Blockchain and Internet of Things in smart cities and drug supply management: Open issues, opportunities, and future directions

Blockchain-based drug supply management (DSM) requires powerful security and privacy procedures for high-level authentication, interoperability, and medical record sharing. Researchers have shown a surprising interest in Internet of Things (IoT)-based smart cities in recent years. By providing a variety of intelligent applications, such as intelligent transportation, industry 4.0, and smart financing, smart cities (SC) can improve the quality of life for their residents. Blockchain technology (BCT) can allow SC to offer a higher standard of security by keeping track of transactions in an immutable, secure, decentralized, and transparent distributed ledger. The goal of this study is to systematically explore the current state of research surrounding cutting-edge technologies, particularly the deployment of BCT and the IoT in DSM and SC. In this study, the defined keywords “blockchain”, “IoT”, drug supply management”, “healthcare”, and “smart cities” as well as their variations were used to conduct a systematic search of all relevant research articles that were collected from several databases such as Science Direct, JStor, Taylor & Francis, Sage, Emerald insight, IEEE, INFORMS, MDPI, ACM, Web of Science, and Google Scholar. The final collection of papers on the use of BCT and IoT in DSM and SC is organized into three categories. The first category contains articles about the development and design of DSM and SC applications that incorporate BCT and IoT, such as new architecture, system designs, frameworks, models, and algorithms. Studies that investigated the use of BCT and IoT in the DSM and SC make up the second category of research. The third category is comprised of review articles regarding the incorporation of BCT and IoT into DSM and SC-based applications. Furthermore, this paper identifies various motives for using BCT and IoT in DSM and SC, as well as open problems and makes recommendations. The current study contributes to the existing body of knowledge by offering a complete review of potential alternatives and finding areas where further research is needed. As a consequence of this, researchers are presented with intriguing potential to further create decentralized DSM and SC apps as a result of a comprehensive discussion of the relevance of BCT and its implementation.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis

Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis

Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans

Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy must be employed to isolate COVID-19. When it comes to determining the identity of COVID-19, one of the most significant obstacles that researchers must overcome is the rapid propagation of the virus, in addition to the dearth of trustworthy testing models. This problem continues to be the most difficult one for clinicians to deal with. The use of AI in image processing has made the formerly insurmountable challenge of finding COVID-19 situations more manageable. In the real world, there is a problem that has to be handled about the difficulties of sharing data between hospitals while still honoring the privacy concerns of the organizations. When training a global deep learning (DL) model, it is crucial to handle fundamental concerns such as user privacy and collaborative model development. For this study, a novel framework is designed that compiles information from five different databases (several hospitals) and edifies a global model using blockchain-based federated learning (FL). The data is validated through the use of blockchain technology (BCT), and FL trains the model on a global scale while maintaining the secrecy of the organizations. The proposed framework is divided into three parts. First, we provide a method of data normalization that can handle the diversity of data collected from five different sources using several computed tomography (CT) scanners. Second, to categorize COVID-19 patients, we ensemble the capsule network (CapsNet) with incremental extreme learning machines (IELMs). Thirdly, we provide a strategy for interactively training a global model using BCT and FL while maintaining anonymity. Extensive tests employing chest CT scans and a comparison of the classification performance of the proposed model to that of five DL algorithms for predicting COVID-19, while protecting the privacy of the data for a variety of users, were undertaken. Our findings indicate improved effectiveness in identifying COVID-19 patients and achieved an accuracy of 98.99%. Thus, our model provides substantial aid to medical practitioners in their diagnosis of COVID-19

Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans

Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy must be employed to isolate COVID-19. When it comes to determining the identity of COVID-19, one of the most significant obstacles that researchers must overcome is the rapid propagation of the virus, in addition to the dearth of trustworthy testing models. This problem continues to be the most difficult one for clinicians to deal with. The use of AI in image processing has made the formerly insurmountable challenge of finding COVID-19 situations more manageable. In the real world, there is a problem that has to be handled about the difficulties of sharing data between hospitals while still honoring the privacy concerns of the organizations. When training a global deep learning (DL) model, it is crucial to handle fundamental concerns such as user privacy and collaborative model development. For this study, a novel framework is designed that compiles information from five different databases (several hospitals) and edifies a global model using blockchain-based federated learning (FL). The data is validated through the use of blockchain technology (BCT), and FL trains the model on a global scale while maintaining the secrecy of the organizations. The proposed framework is divided into three parts. First, we provide a method of data normalization that can handle the diversity of data collected from five different sources using several computed tomography (CT) scanners. Second, to categorize COVID-19 patients, we ensemble the capsule network (CapsNet) with incremental extreme learning machines (IELMs). Thirdly, we provide a strategy for interactively training a global model using BCT and FL while maintaining anonymity. Extensive tests employing chest CT scans and a comparison of the classification performance of the proposed model to that of five DL algorithms for predicting COVID-19, while protecting the privacy of the data for a variety of users, were undertaken. Our findings indicate improved effectiveness in identifying COVID-19 patients and achieved an accuracy of 98.99%. Thus, our model provides substantial aid to medical practitioners in their diagnosis of COVID-19

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images

Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers

Deep learned vectors’ formation using auto-correlation, scaling, and derivations with CNN for complex and huge image retrieval

Abstract Deep learning for image retrieval has been used in this era, but image retrieval with the highest accuracy is the biggest challenge, which still lacks auto-correlation for feature extraction and description. In this paper, a novel deep learning technique for achieving highly accurate results for image retrieval is proposed, which implements a convolutional neural network with auto-correlation, gradient computation, scaling, filter, and localization coupled with state-of-the-art content-based image retrieval methods. For this purpose, novel image features are fused with signatures produced by the VGG-16. In the initial step, images from rectangular neighboring key points are auto-correlated. The image smoothing is achieved by computing intensities according to the local gradient. The result of Gaussian approximation with the lowest scale and suppression is adjusted by the by-box filter with the standard deviation adjusted to the lowest scale. The parameterized images are smoothed at different scales at various levels to achieve high accuracy. The principal component analysis has been used to reduce feature vectors and combine them with the VGG features. These features are integrated with the spatial color coordinates to represent color channels. This experimentation has been performed on Cifar-100, Cifar-10, Tropical fruits, 17 Flowers, Oxford, and Corel-1000 datasets. This study has achieved an extraordinary result for the Cifar-10 and Cifar-100 datasets. Similarly, the results of the study have shown efficient results for texture datasets of 17 Flowers and Tropical fruits. Moreover, when compared to state-of-the-art approaches, this research produced outstanding results for the Corel-1000 dataset

Barriers and facilitators to pharmacovigilance activities in Pakistan:A healthcare professionals-based survey

The timely reporting of adverse drug reactions (ADRs) could improve pharmacovigilance (PV) in a healthcare system. However, in almost all healthcare systems barriers exist that lead to the underreporting of ADRs. The objective of this study was to identify the barriers and facilitators regarding PV activities from the point of view of healthcare professionals (HCPs) in Lahore, Pakistan. A cross-sectional questionnaire-based survey was conducted between September 2018 to January 2019. The data was collected through convenience sampling of physicians, pharmacists, and nurses at tertiary care public hospitals in Lahore. A total of 384 questionnaires were distributed, and 346 HCPs responded to the survey. Over 62% percent of physicians and 54.8% of nurses agreed that they did not know how to report an ADR in their workplace. About 43.2% of pharmacists and 40.1% of nurses disagreed that they were not aware of the need for ADR reporting. Furthermore, 41.6% of nurses identified a lack of financial reimbursement and 51.8% highlighted a lack of support from a colleague as a reason that could lead to the underreporting of ADR. The majority of participants, including 69.6% physicians, 48.6% pharmacists, and 55.3% nurses identified the lack of knowledge about the existence of a national PV centre. Extra time for ADR reporting, incentives, continuous medical education, reminders, and availability of an online ADR reporting system was classed as the facilitators and were agreed upon by the majority of HCPs