A Kp‐Driven Machine Learning Model Predicting the Ultraviolet Emission Auroral Oval by Huiting Feng, Dedong Wang, Yuri Y. Shprits, Artem Smirnov, Deyu Guo, Yoshizumi Miyoshi, Stefano Bianco, Shangchun Teng, Run Shi, Su Zhou, Yongliang Zhang

“…Based on the data spanning from 2005 to 2016 obtained from DMSP/SSUSI, we explore several machine learning algorithms, such as KNN, RF, and XGBoost, to construct an auroral oval prediction model. …”
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Dynamic Heart Rate Variability Vector and Premature Ventricular Contractions Patterns in Adult Hemodialysis Patients: A 48 h Risk Exploration by Gabriel Vega-Martínez, Francisco José Ramos-Becerril, Josefina Gutiérrez-Martínez, Arturo Vera-Hernández, Carlos Alvarado-Serrano, Lorenzo Leija-Salas

“…This study involves 4-h electrocardiographic records from the Telemetric and Holter ECG Warehouse (THEW) project database to analyze the dynamics in heart rate variability (HRV) indices of 51 patients with CKD. It proposes three algorithms to process long-term electrocardiography records: QRS complex and R-wave detection, premature ventricular contraction (PVC) identification, and tachograms. …”
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Predicting the Botanical Origin of Honeys with Chemometric Analysis According to Their Antioxidant and Physicochemical Properties by Anna Maria Kaczmarek, Małgorzata Muzolf-Panek, Jolanta Tomaszewska-Gras, Piotr Konieczny

“…The models based on ANN and C&RT algorithms were characterized by 100% accuracy. Study results demonstrate that the chemometric approach enables high-accuracy classification of honeys according to their botanical origin.…”
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RGB-T Object Detection With Failure Scenarios by Qingwang Wang, Yuxuan Sun, Yongke Chi, Tao Shen

“…Currently, RGB-thermal (RGB-T) object detection algorithms have demonstrated excellent performance, but issues such as modality failure caused by fog, strong light, sensor damage, and other conditions can significantly impact the detector's performance. …”
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Organ‐based clues for diagnosis of inborn errors of immunity: A practical guide for clinicians by Fatemeh Mohammadi, Amirhossein Yadegar, Mahta Mardani, Aryan Ayati, Hassan Abolhassani, Nima Rezaei

“…As a result, it can be the mainstay of diagnostic algorithms, prognosis, and possibly therapeutic interventions in patients with IEI. …”
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A review of deep learning in blink detection by Jianbin Xiong, Weikun Dai, Qi Wang, Xiangjun Dong, Baoyu Ye, Jianxiang Yang

“…By overcoming the challenges identified in this study, the application prospects of deep learning-based blink detection algorithms will be significantly enhanced.…”
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Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma by Feiyu Qi, Guiming Zha, Yanfang Zhang, Sihua Liu, Yuhang Yang, Wanliang Sun, Dongdong Wang, Zhong Liu, Zheng Lu, Dengyong Zhang

“…Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. …”
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Enabling Low-Carbon Transportation: Resilient Energy Governance via Intelligent VPP and Mobile Energy Storage-Driven V2G Solutions by Guwon Yoon, Myeong-in Choi, Keonhee Cho, Seunghwan Kim, Ayoung Lee, Sehyun Park

“…K-means clustering based on spatial proximity and energy demand is followed by heuristic algorithms to improve the efficiency of mobile ESS operation. …”
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Exploring high entropy alloys: A review on thermodynamic design and computational modeling strategies for advanced materials applications by Peter Ifeolu Odetola, Bukola Joseph Babalola, Ayodeji Ebenezer Afolabi, Ufoma Silas Anamu, Emmanuel Olorundaisi, Mutombo Christian Umba, Thabang Phahlane, Olusoji Oluremi Ayodele, Peter Apata Olubambi

“…Central to the present discourse is the application of valence electron concentration (VEC) and cutting-edge strategies, including the CALculation of PHAse Diagrams (CALPHAD) method, first-principles approach, and machine-learning algorithms, which collectively empower the prediction and understanding of HEA behavior. …”
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AI-Based Ransomware Detection: A Comprehensive Review by Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi, Md Zahidul Islam

“…This study contributes significantly to the development of a systematic evaluation framework that evaluates each component of the AI-based detection model framework using specific criteria and methodologies and analyzes how various AI algorithms respond to different ransomware attacks, thereby providing insights for more effective and robust detection methods. …”
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Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna by Md Mahabub Alam, Nurhafizah Abu Talip Yusof, Ahmad Afif Mohd Faudzi, Md Raihanul Islam Tomal, Md Ershadul Haque, Md. Suaibur Rahman

“…A predictive ML framework was developed using six regression algorithms trained on significant geometrical parameters, such as ring slot radius, via diameter, and feedline width. …”
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Android Malware Category and Family Identification Using Parallel Machine Learning by Ahmed Hashem El Fiky, Mohamed Ashraf Madkour, Ayman El Shenawy

“…The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work.  …”
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In silico analysis of atrial fibrillation and hypertension mechanism of action secondary to ibrutinib/acalabrutinib in chronic lymphocytic leukemia by Raúl Córdoba, Antoni Bayés-Genís, Ana Muntañola, Dolors Colomer, Jorge Castro, Carolina Leiva, Esther Álvarez, Eduardo Zatarain-Nicolas

“…These adverse events seem to be derived from off-targets rather than BTK inhibition. Machine learning algorithms were applied to identify targets likely to trigger AF and hypertension in simulated CLL patients receiving acalabrutinib or ibrutinib. …”
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Identifying potential biomarkers and molecular mechanisms related to arachidonic acid metabolism in vitiligo by Xiaoqing Li, Xiaoqing Li, Li Yang, Longfei Zhu, Jingying Sun, Jingying Sun, Cuixiang Xu, Cuixiang Xu, Lijun Sun, Lijun Sun

“…A biomarker-based nomogram was constructed to predict the risk of vitiligo.Results15 overlapping candidate genes were obtained between the DEGs and AAM-RGs. Machine-learning algorithms were used to identify six key genes as PTGDS, PNPLA8, FAAH, ABHD12, PTGS1, and MGLL. …”
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A Usability Pilot Study of a Sensor-Guided Interactive System for Dexterity Training in Parkinson’s Disease by Nic Krummenacher, Stephan M. Gerber, Manuela Pastore-Wapp, Michael Single, Stephan Bohlhalter, Tobias Nef, Tim Vanbellingen

“…Additionally, the feedback shown as a counter, detected through newly developed algorithms, was evaluated by comparing the device-detected repetitions during six exercises to those counted by a supervisor. …”
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Integrating IoT sensors and machine learning for sustainable precision agroecology: enhancing crop resilience and resource efficiency through data-driven strategies, challenges, an... by Val Hyginus Udoka Eze, Esther Chidinma Eze, George Uwadiegwu Alaneme, Pius Erheyovwe BUBU, Ezekiel Oluwaseun Ejiofor Nnadi, Michael Ben Okon

“…Coupled with advanced ML algorithms, this data facilitates predictive analytics and real-time decision-making, optimizing resource utilization for irrigation, pest control, and yield prediction. …”
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Deterministic cascade evolution in coal and gas outbursts: From early acoustic signatures to system-wide failure by Yang Lei, Zhijie Wen, Liang Wang, Ting Ren, Yujun Zuo

“…Our complementary analytical algorithms—the absolute amplitude integral and predominant period function—revealed characteristic step-wise patterns in outburst development. …”
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STAT3/TGFBI signaling promotes the temozolomide resistance of glioblastoma through upregulating glycolysis by inducing cellular senescence by Yanbin Zhang, Xiaohua Xiao, Ge Yang, Xiaobing Jiang, Shujie Jiao, Yingli Nie, Tao Zhang

“…In this study, 101 unique combinations of 10 machine learning algorithms were used to construct prognostic models based on cellular senescence-related genes (CSRGs). …”
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Machine learning approach effectively discriminates between Parkinson’s disease and progressive supranuclear palsy: Multi-level indices of rs-fMRI by Weiling Cheng, Xiao Liang, Wei Zeng, Jiali Guo, Zhibiao Yin, Jiankun Dai, Daojun Hong, Fuqing Zhou, Fangjun Li, Xin Fang

“…We constructed fifteen distinct combinations of indices and selected four machine learning algorithms for model development. Subsequently, different validation templates were employed to assess the classification results and investigate the relationship between the most significant features and clinical assessment scales. …”
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Different oscillatory mechanisms of dementia-related diseases with cognitive impairment in closed-eye state by Talifu Zikereya, Yuchen Lin, Zhizhen Zhang, Ignacio Taguas, Kaixuan Shi, Chuanliang Han

“…We accurately and clearly identified three stable oscillation targets (theta, ∼5 Hz, alpha, ∼10 Hz, and beta, ∼18 Hz) that facilitate differentiation between AD, FTD, and HC both statistically and through classification using machine learning algorithms. Overall, the differences between AD and HC were the most pronounced, with FTD exhibiting intermediate characteristics. …”
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