Vegetation browning as an indicator of drought impact and ecosystem resilience by Ignacio Fuentes, Javier Lopatin, Mauricio Galleguillos, James McPhee

“…The Continuous Change Detection and Classification (CCDC) algorithm identified negative vegetation changes, filtering out non-browning events to reduce uncertainties. …”
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KF-NIPT: K-mer and fetal fraction-based estimation of chromosomal anomaly from NIPT data by Dongin Kim, Ji Yeon Sohn, Jin Hee Cho, Ji-Hye Choi, Gwi-young Oh, Hyun Goo Woo

“…However, current methods to detect anomaly from maternal cell-free DNAs (cfDNAs) that are based on the sequence read counts calculating z-scores face challenges with false positives and negatives. …”
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Comparison of the STANDARD M10 C. difficile, Xpert C. difficile, and BD MAX Cdiff assays as confirmatory tests in a two-step algorithm for diagnosing Clostridioides difficile infec... by Hyunseul Choi, Minhee Kang, Sun Ae Yun, Hui-Jin Yu, Eunsang Suh, Tae Yeul Kim, Hee Jae Huh, Nam Yong Lee

“…This algorithm starts with enzyme immunoassay (EIA) for detecting glutamate dehydrogenase (GDH) and toxins A/B, followed by nucleic acid amplification test (NAAT) for GDH-positive but toxin-negative cases. …”
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Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks by Hongna Tan, Qingxia Wu, Yaping Wu, Bingjie Zheng, Bo Wang, Yan Chen, Lijuan Du, Jing Zhou, Fangfang Fu, Huihui Guo, Cong Fu, Lun Ma, Pei Dong, Zhong Xue, Dinggang Shen, Meiyun Wang

“…Key Points The false positive and negative rates of mammography diagnosis remain high. …”
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Evaluation of Weed Infestations in Row Crops Using Aerial RGB Imaging and Deep Learning by Plamena D. Nikolova, Boris I. Evstatiev, Atanas Z. Atanasov, Asparuh I. Atanasov

“…One of the important factors negatively affecting the yield of row crops is weed infestations. …”
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Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation paramet... by Xin-Yue Song, Xin-Peng Xie, Wen-Jing Xu, Yu-Jia Cao, Bin-Miao Liang

“…Abstract Background The integration of machine learning (ML) algorithms enables the detection of diffusion abnormalities-related respiratory changes in individuals with normal body mass index (BMI), overweight, and obesity based on BMI and pulmonary ventilation parameters. …”
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Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery by Andrii Zaiats, T. Trevor Caughlin, Jennyffer Cruz, David S. Pilliod, Megan E. Cattau, Rongsong Liu, Richard Rachman, Maisha Maliha, Donna Delparte, John D. J. Clare

“…Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. …”
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Convolutional Neural Network-Based Approach for Cobb Angle Measurement Using Mask R-CNN by Marcos Villar García, José-Benito Bouza-Rodríguez, Alberto Comesaña-Campos

“…We propose the use of Mask R-CNN architecture for spine detection and segmentation in response to the first two questions, and a set of algorithms to tackle the third. …”
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Development of Smart Models to Accurately Predict Dynamic Viscosity of CO2-Saturated Polyethylene Glycol by Ayat Hussein Adhab, Morug Salih Mahdi, Bhavesh Kanabar, Anupam Yadav, Ranganathaswamy M K, Rishabh Thakur, Parveen Kumar, Braj Krishna, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

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Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study by Ruifang Hu, Xianping Liu, Yong Zhang, Clement Arthur, Dongguang Qin

“…Diagnostic performance was calculated using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultsThe study revealed superior sensitivity (95.2%) and specificity (96.5%) with AI-enhanced endoscopy compared to conventional endoscopy (89.6%, 92.4%), respectively. …”
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Refining malaria diagnosis in high-transmission areas: a dual-approach with rapid diagnostic tests (RDTs) and dbPCR-NALFIA by Diane Yirgnur Some, Francois Kiemde, Hermann Sorgho, Marc Christian Tahita, Antonia Windkouni Bere, Abdoulaye Ouedraogo, Souleymane Vivien Banao, Kouadjo Bagre, Gérémie Djiri, Georges Some, Toussaint Rouamba, Yeri Esther Hien, Aly Savadogo, Henk D. F. H. Schallig, Halidou Tinto

“…Following confirmation of undetermined sequential interpretation with dbPCR-NALFIA, the sequential algorithm had a sensitivity of 97.9%, a specificity of 94.8%, a positive predictive value of 97.2%, and a negative predictive value of 96.1%. …”
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A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study by Tracy Huang, Chun-Kit Ngan, Yin Ting Cheung, Madelyn Marcotte, Benjamin Cabrera

“…MethodsWe devised a hybrid deep learning–based feature selection approach to support early detection of negative long-term behavioral outcomes in survivors of cancer. …”
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Intrusion Detection System Framework for SDN-Based IoT Networks Using Deep Learning Approaches With XAI-Based Feature Selection Techniques and Domain-Constrained Features by Manlaibaatar Tserenkhuu, Md Delwar Hossain, Yuzo Taenaka, Youki Kadobayashi

“…This study proposes an IDS framework to detect various cyberattacks in SDN-based IoT networks utilizing three deep learning algorithms that incorporate hyperparameter tuning and the feature selection process based on explainable artificial intelligence (XAI), which uses domain-constrained features to improve performance and reduce computational complexity. …”
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Non-invasive diagnosis of esophageal cancer by a simplified circulating cell-free DNA methylation assay targeting OTOP2 and KCNA3: a double-blinded, multicenter, prospective study by Yan Bian, Ye Gao, Han Lin, Chang Sun, Wei Wang, Siyu Sun, Xiuling Li, Zhijie Feng, Jianlin Ren, Hezhong Chen, Chaojing Lu, Jinfang Xu, Jun Zhou, Kangkang Wan, Lei Xin, Zhaoshen Li, Luowei Wang

“…IEsohunter test showed sensitivities of 78.5% (95% CI 69.1–85.6), 87.3% (95% CI 79.4–92.4), 92.5% (95% CI 85.9–96.2), and 96.9% (95% CI 84.3–99.8) for stage I-IV EC, respectively, with an overall sensitivity of 87.4% (95% CI 83.4–90.6) and specificity of 93.3% (95% CI 91.2–94.9) for EC detection. The IEsohunter test status turned negative (100.0%, 47/47) after surgical resection of EC. …”
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Sensitivity Study of Feature Vectors Formed on the Basis of Multiple Scale Transformations of Processed Images by D. V. Vasilieva

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SCH-Hunter: A Taint-Based Hybrid Fuzzing Framework for Smart Contract Honeypots by Haoyu Zhang, Baotong Wang, Wenhao Fu, Leyi Shi

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Assessment of the Phase-to-Ground Fault Apparent Admittance Method with Phase/Ground Boundaries to Detect Types of Electrical Faults for Protective Relays Using Signature Library a... by Emilio C. Piesciorovsky, Marissa E. Morales Rodriguez

“…Protective relays in electric power grids recognize the types of electrical faults in a few seconds. The most common detection method to detect the types of electrical faults is based on measuring the angle between the zero and negative sequence currents. …”
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Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study by Anabel Franco-Moreno, Elena Madroñal-Cerezo, Cristina Lucía de Ancos-Aracil, Ana Isabel Farfán-Sedano, Nuria Muñoz-Rivas, José Bascuñana Morejón-Girón, José Manuel Ruiz-Giardín, Federico Álvarez-Rodríguez, Jesús Prada-Alonso, Yvonne Gala-García, Miguel Ángel Casado-Suela, Ana Bustamante-Fermosel, Nuria Alfaro-Fernández, Juan Torres-Macho

“…Sensitivity, specificity, and negative and positive predictive values were 62%, 94%, 93% and 75%, respectively. …”
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Non-Destructive Early Detection of Drosophila Suzukii Infestation in Sweet Cherries (c.v. <i>Sweet Heart</i>) Based on Innovative Management of Spectrophotometric Multilinear Corre... by Giuseppe Altieri, Mahdi Rashvand Avaei, Attilio Matera, Francesco Genovese, Vincenzo Verrastro, Naouel Admane, Orkhan Mammadov, Sabina Laveglia, Giovanni Carlo Di Renzo

“…The identified procedure of management of regression algorithms allowed the selection of a very performant and robust model using the partial least squares regression algorithm: its false negative rate and false positive rate, after 500 Monte Carlo runs, were 0.004% +/− 0.003 and 0.02% +/− 0.01, respectively, and, in addition, the 50% of samples were used for the external cross-validation set.…”
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Identification of Mattic Epipedon Degradation on the Northeastern Qinghai–Tibetan Plateau Using Hyperspectral Data by Junjun Zhi, Hong Zhu, Jingwen Yang, Qiuchen Yan, Dandan Zhi, Zhongbao Sun, Liangwei Ge, Chengwen Lv

“…The characteristic bands were concentrated in the visible light range (446–450 nm) and short-wave infrared range (2134 nm, 2267–2269 nm), which are closely related to the spectral responses of organic carbon and mineral components. (2) Spectral reflectance was significantly negatively correlated with moisture content, and model accuracy decreased as moisture content increased. (3) After correction using the EPO algorithm, the model accuracy for the high-moisture group improved by 13.2–16.7%, whereas that for the low-moisture group (<15%) decreased by 7.5%, verifying 15% moisture content as the critical threshold for water interference. …”
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