Accurate Empirical Path Loss Models with Route Classification for mmWave Communications by Supachai Phaiboon, Pisit Phokharatkul

“…This paper presents accurate empirical path loss models with route classification for the high band frequency of 5 G wireless. …”
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Classification Based on Pruning and Double Covered Rule Sets for the Internet of Things Applications by Shasha Li, Zhongmei Zhou, Weiping Wang

“…However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. …”
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Erratum to “Chronic active lesions in multiple sclerosis: classification, terminology, and clinical significance”

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An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation by H. M. Ravindu T. Bandara, K. S. Priyanayana, A. G. Buddhika P. Jayasekara, D. P. Chandima, R. A. R. C. Gopura

“…Therefore, this paper proposes a method to create an intelligent gesture classification system with a gesture model which was built based on human studies for every essential motion in domestic navigation with hand gesture variance compensation capability. …”
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A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients by Jun Liu, Siqi Sun, Yang Liu, Jiayang Guo, Hailong Li, Yuan Gao, Jintao Sun, Jing Xiang

“…After optimized configuration, the accuracy, precision, recall, and F1-score of the proposed detector reached 94%, 95%, 94%, and 94%, which were better than other classical machine learning models. In addition, we used the k-fold cross-validation scheme to test the performance consistency of the model. …”
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Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds by Christopher J. Bell, Kaushallya Adhikari, Andrew Brown

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Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain by Alexander Uzhinskiy

Subjects: “…plant disease classification…”
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A dataset of Antarctic ecosystems in ice-free lands: classification, descriptions, and maps by Anikó B. Tóth, Aleks Terauds, Steven L. Chown, Kevin A. Hughes, Peter Convey, Dominic A. Hodgson, Don A. Cowan, John Gibson, Rachel I. Leihy, Nicholas J. Murray, Sharon A. Robinson, Justine D. Shaw, Jonathan S. Stark, Mark I. Stevens, John van den Hoff, Jane Wasley, David A. Keith

“…We present Antarctica’s first comprehensive ecosystem classification and map of ice-free lands, which host most of the continent’s biodiversity. …”
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A composite improved attention convolutional network for motor imagery EEG classification by Wenzhe Liao, Zipeng Miao, Shuaibo Liang, Linyan Zhang, Chen Li

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Skin Lesion Classification Through Test Time Augmentation and Explainable Artificial Intelligence by Loris Cino, Cosimo Distante, Alessandro Martella, Pier Luigi Mazzeo

Subjects: “…skin disease classification…”
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Prediction of Grade Classification of Rock Burst Based on PCA-SSA-PNN Architecture by Zhenyi Wang, Yalei Wang, Xiaoliang Jin

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A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution by Yang Cao, Xiaoli Zhao, Debin Su, Xiang Cheng, Hong Ren

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Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN by Nadikatla Chandrasekhar, Sujatha Canavoy Narahari, Sreedhar Kollem, Samineni Peddakrishna, Archana Penchala, Babji Prasad Chapa

“…Then, the important features are selected using Poisson Distribution Function - Snow Leopard Optimization (PDF-SLO), and the PJM-DJRNN is used to classify the types of disease. The proposed method is more effective than existing research methodologies as it uses both ECG and PCG signals, achieves better input signals, and accurately predicts HD classification. …”
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Classification of Slovenian Dry-Cured Ham – Kraški pršut – According to Texture Profile by Mateja Lušnic Polak, Tomaž Polak, Mojca Kuhar, Iva Zahija Jazbec, Tadej Kaltnekar, Lea Demšar

“…Based on the median for hardness in the sensory analysis, the samples were classified into three ranks of texture using linear discriminant analysis (9 variables, 100% correct classification): optimal (median 4.0; 19% of samples), slightly too soft (median 3.5, 72% of samples), and soft (median 3.0; 9% of samples). …”
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Improved machine classification algorithm for electric rail circuits in train warning systems by I. V. Prisukhina, D. V. Borisenko

“…There are known algorithms that implement the classification of code signals in an electric rail circuit. …”
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Classification of taxes and fees as a method of systematization of environmental taxation in the russian federation by L. S. Samodelko

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Research on Pneumothorax Classification Model of DenseNet Based on Multilayer Network Optimization by Hongliang Huang, Qike Wang, Lidong Wang

“…The classification accuracy is between 80% and 85%. The DenseNet-MNO classification model can accurately detect the condition of pneumothorax, providing technical support for detection technology.…”
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Instability of revised Korean Developmental Screening Test classification in first year of life by Ji Eun Jeong, You Min Kim, Na Won Lee, Gyeong Nam Kim, Jisuk Bae, Jin Kyung Kim

“…Purpose This study aimed to examine the stability of developmental classifications using the revised Korean Developmental Screening Test (K-DST) in healthy term infants aged 4–6 and 10–12 months. …”
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An automatic cervical cell classification model based on improved DenseNet121 by Yue Zhang, Chunyu Ning, Wenjing Yang

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Analysis of the Tick Rule and Bulk Volume Classification algorithms in the Brazilian stock market by Leonardo Souza Siqueira, Laíse Ferraz Correia, Hudson Fernandes Amaral

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