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  1. Article ; Online: Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems

    Malik Mustafa / Marwan Alshare / Deepshikha Bhargava / Rahul Neware / Balbir Singh / Peter Ngulube

    Computational and Mathematical Methods in Medicine, Vol

    2022  Volume 2022

    Abstract: Due to the high amount of electronic health records, hospitals have prioritized data protection. Because it uses parallel computing and is distributed, the security of the cloud cannot be guaranteed. Because of the large number of e-health records, ... ...

    Abstract Due to the high amount of electronic health records, hospitals have prioritized data protection. Because it uses parallel computing and is distributed, the security of the cloud cannot be guaranteed. Because of the large number of e-health records, hospitals have made data security a major concern. The cloud’s security cannot be guaranteed because it uses parallel processing and is distributed. The blockchain (BC) has been deployed in the cloud to preserve and secure medical data because it is particularly prone to security breaches and attacks such as forgery, manipulation, and privacy leaks. An overview of blockchain (BC) technology in cloud storage to improve healthcare system security can be obtained by reading this paper. First, we will look at the benefits and drawbacks of using a basic cloud storage system. After that, a brief overview of blockchain cloud storage technology will be offered. Many researches have focused on using blockchain technology in healthcare systems as a possible solution to the security concerns in healthcare, resulting in tighter and more advanced security requirements being provided. This survey could lead to a blockchain-based solution for the protection of cloud-outsourced healthcare data. Evaluation and comparison of the simulation tests of the offered blockchain technology-focused studies can demonstrate integrity verification with cloud storage and medical data, data interchange with reduced computational complexity, security, and privacy protection. Because of blockchain and IT, business warfare has emerged, and governments in the Middle East have embraced it. Thus, this research focused on the qualities that influence customers’ interest in and approval of blockchain technology in cloud storage for healthcare system security and the aspects that increase people’s knowledge of blockchain. One way to better understand how people feel about learning how to use blockchain technology in healthcare is through the United Theory of Acceptance and Use of Technology (UTAUT). A ...
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 303
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Hindawi Limited
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Blockchain and IPFS Integrated Framework in Bilevel Fog-Cloud Network for Security and Privacy of IoMT Devices

    Abolfazl Mehbodniya / Rahul Neware / Sonali Vyas / M. Ranjith Kumar / Peter Ngulube / Samrat Ray

    Computational and Mathematical Methods in Medicine, Vol

    2021  Volume 2021

    Abstract: Internet of Medical Things (IoMT) has emerged as an integral part of the smart health monitoring system in the present world. The smart health monitoring deals with not only for emergency and hospital services but also for maintaining a healthy lifestyle. ...

    Abstract Internet of Medical Things (IoMT) has emerged as an integral part of the smart health monitoring system in the present world. The smart health monitoring deals with not only for emergency and hospital services but also for maintaining a healthy lifestyle. The industry 5.0 and 5/6G has allowed the development of cost-efficient sensors and devices which can collect a wide range of human biological data and transfer it through wireless network communication in real time. This led to real-time monitoring of patient data through multiple IoMT devices from remote locations. The IoMT network registers a large number of patients and devices every day, along with the generation of huge amount of big data or health data. This patient data should retain data privacy and data security on the IoMT network to avoid any misuse. To attain such data security and privacy of the patient and IoMT devices, a three-level/tier network integrated with blockchain and interplanetary file system (IPFS) has been proposed. The proposed network is making the best use of IPFS and blockchain technology for security and data exchange in a three-level healthcare network. The present framework has been evaluated for various network activities for validating the scalability of the network. The network was found to be efficient in handling complex data with the capability of scalability.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 303
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Hindawi Limited
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article ; Online: Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network

    Can Liu / Sumaya Sanober / Abu Sarwar Zamani / L. Rama Parvathy / Rahul Neware / Abdul Wahab Rahmani

    Security and Communication Networks, Vol

    2022  Volume 2022

    Abstract: Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing ...

    Abstract Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy.
    Keywords Technology (General) ; T1-995 ; Science (General) ; Q1-390
    Subject code 303
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Hindawi-Wiley
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  4. Article ; Online: ECC-Based Authenticated Key Exchange Protocol for Fog-Based IoT Networks

    Ummer Iqbal / Jyoti Bhola / M. Jayasudha / Mohd Wazih Ahmad / Rahul Neware / Arvind R. Yadav / Fraol Waldamichael Gelana

    Security and Communication Networks, Vol

    2022  Volume 2022

    Abstract: Fog computing is one of the prominent technology that bridges the gap between IoT nodes and cloud servers. For increasing the efficiency at the fog level, a fog federation can be employed. Fog federation at the fog level can be controlled by the fog ... ...

    Abstract Fog computing is one of the prominent technology that bridges the gap between IoT nodes and cloud servers. For increasing the efficiency at the fog level, a fog federation can be employed. Fog federation at the fog level can be controlled by the fog coordinator. However, the information exchange between the fog coordinator and IoT nodes needs to be secured. Recently, a lightweight secure key exchange (LKSE) protocol for secure key exchange for fog federation was proposed. In this paper, the cryptanalysis of the LKSE is carried out. The cryptanalysis indicates that LKSE is vulnerable to spoofing and man in the middle attacks. To overcome the limitation of the LKSE, a design of an ECC-based secure key exchange protocol for IoT devices and fog coordinators is proposed. The security strength of the designed method has been evaluated using BAN logic and the random oracle model. Simulations on AVISPA have been performed for automatic security verification of the proposed method. A detailed security and functional comparison of the proposed scheme with LKSE have also been carried out.
    Keywords Technology (General) ; T1-995 ; Science (General) ; Q1-390
    Subject code 660
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Hindawi-Wiley
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article ; Online: Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques

    Abolfazl Mehbodniya / Izhar Alam / Sagar Pande / Rahul Neware / Kantilal Pitambar Rane / Mohammad Shabaz / Mangena Venu Madhavan

    Security and Communication Networks, Vol

    2021  Volume 2021

    Abstract: Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous ... ...

    Abstract Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.
    Keywords Technology (General) ; T1-995 ; Science (General) ; Q1-390
    Subject code 006
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Hindawi-Wiley
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Article ; Online: Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

    Syed Nisar Hussain Bukhari / Amit Jain / Ehtishamul Haq / Moaiad Ahmad Khder / Rahul Neware / Jyoti Bhola / Moslem Lari Najafi

    Journal of Healthcare Engineering, Vol

    2021  Volume 2021

    Abstract: Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide ... ...

    Abstract Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, ...
    Keywords Medicine (General) ; R5-920 ; Medical technology ; R855-855.5
    Subject code 006
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Hindawi Limited
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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