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Local Hearts, Global Minds: Using SEL to Prevent Bullying in Resource- Constrained Establishments
This chapter explores the role of Socio- Emotional Learning in developing bullying prevention strategies in resource constrained educational establishments. By applying established theoretical frameworks such as the CASEL model, Bronfenbrenners ecological systems theory and Banduras social learning theory, SEL is established as a culturally adaptable approach to combat bullying in under- resourced settings. By drawing on case studies from developing and under- developed countries such as India, Zimbabwe, and Ghana, efficacy of simple interventions are highlighted. Techniques such as circle time, storytelling, peer mentoring, and theatre based interventions are proposed as tools to boost empathy, emotional regulation and prosocial behavior in accordance with SEL. Evidence based effectiveness of SEL in resource constrained environments allows for it to be proposed as a simple but efficient method ready to be integrated into programs as a cost effective approach against bullying. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Technological Innovation in Digital Payments: A Survey of Trends, Challenges, and Opportunities
This paper critically examines the impact of technological innovations, particularly in the areas of financial technology (FinTech), artificial intelligence (AI), and machine learning (ML), on digital payments. The aim is to analyze how these advances have transformed traditional payment systems to improve transaction efficiency, reduce costs, and allow real-time analysis, making them essential components of the modern financial ecosystem. In addition, the study explores the crucial role of digital payments in promoting financial inclusion, especially in regions where the banking infrastructure is underdeveloped while addressing the current challenges in rural and remote areas. The article highlights the importance of robust cybersecurity measures, adaptive regulatory frameworks, and digital inclusion policies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
The broad-basing process in India and muslims
Muslims are recognised as numerically the most important among religious minorities in India. Broad-Basing has covered them, but the rate of catching up with the rest is not satisfactory. There has been a faster decline in poverty rate among Muslims than among the rest. The preponderance of the informal economy into which most of the Muslim workers are caught, their lower representation in higher education and gender biases are major stumbling blocks in their progress. The lower work participation of Muslim women is a significant factor in Muslims lagging behind others in employment. Most Muslim converts in India are believed to have come from the lower social groups, particularly artisans. Rural artisans suffered deprivation both during the colonial period due to cheaper imports of manufactured goods from England, and also subsequently after independence due to the rise of modern industry. Most of the artisans were reduced to the status of agricultural labourers. Thus the destiny of Muslims in India is tied up greatly with that of the informal sector. Their Broad-Basing can be promoted with the improvement in the status of the informal sector. 2020 selection and editorial matter, M. V. Nadkarni. -
Development challenges for agriculture in Maharashtra
Maharashtra is heralded as one of the economically advanced states, but this illusion crashed under the attack of COVID-19 virus and economic deterioration is expected to follow. It is argued here that the state policy dished out a raw deal to the agricultural sector and set the sector under severe stress. The path of this retrogression, reasons behind the trends and the possible policy platform for the last six decades are traced. Stagnation has gripped the agricultural sector, and it is losing cultivable land to other uses. This is accompanied by a sharp increase in small and marginal holdings. Surprisingly, the state has no agricultural policy document in place and the sector largely depends on only sporadic firefighting approaches with a policy paralysis. 2020 Economic and Political Weekly. All rights reserved. -
Diagnosis of compromised accounts for online social performance profile network
Proliferation of internet technologies has changed the way content is created and exchanged through the Internet, prompting expansion of online networking applications and administrations. Online networking empower creation and exchanged the clients produced content and design of a scope of Internet-based applications. This development is fueled by more administrations as well as by the rate of their adoption by the users. While determined spammers misuse the built up trust connections between account proprietors and their companions to proficiently spread malignant spam, auspicious discovery of traded off records is quite challenge, because of the fixed trust association among the administration suppliers, account proprietors, and their companions. The proposed paper depicts a novel method to notice the cooperated user account in systems like Facebook and twitter. Our novel scheme consists of statistical method of modelling and detected to identity accounts that behaves a sudden change along with detected the compromised accounts. This paper gives validation of these behavioral elements by gathering and dissecting genuine client clickstreams to an OSN site. Taking into account our estimation study, further devise every client's social behavioral profile (SBP) by joining its separate behavioral element measurements. We assess the capacity of social behavioral profiles in recognizing distinctive OSN clients, and the simulation results demonstrate the social behavioral profiles precisely separate every OSN clients and distinguish traded off records. 2016 IEEE. -
Insights of Evolving Methods Towards Screening of AI-Enhanced Malware in IoT Environment
Internet-of-Things (IoT) has been encountering a series of potential form of threats since past half decades. Artificial Intelligence (AI), which is frequently seen to be adopted to solve various challenges in IoT operation, has now been adopted even by attackers for their malicious purposes. Of all forms of threats, AI-enhanced malwares are one of the most potential forms of threats which has its extensive effectiveness towards the complete operation of the entire IoT environment. Hence, this manuscript discusses existing detection and prevention approaches evolved in current literatures to understand various taxonomies of solution-based methodologies for circumventing such threats. The paper also contributes towards highlighting the potential open-ended issues that are yet to be addressed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Secure framework of authentication mechanism over cloud environment
Cloud computing offers a cost effective virtual infrastructure management along with storage and application-oriented services to its customers. This innovation quickly turns into a generally very widely accepted worldview for conveying administrations through web. In this way, this administration expert provider must be offer the trust and information security, on the grounds that there is a most vital and profitable and most delicate information in extremely secure using cryptographic techniques to secure the data in cloud. So for ensure the privacy of essential information, it must be secured utilizing encryptions algorithms and afterward transferring to cloud. This paper presents a novel technique for electronic distributed computing administrations utilizing two-variable validation (2FA) access control framework. The prime target of the projected framework is to guarantee a optimal security for all the actors involved in the component design of proposed authentication system. Furthermore, property based control in the framework likewise authorize cloud servers to maximum the access to those clients with the same arrangement of properties while saving client privacy. At long last, we additionally do a reproduction to show the practicability of our proposed framework. The assessment work is done by utilizing expense of communication, data transfer capacity and proficiency of the framework as an execution metric. Springer International Publishing AG 2017. -
The nutraceutical properties and health benefits of pseudocereals: a comprehensive treatise
This review article depicts the possible replacement of staple cereal sources with some pseudocereals like Chia, Quinoa, Buckwheat, and Amaranth, which not only provide recommended daily allowance of all nutrients but also help to reduce the chances of many non-communicable infections owing to the presence of several bioactive compounds. These pseudocereals are neglected plant seeds and should be added in our routine diet. Besides, they can serve as nutraceuticals in combating various diseases by improving the health status of the consumers. The bioactive compounds like rutin, quercetin, peptide chains, angiotensin I, and many other antioxidants present in these plant seeds help to reduce the oxidative stress in the body which leads toward better health of the consumers. All these pseudocereals have high quantity of soluble fiber which helps to regulate bowel movement, control hypercholesterolemia (presence of high plasma cholesterol levels), hypertension (high blood pressure), and cardiovascular diseases. The ultimate result of consumption of pseudocereals either as a whole or in combination with true cereals as staple food may help to retain the integrity of the human body which increases the life expectancy by slowing down the aging process. 2022 Taylor & Francis Group, LLC. -
An efficient classification of cirrhosis liver disease using hybrid convolutional neural network-capsule network
Liver cirrhosis is the diffuse and advanced phase of liver disease. Several morphological methods are used for imaging modalities. But, these modalities are biased and lack in higher detection accuracy. Hence, this work introduces automated cirrhosis liver disease classification using an optimized hybrid deep learning model. In this work, Magnetic Resonance Image (MRI) is considered for the process. Initially, an Extended Guided Filter (EGF) is used for eliminating the noise from input MRI images. Binomial thresholding is used to segment the tumor from the image. Then, Feature Extraction (FE) phase is carried out by Grey Level Co-occurrence Matrix (GLCM) and Gray level Run-length Matrix (GRLM). Finally, a hybrid of two Deep Learning (DL) algorithms Convolutional Neural Network and Capsule Network (HCNN-CN) are integrated to classify the Cirrhosis liver disease. Moreover, for fine tuning the parameters of the neural network, an optimization approach Adaptive Emperor Penguin Optimization (AEPO) is used. The proposed HCNN-CN-AEPO is compared over several approaches and depicted accuracy and sensitivity value of 0.993 and 0.986 on the real time dataset. The experimental results proved that the proposed HCNN-CN-AEPO can exactly diagnose the tumour. 2022 Elsevier Ltd -
Sex Work and Caste Governance in India: Between Informal Regulation and Legal Apathy
Sex work in India occupies a paradoxical position tolerated in practice yet criminalized and morally condemned in law and society. However, this paradox is not merely a product of legal ambiguity; it is deeply entrenched in Indias historical caste hierarchies, gender norms, and systems of informal governance. This article critically interrogates the intersection of caste and sex work in India, focusing on how marginalized caste communities engaged in traditional and contemporary forms of sexual labor are governed not only by a punitive legal framework but, more insidiously, through caste-based informal regulation and entrenched social surveillance. The states neglect, combined with a moralized legal discourse, contributes to what this article terms legal apathy- a systemic failure to extend legal rights, protections, or recognition to sex workers, particularly those from oppressed caste locations. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Ethical imperatives and frameworks for responsible AI adoption in digital entrepreneurship
This study explores ethical dimensions in AI adoption for digital entrepreneurship. Thematic analysis highlights transparency, fairness, and accountability. Findings recommend comprehensive ethical guidelines, inclusive decision-making, and robust accountability mechanisms. Practical implications extend to digital ventures, policymakers, and educators. Future research may delve into industry-specific nuances, cross-cultural analyses, and the longitudinal impact of ethical frameworks, contributing significantly to responsible AI adoption discourse in digital entrepreneurship. 2024, IGI Global. All rights reserved. -
Mentha spicata assisted AgCuO nanocomposite enables anti-diabetic and vitamin-C sensing activities
Diabetes mellitus (DM), a multifactorial chronic health condition, affects a sizable portion of the global population, and more people are expected to contract it in the future, according to the World Health Organisation (WHO). Diabetes mellitus can be treated with conventional drugs, but most of the medications have a variety of side effects. The use of nanocomposites (NCs) to treat diabetes has been prioritized in this scenario. In this study, AgCuO NCs were synthesized using a green method using Mentha spicata leaf extract and their physicochemical properties were investigated with a variety of analytical techniques. According to an extensive in vivo and in vitro analysis of the biological activities of as-synthesized AgCuO NCs, AgCuO NCs possess effective antibacterial, anti-diabetic, and anti-hyperlipidemic characteristics. When AgCuO NCs are administered to STZ-induced animals in a concentration-based manner, the blood levels of inflammatory and liver marker enzymes are reduced and antioxidant enzyme levels are increased. Besides, AgCuO NCs exhibit excellent sensing activity with a limit of detection of 86 nM against Vitamin-C. This study reveals that AgCuO NCs derived from Mentha spicata may, therefore, prove to be a very successful anti-diabetic and biosensor candidate in the future. 2024 Elsevier B.V. -
Exploring thermal and entropic behaviors in nanofluid stagnation point flow with nonlinear dynamics
This study investigates the optimization of heat and mass transfer in nanofluid stagnation point flow by analyzing entropy generation and its underlying physical mechanisms. Nanofluid technology, widely applied in thermal energy storage, and heat exchangers represents a significant advancement in modern thermal systems. While nanofluids enhance heat transfer rates, optimizing thermal conductivity through nanoparticle dispersion remains a key challenge. This work also incorporates the effects of a nonlinear chemical reaction to evaluate its impact on coupled heat and mass transport. The governing nonlinear partial differential equations, including momentum, energy, and concentration expressions, are reduced to a system of coupled ordinary differential equations using local similarity transformations. These equations are solved numerically using a Runge-Kutta scheme in MATLAB. The results, presented through tables and graphs, demonstrate how velocity, temperature, and concentration profiles vary with key physical parameters. Entropy generation is shown to increase with higher porosity, while reductions in slip and Williamson fluid parameters decrease it. Furthermore, the skin friction coefficient increases by approximately 7 % when the magnetic parameter M increases from 0 to 0.5, whereas the Nusselt number decreases by nearly 28.6 % as M increases from 0 to 1. Additionally, the local Sherwood number decreases by approximately 16.7 % when the permeability parameter Kp increases from 0 to 0.3. These findings provide practical insights into enhancing nanofluid based heat and mass transfer systems for engineering applications. 2025 The Authors. -
Experimental instigating a counter cultural film platform in Bangalore /
Moving Image Review & Art Journal (MIRAJ), Vol.7, Issue 2, pp.189-297, ISSN No: 2045-6298. -
Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian MutationSpider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset. 2022 by the authors. -
Flexible and cost-effective cryptographic encryption algorithm for securing unencrypted database files at rest and in transit
To prevent unauthorized access to the databases and to ensure that the data of the databases is protected from intruders and insiders, the data is being encrypted at the storage locations. The same goal is achieved with Transparent Data Encryption, a feature that can be found in almost all database products. However, it has been observed that the non-datafiles are being ignored and there is no standard encryption for them like there is for datafiles. Moreover, there was no standard algorithm to encrypt them without relying on third-party tools. Therefore, This study provides a robust algorithm to perform the encryption. This presentation also describes the importance of non-datafiles encryption, and how some non-datafiles can pose a threat to data and infrastructure without encryption. The practical implementation of the non-data file encryption algorithm shows the authentic results. Further, unlike existing algorithms, the proposed algorithm gives the file owner full control over the encryption logic. In the encryption process, two levels of encryption logics are combined with a passcode lock, while the same combination of two levels of reversing encryption and passcode is used in the decryption process to convert encoded data back into text format. 2022 The Author(s) -
Cloud databases: A resilient and robust framework to dissolve vendor lock-in
Vendor lock-in has become a major concern in cloud computing. The term vendor lock-in describes situations where the subscriber cannot move data or services to another cloud vendor. This is due to heavy data volumes, high network bandwidth costs, dependencies, or unacceptable downtime. The proposed vendor lock-in dissolution practice migrates the database effectively in noticeably less time, regardless of database size and with a nominal network bandwidth requirement. Through this new practice, databases can be migrated to very remote regions, even across continents. A real-time implementation of the proposed method presented in this paper. 2024 The Author(s) -
Assimilating sense into disaster recovery databases and judgement framing proceedings for the fastest recovery
The replication between the primary and secondary (standby) databases can be configured in either synchronous or asynchronous mode. It is referred to as out-of-sync in either mode if there is any lag between the primary and standby databases. In the previous research, the advantages of the asynchronous method were demonstrated over the synchronous method on highly transactional databases. The asynchronous method requires human intervention and a great deal of manual effort to configure disaster recovery database setups. Moreover, in existing setups there was no accurate calculation process for estimating the lag between the primary and standby databases in terms of sequences and time factors with intelligence. To address these research gaps, the current work has implemented a self-image looping database link process and provided decision-making capabilities at standby databases. Those decisions from standby are always in favor of selecting the most efficient data retrieval method and being in sync with the primary database. The purpose of this paper is to add intelligence and automation to the standby database to begin taking decisions based on the rate of concurrency in transactions at primary and out-of-sync status at standby. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
A Compatible Hexadecimal Encryption-Booster Algorithm for Augmenting Security in the Advanced Encryption Standard
Among the most prominent encryption algorithms, Advanced Encryption Standard ranks first. Even so, many familiar characters can be seen when an AES encrypted file is opened. As of today, there have been very few contributions to research on suppressing known characters in AES encrypted files. It is possible to identify encrypted files not only by their name and content, but also by their size. As a result, hackers can identify files at source and target locations by comparing their sizes. In this paper, a methodology is presented to address these two research gaps. As a result of the proposed algorithm, almost all characters are transformed into an unintelligible format not only for humans, but also for computer interpreters. As an additional benefit, the proposed method makes the encrypted file appear smaller and conceals its actual size. The proposed Encryption Booster algorithm is also easily integrated with Advanced Encryption Standard. 2023 IEEE. -
Asynchronous Method of Oracle: A Cost-Effective and Reliable Model for Cloud Migration Using Incremental Backups
Cloud Computing has reached a new level in flexibility to provide infrastructure. The proper migration method should be chosen for better cost management and to avoid overpayments to unused resources. So, the migrations from On-Premises to cloud infrastructure is a challenge. The migration can be done in synchronous or asynchronous modes. The synchronous method is mostly used to minimize downtime while doing the cloud migrations. The asynchronous methods can do the migrations in offline mode and very consistently. This paper addresses various issues related to the synchronous mode of Oracle while doing highly transactional database migrations. The proposed methodology provides a solution with a combination of asynchronous and incremental backups for highly transactional databases. This proposed method will be a more cost-effective and reliable model without compromising consistency and integrity. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.