From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers by Heather Shaw, Pinkie Chambers, Matthew Watson, Luke Steventon, James Harmsworth King, Angelo Ercia, Noura Al Moubayed

“…Objectives Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clearance of subsequent treatment is not hindered; however, frequency and timing are not optimal. Model bias and data heterogeneity concerns have hampered the ability of machine learning (ML) to be deployed into clinical practice. …”
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Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study by Audêncio Victor, Francielly Almeida, Sancho Pedro Xavier, Patrícia H.C. Rondó

“…Methods We analyzed data from 1,579 pregnant women enrolled in the Araraquara Cohort, a population-based longitudinal study. …”
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The Comprehensive Effect of Depression, Anxiety, and Headache on Pain Intensity and Painkiller Use in Patients with Headache Analyzed by Unsupervised Clustering Using Machine Learn... by Jong-Ho Kim, Minha Ahn, Jong-Hee Sohn, Sung-Mi Hwang, Jae-Jun Lee, Young-Suk Kwon

“…<b>Background/Objectives</b>: Patients with headache experience depression, anxiety, and reduced quality of life, which are individually associated with pain intensity and painkiller use, but their comprehensive combined effect remains unclear. <b>Methods</b>: Comprehensive patient groups were formed based on unsupervised clustering using machine learning algorithms, and their associations were analyzed via ordinary least square regression. …”
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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

“…Decision Tree, Support Vector Machines (SVM), Ensemble Method, K-Nearest Neighbors, Naïve Bayes and Discriminant Analysis were applied using mean averaged bioimpedance modulus and phase angle spectra from lung tissue across 15 frequencies (15–307 kHz). …”
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Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performance by Md. Ashraful Haque, Md. Sharif Ahammed, Soeung Socheatra, Redwan A. Ananta, Md. Jamal Hossain Nirob, Narinderjit Singh Sawaran Singh, Noorlindawaty Md. Jizat, Saeed Alsowail, Samir Salem Al-Bawri

“…Besides being compact (measuring 37 mm × 24 mm), it boasts an impressive maximum gain of 8.09 dB and an efficiency level of 98.2 %. The methods explored in this research are the RLC equivalent circuit model and simulations with validated measurements. …”
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Blood metabolome shows signatures of metabolic dysregulation in obese and overweight subjects that can be predicted by machine learning applied to heart rate variability by Andrea Di Credico, Andrea Di Credico, Andrea Di Credico, David Perpetuini, Pascal Izzicupo, Giulia Gaggi, Giulia Gaggi, Giulia Gaggi, Claudia Rossi, Claudia Rossi, Arcangelo Merla, Barbara Ghinassi, Barbara Ghinassi, Barbara Ghinassi, Angela Di Baldassarre, Angela Di Baldassarre, Angela Di Baldassarre, Ines Bucci, Ines Bucci

“…The main upregulated metabolites differentiating NW from OB were C6DC and C8:1, while C6DC and C10:2 were higher in OW than NW. Time- and frequency-domain HRV features show a good correlation with the regulated metabolites. …”
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Spectral Components of Honey Bee Sound Signals Recorded Inside and Outside the Beehive: An Explainable Machine Learning Approach to Diurnal Pattern Recognition by Piotr Książek, Urszula Libal, Aleksandra Król-Nowak

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Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data by Gen Long, Sarintip Tantanee, Korakod Nusit, Pitikhate Sooraksa

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A Heavy-Haul Railway Corrugation Diagnosis Method Based on WPD-ASTFT and SVM by Binghuan Xiao, Jinzhao Liu, Ziyuan Zhang

“…This paper presents a heavy-haul railway corrugation diagnosis method incorporating the time-frequency analysis with machine learning methods. …”
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Machine learning assists regulated cell death crucial biomarker selection in adenocarcinoma of the lung: biological data testing and cell assay determination by Han Ning, Ying Jiang, Mengli Zheng, Gao Yang, Lianjun Ma, Yachao Zhao

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Acoustic Signal Aided Detection Method of Spiral Bevel Gears Based on PSO-MCKD by Li Xianshan, Tian Yu, Wei Lilin, Zhao Xiaofeng, Yan Changfeng

“…Aiming at the problems of cumbersome operation and strong subjectivity of the traditional detection method for the transmission accuracy of spiral bevel gears, an acoustic signal-assisted detection method for judging the transmission accuracy of spiral bevel gears is proposed. …”
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Characteristic Analysis of Bending Fatigue Stress of Asymmetric Gear with Small Teeth Number by Cai Youjie

“…The asymmetric and symmetrical gears with small teeth number are high efficiency machined by numerical control machining method. Their bending fatigue strength is tested by high-frequency fatigue testing machine,and the results for different stress level are analyzed and discussed by using the fatigue cumulative failure hypothesis. …”
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Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach by Bada Kang, Min Kyung Park, Jennifer Ivy Kim, Seolah Yoon, Seok-Jae Heo, Chaeeun Kang, SungHee Lee, Yeonkyu Choi, Dahye Hong

“…ObjectiveThis study aims to develop and validate machine learning models to identify and explore factors related to social interaction frequency and loneliness levels among older adults in the predementia stage. …”
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A Multi-Level Integrated EEG-Channel Selection Method Based on the Lateralization Index by Junhong Luo, Qing Liu, Pengrui Tai, Guanglin Li, Yongcheng Li

“…To evaluate the effectiveness of the proposed method, the time and frequency domain features from selected channels were extracted. …”
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Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach by Samrat Kumar Dey, Khandaker Mohammad Mohi Uddin, Arpita Howlader, Md. Mahbubur Rahman, Hafiz Md. Hasan Babu, Nitish Biswas, Umme Raihan Siddiqi, Badhan Mazumder

“…This study aims to develop a robust AI-driven system to detect asphyxia in newborns using ML and DL models, focusing on improving accuracy and efficiency over traditional diagnostic methods. This study explores feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs), where the features are categorized into time and frequency domains. …”
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Miniaturized device to measure urease activity in the soil interstitial fluid using wenner method by Rafaela Cardoso, Thomas Drouinot, Susana Cardoso de Freitas

“…Both square and sinusoidal waves with 5 kHz frequency were selected among other frequencies. The measurements were adjusted during the calibration of the microdevice, done using standard salt solutions with known electrical conductivity measured using an electrical conductivity probe. …”
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Model-Aware XGBoost Method Towards Optimum Performance of Flexible Distributed Raman Amplifier by Anand Prakash, Jaisingh Thangaraj, Sharbani Roy, Shaury Srivastav, Jitendra K. Mishra

“…It is shown that the proposed method can diagnose the fault within 2.3 ms with accuracy of 99.6&#x0025; and has also the highest estimation and efficacy in comparison with other ML based tree models. …”
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From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities. by Shreya Chappidi, Mason J Belue, Stephanie A Harmon, Sarisha Jagasia, Ying Zhuge, Erdal Tasci, Baris Turkbey, Jatinder Singh, Kevin Camphausen, Andra V Krauze

“…Three methods were developed to derive clinical PFS: 1) frequency analysis of corticosteroid prescriptions, 2) natural language processing (NLP) of reports, and 3) computer vision (CV) volumetric analysis of imaging. …”
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Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemic by Eka Miranda, Mediana Aryuni, Mia Ika Rahmawati, Siti Elda Hiererra, Albert Verasius Dian Sano

“…Material and methods: Synthetic Minority Over-sampling Technique (SMOTE) and data augmentation were used to handle imbalanced data from small-scale datasets. …”
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Early warning forecasting of river ecological flow in a typical Karst basin, southwest China: Leveraging baseflow segmentation and interpretable machine learning by Chongxun Mo, Haiyang Wang, Shufeng Lai, Yi Huang, Wei Gao, Lingling Tang, Siting Zhong

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