Detecting Depression via Tweets on Twitter Utilizing Machine Learning and Neural Network Models by Xinyong Lu

“…Eight diverse methods incorporating machine learning (ML) and deep learning (DL) are employed. …”
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Supervised Sentiment Analysis of Indirect Qualitative Student Feedback for Unbiased Opinion Mining by Smitha Bidadi Anjan Prasad, Raja Praveen Kumar Nakka

“…To address the problem of the manual annotation of large volumes of data, this paper proposes a machine learning method that uses the sentiment 140 dataset as the training set to automate the process of annotations of tweets. …”
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Coordinated Control of Second-Order Harmonic Voltage and Current Fluctuations in Cascaded H-Bridge Inverter with Supercapacitor and DC-DC Stage at Variable Output Frequencies by Ye Zhang, Zixin Li, Fanqiang Gao, Jinhao Zhang, Cong Zhao, Yaohua Li

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Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding by Kulecki Bartłomiej, Belter Dominik

“…The manuscript shows that machine learning-based techniques, such as lightweight multilayer perceptrons (MLPs) operating in a low-dimensional feature space, offer a faster alternative for collision checking than traditional methods that rely on geometric approaches, such as triangle-to-triangle intersection tests and Bounding Volume Hierarchies (BVH) for mesh-based models.…”
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Non-invasive detection of Parkinson’s disease based on speech analysis and interpretable machine learning by Huanqing Xu, Wei Xie, Mingzhen Pang, Ya Li, Luhua Jin, Fangliang Huang, Xian Shao

“…This study aims to develop an interpretable machine learning model that uses speech recordings and acoustic features to predict PD.MethodsA dataset of speech recordings from individuals with and without PD was analyzed. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Method and DataIn this study, operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including RF, DT, SVM, LR, NB, and KNN. …”
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Predicting financial default risks: A machine learning approach using smartphone data by Shinta Palupi, Gunawan, Ririn Kusdyawati, Richki Hardi, Rana Zabrina

“…This study leverages machine learning (ML) techniques to predict financial default risks using smartphone data, providing a novel approach to financial risk assessment. …”
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Machine Learning and Artificial Intelligence Techniques in Smart Grids Stability Analysis: A Review by Arman Fathollahi

“…Examination extends to stability, voltage and frequency regulation along with fault detection methods that highlight their applications in increasing smart grid operational boundaries. …”
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From genetic data to kinship clarity: employing machine learning for detecting incestuous relations by Dejan Šorgić, Aleksandra Stefanović, Mladen Popović, Dušan Keckarević

“…Introduction:The aim of the study was to develop a predictive model based on STR profiles of mothers and children for the detection of incestuous conception.Methods:Based on allele frequency data from the USA and Saudi Arabia, STR profiles were generated and used to simulate offspring profiles corresponding to father-child and brother-sister incest scenarios. …”
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Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery by Wallace Jaffray, Ziheng Guo, Andrea Di Falco, Marcello Ferrera

“…Because of this, various methods have been developed to recover a pulse’s electric field profile at these durations, with the frequency-resolved optical gating (FROG) technique being the most common. …”
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Watch Your Callback: Offline Anomaly Detection Using Machine Learning in ROS 2 by Jeonghwan Kang, Kyounghwan Kim, Donghyun Kwon

“…In our experiments, we utilize multiple machine learning models, including Isolation Forest, OCSVM, and Autoencoder, to analyze the temporal characteristics of callbacks, focusing on response time and invocation frequency. …”
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Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques by Hepseeba Kode, Khaled Elleithy, Laiali Almazaydeh

“…This study focuses on classifying time-series data representation of EEG signals with machine learning-based classifiers by tuning parameters and deep learning-based One-Dimensional Convolutional Neural Network (1D CNN) methods. …”
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Fall prediction in a quiet standing balance test via machine learning: Is it possible? by Juliana Pennone, Natasha Fioretto Aguero, Daniel Marczuk Martini, Luis Mochizuki, Alexandre Alarcon do Passo Suaide

“…An innovative approach for fall prediction is the machine learning. Machine learning is a computer-science area that uses statistics and optimization methods in a large amount of data to make outcome predictions. …”
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Automated Detection and Classification of Sleep Spindles using Machine Learning and Signal Processing Techniques by Sindhu Faiza, Rehman Hafiz Zia Ur, Khan Zeashan, Moshayedi Ata Jahangir

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Enhancing eLoran Timing Accuracy via Machine Learning With Meteorological and Terrain Data by Taewon Kang, Seunghyeon Park, Pyo-Woong Son, Jiwon Seo

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Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset nets by Yasser M. Qureshi, Vitaly Voloshin, Amy Guy, Hilary Ranson, Philip J. McCall, James A. Covington, Catherine E. Towers, David P. Towers

“…Future work should extend these methods to other ITNs to further illuminate the complex interplay between mosquito behaviour and insecticidal intervention.…”
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Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus by Tianze Ding, Peijie Liu, Jie Jia, Hui Wu, Jie Zhu, Kefeng Yang

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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…This study aimed to estimate alfalfa crop height through satellite images and machine learning methods within the Google Earth Engine (GEE) Python API. …”
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Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach by Zhengyuan Su, Huadong Jiang, Ying Yang, Xiangqing Hou, Yanli Su, Li Yang

“…MethodsWe collected 525 suicide-related calls from the records of a psychological assistance hotline in a province in northwest China. …”
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Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification by Yong Zhou, Yizhou Tan, Shasha Wang, Hanshu Cai, Ying Gu

“…Currently, there is an urgent need for a rapid, quantifiable, and easily implementable alertness assessment method. Methods Twelve optical stimulation frequencies ranged from 4 to 48 Hz were chosen to induce brainwave entrainment (BWE) for 30 s, respectively, in 40 subjects. …”
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