Identification and validation of diagnostic biomarkers and immune cell abundance characteristics in Staphylococcus aureus bloodstream infection by integrative bioinformatics analys... by Junhong Shi, Li Shen, Yanghua Xiao, Cailing Wan, Bingjie Wang, Peiyao Zhou, Jiao Zhang, Weihua Han, Rongrong Hu, Fangyou Yu, Hongxiu Wang

“…First, after overlapping the differentially expressed genes (DEGs) in S. aureus infection samples from GSE33341-human and GSE33341-mice samples, we detected 63 overlapping genes. Subsequently, the hub genes including DRAM1, PSTPIP2, and UPP1 were identified via three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. …”
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Three-Dimensional Path-Following with Articulated 6DoF Robot and ToF Sensors by Tymon F. Wawrzyniak, Ignacy D. Orłowski, Marek A. Galewski

“…This is achieved by equipping a standard industrial robot with a path detection tool featuring six low-cost Time-of-Flight (ToF) sensors. …”
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Deeper insight into speech characteristics of patients at ultra-high risk using classification and explainability models by Deok-Hee Kim-Dufor, Michel Walter, Marie-Odile Krebs, Yannis Haralambous, Philippe Lenca, Christophe Lemey, Christophe Lemey

“…IntroductionPeculiar use of language and even language deficits are one of the well-known signs of schizophrenia. Different language features analyzed using natural language processing and machine learning have been reported to differentiate patients at ultra-high risk for psychosis. …”
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An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data by Tanisha Bhardwaj, K. Sumangali

“…PPFBXAIO employs Secure Hash Algorithm 256 (SHA-256) for blockchain-backed secure model updates, Min-Max normalization for feature scaling, and the Levy Grasshopper Optimization Algorithm (LGOA) for optimal feature selection and federated model tuning. …”
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An Assessment of the Use of Visual Odometry in Localization by İ. Yakar, R. A. Kucak, S. Bilgi

“…The process commonly involves detecting and matching key points or features in the images, such as corners or edges, and then using algorithms to calculate the camera's motion. …”
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An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition by Zongying Liu, Shaoxi Li, Jiangling Hao, Jingfeng Hu, Mingyang Pan

“…Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. …”
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Machine learning identifies 6-gene signature in peripheral blood for pancreatic cancer diagnosis by Francisco Carrillo-Perez, Octavio Caba, Cristina Jiménez-Luna, Francisco Ortuño, Daniel Castillo-Secilla, Luis Javier Herrera, Jose Prados, Ignacio Rojas

“…We analyzed two distinct datasets: extracellular vesicle long RNA (exLR) and peripheral blood mononuclear cell (PBMC) RNA-Seq. Feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by support vector machine (SVM) classification, identified a 15-gene signature derived from the exLR data. …”
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Pulsed inhibition of corticospinal excitability by the thalamocortical sleep spindle by Umair Hassan, Prince Okyere, Milad Amini Masouleh, Christoph Zrenner, Ulf Ziemann, Til Ole Bergmann

“…Utilizing a recently developed real-time spindle detection algorithm, we applied spindle phase-triggered transcranial magnetic stimulation (TMS) to the primary motor cortex (M1) hand area to characterize the corticospinal excitability profile of spindles via motor evoked potentials (MEP). …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS) with machine learning (ML) algorithms. …”
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Temporal scalability analysis of spectral remote sensing and Machine Learning-Based models for forest mapping in the tropical region of Nigeria by K. Pawłuszek-Filipiak, D. Włodarczyk, K. Stuchły, F.E. Ikuemonisan

“…The integration of machine learning (ML) algorithms with remote sensing (RS) data offers significant potential for enhancing dynamic forest characterization and change detection through high temporal density mapping. …”
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Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks by Adoración Antolí, Adoración Antolí, Francisco Javier Rodriguez-Lozano, José Juan Cañas, Julia Vacas, Julia Vacas, Fátima Cuadrado, Fátima Cuadrado, Araceli Sánchez-Raya, Araceli Sánchez-Raya, Carolina Pérez-Dueñas, Carolina Pérez-Dueñas, Juan Carlos Gámez-Granados

“…Naive Bayes and Logistic Model Trees (LMT) emerged as the most effective algorithms in this study. The resulting model enabled the identification of potential disorder-specific markers, such as the mean duration of visits to objects.ConclusionThese findings highlight the potential of applying XML techniques to eye-tracking data collected through tasks designed to capture features characteristic of neurodevelopmental conditions. …”
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A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment by Zhihong Li, Yang Dong, Yanjie Wen, Han Xu, Jiahao Wu

“…The experimental results show that the tracking accuracy of mAP is higher than 95% both in normal and abnormal data sets and higher than that of the traditional detection algorithm. The detection speed of the normal data set is slightly higher than that of the abnormal data set. …”
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AviaryMOT: Aviary Attention-based adaptive multi-object tracking of cranes and storks in wetlands by Chang Liu, Xuran Ma, Jiahong Zhou, Nini Sun, Hengming Liu

“…To tackle these issues, we propose a novel multi-target tracking algorithm, AviaryMOT, which utilizes a fusion technique that combines shallow and deep features to enhance tracking accuracy and effectiveness. …”
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Improved U-Net for Precise Gauge Dial Segmentation in Substation Inspection Systems: A Study on Enhancing Accuracy and Robustness by Wan Zou, Yiping Jiang, Wenlong Liao, Songhai Fan, Yueping Yang, Jin Hou, Hao Tang

“…To address these challenges, we propose an improved U-Net segmentation algorithm. The key innovation of our approach is the insertion of a layer-hopping connection module between the Encoder and Decoder to capture feature information across multiple scales, enhancing semantic expressiveness and optimizing feature fusion. …”
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Improving T2D machine learning-based prediction accuracy with SNPs and younger age by Cynthia AL Hageh, Andreas Henschel, Hao Zhou, Jorge Zubelli, Moni Nader, Stephanie Chacar, Nantia Iakovidou, Haralampos Hatzikirou, Antoine Abchee, Siobhán O’Sullivan, Pierre A. Zalloua

“…Results: The inclusion of genomic data modestly improved model performance across all algorithms in the discovery dataset. Clinical features such as family history of T2D and hypertension consistently ranked as top features. …”
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Transformer Fault Diagnosis Based on Knowledge Distillation and Residual Convolutional Neural Networks by Haikun Shang, Yanlei Wei, Shen Zhang

“…The approach begins by extracting features from DGA data using the ratio method and Multiscale sample entropy (MSE), then reconstructs the state space of the feature data using recursive plots to generate interpretable two-dimensional image features. …”
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Comparison of five relative radiometric normalization techniques for remote sensing monitoring by DING Li-xia, ZHOU Bin, WANG Ren-chao

“…The root-mean-square error and the dynamic range were employed in comparing and evaluating the images normalized by five methods. A change detection algorithm, i. e., image subtraction, was applied to compare the effects on change detection. …”
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Seasonal to decadal variability and persistence properties of the Euro-Atlantic jet streams characterized by complementary approaches by H. Banderier, H. Banderier, A. Tuel, T. Woollings, O. Martius, O. Martius, O. Martius

“…</p> <p>First, we apply the self-organizing map (SOM) clustering algorithm to create a 2D distance-preserving discrete feature space for the tropopause-level summer wind field over the North Atlantic. …”
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Penerapan Decision Tree dan Random Forest dalam Deteksi Tingkat Stres Manusia Berdasarkan Kondisi Tidur by Sza Sza Amulya Larasati, Elok Nuraida Kusuma Dewi, Brahma Hanif Farhansyah, Fitra Abdurrachman Bachtiar, Fajar Pradana

“…Hyperparameter tuning is done using k-fold cross-validation, and the model is designed using the Decision Tree and Random Forest classification algorithms. The results show that five features: snoring rate, respiration rate, limb movement including eye movement, and heart rate during sleep are directly proportional to the level of stress. …”
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Technology of decision-making in process management of the non-productive sphere by O. M. Kulikova

“…The article describes author's technology of acceptance of administrative decisions in process management in non-production sphere that contains two algorithms: the algorithm of detecting patterns and forecasting, algorithm development management decisions in the form of options. …”
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