FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification by Peixian Zhuang, Xiaochen Zhang, Hao Wang, Tianxiang Zhang, Leiming Liu, Jiangyun Li

“…To address these issues, we present a novel frequency-aware hierarchical Mamba (FAHM) for HSI classification. Specifically, FAHM is comprised of three main parts: a spatial-spectral interaction block (SSIB), a frequency group embedding module (FGEM), and an adaptive bidirectional Mamba block (ABMB). …”
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Dry fruit image classification using stacking ensemble model by Maheen Islam, Mujahidul Islam, Alfe Suny, Abdullah Al Rafi, Abdullahi Chowdhury, Mohammad Manzurul Islam, Saleh Masum, Md Sawkat Ali, Taskeed Jabid, Md Mostofa Kamal Rasel

“…Precise and efficient classification of dry fruit images is critical for enhancing quality control, efficiency, and safety in the agricultural and food industries. …”
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Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches by Hamed Ghaderi, Nasibe Alipour, Hossein Safari

“…The two models include the support vector machine (SVM) and 1D convolutional neural network (1D-CNN), which use ZMs, compared with the other three classification models of 2D-CNN, ResNet50, and VGG16 that apply the features from original images. …”
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IoT Based Meat Freshness Classification Using Deep Learning by Zarif Wasif Bhuiyan, Syed Ali Redwanul Haider, Adiba Haque, Mohammad Rejwan Uddin, Mahady Hasan

“…The custom Convolutional Neural Network (CNN) was trained on a dataset comprising 9,928 images, 6,672 of which were utilized for meat freshness classification and 3,256 for meat species classification. …”
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Binary-Classification Physical Fractal Models in Different Coal Structures by Guangui Zou, Yuyan Che, Tailang Zhao, Yajun Yin, Suping Peng, Jiasheng She

“…In the new constitutive equation, three key fractional orders, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>, emerged. …”
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Comorbidity: a Modern View on the Problem; Classification (first notice) by Abrahamovych O., Fayura O., Abrahamovych U.

“…In Ukrainian literature the authors often use the term “bicausal diagnosis” to describe comorbidity when there are two underlying diseases, to describe polymorbidity – “multicausal diagnosis” (three or more pathological conditions in one individual). …”
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Endoscopic Features of Gastrointestinal Amyloidosis: A Proposed Endoscopic Classification by Joo Hye Song, Hye Mi Jung, Ji Won Kim, Eun Ran Kim, Ga Yeon Lee, Sang Eun Yoon, Seok Jin Kim, Jung-Sun Kim, Dong Kyung Chang, Young-Ho Kim, Eun-Seok Jeon, Kihyun Kim, Sung Noh Hong

“…Methods : The endoscopic findings of 127 patients with GIA were reviewed and classified by three experienced endoscopists. The relationships of the endoscopic classification of GIA with clinical amyloidosis entities, symptoms, and patient outcomes were evaluated. …”
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Machine learning-guided field site selection for river classification by Zhihao Wang, Gregory Brian Pasternack, Yufang Jin, Costanza Rampini, Serena Alexander, Nikhil Kumar, Rune Storesund, K. Martin Perales, Christopher Lim, Stephanie Moreno, Igor Lacan

“…This framework includes three steps: (1) initial field site selection via machine learning from prior datasets, (2) selected field site accessibility evaluation and observation, and (3) additional field site decision and selection via an iterative learning process. …”
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Is the coronal plane alignment of the knee (CPAK) classification useful to plan individualized total knee arthroplasty surgery for the Spanish population? A critical analysis of th... by O. Pujol, P. Hinarejos, A. Pons, E. Famada, A. Zumel, J. Erquicia, J. Leal-Blanquet

“…The CPAK classification can be useful to describe and categorize osteoarthritic patients. …”
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Comparison of different classification of metabolic syndrome in the Kyrgyz ethnic group by Ch. B. Moldokeeva, O. S. Lunegova, A. S. Kerimkulova, A. E. Mirrakhimov, S. S. Abilova, A. A. Bairamukova, Yu. V. Zalesskaya, E. M. Mirrakhimov

“…Goal: Comparison of different classification of metabolic syndrome (MS) in Kyrgyz ethnic group. …”
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Prognostic significance of the Bethesda cytological classification in recurrent thyroid carcinoma by I. M. Zakharova, O. G. Grigoruk, S. A. Terekhova, D. I. Ganov, A. F. Lazarev, I. V. Vikhlyanov, Yu. A. Antonova, E. K. Semeryanova

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Knee phenotypes distribution according to CPAK classification in Turkish population by Vahit Emre Özden, Göksel Dikmen, Kayahan Karaytuğ, Arda Mavi, Yılmaz Onat Köylüoğlu, İsmail Remzi Tözün

“…The mean JLO was 173.7 ± 4.38, and the mean aHKA was 0.15 ± 3.81 in nonarthritic group. Arthritic group CPAK type distribution was 20.7% type I (n=118), 3.1% type II (n=18), 0.17% type III (n=1), 57.1% type IV (n=326), 8.4% type V (n=48), 0.17% type VI (n=1), 7.8% type VII (n=45), 1.4% type VIII (n=8), and 0.8% type IX (n=5). …”
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Genome-wide identification and classification of MYB superfamily genes in peach. by Chunhua Zhang, Ruijuan Ma, Jianlan Xu, Juan Yan, Lei Guo, Juan Song, Ruchao Feng, Mingliang Yu

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Classification and Recognition Method for Bearing Fault based on IFOA-SVM by Wei Zhang, Zhihua Ma

“…In order to identify the nonlinear classification of bearing fault features more accurately， a fault identification method based on IFOA-SVM is proposed. …”
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Classification of Some Fruits using Image Processing and Machine Learning by Dilara Gerdan Koç, Mustafa Vatandaş

“…In the training of artificial classifiers, the success was 93.6% for KNN, 90.3% for DT, 88.3% for Naive Bayes, 92.6% for MLP and 94.3% for RF.…”
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Syndesmotic Loose Bodies in Complex Ankle Fractures: Classification System by Hernán E Coria, Daniela Blanco, Héctor J Masaragian, Luciano Mizdraji, Fernando D Perin, Leonel Rega, Mauricio Rodriguez Acuña, Johann Veizaga Velasco

“…Results: About 26 patients were male and 56 were female, mean age was 59.1 years. We found 73 B3 and 9 C3 fractures. Type I loose bodies were found in 22 patients (27%), type II in 35 patients (43%), type III in 13 patients (15%), and in multiple areas, type IV in 12 patients (15%). …”
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Research on Classification Criteria for the Reducibility and Irreducibility of Intertrochanteric Femoral Fractures by Fenghua Zhu, Jinya Qiu, Liang Han, Hongxing Xu, Longtao Xiao, Qiushun Zhang, Yifeng Zhao

“…Results Logistic regression revealed that the risk factors leading to irreducibility were 31A3, 31A3.3, 31A1 (with obvious separation displacement), 31A2 (with anterior angular exostosis) and 31A2 (with a concomitant proximal femur fracture) fractures. …”
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Enhanced Skin Lesion Segmentation and Classification Through Ensemble Models by Su Myat Thwin, Hyun-Seok Park

“…This study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks. The research employs deep learning ensemble models for both segmentation (using U-Net, SegNet, and DeepLabV3) and classification (using VGG16, ResNet-50, and Inception-V3). …”
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Ulnar polydactyly of the hand: a classification system and clinical series by Dan Chen, Wenyao Zhong, Liying Sun, Zongxuan Zhao, Wen Tian

“…Type 0 was the most common (38 cases), followed by Type 4 (19 cases), Type 3 (4 cases), and Type 1 (3 cases). Our classification system effectively categorized all cases, including rare variants such as Type 1b (duplicated distal phalanx) and Type 4d (duplication originating from the deformed fourth metacarpal), which are not adequately addressed by previous classifications. …”
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Molecular genetic classification of endometrial cancer in the context of personalised medicine by L. N. Lyubchenko, S. V. Mukhtarulina, M. A. Meshkova, Z. A. Sidakova, N. N. Volchenko, E. G. Novikova

“…The renewed FIGO 2023 classification contains 19 endometrial cancer substages: 5 are included in stage I, 3 in stage II, 8 in stage III, 3 in stage IV. …”
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