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Saif S, Das P, Biswas S, Khan S, Haq MA, Kovtun V. A secure data transmission framework for IoT enabled healthcare. Heliyon 2024; 10:e36269. [PMID: 39224301 PMCID: PMC11367544 DOI: 10.1016/j.heliyon.2024.e36269] [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: 02/07/2024] [Revised: 08/07/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024] Open
Abstract
The Internet of Medical Things (IoMT) has transformed healthcare by connecting medical devices, sensors, and patients, significantly improving patient care. However, the sensitive data exchanged through IoMT is vulnerable to security attacks, raising serious privacy concerns. Traditional key sharing mechanisms are susceptible to compromise, posing risks to data integrity. This paper proposes a Timestamp-based Secret Key Generation (T-SKG) scheme for resource-constrained devices, generating a secret key at the patient's device and regenerating it at the doctor's device, thus eliminating direct key sharing and minimizing key compromise risks. Simulation results using MATLAB and Java demonstrate the T-SKG scheme's resilience against guessing, birthday, and brute force attacks. Specifically, there is only a 9 % chance of key compromise in a guessing attack if the attacker knows the key sequence pattern, while the scheme remains secure against brute force and birthday attacks within a specified timeframe. The T-SKG scheme is integrated into a healthcare framework to securely transmit health vitals collected using the MySignals sensor kit. For confidentiality, the Data Encryption Standard (DES) with various Cipher Block modes (ECB, CBC, CTR) is employed.
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Affiliation(s)
- Sohail Saif
- Department of Computer Applications, Maulana Abul Kalam Azad University of Technology, Haringhata, 741249, India
| | - Priya Das
- Department of Computer Science, Chakdaha College, Chakdaha, 741222, India
| | - Suparna Biswas
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Haringhata, 741249, India
| | - Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
- University Centre for Research and Development, Chandigarh University, Mohali, 140413, India
| | - Mohd Anul Haq
- Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
| | - Viacheslav Kovtun
- Computer Control Systems Department, Vinnytsia National Technical University, Vinnytsia, Ukraine
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Aminizadeh S, Heidari A, Dehghan M, Toumaj S, Rezaei M, Jafari Navimipour N, Stroppa F, Unal M. Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artif Intell Med 2024; 149:102779. [PMID: 38462281 DOI: 10.1016/j.artmed.2024.102779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 12/30/2023] [Accepted: 01/14/2024] [Indexed: 03/12/2024]
Abstract
The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.
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Affiliation(s)
- Sarina Aminizadeh
- Medical Faculty, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Arash Heidari
- Department of Software Engineering, Haliç University, Istanbul 34060, Turkiye.
| | - Mahshid Dehghan
- Tabriz University of Medical Sciences, Faculty of Medicine, Tabriz, Iran
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Mahsa Rezaei
- Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran
| | - Nima Jafari Navimipour
- Future Technology Research Center, National Yunlin University of Science and Technology, Douliou 64002, Taiwan; Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Türkiye.
| | - Fabio Stroppa
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Türkiye
| | - Mehmet Unal
- Department of Mathematics, School of Engineering and Natural Sciences, Bahçeşehir University, Istanbul, Turkiye
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Kumar D, Sood SK, Rawat KS. Empowering elderly care with intelligent IoT-Driven smart toilets for home-based infectious health monitoring. Artif Intell Med 2023; 144:102666. [PMID: 37783534 DOI: 10.1016/j.artmed.2023.102666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 10/04/2023]
Abstract
The COVID-19 pandemic highlights the need for effective and non-intrusive methods to monitor the well-being of elderly individuals in their homes, especially for early detection of potential viral infections. Conspicuously, the present paper develops a Multi-scaled Long Short Term Memory (Ms-LSTM) model for the routine health monitoring of elderly patients to detect COVID-19. The proposed method offers home-based health diagnostics through urine analysis by leveraging the IoT-Fog-Cloud paradigm. Mainly, the proposed model constitutes a four-layered architecture: data acquisition, fog layer, cloud layer, and interface layer. Each layer serves distinct functionalities and provides specific services, thereby collectively enhancing the overall effectiveness of the model. The statistical results of the study demonstrate the superior performance of the proposed Ms-LSTM model in comparison to state-of-the-art methods, including Artificial Neural Networks (ANN), K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest, and LSTM. Further, the proposed model attains a mean temporal efficiency of 39.23 seconds. It exhibits high reliability (92.97%), stability (70.06%), and predictive accuracy (93.25%).
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Affiliation(s)
- Dheeraj Kumar
- Central University of Himachal Pradesh, Dharmashala, Himachal Pradesh, India.
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Nigar N, Jaleel A, Islam S, Shahzad MK, Affum EA. IoMT Meets Machine Learning: From Edge to Cloud Chronic Diseases Diagnosis System. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:9995292. [PMID: 37304462 PMCID: PMC10250092 DOI: 10.1155/2023/9995292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/08/2023] [Accepted: 04/15/2023] [Indexed: 06/13/2023]
Abstract
In conventional healthcare, real-time monitoring of patient records and information mining for timely diagnosis of chronic diseases under certain health conditions is a crucial process. Chronic diseases, if not diagnosed in time, may result in patients' death. In modern medical and healthcare systems, Internet of Things (IoT) driven ecosystems use autonomous sensors to sense and track patients' medical conditions and suggest appropriate actions. In this paper, a novel IoT and machine learning (ML)-based hybrid approach is proposed that considers multiple perspectives for early detection and monitoring of 6 different chronic diseases such as COVID-19, pneumonia, diabetes, heart disease, brain tumor, and Alzheimer's. The results from multiple ML models are compared for accuracy, precision, recall, F1 score, and area under the curve (AUC) as a performance measure. The proposed approach is validated in the cloud-based environment using benchmark and real-world datasets. The statistical analyses on the datasets using ANOVA tests show that the accuracy results of different classifiers are significantly different. This will help the healthcare sector and doctors in the early diagnosis of chronic diseases.
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Affiliation(s)
- Natasha Nigar
- Department of Computer Science (RCET), University of Engineering and Technology, Lahore, Pakistan
| | - Abdul Jaleel
- Department of Computer Science (RCET), University of Engineering and Technology, Lahore, Pakistan
| | - Shahid Islam
- Department of Computer Science (RCET), University of Engineering and Technology, Lahore, Pakistan
| | - Muhammad Kashif Shahzad
- Power Information Technology Company (PITC), Ministry of Energy,Power Division, Government of Pakistan, Lahore, Pakistan
| | - Emmanuel Ampoma Affum
- Department of Telecommunication Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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Wang L, Xiao R, Chen J, Zhu L, Shi D, Wang J. A slow feature based LSTM network for susceptibility assessment of acute mountain sickness with heterogeneous data. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Shen J, Ghatti S, Levkov NR, Shen H, Sen T, Rheuban K, Enfield K, Facteau NR, Engel G, Dowdell K. A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine. Front Artif Intell 2022; 5:1034732. [PMID: 36530356 PMCID: PMC9755752 DOI: 10.3389/frai.2022.1034732] [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: 09/02/2022] [Accepted: 11/02/2022] [Indexed: 09/19/2023] Open
Abstract
Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.
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Affiliation(s)
- John Shen
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Siddharth Ghatti
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Nate Ryan Levkov
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Haiying Shen
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Tanmoy Sen
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Karen Rheuban
- School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Kyle Enfield
- School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Nikki Reyer Facteau
- University of Virginia (UVA) Health System, University of Virginia, Charlottesville, VA, United States
| | - Gina Engel
- School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Kim Dowdell
- School of Medicine, University of Virginia, Charlottesville, VA, United States
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Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9112634. [PMID: 35875781 PMCID: PMC9300353 DOI: 10.1155/2022/9112634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/07/2022] [Accepted: 06/21/2022] [Indexed: 12/23/2022]
Abstract
The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images' classification that may be used anywhere, i.e., it is an ubiquitous approach. It was designed in two stages: first, we employ a transfer learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the chaos game optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datsets. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.
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