Annotation of Text Corpora by Sentiment and Presence of Irony within a Project of Citizen Science by Ilya Vyacheslavovich Paramonov, Anatoliy Yurievich Poletaev

“…It was also shown that the results of automatic algorithms of detecting the sentiment of sentences improved by 12–13 % when using a corpus for which all the annotators (from 3 till 5) had the agreement, in comparison with a corpus annotated by only one volunteer.…”
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MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation by Benjamin Voloh, Marcus R Watson, Seth Konig, Thilo Womelsdorf

“…Our modified algorithm shows a significant and marked improvement in saccade detection - showing both more true positives and less false negatives – especially under higher noise levels. …”
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Vital Sign and Biochemical Data Collection Using Non-contact Photoplethysmography and the Comestai Mobile Health App: Protocol for an Observational Study by Gianvincenzo Zuccotti, Paolo Osvaldo Agnelli, Lucia Labati, Erika Cordaro, Davide Braghieri, Simone Balconi, Marco Xodo, Fabrizio Losurdo, Cesare Celeste Federico Berra, Roberto Franco Enrico Pedretti, Paolo Fiorina, Sergio Maria De Pasquale, Valeria Calcaterra

“… BackgroundEarly detection of vital sign changes is key to recognizing patient deterioration promptly, enabling timely interventions and potentially preventing adverse outcomes. …”
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The role of artificial intelligence in breast cancer screening as a supportive tool for radiologists by Agata Król, Katarzyna Kwaterska, Karol Kutyłowski, Paweł Łuckiewicz

“…Authors generally focused on cancer detection rate, false positive or negative results, recall rate and the workload involvement. …”
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Diagnostic Utility of ¹⁸F-FDG PET/CT in Infective Endocarditis by Corina-Ioana Anton, Alice-Elena Munteanu, Mihaela Raluca Mititelu, Militaru Alexandru Ștefan, Cosmin-Alexandru Buzilă, Adrian Streinu-Cercel

“…False-negative results were mostly observed in early post-surgical PVE and native valve endocarditis. …”
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Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds by Wu Qiao, Tong Xie, Jing Lu, Tinghan Jia

“…Background To enhance the accuracy of allergen detection in cosmetic compounds, we developed a co-culture system that combines HaCaT keratinocytes (transfected with a luciferase plasmid driven by the AKR1C2 promoter) and THP-1 cells for machine learning applications. …”
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Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble by Tossapon Boongoen, Natthakan Iam-On, James Mullaney

“…The results with simulated data and algorithms of NB, C4.5 and KNN have shown that the proposed framework can filter out some negative samples quickly, while making classification of the rest more effective. …”
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The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights by Majdi Maabreh, Arwa Maabreh, Basheer Qolomany, Ala Al-Fuqaha

“…The word “robustness” refers to a machine learning algorithm’s ability to cope with hostile situations. …”
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Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway by Esmaeil S. Nadimi, Jan-Matthias Braun, Benedicte Schelde-Olesen, Smith Khare, Vinay C. Gogineni, Victoria Blanes-Vidal, Gunnar Baatrup

“…We developed a family of algorithms based on explainable deep neural networks (DNN) that detect polyps within a sequence of images, feed only those images containing polyps into two parallel independent networks to characterize, and estimate the size of important findings. …”
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Classification of Lung Nodule Using Hybridized Deep Feature Technique by Malin Bruntha, Immanuel Alex Pandian, Siril Sam Abraham

“…The False Positive Rate was found to be 2.637% and False Negative Rate was 9.09%.…”
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Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from mycobacterium tuberculosis by Hong-ling Li, Ri-zeng Zhi, Hua-sheng Liu, Mei Wang, Si-jie Yu

“…The multimodal model contained age, IL-6, and the 2 radiomics features, and the optimal model was from LightGBM algorithm. The optimal multimodal model had the highest AUC value, accuracy, sensitivity, and negative predictive value compared with the optimal clinical or radiomics models, and its’ favorable performance was also verified in the external test dataset (accuracy = 0.745, sensitivity = 0.900). …”
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Longitudinal analysis of behavioral factors and techniques used to identify vaccine hesitancy among Twitter users: Scoping review by Sulaiman Khan, Md. Rafiul Biswas, Zubair Shah

“…SVM, LDA, BERT are the techniques used for topic modeling, while Louvain, NodeXL, and Infomap algorithms are used for community detection. This research is notable for being the first systematic review that emphasizes the dearth of longitudinal studies and the methodological and underlying practical constraints underpinning the lucrative implementation of an explainable and longitudinal behavior analysis system. …”
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Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care. by Youli Jiang, Ao Li, Zhihuan Li, Yanfeng Li, Rong Li, Qingshi Zhao, Guisu Li

“…Predictive models were developed using machine learning algorithms, including Gradient Boosting Machine (GBM), Random Forest (RF), and Logistic Regression (LR). …”
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Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis by Atta Taseh, Souri Sasanfar, Michelle Chan, Evan Sirls, Ara Nazarian, Kayhan Batmanghelich, Jonathan F Bean, Soheil Ashkani-Esfahani

“…The highest F1ICD ConclusionsOur findings showed promising performance with higher accuracy of NLP algorithms compared to the conventional method for detecting fall occurrence and mechanism in developing disease registries using clinical notes. …”
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Pengaruh Word Affect Intensities Terhadap Deteksi Ulasan Palsu by Raga Saputra Heri Istanto, Fitra Abdurrachman Bachtiar, Achmad Ridok

“…These features are then combined with features in previous studies and evaluated using several classification algorithms. The results showed that word affect intensities can be a factor that affects the increased accuracy of fake review detection by 2.1%. …”
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Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning by Mohammad Amin Habibi, Reza Omid, Shafaq Asgarzade, Sadaf Derakhshandeh, Ali Soltani Farsani, Zohreh Tajabadi

“…We systematically reviewed the studies reported the performance of machine learning (ML) algorithms for accurately discrimination of these two entities. …”
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Gamified mHealth System for Evaluating Upper Limb Motor Performance in Children: Cross-Sectional Feasibility Study by Md Raihan Mia, Sheikh Iqbal Ahamed, Samuel Nemanich

“…We applied change point detection (ie, the pruned exact linear time method), signal processing techniques, and other algorithms to calculate movement speed and accuracy from spatiotemporal parameters. …”
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Sentiment Analysis and Classification of User Reviews of the 'Access by KAI' Application Using Machine Learning Methods to Improve Service Quality by Hildegardis Kristina saka, Putri Taqwa Prasetyaningrum

“…Precision, recall, and F1-scores for each model were also evaluated, showing strong performance in detecting negative sentiments but lower performance for neutral and positive sentiments. …”
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Peripherex Home Visual Field Demonstrates High Test-Retest Reliability, Validity by Schweitzer J, Ibach M, Berdahl J, Daoud M, Daoud YA, Kempinski Y, Goldberg JL

“…Test-retest reliability was good, with interclass correlation coefficient of 0.7596. Detection of abnormal fields was excellent, with a sensitivity of 95.7% and a negative predictive value (NPV) of 98.27%. …”
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Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE study protocol by Gabriele Zoppoli, Alberto Ballestrero, Valerio Gaetano Vellone, Piero Fregatti, Francesco Ravera, Gabriella Cirmena, Martina Dameri, Maurizio Gallo, Daniele Friedman, Massimo Calabrese, Alberto Tagliafico, Lorenzo Ferrando

“…Circulating tumour DNA, cell-free methylated DNA and circulating proteins will be assessed in samples collected at t0 from patients with stage I–IIA BC at surgery together with those collected from patients with histologically confirmed benign lesions of similar size and from healthy controls with negative mammography. These analyses will be combined with radiomic variables extracted with freeware algorithms applied to cases and matched controls for which digital mammography is available. …”
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