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Alsharif N, Al-Adhaileh MH, Al-Yaari M, Farhah N, Khan ZI. Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis. Front Med (Lausanne) 2024; 11:1436646. [PMID: 39099594 PMCID: PMC11294196 DOI: 10.3389/fmed.2024.1436646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/05/2024] [Indexed: 08/06/2024] Open
Abstract
Timely and unbiased evaluation of Autism Spectrum Disorder (ASD) is essential for providing lasting benefits to affected individuals. However, conventional ASD assessment heavily relies on subjective criteria, lacking objectivity. Recent advancements propose the integration of modern processes, including artificial intelligence-based eye-tracking technology, for early ASD assessment. Nonetheless, the current diagnostic procedures for ASD often involve specialized investigations that are both time-consuming and costly, heavily reliant on the proficiency of specialists and employed techniques. To address the pressing need for prompt, efficient, and precise ASD diagnosis, an exploration of sophisticated intelligent techniques capable of automating disease categorization was presented. This study has utilized a freely accessible dataset comprising 547 eye-tracking systems that can be used to scan pathways obtained from 328 characteristically emerging children and 219 children with autism. To counter overfitting, state-of-the-art image resampling approaches to expand the training dataset were employed. Leveraging deep learning algorithms, specifically MobileNet, VGG19, DenseNet169, and a hybrid of MobileNet-VGG19, automated classifiers, that hold promise for enhancing diagnostic precision and effectiveness, was developed. The MobileNet model demonstrated superior performance compared to existing systems, achieving an impressive accuracy of 100%, while the VGG19 model achieved 92% accuracy. These findings demonstrate the potential of eye-tracking data to aid physicians in efficiently and accurately screening for autism. Moreover, the reported results suggest that deep learning approaches outperform existing event detection algorithms, achieving a similar level of accuracy as manual coding. Users and healthcare professionals can utilize these classifiers to enhance the accuracy rate of ASD diagnosis. The development of these automated classifiers based on deep learning algorithms holds promise for enhancing the diagnostic precision and effectiveness of ASD assessment, addressing the pressing need for prompt, efficient, and precise ASD diagnosis.
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Affiliation(s)
- Nizar Alsharif
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Department of Computer Engineering and Science, Albaha University, Al Bahah, Saudi Arabia
| | - Mosleh Hmoud Al-Adhaileh
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Deanship of E-learning and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Mohammed Al-Yaari
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Nesren Farhah
- Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Zafar Iqbal Khan
- Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
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Yousef R, Khan S, Gupta G, Albahlal BM, Alajlan SA, Ali A. Bridged-U-Net-ASPP-EVO and Deep Learning Optimization for Brain Tumor Segmentation. Diagnostics (Basel) 2023; 13:2633. [PMID: 37627893 PMCID: PMC10453237 DOI: 10.3390/diagnostics13162633] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/25/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big challenge due to the complexity of brain tumor tissues, and segmenting these tissues from the healthy tissues is an even more tedious challenge when manual segmentation is undertaken by radiologists. In this paper, we have presented an experimental approach to emphasize the impact and effectiveness of deep learning elements like optimizers and loss functions towards a deep learning optimal solution for brain tumor segmentation. We evaluated our performance results on the most popular brain tumor datasets (MICCAI BraTS 2020 and RSNA-ASNR-MICCAI BraTS 2021). Furthermore, a new Bridged U-Net-ASPP-EVO was introduced that exploits Atrous Spatial Pyramid Pooling to enhance capturing multi-scale information to help in segmenting different tumor sizes, Evolving Normalization layers, squeeze and excitation residual blocks, and the max-average pooling for down sampling. Two variants of this architecture were constructed (Bridged U-Net_ASPP_EVO v1 and Bridged U-Net_ASPP_EVO v2). The best results were achieved using these two models when compared with other state-of-the-art models; we have achieved average segmentation dice scores of 0.84, 0.85, and 0.91 from variant1, and 0.83, 0.86, and 0.92 from v2 for the Enhanced Tumor (ET), Tumor Core (TC), and Whole Tumor (WT) tumor sub-regions, respectively, in the BraTS 2021validation dataset.
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Affiliation(s)
- Rammah Yousef
- Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan 173229, India
| | - Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (S.A.A.)
- Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali 140413, India
| | - Gaurav Gupta
- Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan 173229, India
| | - Bader M. Albahlal
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (S.A.A.)
| | - Saad Abdullah Alajlan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (S.A.A.)
| | - Aleem Ali
- Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali 140413, India
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Solomon DD, Khan S, Garg S, Gupta G, Almjally A, Alabduallah BI, Alsagri HS, Ibrahim MM, Abdallah AMA. Hybrid Majority Voting: Prediction and Classification Model for Obesity. Diagnostics (Basel) 2023; 13:2610. [PMID: 37568973 PMCID: PMC10417773 DOI: 10.3390/diagnostics13152610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/26/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity is a major global health concern. Obesity is caused by hereditary, physiological, and environmental factors, as well as poor nutrition and a lack of exercise. Weight loss can be difficult for various reasons, and it is diagnosed via BMI, which is used to estimate body fat for most people. Muscular athletes, for example, may have a BMI in the obesity range even when they are not obese. Researchers from a variety of backgrounds and institutions devised different hypotheses and models for the prediction and classification of obesity using different approaches and various machine learning techniques. In this study, a majority voting-based hybrid modeling approach using a gradient boosting classifier, extreme gradient boosting, and a multilayer perceptron was developed. Seven distinct machine learning algorithms were used on open datasets from the UCI machine learning repository, and their respective accuracy levels were compared before the combined approaches were chosen. The proposed majority voting-based hybrid model for prediction and classification of obesity that was achieved has an accuracy of 97.16%, which is greater than both the individual models and the other hybrid models that have been developed.
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Affiliation(s)
- Dahlak Daniel Solomon
- Yogananda School of AI Computers and Data Sciences, Shoolini University, Solan 173229, India
| | - Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (A.M.A.A.)
- Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali 140413, India
| | - Sonia Garg
- Yogananda School of AI Computers and Data Sciences, Shoolini University, Solan 173229, India
| | - Gaurav Gupta
- Yogananda School of AI Computers and Data Sciences, Shoolini University, Solan 173229, India
| | - Abrar Almjally
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (A.M.A.A.)
| | - Bayan Ibrahimm Alabduallah
- Department of Information System, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11432, Saudi Arabia
| | - Hatoon S. Alsagri
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (A.M.A.A.)
| | - Mandour Mohamed Ibrahim
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (A.M.A.A.)
| | - Alsadig Mohammed Adam Abdallah
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia (A.M.A.A.)
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Khan S, Siddiqui T, Mourade A, Alabduallah BI, Alajlan SA, almjally A, Albahlal BM, Alfaifi A. Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture. THE INTERNATIONAL JOURNAL, ADVANCED MANUFACTURING TECHNOLOGY 2023; 128:1-13. [PMID: 37360660 PMCID: PMC10243703 DOI: 10.1007/s00170-023-11602-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/12/2023] [Indexed: 06/28/2023]
Abstract
Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.
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Affiliation(s)
- Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
- Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali, 140413 India
| | - Tamanna Siddiqui
- Department of Computer Science, Aligarh Muslim University, Aligarh, UP India
| | - Azrour Mourade
- Computer Sciences Department, Faculty of Sciences and Technics, Moulay Ismail University, Meknes, Morocco
| | - Bayan Ibrahimm Alabduallah
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671 Saudi Arabia
| | - Saad Abdullah Alajlan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Abrar almjally
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Bader M. Albahlal
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Amani Alfaifi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
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Ngueilbaye A, Huang JZ, Khan M, Wang H. Data quality model for assessing public COVID-19 big datasets. THE JOURNAL OF SUPERCOMPUTING 2023:1-33. [PMID: 37359333 PMCID: PMC10230148 DOI: 10.1007/s11227-023-05410-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/17/2023] [Indexed: 06/28/2023]
Abstract
For decision-making support and evidence based on healthcare, high quality data are crucial, particularly if the emphasized knowledge is lacking. For public health practitioners and researchers, the reporting of COVID-19 data need to be accurate and easily available. Each nation has a system in place for reporting COVID-19 data, albeit these systems' efficacy has not been thoroughly evaluated. However, the current COVID-19 pandemic has shown widespread flaws in data quality. We propose a data quality model (canonical data model, four adequacy levels, and Benford's law) to assess the quality issue of COVID-19 data reporting carried out by the World Health Organization (WHO) in the six Central African Economic and Monitory Community (CEMAC) region countries between March 6,2020, and June 22, 2022, and suggest potential solutions. These levels of data quality sufficiency can be interpreted as dependability indicators and sufficiency of Big Dataset inspection. This model effectively identified the quality of the entry data for big dataset analytics. The future development of this model requires scholars and institutions from all sectors to deepen their understanding of its core concepts, improve integration with other data processing technologies, and broaden the scope of its applications.
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Affiliation(s)
- Alladoumbaye Ngueilbaye
- Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060 Guangdong China
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060 Guangdong China
| | - Joshua Zhexue Huang
- Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060 Guangdong China
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060 Guangdong China
| | - Mehak Khan
- Department of Computer Science, AI Lab, Oslo Metropolitan University, Oslo, Norway
| | - Hongzhi Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001 Heilongjiang China
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Yousef R, Khan S, Gupta G, Siddiqui T, Albahlal BM, Alajlan SA, Haq MA. U-Net-Based Models towards Optimal MR Brain Image Segmentation. Diagnostics (Basel) 2023; 13:diagnostics13091624. [PMID: 37175015 PMCID: PMC10178263 DOI: 10.3390/diagnostics13091624] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/14/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture's performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.
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Affiliation(s)
- Rammah Yousef
- Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan 173229, India
| | - Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
- Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali 140413, India
| | - Gaurav Gupta
- Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan 173229, India
| | - Tamanna Siddiqui
- Department of Computer Science, Aligarh Muslim University, Aligarh 202001, India
| | - Bader M Albahlal
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Saad Abdullah Alajlan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Mohd Anul Haq
- Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia
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DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms. Diagnostics (Basel) 2023; 13:diagnostics13061093. [PMID: 36980401 PMCID: PMC10047105 DOI: 10.3390/diagnostics13061093] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label the virus one of the world’s top ten public health risks. Dengue hemorrhagic fever can progress into dengue shock syndrome, which can be fatal. Dengue hemorrhagic fever can also advance into dengue shock syndrome. To provide accessible and timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate Dengue and its subcategories in the early stages of illness development. Dengue fever can be predicted in advance, saving one’s life by warning them to seek proper diagnosis and treatment. Predicting infectious diseases such as dengue is difficult, and most forecast systems are still in their primary stages. In developing dengue predictive models, data from microarrays and RNA-Seq have been used significantly. Bayesian inferences and support vector machine algorithms are two examples of statistical methods that can mine opinions and analyze sentiment from text. In general, these methods are not very strong semantically, and they only work effectively when the text passage inputs are at the level of the page or the paragraph; they are poor miners of sentiment at the level of the sentence or the phrase. In this research, we propose to construct a machine learning method to forecast dengue fever.
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Haq AU, Li JP, Kumar R, Ali Z, Khan I, Uddin MI, Agbley BLY. MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:4695-4706. [PMID: 36160944 PMCID: PMC9483375 DOI: 10.1007/s12652-022-04373-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 07/30/2022] [Indexed: 05/25/2023]
Abstract
The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.
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Affiliation(s)
- Amin ul Haq
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731 Sichuan China
| | - Jian Ping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731 Sichuan China
| | - Rajesh Kumar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313001 China
| | - Zafar Ali
- School of Computer Science and Engineering Southeast University, Nanjing, 210096 China
| | - Inayat Khan
- Department of Computer Science, University of Buner, Buner, 19290 Pakistan
| | - M. Irfan Uddin
- Institute of Computing, Kohat University of Science and Technology, Kohat, 26000 Pakistan
| | - Bless Lord Y. Agbley
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731 Sichuan China
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BiCHAT: BiLSTM with deep CNN and hierarchical attention for hate speech detection. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2022.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Padhy S, Dash S, Routray S, Ahmad S, Nazeer J, Alam A. IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2389636. [PMID: 35634091 PMCID: PMC9132636 DOI: 10.1155/2022/2389636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/25/2022] [Accepted: 04/30/2022] [Indexed: 12/11/2022]
Abstract
Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.
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Affiliation(s)
- Sasmita Padhy
- School of Computing Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh, India
| | - Sachikanta Dash
- Department of Computer Science and Engineering, GIET University, Gunupur, Odisha, India
| | - Sidheswar Routray
- Department of Computer Science and Engineering, School of Engineering, Indrashil University, Rajpur, Mehsana, Gujarat, India
| | - Sultan Ahmad
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
| | - Jabeen Nazeer
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
| | - Afroj Alam
- Department of Computer Science, Bakhtar University, Kabul, Afghanistan
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Pinnock H, Murphie P, Vogiatzis I, Poberezhets V. Telemedicine and virtual respiratory care in the era of COVID-19. ERJ Open Res 2022; 8:00111-2022. [PMID: 35891622 PMCID: PMC9131135 DOI: 10.1183/23120541.00111-2022] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 05/04/2022] [Indexed: 11/05/2022] Open
Abstract
The World Health Organization defines telemedicine as “an interaction between a health care provider and a patient when the two are separated by distance”. The COVID-19 pandemic has forced a dramatic shift to telephone and video consulting for follow up and routine ambulatory care for reasons of infection control. Short Message Service (“text”) messaging has proved a useful adjunct to remote consulting allowing transfer of photographs and documents. Maintaining non-communicable diseases care is a core component of pandemic preparedness and telemedicine has developed to enable (for example) remote monitoring of sleep apnoea, telemonitoring of chronic obstructive pulmonary disease, digital support for asthma self-management, remote delivery of pulmonary rehabilitation. There are multiple exemplars of telehealth instigated rapidly to provide care for people with COVID-19, to manage the spread of the pandemic, or to maintain safe routine diagnostic or treatment services.Despite many positive examples of equivalent functionality and safety, there remain questions about the impact of remote delivery of care on rapport and the longer-term impact on patient/professional relationships. Although telehealth has the potential to contribute to universal health coverage by providing cost-effective accessible care, there is a risk of increasing social health inequalities if the “digital divide” excludes those most in need of care. As we emerge from the pandemic, the balance of remote versus face-to-face consulting, and the specific role of digital health in different clinical and healthcare contexts will evolve. What is clear is that telemedicine in one form or another will be part of the “new norm”.
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Pandimurugan V, Rajasoundaran S, Routray S, Prabu AV, Alyami H, Alharbi A, Ahmad S. Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6671234. [PMID: 35571726 PMCID: PMC9106471 DOI: 10.1155/2022/6671234] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 02/27/2022] [Accepted: 04/08/2022] [Indexed: 12/12/2022]
Abstract
Purpose The need for computerized medical assistance for accurate detection of brain hemorrhage from Computer Tomography (CT) images is more mandatory than conventional clinical tests. Recent technologies and advanced computerized algorithms follow Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to improve medical diagnosis platforms. This technology is making the diagnosis practice of brain issues easier for medical practitioners to analyze and identify diseases with an assured degree of precision and performance. Methods As the existing CT image analysis models use standard procedures to detect hemorrhages, the need for DL-based data analysis is essential to provide more accurate results. Generally, the existing techniques are limited with image training efficiency, image filtering procedures, and runtime system tuning modules. On the scope, this work develops a DL-based automated analysis of CT scan slices to find various levels of brain hemorrhages. Notably, this proposed system integrates Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) architectures as Integrated Generative Adversarial-Convolutional Imaging Model (IGACM) for extracting the CT image features for detecting brain hemorrhages. Results This system produces good results and takes lesser training time than existing techniques. This proposed system effectively works over CT images and classifies the abnormalities with more accuracy than current techniques. The experiments and results deliver the optimal detection of hemorrhages with better accuracy. It shows that the proposed system works with 5% to 10% of the better performance compared to other diagnostic techniques. Conclusion The complex nature of CT images leads to noncorrelated feature complexities in diagnosis models. Considering the issue, the proposed system used GAN-based effective sampling techniques for enriching complex image samples into CNN training phases. This concludes the effective contribution of the proposed IGACM technique for detecting brain hemorrhages than the existing diagnosis models.
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Affiliation(s)
- V. Pandimurugan
- School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, India
| | - S. Rajasoundaran
- School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, India
| | - Sidheswar Routray
- Department of Computer Science and Engineering, School of Engineering, Indrashil University, Rajpur, Mehsana, Gujarat, India
| | - A. V. Prabu
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India
| | - Hashem Alyami
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Abdullah Alharbi
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Sultan Ahmad
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
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