Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines by Cheng Yin, Le Thanh Binh, Duong Tran Anh, Son T. Mai, Anh Le, Van-Hau Nguyen, Van-Chien Nguyen, Nguyen Xuan Tinh, Hitoshi Tanaka, Nguyen Trung Viet, Long D. Nguyen, Trung Q. Duong

“…Hence it is crucial to accurately monitor the shoreline changes for better coastal management and reduction of risks for communities. In this paper, we explored a statistical forecasting model, Seasonal Auto-regressive Integrated Moving Average (SARIMA), and two Machine Learning (ML) models, Neural Network Auto-Regression (NNAR) and Long Short-Term Memory (LSTM), to predict the shoreline variations from surveillance camera images. …”
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Improving Voice Spoofing Detection Through Extensive Analysis of Multicepstral Feature Reduction by Leonardo Mendes de Souza, Rodrigo Capobianco Guido, Rodrigo Colnago Contreras, Monique Simplicio Viana, Marcelo Adriano dos Santos Bongarti

“…This study proposes a novel experimental analysis that extensively explores various dimensionality reduction strategies in conjunction with supervised machine learning models to effectively identify spoofed voice signals. …”
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Investigating the Effects of Labeled Data on Parameterized Physics-Informed Neural Networks for Surrogate Modeling: Design Optimization for Drag Reduction over a Forward-Facing Ste... by Erik Gustafsson, Magnus Andersson

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Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment by Rafael Gonçalves, Diogo Magalhães, Rafael Teixeira, Mário Antunes, Diogo Gomes, Rui L. Aguiar

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Using Permutation-Based Feature Importance for Improved Machine Learning Model Performance at Reduced Costs by Adam Khan, Asad Ali, Jahangir Khan, Fasee Ullah, Muhammad Faheem

“…This task is commonly achieved through Machine Learning (ML) techniques, but improving model performance typically incurs significant computational costs. …”
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A novel machine learning model for perimeter intrusion detection using intrusion image dataset. by Shahneela Pitafi, Toni Anwar, I Dewa Made Widia, Zubair Sharif, Boonsit Yimwadsana

“…To address these challenges, a new machine learning model is developed. This model utilizes the pre-trained InceptionV3 for feature extraction on PID intrusion image dataset, followed by t-SNE for dimensionality reduction and subsequent clustering. …”
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Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model by Untari Novia Wisesty, Tjokorda Agung Budi Wirayuda, Febryanti Sthevanie, Rita Rismala

“…In this research, stroke prediction was carried out on the Stroke dataset acquired from the Kaggle dataset using various machine learning models. Then, data sampling techniques are used to handle data imbalance problems in the stroke dataset, which include Random Undersampling, Random Oversampling, and SMOTE techniques. …”
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Using Machine Learning to Develop a Surrogate Model for Simulating Multispecies Contaminant Transport in Groundwater by Thu-Uyen Nguyen, Heejun Suk, Ching-Ping Liang, Yu-Chieh Ho, Jui-Sheng Chen

“…Recent advances in artificial intelligence (AI) offer promising alternatives, particularly data-driven machine learning techniques, for accelerating such simulations. …”
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A Machine Learning-Based Risk Prediction Model During Pregnancy in Low-Resource Settings by Kapil Tomar, Chandra Mani Sharma, Tanisha Prasad, Vijayaraghavan M. Chariar

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Photovoltaic Farm Power Generation Forecast Using Photovoltaic Battery Model with Machine Learning Capabilities by Agboola Benjamin Alao, Olatunji Matthew Adeyanju, Manohar Chamana, Stephen Bayne, Argenis Bilbao

“…This study presents a machine learning-based photovoltaic (PV) model for energy management and planning in a microgrid with a battery system. …”
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Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma by Hehe Wang, Junge Zhang, Peng Cheng, Lujie Yu, Chunlin Li, Yaowen Wang

“…This study aimed to develop robust machine learning (ML)-driven diagnostic models and identify key biomarkers through integrated analysis of multi-cohort transcriptomic data. …”
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Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction by Mehak Gurnani, Konstantinos Patlatzoglou, Joseph Barker, Derek Bivona, Libor Pastika, Ewa Sieliwonczyk, Boroumand Zeidaabadi, Paolo Inglese, Lara Curran, Ahran D. Arnold, Declan O'Regan, Zachary Whinnett, Kenneth C. Bilchick, Nicholas S. Peters, Daniel B. Kramer, Jonathan W. Waks, Arunashis Sau, Fu Siong Ng

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Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques by Ali Mohammed Alsaffar, Mostafa Nouri-Baygi, Hamed Zolbanin

“…To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. …”
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Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models by Pakorn Lonlab, Suparinthon Anupong, Chalita Jainonthee, Sudarat Chadsuthi

“…This study aimed to identify the high- and low-risk HFMD outbreak areas using machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). …”
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Integrating Sentinel-1 SAR and Machine Learning Models for Optimal Soil Moisture Sensor Placement at Catchment Scale by Yi Xie, Guotao Cui, Kaifeng Zheng, Guoping Tang

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Enhancing weather index insurance through surrogate models: leveraging machine learning techniques and remote sensing data by Sachini Wijesena, Biswajeet Pradhan

“…WII products often rely on a single weather index, which fails to encompass the complex nature of weather events. While machine learning models offer the potential to model the multifaceted nature of factors influencing crop growth, their adoption in WII products has been limited due to their lack of transparency, often perceived as ‘black box models’. …”
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X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head by Yaqing He, Yang Chen, Yusen Chen, Pingshi Li, Le Yuan, Maoxiao Ma, Yuhao Liu, Wei He, Wu Zhou, Leilei Chen

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Experimental investigation of solar PVT collector with the dryer on mass and temperature of dried red chili with Machine Learning Models by Miroslav Mahdal, K. Rajathi, Muniyandy Elangovan, Prabhukumar Sellamuthu, Amit Verma

“…The drying process was stop at 11 % moisture content after 6-day testing round of red chilies drying. Machine learning (ML) models, namely the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree (DT), were used to forecast the temperature and mass dryness variables.The RBF model showed the best performance with 0.98, 0.95, and 0.92 for temperature dryness, above MLP and DM. …”
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Empirical and machine learning-based approaches to identify rainfall thresholds for landslide prediction: a case study of Kerala, India by Varun Menon, Sreevalsa Kolathayar

“…Supporting this objective, the present study developed a machine learning (ML) classifier-based threshold model to determine rainfall thresholds for predicting impending landslides in Kerala, India, using historical data. …”
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Advancing wastewater reuse: AI-driven insights into ozone-based organic pollutant reduction by Syed Muzzamil Hussain Shah, Sani I. Abba, Mohamed A. Yassin, Ebrahim Al-Qadami, Dahiru U. Lawal, Imtiaz Afzal Khan, Jamilu Usman, Haris U. Qureshi, Isam H. Aljundi

“…Subsequently, the study further integrated Artificial Intelligence (AI) assisted Machine Learning (ML) for accurate ORP prediction. …”
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