Development Of Learning Applications Research Articles

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.

Ekoland: The Development of Android-Based Learning Media Using iSpring Suite to Improve the Understanding of Ecosystem Material in Elementary School

This research aimed to develop science teaching media in the form of learning applications that use the help of iSpring Suite. The development of ecosystem material science learning applications is made to maximize the use of smartphones owned by students and reduce the various negative impacts of using Android in learning. The Research and Development (RD) method using a procedural model, was applied in this research. The data collection instruments in this study were validation sheets, student response questionnaires, and test questions. The test subjects in this study were 48 fifth-grade students at Ibnu Rusyd Islamic Elementary School. The Ekoland learning media feasibility test result is 71.95% from the percentage criteria of 61%-80% which shows very feasible results. and declared feasible because it is greater than 61%. The effectiveness level of Ekoland learning media is 93.75% from the percentage criteria of 61% -80% which shows decent results, and it is said to be effective because it is greater than 80%. Based on the results of the study, it is stated that the development of Ekoland learning media developed with android-based using the iSpring Suite application influences the increase in student learning outcomes in understanding ecosystem material in science learning and can provide a positive response from the use of android that maximizes the benefits of using android in education. Research has had a positive impact on improving science learning in schools, showing that Ecoland has a positive impact on students' learning outcomes and understanding of ecosystems.

Development of web-based learning application to enhance understanding in Philippine history

This study introduces a web-based learning system designed to enhance the recall and understanding of Philippine history through engaging quizzes and games, aligning with ISO 25010 standards. The system prioritizes functional suitability, usability, reliability, performance efficiency, and portability, with the aim of aiding users, particularly students, in remembering and studying Philippine history. Leveraging interactive features such as game content and quizzes, the system actively engages learners, promoting comprehension and retention of historical facts. Instant feedback mechanisms further contribute to users' ability to identify strengths and weaknesses in understanding Philippine history. To evaluate the system's performance, the researchers applied the Locally Weighted Learning (LWL) algorithm, adhering to ISO 25010 guidelines. The LWL algorithm, combining lazy learning and a decision stump classifier with linear weighting kernels, achieved an impressive 90% accuracy on the "Book1" dataset. This comprehensive evaluation validated the system's reliability and effectiveness, showcasing high accuracy in assessing attributes such as data accuracy, system availability, user-friendliness, responsiveness, and security assurance. The study also includes a regression analysis, indicating a moderate positive linear relationship (multiple R of 0.4243) between independent variables (functional suitability, usability, performance efficiency, reliability, and portability) and the dependent variable (software quality score). While the model explains approximately 22.38% of the variability, the non-significant p-value in the ANOVA table prompts consideration for further refinement. Overall, this research contributes to the field of educational technology by providing valuable insights into the development and evaluation of a history-focused learning application, emphasizing both practical usability and theoretical reliability within the framework of ISO 25010.

Development of Android-based Learning Applications in Mathematic Learning at Madrasah Ibtidaiyah Jayapura City

Development of Android-based Learning Applications in Mathematic Learning at Madrasah Ibtidaiyah Jayapura City

A MOBILE LEARNING APPLICATION TO FOSTER SUSTAINABLE AGRICULTURE THROUGH HYDROPONICS: DEVELOPMENT AND USABILITY TESTING

This study explores the development and usability testing of a mobile learning application tailored to provide an engaging educational experience in hydroponics for school children aged 12 years and below. The challenges faced by the agricultural education sector in capturing the interest and participation of the younger generation, where conventional methods have often struggled to engage and inspire. Embracing the principles of design thinking, this research seeks to bridge the gap in agriculture education, addressing the unique needs and preferences of younger learners in the digital age. A mobile learning application was developed, designed to combine the principles of hydroponics with interactive digital learning experiences. The design thinking methodology guided the iterative development process, placing a strong emphasis on user-centred design. The usability of the mobile learning application was assessed using the Post-Study System Usability Questionnaire (PSSUQ). Results of the usability testing revealed high levels of usability and user satisfaction among participants. The application's usefulness, user-friendly interface, engaging content, that aligned with educational objectives were among the factors contributing to its positive reception. The mobile learning application serves as a promising solution to enhance agriculture education, making hydroponics an accessible and engaging subject for school children. As a result, this research contributes to the broader goal of fostering sustainable agriculture and environmental consciousness among future generations.

A Context-Based Multimedia Vocabulary Learning System for Mobile Users

Vocabulary acquisition and retention is an essential part of learning a foreign language and many learners use flashcard applications to repetitively increase vocabulary retention. However, it can be difficult for learners to remember new words and phrases without any context. In this paper, we propose a system that allows users to acquire new vocabulary with media which gives context to the words. Theoretically, this use of multimedia context should enable users to practice with interest and increased motivation, which has been shown to enhance the effects of contextual language learning. An experiment with 46 English as foreign language learners showed better retention after two weeks with the proposed system as compared to ordinary flashcards. However, the impact was not universally beneficial to all learners. An analysis of participant attributes that were gathered through surveys and questionnaires shows a link between personality and learning traits and affinity for learning with this system. This result indicates that the proposed system provides a significant advantage in vocabulary retention for some users, while other users should stay with traditional flashcard applications. The implications of this study indicate the need for the development of more personalized learning applications.

Development of Android-Based Learning Application Using Articulate Storyline 3

This research aims to create an Android-based learning application assisted by Articulate Storyline 3 software for class VIII students of SMP Negeri 13 Kupang that is valid, practical, and effective. The method used is Research and Development (R&D) with the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. Data collection techniques and instruments include interviews, questionnaires, and tests. The data analysis technique uses qualitative and quantitative descriptive analysis techniques. This research produces an Android-based application product named "Relations and Functions" and has met the criteria for validity, practicality, and effectiveness. For media validity, the average assessment by 3 material experts was 4.37, and the average assessment by 3 media experts was 4.38, so it has the "valid" category. For the practicality of the media in small-scale trials, the student response was 4.72, and the effectiveness of the media through the percentage of learning completion was 100%. Meanwhile, in the large-scale trial, the subject teacher's assessment response was 4.5 and the student response was 4.63, so it was in the "practical" category. For media effectiveness, the learning completion percentage was 83.33% and was categorized as "very effective". Thus, the learning application developed is valid, practical, effective, and suitable for use in learning activities.

Design and Development of Sign Language Learning Application for Special Needs Students Based on Android Using Flutter

Sign language is a communication system used by individuals with hearing impairments to interact and convey messages without using verbal sounds. Sign language has its own unique grammatical structure and vocabulary, enabling sign language users to express ideas and emotions through facial expressions, hand movements, and body positions. In this research, we explore important aspects of sign language, including its uniqueness, complexity, and diversity across different countries and cultures. We also discuss the role of sign language in facilitating communication, education, and social inclusion for the deaf community. This research highlights the significance of introducing and developing sign language competence among the general public, educators, and professionals in the fields of healthcare and social services. The development of the sign language learning media utilizes the Multimedia Development Life Cycle method. The main application used in creating the sign language learning media is Flutter, along with supporting applications such as Adobe Illustrator 2020 and Adobe Photoshop CC 2019. The features included in this application are finger alphabet signs, number signs, video tutorials for numbers and letter signs, quizzes, and learning objectives.

Development of Learning Applications e Mole B in Badminton Games

The absence of technology based learning media in badminton activities has motivated researchers to develop new technology based learning media, so that they are easy to use and practice anywhere. The purpose of this study was to develop an electronic based badminton learning application called e Mole B. The method used in this research is research and development or R&D. Product trials were carried out at SMPN 4 Ciawigebang. With a small sample size of 10 extracurricular student respondents and a large scale test of 35 grade 7 student respondents at SMPN 4 Ciawigebang. The results of this research are based on a small scale test of 85% in the feasible category and a large scale test of 87.50% in the feasible category

Speaking to remember: Model-based adaptive vocabulary learning using automatic speech recognition

Memorizing vocabulary is a crucial aspect of learning a new language. While personalized learning- or intelligent tutoring systems can assist learners in memorizing vocabulary, the majority of such systems are limited to typing-based learning and do not allow for speech practice. Here, we aim to compare the efficiency of typing- and speech based vocabulary learning. Furthermore, we explore the possibilities of improving such speech-based learning using an adaptive algorithm based on a cognitive model of memory retrieval. We combined a response time-based algorithm for adaptive item scheduling that was originally developed for typing-based learning with automatic speech recognition technology and tested the system with 50 participants. We show that typing- and speech-based learning result in similar learning outcomes and that using a model-based, adaptive scheduling algorithm improves recall performance relative to traditional learning in both modalities, both immediately after learning and on follow-up tests. These results can inform the development of vocabulary learning applications that–unlike traditional systems–allow for speech-based input.

Machine learning for leaf disease classification: data, techniques and applications

The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular.

Machine Learning: Models, Challenges, and Research Directions

Machine learning techniques have emerged as a transformative force, revolutionizing various application domains, particularly cybersecurity. The development of optimal machine learning applications requires the integration of multiple processes, such as data pre-processing, model selection, and parameter optimization. While existing surveys have shed light on these techniques, they have mainly focused on specific application domains. A notable gap that exists in current studies is the lack of a comprehensive overview of machine learning architecture and its essential phases in the cybersecurity field. To address this gap, this survey provides a holistic review of current studies in machine learning, covering techniques applicable to any domain. Models are classified into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning. Each of these categories and their models are described. In addition, the survey discusses the current progress related to data pre-processing and hyperparameter tuning techniques. Moreover, this survey identifies and reviews the research gaps and key challenges that the cybersecurity field faces. By analyzing these gaps, we propose some promising research directions for the future. Ultimately, this survey aims to serve as a valuable resource for researchers interested in learning about machine learning, providing them with insights to foster innovation and progress across diverse application domains.

Need Analysis for The Development of Augmented Reality-Based Electronic Design Application in Secondary School Design and Technology (D&T) Subject

Students should be introduced to computer programming in order to generate computer programming experts. However, there are many constraints in introducing students to the basics of programming and microcontrollers in Design and Technology subjects in schools. The use of the latest technology is becoming a demand in implementing the best teaching and learning and it has been an essential component of the educational process. One of the latest technologies used in education is Augmented reality which has a positive effect on students' learning. The study used a Design and Development Research (DDR) design consisting of three phases. This paper discusses the findings of the first phase (needs analysis) of the study, which was conducted to determine challenges in the implementation of the second form Design and Technology curriculum, the potential use of mobile devices in teaching and learning, as well as the need for the development of augmented reality-based Electronic Design learning applications as an aid in teaching in secondary school D&T subjects. Online surveys were employed in this study, and they were sent to second-form RBT teachers via school email. Statistical Packages for Social Science (SPSS) version 27.0 was used to perform a descriptive analysis of the study's data using the mean (M) and standard deviation (SD). Based on the analysis of survey data among 138 respondents, it was discovered that the average mean value for the challenge aspect of T&L implementation for D&T subjects was at a moderate level (M = 2.68, SP =0.52), indicating that there is still much space for improvement in this aspect. Electronic Design is the topic that is the most challenging to implement into the second-form D&T curriculum. High levels of agreement were found in a descriptive examination of the teacher's perceptions of the possible usage of mobile devices (M = 4.10, SD = 0.68). The average mean for the study of the development need of the augmented reality-based electronic design application is high (M = 3.89, SD = 0.55), indicating that it is necessary to develop it as a support material for second-form D&T courses. The findings of this study are crucial in ensuring that the design and development of a mobile application as a teaching aid for microcontroller education are put into practice and have a positive effect in enhancing secondary school students' literacy in programming.

Formal Specification and Testing for Reinforcement Learning

The development process for reinforcement learning applications is still exploratory rather than systematic. This exploratory nature reduces reuse of specifications between applications and increases the chances of introducing programming errors. This paper takes a step towards systematizing the development of reinforcement learning applications. We introduce a formal specification of reinforcement learning problems and algorithms, with a particular focus on temporal difference methods and their definitions in backup diagrams. We further develop a test harness for a large class of reinforcement learning applications based on temporal difference learning, including SARSA and Q-learning. The entire development is rooted in functional programming methods; starting with pure specifications and denotational semantics, ending with property-based testing and using compositional interpreters for a domain-specific term language as a test oracle for concrete implementations. We demonstrate the usefulness of this testing method on a number of examples, and evaluate with mutation testing. We show that our test suite is effective in killing mutants (90% mutants killed for 75% of subject agents). More importantly, almost half of all mutants are killed by generic write-once-use-everywhere tests that apply to any reinforcement learning problem modeled using our library, without any additional effort from the programmer.