A Deep Learning Enabled Chest X-Ray Abnormality Detection Model for Radiology Assistance. by Ssempeebwa, Phillip, Ainembabazi, Patience

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Design and Prototype of a Local Relay Fault Detection System for Three Phase Power Lines. by Lutaama, Samuel

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RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis by S. M. Nuruzzaman Nobel, S M Masfequier Rahman Swapno, Md. Ashraful Hossain, Mejdl Safran, Sultan Alfarhood, Md. Mohsin Kabir, M. F. Mridha

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

“…Machine learning and voice analysis are promising objective screening methods for mental disorders during pregnancy, potentially improving early detection. Conclusion We developed a lightweight machine learning model to analyze pregnant women's voices for screening various mental disorders, achieving high sensitivity and demonstrating the potential of voice analysis as an effective and objective tool in perinatal mental health care.…”
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Prospective evaluation of the diagnostic accuracy of FDG-PET/CT for the detection of sternal wound infection post-sternotomy by Angela Cai, Mehrshad Bakhshi, Yoan Lamarche, Francois Harel, Matthieu Pelletier-Galarneau

“…Sensitivity and specificity of FDG-PET/CT for detection of DSWI were 67% and 66%, respectively and an accuracy of 66% was obtained. …”
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Four Central Nanocavity of Ultra-Sensitive Refractive Index Biosensor for Cancer Detection in Hexagonal Photonic Crystal by William Matsui, Jianqiao Fang

“…We design and analysis of a novel biosensor with four center nanocavity in hexagonal photonic crystal (PhC) is presented. This biosensor can detect cancer cells from normal cells. The main performance of the detection is based on glass nanocavity and the samples refractive index. …”
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The role of multifocal visual evoked potential in detection of minimal hepatic encephalopathy in patients with compensated liver cirrhosis by Reem M. Sabry, Osama Hamad, Hossam Eldin Mohamed Khalil, Sahar Ibrahim Mohammed, Ragaey Ahmad Eid, Hanan Hosny

“…Conclusion mfVEP demonstates promising results for objective early detection of MHE, with a sensitivity of approximately 92.9%.…”
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Altered functional network topology properties to detect anxiety and depression caused by Crohn’s disease and disease severity by Jiaxing Yang, Kechen Yue, Jingwen Sun, Changjie Pan, Xintong Wu, Yongjun Cheng, Xiaomeng Wu, Haifeng Shi

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Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis by Syed Muhammad Ali Imran, Abida Hussain, Nema Salem, Muhammad Arsalan

“…An advanced deep learning method for computer-aided disease diagnosis that detects pigment signs (PS) in RP patients, enabling ophthalmologists to identify and treat the condition on or before schedule. …”
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Enhancing Monkeypox Detection through Data Analytics: A Comparative Study of Machine and Deep Learning Techniques by Kinjal A. Patel, Asadi Srinivasulu, Kuntesh Jani, Goddindla Sreenivasulu

Subjects: “…monkeypox detection…”
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Simulation-based validation of a method to detect changes in SARS-CoV-2 reinfection risk. by Belinda Lombard, Harry Moultrie, Juliet R C Pulliam, Cari van Schalkwyk

“…We performed simulation-based validation of an existing catalytic model designed to detect changes in the risk of reinfection by SARS-CoV-2. …”
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How to Dissect Honey Bees (Apis mellifera L.) to Detect Tracheal Mites (Acarapis woodi Rennie) by John Bonkowski, Ashley N. Mortensen, James D. Ellis

“…ENY-164/IN1072: How to Dissect Honey Bees (Apis mellifera L.) to Detect Tracheal Mites (Acarapis woodi Rennie) (ufl.edu) …”
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Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection by wafiq azizah, soffiana agustin

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Fusion of multi-scale attention for aerial images small-target detection model based on PARE-YOLO by Huiying Zhang, Pan Xiao, Feifan Yao, Qinghua Zhang, Yifei Gong

Subjects: “…Lightweight detection head…”
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Machine learning optimized design of THz piezoelectric perovskite-based biosensor for the detection of formalin in aqueous environments by Jacob Wekalao, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-zahrani

“…Abstract This investigation presents the development and characterization of an advanced piezoelectric perovskite-based biosensing platform optimized for formalin detection in aqueous media through the implementation of Locally Weighted Linear Regression (LWLR) machine learning algorithms. …”
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Detection and subtyping of Blastocystis sp. in human and animal stool samples using high-resolution melting analysis by Ghodratollah Salehi Sangani, Ahmad Hosseini-Safa, Bibi Razieh Hosseini Farash, Pooria Salehi Sangani, Mehdi Zarean, Elham Moghaddas, Saeid Zoghdar Moghadam, Lida Jarahi, Ali Gholizadeh, Fariba Berenji

“…Although the pathogenicity of Blastocystis remains debated, it has been detected in both symptomatic and asymptomatic individuals. …”
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Direct detection and identification of viruses in saliva using a SpecID ionization modified mass spectrometer. by Pierre Alusta, Angel Paredes, Marli Azevedo, Lisa Mullis, Dan Buzatu

“…Polymerase chain reaction (PCR) remains the gold standard for sensitive detection of SARS-CoV-2 infection in biological samples. …”
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A Novel MoCo-Based Self-Supervised Learning Framework for Solar Panel Defect Detection by Jun Huang, Shamsul Arrieya Ariffin, Yongqiang Chen, Jinghui Lin, Wanting Xu

Subjects: “…Solar panel defect detection…”
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Analysis of the corrosion rate of SS 316l metal in a tsunami detection tool in seawater media by Gusti Ayu Arwati I, Alfattah Muhammad, Maryani Yeyen, Suprihatiningsih Wiwit, Yuliarty Popy

“…Indonesia frequently faces tsunami disasters, making the need for early detection tools essential. As a technological innovation, the inabuoy was created. …”
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Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty by Morteza Barkhi, Javad Pourhossein, Seyed Ali Hosseini

“…The proposed method, which performs fault detection and classification together, just requires local information and functions effectively to discriminate faulty from normal conditions considering different uncertainty of resistance faults. …”
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