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Mainstreaming Northeast Tribal Women in India through Financial Education: A Systematic Review
Financial education is required to enhance financial literacy for socio-economic development. This paper aims to understand the financial literacy level among the Scheduled Tribes of India, specifically the Mao-Naga Tribe women of Northeast India. The current paper is based on secondary data and adheres to the steps and process of a systematic review. Prominent authors, times, tribes, countries, journals, and keywords have been identified for the comprehensive analysis. Since the goal of this paper is to review the existing literature regarding financial literacy among tribals, the findings indicate that financial education intervention, socio-cultural practices, social affinity, and early life financial experience affect individuals financial literacy. It has also been observed that a productive pathway to achieve financial literacy and inclusion lies in integrating financial education programs within the socio-cultural practices of tribal women. Thus, financial literacy can enhance the financial well-being that is necessary for socio-economic development among Mao-Naga Tribal women. This paper can help governments, central bank regulators, and researchers know the essential elements of financial literacy and identify the pertinent areas for further empowerment among sub-groups of the population, especially among tribal women of Northeast India. 2022 Journal of International Womens Studies. -
Mainstreaming Reproductive Mental Health of Women: The Unmet Need of the Hour
Background: While the existing research is limited, over recent years, there has been growing awareness to understand the mental health of women during menstruation, menopause, and postpartum. Methodology: A woman's distinct reproductive life stages adversely affect her psychological well-being, aggravated by other underlying social, economic, and cultural factors. Drawing upon the analysis of governing laws and womens reproductive health literature. Results: The existing reproductive health law, educational, and workplace frameworks in India are inadequate for supporting the reproductive mental health of women. Conclusion: It is of critical importance to adopt a holistic approach and call for mainstreaming the reproductive mental health of women through urgent legal and healthcare reforms. The Author(s). -
Making a Difference: Social Responsibilities of Infosys
International Journal of Management, IT and Engineering Vol. 2, Issue 10, pp 336-350, ISSN No. 2249-0558 -
Making of socially sensitive students
Educational institutions have a major role in realising equal representation in the society, writes John J Kennedy. -
Making students future-ready
By all means, let the child bloom. But do not forget to water the roots, writes John J Kennedy -
Making the body public: Implications of the new standards of body-image
[No abstract available] -
Making youth politically aware
Educations sole purpose has become acquiring a degree, preferably with todays much-hyped employability skills, and landing cushy jobs with fat salaries -
Malayalam biopics: From books to films /
This article talks about the difficulties that emerge when considering biographical films that are focused around biographical or autobiographical works of writing utilizing careful investigations of three Malayalam films. The films are an adjustment from their individual books. -
Malayalam kavi Kumaranasan ka yug bodh /
International Journal of Hindi Research, Vol.4, Issue 4, pp.56-58, ISSN No: 2455-2232. -
Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks
In wireless, every device can moves anywhere without any infrastructure also the information can be maintained constantly for routing the traffic. The open issues of wireless Adhoc network the attacks which are chosen the forwarding attack that is dropped by malicious node to corrupt the network performance then the information integrity exposure. Aim of the problem that existing methods in Adhoc network for malicious node detection which cannot assure the traceability of the node as well as the fairness of node detection. In this paper, the proposed heterogeneous cluster based secure routing scheme provides trust based secure network for detection of attacks such as wormhole and black hole caused by malicious nodes presence in wireless Adhoc network. The simulation result shows that the proposed model is detect the malicious nodes effectively in wireless Adhoc networks. The malicious node detection efficiency can be achieved 96% also energy consumption also 10% better than existing method. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Malicious Traffic Classification in WSN using Deep Learning Approaches
Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. 2023 IEEE. -
Malicious URL Detection Using Machine Learning Techniques
Cyber security is a very important requirement for users. With the rise in Internet usage in recent years, cyber security has become a serious concern for computer systems. When a user accesses a malicious Web site, it initiates a malicious behavior that has been pre-programmed. As a result, there are numerous methods for locating potentially hazardous URLs on the Internet. Traditionally, detection was based heavily on the usage of blacklists. Blacklists, on the other hand, are not exhaustive and cannot detect newly created harmful URLs. Recently, machine learning methods have received a lot of importance as a way to improve the majority of malicious URL detectors. The main goal of this research is to compile a list of significant features that can be utilized to detect and classify the majority of malicious URLs. To increase the effectiveness of classifiers for detecting malicious URLs, this study recommends utilizing host-based and lexical aspects of the URLs. Malicious and benign URLs were classified using machine learning classifiers such as AdaBoost and Random Forest algorithms. The experiment shows that Random Forest performs really well when checked using voting classifier on AdaBoost and Random Forest Algorithms. The Random Forest achieves about 99% accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
MALL-Based Writing Instruction: Assessing the Effectiveness of Digital Platforms Among ESL Learners
Nowadays, mobile-assisted language learning (MALL) has emerged as a globally adopted approach that builds on the earlier development of computer-assisted language learning (CALL) by utilizing the accessibility and flexibility of mobile devices to promote independent and self-directed learning. It enables learners to extend lan guage practice beyond classroom boundaries and provides authentic opportunities to engage with English as a Second Language (ESL). This study investigates the potential of digital platforms, specifically WordPress and Hem ingwayEditor, in enhancing the writing skills of non-native English learners within a MALL framework. WordPress offers a collaborative digital space where learners can publish, share, and receive feedback on their writing, while Hemingway Editor provides real-time analytical feedback to improve readability, grammar, and stylistic accuracy. The research adopted a quantitative design with both control and experimental groups to examine the effective ness of these platforms. Participants included ESL learners who engaged in structured writing tasks, with their progress assessed through pre- and post-tests. The findings of the study reveal that learners using WordPress and Hemingway Editor demonstrated notable improvements in writing performance when compared to the control group. The integration of these tools not only improved grammatical accuracy and stylistic clarity but also encour aged active participation, reflection, and learner autonomy. The results emphasize the pedagogical value of incor porating MALL strategies into language instruction, particularly for developing essential writing skills among ESL learners. In conclusion, this research affirms that mobile technologies, when strategically integrated into teaching, significantly enhance learning outcomes and offer sustainable pathways for improving ESL writing proficiency. 2025, Digital Technologies Research and Applications. All rights reserved. -
Malpractice Detection in Examination Hall using Deep Learning
Various institutions administer tests at designated examination locations, chosen third-party and approved centers, and have established standards for installing CCTV cameras and conducting frisking under the supervision of designated personnel. Some institutions are using online proctoring, which enables students to take exams from any location. In all of the aforementioned scenarios, human monitoring is conducted, and maintaining a high level of vigilance may be challenging due to administrative oversight or intentional allowance of malpractice for personal gain. The malpractice detection may be attributed to acts like as plagiarism, unauthorized sharing of papers, and non-verbal communication. The study is conducted by capturing the dataset in the classroom of Christ University. The proposed approach is based on the YOLO framework. The movies are processed in real time to identify hand rotation, paper extraction, and classify the motion. The accuracy for the Head_right class is significantly higher than that of the Head_left class. The system is implemented using the programming language Python and has the potential for future expansion to provide real-time monitoring. 2024 IEEE. -
Mamaearth: from a mothers dilemma to a multi-crore brand
Learning outcomes: The case study has several objectives: to gauge the evaluation of the direct-to-consumer industry in the economy of India, to analyse the competition of the brands, to ascertain the evolution of smaller direct-to-consumer (DTC) brands on the purchasing capacity of consumers, to analyse challenges in branding in Tier 2 and 3 cities and to evaluate the strategic branding decisions of Mamaearth. Case overview/synopsis: During her pregnancy, Ghazal Alagh and her husband Varun Alagh, the co-founders of Mamaearth, were looking for some good and natural products for their babys skincare. However, she could not find products that were 100% safe. Hence, as a concerned mother, she started using a few hands-on home remedies for her baby, which were 100% organic, and then the idea clicked to her to start a baby care brand named Mamaearth, which later also included personal care products. The company started as a DTC/internet-first brand in 2016, which only used to sell products online without any intermediaries when it was still trying to make its way in the market and was aware of the stiff competition by giants such as Hindustan Unilever and Proctor & Gamble, who were ruling the market for decades. When the COVID-19 pandemic hit, the market saw a shift in consumer buying patterns. There was greater use of e-commerce touch points for shopping, as various digital platforms such as the official site of products, social media and mobile platforms were used by consumers during the pandemic, leading to digitalization in buying and digitalization of consumer shopping journey. These technology platforms were expected to play a substantial role in reaching and creating consumer awareness, transaction and retention post-COVID according to reports by Deloitte 2020. Moreover, such a shift in behaviour amidst the COVID-19 pandemic shot up sales of this DTC brand and made itself the big shot it is today, where they were looking to get into an initial public offering in just seven years of its launch. They re-evaluated their strategy, which helped them become the biggest brand in no time. Complexity academic level: This case study is suitable for Doctor of Philosophy students. Supplementary material: Teaching notes are available for educators only. Subject code: CSS 8: Marketing. 2023, Emerald Publishing Limited. -
Management and Sales Forecasting of an E-commerce Information System Using Data Mining and Convolutional Neural Networks
The exponential development of e-commerce in recent decades has enhanced convenience for individuals. Compared to the conventional business environment, e-commerce is characterized by increased dynamism and complexity, resulting in several obstacles. Data mining assists individuals in effectively addressing these difficulties. Traditional data mining cannot efficiently use big data in the power provider industry. It heavily relies on time-consuming and labor-intensive feature engineering, and the resulting model could be more easily scalable. Convolutional Neural Networks (CNN) can efficiently use vast amounts of data and autonomously extract valuable elements from the original input, resulting in increased effectiveness. This article utilizes a CNN to extract valuable insights from e-commerce information to forecast commodities sales accurately and proposes a CNN-based Sales Forecasting Model (CNN-SFM). The findings indicate that using data mining and CNN yields a high level of precision in forecasting forthcoming people buying capacity data. The correlation variable between actual usage information and projected usage information was 0.98, and the highest mean error was just 1.78%. Data mining can effectively extract hidden relevant information and forecast future consumption habits for e-commerce systems. CNN demonstrates proficiency in accurately predicting forthcoming consumption power and trends. The Research Publication. -
Management practices on execution effectiveness of strategies based on Thirukkural
Thirukkural by Thiruvalluvar contains couplets that speak about the morale necessary for an individual based on the roles played in various circumstances of life. These are applied to various fields including management even today. In this chapter, the authors conduct a narrative analysis on two major aspects of management skills to be inculcated in managers for successful progression of the organization. Execution is one such important aspect of management which plays a significant role in constructing effective doable strategies and executing the strategies without delay after proper analysis, thus sustaining the motivation level of the team and progress. 2024, IGI Global. All rights reserved. -
Managing change, growth and transformation: Case studies of organizations in an emerging economy
Purpose: In view of dynamic and widespread economic transformation in emerging economies, managing organizational change and growth in this context deserves more research attention. The purpose of this paper is to examine how three organizations in different industries manage change, growth and transformation in their organizational ecosystem. Design/methodology/approach: The authors conducted in-depth interviews with the leadership of three organizations in different economic sectors in India, a country representing an emerging economy. The authors also reviewed historical data from these organizations. Three case studies illustrating the evolution of these organizations were developed from the data collected. Findings: Lessons and implications from the three case studies suggest the following key elements of effective organizational change mechanisms in an emerging economy: visionary entrepreneurial leadership; program quality excellence; scale growth and scope expansion; network capabilities; and sustainable stakeholders engagement. At the same time, this study also shows how these organizations manage change, growth and transformation in the context of a society with strong traditions and cultural norms. Research limitations/implications: Results and conclusions may be limited by the fact that the study is based on three case studies. Additional studies from a variety of industries with large numbers of participants will be helpful in more fully understanding the ways in which change, growth and transformation can best be developed and deployed in different organizational settings. Practical implications: The proposed model of organizational change in an emerging economy may assist organizational leadership in designing and sustaining their change efforts. Social implications: This study highlights the role of visionary entrepreneurial leadership and the impact of organizational growth mechanisms on organizational value delivery capabilities and organizational reputation. Originality/value: Lessons and implications of five growth steps of outstanding organizations in an emerging economy context provide valuable insight for organizational change, growth and transformation in other emerging contexts. 2019, Emerald Publishing Limited. -
Managing change, growth and transformation: Case studies of organizations in an emerging economy /
Journal of Management Development, Vol.38, Issue 4, pp. 298-311, ISSN No. 0262-1711. -
Managing Climate Risk in Indian Banking: Regulatory Shifts and Institutional Responses
Climate change is widely discussed as a prominent risk aspect which can pose financial risks to financial entities. The risks may range from physical risks and transition risks and impact can be evident across all the global economies. In India, the banking sector is the most important component of its financial system and is entrusted with managing various financial risks and mobilizing funds for sustainable development. The timely guidance of the Reserve Bank of India (the apex regulatory body) has made Indian Banks factor in climate risk factor into their internal control, risk assessment and disclosure processes. This chapter examines the changing role of risk management practices in Indian Banking Sector, where climate change also has a key role now. This study also focusses on new policies, practices and response of individual banks. The large banks of India have set a path of climate risk management by starting a separate ESG and Climate Finance Unit, laying down an ESG financing framework, and adding climate scenario analysis in its risk assessment. Banks such as HDFC Bank, Yes Bank, Kotak Mahindra Bank, and Federal Bank have also advanced through financed emissions reporting, net-zero commitments, climate stress tests, and coal policy exclusions. In spite of these steps, the overall preparedness of the Indian banking sector is still limited, with most institutions still in the process of defining climate risk strategies. The key issues are availability of data, absence of uniform ESG metrics, and dearth of internal expertise. Institutional responses are compared and explained in this chapter and policy gaps that must be addressed in the short run. Through the presentation of regulatory changes and best practices, it emphasizes the importance of Indian banks moving away from reaction-driven risk management to proactive climate leadership and thus contributing to Indias larger environmental and economic objectives. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.