Random Ensemble MARS: Model Selection in Multivariate Adaptive Regression Splines Using Random Forest Approach by Mehmet Ali Cengiz, Dilek Sabancı

“…Multivariate Adaptive Regression Splines (MARS) is a supervised learning model in machine learning, not obtained by an ensemble learning method. …”
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Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization by Shiqi Yu, Bin Mao, Yuanhang Zhou, Yunhong Liu, Chanlin Yi, Fali Li, Dezhong Yao, Peng Xu, X. San Liang, Tao Zhang

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LGLoc as a new language model-driven graph neural network for mRNA localization by Saeedeh Akbari Rokn Abadi, Aref Shahbakhsh, Somayyeh Koohi

“…., cancer, Alzheimer’s) and drug development. While numerous methods have been developed for this purpose, existing approaches face challenges: experimental methods are often costly and time-consuming, while computational methods may lack accuracy and efficiency. …”
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Optimisation-Based Feature Selection for Regression Neural Networks Towards Explainability by Georgios I. Liapis, Sophia Tsoka, Lazaros G. Papageorgiou

“…Regression is a fundamental task in machine learning, and neural networks have been successfully employed in many applications to identify underlying regression patterns. …”
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Prediction and Assessment of Myocardial Infarction Risk on the Base of Medical Report Text Collection by Margaryta Prazdnikova

“…This underscores the significant potential of leveraging textual data and machine learning methods in medical diagnostics. Moreover, the reduction in false predictions highlights the model's reliability and suitability for practical application. …”
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Developing a multi-variate prediction model for COVID-19 from crowd-sourced respiratory voice data by Yuyang Yan, Wafaa Aljbawi, Sami O. Simons, Visara Urovi

“…Methods: We develop a deep learning model to identify COVID-19 from voice recording data. …”
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An Integrated Approach for Emergency Response and Long-Term Prevention for Rainfall-Induced Landslide Clusters by Wenxin Zhao, Yajun Li, Yunfei Huang, Guowei Li, Fukang Ma, Jun Zhang, Mengyu Wang, Yan Zhao, Guan Chen, Xingmin Meng, Fuyun Guo, Dongxia Yue

“…Under the background of global climate change, shallow landslide clusters induced by extreme rainfall are occurring with increasing frequency, causing severe casualties and economic losses. …”
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The relationship between the annual catch of bigeye tuna and climate factors and its prediction by Peng Ding, Hui Xu, Xiaorong Zou, Xiaorong Zou, Xiaorong Zou, Shuyi Ding, Siqi Bai

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Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study by Andrew Houston, Sophie Williams, William Ricketts, Charles Gutteridge, Chris Tackaberry, John Conibear

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AI-Powered System for an Efficient and Effective Cyber Incidents Detection and Response in Cloud Environments by Mohammed Ashfaaq M. Farzaan, Mohamed Chahine Ghanem, Ayman El-Hajjar, Deepthi N. Ratnayake

“…This study tackles this urgent issue by introducing a cutting-edge AI-driven cyber incident response system specifically designed for cloud platforms. Unlike conventional methods, our system employs advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to provide accurate, scalable, and seamless integration with platforms like Google Cloud and Microsoft Azure. …”
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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review by Ikram Bagri, Karim Tahiry, Aziz Hraiba, Achraf Touil, Ahmed Mousrij

“…Combining such methods with time–frequency analysis methods was shown to be an ideal combination for information extraction. …”
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Metabolomics and nutrient intake reveal metabolite–nutrient interactions in metabolic syndrome: insights from the Korean Genome and Epidemiology Study by Minyeong Kim, Suyeon Lee, Junguk Hur, Dayeon Shin

“…Objective This study aimed to characterize the metabolomic profiles and nutrient intake associated with MetS and to examine their relationships in the Ansan-Ansung cohort of the Korean Genome and Epidemiology Study (KoGES). Methods Data from 2,306 middle-aged adults (1,109 men and 1,197 women) in the KoGES Ansan-Ansung cohort were analyzed. …”
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The impact of online food delivery applications on dietary pattern disruption in the Arab region by Radwan Qasrawi, Radwan Qasrawi, Suliman Thwib, Ghada Issa, Malak Amro, Razan AbuGhoush, Maha Hoteit, Maha Hoteit, Sahar Khairy, Narmeen Jamal Al-Awwad, Narmeen Jamal Al-Awwad, Khlood Bookari, Sabika Allehdan, Dalal Alkazemi, Haleama Al Sabbah, Salima Al Maamari, Asma H. Malkawi, Reema Tayyem

“…BackgroundWhile online food delivery applications (OFDAs) offer convenient food accessibility, their impact on dietary behaviors remains insufficiently explored, especially in the Arab region. This study applies machine learning (ML) techniques to identify the key behavioral and nutritional factors contributing to dietary disruption linked to OFD platforms.MethodsWe conducted a cross-sectional study which involved 7,370 adults across 10 Arab countries using a comprehensive online survey. …”
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Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels by Bassel Al Homssi, Chiu C. Chan, Ke Wang, Wayne Rowe, Ben Allen, Ben Moores, Laszlo Csurgai-Horvath, Fernando Perez Fontan, Sithamparanathan Kandeepan, Akram Al-Hourani

“…This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is composed of: 1) forecasting method using model-based deep learning, intended for real-time operation of satellite terminals; and 2) statistical channel simulator that generates a time-series path-loss random process, intended for system design and research. …”
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Considering the effect of non-landslide sample selection on landslide susceptibility assessment by Youchen Zhu, Deliang Sun, Haijia Wen, Qiang Zhang, Qin Ji, Changming Li, Pinggen Zhou, Jianjun Zhao

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Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality by Fernando García-García, Dae-Jin Lee, Mónica Nieves-Ermecheo, Olaia Bronte, Pedro Pablo España, José María Quintana, Rosario Menéndez, Antoni Torres, Luis Alberto Ruiz Iturriaga, Isabel Urrutia, COVID-19 & Air Pollution Working Group

“…Methods Multicentric observational, prospective, longitudinal, cohort study conducted in four hospitals in Spain. …”
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An improved deep learning model for soybean future price prediction with hybrid data preprocessing strategy by Dingya CHEN, Hui LIU, Yanfei LI, Zhu DUAN

“…In the data preprocessing stage, futures price series are decomposed into subsequences using the ICEEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) method. The Lempel-Ziv complexity determination method was then used to identify and reconstruct high-frequency subsequences. …”
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A Study on Denoising Autoencoder Noise Selection for Improving the Fault Diagnosis Rate of Vibration Time Series Data by Jun-gyo Jang, Soon-sup Lee, Se-Yun Hwang, Jae-chul Lee

“…The fault diagnosis rates were calculated using One-Class Support Vector Machines (OC-SVM) for performance comparison. As a result, the model trained with high-frequency noise achieved a 0.0293 higher average F1-score than the Gaussian-based model. …”
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Human motion state recognition based on smart phone built-in sensor by Xiaoling YIN, Xiaojiang CHEN, Qishou XIA, Juan HE, Pengyan ZHANG, Feng CHEN

“…To solve problems of low accuracy and fewer types of human motion state recognized by current smart phones,a method to do hierarchical recognition by using acceleration sensors and gravity sensors was proposed.Firstly,linear acceleration in inertial coordinate system and independent of phone direction was calculated by using the relation between acceleration and gravity acceleration.Secondly,according to the span of human motion frequency and linear acceleration vector,positions of peak and trough of footsteps were determined.Finally,feature vector of linear acceleration in time domain was extracted and human motion states were recognized hierarchically by using hierarchical support vector machine (H-SVM).The experiment shows the method can recognize six usual human motion states,while accuracy rate up to 93.37%.…”
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Human motion state recognition based on smart phone built-in sensor by Xiaoling YIN, Xiaojiang CHEN, Qishou XIA, Juan HE, Pengyan ZHANG, Feng CHEN

“…To solve problems of low accuracy and fewer types of human motion state recognized by current smart phones,a method to do hierarchical recognition by using acceleration sensors and gravity sensors was proposed.Firstly,linear acceleration in inertial coordinate system and independent of phone direction was calculated by using the relation between acceleration and gravity acceleration.Secondly,according to the span of human motion frequency and linear acceleration vector,positions of peak and trough of footsteps were determined.Finally,feature vector of linear acceleration in time domain was extracted and human motion states were recognized hierarchically by using hierarchical support vector machine (H-SVM).The experiment shows the method can recognize six usual human motion states,while accuracy rate up to 93.37%.…”
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