Predicting police and military violence: evidence from Colombia and Mexico using machine learning models by Juan David Gelvez

“…Such misconduct is not random, making its prevention both crucial and challenging due to the difficulty of measuring and detecting these phenomena beforehand. Recent advances in artificial intelligence offer new tools for this task. …”
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A systematic approach to study the effects of acquisition parameters and biological factors on computerized mammography analysis using ex vivo human tissue: A protocol description. by Nicole Hernández, Tomppa Pakarinen, Annukka Salminen, Santiago Laguna Castro, Ulla Karhunen-Enckell, Markus Hannula, Ritva Heljasvaara, Jari Hyttinen, Katriina Joensuu, Otto Jokelainen, Arja Jukkola, Sanna-Maria Karppinen, Auni Lindgren, Eero Lääperi, Emilia Peuhu, Taina Pihlajaniemi, Renata Prunskaite-Hyyryläinen, Kirsi Rilla, Pekka Ruusuvuori, Leena Latonen, Teemu Tolonen, Masi Valkonen, Mira Valkonen, Miska Vuorlaakso, Said Pertuz, Irina Rinta-Kiikka, Otso Arponen

“…<h4>Background</h4>Mammography is the most common imaging modality for the detection of breast cancer. Artificial intelligence algorithms for mammography analysis have shown promising performance for breast cancer risk assessment and lesion detection and classification; however, these models often fail the test of external validation. …”
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Flow cytometry in the diagnosis of plasma cell tumors and assessment of minimal residual disease by L. Yu. Grivtsova, T. Yu. Mushkarina, V. V. Lunin, P. A. Zeynalova

“…Standardized flow cytometric protocols (the Euro-Flow conception) and algorithms for both the diagnosis of plasma cell tumors and the detection of minimal residual disease in plasma cell myeloma are described.…”
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Robust screening of atrial fibrillation with distribution classification by Pierre-François Massiani, Lukas Haverbeck, Claas Thesing, Friedrich Solowjow, Marlo Verket, Matthias Daniel Zink, Katharina Schütt, Dirk Müller-Wieland, Nikolaus Marx, Sebastian Trimpe

“…These strengths result from two main components: (i) preliminary peak detection enabling robust computation of medically relevant features; and (ii) a mathematically principled way of aggregating those features to compare their full distributions. …”
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A deep decentralized privacy-preservation framework for online social networks by Samuel Akwasi Frimpong, Mu Han, Emmanuel Kwame Effah, Joseph Kwame Adjei, Isaac Hanson, Percy Brown

“…Our methodology employs a two-tier architecture: the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm (ePSOGSA) for optimizing feature selection, while the second tier employs an enhanced Non-symmetric Deep Autoencoder (e-NDAE) for anomaly detection. …”
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An automatic generalized Gaussian mixture-based approach for accurate brain tumor segmentation in magnetic resonance imaging analysis by Khalil Ibrahim Lairedj, Zouaoui Chama, Amina Bagdaoui, Samia Larguech, Serge Dzo Mawuefa Afenyiveh, Younes Menni

“…Image segmentation is crucial in medical science for feature extraction, analysis, and interpretation, especially brain tumor segmentation, which is challenging. …”
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Efficient brain tumor grade classification using ensemble deep learning models by Sankar M, Baiju BV, Preethi D, Ananda Kumar S, Sandeep Kumar Mathivanan, Mohd Asif Shah

“…In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. …”
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Exploring the comprehensive integration of artificial intelligence in optimizing HVAC system operations: A review and future outlook by Shengze Lu, Shiyu Zhou, Yan Ding, Moon Keun Kim, Bin Yang, Zhe Tian, Jiying Liu

“…The main algorithms of supervised learning, reinforcement learning, and deep learning are then analyzed in the fields of air conditioning operation optimization, energy consumption prediction and control, indoor environmental protection, and fault detection and diagnosis. …”
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Object Ontologies as a Priori Models for Logical-Probabilistic Machine Learning by D. N. Gavrilin, A.V. Mantsivoda

“…Logical-probabilistic machine learning (LPML) is an AI method able to explicitly work with a priori knowledge represented in data models. This feature significantly complements traditional deep learning knowledge acquiring. …”
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Age- and Post-Based Complex Analyses of Heart Rate Variability in Young Male Handball Players for Potential Prevention of Overload-induced Injuries by Zita Petrovszki, Olivér Czimbalmos, Vera Gal, Gabor Korosi, Edit Nagy, Rita Mikulan, Gyongyi Horvath

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Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data by Hongwei Huang, Shuyi Liu, Mingliang Zhou, Hua Shao, Qingtong Li, Phromphat Thansirichaisree

“…The proposed fusion approach combines image and point cloud data, leveraging the enhanced YOLOv8-seg instance segmentation model for defect identification. The scale-invariant feature transform (SIFT) algorithm is used to match local defect regions in the image data with the corresponding point cloud data, enabling the extraction of 3D coordinates and the integration of defect pixels with the point cloud information. …”
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CONTROL RESEARCH AND AUTOMATION OF STOCHASTIC DEVIATIONS IN ORGANISATIONAL MANAGEMENT PRODUCTION PROCESS SYSTEMS by R. B. Aghajanyan, D. O. Baizhanova, M. V. Markosyan

“…A method is proposed for the automated realisation of deviation from identification processes based on the processing of unstructured messages, the automatic generation of characteristic control parameters and a comparison of registered values with normative ones. The problem of detecting the primary cause of deviations is considered on the basis of the algorithm for processing the cause-effect relationships and probabilistic values of the relationship between the different defined groups of deviations.Results. …”
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Research and Implementation of Travel Aids for Blind and Visually Impaired People by Jun Xu, Shilong Xu, Mingyu Ma, Jing Ma, Chuanlong Li

“…To address embedded device computational constraints, we developed a lightweight object detection and segmentation algorithm. Key innovations include a multi-scale attention feature extraction backbone, a dual-stream fusion module incorporating the Mamba architecture, and adaptive context-aware detection/segmentation heads. …”
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The comparison of distance metrics in descriptor matching methods utilised in TLS-SfM point cloud registration by Markiewicz Jakub

“…Descriptor matching in SfM-TLS typically employs the L2 norm to measure Euclidean distances between features, valued for its simplicity and compatibility with algorithms like SIFT. …”
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Automated Fillet Weld Inspection Based on Deep Learning from 2D Images by Ignacio Diaz-Cano, Arturo Morgado-Estevez, José María Rodríguez Corral, Pablo Medina-Coello, Blas Salvador-Dominguez, Miguel Alvarez-Alcon

“…Following an extensive review of available solutions, algorithms, and networks based on this convolutional strategy, it was determined that the You Only Look Once algorithm in its version 8 (YOLOv8) would be the most suitable for object detection due to its performance and features. …”
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SG-ResNet: Spatially Adaptive Gabor Residual Networks with Density-Peak Guidance for Joint Image Steganalysis and Payload Location by Zhengliang Lai, Chenyi Wu, Xishun Zhu, Jianhua Wu, Guiqin Duan

“…Experimental results obtained with datasets generated by several public steganography algorithms demonstrate that SG-ResNet achieves State-of-the-Art results in terms of detection accuracy, with 0.94, and with a PSNR of 29 between reconstructed and original secret images.…”
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Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis by Faisal Abdulaziz Almisned, Natacha Usanase, Dilber Uzun Ozsahin, Ilker Ozsahin

“…This research aims to analyze six machine learning algorithms, and an ensemble voting classifier, develop hybrid models for the early detection of pancreatic cancer based on several clinical characteristics and interpret their performance with Shapley Additive Explanations (SHAP). …”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. …”
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High-Resolution Computed Tomography of Single Breast Cancer Microcalcifications in Vivo by Kazumasa Inoue, Fangbing Liu, Jack Hoppin, Elaine P. Lunsford, Christian Lackas, Jacob Hesterman, Robert E. Lenkinski, Hirofumi Fujii, John V. Frangioni

“…Two ray-tracing image reconstruction algorithms were tested: a voxel-driven &#x201C;fast&#x201D; cone beam algorithm (FCBA) and a detector-driven &#x201C;exact&#x201D; cone beam algorithm (ECBA). …”
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Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer’s disease: transcriptomics combined with machine learning analysis by Meiling Wang, Aojie He, Yubing Kang, Zhaojun Wang, Yahui He, Kahleong Lim, Chengwu Zhang, Li Lu

“…In conclusion, this study detected changes in intercellular communication between endothelial cells and pericytes in the middle temporal gyrus and identified four novel feature genes related to middle temporal gyrus and vascular functioning in patients with Alzheimer’s disease. …”
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