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Fine-Tuning ResNet50 for Land Use and Land Cover Classification in the Philippines 6

Fine-Tuning ResNet50 for Land Use and Land Cover Classificat...

引用

6th IEEE International Conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Cahapin, Erwin Fajardo, Arnel Graduate Programs Technological Institute of the Philippines Manila Philippines College of Computing Science Information and Communication Technology Isabela State University Isabela Philippines

ISBN: (纸本)9798350306613

This study focuses on employing ResNet50 as a base model for classifying LULC from the satellite dataset. Unlike conventional ML methods such as decision trees, RF and SVM, convolutional neural network (CNN) has emerged as a viable solution for image classification, with its automatic feature extraction and effective spatial feature capture. However, the complexity of building models from scratch can lead to overfitting, prompting the adoption of transfer learning. Transfer learning involves retraining a pre-trained model base, significantly enhancing CNN model training accuracy, while reducing the need for a large dataset. The objectives of this study include preprocessing Sentinel-1 synthetic aperture radar (SAR) dataset, fine-tuning the ResNet50 model, and assessing model performance using confusion matrix together with other performance metrics. The model-building pipeline includes ResNet50 input preparation, adding custom layers on top of the base model, compiling the model, and implementing early stopping during training. Hyperparameter tuning is performed using the keras tuner. Results exhibit the efficacy of the method, indicated by high training and testing accuracies and low losses. The final evaluation metrics indicate a well-performing model that satisfies the evaluation metrics. This study successfully applies transfer learning with ResNet50 to Sentinel-1 SAR data for LULC classification, providing insights into effective remote sensing applications. © 2024 IEEE.

关键词： fine-tuning hyperparameters keras tuner LULC ResNet50

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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...

引用

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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Towards Designing an Intelligent Recommender System using Adaptive Collaborative Filtering Technique 3

Towards Designing an Intelligent Recommender System using Ad...

引用

3rd International Symposium on Computer Technology and Information Science, ISCTIS 2023

作者： Zhang, Qi Abisado, Mideth College of Computing and Information Technologies National University Graduate Programs Department Manila Philippines TangShan Normal University Hebei China

ISBN: (纸本)9798350325386

In recent years, intelligent recommendation systems using adaptive collaborative filtering have received great attention due to their ability to provide personalized and relevant recommendations. The innovation of this study lies in proposing an adaptive collaborative intelligent recommendation model (ACIR), which combines adaptive collaborative filtering with context, user behavior, and movie feature information. It utilizes automatic encoders and deep learning algorithms to capture the characteristic relationships of the context, explore the internal connections between the context and users, and improve the accuracy and interpretability of the recommendation system to meet the personalized needs of users. The aim of this study is to predict user preferences by considering the characteristics of users, projects, and contexts, and to evaluate the potential changes in user preferences over time. In the experimental stage, LDOS CoMoDa was used to test and train the dataset, and the model was validated. Choose traditional collaborative recommendation, k-means algorithm, CAMF, and CAMF_ CM algorithm for comparison. After experimental verification, this article proposes that compared with other traditional recommendation algorithms, ACIR has higher accuracy and stronger application value © 2023 IEEE.

关键词： Recommender systems

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Repurposing and Analysis of Used LiFePO4 Battery Cells Using Electrochemical Impedance Spectroscopy

Repurposing and Analysis of Used LiFePO4 Battery Cells Using...

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2024 Multimedia University Engineering Conference, MECON 2024

作者： Cantal, Jed Allan M. Fajardo, Arnel C. Ozaeta, Mark Anthony A. Technological Institute of the Philippines-Manila Graduate Programs Metro Manila Philippines College of Computing Studies Information and Communication Technology Isabela State University-Cauayan Isabela Philippines

ISBN: (纸本)9798331530747

Lithium-ion batteries (LIBs) have become integral to modern energy solutions, yet their widespread usage poses challenges in end-of-life management and recycling. This study addresses these challenges by analyzing and repurposing used Lithium-iron Phosphate (LiFePO4) battery cells from electric vehicles. Through Electrochemical Impedance Spectroscopy (EIS) and cycle aging tests, battery health and performance are evaluated. Additionally, a power bank application is developed using the evaluated cells. The experimental setup involves an electrochemical workstation, computer interface, and battery test system, enabling accurate measurements. Testing procedures include cycle aging tests and EIS analysis. Subsequently, a power bank is designed using the evaluated LiFePO4 cells, providing a portable energy solution. Results demonstrate impedance behavior, capacity fade, and discharge capacity trends, guiding decisions on repurposing or recycling battery cells. The power bank exhibits efficient charging capabilities and compatibility with modern devices, highlighting the viability of LiFePO4-based solutions. This study underscores the potential of LiFePO4-based power banks to offer reliable, efficient, and environmentally friendly charging solutions while extending the lifespan of used battery cells. © 2024 IEEE.

关键词： Electrochemical impedance spectroscopy

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Land Use and Land Cover Mapping using Multi-temporal Sentinel-1 Data and Machine Learning

Land Use and Land Cover Mapping using Multi-temporal Sentine...

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2023 IEEE Symposium on Wireless Technology and Applications, ISWTA 2023

作者： Cahapin, Erwin L. Fajardo, Arnel C. Technological Institute of the Philippines Graduate Programs Manila Philippines Isabela State University College of Computing Science Information and Communication Technology Isabela Philippines

ISBN: (纸本)9798350310955

Land use and land cover (LULC) maps are essential for effective policymaking and resource management. However, in many developing countries, including the Philippines, the lack of infrastructure, capacity, and technology poses significant challenges to generating reliable and timely LULC maps. With the availability of abundant Sentinel-1 data, various classification techniques have been explored to map LULC. This study focused on the proper preprocessing, feature extraction, and the use of RF50 for the classification of multi-temporal Sentinel-1A imagery. It also aimed to assess the performance of RF50 in LULC classification and mapping in the Philippine context. The methodology involved preprocessing the Sentinel-1 data, training and testing RF50 using a set of LULC classes, and developing an LULC map. The performance of RF50 is assessed using various metrics such as accuracy, precision, correlation and error rate. © 2023 IEEE.

关键词： Developing countries

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Improving Lane Annotation in Autonomous Driving Data Sets with Classical Computer Vision Techniques

Improving Lane Annotation in Autonomous Driving Data Sets wi...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Stojanovic, Dimitrije Cetic, Nenad Kocic, Jelena Pavkovic, Bogdan University of Novi Sad Faculty of Technical Sciences Departmant of Computing and Control Engineering Novi Sad Serbia Customer Programs TTTech Auto Belgrade Serbia

ISBN: (纸本)9798350347722

Autonomous driving systems rely on accurate and reliable lane detection to safely navigate roads. In this paper, we propose a method for improving lane annotation in autonomous driving data sets using classical computer vision techniques. The proposed method combines the Hough transform and linear curve fitting to detect and smooth the positions of lane markings in a video stream. We evaluate the performance of the proposed method on the Berkeley DeepDrive (BDD) dataset and compare it to the ground truth annotations. Results show that the proposed method achieves a high level of accuracy and robustness in lane detection, and can effectively improve lane annotation in autonomous driving data sets. Our method provides a valuable tool for training and evaluating autonomous driving systems, and can also be applied to improve annotation in different datasets. © 2023 IEEE.

关键词： Hough transforms

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Philippine Stock Exchange Index Forecasting Using a Tuned Artificial Neural Network Model with a Modified Firefly Algorithm 6

Philippine Stock Exchange Index Forecasting Using a Tuned Ar...

引用

6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Villaruz, Jolitte A. Gerardo, Bobby D. Medina, Ruji P. Technological Institute of the Philippines Graduate Programs Quezon City Philippines Northern Iloilo State University College of Information and Computing Studies Estancia Iloilo Philippines

ISBN: (纸本)9798350325485

Forecasting stock market index fluctuations is crucial for financial market participants. However, analyzing time-series datasets from stock markets is challenging due to their noisy and dynamic nature. Artificial neural network (ANN) models have shown promising results in time-series forecasting, but their success heavily relies on carefully selecting and configuring their hyperparameters. This process often involves time-consuming trial-And-error techniques, default parameter usage, or reliance on limited existing studies that may have biases and errors. To address this, this research proposes a novel approach using a modified Firefly algorithm to automatically find optimal hyperparameters for the ANN model. The method is applied to forecast the Philippine Stock Exchange Composite Index (PSEi) closing price for the next day using data from May 11, 2018, to May 10, 2023. Results show minimal error rates, confirming the approach's effectiveness in improving forecasting techniques and enhancing decision-making in the ever-changing stock market landscape. © 2023 IEEE.

关键词： Optimization

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Modifying Blowfish Algorithm Using Permutation Techniques 24

Modifying Blowfish Algorithm Using Permutation Techniques

引用

24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Wenceslao, Jon Laurence B. Wenceslao, Felicisimo V. Cerna, Patrick D. Northern Iloilo State University College of Information and Computing Studies Estancia Iloilo Philippines State University of Northern Negros Graduate Programs Negros Occidental Philippines

ISBN: (纸本)9798331515799

This paper proposes a modified version of the Blowfish Algorithm using permutation techniques. These techniques are key parts of the F-function of the algorithm. The first modification is in the bit operations while the second part employs the cyclical Shift Rows operation. We conducted simulations to test the security strength of the modified Blowfish Algorithm in changing one bit from two input strings. The avalanche effect showed 54.687% and 53.125% changes in the output bits thus making our model secure. We also tested the execution performance of the original Blowfish Algorithm and the modified Blowfish Algorithm in the encryption and decryption processes. While the original version performs better than the modified version, no statistical difference was found between the two versions. © 2025 IEEE.

关键词： Encryption algorithms

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Twitter Sentiment Analysis with Maximum Entropy and Naive Bayes Using N -gram Approach 8

Twitter Sentiment Analysis with Maximum Entropy and Naive Ba...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Atillo, Guilbert Nicanor A. Gerardo, Bobby D. Medina, Ruji P. Graduate Programs Technological Instituteof the Philippines Quezon City1109 Philippines College of Information and Computing Studies Northern Iloilo State University Estancia Iloilo5017 Philippines

ISBN: (纸本)9798350326093

Finding out what people think and feel has always been common regarding social and political opinions, products, and services experiences. Due to the sheer amount of information on the internet, different machine learning algorithms are implemented to classify information better. The Maximum Entropy and Nave Bayes employing the n-grams approach will be assessed in this research study along with their accuracy, recall, f-1 score, precision, and time complexity. Instead of the customary product and service review, this study will investigate the opinions of Twitter users regarding the Death Penalty. In comparison to prior literature, the experiment's findings show improved accuracy. The findings reveal that Maximum Entropy with n-grams performs better when compared to Naïve Bayes with n-grams, with an average accuracy result of 92.67% in classifying the death penalty data. However, the Naïve Bayes using n-grams outperform Maximum Entropy regarding time complexity with an average of 69.83 milliseconds on an average input length of 97.98 Kb. The trade-off is when the basis is accuracy, the Maximum Entropy with n-grams is recommended. But regarding time complexity, the Naïve Bayes using n-gram is favored for smaller input length. © 2023 IEEE.

关键词： Sentiment analysis

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Sentiment Analysis in Product Reviews with Maximum Entropy and Naïve Bayes Using N-gram Method 6

Sentiment Analysis in Product Reviews with Maximum Entropy a...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Atillo, Guilbert Nicanor A. Gerardo, Bobby D. Medina, Ruji P. Graduate Programs Technological Institute of the Philippines Quezon City1109 Philippines Northern Iloilo State University Estancia College of Information and Computing Studies Iloilo5017 Philippines

ISBN: (纸本)9798350315639

Online shoppers now primarily rely on product reviews and user experiences shared on social media platforms before making an online purchase. The opportunity to capture product sentiments on social media has drawn interest in data mining analysis as it provides exciting ideas to support business growth and success. The two biggest online retailers, Amazon and Shein product reviews will be analyzed with the Maximum Entropy and Naive Bayes using the n-grams approach based on precision, recall, f-1 score, and accuracy. This study will also evaluate their time complexities depending on hardware and software requirements. Findings reveal that Maximum Entropy with n-grams generated improved accuracy performance, particularly with unigram, with an average accuracy rating of 91.5% compared to Naive Bayes with n-grams. However, the Naïve Bayes performs better when tested on different hardware and software specifications. Thus, the Naive Bayes algorithm performs better in execution time, and the Maximum Entropy using the n-gram approach resulted in improved accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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