Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification by Dian Kurniasari, Muhammad Dwi Pratama, Akmal Junaidi, Ahmad Faisol

“…However, some challenges remain, particularly in the classification between “Non-Demented” and “Very Mild Demented” cases. …”
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Robust Forest Sound Classification Using Pareto-Mordukhovich Optimized MFCC in Environmental Monitoring by Ahmad Qurthobi, Robertas Damasevicius, Vytautas Barzdaitis, Rytis Maskeliunas

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Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model by Suvashisa Dash, Mohammed Siddique, Satyasis Mishra, Demissie J. Gelmecha, Sunita Satapathy, Davinder Singh Rathee, Ram Sewak Singh

“…The detection and classification of brain tumors play a crucial role in early diagnosis and treatment planning. …”
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Machine Learning for Lung Cancer Subtype Classification: Combining Clinical, Histopathological, and Biophysical Features by Aiga Andrijanova, Lasma Bugovecka, Sergejs Isajevs, Donats Erts, Uldis Malinovskis, Andis Liepins

“…This study investigates whether integrating atomic force microscopy (AFM) measurements with conventional clinical and histopathological data can improve lung cancer subtype classification. <b>Methods:</b> We developed and analyzed a novel dataset combining clinical, histopathological, and AFM-derived biophysical characteristics from 37 lung cancer patients. …”
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Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software by Amr Abd-Elrahman, Katie Britt, Tao Liu

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New System for the Classification of Epiphyseal Separation of the Coracoid Process: Evaluation of Nine Cases and Review of the Literature by Takamitsu Mondori, Yoshiyuki Nakagawa, Shimpei Kurata, Shuhei Fujii, Takuya Egawa, Kazuya Inoue, Yasuhito Tanaka

“…Patients/Participants. A total of 37 patients were included in the analysis. Data on sex, age, cause and mechanism of injury, separation type, concomitant injury around the shoulder girdle, treatment, and functional outcomes were obtained. …”
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Integrating Wearable Sensor Signal Processing with Unsupervised Learning Methods for Tremor Classification in Parkinson’s Disease by Serena Dattola, Augusto Ielo, Angelo Quartarone, Maria Cristina De Cola

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Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography by Lilei Sun, Jie Wen, Junqian Wang, Zheng Zhang, Yong Zhao, Guiying Zhang, Yong Xu

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Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

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Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders by Souhaila Khalfallah, William Puech, Mehdi Tlija, Kais Bouallegue

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BanglaBlend: A large-scale nobel dataset of bangla sentences categorized by saint and common form of bangla languageMendeley Data by Umme Ayman, Chayti Saha, Azmain Mahtab Rahat, Sharun Akter Khushbu

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Postoperative symptom changes following uterine artery embolization for uterine fibroid based on FIGO classification by Yoshimi Nozaki, Shiori Takeuchi, Masafumi Arai, Yoshiki Kuwatsuru, Hiroshi Toei, Shingo Okada, Hitomi Kato, Naoko Saito, Takamichi Nobushima, Keisuke Murakami, Mari Kitade, Ryohei Kuwatsuru

“…Abstract Background Classifying uterine fibroid using the International Federation of Gynecology and Obstetrics (FIGO) classification system assists treatment decision-making and planning. …”
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Myocardial Infarction-Associated Shock: A Comprehensive Analysis of Phenotypes, SCAI Classification, and Outcome Assessment by Stanislav Dil, Maria Kercheva, Oleg Panteleev, Sergey Demianov, Aleksandr Kanev, Nina Belich, Boris Kornienko, Vyacheslav Ryabov

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Application of an intelligent English text classification model with improved KNN algorithm in the context of big data in libraries by Qinwen Xu

“…The results showed that the improved K-nearest neighbor algorithm achieved significant improvement in the precision, recall, and F1 values, reaching 90.50 %, 89.95 %, and 89.37 %, respectively. The classification time was significantly reduced to 1034.57 s. …”
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Performance Evaluation of Deep Learning Image Classification Modules in the MUN-ABSAI Ice Risk Management Architecture by Ravindu G. Thalagala, Oscar De Silva, Dan Oldford, David Molyneux

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The Place of Vocational Pedagogy in Pedagogical Science System by N. V. Ronzhina

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Regression-Based Classification of the Middle-Latency Auditory-Evoked Potentials in Vestibular Migraine and Concussion Patients with Dizziness by Carolina Beppi, Daniel Agostino, Antonella Palla, Nina Feddermann-Demont, Julia Dlugaiczyk, Dominik Straumann

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RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis by S. M. Nuruzzaman Nobel, S M Masfequier Rahman Swapno, Md. Ashraful Hossain, Mejdl Safran, Sultan Alfarhood, Md. Mohsin Kabir, M. F. Mridha

“…The research is fully committed to identifying abnormalities within this complex environment, going beyond the classification of subtypes of ovarian cancer. We proposed a new Attention Embedder, a state-of-the-art model with effective results in ovarian cancer subtype classification and outlier detection. …”
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Interrelation between hypoxic liver injury and Killip classification in ST-segment elevation myocardial infarction patients by Seong Huan Choi, Ji-Hun Jang, Dae-Young Kim, Young Ju Suh, Yong-Soo Baek, Sung-Hee Shin, Seong-Ill Woo, Dae-Hyeok Kim, Jeonggeun Moon, Jon Suh, WoongChol Kang, Sang-Don Park, Sung Woo Kwon

“…The incidence of HLI showed incremental tendency with respect to the Killip classification (19.5%, 19.4%, 34.6%, and 37.8%, respectively; p &lt; 0.001). …”
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Agricultural and Related Pest Control Applicator License Classifications under the Florida Department of Agriculture and Consumer Services (FDACS) by Frederick M. Fishel

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