1
|
Ai Y, Chen CL, Weng W, Chiang ML, Deng YY, Lim ZY. A Traceable Vaccine Supply Management System. SENSORS (BASEL, SWITZERLAND) 2022; 22:9670. [PMID: 36560039 PMCID: PMC9785215 DOI: 10.3390/s22249670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Everyone should be vaccinated, but the eligibility and safety of the vaccine are always overlooked by most people. The outbreak of COVID-19 has led many countries to intensify the development and production of the COVID-19 vaccine. and some countries have even required universal vaccination against this epidemic. However, such popularization of vaccination has also exposed various flaws in vaccine management that existed in the past, and vaccinators have become more concerned about the effectiveness of their vaccinations. In this paper, we propose a blockchain-based traceable vaccine management system. First, the system uses smart contracts to store the records generated during the whole process, from vaccine production to vaccination. Second, the proposed scheme uses the Edwards-curve digital signature algorithm (EdDSA) to guarantee the security and integrity of these data. Third, the system participants can access the corresponding data according to their authority to ensure the transparency of the whole system operation process. Finally, this paper will also conduct a security analysis of the whole system to ensure that the system can resist potential attacks by criminals.
Collapse
Affiliation(s)
- Yaohong Ai
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Chin-Ling Chen
- School of Information Engineering, Changchun Sci-Tech University, Changchun 130600, China
- Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
| | - Wei Weng
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Mao-Lun Chiang
- Bachelor Degree Program of Artificial Intelligence, National Taichung University of Science and Technology, Taichung 40401, Taiwan
| | - Yong-Yuan Deng
- Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
| | - Zi-Yi Lim
- Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
| |
Collapse
|
2
|
Imran, Iqbal N, Kim D. Intelligent Material Data Preparation Mechanism Based on Ensemble Learning for AI‐Based Ceramic Material Analysis. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202200517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Imran
- Department of Biomedical Engineering Gachon University Incheon 21936 Republic of Korea
| | - Naeem Iqbal
- Department of Computer Engineering Jeju National University Jeju 63243 Republic of Korea
| | - Do‐Hyeun Kim
- Department of Computer Engineering Jeju National University Jeju 63243 Republic of Korea
- Advanced Technology Research Institute Jeju National University Jeju 63243 Republic of Korea
| |
Collapse
|
3
|
Dhanke J, Rathee N, Vinmathi MS, Janu Priya S, Abidin S, Tesfamariam M. Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3564482. [PMID: 36254205 PMCID: PMC9569225 DOI: 10.1155/2022/3564482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
It is essential to change health services from a hospital to a patient-centric platform since medical costs are steadily growing and new illnesses are emerging on a worldwide scale. This study provides an optimal decision support system based on the cloud and Internet of Things (IoT) for identifying Chronic Kidney Disease (CKD) to provide patients with efficient remote healthcare services. To identify the presence of medical data for CKD, the proposed technique uses an algorithm named Improved Simulated Annealing-Root Mean Square -Logistic Regression (ISA-RMS-LR). The four subprocesses that make up the proposed model are a collection of data, preprocessing, feature selection, and classification. The incorporation of Simulated Annealing (SA) during Feature Selection (FS) enhances the ISA-RMS-LR model's classifier outputs. Using the CKD benchmark dataset, the ISA-RMS-LR model's efficacy has been verified. According to the experimental findings, the proposed ISA-RMS-LR model effectively classifies patients with CKD, with high sensitivity at 99.46%, accuracy at 99.26%, Specificity at 98%, F-score at 99.63%, and kappa value at 98.29%. The proposed system has many benefits including the fast transmission of medical data to the medical personnel, real-time tracking, and registration condition of the patient through a medical record. Potential enhancement of the performance measures the provider system's hospital capacity and monitoring of a significant number of patients with a concentrated average delay.
Collapse
Affiliation(s)
- Jyoti Dhanke
- Department of Engineering Science (Mathematics), Bharati Vidyapeeth's College of Engineering Lavale, Pune 412115, Maharashtra, India
| | - Naveen Rathee
- Department of Electronics and Communication Engineering, IIMT College of Engineering, Greater Noida 201310, Uttar Pradesh, India
| | - M. S. Vinmathi
- Department of CSE, Panimalar Engineering College, Bangalore Trunk Road, Nazarethpet, Poonamallee, Chennai 600123, Tamilnadu, India
| | - S. Janu Priya
- Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering, Samayapuram, Tiruchirappalli, Tamilnadu 621112, India
| | - Shafiqul Abidin
- Department of Computer Science, Aligarh Muslim University, Aligarh 202002, Uttar Pradesh, India
| | - Mikiale Tesfamariam
- Department of Software Engineering, College of Computing and Informatics, Haramaya University, Dire Dawa, Ethiopia
| |
Collapse
|
4
|
Ullah N, Khan MS, Khan JA, Choi A, Anwar MS. A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:7575. [PMID: 36236674 PMCID: PMC9570935 DOI: 10.3390/s22197575] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/01/2022] [Accepted: 10/02/2022] [Indexed: 06/16/2023]
Abstract
Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient's brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.
Collapse
Affiliation(s)
- Naeem Ullah
- Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
| | - Mohammad Sohail Khan
- Department of Computer Software Engineering, University of Engineering and Technology Mardan, Mardan 23200, Pakistan
| | - Javed Ali Khan
- Department of Software Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan
| | - Ahyoung Choi
- Department of AI, Software Gachon University, Seongnem-si 13120, Korea
| | | |
Collapse
|
5
|
Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136772] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Regression testing ensures that modified software code changes have not adversely affected existing code modules. The test suite size increases with modification to the software based on the end-user requirements. Regression testing executes the complete test suite after updates in the software. Re-execution of new test cases along with existing test cases is costly. The scientific community has proposed test suite prioritization techniques for selecting and minimizing the test suite to minimize the cost of regression testing. The test suite prioritization goal is to maximize fault detection with minimum test cases. Test suite minimization reduces the test suite size by deleting less critical test cases. In this study, we present a four-fold methodology of test suite prioritization based on reinforcement learning. First, the testers’ and users’ log datasets are prepared using the proposed interaction recording systems for the android application. Second, the proposed reinforcement learning model is used to predict the highest future reward sequence list from the data collected in the first step. Third, the proposed prioritization algorithm signifies the prioritized test suite. Lastly, the fault seeding approach is used to validate the results from software engineering experts. The proposed reinforcement learning-based test suite optimization model is evaluated through five case study applications. The performance evaluation results show that the proposed mechanism performs better than baseline approaches based on random and t-SANT approaches, proving its importance for regression testing.
Collapse
|
6
|
Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications. ELECTRONICS 2022. [DOI: 10.3390/electronics11121893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.
Collapse
|