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Articles by Lee SI
Explainable fraud detection under fixed false positive rate
Explainable fraud detection under fixed false positive rate Duluth, Georgia, United States DOI 10.7717/peerj-cs.4007 Academic Editor Ankit Vishnoi Subject Areas Algorithms and Analysis of Algorithms, Artificial Intelligence, Data Mining and Machine Learning, Data Science, Neural Networks Keywords Fraud detection, Explainable artificial intelligence (XAI), Machine learning, Imbalanced classification, False positive rate, Operational machine learning, SHAP, Model interpretability, Risk scoring,...
Multi-scale predictive modeling of phenology and carotenoid content in carrots using spectral techniques, colorimetry, and artificial intelligence
The results of this study validate the hypothesis that the integration of spectral and colorimetric techniques with artificial intelligence models enables the non-destructive estimation of carotenoid content, physicochemical and phenological parameters, and high-accuracy classification of damage in carrots.
Interpretable prediction of private brand purchases by pet type in e-commerce for consumer behavior analysis using real-world transaction data
1Institute of IT Convergence Technology, Seoul National University of Science and Technology, Seoul, Republic of South Korea 2Department of Information Systems, Hanyang University, Seoul, Republic of South Korea 3Department of Artificial Intelligence, Hanyang University, Seoul, Republic of South Korea DOI 10.7717/peerj-cs.3795 Academic Editor Davide Chicco Subject Areas Algorithms and Analysis of Algorithms, Artificial Intelligence, Data Mining and Machine Learning, Data Science, Neural...
Adaptive modeling combined with reinforcement learning to handle text multi-categorization tasks in news pushing
Adaptive modeling combined with reinforcement learning to handle text multi-categorization tasks in news pushing 1School of Journalism and Communication, Wuhan University of Communication, Wuhan, China 2Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan DOI 10.7717/peerj-cs.3759 Academic Editor Bilal Alatas Subject Areas Computational Biology, Algorithms and Analysis of Algorithms, Data Mining and Machine...
Assessing COVID-19 vaccine hesitancy in Malaysian pregnant women: prevalence and influencing factors
Norhidayu Ginon conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. Zainab Mat Yudin conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
OneSTOP (One-Stop Telehealth Obesity Program): study protocol for an open-label randomized controlled trial on the use of telecare and digital platforms for multidisciplinary management of weight management and related comorbidities
Pantalone KM, Hobbs TM, Chagin KM, Kong SX, Wells BJ, Kattan MW, et al. Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system. BMJ Open. 2017;7(11):e017583. https://doi.org/10.1136/bmjopen-2017-017583. Google Scholar Chen K, Sng WK, Quah JH, Liu J, Chong BY, Lee HK, et al. Clinical spectrum of non-alcoholic fatty liver disease in patients with diabetes mellitus. PLoS One.
Artificial intelligence-enhanced CSI-based Wi-Fi sensing for non-contact vital sign monitoring: a systematic review
1Abu Dhabi University, Abu Dhabi, United Arab Emirates 2Cleveland Clinic Hospital, Abu Dhabi, United Arab Emirates 3Leeds Beckett University, Leeds, United Kingdom 4Bradford Institution for Health Research, Bradford, United Kingdom 5Department of Government Enablement, Abu Dhabi, United Arab Emirates DOI 10.7717/peerj-cs.3375 Academic Editor Sara Comai Subject Areas Artificial Intelligence, Data Mining and Machine Learning, Emerging Technologies, Security and Privacy, Internet of Things...
Hybrid GNN-Transformer model for multi-omic cancer classification with interpretable pathway-driven feature selection
Cancer is one of the most leading causes of mortality worldwide, and successful therapy remains a real challenge despite enormous advancement in healthcare over the past century. A primary method of cancer categorization is based on the anatomical site (tissue or organ) of origin, for instance, breast cancer and prostate cancer.
Enhancing heatstroke prediction accuracy with interpretable machine learning: a multi-center data-driven approach
Enhancing heatstroke prediction accuracy with interpretable machine learning: a multi-center data-driven approach 1The 908th Hospital of Chinese PLA Logistic Support Force, Nanchang, China 2Nanchang Hongdu Hospital of Traditional Chinese Medicine, Nanchang, China 3Nanchang Key Laboratory of Thrombosis and Hemostasis, Nanchang, China DOI 10.7717/peerj.20377 Academic Editor Luigi Di Biasi Subject Areas Emergency and Critical Care, Public Health, Sports Medicine Keywords Machine learning,...
A hybrid CNN-GRU model with XAI-Driven interpretability using LIME and SHAP for static analysis in malware detection
With the rapid advancement of computer technologies, malware has become a significant threat to cybersecurity for individuals, organizations, and even entire nations. Therefore, it is imperative to detect and eliminate malware as soon as possible. Malware analysis is the process of understanding the behavior of malware or malicious code and determining the impact of malware on systems.
Stock assessment and management implications of sailfin sandfish (Arctoscopus japonicus) caught by multiple fishing gears with different selectivity in Korean waters
The Korean government has been promoting a resource recovery project since the mid-2000s to restore declining sailfin sandfish resources. In 2009, a total allowable catch (TAC) system was implemented for two fisheries, namely the East Sea mid-sized Danish seine (DS) and East Sea mid-sized trawl fisheries, to manage the sandfish population. Nonetheless, sandfish resources have experienced a decline in recent years (Fig. 1A), necessitating integrated management at the macro level (Lee et al., 2024).
An interpretable credit risk assessment model with boundary sample identification
1School of Economics, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China 2Courant Institute of Mathematical Sciences, New York University, New York, New York, United States 3Social Sciences, Franklin and Marshall College, Lancaster City, Pennsylvania, United States DOI 10.7717/peerj-cs.2988 Academic Editor Siddhartha Bhattacharyya Subject Areas Algorithms and Analysis of Algorithms, Artificial Intelligence, Data Mining and Machine Learning, Data Science, Neural...
Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model
This study developed and validated an LGBM model based on routine laboratory data to diagnose noninvasive TPE. By incorporating SHAP analysis, the model’s interpretability was enhanced, allowing clinicians to understand the contribution of each feature to the diagnostic outcome, thus addressing the “black box” problem typically associated with machine learning (Gunning et al., 2019; Holzinger et al., 2017).
LASSO-derived nomogram prediction model for lymph node metastasis in colorectal cancer: a retrospective analysis
CRC remains an important public health problem worldwide, and LNM is an important prognostic factor for CRC. LNM in CRC is a multifaceted process involving tumor cell invasion, endocytosis, transport through the lymphatic system, retention in the lymph nodes, extravasation, proliferation, angiogenesis, and interaction with the immune system (Cao et al., 2022; Huang & Chen, 2017; Jiang et al., 2013; Wang et al., 2021; Yan, Su & Qin, 2020).
A method for explaining individual predictions in neural networks
The proposed NNexplainer explains the prediction results generated by a neural-network-based prediction model. The prediction can be decomposed into the contributions of each input feature value. Using the interactive contribution plot, we observe the interactions among feature values. In this section, we discuss the reliability of the proposed method and then compare NNexplainer with LIME and SHAP. We also discuss the explanation for the classification model based on neural networks.
EMF-Portal | Photobiostimulation Therapy (PBST) and Pulsed Electromagnetic Field Therapy (PEMF) in Hair Loss Treatment: Multicentric, Retrospective, Case-Control Study
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TaZFP 23, a new Cys2/His2-type zinc-finger protein, is a regulator of wheat (Triticum aestivum L.) growth and abiotic stresses
TaZFP 23, a new Cys2/His2-type zinc-finger protein, is a regulator of wheat (Triticum aestivum L.) growth and abiotic stresses 1College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, China 2College of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha, China DOI 10.7717/peerj.18956 Academic Editor Imren Kutlu Subject Areas Agricultural Science, Bioinformatics, Biotechnology, Plant Science Keywords Wheat (Triticum aestivum L.), Drought,...
Enhancing intrusion detection performance using explainable ensemble deep learning
This section begins by presenting the recent intrusion detection methods based on machine learning. Then, gives the state of the art of application of deep learning methods for intrusion detection and finally describes explainable artificial intelligence techniques for intrusion detection. Machine learning models were widely used for intrusion detection (Ahmad et al., 2021; Liu & Lang, 2019).
Improving prediction of maternal health risks using PCA features and TreeNet model
Leila Jamel conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft. Muhammad Umer conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.
Transcriptomic and targeted metabolome analyses revealed the regulatory mechanisms of the synthesis of bioactive compounds in Citrus grandis ‘tomentosa’
The ‘Huanglong’ variety of C. grandis ‘tomentosa’ was sampled from the planting base of Xiangxiu Huajuhong Industrial Co., Ltd (Linchen Town, Huazhou City, 22°49′11″N, 110°36′11″E). The fruits (F, 30-day-old), petals (P, just blooming), and tender leaves (L, 20-day-old) were sampled on March 2, 2018. The plant materials, including 10 fruits, 20 leaves, and 50 flowers, were obtained each of nine 10-year-old individuals, , and the samples of three individuals were obtained as biological repeats.
Particulate matter 10 induces oxidative stress and apoptosis in rhesus macaques skin fibroblast
Particulate matter 10 induces oxidative stress and apoptosis in rhesus macaques skin fibroblast 1Primate Resources Center, Korea Research Institute of Bioscience and Biotechnology, Jeongup-si, Republic of Korea 2Department of Animal Science, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea DOI 10.7717/peerj.16589 Academic Editor Xinfeng Wang Subject Areas Biochemistry, Cell Biology, Molecular Biology, Toxicology Keywords Non-human primate, Particulate matter 10, Toxicity,...
Recent Advances in Basic and Clinical Aspects of Rheumatoid Arthritis-associated Interstitial Lung Diseases
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Nutritional indices for screening sarcopenia before adult cardiac surgery
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Estimation of ataxia severity in children with ataxia-telangiectasia using ankle-worn sensors
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Fostering transparent medical image AI via an image-text foundation model grounded in medical literature
This is a preprint. It has not yet been peer reviewed by a journal. The National Library of Medicine is running a pilot to include preprints that result from research funded by NIH in PMC and PubMed. Display options Abstract Building trustworthy and transparent image-based medical AI systems requires the ability to interrogate data and models at all stages of the development pipeline: from training models to post-deployment monitoring.
Efficacy of fucoxanthin extract from Sargassum horneri on 3T3-L1 pre-adipocyte differentiation
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Sex Cord-Stromal Tumors of the Ovary
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Dissection of medical AI reasoning processes via physician and generative-AI collaboration
This is a preprint. It has not yet been peer reviewed by a journal. The National Library of Medicine is running a pilot to include preprints that result from research funded by NIH in PMC and PubMed. Display options Abstract Despite the proliferation and clinical deployment of artificial intelligence (AI)-based medical software devices, most remain black boxes that are uninterpretable to key stakeholders including patients, physicians, and even the developers of the devices.
Optimal Dose Warfarin for Asian Patients after Mitral Valve Replacement
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Adult intussusception: a challenge to laparoscopic surgery?
Discussion Intussusception is considered a rare condition in adults. The variability of the clinical manifestations makes diagnosis difficult. Surgical resection of intestinal lesions is the main treatment for adult intussusception laparoscopic surgery is emerging as an alternative to open surgery; however, it is controversial in the treatment of adult intussusception. Briggs et al.
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