XModNN: Explainable Modular Neural Network to Identify Clinical Parameters and Disease Biomarkers in Transcriptomic Datasets by Jan Oldenburg, Jonas Wagner, Sascha Troschke-Meurer, Jessica Plietz, Lars Kaderali, Henry Völzke, Matthias Nauck, Georg Homuth, Uwe Völker, Stefan Simm

“…The combination of this workflow with layer-wise relevance propagation ensures a robust post hoc explanation of the individual module contribution. …”
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Sieving with Streaming Memory Access by Ziyu Zhao, Jintai Ding, Bo-Yin Yang

“…In particular, a dimension-n BGJ sieve uses only 20.2075n+o(n) streaming (non-random) main memory accesses. A key insight: Bucket sizes decrease by orders of magnitude after each BGJ filtering layer, so that sub-buckets fit into successively much smaller (hence faster) storage areas. …”
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Machine Learning-Enabled Attacks on Anti-Phishing Blacklists by Wenhao Li, Shams Ul Arfeen Laghari, Selvakumar Manickam, Yung-Wey Chong, Binyong Li

“…This study presents a comprehensive security analysis of anti-phishing blacklists and introduces two novel cloaking attacks—Feature-Driven Cloaking and Transport Layer Security (TLS)-Based Cloaking—that exploit vulnerabilities in the automated detection systems of anti-phishing entities (APEs). …”
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Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) by Gordan Mimić, Amit Kumar Mishra, Miljana Marković, Branislav Živaljević, Dejan Pavlović, Oskar Marko

“…Support Vector Machine and Multi-layer Perceptron were used as classification algorithms for harvest detection, with the former achieving higher accuracies of 79.65% for wheat, 83.41% for maize and 95.97% for soybean. …”
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Performance Evaluation of 5G New Radio V2X Sidelink for Coexisting Traffic by Joao Guerra, Miguel Luis, Pedro Rito

“…To support the direct vehicle-to-vehicle communication, without the need to rely on the cellular network, dynamic scheduling and random resource selection channel access techniques must be adopted. …”
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Predictive modeling of rapid glaucoma progression based on systemic data from electronic medical records by Richul Oh, Hyunjoong Kim, Tae-Woo Kim, Eun Ji Lee

“…Abstract This study investigated the baseline systemic features that predict rapid thinning of the retinal nerve fiber layer (RNFL) in patients with primary open-angle glaucoma (POAG). …”
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A longitudinal investigation of gut microbiota dynamics in laying hens from birth to egg-laying stages by Seojin Choi, Eun Bae Kim

“…DNA was extracted from the samples and the V4 region of the 16S rRNA gene was sequenced using an Illumina MiSeq platform. The Random Forest algorithm was applied to identify microbial predictors and explore their relationships with age. …”
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Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

“…This study investigates 12 machine learning models—logistic regression (LR), support vector machine (SVM, linear/RBF), K-nearest neighbor (KNN), Naïve bayes (NB), decision tree (DT), random forest (RF), extra trees (ET), gradient boosting (GbBoost), extreme gradient boosting (XgBoost), adaboost, and multi-layer perceptron (MLP)—to develop a robust ML model capable of reliably identifying PD cases. …”
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Research on Multi-Parameter Error Model of Backcalculated Modulus Using Abaqus Finite Element Batch Modeling Based on Python Language by Chunlong Xiong, Jiangmiao Yu, Xiaoning Zhang, Chuanxi Luo

“…When the errors of multiple parameters are combined randomly, the modulus errors range from −100% to 595%, and the probability of the modulus errors being less than 15% is highest in the asphalt surface layer, followed by the subgrade, and then the base and subbase layers. …”
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IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target Recognition by George Karantaidis, Athanasios Pantsios, Ioannis Kompatsiaris, Symeon Papadopoulos

“…To mitigate the speckle noise inherent in SAR images, we employ a denoising module based on a neural network approximation of Robust Principal Component Analysis (RPCA), leveraging a simple neural network for efficient noise reduction in SAR imagery. Moreover, a random projection layer improves the linear separability of features, and a variant of Linear Discriminant Analysis (LDA) decorrelates extracted class prototypes for better generalization. …”
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Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning Algorithm by Yang Yang, Huiwen Hou, Gang Yao, Bo Wu

“…Compared with small-span bridges and single-layer main girder forms, its lightweight design and low damping characteristics make it more prone to vortex-induced vibration (VIV). …”
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Transformer-Based Vulnerability Detection in IoT Firmware Binaries Using Opcode Sequences by M. Nandish, Jalesh Kumar, H. G. Mohan, M. V. Manoj Kumar

“…The classifiers used in the proposed approach are Random Forest, Multi-Layer Perceptron, and GAN-based classifier, which operate on the DeBERTa-generated embeddings. …”
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Spatial and Temporal Variability of Chlorophyll-a and the Modeling of High-Productivity Zones Based on Environmental Parameters: a Case Study for the European Arctic Corridor by Kuzmina Sofia, Lobanova Polina, Chepikova Svetlana Sergeevna

“…Firstly, we use remotely sensed data to assess spatial and temporal changes in correlation between Chl-a and environmental parameters that could influence Chl-a in the region – Photosynthetically Active Radiation (PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS) – over the 2010–2019 time period. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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DEVELOPMENT OF DATA MESH DATA PLATFORM WITH ML DOMAIN OF DATA ANALYSIS by M. Fostyak, L. Demkiv

“…Additionally, two new columns were derived to represent the net difference between income and expenditures. The layer of data analysis includes the ML model domain. …”
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Monitoring Gypsiferous Soils by Leveraging Advanced Spaceborne Hyperspectral Imagery via Spectral Indices and a Machine Learning Approach by Najmeh Rasooli, Saham Mirzaei, Stefano Pignatti

“…Enhancing the spatial resolution of gypsiferous soil detection, as a valuable baseline information layer, is beneficial for investigating agroecological processes and tackling land degradation in semi-arid environments. …”
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Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting by Mojtaba Khakpour Komarsofla, Kavian Khosravinia, Amirkianoosh Kiani

“…Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. …”
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Prevention and treatment of CCV in patients undergoing cataract phacoemulsification by E. Yu. Yazykova, L. Sh. Ramazanova

“…After the first survey the patients were divided into two groups by means of random choice (15 men and 15 women in each group). …”
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Plant leaf classification using the multiscale entropy of curvature and feature aggregation by Raphael G. Pinheiro, José G.F. Lopes, Marcelo M.S. Souza, Fátima N.S. Medeiros

“…We compare our handcrafted descriptors with deep features from various CNNs in multiclass classification using the random forest classifier, replacing the fully connected layer of the CNNs with this classifier. …”
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Machine-learning-based analytics for risk forecasting of anaphylaxis during general anesthesia by Shuang Liu, Yasuyuki Suzuki, Toshihiro Yorozuya, Masaki Mogi

“…After data pre-processing, the performance of five classification methods: Logistic Regression Analysis, Support Vector Machine, Random Forest, Linear Discriminant Analysis, and Naïve Bayes), which were integrated with four feature selection methods (Recursive Feature Elimination, Chi-Squared Method, Correlation-based Feature Selection, and Information Gain Ratio), was evaluated using two-layer cross-validation. …”
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