Optimization method improvement for nonlinear constrained single objective system without mathematical models by HOU Gong-yu, XU Zhe-dong, LIU Xin, NIU Xiao-tong, WANG Qing-le

“…Then, the immune particle swarm algorithm, which incorporates the self-regulatory mechanism of the immune system, is used to optimize the system based on the predicted model. …”
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Programmed cell death signatures-driven microglial transformation in Alzheimer’s disease: single-cell transcriptomics and functional validation by Mi-Mi Li, Ying-Xia Yang, Ya-Li Huang, Shu-Juan Wu, Wan-Li Huang, Li-Chao Ye, Ying-Ying Xu

“…BackgroundThis study aims to develop and validate a programmed cell death signature (PCDS) for predicting and classifying Alzheimer’s disease (AD) using an integrated machine learning framework. …”
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The Growing Impact of Natural Language Processing in Healthcare and Public Health by Aadit Jerfy, Owen Selden, Rajesh Balkrishnan PhD

“…Automating labor intensive and tedious tasks with language processing algorithms, using text analytics systems and machine learning to analyze social media data and extracting insights from unstructured data allows for better public sentiment analysis, enhancement of risk prediction models, improved patient communication, and informed treatment decisions. …”
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Universal multilayer network embedding reveals a causal link between GABA neurotransmitter and cancer by Léo Pio-Lopez, Michael Levin

“…Our model made new predictions about a link between GABA and cancer that we verified experimentally in the Xenopus laevis model. …”
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Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads by Mostafa M. Radwan, Sundus A. Faris, Ahmed Y. Barakat, Ahmad Mousa

“…Machine and deep learning algorithms have recently been more instrumental for forecasting pavement conditions. …”
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Near-Field Nulling Control Beamfocusing Optimization for Multi-User Interference Suppression by Yuanzhe Gong, Mohammadhossein Karimi, Tho Le-Ngoc

“…The model achieves a phase error of less than 0.021 radians and a magnitude error of 0.17 dB in the predicted feeding weights. The resulting near-field beam patterns using the LCMV-based vector and the DNN-predicted vector show good agreement.…”
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Point-of-care prognostication in moderate Covid-19: Analytical validation and prognostic accuracy of a soluble urokinase plasminogen activator receptor (suPAR) rapid test. by Arjun Chandna, Raman Mahajan, Priyanka Gautam, Lazaro Mwandigha, Sabine Dittrich, Vikash Kumar, Jennifer Osborn, Pragya Kumar, Constantinos Koshiaris, George M Varghese, Yoel Lubell, Sakib Burza

“…AUC of ELISA = 0.70 [95% CI = 0.63 to 0.76]; p = 0.12) or as part of a published multivariable prediction model (AUC of RDT-based model = 0.74 [95% CI = 0.66 to 0.83] vs. …”
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Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis. by Jessica L Nielson, Shelly R Cooper, John K Yue, Marco D Sorani, Tomoo Inoue, Esther L Yuh, Pratik Mukherjee, Tanya C Petrossian, Jesse Paquette, Pek Y Lum, Gunnar E Carlsson, Mary J Vassar, Hester F Lingsma, Wayne A Gordon, Alex B Valadka, David O Okonkwo, Geoffrey T Manley, Adam R Ferguson, TRACK-TBI Investigators

“…<h4>Conclusions</h4>TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. …”
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Visual analysis of research trends in diabetes-associated dry eye via bibliometrics by Zhe Yang, Jia-Yi Jiang, Xiao-Hui Zhang

“…With high prevalence of dry eye in Asia, valuable resources like the Korea National Health and Nutrition Examination Survey (KNHANES) database offer crucial data for developing risk prediction models for DADE. Building risk prediction models using machine learning algorithms is a promising future research direction, enabling physicians to identify high-risk individuals and implement early interventions.…”
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Assessment of copeptin and obestatin levels in coronary artery disease patients by Saraa Ali, Fatma F. Abdel Hamid, Samia Hussein, Shaimaa Wageeh, Doaa M. Ibrahim

“…Also, it may be used for prediction of the course of stable angina as it was positively correlated with copeptin and Gensini score.…”
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Cotton Crop Classification using Optical and Microwave Remote Sensing Datasets in Google Earth Engine by B. Meerasha, M. Sagayam

“…This study demonstrates how combining optical and microwave remote sensing data, the GEE platform, transfer learning, and cotton cropland mapping algorithms can enhance insights into precision agricultural systems.…”
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Secondary Migration Trend Based on Basin Modeling: A Case Study of the Cambrian Petroleum System in the Tarim Basin by Yong Yue, Bin Li, Peng Wei, Xin Zhang, Kun Zhang, Yang Suju, Xu Qingqi

“…While previous studies have relied on geological and fluid geochemical characteristics to predict migration direction, these results are often limited by the number of samples. …”
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Poisson random measure noise-induced coherence in epidemiological priors informed deep neural networks to identify the intensity of virus dynamics by Saima Rashid, Ayesha Siddiqa, Fekadu Tesgera Agama, Nazeran Idrees, Mohammed Shaaf Alharthi

“…Compartmental models have estimates of parameter complications, whereas machine learning algorithms struggle to understand MPV’s progression and lack elucidation. …”
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Evaluation of eco-friendly soil slope stabilization techniques for forest roads by using an Artificial Neural Network (ANN) by Kıvanç Yüksel, Neşe Gülci, Abdullah Emin Akay, Sercan Gülci

“…In the solution process, an Artificial Neural Network (ANN) model, which is one of the machine learning algorithms, was used to predict sediment yield from forest road cut slopes. …”
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Accurate and affordable multi-cancer early detection and localization via plasma cfDNA multi-omic profiling by Pin Cui, Weihuang He, Mingji Feng, Hanming Lai

“…By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites, based on which we built in silico classifiers for prediction of each of the four groups previously mentioned, achieving considerable performance of AUC ranging from 0.8896 to 0.959. …”
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Novel candidate genes may be possible predisposing factors revealed by whole exome sequencing in familial esophageal squamous cell carcinoma by Narjes Forouzanfar, Ancha Baranova, Saman Milanizadeh, Alireza Heravi-Moussavi, Amir Jebelli, Mohammad Reza Abbaszadegan

“…A more advanced method to identify new molecular biomarkers predicting disease prognosis can be whole exome sequencing. …”
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Oxygen isotope values of charred tree bark as an indicator of forest fire severity by Elizabeth McDonald, Elizabeth A. Webb, Jeffery P. Dech

“…We also analyzed pre- and post-fire Sentinel-2 imagery of the burn area to compute various Normalized Burn Ratio (NBR)-based change detection algorithms, which are known to produce reliable predictions of CBI. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. …”
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An immunoinformatic investigation on Rift Valley fever virus protein reveals possible epitopes for vaccines by Tanjir Hosen, Saaimatul Huq, Mohammad Abdullah-Al-Shoeb, Shahidul Islam, Muhammad Abul Kalam Azad

“…Gn/Gc protein sequences were analyzed with bioinformatics tools and algorithms. The predicted T-cell and B-cell epitopes were evaluated for antigenicity, allergenicity, and toxicity by the VaxiJen v2.0 system, AllerTop v2.0, and ToxinPred server, respectively. …”
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Semantic Segmentation with Multispectral Satellite Images of Waterfowl Habitat by Mateo Gannod, Nicholas Masto, Collins Owusu, Cory Highway, Katherine Brown, Abigail Blake-Bradshaw, Jamie Feddersen, Heath Hagy, Douglas Talbert, Bradley Cohen

“…Therefore, conservation planners rely on accurate predictions of wetland habitats for waterfowl at regional scales. …”
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