Application of an improved pelican optimization algorithm based on comprehensive strategy in PV parameter identification by Xu Yong, Sang Bicong, Zhang Yi

“…Secondly, the position update formula of the Pelican optimization algorithm in the global detection phase is replaced by the position update formula of the red-tailed Eagle optimization algorithm in the soaring phase to obtain the adequacy of the Pelican optimization algorithm in solution space search. …”
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Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System by Pavol Partila, Miroslav Voznak, Jaromir Tovarek

“…The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. …”
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ADAPTATION OF “DRIED BLOOD DROP” METHOD FOR THERAPEUTIC DRUG MONITORING by V. I. Petrov, I. S. Anikeev, T. E. Zayachnikova, A. V. Strygin, A. M. Dotsenko

“…To obtain the adequate quality samples, the developed protocols have been tested and optimized at the stages of selection and storage. By high-performance liquid chromatography with mass spectrometric detection (HPLC-MS/MS), using a “dried blood drop” as a sample preparation, drug validation protocols have been optimized to ensure that acceptable validation characteristics were achieved, and subsequent Therapeutic Drug Monitoring was performed.Results. …”
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CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors by Yao Zhang, Pengfei Zhang, Wenchao Zhang, Mingwei Wang

“…For early fault detection, the Mahalanobis distance-based function mapping method is established, along with the PMT monitoring index, early warning, and alarm threshold. …”
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Research on Data-driven PET-CT Gating Imaging Method by XU Shidong1, 2, 3, 4, SUN Xiaoli1, 3, 4, , LIU Shuangquan1, 3, 4, ZHENG Yushuang1, 3, 4, LI Mohan1, 3, 4, WEI Cunfeng1, 2, 3, 4

Optimized protocol for direct extraction of SARS-CoV-2 RNA from raw wastewater samples (ANRS 0160) by Ahlam Chaqroun, Ghina El Soufi, Zuzana Gerber, Julie Loutreul, Nicolas Cluzel, Julie Challant, Damien Delafoy, Florian Sandron, Léo Di Jorio, Stéphanie Raffestin, Vincent Maréchal, Christophe Gantzer, Robert Olaso, Jean-François Deleuze, Olivier Rohr, Nicolas Boudaud, Clémentine Wallet, Isabelle Bertrand

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Evaluating Sparse Feature Selection Methods: A Theoretical and Empirical Perspective by Monica Fira, Liviu Goras, Hariton-Nicolae Costin

“…The mathematical foundations of feature selection methods inspired by compressed detection are presented, highlighting how the principles of sparse signal recovery can be applied to identify the most relevant features. …”
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Architectural design methods based on image recognition technology and virtual VR by Feng Li

“…In practical analysis of architectural design, the average accuracy and intersection over union of the improved YOLOv4 model confirmed the good detection performance of this method. The application of virtual reality technology in building information models has significantly improved the visualization delay rate, and the subjective evaluation of users was relatively high. …”
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Enhancing Latent Defect Detection in Built-In Spindle Assembly Lines Through Vibration Data Analysis by Kuo-Hao Li, Chao-Nan Wang, Yao-Chi Tang

“…This automated selection of the optimal k value enhanced the stability and reliability of the defect detection process. …”
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Vascular complications after liver transplantation: contemporary approaches to detection and treatment. A literature review by K. O. Semash, T. A. Dzhanbekov, M. M. Akbarov

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Systematic review and meta-analysis of detecting galactomannan in bronchoalveolar lavage fluid for diagnosing invasive aspergillosis. by Mingxiang Zou, Lanhua Tang, Shushan Zhao, Zijin Zhao, Luyao Chen, Peng Chen, Zebing Huang, Jun Li, Lizhang Chen, Xuegong Fan

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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Comparative study of Taqman-based qPCR assay for the detection of Anisakis simplex and Pseudoterranova decipiens. by Mi-Gyeong Kim, Min Ji Hong, Doo Won Seo, Hyun Mi Jung, Hyun-Ja Han, Seung Hwan Kim, Insun Joo

“…Consequently, there is a necessity to compare and analyze the optimal detection methods with a view to preventing Anisakis outbreaks. …”
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CFRNet: Cross-Attention-Based Fusion and Refinement Network for Enhanced RGB-T Salient Object Detection by Biao Deng, Di Liu, Yang Cao, Hong Liu, Zhiguo Yan, Hu Chen

“…Existing deep learning-based RGB-T salient object detection methods often struggle with effectively fusing RGB and thermal features. …”
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Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…This paper concludes with a review of the progress in fault identification in ICE components and prospects, highlighted by an experimental investigation using 16 machine learning algorithms with seven feature selection techniques under three load conditions to detect faults in a four-cylinder ICE. Additionally, this study incorporates advanced deep learning techniques, including a deep neural network (DNN), a one-dimensional convolutional neural network (1D-CNN), Transformer and a hybrid Transformer and DNN model which demonstrate superior performance in fault detection compared to traditional machine learning methods.…”
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Optimization of a Newly Developed Chamber Setup for Spatial Dust Measurements in the Context of Containment by Hendrik Küllmar, Martin Schöler, Claudia S. Leopold

“…The optimization was aimed at a maximization of the amount of detected dust and a minimization of the required sample mass. …”
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Optimization of serious bacterial infections intensive therapy in children in Anesthesiology and Intensive Care Department by M. Yu. Kurochkin, A. H. Davydova, Yu. V. Horodkova

“…Materials and methods. We investigated respiratory tract microflora by bacteriological method in 120 newborns and 30 children from 1 month with severe bacterial infections at admission and during prolonged stay in AICU. …”
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At what cycle threshold level are dogs able to detect SARS-CoV-2 in humans? by Víctor M Vidal-Martínez, Juan Manuel Mancilla-Tapia, Lilia C Soler-Jiménez, Iván Velázquez-Abunader, Matilde Jiménez-Coello, Antonio Ortega-Pacheco, David Hernández-Mena

“…These performance values concur well with those reported for commercial rapid antigen tests for detecting SARS-CoV-2. Consequently, it is considered that using properly trained animals could offer a viable option to supplement existing diagnostic methods, allowing for rapid diagnosis while optimizing time and economic resources. …”
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Comparative Study on Rail Damage Recognition Methods Based on Machine Vision by Wanlin Gao, Riqin Geng, Hao Wu

“…This study systematically evaluated three object detection models—YOLOv8, SSD, and Faster R-CNN—in terms of detection accuracy (<i>mAP</i>), missed detection rate (<i>mAR</i>), and training efficiency. …”
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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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