Mixed signal modulation recognition method based on temporal depth residual shrinkage network by LIU Jinghua, WEI Xianglin, FAN Jianhua, HU Yongyang, WANG Xiaobo, YU Bing

“…Notably, the recognition accuracy for 12 out of 21 types of aliased signals approaches 100%.…”
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Federated Learning: A Distributed Shared Machine Learning Method by Kai Hu, Yaogen Li, Min Xia, Jiasheng Wu, Meixia Lu, Shuai Zhang, Liguo Weng

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An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection by zahra asgari varzaneh, soodeh hosseini

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Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons by K. Jayasree, Malaya Kumar Hota

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Tilted-Mode All-Optical Diffractive Deep Neural Networks by Mingzhu Song, Xuhui Zhuang, Lu Rong, Junsheng Wang

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Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes by Xiangyu Zhang, Claudia Baumann, Rabindranath De La Fuente

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High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition by Ivan Grcić, Hrvoje Pandžić

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4G network capacity expansion system based on service perception by Limin TANG, Deping XU, Ritao CHENG

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Comparative analysis methods in informatics by Jelena Gasperovič, Albertas Čaplinskas

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Differential geometry of nonholonomic complexes NGr(1, n, 2n − 4) in n-dimensional affine space by Kazimieras Navickis

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Multidimensional analogs of A. Bicardze system by Eugenijus Paliokas

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Analisis Kinerja Algoritma CART dan Naive Bayes Berbasis Particle Swarm Optimization (PSO) untuk Klasifikasi Kelayakan Kredit Koperasi by Eko Arif Riyanto, Tri Juninisvianty, Doddy Ferdian Nasution, Risnandar Risnandar

“…The accuracy value obtained from this study was 96.43%, the recall value was 94.12%, and the precision value is 100%. AUC value of 0.963 indicates that this study is included in the good classification. …”
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Staging of esophageal cancer using PET/MRI: a systematic review with head-to-head comparison by Alisa Mohebbi, Saeed Mohammadzadeh, Zahra Moradi, Afshin Mohammadi, Hossein Poustchi, Seyed Mohammad Tavangar

“…For T, N, and M staging, PET/MRI showed 9.1%, 2.0%, and 10.7% upstaging than the histopathological evaluation while these numbers were 19.4%, 12.4%, and 5.3% for downstaging. For direct comparison with PET/CT, PET/MRI showed 0.7% and 5.6% less downstaging and upstaging for N staging and 2.5% and 4.0% for M staging. …”
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Concept of Tributalism: a comparative Analysis of S. Amin 's, J. Haldon's and H. H. Stahl's approaches by Nerijus Babinskas

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Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice by Christopher Harrison, Helena Britt, Graeme Miller, Joan Henderson

“…When multimorbidity was defined as 3+ disease entities: counting individual chronic conditions produced significantly higher estimates than counting groups of chronic conditions; the 12 most prevalent chronic conditions identified only two-thirds of patients identified using all chronic conditions.Conclusions Multimorbidity defined as 2+ disease entities can be measured using different definitions of disease entity with as few as 12 prevalent chronic conditions, but lacks specificity to be useful, especially in older people. …”
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Clinical severity grading of NF2-related schwannomatosis by Anna C. Lawson McLean, Denise Löschner, Said Farschtschi, Nora F. Dengler, Steffen K. Rosahl

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Social Attitudes toward Cerebral Palsy and Potential Uses in Medical Education Based on the Analysis of Motion Pictures by Marek Jóźwiak, Brian Po-Jung Chen, Bartosz Musielak, Jacek Fabiszak, Andrzej Grzegorzewski

“…The CP incidences of different Gross Motor Function Classification System (GMFCS) levels in real world and in movies, respectively, are 40–51%, 47% (Level I + II); 14–19%, 12% (Level III); 34–41%, 41% (Level IV + V). …”
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Three-dimensional printed models improve orthopedic residents’ understanding of adolescent idiopathic scoliosis by Ruiqi Liu, Hong Ma

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EEG-RegNet: Regressive Emotion Recognition in Continuous VAD Space Using EEG Signals by Hyo Jin Jon, Longbin Jin, Hyuntaek Jung, Hyunseo Kim, Eun Yi Kim

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Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data by Lella Kranthi Kumar, K. G. Suma, Pamula Udayaraju, Venkateswarlu Gundu, Srihari Varma Mantena, B. N. Jagadesh

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