Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

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Autonomous Driving Road Environment Recognition with Multiscale Object Detection by Jeny J.R.V., Divya Phulari, Varsha Kolanu, Mrunalini Anantha, Irfan S.K.M.

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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

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Environmental Risk Assessment of Wuhan Qingshan Industrial Park Based on Multi-Source Data Fusion and FAHP by Yang Yangyi

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THE POWER OF INFORMATION: HOW BUSINESS INTELLIGENCE SHAPES A COMPANY'S ECONOMIC SUSTAINABILITY by Gia Zoidze, Ramaz Otinashvili, Shota Veshapidze

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Evaluation of novel candidate variations and their interactions related to bipolar disorders: Analysis of GWAS data by Acikel C, Aydin Son Y, Celik C, Gul H

“…The reduced number of associated SNPs discovered by MDR, without loss in the classification performance, would facilitate validation studies and decision support models, and would reduce the cost to develop predictive and diagnostic tests. …”
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The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical Data by Valentino Blanco, Aitana Iglesias

“…This data was obtained from the UCI website's data warehouse and encompasses 14 distinct variables. The three models, namely "k-means, support vector machine, and neural network," are extensively used classification methods in the domains of data mining and machine learning. …”
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A XGBoost-Based Prediction Method for Meat Sheep Transport Stress Using Wearable Photoelectric Sensors and Infrared Thermometry by Ruiqin Ma, Runqing Chen, Buwen Liang, Xinxing Li

“…The accuracy of the assessment of the transport stress state of meat sheep after the optimization of three parameters was 100%, 90.91%, and 93.33%, and the classification accuracy of the overall model reached 94.92%. …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…One hundred twenty-nine online exam data were analyzed by the researcher with three different scenarios to reveal the best model performance in regression and classification. …”
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Optimizing EEG - based Emotion Recognition with a multi-modal ensemble approach by Kavitha K V, L R Sudha, J S Jayasudha

“…This ensemble seeks to enhance classification accuracy by utilizing the complimentary capabilities of each model. …”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…Once the model fine-tunes the eGFR estimations, it feeds them into various algorithms for CKD stage classification, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). …”
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Mixed signal modulation recognition method based on temporal depth residual shrinkage network by LIU Jinghua, WEI Xianglin, FAN Jianhua, HU Yongyang, WANG Xiaobo, YU Bing

“…There were three key modules in the model: a residual module, a shrinkage module, and a LSTM module. …”
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A Fourth-Order Fuzzy Support Vector Machine Based on Class Center by Yongqiang Zhou, Zhenggang Song

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Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis by Cunhao Li, Zhongjian Gao, Xiaomei Chen, Xuqiang Zheng, Xiaoman Zhang, Chih-Yang Lin

“…MCI, and 94.83% for MCI vs. AD across the three classification tasks.…”
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Assessment of flight fatigue using heart rate variability and machine learning approaches by Dalong Guo, Cong Wang, Yufei Qin, Lamei Shang, Aijing Gao, Baosen Tan, Yubin Zhou, Guangyun Wang

“…These models were applied to perform a three-level classification of flight fatigue. …”
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Research on the Evaluation of the Node Cities of China Railway Express Based on Machine Learning by Chenglin Ma, Mengwei Zhou, Wenchao Kang, Haolong Wang, Jiajia Feng

“…Feature importance analysis identified 11 decisive indicators from node city evaluation empirical indicators, where CR Express trade volume (weight = 0.1269), logistics hub classification (weight = 0.1091), and operational frequency (weight = 0.0980) emerged as the top three predictors. …”
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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

“…Several classifiers, including Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), are trained, and their predictions are aggregated using Logistic Regression (LR) as the meta-classifier. The resulting model has exceptional diagnostic performance, with the LR ensemble achieving an accuracy of 98.98% in differentiating the three subject groups. …”
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Facial Anti-Spoofing Using “Clue Maps” by Liang Yu Gong, Xue Jun Li, Peter Han Joo Chong

“…Finally, the auxiliary classifier is adopted to assist the model in making the decision, which regards this result as one preliminary result. …”
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Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data by Gulhan Kilicarslan, Dilber Cetintas, Taner Tuncer, Muhammed Yildirim

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Improving myocardial infarction diagnosis with Siamese network-based ECG analysis. by Vaibhav Gadag, Simrat Singh, Anshul Harish Khatri, Shruti Mishra, Sandeep Kumar Satapathy, Sung-Bae Cho, Abishi Chowdhury, Amrit Pal, Sachi Nandan Mohanty

“…It is then trained using the Siamese Network Model.<h4>Results</h4>The classification accuracy comes out to be 98%. …”
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