Automated Crack Width Measurement in 3D Models: A Photogrammetric Approach with Image Selection by Huseyin Yasin Ozturk, Emanuele Zappa

“…Then, a subset of images are automatically selected based on camera orientation and distance, and a deep learning algorithm is applied to detect cracks in 2D images. …”
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Developing and validating an artificial intelligence-based application for predicting some pregnancy outcomes: a multi-phase study protocol by Fatemeh Shabani, Ata Jodeiri, Sakineh Mohammad‑Alizadeh‑Charandabi, Fatemeh Abbasalizadeh, Jafar Tanha, Mojgan Mirghafourvand

“…By integrating retrospective data analysis, machine learning, and prospective validation, the study aims to improve early risk detection and maternal care. …”
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A Micro-Topography Enhancement Method for DEMs: Advancing Geological Hazard Identification by Qiulin He, Xiujun Dong, Haoliang Li, Bo Deng, Jingsong Sima

“…This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. …”
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Deep Learning Unravels Differences Between Kinematic and Kinetic Gait Cycle Time Series from Two Control Samples of Healthy Children Assessed in Two Different Gait Laboratories by Alfonso de Gorostegui, Damien Kiernan, Juan-Andrés Martín-Gonzalo, Javier López-López, Irene Pulido-Valdeolivas, Estrella Rausell, Massimiliano Zanin, David Gómez-Andrés

“…Our study emphasizes the importance of standardized protocols and robust data pre-processing to enhance the transferability of machine learning models across clinical settings, particularly for deep learning approaches.…”
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Analysis and Identification of Factors Influencing the Survival of Burn Injury Patients with an Artificial Intelligence Approach by Sara Hashemi, Shahla Faramarzi, Laya Rahmani Pirouz, Azita Yazdani

“…Conclusion: The use of machine learning algorithms in predicting the survival of burn patients is promising. …”
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Steganography in IoT: A Comprehensive Survey on Approaches, Challenges, and Future Directions by Maha Driss, Lamia Berriche, Safa Ben Atitallah, Siwar Rekik

“…Steganography is an intriguing approach that allows hiding sensitive information within seemingly ordinary data, preventing unauthorized parties from detecting and accessing it. …”
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TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells by Linlin Tang, Yangli Jin, Jinxu Wang, Xiuyan Lu, Mengque Xu, Mingwei Xiang

“…Univariate Cox regression analysis was conducted to screen prognostic genes, and 10 machine learning algorithms were combined to find the optimal strategy to evaluate the prognosis of the patients based on the prognosis-related genes. …”
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Advancing Breast Cancer Diagnosis: A Comprehensive Machine Learning Approach for Predicting Malignant and Benign Cases with Precision and Insight in a Neutrosophic Environment usin... by Nihar Ranjan Panda, R. Rajalakshmi, Surapati Pramanik, Mana Donganont, Prasanta Kumar Raut

“…Four top machine learning algorithms are trained and evaluated with a series of performance measures such as accuracy, positive predictive value (PPV), negative predictive value (NPV), F1-score, etc. …”
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Comparing 2D and 3D Feature Extraction Methods for Lung Adenocarcinoma Prediction Using CT Scans: A Cross-Cohort Study by Margarida Gouveia, Tânia Mendes, Eduardo M. Rodrigues, Hélder P. Oliveira, Tania Pereira

“…Machine learning algorithms have already shown the potential to recognize patterns in CT scans to classify the cancer subtype. …”
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A Machine Vision Perspective on Droplet‐Based Microfluidics by Ji‐Xiang Wang, Hongmei Wang, Huang Lai, Frank X. Liu, Binbin Cui, Wei Yu, Yufeng Mao, Mo Yang, Shuhuai Yao

“…To address the long‐standing challenges associated with the accurate and efficient identification, sorting, and analysis of the morphology and generation rate of single and double emulsion droplets, a novel machine vision approach utilizing the deformable detection transformer (DETR) algorithm is proposed. …”
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Multi-model machine learning framework for lung cancer risk prediction: A comparative analysis of nine classifiers with hybrid and ensemble approaches using behavioral and hematolo... by Vinod Kumar, Chander prabha, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

“…LC continues to be the most prevalent cause of cancer deaths worldwide, which calls for sophisticated detection strategies. The present study investigates 34 demographic, behavioral, and hematological risk factors based on a sample of 2,000 patient data records. …”
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Advancing Neurodegenerative Disease Management: Technical, Ethical, and Regulatory Insights from the NeuroPredict Platform by Marilena Ianculescu, Lidia Băjenaru, Ana-Mihaela Vasilevschi, Maria Gheorghe-Moisii, Cristina-Gabriela Gheorghe

“…Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. …”
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Artificial Intelligence in Chest Radiography—A Comparative Review of Human and Veterinary Medicine by Andrea Rubini, Roberto Di Via, Vito Paolo Pastore, Francesca Del Signore, Martina Rosto, Andrea De Bonis, Francesca Odone, Massimo Vignoli

“…However, AI is still supplementary to clinical expertise due to challenges such as data limitations, algorithmic biases, and the need for extensive validation. …”
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Development and validation of a machine learning-based predictive model for compassion fatigue in Chinese nursing interns: a cross-sectional study utilizing latent profile analysis by Lijuan Yi, Ting Shuai, Jingjing Zhou, Liang Cheng, Maria F. Jiménez-Herrera, Xu Tian

“…Latent profile analysis was used to classify compassion fatigue levels, and potential predictors were identified through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Eight machine learning algorithms were applied to predict compassion fatigue, with performance assessed through cross-validation, calibration, and discrimination metrics. …”
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Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to wh... by Akira Ono, Yukihiro Terada, Takuya Kawata, Masakuni Serizawa, Mitsuhiro Isaka, Takanori Kawabata, Toru Imai, Keita Mori, Koji Muramatsu, Isamu Hayashi, Hirotsugu Kenmotsu, Keiichi Ohshima, Kenichi Urakami, Takeshi Nagashima, Masatoshi Kusuhara, Yasuto Akiyama, Takashi Sugino, Yasuhisa Ohde, Ken Yamaguchi, Toshiaki Takahashi

“…IME profiles, including PD‐L1 tumor proportion score (TPS), stromal CD8 tumor‐infiltrating lymphocyte (TIL) density, and stromal Foxp3 TIL density, were quantified by digital pathology using a machine learning algorithm. To detect factors associated with TMB, clinical data, and IME factors were assessed by means of a multiple regression model. …”
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Edge_MVSFormer: Edge-Aware Multi-View Stereo Plant Reconstruction Based on Transformer Networks by Yang Cheng, Zhen Liu, Gongpu Lan, Jingjiang Xu, Ren Chen, Yanping Huang

“…This model integrates an edge detection algorithm to augment edge information as input to the network and introduces an edge-aware loss function to focus the network’s attention on a more accurate reconstruction of edge regions, where depth estimation errors are obviously more significant. …”
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IoT-MFaceNet: Internet-of-Things-Based Face Recognition Using MobileNetV2 and FaceNet Deep-Learning Implementations on a Raspberry Pi-400 by Ahmad Saeed Mohammad, Thoalfeqar G. Jarullah, Musab T. S. Al-Kaltakchi, Jabir Alshehabi Al-Ani, Somdip Dey

“…The investigation proposes a framework called IoT-MFaceNet (Internet-of-Things-based face recognition using MobileNetV2 and FaceNet deep-learning) utilizing pre-existing deep-learning methods, employing the MobileNetV2 and FaceNet algorithms on both ImageNet and FaceNet databases. …”
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An improved hybrid approach involving deep learning for urban greening tree species classification with Pléiades Neo 4 imagery—A case study from Nanjing, Eastern China by Min Sun, Stephane G.P. Debulois, Zhengnan Zhang, Xiaolei Cui, Zhili Chen, Mingshi Li

“…Future work will integrate multi-source data, multi-seasonal observations, and adaptive algorithms to further enhance classification performance and improve model robustness across diverse urban environments.…”
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Improving Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases by Pareza Alam Jusia, Abdul Rahim, Herti Yani, Jasmir Jasmir

“…In recent years, the volume of medical data related to heart disease has increased rapidly, and various heart disease data has collaborated with information technology such as machine learning to detect, predict, and classify diseases. …”
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Automated Foveal Avascular Zone Segmentation in Optical Coherence Tomography Angiography Across Multiple Eye Diseases Using Knowledge Distillation by Peter Racioppo, Aya Alhasany, Nhuan Vu Pham, Ziyuan Wang, Giulia Corradetti, Gary Mikaelian, Yannis M. Paulus, SriniVas R. Sadda, Zhihong Hu

“…Although several automated FAZ detection and segmentation algorithms have been developed for use with OCTA, their performance can vary significantly due to differences in data accessibility of OCTA in different retinal pathologies, and differences in image quality in different subjects and/or different OCTA devices. …”
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