Diabetic retinopathy is a complication of diabetes that targets the eyes by damaging the retinal blood vessels. Initially it is asymptomatic or causes fluctuating vision problems. As it becomes severe, it affects both the eyes and eventually causes partial or complete vision loss. Primarily occurs when the blood sugar level is unmanageable. Therefore, the person with diabetes mellitus is always at a high risk of acquiring this disease. The early detection can deter the contingency of complete and permanent blindness. Thus, requires an efficient screening system. The present work considers a deep learning methodology specifically a Densely Connected Convolutional Network DenseNet-169, which is applied for the early detection of diabetic retinopathy. It classifies the fundus images based on its severity levels as No DR, Mild, Moderate, Severe and Proliferative DR. The datasets that are taken into consideration are Diabetic Retinopathy Detection 2015 and Aptos 2019 Blindness Detection which are both obtained from Kaggle. The proposed method is accomplished through various steps: Data Collection, Preprocessing, Augmentation and modelling. Our proposed model achieved 90% of accuracy. The Regression model was also employed, manifested up an accuracy of 78%. The main aim of this work is to develop a robust system for detecting DR automatically.

The overwhelming popularity of technology-based solutions and innovations to address day-to-day processes has significantly contributed to the emergence of smart cities. where millions of interconnected devices and sensors generate and share huge volumes of data. The easy and high availability of rich personal and public data generated in these digitalized and automated ecosystems renders smart cities vulnerable to intrinsic and extrinsic security breaches. Today, with fast-developing technologies, the classical username and password approaches are no longer adequate to secure valuable data and information from cyberattacks. Multi-factor authentication (MFA) can provide an effective solution to minimize the security challenges associated with legacy single-factor authentication systems (both online and offline). This paper identifies and discusses the role and need of MFA for securing the smart city ecosystem. The paper begins by describing the notion of smart cities and the associated security threats and privacy issues. The paper further provides a detailed description of how MFA can be used for securing various smart city entities and services. A new concept of blockchain-based multi-factor authentication named “BAuth-ZKP” for securing smart city transactions is presented in the paper. The concept focuses on developing smart contracts between the participating entities within the smart city and performing the transactions with zero knowledge proof (ZKP)-based authentication in a secure and privacy-preserved manner. Finally, the future prospects, developments, and scope of using MFA in smart city ecosystem are discussed.

Background: Curcumin has been widely used to treat a variety of diseases and disorders since ancient times, most notably for the purpose of healing wounds. Despite the large number of available reviews on this topic, a bibliometric tool-based meta-analysis is missing in the literature. Scope and approach: To evaluate the influence and significance of the countries, journals, organizations and authors that have contributed the most to this topic, the popular bibliometric markers, including article count, citation count, and Hirsch index (H-index), are taken into account. Their collaborative networks and keyword co-occurrence along with the trend analysis are also sketched out using the VOSviewer software. To the best of our knowledge, this is the first bibliometric review on the topic and hence it is envisaged that it will attract researchers to explore future research dimensions in the related field. Key findings and conclusions: India provided the most articles, making up more than 27.49 percent of the entire corpus. The International Journal of Biological Macromolecules published the most articles (44), and it also received the most citations (2012). The Journal of Ethnopharmacology (28 articles) and Current Pharmaceutical Design (20 articles) were the next most prolific journals with 1231 and 812 citations, respectively. The results indicate a significant increase in both research and publications on the wound-healing properties of curcumin. Recent studies have concentrated on creating novel medicine-delivery systems that use nano-curcumin to boost the effect of the curcumin molecule in therapeutic targeting. It has also been observed that genetic engineering and biotechnology have recently been employed to address the commercial implications of curcumin.

A large section of the population around the globe is migrating towards urban settlements. Nations are working toward smart city projects to provide a better wellbeing for the inhabitants. Cyber-physical systems are at the core of the smart city setups. They are used in almost every system component within a smart city ecosystem. This paper attempts to discuss the key components and issues involved in transforming conventional cities into smart cities with a special focus on cyber-physical systems in the Indian context. The paper primarily focuses on the infrastructural facilities and technical knowhow to smartly convert classical cities that were built haphazardly due to overpopulation and ill planning into smart cities. It further discusses cyber-physical systems as a core component of smart city setups, highlighting the related security issues. The opportunities for businesses, governments, inhabitants, and other stakeholders in a smart city ecosystem in the Indian context are also discussed. Finally, it highlights the issues and challenges concerning technical, financial, and other social and infrastructural bottlenecks in the way of realizing smart city concepts along with future research directions.

BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a subject with no available training statistics. Deep learning frameworks are popular among researchers for analyzing both spatial and time series data, making them well-suited for classifying EEG signals. METHOD. In this paper, a deep neural network model is proposed for mental task classification for an imagined task from EEG signal data. Pre-computed features of EEG signals were obtained after raw EEG signals acquired from the subjects were spatially filtered by applying the Laplacian surface. To handle high-dimensional data, principal component analysis (PCA) was performed which helps in the extraction of most discriminating features from input vectors. RESULT. The proposed model is non-invasive and aims to extract mental task-specific features from EEG data acquired from a particular subject. The training was performed on the average combined Power Spectrum Density (PSD) values of all but one subject. The performance of the proposed model based on a deep neural network (DNN) was evaluated using a benchmark dataset. We achieved 77.62% accuracy. CONCLUSION. The performance and comparison analysis with the related existing works validated that the proposed cross-subject classification framework outperforms the state-of-the-art algorithm in terms of performing an accurate mental task from EEG signals.