Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review by Aqsha Nur, Defin Yumnanisha, Sydney Tjandra, Adang Bachtiar, Dante Saksono Harbuwono

“…AI models (i.e., machine learning and deep learning) have yielded prediction performances of up to 98% in various diseases. …”
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Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data by Arnaud Ferré, Mouhamadou Ba, Robert Bossy

“…An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Mohammad Aljanabi, Mohd Arfian Ismail, Vitaly Mezhuyev

“…ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). …”
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Predicting CTCF cell type active binding sites in human genome by Lu Chai, Jie Gao, Zihan Li, Hao Sun, Junjie Liu, Yong Wang, Lirong Zhang

“…This study provides a deeper understanding of the regulatory functions of CTCF via machine learning framework.…”
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A Bayesian Inference-Based Method for Uncertainty Analysis in Raman Spectroscopy by Hanxuan Zhou

“…In experiments on an animal blood Raman spectroscopy dataset (both reflection and transmission modes), the proposed method achieves classification accuracies of 95.36% and 94.83%, respectively, showing slight improvements over other classical machine learning and neural network methods. Furthermore, by generating prediction confidence heatmaps, BayesianVGG effectively addresses the uncertainty analysis of unknown samples, thereby improving the interpretability of the prediction results.…”
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Automatic speech recognition predicts contemporaneous earthquake fault displacement by Christopher W. Johnson, Kun Wang, Paul A. Johnson

“…Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. …”
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The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects by Li Minghui, Zhou Decheng, Zhang Shiqi

“…This article summarizes three traditional methods of TFP: parameter-based prediction, shallow machine learning-based prediction, and deep learning (DL)-based prediction. …”
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Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism by Fayez Saud Alreshidi, Mohammad Alsaffar, Rajeswari Chengoden, Naif Khalaf Alshammari

“…We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. …”
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Language experience influences performance on the NIH Toolbox Cognition Battery: A cluster analysis by Ashley Chung-Fat-Yim, Sayuri Hayakawa, Viorica Marian

“…The present study addresses this limitation by using a machine learning algorithm, known as cluster analysis, to identify naturally occurring subgroups of participants with similar language profiles. …”
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Design of Intelligent Recognition English Translation Model Based on Deep Learning by Yuexiang Ruan

“…Additionally, a new and popular branch of machine learning is deep learning which has achieved excellent results in research fields such as natural language processing. …”
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Refining breast cancer classification: Customized attention integration approaches with dense and residual networks for enhanced detection by Mohammad Sakif Alam, Anwar Hossain Efat, SM Mahedy Hasan, Md Palash Uddin

“…This study aims to develop an accurate and robust framework for classifying breast cancer into benign and malignant categories using a novel machine learning architecture. Methods We propose a dense-ResNet attention integration (DRAI) architecture that combines DenseNet and ResNet models with three attention mechanisms to enhance feature extraction from the BreakHis dataset. …”
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Science Mapping the Knowledge Base on Student Retention in Higher Education: A Bibliometric Review of Research Papers from 1914-2022 by Murat Öztürk, Mehmet Şükrü Bellibaş, Bekir S. Gür, Enes Gök

“…Moreover, the term higher education is associated with dropout, completion, and academic performance, as well as new methodological terms like data mining, machine learning, learning analytics, and logistic regression. …”
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Analysis of Acoustic Signals of Footsteps from the Piezoelectric Sensor by Bilge Çiğdem Çiftçi, Gamze Kaya, Mustafa Kurt

“…The source of the acoustic signal can be determined by matching it with the existing database using machine learning algorithms like face recognition systems for future goals.…”
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COVID-19 Diagnosis from Blood Gas Using Multivariate Linear Regression by Faruk Ayata, Ebubekir Seyyarer

“…In this study, it is aimed to obtain a function that finds the positive or negative COVID-19 test from the blood gas values of individuals by using Machine Learning methods to contribute to the outbreak management. …”
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Radiative forcing from the 2020 shipping fuel regulation is large but hard to detect by Jianhao Zhang, Yao-Sheng Chen, Edward Gryspeerdt, Takanobu Yamaguchi, Graham Feingold

“…The strict sulfur regulation on shipping fuel implemented in 2020 (IMO2020) presents an opportunity to assess the potential impacts of such emission regulations and the detectability of deliberate aerosol perturbations for climate intervention. Here we employ machine learning to capture cloud natural variability and estimate a radiative forcing of +0.074 ±0.005 W m−2 related to IMO2020 associated with changes in shortwave cloud radiative effect over three low-cloud regions where shipping routes prevail. …”
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Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment by An Zichen, Li Fan

“…Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. …”
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A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model by Chengyao Lv, Deng Pan, Yaxiong Li, Jianxin Li, Zong Wang

“…Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. …”
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An Improved Stochastic Configuration Networks With Compact Structure and Parameter Adaptation by Sanyi Li, Hongyu Guan, Peng Liu, Weichao Yue, Qian Wang

“…Stochastic Configuration Networks (SCNs) perform well in machine learning and data mining tasks in complex data environments. …”
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The first geospatial dataset of irrigated fields (2020–2024) in Vojvodina (Serbia) by Mirjana Radulović, Miljana Marković, Sanja Brdar, Ioannis Athanasiadis, Gordan Mimić

“…This study’s goal is to give accessibility to our dataset which further can be explored and used for building or fine-tuning machine learning and deep learning models for the automatic detection of irrigated fields using satellite imagery.…”
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Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes by Zhimin Chen, Tao Du, N. M. Anoop Krishnan, Yuanzheng Yue, Morten M. Smedskjaer

“…We further establish bridges between dynamical characteristics, local structural features, and atomic rearrangements by applying a machine learning-based structure fingerprint termed “softness”. …”
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