An automated adaptive trading system for enhanced performance of emerging market portfolios by Cristiana Tudor, Robert Sova

“…The system incorporates an Autoregressive Moving Average-Generalized AutoRegressive Conditional Heteroskedasticity model that offers an interpretability advantage over machine-learning methods. The main strength of the AATS is its ability to allow the embedded hybrid forecasting model to adapt to the changing environments that characterize EMs. …”
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Blood from septic patients with necrotising soft tissue infection treated with hyperbaric oxygen reveal different gene expression patterns compared to standard treatment by Julie Vinkel, Alfonso Buil, Ole Hyldegaard

“…Gene expression profiles were analysed using machine learning techniques to identify sepsis endotypes, treatment response endotypes and clinically relevant transcriptomic signatures of response to treatment. …”
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Integrative single-cell and multi-omics analyses reveal ferroptosis-associated gene expression and immune microenvironment heterogeneity in gastric cancer by Shupeng Zhang, Zhaojin Li, Gang Hu, Hekai Chen

“…In the gene selection and model validation section, critical genes were identified using machine learning algorithms, constructing a model with high predictive accuracy. …”
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Economic Evaluation of a Novel Lung Cancer Diagnostic in a Population of Patients with a Positive Low-Dose Computed Tomography Result by Michael J. Morris, Sheila A. Habib, Maggie L. Do Valle, John E. Schneider

“…**Objectives:** This study evaluated the potential cost savings for US payers of CyPath® Lung, a novel diagnostic tool utilizing flow cytometry and machine learning for the early detection of lung cancer, in patients with positive LDCT scans with indeterminate pulmonary nodules (IPNs) ranging from 6 to 29 mm. …”
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Retrieval of water quality parameters based on IOA-ML models and their response to short-term hydrometeorological factors by Wentong Hu, Donghao Miao, Chi Zhang, Zixian He, Wenquan Gu, Dongguo Shao

“…Study focus: Large-scale and high-precision estimation of water quality parameters (WQPs) is critical in explaining the spatiotemporal dynamics and clarifying their response to short-term hydrometeorological factors. Six machine learning models optimized by intelligent optimization algorithms (IOA-ML) were developed to retrieve WQPs using paired in situ measurements and near-synchronous Sentinel-2 reflectance (Rrs). …”
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Selective inhibition of TGF-β-induced epithelial-mesenchymal transition overcomes chemotherapy resistance in high-risk lung squamous cell carcinoma by Liangdong Sun, Jue Wang, Huansha Yu, Xinsheng Zhu, Jing Zhang, Junjie Hu, Yilv Yan, Xun Zhang, Yuming Zhu, Gening Jiang, Ming Ding, Peng Zhang, Lele Zhang

“…The integrative development of machine learning-based models reveals a random survival forest (RSF) prognostic model for LUSC. …”
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Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy by Shao-Hsun Chang, Kai-Chao Yao, Yao-Ting Chen, Cheng-Yang Chung, Wei-Lun Huang, Wei-Sho Ho

“…Data were collected through a stratified random sampling method from 19 universities in Taiwan, involving 200 students who had completed five core AI-related courses, including artificial intelligence, machine learning, internet of things, big data, and robotics. …”
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A Hybrid Transformer Architecture for Multiclass Mental Illness Prediction Using Social Media Text by Adnan Karamat, Muhammad Imran, Muhammad Usman Yaseen, Rasool Bukhsh, Sheraz Aslam, Nouman Ashraf

“…Mental illness prediction through text involves employing natural language processing (NLP) techniques and deep learning algorithms to analyze textual data for the identification of mental disorders. Therefore, machine learning and deep learning algorithms have been utilized in the existing literature for the detection of mental illness. …”
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Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments by Somayajulu L.N. Dhulipala, Nicholas Casaprima, Audrey Olivier, Bjorn C. Vaagensmith, Timothy R. McJunkin, Ryan C. Hruska

“…Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. …”
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Drawing-Aware Parkinson’s Disease Detection Through Hierarchical Deep Learning Models by Ioannis Kansizoglou, Konstantinos A. Tsintotas, Daniel Bratanov, Antonios Gasteratos

“…To that end, various machine learning (ML) and deep learning (DL) approaches have been explored for early detection. …”
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Development of risk models for early detection and prediction of chronic kidney disease in clinical settings by Pegah Bahrami, Davoud Tanbakuchi, Monavar Afzalaghaee, Majid Ghayour-Mobarhan, Habibollah Esmaily

“…Therefore, this study aims to utilize machine learning techniques to predict CKD at early stages. …”
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Multilocus Sequence Typing for Interpreting Blood Isolates of Staphylococcus epidermidis by Prannda Sharma, Ashley E. Satorius, Marika R. Raff, Adriana Rivera, Duane W. Newton, John G. Younger

“…Here, we examined 100 isolates of this species (50 blood isolates representing true bacteremia, 25 likely contaminant isolates, and 25 skin isolates) and the ability of sequence typing to differentiate them. Three machine learning algorithms (classification regression tree, support vector machine, and nearest neighbor) were employed. …”
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Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images by Muhammad Asad Arshed, Muhammad Ibrahim, Shahzad Mumtaz, Tenvir Ali, Gyu Sang Choi

“…The achieved results of the proposed model are compared against established transfer learning models like VGG16, DenseNet121 and some previously used traditional machine learning models like SVM and KNN. The collected results indicate a significant advancement in predicting drug side effects and offer a promising avenue for streamlining the drug development process.…”
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Representing and Quantifying Conformational Changes of Kinases and Phosphatases Using the TSR-Based Algorithm by Tarikul I. Milon, Krishna Rauniyar, Sara Furman, Khairum H. Orthi, Yingchun Wang, Vijay Raghavan, Wu Xu

“…TSR keys are effective when used as features for unsupervised machine learning and for key searches. If discriminative TSR keys are identified, they can be mapped back to atomic details within the amino acids involved. …”
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Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-rays by Yudong Wang, Zongjin Qu, Zhengjun Dai, Yuhong Li, Yanyan Liu, Wei Wang, Lianxiang Xiao, Yi Zhang

“…To establish these radiomics models, six machine learning models were utilized in the study. The performance was evaluated using the area under the receiver operating characteristic curve (AUC). …”
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Large language models improve the identification of emergency department visits for symptomatic kidney stones by Cosmin A. Bejan, Amy M. Reed, Matthew Mikula, Siwei Zhang, Yaomin Xu, Daniel Fabbri, Peter J. Embí, Ryan S. Hsi

“…In this study, we investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were due to symptomatic kidney stones. …”
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Neural Determinants of Sedentary Lifestyle in Older Adults: A Brain Network Analysis by Mohsen Bahrami, Jonathan H Burdette, Paul J Laurienti, Barbara J Nicklas, W Jack Rejeski, Jason Fanning

“…Method The goal of the current study, using baseline fMRI and accelerometry data from 36 participants and advanced machine learning tools, was to determine if we could identify complex brain circuits underlying variability associated with changes in sitting time and daily steps during the 6‐month intensive phase among participants randomized to the WL + SitLess treatment condition. …”
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Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study by Maria Munoz-Novoa, Morten B. Kristoffersen, Katharina S. Sunnerhagen, Autumn Naber, Max Ortiz-Catalan, Margit Alt Murphy

“…Abstract Background Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…<h4>Conclusions</h4>This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. …”
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Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells by Nida Aslam, Irfan Ullah Khan, Aisha Alansari, Marah Alrammah, Atheer Alghwairy, Rahaf Alqahtani, Razan Alqahtani, Maryam Almushikes, Mohammed AL Hashim

“…Additionally, it can lead to several faulty events that could increase costs and production losses since the engineers tend to focus on the analysis rather than detecting the faulty events. Recently, machine learning (ML) techniques have significantly solved enormous real-time data anomaly problems by decreasing the data engineers’ interaction processes. …”
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