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Ashwini K, Keshava Murthy GN, Raviraja S, Srinidhi GA. A Novel Multimodal Biometric Person Authentication System Based on ECG and Iris Data. BIOMED RESEARCH INTERNATIONAL 2024; 2024:8112209. [PMID: 38884018 PMCID: PMC11178413 DOI: 10.1155/2024/8112209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/01/2024] [Accepted: 03/13/2024] [Indexed: 06/18/2024]
Abstract
Existing security issues like keys, pins, and passwords employed presently in almost all the fields that have certain limitations like passwords and pins can be easily forgotten; keys can be lost. To overcome such security issues, new biometric features have shown outstanding improvements in authentication systems as a result of significant developments in biological digital signal processing. Currently, the multimodal authentications have gained huge attention in biometric systems which can be either behavioural or physiological. A biometric system with multimodality club data from many biometric modalities increases each biometric system's performance and makes it more resistant to spoof attempts. Apart from electrocardiogram (ECG) and iris, there are a lot of other biometric traits that can be captured from the human body. They include face, fingerprint, gait, keystroke dynamics, voice, DNA, palm vein, and hand geometry recognition. Electrocardiograms (ECG) have recently been employed in unimodal and multimodal biometric recognition systems as a novel biometric technology. When compared to other biometric approaches, ECG has the intrinsic quality of a person's liveness, making it difficult to fake. Similarly, the iris also plays an important role in biometric authentication. Based on these assumptions, we present a multimodal biometric person authentication system. The projected method includes preprocessing, segmentation, feature extraction, feature fusion, and ensemble classifier where majority voting is presented to obtain the final outcome. The comparative analysis shows the overall performance as 96.55%, 96.2%, 96.2%, 96.5%, and 95.65% in terms of precision, F1-score, sensitivity, specificity, and accuracy.
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Affiliation(s)
- K Ashwini
- Siddaganga Institute of Technology, Tumakuru Affiliated to Visvesvaraya Technological University, Belagavi 590018, Karnataka, India
| | - G N Keshava Murthy
- Siddaganga Institute of Technology, Tumakuru Affiliated to Visvesvaraya Technological University, Belagavi 590018, Karnataka, India
| | - S Raviraja
- University of Garden City, Khartoum, Sudan
| | - G A Srinidhi
- Sri Siddhartha Institute of Technology, Tumakuru, India
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Özen F. Random forest regression for prediction of Covid-19 daily cases and deaths in Turkey. Heliyon 2024; 10:e25746. [PMID: 38370220 PMCID: PMC10869860 DOI: 10.1016/j.heliyon.2024.e25746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/18/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024] Open
Abstract
During pandemic periods, there is an intense flow of patients to hospitals. Depending on the disease, many patients may require hospitalization. In some cases, these patients must be taken to intensive care units and emergency interventions must be performed. However, finding a sufficient number of hospital beds or intensive care units during pandemic periods poses a big problem. In these periods, fast and effective planning is more important than ever. Another problem experienced during pandemic periods is the burial of the dead in case the number of deaths increases. This is also a situation that requires due planning. We can learn some lessons from Covid 19 pandemic and be prepared for the future ones. In this paper, statistical properties of the daily cases and daily deaths in Turkey, which is one of the most affected countries by the pandemic in the World, are studied. It is found that the characteristics are nonstationary. Then, random forest regression is applied to predict Covid-19 daily cases and deaths. In addition, seven other machine learning models, namely bagging, AdaBoost, gradient boosting, XGBoost, decision tree, LSTM and ARIMA regressors are built for comparison. The performance of the models are measured using accuracy, coefficient of variation, root-mean-square score and relative error metrics. When random forest regressors are employed, test data related to daily cases are predicted with an accuracy of 92.30% and with an r2 score of 0.9893. Besides, daily deaths are predicted with an accuracy of 91.39% and with an r2 score of 0.9834. The closest rival in predictions is the bagging regressor. Nevertheless, the results provided by this algoritm changed in different runs and this fact is shown in the study, as well. Comparisons are based on test data. Comparisons with the earlier works are also provided.
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Affiliation(s)
- Figen Özen
- Department of Electrical and Electronics Engineering, Haliç University, Istanbul, Turkey
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Ismail WN. Snake-Efficient Feature Selection-Based Framework for Precise Early Detection of Chronic Kidney Disease. Diagnostics (Basel) 2023; 13:2501. [PMID: 37568865 PMCID: PMC10417271 DOI: 10.3390/diagnostics13152501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
Chronic kidney disease (CKD) refers to impairment of the kidneys that may worsen over time. Early detection of CKD is crucial for saving millions of lives. As a result, several studies are currently focused on developing computer-aided systems to detect CKD in its early stages. Manual screening is time-consuming and subject to personal judgment. Therefore, methods based on machine learning (ML) and automatic feature selection are used to support graders. The goal of feature selection is to identify the most relevant and informative subset of features in a given dataset. This approach helps mitigate the curse of dimensionality, reduce dimensionality, and enhance model performance. The use of natural-inspired optimization algorithms has been widely adopted to develop appropriate representations of complex problems by conducting a blackbox optimization process without explicitly formulating mathematical formulations. Recently, snake optimization algorithms have been developed to identify optimal or near-optimal solutions to difficult problems by mimicking the behavior of snakes during hunting. The objective of this paper is to develop a novel snake-optimized framework named CKD-SO for CKD data analysis. To select and classify the most suitable medical data, five machine learning algorithms are deployed, along with the snake optimization (SO) algorithm, to create an extremely accurate prediction of kidney and liver disease. The end result is a model that can detect CKD with 99.7% accuracy. These results contribute to our understanding of the medical data preparation pipeline. Furthermore, implementing this method will enable health systems to achieve effective CKD prevention by providing early interventions that reduce the high burden of CKD-related diseases and mortality.
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Affiliation(s)
- Walaa N Ismail
- Department of Management Information Systems, College of Business Administration, Al Yamamah University, Riyadh 11512, Saudi Arabia
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Ortiz-Barrios M, Arias-Fonseca S, Ishizaka A, Barbati M, Avendaño-Collante B, Navarro-Jiménez E. Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study. JOURNAL OF BUSINESS RESEARCH 2023; 160:113806. [PMID: 36895308 PMCID: PMC9981538 DOI: 10.1016/j.jbusres.2023.113806] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 01/18/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.
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Affiliation(s)
- Miguel Ortiz-Barrios
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Sebastián Arias-Fonseca
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Alessio Ishizaka
- NEOMA Business School, 1 rue du Maréchal Juin, Mont-Saint-Aignan 76130, France
| | - Maria Barbati
- Department of Economics, University Ca' Foscari, Cannaregio 873, Fondamenta San Giobbe, 30121 Venice, Italy
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Duan Y, Huo J, Chen M, Hou F, Yan G, Li S, Wang H. Early prediction of sepsis using double fusion of deep features and handcrafted features. APPL INTELL 2023; 53:1-17. [PMID: 36685641 PMCID: PMC9843111 DOI: 10.1007/s10489-022-04425-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2022] [Indexed: 01/19/2023]
Abstract
Sepsis is a life-threatening medical condition that is characterized by the dysregulated immune system response to infections, having both high morbidity and mortality rates. Early prediction of sepsis is critical to the decrease of mortality. This paper presents a novel early warning model called Double Fusion Sepsis Predictor (DFSP) for sepsis onset. DFSP is a double fusion framework that combines the benefits of early and late fusion strategies. First, a hybrid deep learning model that combines both the convolutional and recurrent neural networks to extract deep features is proposed. Second, deep features and handcrafted features, such as clinical scores, are concatenated to build the joint feature representation (early fusion). Third, several tree-based models based on joint feature representation are developed to generate the risk scores of sepsis onset that are combined with an End-to-End neural network for final sepsis detection (late fusion). To evaluate DFSP, a retrospective study was conducted, which included patients admitted to the ICUs of a hospital in Shanghai China. The results demonstrate that the DFSP outperforms state-of-the-art approaches in early sepsis prediction.
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Affiliation(s)
- Yongrui Duan
- School of Economics & Management, Tongji University, Shanghai, China
| | - Jiazhen Huo
- School of Economics & Management, Tongji University, Shanghai, China
| | - Mingzhou Chen
- School of Economics & Management, Tongji University, Shanghai, China
| | - Fenggang Hou
- Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Guoliang Yan
- Department of Geriatrics, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Shufang Li
- Emergency Department, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Haihui Wang
- Department of Geriatrics, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, China
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Hassan MM, AlQahtani SA, Alelaiwi A, Papa JP. Explaining COVID-19 diagnosis with Taylor decompositions. Neural Comput Appl 2022; 35:1-14. [PMID: 36415284 PMCID: PMC9672580 DOI: 10.1007/s00521-022-08021-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 10/26/2022] [Indexed: 11/19/2022]
Abstract
The COVID-19 pandemic has devastated the entire globe since its first appearance at the end of 2019. Although vaccines are now in production, the number of contaminations remains high, thus increasing the number of specialized personnel that can analyze clinical exams and points out the final diagnosis. Computed tomography and X-ray images are the primary sources for computer-aided COVID-19 diagnosis, but we still lack better interpretability of such automated decision-making mechanisms. This manuscript presents an insightful comparison of three approaches based on explainable artificial intelligence (XAI) to light up interpretability in the context of COVID-19 diagnosis using deep networks: Composite Layer-wise Propagation, Single Taylor Decomposition, and Deep Taylor Decomposition. Two deep networks have been used as the backbones to assess the explanation skills of the XAI approaches mentioned above: VGG11 and VGG16. We hope that such work can be used as a basis for further research on XAI and COVID-19 diagnosis for each approach figures its own positive and negative points.
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Affiliation(s)
- Mohammad Mehedi Hassan
- College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
| | - Salman A. AlQahtani
- College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
| | - Abdulhameed Alelaiwi
- College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
| | - João P. Papa
- Department of Computing, São Paulo State University, Bauru, Brazil
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Zhu J, Wei S, Xie X, Yang C, Li Y, Li X, Hu B. Content-based multiple evidence fusion on EEG and eye movements for mild depression recognition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107100. [PMID: 36162244 DOI: 10.1016/j.cmpb.2022.107100] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/28/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Depression is a serious neurological disorder that has become a major health problem worldwide. The detection of mild depression is important for the diagnosis of depression in early stages. This research seeks to find a more accurate fusion model which can be used for mild depression detection using Electroencephalography and eye movement data. METHODS This study proposes a content-based multiple evidence fusion (CBMEF) method, which fuses EEG and eye movement data at decision level. The method mainly includes two modules, the classification performance matrix module and the dual-weight fusion module. The classification performance matrices of different modalities are estimated by Bayesian rule based on confusion matrix and Mahalanobis distance, and the matrices were used to correct the classification results. Then the relative conflict degree of each modality is calculated, and different weights are assigned to the above modalities at the decision fusion layer according to this conflict degree. RESULTS The experimental results show that the proposed method outperforms other fusion methods as well as the single modality results. The highest accuracies achieved 91.12%, and sensitivity, specificity and precision were 89.20%, 93.03%, 92.76%. CONCLUSIONS The promising results showed the potential of the proposed approach for the detection of mild depression. The idea of introducing the classification performance matrix and the dual-weight model to multimodal biosignals fusion casts a new light on the researches of depression recognition.
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Affiliation(s)
- Jing Zhu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shiqing Wei
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiannian Xie
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Changlin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yizhou Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; Shandong Academy Of Intelligent Computing Technoloy, Shandong, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, China; School of Medical Technology, Beijing Institute of Technology, Beijing, China.
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Daniel J, Irin Sherly S, Ponnuramu V, Pratap Singh D, Netra SN, Alonazi WB, Almutairi KMA, Priyan KSA, Abera Y. Recurrent Neural Networks for Feature Extraction from Dengue Fever. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:5669580. [PMID: 35722151 PMCID: PMC9203200 DOI: 10.1155/2022/5669580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 04/29/2022] [Indexed: 11/22/2022]
Abstract
Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.
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Affiliation(s)
- Jackson Daniel
- Department of Electronics and Instrumentation Engineering, National Engineering College, Kovilpatti, Nallatinputhur, Tamil Nadu 628503, India
| | - S. Irin Sherly
- Department of Information Technology, Panimalar Institute of Technology, Chennai, Tamil Nadu 600123, India
| | - Veeralakshmi Ponnuramu
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu 600124, India
| | - Devesh Pratap Singh
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand 248002, India
| | - S. N. Netra
- Department of Information Science and Engineering, East Point College of Engineering and Technology, Bengaluru, Karnataka 560049, India
| | - Wadi B. Alonazi
- Health Administration Department, College of Business Administration, King Saud University, P. O. Box: 71115, Riyadh 11587, Saudi Arabia
| | - Khalid M. A. Almutairi
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, P. O. Box: 10219, Riyadh 11433, Saudi Arabia
| | - K. S. A. Priyan
- Department of Biotechnology, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Yared Abera
- Department of Technology and Informatics, Ambo University, Woliso Campus, Ambo, Ethiopia
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