We report the green synthesis of novel magnetically separable MgFe2O4 nanoparticles using Cajanus cajan (L.) Millsp leafs via combustion method. The MgFe2O4 were characterized by powder X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), and UV-diffuse reflectance (UV-DRS) spectroscopy. The crystalline structure of MgFe2O4 was confirmed via XRD studies and TEM showed that the MgFe2O4 NPs were distorted spherical particles with particle size ranging between 5 and 15 nm. UV-DRS study showed the optical band gap of MgFe2O4 NPs to be 1.8 eV. Microwave-assisted (MW) degradation of PCM-dolo drug using MgFe2O4 as catalyst was performed at different operating parameters such as time (30 min), drug concentration (PCM-dolo 50 mg/L), initial concentration of MgFe2O4 (0–110 mg/L), and microwave power (100–600 W) to obtained the degraded fragments of the drug. Experimental data was used to compute the degradation efficiency of PCM-dolo on MgFe2O4. The enhanced catalytic performance could be ascribed to the production of MW-induced active species, such as holes (h+), superoxide radicals (?O2?) and hydroxyl radicals (?OH) in the degradation process. A possible degradation mechanism and pathway was proposed. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Joint spectral-spatial feature extraction has been proven to be the most effective part of hyperspectral image (HSI) classification. But, due to the mixing of informative and noisy bands in HSI, joint spectral-spatial feature extraction using convolutional neural network (CNN) may lead to information loss and high computational cost. More specifically, joint spectral-spatial feature extraction from excessive bands may cause loss of spectral information due to the involvement of convolution operation on non-informative spectral bands. Therefore, we propose a simple yet effective deep learning model, named deep hierarchical spectral-spatial feature fusion (DHSSFF), where spectral-spatial features are exploited separately to reduce the information loss and fuse the deep features to learn the semantic information. It makes use of abundant spectral bands and few informative bands of HSI for spectral and spatial feature extraction, respectively. The spectral and spatial features are extracted through 1D CNN and 3D CNN, respectively. To validate the effectiveness of our model, the experiments have been performed on five well-known HSI datasets. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods and achieved 99.17%, 98.84%, 98.70%, 99.18%, and 99.24% overall accuracy on Kennedy Space Center, Botswana, Indian Pines, University of Pavia, and Salinas datasets, respectively.

The purpose of this chapter is to examine the relationship between blockchain technology and sustainable development, aiming as to how blockchain technology (BT) could be effectively implemented to achieve different sustainable development goals (SDGs). Tokenization is one of the methods of blockchain technology which seems to be viable strategy to overcome the bottlenecks in terms of economic, social and governance (ESG) by engagement and participation of different stakeholders such as customers, businesses and government. The present chapter thoroughly reviews the fundamental and conceptual framework as well as implementation of blockchain technology to comprehend sustainable development goals (SDGs). This chapter provides understanding of functional approach promoting green practices viz. energy conservation, circular economy, recycling etc. It also investigates various problems connected with the implementation of blockchain technology and sustainable development. Furthermore, the study strives to create awareness amongst stakeholders includes industry, academia, researcher and policymakers as in what manners blockchain technology is beneficial in attaining sustainable development goals (SDGs) and build more ecologically friendly conscious society.

In recent years, there has been an enormous increase in the number of companies and of customers for almost every industry. The increment in the number of companies has also provided the choices to the customer but in turn it has also created new challenges. Thus, the companies must work not only to improve their products or services but to sustain customers in the competitive world. Churn prediction is the prediction of customers who are at a potential risk of discontinuing the product or service of the company. Thus, in today’s competitive world, churn prediction is more relevant. In the present work, we have employed various machine learning models for an early prediction of churns, to mitigate the potential risk of losing the customers. The authors have chosen ensemble models for this task. Finally, the models are trained on the dataset. The results for various models are compared using accuracy, precision, recall, and F1 score. Moreover, it is also observed that for our dataset XGBoost outperformed over other models

Skin Melanoma is a lethal type of cancer. The early diagnosis of which is crucial to improve the survival rate of the patients. Convolution neural networks are at the heart of the deep learning algorithms. In the present work authors have experimentally compared 2D and 3D Convolution Neural Network (CNN) models to identify the melanoma. We have employed three different types of datasets namely PH2, ISIC archive, and ISIC skin cancer datasets. We applied the two models on each of the datasets to determine their accuracy, precision, recall, f1 score and ROC curves. The experimental results provide the insights about the advantages and limitations of using 2D and 3D CNN models for the identification of skin melanoma. The authors have observed that 2D CNN model shows enhanced capabilities to detect skin lesion structures compared to 3D CNN. Moreover, the classification accuracy of the 2D CNN is also found better than 3D CNN

Blockchain technology is in fact a public ledger that gathers data in a chain of blocks, which gradually improves security, trust, transparency, quality, decentralization, and immutability while operating businesses. In the present scenario of business, the organization is not only concentrating on improving the activities related to operational aspects, but it also needs to meet the expectations of various stakeholders. Corporate social responsibility (CSR) is such a concept which facilitates the organization to cater the information related to various social and environmental concerns arising out of the business operations. It is now the liability on the part of the organization to communicate these CSR-related concerns in such a way that they effectively meet the expectations of stakeholders. CSR communication has become an integral part of the organization’s marketing strategy not only through the rise of public awareness on environmental and social issues but also because there is a demand for the correct use of CSR communication. However, organizations face difficulties in their CSR activities and actions, and due to this challenging situation, there is a rampant need for a solution. Blockchain is one of the most rewarding technology because it stores and records information in such a way that it makes it practically impossible to change or cheat the system. In fact, blockchain provides the desire transparency, traceability, decentralization, and accountability that CSR communication lacks recently. Therefore, this study identifies those common difficulties of CSR communication based on a literature review and proposes implementing blockchain as a solution for these problems. Finally, the objective of this study is to investigate what are the common problems or difficulties in CSR communication, and furthermore, what are the usefulness and benefits of blockchain, and could these benefits really overcome the identified difficulties

The study aims to provide a scientometric review of the literature based on ESG for the period starting from 2011 to 2022. The objective is to identify annual trends, research trajectories, geographical spread, trending research areas, and significant research outlets in the field of ESG. The data was collected from the SCOPUS database using relevant keywords. The VOSviewer (version 1.6.18) and R-Studio were used to analyze the selected 380 publications. The result depicts that from 2015 to 2022, there is a steady upward trend, with the year 2021 seeing a significant rise in publications. The study highlights that the US, UK, and Australia are among the top three contributing countries. Keywords analysis signifies fields such as sustainable development, corporate social responsibilities, and ESG investing, which are currently focused research areas based on ESG. The research might provide the researchers interested in this area with an extensive wealth of information to perform future investigations on ESG. The study adopted a scientometric approach to map out the research focus in recent ESG-related studies. The study gives significant insights into past and present trends of ESG-based studies and provides a roadmap for future studies

The present work develops a method for identity authentication and verification of static signatures stored in the database employing artificial neural network. The present method uses mathematical moments for feature extraction such as mean, variance, skewness and kurtosis. First of all, the method suggests to scan the signature images, then after a sequence of preprocessing steps the resulting images are subjected to feature extraction, however, at present already existing standard databases have been used. Subsequently, the system is trained from the genuine signature of individuals, and then an ANN is used to classify the signature images. The suggested method's effectiveness has been demonstrated by comparison with the four current methodologies and experimental findings.