HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network by D. Santhakumar, K. Dhana Shree, M. Buvanesvari, A. Saran Kumar, Ayodeji Olalekan Salau

“…Examining ECG signals recorded on the body’s surface may not be an effective method for studying and diagnosing diabetic issues. The study introduces a research proposal utilizing a high-density resolution electrocardiogram (ECG) system with a minimum of 64 channels and multi-view convolutional neural network classification (HD-MVCNN) to address the mentioned challenges. …”
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GreenNet: A dual-encoder network for urban green space classification using high-resolution remotely sensed images by Ke Chen, Yang Wang, Cunrui Huang, Jing Wang, Sabrina L. Li, Haiyan Guan, Lingfei Ma

“…However, existing deep learning networks for large-scale high-resolution remote sensing images often face limited receptive fields and insufficient extraction of global information, making it challenging to achieve satisfactory performance on urban green space classification tasks. To address these issues, this paper presents a novel dual-encoder network, termed GreenNet, specifically designed for urban green space classification from high-resolution remotely sensed images. …”
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ActBeCalf: Accelerometer-based multivariate time-series dataset for calf behavior classificationZenoob - EU Open Research Repository by Oshana Iddi Dissanayake, Sarah E. McPherson, Joseph Allyndrée, Emer Kennedy, Pádraig Cunningham, Lucile Riaboff

“…The code utilized for the classification is publicly available in the dataset repository. …”
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Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data by Suling He, Yuncheng Deng, Jinliang Wang, Mengjia Luo

“…However, many existing mine-mapping studies tend to classify all vegetation into a single class, overlooking inter-species differentiation and thereby limiting the precision of ecological evaluations. To address this issue, this study proposed a multi-scale hierarchical classification (MSHC) method for fine-scale mapping of vegetation types in open phosphate mining areas by combining unmanned aerial vehicle (UAV) RGB remote sensing imagery and light detection and ranging (LiDAR) data with the support of object-oriented methods. …”
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Machine learning for automated cause-of-death classification from 2021 to 2022 in Korea: development and validation of an ICD-10 prediction model by Seokmin Lee, Gyeongmin Im

“…Free-text fields for immediate, antecedent, and contributory causes were concatenated and fine-tuned with KM-BERT. Three classification models were developed: (1) final underlying cause prediction (International Classification of Diseases, 10th Revision [ICD-10] code) from certificate inputs, (2) tentative underlying cause selection based on ICD-10 Volume 2 rules, and (3) classification of individual cause-of-death entries. …”
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Des régions territoriales aux régions fonctionnelles, une proposition de classification des régions de l’Union Européenne by François-Olivier Seys

“…The regions’ issue is recent in Europe on a historical scale. …”
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Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features by Chaolu Meng, Yongqi Hou, Quan Zou, Lei Shi, Xi Su, Ying Ju

“…To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. …”
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Automatic Change Detection of High Resolution Remote Sensing Images Based on Level Set Evolution and Support Vector Machine Classification by YAN Ming, CAO Guo, XIA Meng

“…We propose a method for change detection in highresolution remote sensing images by means of level set evolution and Support Vector Machine (SVM) classification, which combined both pixellevel method and objectlevel method Both pixelbased change features and objectbased ones are extracted to improve the discriminability between the changed class and the unchanged classAt the pixellevel, the change detection problem is formulated as a segmentation issue using level set evolution in the difference image At the objectlevel, potential training samples are selectedfrom the segmentation results without manual intervention into SVM classifier Thereafter, the final changes are obtained by combining the pixelbased changes and the objectbased changes A chief advantage of our approach is being able to select appropriate samples for SVM classifier training Furthermore, our proposed method helps improving the accuracy and the degree of automation We systematically evaluated it with a variety of SPOT5 images and aerial images Experimental results demonstrated the accuracy of our proposed method…”
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A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System Measurements by Wei Yuan, Phawis Thammasorn, Lingxiao Wang, Shih Mo

“…However, implementing this technology in real-world settings remains challenging due to difficulties in accurately modeling wave scattering, which often leads to suboptimal performance of classification systems. Our proposed deep learning framework addresses this issue by bypassing complex modeling tasks and directly extracting stroke signatures from wavelet-filtered microwave signals, combined with clinical variables and antenna measurements. …”
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Edge and Node Enhancement Graph Convolutional Network: Imbalanced Graph Node Classification Method Based on Edge-Node Collaborative Enhancement by Jiadong Tian, Jiali Lin, Dagang Li

“…In addressing the issue of node classification with imbalanced data distribution, traditional models exhibit significant limitations. …”
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Enhancing E-Nose Performance via Metal-Oxide Based MEMS Sensor Arrays Optimization and Feature Alignment for Drug Classification by Ruiwen Kong, Wenfeng Shen, Yang Gao, Dawu Lv, Ling Ai, Weijie Song, Ruiqin Tan

“…This article introduces a novel approach to improve electronic nose classification accuracy by optimizing sensor arrays and aligning features. …”
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Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area by Aicha Moumni, Abderrahman Lahrouni

“…The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. …”
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NLP based text classification using TF-IDF enabled fine-tuned long short-term memory: An empirical analysis by Pratiyush Guleria, Jaroslav Frnda, Parvathaneni Naga Srinivasu

“…While these platforms have become vital channels for sharing news, advertisements, and crucial updates, they also pose challenges in verifying the authenticity of the information in real-time. Addressing this issue, this study proposes a novel Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM) model designed for the classification of fake news articles. …”
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Robust self management classification via sparse representation based discriminative model for mild cognitive impairment associated with diabetes mellitus by Yun-xian Wang, Rong Lin, Hao Liang, Yuan-jiao Yan, Ji-xing Liang, Ming-feng Chen, Hong Li

“…Thus, the inclusion and discrimination of subsequent interventions according to their self-management is an urgent issue. A Sparse-representation-based Discriminative Classification model (SDC) is proposed in this paper to correctly classify MCI-DM patients based on their self-management ability. …”
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Fine-scale forest classification with multi-temporal sentinel-1/2 imagery using a temporal convolutional neural network by Rongfei Duan, Chunlin Huang, Peng Dou, Jinliang Hou, Ying Zhang, Juan Gu

“…However, spectral similarity between different vegetation types and the issue of mixed pixels in medium-resolution satellite imagery remain significant challenges for fine-scale forest classification. …”
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Classification of fashion e-commerce products using ResNet-BERT multi-modal deep learning and transfer learning optimization. by In-Jae Seo, Yo-Han Lee, Beakcheol Jang

“…To resolve this problem, this study proposes a methodology for fashion e-commerce product classification based on multi-modal deep learning and transfer learning. …”
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CLPM: A Hybrid Network With Cross-Space Learning and Perception-Driven Mechanism for Long-Tailed Remote Sensing Image Classification by Lei Zhang, Min Kong, Changfeng Jing, Xing Xing

“…Long-tailed distribution is a common issue in remote sensing image classification (RSIC), and many datasets suffer from severe class imbalance. …”
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Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data by Mazin Al-Mahrouqi, Abdollah Shafieezadeh, Jieun Hur, Jae-Wook Jung, Jeong-Gon Ha, Daegi Hahm

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Enhanced sentiment analysis framework: Ensemble attention enhanced bidirectional long-short-term encoder for accurate classification of consumer reviews by Ali Jaber Almalki

“…To rectify this type of issue, current work focuses on developing a sentiment analysis framework by progressive techniques. …”
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A preliminary report on the feasibility of regression-based alignment of diagnostic thresholds for harmonized use of international classification criteria for antiphospholipid synd... by Yukari Motoki, Risa Kaneshige, Yuichiro Fujieda, Kenji Oku, Eriko Morishita, Masahiro Ieko, Tatsuya Atsumi, Kiyoshi Ichihara, Junzo Nojima

“…<h4>Background</h4>In 2023, international rheumatology societies issued new classification criteria for antiphospholipid syndrome (APS). …”
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