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8th International Conference on E-Society, E-Education and E-Technology, ESET 2024

作者： Lacorte, Alice Maldonado Gutierrez Calvez, Jeanne Pauline Atanacio, Marvin Menisis, Archie Natividad, Arniel Esguerra, Dave School of Architecture Computing and Engineering National University Philippines Batangas Lipa City Philippines Ninjavan Company Laguna Cabuyao Philippines Laguna Cabuyao Philippines

ISBN: (纸本)9798400707094

This paper introduces a mobile application designed to facilitate the learning and practice of the Baybayin script-an ancient writing system indigenous to the Philippines into a more interactive and engaging way. The scope delineates in the need for more comprehensive Baybayin learning resources, emphasizing the need for an interactive mobile application that will engage students or users to learn the said ancient writings. The methodology highlights the use of spiral model for software development and descriptive type of research for data gathering. Based on the results of the study, the application’s features include interactive lessons, quizzes, a progress tracker, and practice writing, with a distinctive character recognition system allowing users to receive real-time feedback on their writing skills. The overarching goals are to promote cultural awareness, improve Baybayin writing skills, deepen historical appreciation, and provide an engaging learning experience. The character recognition accuracy, employing machine learning algorithms, enhances the learning experience. It presented a detailed discussion of the results, encompassing cultural awareness, interactive way of familiarizing the writing system, improved writing skills, engaging learning experiences, and accessibility, positioning the app as a dynamic and valuable tool for Baybayin enthusiasts and learners to value its historical significance. © 2024 Copyright held by the owner/author(s).

关键词： Mobile applications

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Application of Clustering Algorithms to enhance Personalized Learning through Recommendation Model

Application of Clustering Algorithms to enhance Personalized...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, Mhd Saeed Tamang, Madhav Theeng Mani, Dyuthi Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

Technology and education have recently changed student involvement with learning resources and educational experiences. It is very important to have a platform which can provide personalized learning resources and group students by learning patterns. The research develops recommendation systems and grouping algorithms to help students with their learning patterns and performance measures. To record multidimensional student learning experiences, demographics, learning activities, problem-solving behaviours, and performance measures are extracted and processed. This system makes personalised student suggestions via collaborative filtering, especially Alternating Least Squares (ALS). The model predicts learning materials and problem sets based on student preferences and ability levels by analysing historical student interactions with learning resources. The research also uses clustering methods like K-means clustering to group students with similar learning and performance patterns. Clustering analysis lets instructors discover student group traits and tailor interventions and support techniques. This research also examines temporal relationships in student learning sequences using sequential models. Sequential learning activities and problem-solving behaviour of students help recurrent neural networks (RNNs), or sequential pattern mining algorithms predict their next activities. This research used a huge dataset of over sixteen million exercise logs and applied collaborative filtering (ALS) and K-means clustering to find learning patterns. The ALS-based recommendation system achieved a Mean Squared Error (MSE) of 0.23, showed excellent predictive accuracy. Clustering grouped students by learning behaviours, enabling targeted interventions. This research improves personalised learning using machine learning and data analytics in education. The recommendation system can help instructors tailor training, give individualised support, and boost academic success for various

关键词： Federated learning

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A Comparative Study of Sales Prediction Using Machine Learning Models: Integration of PySpark and Power BI

A Comparative Study of Sales Prediction Using Machine Learni...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, MhD Saeed Tamang, Madhav Theeng Nepal, Anup Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

Retail management requires accurate sales forecasting for strategic planning, inventory control, and revenue maximisation. In this research, we are predicting sales using PySpark-built ML models and the Big Mart dataset. We evaluate and demonstrate the prediction skills of numerous machine learning algorithms, concentrating on XGBoost. Big Mart offers item weight, visibility, type, and outlet data. We use these properties as prediction model features. PySpark, a strong distributed computing platform, manages massive datasets for analysis and model training. In massive trials, we test decision trees (DT), XGBoost, linear regressions (LR), and random forests (RF). Training and testing enhance model accuracy. RMSE and R-squared measure our model quality. Our metrics evaluate the model’s data fit and prediction accuracy. XGBoost performed best with an RMSE of 1081 and an R-squared of 0.59. The XGBoost algorithm accurately predicts Big Mart sales. The model performs well because of its ensemble learning and understanding of intricate dataset links. We also used Power BI to present analytical insights, helping decision-makers design sales-estimated business plans. This study employed several machine learning algorithms, XGBoost gave the best performance. This research provides insights into how organisations might use these technologies to improve resource allocation and inventory management decision-making. ©2024 IEEE.

关键词： Prediction models

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Revolutionizing Loan Approval: Harnessing the Power of K-Nearest Neighbors for Predictive Eligibility

Revolutionizing Loan Approval: Harnessing the Power of K-Nea...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, Mhd Saeed Tamang, Madhav Theeng Hussain, Niyas Jaheer Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

A bank has become a basic need of our daily lives. People use banks for several tasks, such as depositing or withdrawing money and borrowing loans. The number of loan applications is rising significantly. It is crucial for the bank and financial sectors to determine loan eligibility and approve loans to suitable applicants only. This study attempts to design a system which can easily predict loan eligibility with the application of several machine learning (ML) algorithms. We employed the five different machine learning algorithms to achieve this research’s aim. These algorithms were trained using the pre-processed dataset to create predictive models. F1 score, accuracy, Recall and Precision are used to evaluate the performance of the proposed approaches. The result showed that K-nearest neighbor (KNN) demonstrated outstanding performance, with an accuracy rate of 88.89%. The Random Forest (RF) model had an accuracy of 84.44%, whereas the Support Vector Machine (SVM) and Linear regression (LR) models achieved 82.22% and 80% accuracy, respectively. The Decision Tree (DT) algorithms showed the worst performance among the five algorithms, with an accuracy of 79.88%. The achievement of this research focuses on the possibility of machine learning algorithms as an efficient method for predicting loan eligibility in banks, contributing to the advancement of financial technology by automating and optimizing key banking processes, reducing processing times, and enhancing overall bank efficiency. ©2024 IEEE.

关键词： Nearest neighbor search

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Efficiency of a cylindrical wave energy converter with different bottom shapes in a chambered breakwater

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Marine Systems and Ocean Technology 2025年第2期20卷 1-15页

作者： Aiswaria, K. Balaji, Ramakrishnan Krishnendu, P. Jodha, Shweta Department of Civil Engineering Indian Institute of Technology Bombay Mumbai400076 India School of Engineering Computing and Mathematics University of Plymouth Drake Circus PlymouthPL48AA United Kingdom Naval Architecture and Ocean Engineering Indian Maritime University Visakhapatnam Andhra Pradesh Visakhapatnam531035 India

Ocean waves are one of the high-density renewable energy resources and can work as a generous, sustainable, and clean energy source. The energy capturing of a floating point absorber system is designed to be maximum when it is close to resonance. Integrating coastal protection structures with a wave energy converter can be a promising solution to extract wave energy economically. This paper investigates the influence of bottom shapes on the hydrodynamic performance of a cylindrical heaving floating point absorber wave energy converter stand-alone and integrated with breakwater conditions. The study is carried out for constant mass and constant draft model cases, providing identical conditions for comparison. It is seen that a cylinder with a 90° conical bottom has better performance for the same waterline diameter than all the other models. After the integration with a chambered breakwater, the power performance of the wave energy device was found to increase by a maximum of 3.5 times more than that of the stand-alone case for all the models at quasi-resonance conditions, especially when the WEC is positioned at locations of amplified wave elevations inside the chamber. © The Author(s), under exclusive licence to Sociedade Brasileira de Engenharia Naval 2025.

关键词： Wave power

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Performance of Enhanced CNN-LSTM Prediction Model for Vehicle-Mounted Air Quality Monitoring 15

Performance of Enhanced CNN-LSTM Prediction Model for Vehicl...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Llamelo, Ceferino Medina, Ruji Fajardo, Arnel Graduate Programs Technological Institute of the Philippines Quezon City Philippines School of Architecture Computing and Engineering University of Saint Louis Tuguegarao City Philippines

ISBN: (纸本)9798350364637

CNN-LSTM has been established for predictive modeling in urban air pollution. However, while the method is accurate for time series forecasting, it struggles with accurately capturing spatial variability in air quality data. To address this limitation, we propose an Enhanced CNN-LSTM Prediction Model for vehicle-mounted air quality monitoring systems. This model integrates Convolutional Neural Networks (CNNs), spatial attention mechanisms, and Long Short-Term Memory (LSTM) networks to enhance the capture of both spatial and temporal dependencies in the data. Performance evaluation shows a Mean Absolute Error (MAE) of 5.2 μg/m3for PM2.5, 1.3 μg/m3for PM10, 0.1 ppm for CO, 0.1 ppm for VOCs, and 0.68°C for temperature, with Root Mean Squared Error (RMSE) values of 7.8μg/m3for PM2.5, 1.5μg/m3for PM10, 0.10 ppm for CO, 0.10 ppm for VOCs, and 0.75°C for temperature. Compared to traditional models like Linear Regression and standalone LSTM networks, the Enhanced CNN-LSTM model demonstrates substantial improvements in prediction accuracy. These findings highlight the model's superior performance in delivering accurate, real-time air quality predictions, offering a robust framework for pollution management and public health interventions. © 2024 IEEE.

关键词： Convolutional neural networks

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Gender-Specific Speech Enhancement architecture for Improving Deep Neural Networks Learning

Gender-Specific Speech Enhancement Architecture for Improvin...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Nossier, Soha A. Sharif, Mhd Saeed Biomedical Engineering Department Medical Research Institute Alexandria University Alexandria Egypt Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (纸本)9798331533137

Deep learning techniques for speech enhancement rely on training a deep neural network to process noisy speech, regardless the gender of the speaker. However, research shows that the speech of male and female stimulates different parts in human brain, and that female speech requires more complex analysis. This implies that different processing is applied on the speech, based on the speaker gender. In this work, we argue that male and female speeches have different features that can help in the learning process of speech enhancement deep neural networks if the training is performed on male and female speech data, independently, and using two different deep neural networks, specifically implemented for enhancing the noisy speech signal of the target gender. This work presents a gender-specific speech enhancement architecture, which consists of a front-end binary classifier to detect the speaker gender. Based on the classifier decision, the noisy speech is enhanced using either a male or female speech enhancement model. One-stage and two-stage speech enhancement approaches are used to process male and female speeches, respectively. The results reveal that gender-specific speech enhancement has positive impact on the enhanced speech by deep neural networks. Additionally, the developed architecture achieved classifier accuracy 96.9% and 0.11 increase in Covl speech quality metric for the test data, in comparison to other best-performing networks. ©2024 IEEE.

关键词： Deep neural networks

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Autonomous Navigation For TurtleBot3 Robots in Gazebo Simulation Environment

Autonomous Navigation For TurtleBot3 Robots in Gazebo Simula...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Tamang, Madhav Theeng Maheriya, Darshana Sharif, Mhd Saeed Sutharssan, Thamo Intelligent Technologies Research Group School of Architecture Computing and Engineering University of East London London United Kingdom Department of Engineering University of East London London United Kingdom

ISBN: (纸本)9798331533137

The fast-paced growth in the field of robotics has driven the creation of autonomous navigation systems that are necessary for robots to work autonomously in diverse environments. This research targets TurtleBot3, an immensely popular robotic platform that is known for its affordability and adaptability. The objective of TurtleBot3 is to improve the capabilities within the Gazebo simulation environment, an open-source robotic simulator that provides a realistic virtual environment for testing and developing navigation algorithms for robotics. The combination of intricate sensor technologies, leading-edge control systems, and innovative artificial intelligence strategies is the core of this research, enabling autonomous navigation in complicated surroundings. The initial focus of the study is the application and utilization of path planning algorithms, such as A* (A Star) and Rapidly exploring Random Tree (RRT) and using Simultaneous Localization and Mapping (SLAM) algorithms. Results from simulations demonstrate that the A* algorithm achieved a path accuracy rate of over 95% in static environments, while the RRT algorithm proved effective in dynamic, multi-dimensional spaces. The aim of this analysis is to enhance autonomous navigation algorithms using the Gazebo simulation environment. Conducting tests in a virtual setting minimizes the risks and costs compared to real-time testing. The outcomes of this research are anticipated to advance the field of robotics and hold significant value for applications in autonomous systems, including engineering, urban mobility, and domestic assistance. ©2024 IEEE.

关键词： Robot Operating System

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rECOnnect: Smart Waste Bin for Internet Access

rECOnnect: Smart Waste Bin for Internet Access

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2024 International Conference on Computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Acorda, Gladwin Lorenzo, Ariel M. Avila, Marianne Realin, Kathlene Solmayor, Jonathan Mabborang, Godwin Sam Calalo, Izella Mae School of Architecture Computing and Engineering University of Saint Louis Tuguegarao City Philippines University Research and Innovation University of Saint Louis Tuguegarao City Philippines

ISBN: (纸本)9798350368802

This study introduces an innovative waste disposal method by incorporating technology into a plastic bottle trash bin that offers Wi-Fi access as a reward for proper waste management. The 'smart bin' is designed to collect plastic bottles and grant internet time to users. It operates using a microcontroller with sensors that detect deposited bottles, displaying the number collected and the corresponding internet access time. Users can press a button to transfer this time to a TP-Link module, which provides a voucher code for internet access. Testing revealed that the bin's infrared (IR) sensor effectively counted bottles when inserted one at a time but struggled with multiple bottles, often registering just one. After five bottles, the system successfully generated a voucher code and reset the count. The study concluded that the smart bin accurately tracks bottles and provides corresponding internet access, ensuring a reliable user experience. © 2024 IEEE.

关键词： Wi-Fi

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