Exploiting self-organization and fault tolerance in wireless sensor networks: A case study on wildfire detection application by Felipe Taliar Giuntini, Delano Medeiros Beder, Jó Ueyama

“…The results were classified as True (partial or absolute) or False (partial or absolute). …”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

“…Fabric defect detection plays a vital role in ensuring the production quality of the textile manufacturing industry. …”
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Conservation in action: Cost-effective UAVs and real-time detection of the globally threatened swamp deer (Rucervus duvaucelii) by Ravindra Nath Tripathi, Karan Agarwal, Vikas Tripathi, Ruchi Badola, Syed Ainul Hussain

“…To improve the accuracy of animal recognition with UAVs, we used single-stage detectors YOLO (You Only Look Once) V3, V5, V7, V8, Object detection V3 and DETR (DEtection TRansformer). We trained our model using 48,957 augmented images derived from a dataset of 8210 original true dataset. …”
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A Liquid-Based Cytology System, without the Use of Cytocentrifugation, for Detection of Podocytes in Urine Samples of Patients with Diabetic Nephropathy by Moritsugu Kimura, Masao Toyoda, Nobumichi Saito, Noriko Kaneyama, Han Miyatake, Eitaro Tanaka, Hirotaka Komaba, Masanori Hara, Masafumi Fukagawa

“…Unlike the rapidly progressive glomerular damage in glomerulonephritis, only a few desquamated podocytes are usually detected in diabetic nephropathy (DN). It is not clear whether the low podocyte count in DN is due to limitation of the conventional method or true pathological feature. …”
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Well done! Or how to Avoid Dangers of Pseudoscience: Common Standard for Research in Behavioural Analysis and Deception Detection in Aviation Security by Jenny K. Krüger, María C. Feijoo-Fernández, Signe M. Ghelfi

“…It is the inherent goal of security to detect and prevent unlawful events to happen. This is especially true for aviation security as airports continue to constitute attractive targets for terrorist attacks. …”
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A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN by Fei Xie, Ming Zhang, Jing Zhao, Jiquan Yang, Yijian Liu, Xinyue Yuan

“…Secondly, the candidate regions of license plate are checked to verify the true plate, and the license plate image is located accurately by the integral projection method. …”
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Serological Detection of Rh-Del Phenotype among Rh-Negative Blood Donors at National Blood Center, Yangon, Myanmar by Saw Thu Wah, Saung Nay Chi, Kyi Kyi Kyaing, Aye Aye Khin, Thida Aung

“…Serologically, Rh-Del type can only be detected by an adsorption-elution technique, and it might be mistyped as Rh-negative. …”
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Detecting gene copy number alterations by Oncomine Comprehensive genomic profiling in a comparative study on FFPE tumor samples by Fabio Bozzi, Elena Conca, Marco Silvestri, Gianpaolo Dagrada, Alice Ardore, Donata Penso, Daniele Lorenzini, Chiara Costanza Volpi, Desirè Viola Trupia, Adele Busico, Iolanda Capone, Federica Perrone, Elena Tamborini, Andrea Vingiani, Luca Agnelli, Giancarlo Pruneri

“…OCA+, a targeted DNA panel, showed lower CNAs detection sensitivity and equal specificity for gains and losses. …”
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Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset by Asadi Srinivasulu, Clement Varaprasad Karu, G Sreenivasulu, Gayathri R

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Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System by Roya Alizadeh, Yvon Savaria, Chahe Nerguizian

“…Robust methods are needed to detect how people are moving in smart public transportation systems. …”
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Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax by Diego A. Ramos-Briceño, Alessandro Flammia-D’Aleo, Gerardo Fernández-López, Fhabián S. Carrión-Nessi, David A. Forero-Peña

“…Previous models efficiently detected malaria parasites in red blood cells but had difficulty differentiating between species. …”
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Soft-Label Supervised Meta-Model with Adversarial Samples for Uncertainty Quantification by Kyle Lucke, Aleksandar Vakanski, Min Xian

“…To solve this issue, uncertainty quantification (UQ) models have been developed to allow the detection of misclassifications. Meta-model-based UQ methods are promising due to the lack of predictive model re-training and low resource requirement. …”
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Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation... by Peranut Chotcomwongse, Paisan Ruamviboonsuk, Chaiwat Karavapitayakul, Koblarp Thongthong, Anyarak Amornpetchsathaporn, Methaphon Chainakul, Malee Triprachanath, Eckachai Lerdpanyawattananukul, Niracha Arjkongharn, Varis Ruamviboonsuk, Nattaporn Vongsa, Pawin Pakaymaskul, Turean Waiwaree, Hathaiphan Ruampunpong, Richa Tiwari, Viroj Tangcharoensathien

“…Results Of 645 patients screened by AI, 201 (31.2%) were referrals, 129 (64.2%) of which were true positives agreeable by over-reading; 115 of these true positives (89.1%) had referral adherence. …”
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Comparison of modularity-based approaches for nodes clustering in hypergraphs by Poda, Veronica, Matias, Catherine

“…Subsequently, we provide an overview of the state-of-the-art codes available to maximize hypergraph modularities for detecting node communities in hypergraphs. Through exploration of various simulation settings with controlled ground truth clustering, we offer a comparison of these methods using different quality measures, including true clustering recovery, running time, (local) maximization of the objective, and the number of clusters detected. …”
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Which Emotions of Social Media Users Lead to Dissemination of Fake News: Sentiment Analysis Towards Covid-19 Vaccine by Maide Feyza Er, Yonca Bayrakdar Yılmaz

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Maternal Perception of Decreased Fetal Movement in One Twin: A Clue Leading to the Early Detection of Absent Variability due to Acute Twin-to-Twin Transfusion Syndrome by Hirotada Suzuki, Tomoyuki Kuwata, Akihide Ohkuchi, Yukari Yada, Shigeki Matsubara, Mitsuaki Suzuki

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Enhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Ge... by Jemyoung Lee, Minbeom Kim, Heejun Park, Zepa Yang, Ok Hee Woo, Woo Young Kang, Jong Hyo Kim

“…Results: TSVD_SD outperformed all other methods, achieving the highest sensitivity (84.46%) and accuracy (95.05%), making it particularly effective for identifying true positives. The complementary use of DL methods with HLR further improved detection performance. …”
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Culture-independent detection of Mycobacterium tuberculosis complex DNA using targeted next generation sequencing in African buffalo (Syncerus caffer) oronasal swabs in South Afric... by Sinegugu Kholeka Mhlophe, Charlene Clarke, Giovanni Ghielmetti, Giovanni Ghielmetti, Megan Matthews, Tanya Jane Kerr, Michele Ann Miller, Wynand Johan Goosen, Wynand Johan Goosen

“…Controlling aTB in South Africa relies on mycobacterial culture as the gold standard for M. bovis confirmation, with the single intradermal comparative cervical test (SICCT) and Bovigam™ assays as validated cell-mediated immunological assays for detection. However, these methods are not without their shortfalls, with a suboptimal ability to discern true positive results amidst certain non-tuberculous mycobacteria (NTM) interference. …”
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Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements by Brady Metherall, Anna K. Berryman, Georgia S. Brennan

“…Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different sets of clinical features remains under-explored. …”
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SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden by Bethany K. Hughes, Andrew Davis, Deborah Milligan, Ryan Wallis, Federica Mossa, Michael P. Philpott, Linda J. Wainwright, David A. Gunn, Cleo L. Bishop

“…Results Using scRNA-seq of both 2D and 3D deeply senescent fibroblasts, the model predicts intra-experimental fibroblast senescence to a high degree of accuracy (> 99% true positives). Applying SenPred to in vivo whole skin scRNA-seq datasets reveals that cells grown in 2D cannot accurately detect fibroblast senescence in vivo. …”
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