Fault Diagnostic Method for Pump Running Conditions Based on Process Modeling and Neural Network by T. Uchiyama, S. Kallweit, H. Siekmann

“…A multi-layer neural network is employed for the classification. Three running conditions of a drainage pump are clearly detected by this method. …”
Get full text

Effect of pillar size and joint dip on stability of porous limestone cellars by Jalal Zenah, Péter Görög

“…Material parameters were determined through various laboratory tests such as UCS, triaxial, and discontinuity shear strength tests, as well as rock mass classification. Three-dimensional finite element software (RS3) was used to evaluate the stability of the studied pillar. …”
Get full text

Spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification by Xiong Tan, Zhixiang Xue

“…Furthermore, global spectral characteristics and local spatial features are integrated to perform the hyperspectral image spectral-spatial classification. Three benchmark hyperspectral datasets are employed for comparative classification experiments and ablation study, and experimental results certify the effectiveness and advancement of the proposed model in terms of collaborative classification accuracy.…”
Get full text

Automatic Characterization of the Physiological Condition of the Carotid Artery in 2D Ultrasound Image Sequences Using Spatiotemporal and Spatiospectral 2D Maps by Hamed Hamid Muhammed, Jimmy C. Azar

“…Spatiotemporal and spatiospectral 2D maps describing these patterns (in both the spatial and the frequency domains, resp.) were generated and analyzed by visual inspection as well as automatic feature extraction and classification. Three categories of cases were considered: pathological elderly, healthy elderly, and healthy young cases. …”
Get full text

Skin involvement in children with non-Hodgkin's lymphoma by M Büyükpamukçu, I Ilhan, M Cağlar, C Akyüz, S Berberoğlu, E Kotiloğlu

“…According to Murphy's clinical classification, three cases with primary cutaneous lymphoma were in stage I E, two of the remaining five patients were in stage III and three patients with organ involvement were in stage IV. …”
Get full text

Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques by B.P. Pradeep kumar, Pramod K.B. Rangaiah, Robin Augustine

“…Capsule Networks were utilized for feature extraction and Extreme Learning Machines (ELM) for diagnostic classification. Three datasets were evaluated, including DRISHTI-GS, DRIONS-DB, and HRF, utilizing important parameters such as accuracy, sensitivity, and specificity. …”
Get full text

Interobserver and intraobserver reliability of a new prognostic classification for foot and ankle charcot arthropathy by Mohamed Abdelaziz Elghazy, Samer Ali, Ahmed El-Hawary, Hani El-Mowafi, Yasser Roshdy Kandil

“…Thereafter, twenty cases of foot and ankle Charcot were presented to the raters who were asked to rate each case according to the classification. Three weeks after the initial evaluation, six raters from a single institution repeated the rating of the cases. …”
Get full text

An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection by Yexin Tian, Shuo Xu, Yuchen Cao, Zhongyan Wang, Zijing Wei

“…In this study, we systematically evaluate the mathematical foundations and empirical performance of five representative models for automated fake news classification: three classical machine learning algorithms (Logistic Regression, Random Forest, and Light Gradient Boosting Machine) and two state-of-the-art deep learning architectures (A Lite Bidirectional Encoder Representations from Transformers—ALBERT and Gated Recurrent Units—GRUs). …”
Get full text

Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach. by Huadi Zhou, Mei Xie, Hemiao Shi, Chenhan Shou, Meng Tang, Yue Zhang, Yue Hu, Xiao Liu

“…Radiomic features from both the tumor and peritumoral regions were extracted, and a random forest model was used to select the most contributive features for classification. Three machine learning models-Random Forest, XGBoost, and Extra Trees-were then constructed and trained on four different feature combinations (tumor ADC, tumor T2, tumor ADC+T2, and tumor + peritumoral ADC+T2).…”
Get full text

Innovative fast and low-cost method for the detection of living bacteria based on trajectory by Paul Perronno, Julie Claudinon, Carmen Senin, Serap Elçin-Guinot, Lena Wolter, Olga N. Makshakova, Norbert Dumas, Dimitri Klockenbring, Joseph Lam-Weil, Vincent Noblet, Siegfried Steltenkamp, Winfried Römer, Morgan Madec

“…It combines optical imaging, object detection and tracking, and machine-learning-based classification. Three of the most common bacteria are selected for this study. …”
Get full text

Genetic and Agromorphological Characterization of Sesame (Sesamum indicum L.) Genotypes for Dehiscence-Related Traits and Grain Yield Performances by Gazali B. T. A. Sanni, Vincent Ezin, Antoine Abel Missihoun, Quenum Florent, Adam Ahanchede

“…Descriptive and inferential statistics were performed. After classification, three clusters were identified. The first group with less resistance to shattering had shorter sizes, lower yields, fewer capsules, and smaller capsular zones. …”
Get full text

Multi-label classification with deep learning techniques applied to the B-Scan images of GPR by El Karakhi, Soukayna, Reineix, Alain, Guiffaut, Christophe

“…In this study, a multi-label classification (MLC) model based on transfer learning and data augmentation has been developed to generate multiple information elements on the same image and to realize classification. Three deep learning models: VGG-16, ResNet-50 and adapted CNN were used as pre-trained models for transfer learning. …”
Get full text

Remote Sensing Surveillance Using Multilevel Feature Fusion and Deep Neural Network by Laiba Zahoor, Haifa F. Alhasson, Mohammed Alnusayri, Mohammed Alatiyyah, Dina Abdulaziz Alhammadi, Ahmad Jalal, Hui Liu

“…In the end, a deep neural network was trained to perform the action classification. Three benchmark datasets, the UAV Gesture, UAV Human, and UCF-ARG datasets, were used for our experiments and system testing. …”
Get full text

Comparative Analysis of Adhesive Retention and Denture Weight in Different Residual Ridge Morphologies: A Cross‐Over Randomized‐Controlled Trial by Naseer Ahmed, Maria Shakoor Abbasi, Asra Salahuddin, Lareb Tariq, Sarrah Siraj, Gotam Das, Ghazala Suleman, Fahim Vohra, Artak Heboyan

“…Materials and Methods In this crossover randomized‐controlled trial, the patients were randomly and equally divided into 3 groups based on clinical features and radiographic findings according to the Wical–Swoope classification. Three forms of denture adhesives were used, including powder, cream, and strips, for three residual ridge types. …”
Get full text

Integration of VIS–NIR Spectroscopy and Multivariate Technique for Soils Discrimination Under Different Land Management by Mohamed S. Shokr, Abdel-rahman A. Mustafa, Talal Alharbi, Jose Emilio Meroño de Larriva, Abdelbaset S. El-Sorogy, Khaled Al-Kahtany, Elsayed A. Abdelsamie

“…By utilizing the morphological, physical, and chemical characteristics of representative pedogenetic profiles established in various soils of the Sohag governorate, Egypt, the current research addresses the characterization of surface reflectance spectra and links them with the corresponding soil classification. Three primary areas were identified: recently cultivated, old cultivated, and bare soils. …”
Get full text

Simulation and Validation of Dose Distribution in BNCT Treatment Planning System by LYU Tianqi, MENG Siqin, WANG Hongliang, HE Linfeng, RUAN Shihao, WANG Tianyun, WU Meimei, CHEN Jun, SHI Bin, GUAN Fengping, LI Yuqing, LI Tianfu, HAO Lijie, LIU Yuntao

“…In this paper, a program was designed independently using the Python language, which implemented the primary functions of BNCT-TPS, including medical data processing, organizational classification, three-dimensional reconstruction, particle transport simulation, dose distribution output, and data fusion. …”

Risk stratification and clinical classification for postoperative neurological complications in post-tuberculosis kyphosis: a retrospective cohort study by Jianqiang Wang, Yong Hai, Haoshuang Geng, Zhangfu Li, Yuzeng Liu, Yangpu Zhang, Lijin Zhou

“…Multivariate logistic regression analysis revealed that the Baltalimani sign, spinal cord MRI type, Rajasekaran classification, three-column osteotomy, C-reactive protein (CRP), and SCA were significant risk factors for postoperative neurological complications. …”
Get full text

Recurrence-Based Techniques for Data-Driven Fault Diagnosis and Monitoring in Neutral-Point-Clamped Inverters by Lincoln Moura De Oliveira, Menaouar Berrehil El Kattel, Esio Eloi Dos Santos Filho, Juan C. Vasquez, Josep M. Guerrero, Fernando Luiz Marcelo Antunes

“…The proposed approach is experimentally validated using a laboratory prototype, confirming its effectiveness in fault detection and classification. Three operating conditions were evaluated: an RL load, an induction motor, and an open-circuit fault scenario. …”
Get full text

Aortic Valve Reinterventions after Ozaki: Clinical Case Series from Four Centers by S. T. Enginoev, I. I. Chernov, R. N. Komarov, V. A. Belov, V. B. Arutyunyan, B. K. Kadyraliev, A. P. Semagin, D. V. Kuznetsov, A. A. Zybin, A. M. Ismailbaev, U. K. Abdulmedzhidova, B. M. Tlisov, A. B. Gamzaev

“…Four patients had class III-IV CHF, according to NYHA classification. Three patients had previously been operated on for infective endocarditis (IE), and five patients had bicuspid aortic valve. …”
Get full text

Caracterização hidrodinâmica de solos: aplicação do método Beerkan Hydrodynamic characterization of soils: application of the Beerkan method by Eduardo S. de Souza, Antonio C. D. Antonino, Rafael Angulo-Jaramillo, André Maciel Netto

“…This method was applied to two soils with different textural classification (three of an Oxisoil and three of an Alluvial Soil). …”
Get full text