Objective : Diabetic Retinopathy (DR) is a retinal disease that can cause damage to blood vessels in the eye, that is the major cause of impaired vision or blindness, if not treated early. Manual detection of diabetic retinopathy is time-consuming and prone to human error due to the complex structure of the eye. Methods & Results : various automatic techniques have been proposed to detect diabetic retinopathy from fundus images. However, these techniques are limited in their ability to capture the complex features underlying diabetic retinopathy, particularly in the early stages. In this study, we propose a novel approach to detect diabetic retinopathy using a convolutional neural network (CNN) model. The proposed model extracts features using two different deep learning (DL) models, Resnet50 and Inceptionv3, and concatenates them before feeding them into the CNN for classification. The proposed model is evaluated on a publicly available dataset of fundus images. The experimental results demonstrate that the proposed CNN model achieves higher accuracy, sensitivity, specificity, precision, and f1 score compared to state-of-the-art methods, with respective scores of 96.85%, 99.28%, 98.92%, 96.46%, and 98.65%.

When designed, technologies and frameworks are not created to be as dynamic and flexible as to cater to the requirements of other domains, and so is the case with Blockchain technology. Specifically designed for cryptocurrency, Blockchain was not intended to be used in other domains. However, during the past few years, critics argued that Blockchain has the potential to deal with some unique requirements like confidentiality and immutability and can therefore be deployed in several areas other than cryptocurrency. The use of Blockchain to support Accounting Information Systems (AIS) through enterprise resource planning (ERP) is another motivating domain to investigate in this research. ERP is another promising technology that has gained significant attention across the globe. In this research, a hybrid solution is proposed to ensure AIS data integrity against any deliberate attempt or mala-fide intention for alteration or deletion from the database that can be verified at any later stage. Since Blockchain can be used to prevent any mutability in the stored data, the proposed solution presents a concept of Data Vaults backed by the Blockchain. To this end, we apply cryptographic primitives like SHA256 on the data inside the block and then chain that block to secure data vaults. So far, Blockchain has not yet proven itself as an alternative to any traditional database system. However, it can be applied in conjunction with the Relational Database Management Systems (RDBMS) to provide cost-effective yet robust solutions. This research demonstrates the application of a simple and lean version of Blockchain to assist enterprises in storing their financial and accounting data into data vaults, ensuring their data integrity against any alterations. The suggested cost-effective framework can be easily integrated into AIS and ERP systems to identify data breaches.

Data preprocessing is crucial in the Machine Learning pipeline because the models’ learning ability directly affects the quality of data and the underlying information acquired from this stage. Nevertheless, surprisingly, there are many alternatives for each transformation task, which makes an inexperienced user overwhelmed. A simple Python-based Auto-preprocessing architecture for Automated Machine Learning is developed to offer automated, interactive, and data-driven support to help the users perform data preprocessing tasks efficiently. The suggested method provides valuable insights into a dataset and can handle standard data preprocessing tasks adeptly. Initially, it detects the data problem and presents it to the end-user using compelling visualizations. Then, it recommends the most effective data cleaning and preparation method to the user after evaluating the state-of-the-art candidate techniques. For evaluation, the proposed architecture is employed on ten different and diverse datasets for automatic data preprocessing before passing it to an ML algorithm. The results are then compared with the results generated by the same ML algorithm but implemented on manually preprocessed data. The results have shown that not only did this approach make the whole process uncomplicated and facile, but it was also able to improve the performance of the model significantly.

The recent two decades have witnessed tremendous growth in Internet of things (IoT) applications. There are more than 50 billion devices connected globally. IoT applications’ connectivity with the Internet persistently victimized them with a divergent range of traditional threats, including viruses, worms, malware, spyware, Trojans, malicious code injections, and backdoor attacks. Traditional threats provide essential services such as authentication, authorization, and accountability. Authentication and authorization are the process of verifying that a subject is bound to an object. Traditional authentication and authorization mechanisms use three different factors to identity a subject to verify if the subject has the right capability to access the object. Further, it is defined that a computer virus is a type of malware. Malware includes computer viruses, worms, Trojan horses, spyware, and ransomware. There is a high probability that IoT systems can get infected with a more sophisticated form of malware and high-frequency electromagnetic waves. Purpose oriented with distinct nature IoT devices is developed to work in a constrained environment. So there is a dire need to address these security issues because relying on existing traditional techniques is not good. Manufacturers and researchers must think about resolving these security and privacy issues. Most importantly, this study identifies the knowledge and research gap in this area. The primary objective of this systematic literature review is to discuss the divergent types of threats that target IoT systems. Most importantly, the goal is to understand the mode of action of these threats and develop the recovery mechanism to cover the damage. In this study, more than 170 research articles are systematically studied to understand security and privacy issues. Further, security threats and attacks are categorized on a single platform and provide an analysis to explain how and to what extent they damage the targeted IoT systems. This review paper encapsulates IoT security threats and categorizes and analyses them by implementing a comparative study. Moreover, the research work concludes to expand advanced technologies, e.g., blockchain, machine learning, and artificial intelligence, to guarantee security, privacy, and IoT systems.

In today’s world, services are improved and advanced in every field of life. Especially in the health sector, information technology (IT) plays a vigorous role in electronic health (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment’s multiple benefits, privacy and security loopholes exist. As the number of users grows, the Electronic Healthcare System’s (EHS) response time becomes slower. This study presented a trust mechanism for access control (AC) known as role-based access control (RBAC) to address this issue. This method observes the user’s behavior and assigns roles based on it. The AC module has been implemented using SQL Server, and an administrator develops controls for users with roles and access to multiple EHS modules. To validate the user’s trust value, A .net-based framework has been introduced. The framework of e-health proposed in this research ensures that users can protect their data from intruders and other security threats.