Image Classification of Indonesian Snacks using Convolutional Neural Network by Kunti Eliyen, Abidatul Izzah, Fikha Rizky Aullia

“…One way to address this issue is through image classification of Indonesian traditional snacks using Convolutional Neural Networks (CNN). …”
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Spectral Pattern Classification in Lidar Data for Rock Identification in Outcrops by Leonardo Campos Inocencio, Mauricio Roberto Veronez, Francisco Manoel Wohnrath Tognoli, Marcelo Kehl de Souza, Reginaldo Macedônio da Silva, Luiz Gonzaga Jr, César Leonardo Blum Silveira

“…The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from terrestrial laser scanner in order to identify the presence of different rocks in outcrops and to generate a digital outcrop model. …”
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Research on the Application of Deep Learning Methods in the Field of Image Classification by Peng Yuhui

“…This paper reviews the current research status of image classification models, focusing on the application of DenseNet-201, Xception, MobileNetV3-Small and ResNet-50 models in the fruit field. …”
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Spectral-Spatial Convolutional Hybrid Transformer for Hyperspectral Image Classification by Haixin Sun, Jingwen Xu, Fanlei Meng, Mengdi Cheng, Qiuguang Cao

“…To address this issue, the Spectral-spatial convolutional hybrid Transformer (SSCHFormer) hyperspectral classification model is proposed in this article. First, the spectral pyramid 3D convolution and 2D convolution are combined to extract joint and detailed spectral-spatial features. …”
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Application of BigML in the Classification Evaluation of Top Coal Caving by Meng Wang, Caiwang Tai, Qiaofeng Zhang, Zongwei Yang, Jiazheng Li, Kejun Shen, Kang Wang

“…In order to realize the classification evaluation of top coal caving, this article introduces the method of using BigML to establish the classification evaluation model of top coal caving. …”
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Aura phenomenon: a proposal for an etiology-based clinical classification by Umberto Pensato, Andrew M. Demchuk, Jens P. Dreier, Kevin C. Brennan, Simona Sacco, Michele Romoli

“…Main body We propose the following terminology and etiology-based clinical classification for the aura phenomenon: (i) Migrainous Aura (when the etiology is migraine), (ii) Non-migrainous Aura (when there is an alternative etiology), (iii) Aura of uncertain clinical etiology (when etiology is unclear), and (iv) Migrainous Infarction (a typical migrainous aura in a patient with migraine with aura associated with an infarction in a corresponding anatomical brain region). …”
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Clouds Height Classification Using Texture Analysis of Meteosat Images by Baghdad Science Journal

“…The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. …”
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BLE Signal Processing and Machine Learning for Indoor Behavior Classification by Yi-Shiun Lee, Yong-Yi Fanjiang, Chi-Huang Hung, Yung-Shiang Huang

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Deep Metric Learning-Based Classification for Pavement Distress Images by Yuhui Li, Jiaqi Wang, Bo Lü, Hang Yang, Xiaotian Wu

“…This study proposes a deep metric learning-based pavement distress classification method to address critical limitations in conventional approaches, including their dependency on large training datasets and inability to incrementally learn new categories. …”
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Machine learning classification meets migraine: recommendations for study evaluation by Igor Petrušić, Andrej Savić, Katarina Mitrović, Nebojša Bačanin, Gabriele Sebastianelli, Daniele Secci, Gianluca Coppola

“…This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility. …”
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MLPNN and Ensemble Learning Algorithm for Transmission Line Fault Classification by Tanbir Rahman, Talab Hasan, Arif Ahammad, Imtiaz Ahmed, Nainaiu Rakhaine

“…The power transmission system is modeled using Simulink and the machine learning algorithms. In the IEEE 3-bus system, all of the learning types achieve approximately 99% accuracy in imbalanced and noisy data states, respectively, except CatBoost and decision tree, in the classification of line to line, line to line to line, line to line to ground, line to ground types of faults, and no fault. …”
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Quantifying CineECG Output for Enhancing Electrocardiography Signals Classification by MHD Jafar Mortada, Agnese Sbrollini, Ilaria Marcantoni, Erica Iammarino, Laura Burattini, Peter Van Dam

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Microwave Brain Lesion Detection and Localization Using Classification Algorithms by H. Şahintürk, R. Kıymaz, B. Aslanyürek, H.G. Süngülü

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Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances by Fatema A. Albalooshi, M. R. Qader

“…The results demonstrate promising classification performance in terms of simplicity and accuracy, highlighting the potential of this approach to improve PQ analysis and disturbance identification.…”
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Application of Ultrasound Classification of Hepatic Hydatid Cyst in Iraqi Population by Inas M. Al-Ani, Mudhaffar B. Mahdi, Ghassan M. Khalaf

“…The standardized WHO classification of CE was used in this study. Results: CE class I was noticing in (35%), class II (35%), class III (17%), class IV (7%),  and class V (6%). …”
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The Morphological Classification of Galaxy Clusters: Algorithms for Applying the Numerical Criteria by Elena Panko

“…It is possible in future to adapt the algorithms for the 3D case. The results of statistically valid morphological classification are useful for studies of the evolution of galaxy clusters.…”
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Research on Aerospace Text Classification Based on BERT-LSTM Model by AN Rui, CHEN Hailong, AI Siyu, CUI Xinying

“…Self attention is used to capture key information in the global information, further improving the weight of key features in text classification. Comparison tests are conducted with TextCNN, TextRNN, DPCNN and other models for aerospace text categorization task, and the results show that the proposed model based on bi-directional long and short-term memory networks fused with the attention mechanism improves the accuracy by 25. 3% , 25. 8% , and 18. 4% compared with the other models on aerospace text categorization task, respectively.…”
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Explainability Feature Bands Adaptive Selection for Hyperspectral Image Classification by Jirui Liu, Jinhui Lan, Yiliang Zeng, Wei Luo, Zhixuan Zhuang, Jinlin Zou

“…Hyperspectral remote sensing images are widely used in resource exploration, urban planning, natural disaster assessment, and feature classification. Aiming at the problems of poor interpretability of feature classification algorithms for hyperspectral images, multiple feature dimensions, and difficulty in effectively improving classification accuracy, this paper proposes a feature band adaptive selection method for hyperspectral images. …”
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Optimized ensemble learning for non-destructive avocado ripeness classification by Panudech Tipauksorn, Prasert Luekhong, Minoru Okada, Jutturit Thongpron, Chokemongkol Nadee, Krisda Yingkayun

“…Grid Search achieved the best classification performance, reaching an accuracy of 82.5% and an F1-score of 85.3%, highlighting the benefits of weight-optimized ensemble learning compared to single classifiers. …”
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Weakly supervised veracity classification with LLM-predicted credibility signals by João A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva, Carolina Scarton

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