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Angaitkar P, Ram Janghel R, Prasad Sahu T. An MCDM approach for Reverse vaccinology model to predict bacterial protective antigens. Vaccine 2024:S0264-410X(24)00517-6. [PMID: 38704249 DOI: 10.1016/j.vaccine.2024.04.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/26/2024] [Accepted: 04/20/2024] [Indexed: 05/06/2024]
Abstract
Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets.
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Affiliation(s)
- Pratik Angaitkar
- Department of Information Technology, National Institute of Technology, Raipur, G.E.Road Raipur, C.G. -492010, India.
| | - Rekh Ram Janghel
- Department of Information Technology, National Institute of Technology, Raipur, G.E.Road Raipur, C.G. -492010, India.
| | - Tirath Prasad Sahu
- Department of Information Technology, National Institute of Technology, Raipur, G.E.Road Raipur, C.G. -492010, India.
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2
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Perets O, Stagno E, Yehuda EB, McNichol M, Anthony Celi L, Rappoport N, Dorotic M. Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias. medRxiv 2024:2024.04.09.24305594. [PMID: 38680842 PMCID: PMC11046491 DOI: 10.1101/2024.04.09.24305594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
Objectives 1.1Biases inherent in electronic health records (EHRs), and therefore in medical artificial intelligence (AI) models may significantly exacerbate health inequities and challenge the adoption of ethical and responsible AI in healthcare. Biases arise from multiple sources, some of which are not as documented in the literature. Biases are encoded in how the data has been collected and labeled, by implicit and unconscious biases of clinicians, or by the tools used for data processing. These biases and their encoding in healthcare records undermine the reliability of such data and bias clinical judgments and medical outcomes. Moreover, when healthcare records are used to build data-driven solutions, the biases are further exacerbated, resulting in systems that perpetuate biases and induce healthcare disparities. This literature scoping review aims to categorize the main sources of biases inherent in EHRs. Methods 1.2We queried PubMed and Web of Science on January 19th, 2023, for peer-reviewed sources in English, published between 2016 and 2023, using the PRISMA approach to stepwise scoping of the literature. To select the papers that empirically analyze bias in EHR, from the initial yield of 430 papers, 27 duplicates were removed, and 403 studies were screened for eligibility. 196 articles were removed after the title and abstract screening, and 96 articles were excluded after the full-text review resulting in a final selection of 116 articles. Results 1.3Systematic categorizations of diverse sources of bias are scarce in the literature, while the effects of separate studies are often convoluted and methodologically contestable. Our categorization of published empirical evidence identified the six main sources of bias: a) bias arising from past clinical trials; b) data-related biases arising from missing, incomplete information or poor labeling of data; human-related bias induced by c) implicit clinician bias, d) referral and admission bias; e) diagnosis or risk disparities bias and finally, (f) biases in machinery and algorithms. Conclusions 1.4Machine learning and data-driven solutions can potentially transform healthcare delivery, but not without limitations. The core inputs in the systems (data and human factors) currently contain several sources of bias that are poorly documented and analyzed for remedies. The current evidence heavily focuses on data-related biases, while other sources are less often analyzed or anecdotal. However, these different sources of biases add to one another exponentially. Therefore, to understand the issues holistically we need to explore these diverse sources of bias. While racial biases in EHR have been often documented, other sources of biases have been less frequently investigated and documented (e.g. gender-related biases, sexual orientation discrimination, socially induced biases, and implicit, often unconscious, human-related cognitive biases). Moreover, some existing studies lack causal evidence, illustrating the different prevalences of disease across groups, which does not per se prove the causality. Our review shows that data-, human- and machine biases are prevalent in healthcare and they significantly impact healthcare outcomes and judgments and exacerbate disparities and differential treatment. Understanding how diverse biases affect AI systems and recommendations is critical. We suggest that researchers and medical personnel should develop safeguards and adopt data-driven solutions with a "bias-in-mind" approach. More empirical evidence is needed to tease out the effects of different sources of bias on health outcomes.
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Ejiyi CJ, Qin Z, Ukwuoma CC, Nneji GU, Monday HN, Ejiyi MB, Ejiyi TU, Okechukwu U, Bamisile OO. Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms. Network 2024:1-38. [PMID: 38511557 DOI: 10.1080/0954898x.2024.2331506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 03/13/2024] [Indexed: 03/22/2024]
Abstract
Interpretable machine learning models are instrumental in disease diagnosis and clinical decision-making, shedding light on relevant features. Notably, Boruta, SHAP (SHapley Additive exPlanations), and BorutaShap were employed for feature selection, each contributing to the identification of crucial features. These selected features were then utilized to train six machine learning algorithms, including LR, SVM, ETC, AdaBoost, RF, and LR, using diverse medical datasets obtained from public sources after rigorous preprocessing. The performance of each feature selection technique was evaluated across multiple ML models, assessing accuracy, precision, recall, and F1-score metrics. Among these, SHAP showcased superior performance, achieving average accuracies of 80.17%, 85.13%, 90.00%, and 99.55% across diabetes, cardiovascular, statlog, and thyroid disease datasets, respectively. Notably, the LGBM emerged as the most effective algorithm, boasting an average accuracy of 91.00% for most disease states. Moreover, SHAP enhanced the interpretability of the models, providing valuable insights into the underlying mechanisms driving disease diagnosis. This comprehensive study contributes significant insights into feature selection techniques and machine learning algorithms for disease diagnosis, benefiting researchers and practitioners in the medical field. Further exploration of feature selection methods and algorithms holds promise for advancing disease diagnosis methodologies, paving the way for more accurate and interpretable diagnostic models.
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Affiliation(s)
- Chukwuebuka Joseph Ejiyi
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhen Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Chiagoziem Chima Ukwuoma
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Grace Ugochi Nneji
- Software Engineering Department, Sino-British Collaborative Education, Chengdu University of Technology, Oxford Brookes University, Chengdu, China
| | - Happy Nkanta Monday
- Software Engineering Department, Sino-British Collaborative Education, Chengdu University of Technology, Oxford Brookes University, Chengdu, China
| | | | - Thomas Ugochukwu Ejiyi
- Department of Pure and Industrial Chemistry, University of Nigeria Nsukka, Enugu, Nigeria
| | | | - Olusola O Bamisile
- Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Centre, Chengdu University of Technology, Chengdu, China
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Aksoy G, Cattan G, Chakraborty S, Karabatak M. Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records. J Med Syst 2024; 48:29. [PMID: 38441727 PMCID: PMC10914922 DOI: 10.1007/s10916-024-02048-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/18/2024] [Indexed: 03/07/2024]
Abstract
Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare.
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Affiliation(s)
- Gamzepelin Aksoy
- Department of Software Engineering, Firat University, Elazig, Türkiye.
| | | | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2351, Australia
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, 2007, Australia
- Griffith Business School, Griffith University, Brisbane, QLD, 4111, Australia
| | - Murat Karabatak
- Department of Software Engineering, Firat University, Elazig, Türkiye
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Salsone M, Vescio B, Quattrone A, Marelli S, Castelnuovo A, Casoni F, Quattrone A, Ferini-Strambi L. Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study. Diagnostics (Basel) 2024; 14:363. [PMID: 38396401 PMCID: PMC10888394 DOI: 10.3390/diagnostics14040363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/20/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024] Open
Abstract
Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes.
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Affiliation(s)
- Maria Salsone
- Institute of Molecular Bioimaging and Physiology, National Research Council, 20054 Segrate, Italy
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
| | - Basilio Vescio
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), 88100 Catanzaro, Italy;
- Biotecnomed S.C.aR.L., c/o Magna Graecia University, G Building, lev.1, 88100 Catanzaro, Italy
| | - Andrea Quattrone
- Institute of Neurology, Magna Graecia University, 88100 Catanzaro, Italy;
| | - Sara Marelli
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
| | - Alessandra Castelnuovo
- Sleep Disorders Center, Division of Neuroscience, Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Francesca Casoni
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
| | - Aldo Quattrone
- Neuroscience Research Center, Magna Graecia University, 88100 Catanzaro, Italy
| | - Luigi Ferini-Strambi
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
- Sleep Disorders Center, Division of Neuroscience, Vita-Salute San Raffaele University, 20132 Milan, Italy;
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Çetin E, Bilgin S, Bilgin G. A novel wearable ERP-based BCI approach to explicate hunger necessity. Neurosci Lett 2024; 818:137573. [PMID: 38036086 DOI: 10.1016/j.neulet.2023.137573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023]
Abstract
This study aimed to design a Brain-Computer Interface system to detect people's hunger status. EEG signals were recorded in various scenarios to create a database. We extracted the time-domain and frequency-domain features from these signals and applied them to the inputs of various Machine Learning algorithms. We compared the classification performances and reached the best-performing algorithm. The highest success score of 97.62% was achieved using the Multilayer Perceptron Neural Network algorithm in Event-Related Potential analysis.
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Affiliation(s)
- Egehan Çetin
- Distance Education Application and Research Center, Burdur Mehmet Akif Ersoy University, Burdur, Turkey.
| | - Süleyman Bilgin
- Department of Electrical & Electronics Engineering, Faculty of Engineering, Akdeniz University, Antalya, Turkey.
| | - Gürkan Bilgin
- Department of Electrical & Electronics Engineering, Faculty of Engineering and Architecture, Burdur Mehmet Akif Ersoy University, Burdur, Turkey.
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7
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Iancu A, Leb I, Prokosch HU, Rödle W. Machine learning in medication prescription: A systematic review. Int J Med Inform 2023; 180:105241. [PMID: 37939541 DOI: 10.1016/j.ijmedinf.2023.105241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/17/2023] [Accepted: 09/27/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Medication prescription is a complex process that could benefit from current research and development in machine learning through decision support systems. Particularly pediatricians are forced to prescribe medications "off-label" as children are still underrepresented in clinical studies, which leads to a high risk of an incorrect dose and adverse drug effects. METHODS PubMed, IEEE Xplore and PROSPERO were searched for relevant studies that developed and evaluated well-performing machine learning algorithms following the PRISMA statement. Quality assessment was conducted in accordance with the IJMEDI checklist. Identified studies were reviewed in detail, including the required variables for predicting the correct dose, especially of pediatric medication prescription. RESULTS The search identified 656 studies, of which 64 were reviewed in detail and 36 met the inclusion criteria. According to the IJMEDI checklist, five studies were considered to be of high quality. 19 of the 36 studies dealt with the active substance warfarin. Overall, machine learning algorithms based on decision trees or regression methods performed superior regarding their predictive power than algorithms based on neural networks, support vector machines or other methods. The use of ensemble methods like bagging or boosting generally enhanced the accuracy of the dose predictions. The required input and output variables of the algorithms were considerably heterogeneous and differ strongly among the respective substance. CONCLUSIONS By using machine learning algorithms, the prescription process could be simplified and dosing correctness could be enhanced. Despite the heterogenous results among the different substances and cases and the lack of pediatric use cases, the identified approaches and required variables can serve as an excellent starting point for further development of algorithms predicting drug doses, particularly for children. Especially the combination of physiologically-based pharmacokinetic models with machine learning algorithms represents a great opportunity to enhance the predictive power and accuracy of the developed algorithms.
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Affiliation(s)
- Alexa Iancu
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Ines Leb
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Wolfgang Rödle
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany.
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8
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Taseva AR, Persoons T, D'Arcy DM. Application of an AI image analysis and classification approach to characterise dissolution and precipitation events in the flow through apparatus. Eur J Pharm Biopharm 2023; 189:36-47. [PMID: 37120067 DOI: 10.1016/j.ejpb.2023.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/19/2023] [Accepted: 04/22/2023] [Indexed: 05/01/2023]
Abstract
Imaging and artificial intelligence (AI) approaches have been used with increasing frequency in pharmaceutical industry in recent years. Characterisation of processes such as drug dissolution and precipitation is vital in quality control testing and drug manufacture. To support existing techniques like in vitro dissolution testing, novel process analytical technologies (PATs) can give an insight into these processes. The aim of this study was to create and explore the potential of an automated image classification model based on image analysis to identify events (dissolution and precipitation) occurring in the flow-through apparatus (FTA) test cell, and the ability to characterise a dissolution process over time. Several precipitation conditions were tested in a USP 4 FTA test cell with images recorded during early (plume formation) and late (particulate re-formation) stages of precipitation. An available MATLAB code was used as a base to develop and validate an anomaly classification model able to detect different events occurring during the precipitation process in the dissolution cell. Two variants of the model were tested on images from a dissolution test in the FTA, with a view to application of the image analysis system to quantitative characterization of the dissolution process over time. It was found that the classification model is highly accurate (>90%) in detecting events occurring in the FTA test cell. The model showed potential to be used to characterise the stages of dissolution and precipitation processes, and as a proof of concept demonstrates potential for deep machine learning image analysis to be applied to kinetics of other pharmaceutical processes.
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Affiliation(s)
- Alexandra R Taseva
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| | - Tim Persoons
- Department of Mechanical, Manufacturing & Biomedical Engineering, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
| | - Deirdre M D'Arcy
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland; SSPC, The Science Foundation Ireland Research Centre for Pharmaceuticals, Trinity College Dublin, Ireland.
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9
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Du Y, Hua Z, Liu C, Lv R, Jia W, Su M. ATR-FTIR combined with machine learning for the fast non-targeted screening of new psychoactive substances. Forensic Sci Int 2023; 349:111761. [PMID: 37327724 DOI: 10.1016/j.forsciint.2023.111761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/15/2023] [Accepted: 06/06/2023] [Indexed: 06/18/2023]
Abstract
Due to the diversity and fast evolution of new psychoactive substances (NPS), both public health and safety are threatened around the world. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), which serves as a simple and rapid technique for targeted NPS screening, is challenging with the rapid structural modifications of NPS. To achieve the fast non-targeted screening of NPS, six machine learning (ML) models were constructed to classify eight categories of NPS, including synthetic cannabinoids, synthetic cathinones, phenethylamines, fentanyl analogues, tryptamines, phencyclidine types, benzodiazepines, and "other substances" based on the 1099 IR spectra data items of 362 types of NPS collected by one desktop ATR-FTIR and two portable FTIR spectrometers. All these six ML classification models, including k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), extra trees (ET), voting, and artificial neural networks (ANNs) were trained through cross validation, and f1-scores of 0.87-1.00 were achieved. In addition, hierarchical cluster analysis (HCA) was performed on 100 synthetic cannabinoids with the most complex structural variation to investigate the structure-spectral property relationship, which leads to a summary of eight synthetic cannabinoid sub-categories with different "linked groups". ML models were also constructed to classify eight synthetic cannabinoid sub-categories. For the first time, this study developed six ML models, which were suitable for both desktop and portable spectrometers, to classify eight categories of NPS and eight synthetic cannabinoids sub-categories. These models can be applied for the fast, accurate, cost-effective, and on-site non-targeted screening of newly emerging NPS with no reference data available.
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Affiliation(s)
- Yu Du
- China Pharmaceutical University, Nanjing 210009, Jiangsu, PR China
| | - Zhendong Hua
- Key Laboratory of Drug Monitoring and Control, Drug Intelligence and Forensic Center, Ministry of Public Security, PR China; National Anti-Drug Laboratory of China, Beijing 100193, PR China
| | - Cuimei Liu
- Key Laboratory of Drug Monitoring and Control, Drug Intelligence and Forensic Center, Ministry of Public Security, PR China; National Anti-Drug Laboratory of China, Beijing 100193, PR China.
| | - Rulin Lv
- College of Forensic Science, People's Public Security University of China, Beijing, PR China
| | - Wei Jia
- Key Laboratory of Drug Monitoring and Control, Drug Intelligence and Forensic Center, Ministry of Public Security, PR China; National Anti-Drug Laboratory of China, Beijing 100193, PR China
| | - Mengxiang Su
- China Pharmaceutical University, Nanjing 210009, Jiangsu, PR China.
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10
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Kyparissidis Kokkinidis I, Logaras E, Rigas ES, Tsakiridis I, Dagklis T, Billis A, Bamidis PD. Towards an Explainable AI-Based Tool to Predict Preterm Birth. Stud Health Technol Inform 2023; 302:571-575. [PMID: 37203750 DOI: 10.3233/shti230207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Preterm birth (PTB) is defined as delivery occurring before 37 weeks of gestation. In this paper, Artificial Intelligence (AI)-based predictive models are adapted to accurately estimate the probability of PTB. In doing so, pregnant women' objective results and variables extracted from the screening procedure in combination with demographics, medical history, social history, and other medical data are used. A dataset consisting of 375 pregnant women is used and a number of alternative Machine Learning (ML) algorithms are applied to predict PTB. The ensemble voting model produced the best results across all performance metrics with an area under the curve (ROC-AUC) of approximately 0.84 and a precision-recall curve (PR-AUC) of approximately 0.73. An attempt to provide clinicians with an explanation of the prediction is performed to increase trustworthiness.
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Affiliation(s)
- Ilias Kyparissidis Kokkinidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Evangelos Logaras
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Emmanouil S Rigas
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Ioannis Tsakiridis
- 3rd Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Greece
| | - Themistoklis Dagklis
- 3rd Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Greece
| | - Antonis Billis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
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Singh AV, Varma M, Laux P, Choudhary S, Datusalia AK, Gupta N, Luch A, Gandhi A, Kulkarni P, Nath B. Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review. Arch Toxicol 2023; 97:963-979. [PMID: 36878992 PMCID: PMC10025217 DOI: 10.1007/s00204-023-03471-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/20/2023] [Indexed: 03/08/2023]
Abstract
The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure-activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589, Berlin, Germany.
| | - Mansi Varma
- Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER-Raebareli), Lucknow, 229001, India
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589, Berlin, Germany
| | - Sunil Choudhary
- Department of Radiotherapy and Radiation Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, India
| | - Ashok Kumar Datusalia
- Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER-Raebareli), Lucknow, 229001, India
| | - Neha Gupta
- Department of Radiation Oncology, Apex Hospital, Varanasi, 221005, India
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589, Berlin, Germany
| | - Anusha Gandhi
- Elisabeth-Selbert-Gymnasium, Tübinger Str. 71, 70794, Filderstadt, Germany
| | - Pranav Kulkarni
- Seeta Nursing Home, Shivaji Nagar, Nashik, Maharashtra, 422002, India
| | - Banashree Nath
- Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, Raebareli, Uttar Pradesh, 229405, India
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12
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Li H, Jiao Y, Davey K, Qiao SZ. Data-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts. Angew Chem Int Ed Engl 2023; 62:e202216383. [PMID: 36509704 DOI: 10.1002/anie.202216383] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts.
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Affiliation(s)
- Haobo Li
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Yan Jiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Kenneth Davey
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Shi-Zhang Qiao
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
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13
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Fernández–Calvillo MG, Goya–Esteban R, Cruz–Roldán F, Hernández–Madrid A, Blanco–Velasco M. Machine Learning approach for TWA detection relying on ensemble data design. Heliyon 2023; 9:e12947. [PMID: 36699267 PMCID: PMC9868537 DOI: 10.1016/j.heliyon.2023.e12947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/23/2022] [Accepted: 01/10/2023] [Indexed: 01/17/2023] Open
Abstract
Background and objective T-wave alternans (TWA) is a fluctuation of the ST-T complex of the surface electrocardiogram (ECG) on an every-other-beat basis. It has been shown to be clinically helpful for sudden cardiac death stratification, though the lack of a gold standard to benchmark detection methods limits its application and impairs the development of alternative techniques. In this work, a novel approach based on machine learning for TWA detection is proposed. Additionally, a complete experimental setup is presented for TWA detection methods benchmarking. Methods The proposed experimental setup is based on the use of open-source databases to enable experiment replication and the use of real ECG signals with added TWA episodes. Also, intra-patient overfitting and class imbalance have been carefully avoided. The Spectral Method (SM), the Modified Moving Average Method (MMA), and the Time Domain Method (TM) are used to obtain input features to the Machine Learning (ML) algorithms, namely, K Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machine and Multi-Layer Perceptron. Results There were not found large differences in the performance of the different ML algorithms. Decision Trees showed the best overall performance (accuracy 0.88 ± 0.04 , precision 0.89 ± 0.05 , Recall 0.90 ± 0.05 , F1 score 0.89 ± 0.03 ). Compared to the SM (accuracy 0.79, precision 0.93, Recall 0.64, F1 score 0.76) there was an improvement in every metric except for the precision. Conclusions In this work, a realistic database to test the presence of TWA using ML algorithms was assembled. The ML algorithms overall outperformed the SM used as a gold standard. Learning from data to identify alternans elicits a substantial detection growth at the expense of a small increment of the false alarm.
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Affiliation(s)
| | - Rebeca Goya–Esteban
- Department of Teoría de la Señal y Comunicaciones, Universidad Rey Juan Carlos, Madrid, Spain
| | - Fernando Cruz–Roldán
- Department of Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Madrid, Spain
| | | | - Manuel Blanco–Velasco
- Department of Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Madrid, Spain,Corresponding author.
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14
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Noorain, Srivastava V, Parveen B, Parveen R. Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects. Curr Drug Metab 2023; 24:622-634. [PMID: 37779408 DOI: 10.2174/0113892002265786230921062205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023]
Abstract
Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug formulation and development is no exception. This review article aims to provide an overview of the applications of AI in drug formulation and development and explore its future prospects. The article begins by introducing the fundamental concepts of AI, including machine learning, deep learning, and artificial neural networks and their relevance in the pharmaceutical industry. Furthermore, the article discusses the network and tools of AI and its applications in the pharmaceutical development process, including various areas, such as drug discovery, manufacturing, quality control, clinical trial management, and drug delivery. The utilization of AI in various conventional as well as modified dosage forms has been compiled. It also highlights the challenges and limitations associated with the implementation of AI in this field, including data availability, model interpretability, and regulatory considerations. Finally, the article presents the future prospects of AI in drug formulation and development, emphasizing the potential for personalized medicine, precision drug targeting, and rapid formulation optimization. It also discusses the ethical implications of AI in this context, including issues of privacy, bias, and accountability.
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Affiliation(s)
- Noorain
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India
| | - Varsha Srivastava
- Department of Pharmacognosy and Phytochemistry, Centre of Excellence in Unani Medicine (Pharmacognosy and Pharmacology), School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India
| | - Bushra Parveen
- Department of Pharmacology, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India
| | - Rabea Parveen
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India
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15
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Trifi M, Gasmi A, Carbone C, Majzlan J, Nasri N, Dermech M, Charef A, Elfil H. Machine learning-based prediction of toxic metals concentration in an acid mine drainage environment, northern Tunisia. Environ Sci Pollut Res Int 2022; 29:87490-87508. [PMID: 35809167 DOI: 10.1007/s11356-022-21890-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/02/2022] [Indexed: 06/15/2023]
Abstract
In northern Tunisia, Sidi Driss sulfide ore valorization had produced a large waste amount. The long tailings exposure period and in situ minerals interactions produced an acid mine drainage (AMD) which contributed to a strong increase in the mobility and migration of huge heavy metal (HM) quantities to the surrounding soils. In this work, the soil mineral proportions, grain sizes, physicochemical properties, SO42- and S contents, and Machine Learning (ML) algorithms such as the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models were used to predict the soil HM quantities transferred from Sidi-Driss mine drainage to surrounding soils. The results showed that the HM concentrations had significantly increased with the increase of decomposition and oxidation of galena, marcasite, pyrite, and sphalerite-marcasite and Fe-oxide-hydroxides quantities and the sulfate dissolution (marked with SO42- ions increase) that produced the decreased soil pH. Compared to SVM, and ANN models outputs, the RF model that revealed higher R2val, RPD, RPIQ, and lower error indices had satisfactorily predicted the soil HM accumulation coming from the AMD environment.
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Affiliation(s)
- Mariem Trifi
- Georesources Laboratory, Water Research and Technology Center (CERTE), Borj-Cedria Technopole, B.P. 273, Soliman, 8020, Tunisia.
| | - Anis Gasmi
- Laboratory Desalination and Natural Water Valorization (LaDVEN), Water Research and Technology Center (CERTE), Borj-Cédria Technopole, B.P. 273, Soliman, 8020, Tunisia
- Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University (UM6P), 43150, Ben Guerir, Morocco
| | - Cristina Carbone
- Departiment of Earth, Environment and Life Sciences (DISTAV), University of Genoa, 26 Corso Europa, 16132, Genoa, Italy
| | - Juraj Majzlan
- Institute of Geosciences, Friedrich-Schiller University, Burgweg 11, 07749, Jena, Germany
| | - Nesrine Nasri
- Higher Institute of Environmental Technologies, Urban Planning and Construction, University of Carthage, Charguia II, 2035, Tunis, Tunisia
- Laboratory in Hydraulic and Environmental Modelling, National Engineering School of Tunis, University of Tunis, Tunis, Tunisia
| | - Mohja Dermech
- Mineral Resources and Environment Laboratory, LR01ES06, Sciences Faculty of Tunis, Tunis El Manar University, 1092, Tunis, Tunisia
| | - Abdelkrim Charef
- Georesources Laboratory, Water Research and Technology Center (CERTE), Borj-Cedria Technopole, B.P. 273, Soliman, 8020, Tunisia
| | - Hamza Elfil
- Laboratory Desalination and Natural Water Valorization (LaDVEN), Water Research and Technology Center (CERTE), Borj-Cédria Technopole, B.P. 273, Soliman, 8020, Tunisia
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16
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Chiappelli F, Penhaskashi J. Toward a fractalomic idiotype/anti-idiotypic paradigm. Bioinformation 2022; 18:730-733. [PMID: 37426493 PMCID: PMC10326330 DOI: 10.6026/97320630018730] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 09/30/2022] [Accepted: 09/30/2022] [Indexed: 10/28/2023] Open
Abstract
The CoViD-19 pandemic has demonstrated the need for future developments in anti-viral immunology. We propose that artificial intelligence (AI) and machine learning, and in particular fractal analysis could play a crucial role in that context. Fractals - never-ending repeats of self-similar shapes whose composite tend to resemble the whole - are found in most natural biological structures including immunoglobulin and antigenic epitopes. Increased knowledge of the fractalomic properties of the idiotype/anti-idiotypic paradigm should help develop a novel and improved simplified artificial model of the immune system. Case in point, the regulation and dampening of antibodies as well as the synergetic recognition of an antigen by multiple idiotypes are both immune mechanisms that require further analysis. An enhanced understanding of these complexities could lead to better data analysis for novel vaccines to improve their sensitivity and specificity as well as open other new doors in the field of immunology.
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Affiliation(s)
| | - Jaden Penhaskashi
- West Valley Dental Implant Center, Encino, CA 91316 (minimallyinvasiveperio.com)
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17
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Baghdadi NA, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. An optimized deep learning approach for suicide detection through Arabic tweets. PeerJ Comput Sci 2022; 8:e1070. [PMID: 36092010 PMCID: PMC9455273 DOI: 10.7717/peerj-cs.1070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%.
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Affiliation(s)
- Nadiah A. Baghdadi
- Nursing Management and Education Department, College of Nursing, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amer Malki
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
| | - Hossam Magdy Balaha
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Yousry AbdulAzeem
- Computer Engineering Department, Misr Higher Institute for Engineering and Technology, Mansoura, Egypt
| | - Mahmoud Badawy
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mostafa Elhosseini
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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18
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Tropea M, Fedele G, De Luca R, Miriello D, De Rango F. Automatic Stones Classification through a CNN-Based Approach. Sensors (Basel) 2022; 22:6292. [PMID: 36016053 PMCID: PMC9415546 DOI: 10.3390/s22166292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/04/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of "Sistema per l'Identificazione di Lapidei Per Immagini"), financed by POR Calabria FESR-FSE 2014-2020. Our study is based on the Convolutional Neural Network (CNNs) that is used in literature for many different tasks such as speech recognition, neural language processing, bioinformatics, image classification and much more. In particular, we propose a two-stage hybrid approach based on the use of a model of Deep Learning (DL), in our case the CNN, in the first stage and a model of Machine Learning (ML) in the second one. In this work, we discuss a possible solution to stones classification which uses a CNN for the feature extraction phase and the Softmax or Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB) ML techniques in order to perform the classification phase basing our study on the approach called Transfer Learning (TL). We show the image acquisition process in order to collect adequate information for creating an opportune database of the stone typologies present in the Calabrian quarries, also performing the identification of quarries in the considered region. Finally, we show a comparison of different DL and ML combinations in our Two-Stage Hybrid Model solution.
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Affiliation(s)
- Mauro Tropea
- Department of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, Via P. Bucci, 87036 Rende, Italy
| | - Giuseppe Fedele
- Department of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, Via P. Bucci, 87036 Rende, Italy
| | - Raffaella De Luca
- Department of Biology, Ecology and Earth Sciences (DiBEST), University of Calabria, Via P. Bucci, 87036 Rende, Italy
| | - Domenico Miriello
- Department of Biology, Ecology and Earth Sciences (DiBEST), University of Calabria, Via P. Bucci, 87036 Rende, Italy
| | - Floriano De Rango
- Department of Informatics, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, Via P. Bucci, 87036 Rende, Italy
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19
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Raji H, Tayyab M, Sui J, Mahmoodi SR, Javanmard M. Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review. Biomed Microdevices 2022; 24:26. [PMID: 35953679 DOI: 10.1007/s10544-022-00627-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2022] [Indexed: 12/16/2022]
Abstract
Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.
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Affiliation(s)
- Hassan Raji
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Muhammad Tayyab
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Seyed Reza Mahmoodi
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
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20
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Paolini A, Baldassarre A, Bruno SP, Felli C, Muzi C, Ahmadi Badi S, Siadat SD, Sarshar M, Masotti A. Improving the Diagnostic Potential of Extracellular miRNAs Coupled to Multiomics Data by Exploiting the Power of Artificial Intelligence. Front Microbiol 2022; 13:888414. [PMID: 35756065 PMCID: PMC9218639 DOI: 10.3389/fmicb.2022.888414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/11/2022] [Indexed: 12/15/2022] Open
Abstract
In recent years, the clinical use of extracellular miRNAs as potential biomarkers of disease has increasingly emerged as a new and powerful tool. Serum, urine, saliva and stool contain miRNAs that can exert regulatory effects not only in surrounding epithelial cells but can also modulate bacterial gene expression, thus acting as a “master regulator” of many biological processes. We think that in order to have a holistic picture of the health status of an individual, we have to consider comprehensively many “omics” data, such as miRNAs profiling form different parts of the body and their interactions with cells and bacteria. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) algorithms coupled to other multiomics data (i.e., big data) could help researchers to classify better the patient’s molecular characteristics and drive clinicians to identify personalized therapeutic strategies. Here, we highlight how the integration of “multiomic” data (i.e., miRNAs profiling and microbiota signature) with other omics (i.e., metabolomics, exposomics) analyzed by AI algorithms could improve the diagnostic and prognostic potential of specific biomarkers of disease.
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Affiliation(s)
- Alessandro Paolini
- Research Laboratories, Bambino Gesù Children's Hospital-IRCCS, Rome, Italy
| | | | - Stefania Paola Bruno
- Research Laboratories, Bambino Gesù Children's Hospital-IRCCS, Rome, Italy.,Department of Science, University Roma Tre, Rome, Italy
| | - Cristina Felli
- Research Laboratories, Bambino Gesù Children's Hospital-IRCCS, Rome, Italy
| | - Chantal Muzi
- Research Laboratories, Bambino Gesù Children's Hospital-IRCCS, Rome, Italy
| | - Sara Ahmadi Badi
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran.,Mycobacteriology and Pulmonary Research Department, Pasteur Institute of Iran, Tehran, Iran
| | - Seyed Davar Siadat
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran.,Mycobacteriology and Pulmonary Research Department, Pasteur Institute of Iran, Tehran, Iran
| | - Meysam Sarshar
- Research Laboratories, Bambino Gesù Children's Hospital-IRCCS, Rome, Italy
| | - Andrea Masotti
- Research Laboratories, Bambino Gesù Children's Hospital-IRCCS, Rome, Italy
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21
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Cruz C, Matatagui D, Ramírez C, Badillo-Ramirez I, de la O-Cuevas E, Saniger JM, Horrillo MC. Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO 2. Sensors (Basel) 2022; 22:s22031261. [PMID: 35162005 PMCID: PMC8840179 DOI: 10.3390/s22031261] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/18/2022] [Accepted: 02/01/2022] [Indexed: 01/27/2023]
Abstract
In this research, a compact electronic nose (e-nose) based on a shear horizontal surface acoustic wave (SH-SAW) sensor array is proposed for the NO2 detection, classification and discrimination among some of the most relevant surrounding toxic chemicals, such as carbon monoxide (CO), ammonia (NH3), benzene (C6H6) and acetone (C3H6O). Carbon-based nanostructured materials (CBNm), such as mesoporous carbon (MC), reduced graphene oxide (rGO), graphene oxide (GO) and polydopamine/reduced graphene oxide (PDA/rGO) are deposited as a sensitive layer with controlled spray and Langmuir–Blodgett techniques. We show the potential of the mass loading and elastic effects of the CBNm to enhance the detection, the classification and the discrimination of NO2 among different gases by using Machine Learning (ML) techniques (e.g., PCA, LDA and KNN). The small dimensions and low cost make this analytical system a promising candidate for the on-site discrimination of sub-ppm NO2.
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Affiliation(s)
- Carlos Cruz
- Grupo de Tecnología de Sensores Avanzados (SENSAVAN), Instituto de Tecnologías Físicas y de la Información (ITEFI), CSIC, 28006 Madrid, Spain;
- Department of Electronics, University of Alcala, 28871 Alcala de Henares, Madrid, Spain
- Correspondence: (C.C.); (D.M.)
| | - Daniel Matatagui
- Grupo de Tecnología de Sensores Avanzados (SENSAVAN), Instituto de Tecnologías Físicas y de la Información (ITEFI), CSIC, 28006 Madrid, Spain;
- Dpto. de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
- Correspondence: (C.C.); (D.M.)
| | - Cristina Ramírez
- Institute of Ceramics and Glass, ICV-CSIC, Kelsen 5, Cantoblanco, 28049 Madrid, Spain;
| | - Isidro Badillo-Ramirez
- Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Circuito Exterior S/N, Ciudad Universitaria, Ciudad de Mexico 04510, Mexico; (I.B.-R.); (E.d.l.O.-C.); (J.M.S.)
- Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics (IDUN), Department of Health Technology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Emmanuel de la O-Cuevas
- Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Circuito Exterior S/N, Ciudad Universitaria, Ciudad de Mexico 04510, Mexico; (I.B.-R.); (E.d.l.O.-C.); (J.M.S.)
- Unidad Académica de Física, Universidad Autónoma de Zacatecas, Zacatecas 98068, Mexico
| | - José M. Saniger
- Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Circuito Exterior S/N, Ciudad Universitaria, Ciudad de Mexico 04510, Mexico; (I.B.-R.); (E.d.l.O.-C.); (J.M.S.)
| | - Mari Carmen Horrillo
- Grupo de Tecnología de Sensores Avanzados (SENSAVAN), Instituto de Tecnologías Físicas y de la Información (ITEFI), CSIC, 28006 Madrid, Spain;
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22
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Site A, Lohan ES, Jolanki O, Valkama O, Hernandez RR, Latikka R, Alekseeva D, Vasudevan S, Afolaranmi S, Ometov A, Oksanen A, Martinez Lastra J, Nurmi J, Fernandez FN. Managing Perceived Loneliness and Social-Isolation Levels for Older Adults: A Survey with Focus on Wearables-Based Solutions. Sensors (Basel) 2022; 22:s22031108. [PMID: 35161852 PMCID: PMC8839843 DOI: 10.3390/s22031108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/24/2022] [Accepted: 01/28/2022] [Indexed: 05/02/2023]
Abstract
As an inevitable process, the number of older adults is increasing in many countries worldwide. Two of the main problems that society is being confronted with more and more, in this respect, are the inter-related aspects of feelings of loneliness and social isolation among older adults. In particular, the ongoing COVID-19 crisis and its associated restrictions have exacerbated the loneliness and social-isolation problems. This paper is first and foremost a comprehensive survey of loneliness monitoring and management solutions, from the multidisciplinary perspective of technology, gerontology, socio-psychology, and urban built environment. In addition, our paper also investigates machine learning-based technological solutions with wearable-sensor data, suitable to measure, monitor, manage, and/or diminish the levels of loneliness and social isolation, when one also considers the constraints and characteristics coming from social science, gerontology, and architecture/urban built environments points of view. Compared to the existing state of the art, our work is unique from the cross-disciplinary point of view, because our authors' team combines the expertise from four distinct domains, i.e., gerontology, social psychology, architecture, and wireless technology in addressing the two inter-related problems of loneliness and social isolation in older adults. This work combines a cross-disciplinary survey of the literature in the four aforementioned domains with a proposed wearable-based technological solution, introduced first as a generic framework and, then, exemplified through a simple proof of concept with dummy data. As the main findings, we provide a comprehensive view on challenges and solutions in utilizing various technologies, particularly those carried by users, also known as wearables, to measure, manage, and/or diminish the social isolation and the perceived loneliness among older adults. In addition, we also summarize the identified solutions which can be used for measuring and monitoring various loneliness- and social isolation-related metrics, and we present and validate, through a simple proof-of-concept mechanism, an approach based on machine learning for predicting and estimating loneliness levels. Open research issues in this field are also discussed.
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Affiliation(s)
- Aditi Site
- Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland; (D.A.); (A.O.); (J.N.)
- Correspondence: (A.S.); (E.S.L.)
| | - Elena Simona Lohan
- Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland; (D.A.); (A.O.); (J.N.)
- Correspondence: (A.S.); (E.S.L.)
| | - Outi Jolanki
- Faculty of Social Sciences, Tampere University, 33720 Tampere, Finland; (O.J.); (O.V.); (R.L.); (A.O.)
| | - Outi Valkama
- Faculty of Social Sciences, Tampere University, 33720 Tampere, Finland; (O.J.); (O.V.); (R.L.); (A.O.)
| | - Rosana Rubio Hernandez
- Faculty of Built Environment, Tampere University, 33720 Tampere, Finland; (R.R.H.); (F.N.F.)
| | - Rita Latikka
- Faculty of Social Sciences, Tampere University, 33720 Tampere, Finland; (O.J.); (O.V.); (R.L.); (A.O.)
| | - Daria Alekseeva
- Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland; (D.A.); (A.O.); (J.N.)
| | - Saigopal Vasudevan
- Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland; (S.V.); (S.A.); (J.M.L.)
| | - Samuel Afolaranmi
- Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland; (S.V.); (S.A.); (J.M.L.)
| | - Aleksandr Ometov
- Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland; (D.A.); (A.O.); (J.N.)
| | - Atte Oksanen
- Faculty of Social Sciences, Tampere University, 33720 Tampere, Finland; (O.J.); (O.V.); (R.L.); (A.O.)
| | - Jose Martinez Lastra
- Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland; (S.V.); (S.A.); (J.M.L.)
| | - Jari Nurmi
- Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland; (D.A.); (A.O.); (J.N.)
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23
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Abstract
The potential value of AI to healthcare, and nursing in particular, ranges from improving quality and efficiency of care to delivering on the promise of personalized and precision medicine. AI systems may become virtually indispensable as ever more data is amassed about every aspect of health. AI can help reduce variability in care, while improving precision, accelerating discovery and reducing disparities. AI can empower patients and potentially allow healthcare professionals to relate to their patients as healers supported by the combined wisdom of the best medical research and analytic technology. There are, however, many challenges to understanding the optimal uses of AI; addressing the technological, systemic, regulatory and attitudinal roadblocks to successful implementation; and integrating AI into the fabric of health care. This paper provides a grounding in the origins and fundamental building blocks of AI, applications in healthcare and for nursing, and the critical challenges facing implementation in healthcare.
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Affiliation(s)
- Eileen Koski
- IBM TJ Watson Research Center, Yorktown Heights, New York, USA
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24
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Ara Shaikh A, Kumar A, Jani K, Mitra S, García-Tadeo DA, Devarajan A. The Role of Machine Learning and Artificial Intelligence for making a Digital Classroom and its sustainable Impact on Education during Covid-19. Mater Today Proc 2021; 56:3211-3215. [PMID: 35464152 PMCID: PMC9015108 DOI: 10.1016/j.matpr.2021.09.368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
During the Disease outbreak and in the future, there will be a lot of learning. Since the pandemic has interrupted global schooling, remote learning has emerged as a viable option, depending on machine learning to accomplish its goals. Using the example of ten international science journals that speak out about artificial intelligence in education today and the future of earning, we hope to gain a better understanding of the large extend of the power of artificial intelligence in education, both during the COVID-19 period and during the future learning time frame. Additionally, in addition to evaluating 10 articles, we used an internet search engine to look for relevant material. We conducted searches using terms such as artificial intelligence, learning during a pandemic, and Machine learning, among other things. After that, we used a phenomenological technique to confirm that our results answered the research questions, which was done in accordance with a qualitative approach. Our findings can be summarized by taking into account the evidence from research and literature. Among our findings are that the detailed assessment of artificial intelligence in education, the use of AI in education, typical learning in the pandemic era, and the role of artificial intelligence (AI) disease outbreak learning are all important for both current and future residents. While statistical methods and automated based on learning jobs that are smarter than normal continue to be important, learning is becoming more automated. It helps individuals to be more concentrated on their learning opportunities and to recognize when they do not grasp a subject completely. First and foremost, the instructors provide valuable assistance throughout the assessment process of student learning outcomes.
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Affiliation(s)
- Asmat Ara Shaikh
- Bharati Vidyapeeth's Institute of Management Studies and Research, Navi Mumbai, India
| | - Anuj Kumar
- Apeejay School of Management, Dwarka, Delhi, India
| | - Kruti Jani
- Chimanbhai Patel Post Graduate Institute of Computer Application, Ahmedabad, Gujrat, India
| | | | - Diego A García-Tadeo
- Department of Civil Engineering, Universidad Nacional Santiago Antúnez de Mayolo, Huaraz, Peru
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25
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Cormerais R, Duclos A, Wasselynck G, Berthiau G, Longo R. A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks. Sensors (Basel) 2021; 21:2598. [PMID: 33917240 DOI: 10.3390/s21082598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 03/29/2021] [Accepted: 04/03/2021] [Indexed: 11/16/2022]
Abstract
In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs.
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26
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Hegde N, Shishir M, Shashank S, Dayananda P, Latte MV. A Survey on Machine Learning and Deep Learning-based Computer-Aided Methods for Detection of Polyps in CT Colonography. Curr Med Imaging 2021; 17:3-15. [PMID: 32294045 DOI: 10.2174/2213335607999200415141427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 02/09/2020] [Accepted: 02/27/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Colon cancer generally begins as a neoplastic growth of tissue, called polyps, originating from the inner lining of the colon wall. Most colon polyps are considered harmless but over the time, they can evolve into colon cancer, which, when diagnosed in later stages, is often fatal. Hence, time is of the essence in the early detection of polyps and the prevention of colon cancer. METHODS To aid this endeavor, many computer-aided methods have been developed, which use a wide array of techniques to detect, localize and segment polyps from CT Colonography images. In this paper, a comprehensive state-of-the-art method is proposed and categorize this work broadly using the available classification techniques using Machine Learning and Deep Learning. CONCLUSION The performance of each of the proposed approach is analyzed with existing methods and also how they can be used to tackle the timely and accurate detection of colon polyps.
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Affiliation(s)
- Niharika Hegde
- JSS Academy of Technical Education, Bangalore-560060, Karnataka, India
| | - M Shishir
- JSS Academy of Technical Education, Bangalore-560060, Karnataka, India
| | - S Shashank
- JSS Academy of Technical Education, Bangalore-560060, Karnataka, India
| | - P Dayananda
- JSS Academy of Technical Education, Bangalore-560060, Karnataka, India
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27
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Demrozi F, Pravadelli G, Bihorac A, Rashidi P. Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey. IEEE Access 2020; 8:210816-210836. [PMID: 33344100 PMCID: PMC7748247 DOI: 10.1109/access.2020.3037715] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.
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Affiliation(s)
| | | | - Azra Bihorac
- Division of Nephrology, Hypertension, & Renal Transplantation, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
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28
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Abstract
The Internet of Things (IoT) primary objective is to make a hyper-connected world for various application domains. However, IoT suffers from a lack of interoperability leading to a substantial threat to the predicted economic value. Schema.org provides semantic interoperability to structure heterogeneous data on the Web. An extension of this vocabulary for the IoT domain (iot.schema.org) is an ongoing research effort to address semantic interoperability for the Web of Things (WoT). To design this vocabulary, a central challenge is to identify the main topics (concepts and properties) automatically from existing knowledge in IoT applications. We designed KE4WoT (Knowledge Extraction for the Web of Things) to automatically identify the most important topics from literature ontologies of 3 different IoT application domains - smart home, smart city and smart weather - based on our corpus consisting of 4500 full-text conference and journal articles to utilize domain-specific knowledge encoded within IoT publications. Despite the importance of automatically identifying the relevant topics for iot.schema.org, up to know there is no study dealing with this issue. To evaluate the extracted topics, we compare the descriptiveness of these topics for the 10 most popular ontologies in the 3 domains with empirical evaluations of 23 domain experts. The results illustrate that the identified main topics of IoT ontologies can be used to sufficiently describe existing ontologies as keywords.
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29
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Nazir HM, Hussain I, Ahmad I, Faisal M, Almanjahie IM. An improved framework to predict river flow time series data. PeerJ 2019; 7:e7183. [PMID: 31304058 PMCID: PMC6610541 DOI: 10.7717/peerj.7183] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 05/24/2019] [Indexed: 11/20/2022] Open
Abstract
Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.
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Affiliation(s)
| | - Ijaz Hussain
- Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
| | - Ishfaq Ahmad
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Muhammad Faisal
- Faculty of Health Studies, University of Bradford, Bradford, United Kingdom.,Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Bradford, Bradford, United Kingdom
| | - Ibrahim M Almanjahie
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
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