Voice analysis and deep learning for detecting mental disorders in pregnant women: a cross-sectional study by Hikaru Ooba, Jota Maki, Hisashi Masuyama

“…Traditional screening tools, such as the Edinburgh Postnatal Depression Scale (EPDS), present limitations due to subjectivity and time constraints in clinical settings. Recent advances in voice analysis and machine learning have shown potential for providing more objective screening methods. …”
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Diagnostic dilemma: application of real-time PCR assays for the detection of Dientamoeba fragilis in medical and veterinary specimens by Luke M. Hall, John T. Ellis, Damien J. Stark

“…Abstract Background Real-time PCR (qPCR) diagnostics developed for use in human clinical settings have been implemented to identify new animal hosts of the gastrointestinal protozoan Dientamoeba fragilis. …”
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A combined feature selection approach for malicious email detection based on a comprehensive email dataset by Han Zhang, Yong Shi, Ming Liu, Libo Chen, Songyang Wu, Zhi Xue

“…In addition, outdated data makes it difficult to detect new types of malicious email attacks. (2) There are limitations in feature selection and extraction. …”
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Fast Anomaly Detection for Vision-Based Industrial Inspection Using Cascades of Null Subspace PCA Detectors by Muhammad Bilal, Muhammad Shehzad Hanif

“…While recently several methods have been reported in the literature that have demonstrated impressive detection performance on standard benchmarks, they necessarily rely on computationally intensive CNN architectures and post-processing techniques, necessitating access to high-end GPU hardware and limiting practical deployment in resource-constrained settings. …”
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Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review by Washington Ramírez, Verónica Pillajo, Eileen Ramírez, Ibeth Manzano, Doris Meza

“…While substantial improvements in detection accuracy and sensitivity are noted, significant challenges persist in sensor optimization, data integration, and adaptation into clinical settings. …”
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Development of a Tandem Mass Spectral Library for the Detection of Triterpenoids in Plant Metabolome Based on Reference Standards by Bibi Zareena, Syed Usama Y. Jeelani, Adeeba Khadim, Arslan Ali, Jalal Uddin, Satyajit D. Sarker, Matthias Rainer, Shaden A. M. Khalifa, Hesham R. El-Seedi, Muhammad Ramzan, Syed Ghulam Musharraf

“…For drug development, lead optimization, better knowledge of biological pathways, and high-throughput detection of secondary metabolites in plant extracts are crucial. …”
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Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach by Osama F. Hassan, Ahmed F. Ibrahim, Ahmed Gomaa, M. A. Makhlouf, B. Hafiz

“…The proposed framework sets a new benchmark in drowsiness detection, highlighting its potential for widespread adoption in advanced driver assistance systems.…”
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Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy by Miguel Martins, Miguel Mascarenhas, Maria João Almeida, João Afonso, Tiago Ribeiro, Pedro Cardoso, Francisco Mendes, Joana Mota, Patrícia Andrade, Hélder Cardoso, Miguel Mascarenhas-Saraiva, João Ferreira, Guilherme Macedo

“…Deep-learning (DL) algorithms were developed for the detection of enteric and gastric lesions. Nonetheless, their application in the esophagus lacks evidence. …”
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A comparative analysis of imaging-based algorithms for detecting focal cortical dysplasia type II in children by Jan Šanda, Zuzana Holubová, David Kala, Kateřina Jiránková, Martin Kudr, Tomáš Masák, Anežka Bělohlávková, Pavel Kršek, Jakub Otáhal, Martin Kynčl

“…Automated algorithms, especially those targeting junction blurring, enhance FCD detection in pediatric populations. These algorithms may serve as valuable decision-support tools, particularly in settings where neuroradiological expertise is limited.…”
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Seroprevalence of Antibodies to Plasmodium spp. among Individuals without Detectable Infection in Etsako, Edo State, Nigeria by Mathew Folaranmi Olaniyan, Felix Abayomi Adeoye, Wakili Adelani Tijani, Tolulope Busayo Olaniyan, Kemi Felicia Ajiboye, Medinat Taiwo Adeniran, Christian Onosetale Ugege, David Olufemi Adebo, Obataze Josephine Akpoyovwere, Odekunle Bola Odegbemi

“…Tailored malaria control strategies considering local immunity dynamics are crucial for sustaining progress in malaria elimination efforts in similar settings.…”
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Detection of structural variants linked to mutton flavor and odor in two closely related black goat breeds by Lingle Chang, Xi Niu, Shihui Huang, Derong Song, Xueqin Ran, Jiafu Wang

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Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. …”
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Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence by Kazi Ahnaf Alavee, Mehedi Hasan, Abu Hasnayen Zillanee, Moin Mostakim, Jia Uddin, Eduardo Silva Alvarado, Isabel de la Torre Diez, Imran Ashraf, Md Abdus Samad

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AI-enhanced infrared thermography for reliable detection and spatial mapping of temperature patterns in calf eyes and muzzles by Sueun Kim, Norio Yamagishi, Shingo Ishikawa, Shinobu Tsuchiaka

“…However, previous methods often suffered from limitations such as environmental interference, instantaneous measurement, and inaccurate region of interest (ROI) selection due to manual settings. To overcome these limitations, studies have combined infrared cameras with AI-based segmentation to enable accurate ROI detection and to capture temporal temperature change patterns in cattle. …”
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High-throughput clinical antimicrobial susceptibility testing and drug-resistant subpopulation detection in Gram-negative bacteria by QingFeng Hu, Yuhan Zhou, Yongze Zhu, Zixuan Ma, Tongtong Li, Shuhang Yao, Ju Pan, Mengyan Shi, Fang Su, Beiqiong Shen, Xi Li, Benfang Helen Ruan, Jiaxue Wang

“…Due to the small number of drug-resistant bacteria in these subpopulations, their corresponding drug resistance is often challenging to detect in MIC data. AST remains a critical method for guiding the use of antibacterial agents. …”
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Validation of the cancer-specific utility measure EORTC QLU-C10D using evidence from four lung cancer trials covering six country value sets by Simone Seyringer, Micha J. Pilz, Imad Al-Naesan, Madeleine T. King, Andrew Bottomley, Richard Norman, Lisa Schlosser, Tobias Hell, Eva M. Gamper, EORTC Quality of Life Group

“…The QLU-C10D produced homogenous results across six country value sets and detected differences/changes in alignment with clinical expectations. …”
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Automated Localization and Classification of Expressway Pole-Like Road Facilities from Mobile Laser Scanning Data by Tran Thanh Ha, Taweep Chaisomphob

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A comprehensive image dataset for the identification of lemon leaf diseases and computer vision applicationsMendeley Data by A K M Fazlul Kobir Siam, Prayma Bishshash, Md. Asraful Sharker Nirob, Sajib Bin Mamun, Md Assaduzzaman, Sheak Rashed Haider Noori

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Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics by Tian Zhang, Ying Deng, Wentao Wang, Zhe Zhao, Yiling Wu, Haoqian Wang, Shutao Xia, Weifang Liao, Weijie Liao

“…To identify host gene expression signatures associated with infection, we screened characteristic genes from human metatranscriptomic data by comparing 70% of patients with confirmed infections vs. non-infections. …”
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Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining by Mingshe Sun, Song Chen, Song Chen, Huafei He, Wenzheng Wang, Kezhi Song, Xuebing Lin

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