Learning System Research Articles

Aplikasi Survei Kepuasan Peserta Diklat Berbasis Mobile Untuk Meningkatkan Pelayanan Diklat

Balai Diklat Keuangan Medan There is an Organizing and Learning sector which is tasked with carrying out learning related to State finances, facilitating the implementation of the Learning system and facilitating technical support for implementing learning needs analysis, learning evaluation, certification and competency tests as well as providing assistance services for implementing knowledge processes and learning organizations in the work area of the Financial Education and Training Center. This research was created to create a mobile-based training participant satisfaction survey system to improve training services. The aim is to improve training services that can be accessed easily by training participants. With the survey application, participant satisfaction data can be accessed and analyzed in real-time and this application makes the work of organizers easier in calculating satisfaction surveys and can provide data to the head of the Financial Training Center quickly. By using the waterfall method with Likert Scale calculations, this research produces an efficient information system through the stages of needs analysis, design, implementation, testing, maintenance and Likert scale calculations. The results obtained from this research are that the participant satisfaction survey application can make it easier for organizers to calculate survey results and make it easier for offices to improve office quality quickly.

The Impact of Blockchain Technology Effectiveness in Indonesia's Learning System

This research investigates the integration of blockchain technology in vocational education in Indonesia, evaluating its impact on database system learning outcomes. Involving 86 Indonesian students, this study aims to assess the effectiveness of blockchain in improving students’ understanding, skills, and academic performance. Using a quasi-experimental design, the findings of this study showed a significant improvement in learning outcomes, as evidenced by the analysis of pre-test and post-test scores. The measure of the effectiveness of the improvement in learning outcomes, with a substantial increase of approximately 46.50%, emphasizes the positive influence of blockchain technology on student achievement in the database systems course. The findings of this study will contribute valuable information to educational institutions, policy makers, and educators who wish to incorporate new technologies such as blockchain into their learning systems. The implication of this research is to provide recommendations in optimizing the integration of blockchain technology, which significantly affects learning outcomes in database systems. This is realized through facilitating fast and efficient access to learning materials, fostering a more interactive and engaging learning environment, and enhancing students’ understanding of industry practices. The integration of blockchain into database systems learning not only improves the quality of learning but also equips students for a successful career in information technology.

Pros and cons of E- learning

This is the most important area where majority of the students get confused and have number of doubts. Therefore, it is advised that the student should discuss the topic with the concerned guidesA learning system based on formalised teaching but with the help of electronic resources is known as E-learning. While teaching can be based in or out of the classrooms, the use of A learning system based on formalised teaching but with the help of electronic resources is known as E-learning. While teaching can be based in or out of the classrooms, the use of computers and the Internet forms the major compon INTRODUCTION: E-learning, short for electronic learning, is the process of learning via electronic devices, typically over the internet. It encompasses various forms of educational activities conducted online, including: Online Courses: These are courses delivered entirely over the internet, often through platforms like Coursera, Udemy, or Khan Academy. They can cover a wide range of subjects and are usually self-paced. Virtual Classrooms: These simulate traditional classrooms but take place online. Students and teachers interact in real-time through video conferencing tools like Zoom or Google Meet. Webinars: These are seminars conducted over the web, usually in the form of presentations, workshops, or lectures. Participants can interact with the presenter through chat or Q&A sessions. Mobile Learning: Learning materials are delivered through mobile devices, such as smartphones and tablets, allowing learners to access content anytime, anywhere. Gamification: Educational content is presented in the form of games to engage learners and enhance retention.

ALEKS in High School Mathematics Classrooms: Exploring Teachers’ Perceptions and Use of this Tool

AbstractThere has been a limited amount of research that has attempted to determine teaching strategies using adaptive learning systems. Most studies have attempted to measure success of the use of these technologies based on improvements in students’ test scores but have lacked to provide any information regarding the pedagogy implemented while using the tool or teachers’ perceptions of it. A basic qualitative study was conducted where five Chicagoland high school mathematics teachers, who used the Assessment and Learning in Knowledge Spaces (ALEKS) system, were interviewed three times during one academic school year. This study asked participants to share first-hand experiences and perceptions of using ALEKS as the main learning tool. Teachers used ALEKS for assessing student understanding through its quizzes and assignments, analyzing student progress, and allowing students to practice and receive feedback on mathematical concepts. The findings of this study indicate that teachers found ALEKS to be easy to use and useful in their teaching. Specifically, teachers cited the assessment tools, built-in feedback, ability to personalize learning, and the accessibility of learning tools for students as useful in their teaching.

Learning with Asynchronous Labels

Learning with data streams has attracted much attention in recent decades. Conventional approaches typically assume that the feature and label of a data item can be timely observed at each round. In many real-world tasks, however, it often occurs that either the feature or the label is observed firstly while the other arrives with delay. For instance, in distributed learning systems, a central processor collects training data from different sub-processors to train a learning model, whereas the feature and label of certain data items can arrive asynchronously due to network latency. The problem of learning with asynchronous feature or label in streams encompasses many applications but still lacks sound solutions. In this paper, we formulate the problem and propose a new approach to alleviate the negative effect of asynchronicity and mining asynchronous data streams. Our approach carefully exploits the timely arrived information and builds an online ensemble structure to adaptively reuse historical models and instances. We provide the theoretical guarantees of our approach and conduct extensive experiments to validate its effectiveness.

Abstract PO4-07-03: Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study

Abstract Background and Aim Some studies have reported the use of mammogram AI deep learning algorithms to accurately predict the risk of future breast cancer development in women. In these studies, random and unrelated mammograms were independently used for training of the AI model. We propose a novel deep learning system using sequential past mammograms instead of random studies and aimed to determine if it can further improve risk prediction in a pilot study. Methods Pre-training of the backbone of a Swin-transformer machine learning model was performed by applying region-of-interest lesional masks of benign and malignant on a training set of 1644 mammograms derived from the open-access Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset. To further develop the model, deep learning training and validation were performed on the Cohort of Screen-age Women (CSAW) open-access screening mammogram set which was derived from the long term data of 873 women who developed breast cancer for the first time and 7850 non-cancer women. We then compared the diagnostic accuracy of a model using sequential past mammograms for training against the standard, non-sequential model. For the sequential model, imaging segmentation of the potential cancer site on mammography was performed on all past, pre-cancer mammograms obtained within 60 to 2555 days of cancer diagnosis in the pre-processing stage for breast cancer cases. AI training was then carried out by combining the pre-cancer mammograms belonging to the same woman in a temporal sequence using a transformer encoder. The learning rate started from 1e-6 and decayed 10 times every 10 epochs. For the non-sequential model, we removed the temporal encoder and used random, unrelated mammogram studies for training. Validation was performed on a dataset consisting of 75 pre-cancer mammograms from breast cancer cases and 69 mammograms from non-cancer cases. Results Sensitivity, specificity and overall accuracy on the validation set were 77.3% (58/75), 68.1% (47/69) and (105/144) 72.9% respectively for the sequential model compared to 69.3% (52/75), 69.6% (48/69) and 69.4% (100/144) respectively for the non-sequential model. Conclusion The study showed that deep learning breast cancer risk prediction can be further improved by using sequential past mammograms instead of random mammograms for AI model training. This can also potentially enhance other AI risk prediction models that employ combined mammogram and traditional breast cancer clinical risk factors. A larger validation study will be useful. Contact information: email Lester Leong at lester.leong.c.h@singhealth.com.sg ROC curve for breast cancer risk prediction using AI deep learning training with sequential past mammograms. Citation Format: Lester Leong, Mingjie Xu, Weimin Huang, Erli Zhang, Engracia Loh, Sze Yiun Teo, Geok Hoon Lim, Veronique Kiak Mien Tan, Yirong Sim, Fredrik Strand, Ryan Tan. Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-03.

Attack-model-agnostic defense against model poisonings in distributed learning

The distributed nature of distributed learning renders the learning process susceptible to model poisoning attacks. Most existing countermeasures are designed based on a presumed attack model, and can only perform under the presumed attack model. However, in reality a distributed learning system typically does not have the luxury of knowing the attack model it is going to be actually facing in its operation when the learning system is deployed, thus constituting a zero-day vulnerability of the system that has been largely overlooked so far. In this paper, we study the attack-model-agnostic defense mechanisms for distributed learning, which are capable of countering a wide-spectrum of model poisoning attacks without relying on assumptions of the specific attack model, and hence alleviating the zero-day vulnerability of the system. Extensive experiments are performed to verify the effectiveness of the proposed defense.

A novel approach for identification of zoonotic trypanosome utilizing deep metric learning and vector database-based image retrieval system

A novel approach for identification of zoonotic trypanosome utilizing deep metric learning and vector database-based image retrieval system

Machine learning for network defense: automated DDoS detection with telegram notification integration

As the prevalence and sophistication of distributed denial of service (DDoS) attacks escalate, the imperative for advanced defense mechanisms becomes paramount, especially in rapidly growing digital landscapes like Indonesia. This research presents the development of an innovative intrusion detection system (IDS) that harnesses machine learning (ML) algorithms to automate the detection of DDoS attacks in real time. By monitoring TCP streams, the system utilizes ML-enhanced IDS components to identify malicious traffic patterns indicative of DDoS activities. An automatic alert is dispatched to network administrators via Telegram upon detection, ensuring immediate awareness and facilitating swift countermeasures. Additionally, the system embodies a self-improving architecture by retraining its ML model with newly encountered attack data, thus continuously refining its detection capabilities. The system's efficacy, marked by its adaptive learning and proactive notification system, not only contributes to the fortification of network security but also underscores the potential for ML in cybersecurity within Indonesia’s expanding digital domain. The deployment of this system is anticipated to significantly bolster cybersecurity infrastructure by addressing the urgent need for advanced and responsive defense strategies against the evolving landscape of cyber threats.

Comparative analysis of real issues in open-source machine learning projects

ContextIn the last decade of data-driven decision-making, Machine Learning (ML) systems reign supreme. Because of the different characteristics between ML and traditional Software Engineering systems, we do not know to what extent the issue-reporting needs are different, and to what extent these differences impact the issue resolution process.ObjectiveWe aim to compare the differences between ML and non-ML issues in open-source applied AI projects in terms of resolution time and size of fix. This research aims to enhance the predictability of maintenance tasks by providing valuable insights for issue reporting and task scheduling activities.MethodWe collect issue reports from Github repositories of open-source ML projects using an automatic approach, filter them using ML keywords and libraries, manually categorize them using an adapted deep learning bug taxonomy, and compare resolution time and fix size for ML and non-ML issues in a controlled sample.Result147 ML issues and 147 non-ML issues are collected for analysis. We found that ML issues take more time to resolve than non-ML issues, the median difference is 14 days. There is no significant difference in terms of size of fix between ML and non-ML issues. No significant differences are found between different ML issue categories in terms of resolution time and size of fix.ConclusionOur study provided evidence that the life cycle for ML issues is stretched, and thus further work is required to identify the reason. The results also highlighted the need for future work to design custom tooling to support faster resolution of ML issues.

Multi-task Information Enhancement Recommendation model for educational Self-Directed Learning System

In Self-Directed Learning Systems (SDLS), suggesting specific exercises to students after viewing educational videos is crucial, which promotes the integration of learning resources and improves learning efficiency. Given that exercises and educational videos are distinct learning resources, suggesting exercises centered on educational videos containing knowledge concepts is referred to as cross-type recommendations. Traditional cross-type recommendation methods usually separate data representation and recommendation tasks, which hampers effective knowledge transfer and information fusion. Furthermore, educational resources frequently feature an irregular distribution of information, posing a significant challenge to existing models in extracting crucial information. Besides, due to the inadequate data, the cross-type recommendation system needs help in developing a robust model. To address these challenges, we propose a Multi-task Information Enhancement Recommendation (MIER) Model in this paper. This model provides a comprehensive recommendation capability by integrating resource representation and merging recommendations. Multi-task learning improves the capacity to integrate information and transfer knowledge. Then, we propose an Information Enhancement Encoder (IEE) module that utilizes the attention mechanism to enhance the model’s ability to extract essential information from educational videos and exercises. Finally, to address the problem of inadequate data, we design a fusion structure that incorporates the concept knowledge graph as expert information into the model. To evaluate the effectiveness of the MIER, we compare it with other models on both predicting concepts and recommending exercises task. The corresponding results demonstrate that the MIER outperforms all the other models. We comprehensively evaluated the MIER model’s performance across a range of datasets varying in labeled data quantity. The findings demonstrate that the model consistently achieves outstanding performance and exhibits robustness, even under conditions of limited label availability.

IEEE Transactions on Neural Networks and Learning Systems Publication Information

IEEE Transactions on Neural Networks and Learning Systems Publication Information

Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.

Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is closely associated with cancer prognosis, but ST has limited clinical availability. In this study, we provide a powerful deep learning system to augment TME information based on histological images for patients without ST data, thereby empowering precise cancer prognosis. The system provides two connections to bridge existing gaps. The first is the integrated graph and image deep learning (IGI-DL) model, which predicts ST expression based on histological images with a 0.171 increase in mean correlation across three cancer types compared with five existing methods. The second connection is the cancer prognosis prediction model, based on TME depicted by spatial gene expression. Our survival model, using graphs with predicted ST features, achieves superior accuracy with a concordance index of 0.747 and 0.725 for The Cancer Genome Atlas breast cancer and colorectal cancer cohorts, outperforming other survival models. For the external Molecular and Cellular Oncologycolorectal cancer cohort, our survival model maintains a stable advantage.

IEEE Transactions on Neural Networks and Learning Systems Information for Authors

IEEE Transactions on Neural Networks and Learning Systems Information for Authors

A novel life prediction method of RF circuits based on the improved recurrent broad learning system

A novel life prediction method of RF circuits based on the improved recurrent broad learning system

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements.

PENGEMBANGAN BAHAN PELATIHAN DESAIN SISTEM PEMBELAJARAN BAGI GURU BAHASA INDONESIA SMA

Purpose of this research is produce "Training Material of Study System Design for Senior High School Indonesia Language Teacher" as according (1) requirement of teacher; (2) condition of expansion; (3) effectif, (4) can increase teacher interest; and (5) earns for teacher training. This research adopts expansion models Dick and Carey (2009). The sequence: determination of general purpose, study analysis, identification of initial ability, writing of purpose of special, assessment expansion of directive reference, strategy expansion, election and matter expansion, scheme and expansion of formative evaluation, revision, and scheme and expansion of evaluation sumatif. Result of this research, in general shows product " Training Material", very good. Suggestion, that product exploited by Senior High School Indonesia Language Teacher and exploited for further education expansion. Keyword: Development, training materials, design, learning systems

EVALUASI PELAKSANAAN PROGRAM REMEDIAL DENGAN MENGGUNAKAN MODEL FORMATIF-SUMATIF PADA PELAJARAN MATEMATIKA KELAS V

The remedial is a learning system which is taken based on a comprehensive diagnosis referred to find what is necessary to the students. One of the remedial activity is called an improvement given to the students. SDN 47 Kota Jambi is one of the school that remedial program is applied. This research examines the implementation of remedial program on mathematic subject, obstacles to the implementation of remedial program, intentions, and the effectiveness of teaching remedial on mathematics. This research applies descriptive approach. Interview, observation, and documentation were used to collect data. Interactive analysis model are connected with four interrelated components such as data collection, data reduction, data presentment and conclusion. These four criteria were used as validity data checking: level of credence, diversions, dependence, and the level of certainty. The results of this research have shown that (1) The implementation of remedial formative and summative program in SD 47 Kota Jambi has been accomplished as well. The school is also support the implementation of this program. As for the parents, the remedial program means giving a chance to the students to improve their mark. (2) This research found that student’s character, family, and society are the problems in the implementation of remedial program. As for the teachers, it is quite difficult to set the implementation time whereas as for the students, they are facing difficulties when dealing with narrative text, have lack of attention to the subject explained by the teacher and some of them are less-prepared when facing the remedial. (3) This remedial program has produced students who are diligent and built confident within themselves, getting more and more accurate when they have to dealing with their task and problems. (4) Still there are some improvements that needs to be achieved such as they are not develop their attitude and habits that encourage the sense of self-studious and confidence. Thus, sometimes they did not do their homework given by their teacher. (5) Remedial teaching on mathematics is very effective and brings good effect to the students both in school and home. Keyword : program evaluation, remedial, formative-summative model

Acceptance Analysis of Learning Management System in Project-based Learning

Acceptance Analysis of Learning Management System in Project-based Learning

An Interview System Using AI Technology

The rapid advancement of artificial intelligence (AI) has led to innovative applications across various industries, and one such application is the development of an AI-based interview system. This system aims to enhance and streamline the traditional interview process by leveraging AI technologies to assess and interact with candidates. The AI- based interview system incorporates natural language processing (NLP), machine learning (ML), and sentiment analysis algorithms to analyze both verbal and non-verbal cues during interviews. By employing AI, the system can assess a candidate's communication skills, problem-solving abilities, and cultural fit within the organization. The system's machine learning algorithms continuously learn and improve over time, enhancing their ability to accurately evaluate candidates and predict their potential success within the company. The benefits of an AI-based interview system include increased efficiency in the hiring process, reduction in human bias, and the ability to handle a large number of interviews simultaneously. Furthermore, the system can provide data-driven insights to recruiters, aiding in evidence-based decision-making. While the development and implementation of an AI-based interview system pose ethical considerations and challenges related to privacy and bias, ongoing research and ethical guidelines are essential to address these concerns and ensure fair and transparent use of the technology. As technology continues to evolve, further research and collaboration between AI developers and human resource professionals are crucial to harness the full potential of this innovative tool in the hiring process.