Application of ConvNeXt with transfer learning and data augmentation for malaria parasite detection in resource-limited settings using microscopic images by Outlwile Pako Mmileng, Albert Whata, Micheal Olusanya, Siyabonga Mhlongo

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Automated radiographic assessment of lower limb alignment using deep learning in a data-constrained clinical setting by Luca Häfliger, Andrea Cina, Louis Leuthard, Hannes A. Rüdiger, Vincent A. Stadelmann

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Defining Urban Centres using Alternative Data Sets by Emily Moylan, Somwrita Sarkar

“…Further, most top-order centres that are detected are clustered spatially. Thus, we find that a comparison of the data sets does not support the polycentric model but instead a legacy monocentric model combined with dispersal.…”
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OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data. by Raga Krishnakumar, Anne M Ruffing

“…We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. …”
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Evidence Gaps and Lessons in the Early Detection of Atrial Fibrillation: A Prospective Study in a Primary Care Setting (PREFATE Study) by Josep L. Clua-Espuny, Alba Hernández-Pinilla, Delicia Gentille-Lorente, Eulàlia Muria-Subirats, Teresa Forcadell-Arenas, Cinta de Diego-Cabanes, Domingo Ribas-Seguí, Anna Diaz-Vilarasau, Cristina Molins-Rojas, Meritxell Palleja-Millan, Eva M. Satué-Gracia, Francisco Martín-Luján

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An Advanced LSTM Framework for Pest Detection and Classification in Agricultural Settings by Bhoi Manjulata, Sharma Priti

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Data Fusion of Medical Records and Clinical Data to Enhance Tuberculosis Diagnosis in Resource-Limited Settings by Alvaro D. Orjuela-Cañón, Andrés F. Romero-Gómez, Andres L. Jutinico, Carlos E. Awad, Erika Vergara, Maria A. Palencia

“…This paper explores the use of natural language processing (NLP) techniques and machine learning (ML) models to facilitate TB diagnosis in settings where robust data infrastructure is unavailable. …”
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Parallel clustering algorithm for large-scale biological data sets. by Minchao Wang, Wu Zhang, Wang Ding, Dongbo Dai, Huiran Zhang, Hao Xie, Luonan Chen, Yike Guo, Jiang Xie

“…The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.…”
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SS-OPDet: A Semi-Supervised Open-Set Detection Framework for Dead Pine Wood Detection by Xiaojian Lu, Shiguo Huang, Songqing Wu, Feiping Zhang, Mingqing Weng, Jianlong Luo, Xiaolin Li

“…To overcome these challenges, we propose SS-OPDet, a semi-supervised open-set detection framework that leverages a small amount of labeled data along with abundant unlabeled data. …”
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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets by Hussam Elbehiery, Hanaa fathi, Mohamed Eassa, Ahmed Abdelhafeez, Mohamed Refaat Abdellah, Hadeer Mahmoud

“…But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. …”
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P657: Real-world evaluation of ExpansionHunter for detecting STR expansions in whole-exome sequencing data: Insights from a clinical diagnostic setting by Matheus Castro, João Paulo Kitajima, Fabíola Monteiro, David Schlesinger, Fernando Kok

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Clustering and Detecting Network Intrusion Based on Fuzzy Algorithms by Manar Kashmola, Bayda Khaleel

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Network-based malcode detection technology by WU Bing1, YUN Xiao-chun2, GAO Qi1

“…Following the analysis for traditional distributed IDS,disadvantages that applying structure of multiple engine and small rules set to detect network-level malcode were pointed out,which is based on detailed protocol decoding.Detection model and anti-malcode markup language of network-level malcode were designed for single engine and big rules set.The characteristics of network data flow were analyzed.By optimization of patterns,frequent collisions between suffix with data flow and unbalanced branched of chained list were avoided.The efficiency by using WM algorithm to detect malcode on network level can be remarkably increased.…”
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Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing by Anish Bhattarai, Gonzalo J. Scarpin, Amrinder Jakhar, Wesley Porter, Lavesta C. Hand, John L. Snider, Leonardo M. Bastos

“…Light Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (<i>Gossypium hirsutum</i> L.), but standardized data acquisition and processing guidelines are lacking. …”
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Multimodal Detection of Agitation in People With Dementia in Clinical Settings: Observational Pilot Study by Abeer Badawi, Somayya Elmoghazy, Samira Choudhury, Sara Elgazzar, Khalid Elgazzar, Amer M Burhan

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Unobtrusive stress detection using wearables: application and challenges in a university setting by Peter Neigel, Andrew Vargo, Benjamin Tag, Koichi Kise

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Guest Editorial: Anomaly detection and open‐set recognition applications for computer vision by Hakan Cevikalp, Robi Polikar, Ömer Nezih Gerek, Songcan Chen, Chuanxing Geng

“…Abstract Anomaly detection is a method employed to identify data points or patterns that significantly deviate from expected or normal behaviour within a dataset. …”
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Mammography Images Segmentation Based on Fuzzy Set and Thresholding by Ali Mohammed Salih, Mohammed Y. Kamil

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Unique Method for Prognosis of Risk of Depressive Episodes Using Novel Measures to Model Uncertainty Under Data Privacy by Barbara Pękala, Dawid Kosior, Wojciech Rząsa, Katarzyna Garwol, Janusz Czuma

Subjects: “…interval-valued fuzzy set theory…”
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Product data set generation network based on SAM and pix2pix by Yu Huijun, Zou Zhihao, Kang Shuai

“…The data set generation test was carried out on Retail Product Checkout Dataset(RPC) set, and the improvement of the generated data set on target detection effect was further verified on YOLOv7, Fast R-CNN and AlexNet target detection networks. …”
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