DETERMINATION OF BANK INDONESIA SCHOLARSHIP RECIPIENTS USING NAÏVE BAYES CLASSIFIER by Fera Malianis Febri, Devni Prima Sari

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Stroke Prediction Analysis using Machine Learning Classifiers and Feature Technique by Md. Monirul Islam, Sharmin Akter, Md. Rokunojjaman, Jahid Hasan Rony, Al Amin, Susmita Kar

“…In this paper, a machine learning model is approached for predicting the existence of stroke of a patient where the Random forest classifier outperforms the state-of-the-art models, including Logistic Regression, Decision Tree Classifier (DTC), K-NN. …”
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STTMC: A Few-Shot Spatial Temporal Transductive Modulation Classifier by Yunhao Shi, Hua Xu, Zisen Qi, Yue Zhang, Dan Wang, Lei Jiang

“…Notably, STTMC classifies a group of test signals simultaneously to increase stability of few-shot model with an episode training strategy. …”
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Use of Vision Transformer to Classify Sea Surface Phenomena in SAR Imagery by Junfei Xia, Roland Romeiser, Wei Zhang, Tamay Ozgokmen

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A protein-based classifier for differentiating follicular thyroid adenoma and carcinoma by Yaoting Sun, He Wang, Lu Li, Jianbiao Wang, Wanyuan Chen, Li Peng, Pingping Hu, Jing Yu, Xue Cai, Nan Yao, Yan Zhou, Jiatong Wang, Yingrui Wang, Liqin Qian, Weigang Ge, Mengni Chen, Feng Yang, Zhiqiang Gui, Wei Sun, Zhihong Wang, Minghua Ge, Yi He, Guangzhi Wang, Yongfu Zhao, Huanjie Chen, Xiaohong Wu, Yuxin Du, Wenjun Wei, Fan Wu, Dingcun Luo, Xiangfeng Lin, Haitao Zheng, Xin Zhu, Bei Wei, Jiafei Shen, Jincao Yao, Zhennan Yuan, Tong Liu, Jun Pan, Yifeng Zhang, Yangfan Lv, Qiaonan Guo, Qijun Wu, Tingting Gong, Ting Chen, Shu Zheng, Jingqiang Zhu, Hanqing Liu, Chuang Chen, Hong Han, Sathiyamoorthy Selvarajan, Michael Mingzhao Xing, Kennichi Kakudo, Erik K Alexander, Yijun Wu, Yu Wang, Dong Xu, Hao Zhang, Xiu Nie, Oi Lian Kon, N Gopalakrishna Iyer, Zhiyan Liu, Yi Zhu, Haixia Guan, Tiannan Guo, We-TEC Investigators

“…A subsequent 24-protein classifier, developed via targeted mass spectrometry and validated in three independent sets, showed high performance in retrospective cohorts (AUROC 0.871 [95% CI, 0.833–0.910] and 0.853 [95% CI, 0.772–0.934]) and prospective biopsies (AUROC 0.781 [95% CI, 0.563–1.000]). …”
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Performance of the MOLES and TFSOM-DIM scores in classifying choroidal nevi and melanoma by David Jahnke, Carsten Grohmann, Bettina Fuisting, Christos Skevas, Martin S. Spitzer, Johannes Birtel

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Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis by Sebastian Sanduleanu, Koray Ersahin, Johannes Bremm, Narmin Talibova, Tim Damer, Merve Erdogan, Jonathan Kottlors, Lukas Goertz, Christiane Bruns, David Maintz, Nuran Abdullayev

“…Results: There was agreement between the surgeon’s decision and GPT-3.5 in 102 of 113 cases, and all cases where the surgeon decided upon conservative treatment were correctly classified by GPT-3.5. …”
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Classifying vocal responses of broilers to environmental stressors via artificial neural network by T. Lev-ron, Y. Yitzhaky, I. Halachmi, S. Druyan

“…Additionally, the study evaluated the impact of majority vote decision-making across the three ANN model sizes, showing improvement in mean average precision (mAP), particularly for models with shorter audio inputs. …”
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Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers by Krzysztof Przybył, Daria Cicha-Wojciechowicz, Natalia Drabińska, Małgorzata Anna Majcher

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Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques by Manjog Padhy, Umar Muhammad Modibbo, Rasmita Rautray, Subhranshu Sekhar Tripathy, Sujit Bebortta

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An effective brain stroke diagnosis strategy based on feature extraction and hybrid classifier by Maha Samir Elsayed, Gehad Ahmed Saleh, Ahmed I. Saleh, Abeer Twakol Khalil

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Evaluating Security Anomalies by Classifying Traffic Using a Multi-Layered Model by Mohammadreza Samadzadeh, Najmeh Farajipour Ghohroud

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Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis by Ahmed Alfakeeh, Mhd Saeed Sharif, Abin Daniel Zorto, Thiago Pillonetto

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Machine learning methods in the differential diagnosis of difficult-to-classify types of diabetes mellitus by N. V. Rusyaeva, I. I. Golodnikov, I. V. Kononenko, T. V. Nikonova, M. V. Shestakova

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Optimizing the STB/BBNJ arrangements: based on comparing three STB operational modalities by You Li, Xiaoyi Jiang

“…Based on the three effectiveness factors of saliency, credibility and legitimacy, we classify the existing STB operational modalities into evaluation-oriented modality, recommendation-oriented modality, and decision-oriented modality. …”
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EEG SIGNAL ANALYSIS FOR CLASSIFYING ALZHEIMER’S AND FRONTOTEMPORAL DEMENTIA DISORDERS USING ENSEMBLE METHODS by G Sudha, N Mohankumar, Gousia Thahniyath, Raja Thimmarayan

“…The resulting model has exceptional diagnostic performance, with the LR ensemble achieving an accuracy of 98.98% in differentiating the three subject groups. Results proved the capability of combining EEG signal analysis with ensemble learning techniques to enhance clinical decision making for the early identification of dementia-related disorders.…”
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Self-deception in Financial Decisions by Marta Maciejasz-Świątkiewicz, Mateusz Musiał

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Classifying Flash Flood Disasters From Disaster‐Prone Environments to Support Mitigation Measures by Xiaoyan Zhai, Yongyong Zhang, Yongqiang Zhang, Ronghua Liu, Changjun Liu, Xiaoxiang Zhang, Yuehong Chen, Xiekang Wang, Nigel Wright

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Physiological Signals as Predictors of Mental Workload: Evaluating Single Classifier and Ensemble Learning Models by Nailul Izzah, Auditya Purwandini Sutarto, Ade Hendi, Maslakhatul Ainiyah, Muhammad Nubli bin Abdul Wahab

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Indication of Economic Populism in Local Governance: A New Approach for Classifying Populist Behaviour by Pavel Zdražil, Jan Fuka, Bohuslav Pernica, Robert Baťa

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