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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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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An international multicenter retrospective analysis of repeated anti-ENA testing in ANA-associated rheumatic diseases, a data-driven proposal to increase testing efficacy by Wim H.M. Vroemen, Maria Infantino, Mariangela Manfredi, Joyce J.B.C. van Beers, Carolien Bonroy, Jan G.M.C. Damoiseaux

“…In 76 (1.8 %) patients a positive anti-ENA test was obtained after an initial negative anti-ENA test result, while in 167 (4.0 %) patients additional autoantibodies were detected after an initial positive anti-ENA result. …”
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Integration of YOLOv9 Segmentation and Monocular Depth Estimation in Thermal Imaging for Prediction of Estrus in Sows Based on Pixel Intensity Analysis by Iyad Almadani, Aaron L. Robinson, Mohammed Abuhussein

“…Furthermore, without addressing distance variations, the model’s generalizability diminishes, increasing the likelihood of false positives and negatives and ultimately reducing the effectiveness of estrus detection. …”
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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

“…XGBoost, LightGBM, and CatBoost algorithms were used to train different prediction models, which were subsequently validated in a hold-out dataset. …”
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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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Interpretation of elevated baseline concentrations and serial changes of high‐sensitivity cardiac troponin T in confirmed muscular dystrophies by Mustafa Yildirim, Christoph Reich, Christian Salbach, Regina Pribe‐Wolferts, Barbara Ruth Milles, Tobias Täger, Matthias Mueller‐Hennessen, Markus Weiler, Benjamin Meder, Norbert Frey, Evangelos Giannitsis

“…Applying the criteria of the ESC 0/1 h algorithm for triage of suspected acute coronary syndrome (ACS) showed poor overall effectiveness of baseline hs‐cTnT values. …”
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Identification Method for Incipient Intermittent Arc Ground Fault of High-Voltage Cables by Zhengxiong ZHOU, Xiangyang XIA, Peng ZHU, Mingde LI, Hai HUANG, Shanqiu CHEN, Junshan XIA, Ruiqi WANG

“…Firstly, the non-parametric bilateral CUSUM algorithm is used to monitor the cumulative sum of the positive and negative offsets of the three-phase current to determine the abnormal phase. …”
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Accuracy improvement in financial sanction screening: is natural language processing the solution? by Seihee Kim, ShengYun Yang

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Gradient Flow Decoding by Tadashi Wadayama, Lantian Wei

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Monitoring land-use changes and predicting their spatio-temporal trends in Hamedan City by Naser Shafiei Sabet, Faranak Feyzbabaei cheshmeh sefidi

“…After land use detection and its changes, the trend of these changes was predicted in 2050 using the automatic cell model and Markov chain due to its high ability to detect spatial-spatial component changes.Results and discussion: Results indicated that the growth and development of urbanization in this metropolis have led to the city's expansion in this area. …”
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Automating areas of interest analysis in mobile eye tracking experiments based on machine learning by Julian Wolf, Stephan Hess, David Bachmann, Quentin Lohmeyer, Mirko Meboldt

“…The new algorithm’s performance is validated against a manual fixation-by-fixation mapping, which is considered as ground truth, in terms of true positive rate (TPR), true negative rate (TNR) and efficiency. …”
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Can artificial intelligence and contrast-enhanced mammography be of value in the assessment and characterization of breast lesions? by Lamiaa Mohamed Bassam Hashem, Heba Monir Azzam, Ghadeer Saad Abd El-Shakour El-Gamal, MennatAllah Mohamed Hanafy

“…The resulting mammographic images were processed using AI algorithm. In our study, CEM demonstrated a sensitivity of 98.33%, specificity of 92.86%, positive predictive value (PPV) of 98.34%, negative predictive value (NPP) of 92.85%, and accuracy of 97.3%. …”
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Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model by Victoria A. O'Brien, Vittal S. Rao, Rodrigo D. Trevizan

“…Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.…”
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Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis by Ioannis Stathopoulos, Roman Stoklasa, Maria Anthi Kouri, Georgios Velonakis, Efstratios Karavasilis, Efstathios Efstathopoulos, Luigi Serio

“…Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. …”
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The relationship between activities of daily living and speech impediments based on evidence from statistical and machine learning analyses by Liu Jun, Hongguo Li, Yu Mao, Lan Hu, Dan Wu

“…The inclusion of BI in the models improved the overall predictive performance, highlighting its positive impact on SI prediction.ConclusionThe study employed various statistical methodologies to demonstrate a significant negative correlation between ADL and SI, a finding further corroborated by machine learning algorithms. …”
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FragMangro: A cross-domain zero-shot model for monitoring fragmented mangrove ecosystems by Ruoxin Zhang, Angela An, Mingcan Cen, Chong Zhang, Aimee Huangfu, Sunny Vinnakota, Shuxiang Song

“…Abstract Monitoring fragmented mangrove ecosystems presents significant challenges due to their sparse distribution and the limitations of traditional detection methods, which often suffer from poor convergence and high rates of false positives and false negatives. …”
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UEF-HOCUrdu: Unified Embeddings Ensemble Framework for Hate and Offensive Text Classification in Urdu by Kifayat Ullah, Muhammad Aslam, Muhammad Usman Ghani Khan, Faten S. Alamri, Amjad Rehman Khan

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Research on multi-target recognition method based on WSN and blind source separation by Pengju HE, Gangyi LIU, Siyi LIU

“…Aiming at the problem of signal aliasing in multi-target detection and recognition using wireless sensor network (WSN),a blind source separation algorithm was proposed,which can determine the number of targets and obtain the accurate source signals.In this algorithm,the multichannel mixed signal was used as the analysis object,the number of source signals was determined based on the eigenvalue method and then the blind source separation algorithm based on the non-negative matrix factorization was used to obtain the separation signals.The experimental results indicate that the number of targets can be determined and the accurate separation signals can be obtained by the proposed scheme.It can be applied to solve the problem of signal aliasing in multi-target detection and recognition.…”
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Time series segmentation for recognition of epileptiform patterns recorded via microelectrode arrays in vitro. by Gabriel Galeote-Checa, Gabriella Panuccio, Angel Canal-Alonso, Bernabe Linares-Barranco, Teresa Serrano-Gotarredona

“…The ZdensityRODE algorithm showcased a precision and recall of 93% for ictal event detection and 42% precision for interictal event detection, while the AMPDE algorithm attained a precision of 96% and recall of 90% for ictal event detection and 54% precision for interictal event detection. …”
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