Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model by Marghany Hassan Mohamed, Botheina Hussein Ali, Ahmed Ibrahim Taloba, Ahmad O. Aseeri, Mohamed Abd Elaziz, Shaker El-Sappagah, Nora Mahmoud El-Rashidy

“…Several data preprocessing techniques are employed to optimize the accuracy of the proposed work, including data encoding, data cleaning, data scaling, data skewing transformation, data balancing, and feature selection. The choices of single model ML are logistic regression (LR), K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), and multilayer perceptron (MLP). …”
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Features and Practical Aspects of Radiochemical Purity Determination of Receptor-Specific Lu-177 Radiopharmaceuticals as Exemplified by [¹⁷⁷Lu]Lu–PSMA-617 by A. A. Larenkov, Yu. A. Mitrofanov, M. G. Rakhimov

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The analysis of events in Biblical narratives by D F Tolmie

“…In many of these analyses one can detect a tendency to reduce the analysis of events to a discussion of only one or two aspects. …”
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SmartScanPCOS: A feature-driven approach to cutting-edge prediction of Polycystic Ovary Syndrome using Machine Learning and Explainable Artificial Intelligence by Umaa Mahesswari G, Uma Maheswari P

“…Moreover, to identify essential features for PCOS prediction three feature selection methods: Threshold-driven Optimized Principal Component Analysis (TOPCA), Optimized Salp Swarm (OSSM), and Threshold-driven Optimized Mutual Information Method (TOMIM) were fine-tuned through thresholding and improvisation to detect diverse attribute sets with varying numbers and combinations. …”
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KERF: a knowledge-enhanced relearning framework for tailings pond detection from high-resolution remote sensing images by Bingjie Liu, Wei Wu, Hao Wu, Ailong Ma, Shaohua Hu, Nianchun Du, Xuan Cui, Junyang Xie

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Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device by Kei Suzuki, Tipporn Laohakangvalvit, Midori Sugaya

“…In cross-validation, the independence of test and training data was ensured to avoid excessively calculated score; <b>Results</b>: The results showed that the Macro F1 score was 91.59%, suggesting that a consumer-grade EEG can detect depression. In addition, analysis of the EEG indices selected by feature selection indicated that the Macro F1 score was about 80% for single EEG indices such as differential entropy in the frequency band β and functional connectivity in the left frontal region in the frequency band 1–128 Hz; <b>Conclusions</b>: Although the data were obtained from a consumer-grade EEG, the results suggest that these EEG indices are promising for detection depression.…”
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SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n by Yue Fang, Chenbo Yang, Jie Li, Jingmin Tu

“…Second, a feature fusion module (C2f-Star) is integrated into the backbone to enhance the feature representation capability of the neck through expanded spatial dimensions, mitigating the impact of occluded regions on detection performance. …”
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MRI Parameters and Clinicopathologic Features in Predicting Hepatocellular Carcinomas With Aggressive-Related Marker Expression, Defined by Epithelial Cell Adhesion Molecule and Gl... by Li Y, Huo L, Liu L, Jia N, Song B

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Two complementing in vivo selection systems based on CCA-trimming exonucleases as a tool to monitor, select and evaluate enzymatic features of tRNA nucleotidyltransferases by Karolin Wellner, Josefine Gnauck, Dorian Bernier, Stephan H. Bernhart, Heike Betat, Mario Mörl

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Deep-water seafloor geomorphic features of the Santos Basin, Southeastern Brazilian Margin, shown by analyses and integration of an extensive 3-D seismic data set by Cízia Mara Hercos, Simone Schreiner, Eugênio Taira Inácio Ferreira

“…Edge detection seismic attributes enhance gradient contrast, which, in turn, can map innumerous medium- to smallscale geomorphic features (features solved in maps at a 1:1,000,000 scale or larger), such as canyons, channels, ravines, pockmark units, pockmark fields, lineaments, carbonate and coral mounds, salt-related features (crests, minibasins, and crestal grabens), scars and rugous relief associated with mass-transport deposits, and bottom current-related features (depressions and furrows). …”
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Deep-water seafloor geomorphic features of the Santos Basin, Southeastern Brazilian Margin, shown by analyses and integration of an extensive 3-D seismic data set by Cízia Mara Hercos, Simone Schreiner, Eugênio Taira Inácio Ferreira

“…Edge detection seismic attributes enhance gradient contrast, which, in turn, can map innumerous medium- to smallscale geomorphic features (features solved in maps at a 1:1,000,000 scale or larger), such as canyons, channels, ravines, pockmark units, pockmark fields, lineaments, carbonate and coral mounds, salt-related features (crests, minibasins, and crestal grabens), scars and rugous relief associated with mass-transport deposits, and bottom current-related features (depressions and furrows). …”
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Advancing diabetic retinopathy diagnosis with fundus imaging: A comprehensive survey of computer-aided detection, grading and classification methods by S. Prathibha, Siddappaji

“…Our exhaustive review indicates that most existing methodologies predominantly concentrate on isolated diabetic retinopathy types, employing localized spatiotextural feature analysis for classification. Such specificity often results in limited accuracy and generalizability, restricting practical real-world application. …”
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Advanced object detection for smart accessibility: a Yolov10 with marine predator algorithm to aid visually challenged people by Mahir Mohammed Sharif Adam, Hussah Nasser AlEisa, Samah Al Zanin, Radwa Marzouk

“…They face problems with such actions and object detection should be an essential feature they can rely on a regular basis. …”
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Early detection of vascular catheter-associated infections employing supervised machine learning - a case study in Lleida region by Radu Spaimoc, Jordi Mateo, Francesc Solsona, Alfredo Jover-Sáenz, Fernando Barcenilla, María Ramírez-Hidalgo, Marcos Serrano, Miquel Mesas, Dídac Florensa

“…This study highlights that strategic feature engineering with the GB classifier is sufficient to obtain robust VCAI detection before the appearance of a probable sepsis.…”
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Quantum-classical deep learning hybrid architecture with graphene-printed low-cost capacitive sensor for essential tremor detection by Javier Villalba-Díez, Ana González-Marcos

“…Abstract This study presents a novel hardware and software architecture combining capacitive sensors, quantum-inspired algorithms, and deep learning applied to the detection of Essential Tremor. At the core of this architecture are graphene-printed capacitive sensors, which provide a cost-effective and efficient solution for tremor data acquisition. …”
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Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework by Songxi Yang, Bo Peng, Tang Sui, Meiliu Wu, Qunying Huang

“…Building change detection and building damage assessment are two essential tasks in post-disaster analysis. …”
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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

“…This feature integration and data analysis scheme are then deployed to provide a tomato leaf disease detection Android app. …”
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A Dual-Branch Deep Learning Framework Combining Xception and ResNet for Accurate Lung and Colon Cancer Detection by Chandrasekar Venkatachalam, Priyanka Shah

“…Lung and colon cancers are among the leading causes of cancer-related deaths worldwide. Early detection significantly enhances survival rates, but traditional diagnostic methods, which rely on manual analysis of histopathological images, are labor-intensive, error-prone, and inconsistent. …”
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MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects by Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Rossella Donghia, Gianluigi Giannelli, Angela Lombardi, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci

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Effective multimodal hate speech detection on Facebook hate memes dataset using incremental PCA, SMOTE, and adversarial learning by Emmanuel Ludivin Tchuindjang Tchokote, Elie Fute Tagne

“…Link to the code and dataset below:https://github.com/ludivintchokote/HatePostDetection…”
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