Classification of Incidental Carcinoma of the Prostate Using Learning Vector Quantization and Support Vector Machines by Torsten Mattfeldt, Danilo Trijic, Hans‐Werner Gottfried, Hans A. Kestler

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Machine learning-based classification of valvular heart disease using cardiovascular risk factors by Muhammad Usman Aslam, Songhua Xu, Sajid Hussain, Muhammad Waqas, Nafiu Lukman Abiodun

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Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery by Adam Cuellar, Daniel Brignac, Abhijit Mahalanobis, Wasfy Mikhael

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Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach by Muhammad Saud Ul Hassan, Kashif Liaqat, Laura Schaefer, Alexander J. Zolan

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Remote Sensing of Grasslands: Performance Comparison of Radar and Optical Data in Machine Learning Classification by K. Christofi, C. Chrysostomou, I. Tsardanidis, M. Mavrovouniotis, G. Guerrisi, C. Kontoes, D. G. Hadjimitsis, D. G. Hadjimitsis

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Paraptosis-related classification and risk signature for prognosis prediction and immunotherapy assessment in gastric cancer by Kai Zhou, Ruyue Chen

“…Results Our results revealed distinct subgroups (C1, C2, and C3) among gastric cancer patients through consensus clustering based on 65 paraptosis-related genes. …”
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An improved deep CNN-based freshwater fish classification with cascaded bio-inspired networks by Asadullah Shaikh, Wahidur Rahman, Kaniz Roksana, Tarequl Islam, Mohammad Motiur Rahman, Hani Alshahrani, Adel Sulaiman, Mana Saleh Al Reshan

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Multi-Sensor Integration and Machine Learning for High-Resolution Classification of Herbivore Foraging Behavior by Bashiri Iddy Muzzo, Kelvyn Bladen, Andres Perea, Shelemia Nyamuryekung’e, Juan J. Villalba

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Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification by Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed

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NoiseAugmentNet-HHO: Enhancing Histopathological Image Classification Through Noise Augmentation by Prem Purusottam Jena, Debahuti Mishra, Kaberi Das, Sashikala Mishra

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EEG-Based Classification of Parkinson’s Disease With Freezing of Gait Using Midfrontal Beta Oscillations by Shotabdi Roy, Joseph Nuamah, Taylor J. Bosch, Richa Barsainya, Maximilian Scherer, Thomas Koeglsperger, KC Santosh, Arun Singh

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A Comparative Study and Machine Learning Enabled Efficient Classification for Multispectral Data in Agriculture by Priyanka Gupta, Shruti Kanga, Varun Narayan Mishra

“…Now, cloud-based platforms have gained a lot of attention for crop classification over large regions. The main goal of the research is to analyze crop classification using various machine learning (ML) such as Support Vector Machine (SVM), Gradient Tree Boosting (GTB), Random Forest (RF), Decision Tree (DT) as well as Classification and Regression Trees (CART) on Google Earth Engine platform. …”
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Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data by Haniyeh Rafiepoor, Alireza Ghorbankhanloo, Kazem Zendehdel, Zahra Zangeneh Madar, Sepideh Hajivalizadeh, Zeinab Hasani, Ali Sarmadi, Behzad Amanpour‐Gharaei, Mohammad Amin Barati, Mozafar Saadat, Seyed‐Ali Sadegh‐Zadeh, Saeid Amanpour

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Mapping the genetic landscape of immune-mediated disorders: potential implications for classification and therapeutic strategies by Vera Fominykh, Alexey A. Shadrin, Alexey A. Shadrin, Alexey A. Shadrin, Piotr Jaholkowski, Julian Fuhrer, Nadine Parker, Erik D. Wiström, Oleksandr Frei, Oleksandr Frei, Olav B. Smeland, Olav B. Smeland, Helga Sanner, Helga Sanner, Helga Sanner, Srdjan Djurovic, Srdjan Djurovic, Srdjan Djurovic, Ole A. Andreassen, Ole A. Andreassen, Ole A. Andreassen

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Among Artificial Intelligence/Machine Learning Methods, Automated Gradient-Boosting Models Accurately Score Intraoral Plaque in Non-Standardized Images by Eric Coy, William Santo, Bonnie Jue, Helen Betts, Francisco Ramos-Gomez, Stuart A. Gansky

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A Comparative Analysis of Machine Learning Algorithms for Classification of Diabetes Utilizing Confusion Matrix Analysis by Maad M. Mijwil, Mohammad Aljanabi

“…In this regard, the author opted to compare the performance of three algorithms (logistic regression, Adaboost, and naïve bayes) through the correct classification rate for diabetes prediction in order to ensure the effectiveness of accurate diagnosis. …”
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Gender classification performance optimization based on facial images using LBG-VQ and MB-LBP by Faruq Abdul Hakim, Tio Dharmawan, Muhamad Arief Hidayat

“…The extracted features are then used as training material for several classification methods, namely Naïve Bayes, SVM, KNN, Random Forest, and Logistic Regression. …”
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Evaluation of Hospitalized Patients with Diabetes Mellitus with the International Classification of Functioning, Disability and Health Rehabilitation Set by LU Weiyan, TANG Dandan, ZHU Junzhi, LIU Yingfen, DU Lishi, WANG Haoxiang

“…The model showed that creatinine (<italic>β</italic>=0.010, <italic>t</italic>=7.272, <italic>P</italic>&lt;0.001), age (<italic>β</italic>=0.183, <italic>t</italic>=4.454, <italic>P</italic>&lt;0.001), weeklywalking time (<italic>β</italic>=-0.336, <italic>t</italic>=-3.538, <italic>P</italic>=0.001), weeklyexercise (<italic>β</italic>=-0.378, <italic>t</italic>=-2.566, <italic>P</italic>=0.011) and education level (<italic>β</italic>=-1.338, <italic>t</italic>=-2.426, <italic>P</italic>=0.016) were independent factors that affected functions of patients with diabetes mellitus.ConclusionThree dimensions (body function, activity and participation) are all affected, and creatinine, age, weeklywalking time,weekly exercise and education level are independent factors that affect functions of patients with diabetes mellitus. …”
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Classifications for radiographic evaluation of radiolucent bone lesions have poor inter- and intra-observer agreement by Taylor J. Willenbring, Sarah M. Papa, Kenneth A. Mann, Salvatore M. Cavallaro, Timothy A. Damron

“…We studied the interobserver reliability and intra-observer reproducibility of three classification systems of radiographic radiolucent lesions: (1) original Lodwick classification, (2) modified Lodwick classification, and (3) Enneking classification for benign tumors. …”
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Early Remaining Useful Life Prediction for Lithium-Ion Batteries Using a Gaussian Process Regression Model Based on Degradation Pattern Recognition by Linlin Fu, Bo Jiang, Jiangong Zhu, Xuezhe Wei, Haifeng Dai

“…Based on these extracted features, clustering and classification techniques are employed to categorize the batteries into three distinct degradation patterns. …”
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