automated screening, diagnosis, prognosis, or intervention planning in ND clinical decision-making. Future research should focus on validating these findings across diverse populations integrating additional features such as biometric data and longitudinal tracking to enhance the accuracy and reliability of automated detection systems. ClinicalTrials.gov NCT06633874; https://clinicaltrials.gov/study/NCT06633874. RR2-https://doi.org/10.3390/signals4020021.

The Affordable Virtual Learning Technology of Sea Salt Farming across Multigenerational Users through Improving Fitts’ Law

Sea salt farms are the source of salt, an indispensable essential ingredient in various foods and products. Further, they act as frontlines to protect marine disasters from entering domestic residential areas. However, sea salt farming has decreased in popularity among recent-generation farmers in Samut Songkhram Province, Thailand. This paper presents the development of an affordable virtual learning (AVL) application for Thailand’s sea salt farming. Fitts’ law was applied to optimize user-interactive objects’ size and placement to reduce selection time. The sample consisted of 127 multigenerational users, namely those in Generation X, Generation Y, and Generation Z. This study found that the developed AVL applied Fitts’ law to be used harmoniously with VR economical equipment. A total of 90.55% of multigenerational users were satisfied with the developed AVL. The “Looking” and “Executing by eye focus” activities were enjoyed by 100% of participants. The following activities were “Walking” and “Listening”, with 82.68% and 77.95% enjoying them, respectively. Generation Z users responded more to the “Walking” activity than Generation X and Y users. In addition, the hypothesis testing result of learning outcomes through AVL was consistent among multigenerational users. Therefore, the developed AVL should be used as a medium to conserve sea salt farming in Thailand.

A framework for virtual learning in industrial engineering education: development of a reconfigurable virtual learning factory application

Advances in digital factory technologies are offering great potential to innovate higher education, by enabling innovative learning approaches based on virtual laboratories that increase the involvement of students while delivering realistic experiences. This article introduces a framework for the development of virtual learning applications by addressing multidisciplinary requirements. The implementation of the framework can be eased by the use of the proposed virtual learning factory application (VLFA), an open-source solution that takes advantage of virtual reality to support innovative higher-education learning activities in industrial engineering. A complete design and development workflow is described, starting from the identification of the requirements, to the design of software modules and underlying technologies, up to the final implementation. The framework and the VLFA have been tested to implement a serious game related to the design and analysis of manufacturing systems, also collecting the feedback of students and teachers.

Multimodal Eye Imaging, Retina Characteristics, and Psychological Assessment Dataset

The eyes provide insights into psychology, potentially offering a distinctive perspective for psychological health profiles. However, there exist a notable deficiency in datasets that simultaneously encompass eye features and psychological assessments. To address this gap, our study presents a dataset that included Fundus Photography, Psychological Assessment, Retina Characteristics, and Multimodal Imaging (FPRM). FPRM dataset comprise fundus images at different wavelengths (548 nm and 605 nm), image of oxygen saturation for the retina and 8 specific retinal vessels, videos of retinal blood flow and pupillary light reflex, along with 61 items of multimodal quantitative measurement from 384 participants. Additionally, it features psychological assessments across five dimensions (geriatric depression, generalized anxiety disorder, insomnia, activities of daily living, and deterioration), accompanied by fundus photographs and 6 items of retina characteristics from 1683 participants. FPRM dataset is the first to integrate multimodal ophthalmic data and psychological assessments, not only advancing the development of machine learning applications but also facilitating in-depth research into the relationship between eye health and psychological health profiles.

Developing Website English Learning Application with Google Site to Enhance Grammar Understanding

Grammar understanding is the main point to get the English skill. This study explores the development of a website-based English learning application aimed at enhancing grammar understanding, utilizing Google Sites as the platform. The application was developed following the System Development Life Cycle (SDLC) - Waterfall Model, encompassing phases of requirements analysis, system design, implementation, testing, and deployment. The primary objective was to create an accessible, user-friendly platform that provides comprehensive grammar tutorials, interactive exercises, and quizzes to reinforce learning. During the development process, key user needs were identified, leading to the design of a structured and responsive website. The implementation phase focused on integrating multimedia content and interactive elements to engage learners and facilitate effective grammar practice. Testing involved both functionality and user experience assessments, with feedback indicating that the application successfully supported grammar learning, though some improvements were recommended for clarity and content breadth. The study concludes that using Google Sites for educational purposes offers a practical solution for creating accessible learning tools. The application demonstrated significant potential in enhancing grammar understanding among users, with future improvements suggested to expand the range of topics and interactive features. This project provides a foundation for further exploration of web-based language learning applications using low-cost, widely available platforms.

The Development of 2D Android Learning Application on the Topic of Light Properties for Elementary School Science Subject

The Development of 2D Android Learning Application on the Topic of Light Properties for Elementary School Science Subject

Machine Learning-Based Prediction of Cyclic Voltammetry Behavior of Substitution of Zinc and Cobalt in BiFeO3/Bi25FeO40 for Supercapacitor Applications.

Artificial intelligence and machine learning have become indispensable tools across various disciplines in the present century. In that way, the role of artificial intelligence and machine learning in energy storage devices was investigated. As a preliminary study, the data derived from electrochemical studies were used for the prediction. The prediction of current from cyclic voltammetry (CV) studies was undertaken for bismuth ferrite (BFO), substitution of zinc in BFO (BFZO), and substitution of cobalt in BFO composite (BFCO). CV is a vital electrochemical technique used for studying the electrochemical behavior of any material. The electrochemical study provides insights into the energy storage behavior of the material through the specific capacitance. The machine learning models, such as Artificial Neural Network (ANN), Random Forest (RF), and XGBoost (XGB), are trained and implemented to predict current at different scan rates. These models are trained and validated using the data collected from a CHI 600E electrochemical workstation. Multiple trials of experiments were performed to build the most optimum model for the material. The predicted values provide promising results and align well with the experimental data. The XGBoost, ANN and RF models perform well for the CV data set with an average testing accuracy >97%. Also, a meta-model was created using stacking of the above three machine learning models which further improved the predictive performance, achieving a slightly higher average testing accuracy of over 97.73%. The outcomes from the models can promote the development of machine learning applications in the field of electrochemistry and provide insights into optimizing supercapacitor performance and design through data-driven approaches.

Development of Game-Based Learning Applications to Increase Students’ Learning Motivation

In the continuously evolving digital era, education faces new challenges in maintaining students' interest and learning motivation. One approach that has been introduced to address this challenge is the use of technology in learning, specifically through game-based learning applications. The aim of this study is to develop an effective game-based learning application that enhances students' learning motivation. The research method employed is the Research & Development (R&D) method. The development stages include needs analysis, application design, implementation, evaluation, and revision. The application was developed with consideration to attractive game design principles that motivate and take into account curriculum needs. The research findings indicate that the development of the game-based learning application successfully increased students' learning motivation. Assessments of the application showed positive responses from students to the learning experience provided. Additionally, there was a significant increase in the level of student participation and engagement in the learning process. The conclusion of this study is that the development of game-based learning applications is an effective approach to enhancing students' learning motivation. This application transforms the learning experience into something more engaging and challenging for students, thereby increasing their interest and activity in the learning process. The implication of this study is that the integration of technology in learning can be an effective strategy to motivate students and enhance the overall quality of education.

Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

Abstract This study presents a machine learning application for generating synthetic building electrical load profiles. The implementation followed the Cross Industry Standard Process for the development of Machine Learning Applications with Quality assurance methodology, or CRISP-ML(Q) framework, to ensure a systematic machine learning development process. The model training performance was evaluated using the mean absolute error (MAE), root mean squared error (RSME), and coefficient of determination (R2) which were observed to be 0.0739, 0.1119, and 0.5728, respectively. These metrics remained consistent during the model testing phase, suggesting robust model performance. During the initial simulation experiment, the MAE and RMSE of the generated synthetic load profile were found to be 0.014 and 0.016, respectively, underscoring high model accuracy. Additional evaluation experiments showed that the developed machine learning application can generate realistic building load profiles using high-level parameters such as building type, average daily load, and peak demand. This study can aid in the development of demand-side management strategies and building energy management systems by providing realistic building electrical load profiles especially when real-world data is limited. For future work, researchers can consider integrating additional model features, refining data processing methods, and developing an agile version of the CRISP-ML(Q) framework.

Development of Android Learning Application Based on Smart Apps Creator “BIMA” (Belajar Ingat Malaikat Allah) in Learning Pai Grade 2 Elementary Schoo

Development of Android Learning Application Based on Smart Apps Creator “BIMA” (Belajar Ingat Malaikat Allah) in Learning Pai Grade 2 Elementary Schoo

Development of Interactive Learning Application “Smart Educa” as Learning Media for Introductory Business Courses

Development of Interactive Learning Application “Smart Educa” as Learning Media for Introductory Business Courses

A new dimension in learning badminton refereeing: A 3D simulation android app with virtual reality integration

Virtual reality (VR) technology has become increasingly popular and widely used in sports environments in higher education, but there are still some challenges that need to be overcome. This research aims to create an Android-based 3D simulation application with the help of virtual reality as a learning tool for badminton refereeing. The methodology used is the Multimedia Development Life Cycle (MDLC). The multimedia development methodology consists of six stages, namely concept, design, material collection, creation, testing, and distribution. This application shows great potential for supporting the development of human resources in the field of badminton sports, both for novice referees and players. However, it should be noted that the current research is still at the application creation stage, with effectiveness and distribution tests still to be conducted. Through the optimisation of rendering techniques, test results show good simulation performance on smartphones with a resolution of 1920 x 1080, although the use of VR Box devices may cause eye fatigue in long-term use. This research makes an important contribution to the development of VR-based learning applications for badminton refereeing, with the hope of improving understanding and skills in sports and becoming the basis for the development of sports education technology. Future research should focus on completing the pending stages as well as expanding insights into user interaction and the positive impact that apps can have

The ASAM.ID Application as a Learning Media for Environmental Literacy

Garbage is a crucial problem that must be resolved by all parties, including universities. Universities can contribute to efforts to increase community literacy in waste management through the development of learning applications. This paper aimed to describe the ASAM.ID application development process and its feasibility to be used as an alternative learning media in increasing students’ environmental literacy in waste management. The application development process began with making flowcharts and developing application designs and content. The validation results of content and media experts showed that the ASAM.ID application can be used as an alternative learning media to increase students’ environmental literacy. This study recommends this application be tested in science learning to see its effectiveness and implemented on a broader scale.
 Keywords: ASAM.ID Application, Learning Media, Environmental Literacy

Research on the application of machine learning in business analytics: Cases of Amazon and eBay

With the rapid development of the Internet and the rise of e-commerce, commercial enterprises are faced with a large amount of data and a complex market environment. In this situation, machine learning, as a powerful tool, is widely used in the field of business analysis. In this dissertation, we take Amazon and eBay as examples to study the application of machine learning in the company's business analytics, focusing on its role in market prediction, customer behavior analysis and operation optimization. By analyzing the relevant cases, we find that machine learning technology plays an important role in helping companies make more accurate decisions and improve efficiency. Studying the application of Amazon machine learning in business analytics can promote in-depth research on the application of machine learning in business in academia, and promote the application and development of machine learning technology in other business scenarios. Overall, the application of machine learning in business analytics can help companies understand customer behavior, optimize operations, and improve sales results. However, there are still some challenges, such as data quality, algorithm selection and privacy protection. Therefore, further research and innovation are necessary to advance the development of machine learning applications in business analytics.

Abstract 6210: The cancer omics and drug experimental response dataset (CODERData): A harmonized benchmark dataset for machine learning models of drug response prediction

Abstract Background: Determining a patient’s response to a specific therapy is a vital step in developing personalized cancer treatment. Personalized treatment relies on two key technological advancements: algorithms tailored to each patient’s molecular profiles, and comprehensive datasets detailing the effects of patient diversity on drug response. Despite recent advancements in machine learning-based models, each new algorithm requires curation of numerous datasets, which poses a significant burden to researchers who need to evaluate and compare various algorithms. Currently, datasets include extensive genomics, transcriptomics, proteomics and metabolomics measurements along with drug response data (i.e., cell viability) for each cell line, collected through the Cancer Cell Line Encyclopedia (CCLE) and other initiatives. Additional omics data is available for tumor organoids and patient samples generated by consortia such as Human Cancer Model Initiative (HCMI) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). However, to date there has been little public effort to annotate and harmonize these data across repositories. Methods: To address this need, we developed the Python package coderdata that collates the most up-to-date cancer omics and drug response data. We curated four public cancer datasets, establishing reproducible pipelines that standardize data retrieval, formatting, and integration so the datasets can be built on a cluster if needed. The datasets undergo bi-monthly synchronization with FigShare, ensuring the data is up to date and available. Scientists can use coderdata to retrieve and format data on the command line interface as well as within Python scripts. Results: Coderdata is a straightforward Python package that will enable the development and benchmarking of diverse machine learning applications in cancer research. At its core, it represents reproducible data harmonization that enables heterogeneous datasets to be analyzed in bulk. This Python package downloads and standardizes data from multiple consortia-related resources including HCMI, CPTAC, CCLE, and BeatAML, together representing 4931 multi-omics samples from across 250+ cancer types. These datasets include cancer data metrics such as copy number, mutation, transcriptomics, proteomics, miRNA, methylation, and drug response data. Conclusions: The applications of machine learning in cancer biology are hampered by the distributed nature of existing datasets. As such, the collection and standardization of data by coderdata substantially reduces the time investment required for data curation in cancer research. Direct access to benchmark multi-omics and drug response datasets enables scientists to focus on algorithm development for tumor drug response prediction, potentially accelerating the discovery of therapeutic strategies. Citation Format: Jeremy Jacobson, Sydney Schwartz, M. Ryan Weil, Neeraj Kumar, Sara Gosline. The cancer omics and drug experimental response dataset (CODERData): A harmonized benchmark dataset for machine learning models of drug response prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6210.

Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models

Large Language Models (LLMs) are reshaping the landscape of Machine Learning (ML) application development. The emergence of versatile LLMs capable of undertaking a wide array of tasks has reduced the necessity for intensive human involvement in training and maintaining ML models. Despite these advancements, a pivotal question emerges: can these generalized models negate the need for task-specific models? This study addresses this question by comparing the effectiveness of LLMs in detecting phishing URLs when utilized with prompt-engineering techniques versus when fine-tuned. Notably, we explore multiple prompt-engineering strategies for phishing URL detection and apply them to two chat models, GPT-3.5-turbo and Claude 2. In this context, the maximum result achieved was an F1-score of 92.74% by using a test set of 1000 samples. Following this, we fine-tune a range of base LLMs, including GPT-2, Bloom, Baby LLaMA, and DistilGPT-2—all primarily developed for text generation—exclusively for phishing URL detection. The fine-tuning approach culminated in a peak performance, achieving an F1-score of 97.29% and an AUC of 99.56% on the same test set, thereby outperforming existing state-of-the-art methods. These results highlight that while LLMs harnessed through prompt engineering can expedite application development processes, achieving a decent performance, they are not as effective as dedicated, task-specific LLMs.

Preface

The International Conference on Machine Learning and Automation (CONF-MLA) is an annual conference focusing on research areas including the development of engineering and machine learning applications. It aims to establish a broad and interdisciplinary platform for experts, researchers, and students worldwide to present, exchange, and discuss the latest advances and developments in relevant fields.This volume contains the papers of the 2023 International Conference on Machine Learning and Automation (CONF-MLA 2023). Each of these papers has gained a comprehensive review by the editorial team and professional reviewers. Each paper has been examined and evaluated for its theme, structure, method, content, language, and format.Cooperating with prestigious universities, CONF-MLA 2023 organized five workshops in Chicago, Auckland, Oxford, Glasgow, and Adana. Dr. Marwan Omar chaired the workshop “The Role of Machine Learning in Advancing Cyber Security”, which was held at the Illinois Institute of Technology. Dr. Alan Wang chaired the workshop “Workshop on Machine Learning for Medicine 2023 (WMLM2023)” at the University of Auckland. Prof. Hong Zhu chaired the workshop “Automation of Testing Machine Learning” at Oxford Brookes University. Prof. Anil Fernando chaired the workshop “Autoencoder Based Semantic Communications for Image Transmission on Error Prone Channels” at University of Strathclyde. Dr. Mustafa ISTANBULLU chaired the workshop “Process Design of Semiconductor Device Fabrication: From Concept to Production” at Çukurova University.Besides these workshops, CONF-MLA 2023 also held an online session. Eminent professors from top universities worldwide were invited to deliver keynote speeches in this online session, including Dr. Abdullahi Arabo from University of the West of England, Dr. Roman Bauer from University of Surrey, Dr. Ali Darejeh from UNSW Sydney, etc. They have given keynote speeches on related topics of machine learning, software engineering, automation, and other engineering areas.On behalf of the committee, we would like to give sincere gratitude to all authors and speakers who have made their contributions to CONF-MLA 2023, editors and reviewers who have guaranteed the quality of papers with their expertise, and the committee members who have devoted themselves to the success of CONF-MLA 2023.Dr. Marwan OmarGeneral Chair of Conference CommitteeList of Committee Members CONF-MLA 2023 is available in this pdf.

A Survey of Robustness and Safety of 2D and 3D Deep Learning Models against Adversarial Attacks

Benefiting from the rapid development of deep learning, 2D and 3D computer vision applications are deployed in many safe-critical systems, such as autopilot and identity authentication. However, deep learning models are not trustworthy enough because of their limited robustness against adversarial attacks. The physically realizable adversarial attacks further pose fatal threats to the application and human safety. Lots of papers have emerged to investigate the robustness and safety of deep learning models against adversarial attacks. To lead to trustworthy AI, we first construct a general threat model from different perspectives and then comprehensively review the latest progress of both 2D and 3D adversarial attacks. We extend the concept of adversarial examples beyond imperceptive perturbations and collate over 170 papers to give an overview of deep learning model robustness against various adversarial attacks. To the best of our knowledge, we are the first to systematically investigate adversarial attacks for 3D models, a flourishing field applied to many real-world applications. In addition, we examine physical adversarial attacks that lead to safety violations. Last but not least, we summarize present popular topics, give insights on challenges, and shed light on future research on trustworthy AI.

Using Auto-ML on Synthetic Point Cloud Generation

Automated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper, we introduce a novel approach that applies Auto-ML methods to the process of generating synthetic datasets for training machine learning models. Our approach addresses the problem that generating synthetic datasets requires a complex data generator, and that developing and tuning a data generator for a specific scenario is a time-consuming and expensive task. Being able to reuse this data generator for multiple purposes would greatly reduce the effort and cost, once the process of tuning it to the specific domains of each task is automated. To demonstrate the potential of this idea, we have implemented a point cloud generator for simple scenes. The scenes from this generator can be used to train a neural network to semantically segment cars from the background. The simple composition of the scene allows us to reuse the generator for several different semantic segmentation tasks. The models trained on the datasets with the optimized domain parameters easily outperform a model without such optimizations, while the optimization effort is minimal due to our Auto-ML approach. Although the development of such complex data generators requires considerable effort, we believe that using Auto-ML for dataset creation has the potential to speed up the development of machine learning applications in domains where high-quality labeled data is difficult to obtain.

A new benchmark dataset for machine learning applications in discrete manufacturing: CiP-DMD

The development of machine learning applications in manufacturing depends primarily on the availability of meaningful and reliable data. In this paper, we present the Center for industrial Productivity - Discrete Manufacturing Dataset (CiP-DMD), the first open-source discrete manufacturing dataset of a multi-step machining process for which detailed documentation is available. The dataset encompasses a comprehensive description of the individual machining processes, the quality controls and the traceability concept used to match the process and quality data corresponding to the production of 847 pneumatic cylinders. With this new benchmark dataset, we aim to contribute to the development of new approaches and foment the permeation of machine learning in manufacturing.