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A Feminist Perspective on the Food and Gender based Marketing Narrative
Nutrition to the body is a basic element for sustenance and growth biologically, provided through food. This paper aims to understand why there is a difference between foods that are marketed gender-specifically to males and females separately. There have been a lot of participative changes in the household kitchen activities since the birth of the concept. However, certain things have continued to remain the same either as a result of tradition, preference, or systemic societal loop. This paper aims to categorically understand this patterned behaviour behind gender based food marketing and the consequent consumptions so as to find a more sustainable and inclusive approach for food marketing for the firms of this industry. The aim is also to shed light on the impact of such practices on the psychological level of the individual buyer that stems to form a pattern, creating a recurring practice out of habit, over internal choice. The Electrochemical Society -
Blockchain Technology for Mitigating Copyright Infringement on OTT Platforms through the PRISMA Model
Copyright infringement has been a rampant issue on OTT (Over-the-Top) platforms and post-COVID-19 pandemic. Continuous infringement, illegal streaming, and piracy have contributed to 87 billion INR (Indian rupees) in the piracy economy through OTT platforms. Copyright and its protection regime need upscaling through Blockchain technology as a highly monetized and important asset. This paper identifies key gaps in the ongoing research on applying Blockchain technology to mitigate copyright infringement on OTT platforms through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model. It creates a techno-legal framework to enhance this application and make it more compliant with the existing copyright laws. The study further showcases the absence of legal implications in the existing studies surrounding Blockchain technology, making them a base for the proposed framework. The proposed framework can be further used to create a robust copyright portfolio for OTT platforms while testing their relevant advancements in Blockchain technology in line with legal and commercial necessities. 2025 IEEE. -
Tourism Crossroads and Tribal Knowledge System: A Narrative of Vulnerabilities and SocioEthnic Dynamism in Mizoram
Tourism development trajectories and indigenous knowledge systems meet in discordant ways among tribal communities in Mizoram, Northeast India, and have advantages and disadvantages in the perpetuation of culture. Qualitative analysis and case study methodology have been used to examine the sociocultural mechanisms employed by the Mizo community in the negotiations with external tourism pressures that help maintain cultural integrity. Tlawmngaihna (selflessness) is the ethical philosophy that guides Mizo society's adaptation to external influences. The complete ethical system is applied to hospitality and all aspects of interpersonal relationships, conflict resolution, resource management, and cultural traditions. Findings show manifestations of great transformation in cultural expressionstraditional festivals like Chapchar Kut and indigenous dances must pay attention to tourism calls. Risks to indigenous intellectual property, lack of tribal autonomy, skewed economic outcomes, and disruption of knowledge transmission systems are specific to tribal territories as tourist destinations. Community-based approaches oriented towards local ownership, governance structure, equitable benefit sharing, and indigenous intellectual property protection are crucial for sustainable tourism development. It highlights the socio-ethnic dynamics of cultural transformation in the Indian communities within the tribal setting, with changing relationship dynamics of power generation, gender roles, and generational ties. Proposals are made for practical frameworks for balancing economic development and maintaining sophisticated tribal knowledge systems in tourism contexts. By analyzing the results, the government (policymakers), tourism stakeholders, and tribal communities involved in the difficult decision to balance cultural preservation with tourism revenue will be informed of the delicate balance that must be reached in an increasingly globalized tourism landscape. 2026 selection and editorial matter, Debanjana Nag and Kusum; individual chapters, the contributors. -
SemKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization
The availability of digital devices has increased throughout the world exponentially owing to which the average reader has shifted from offline media to online sources. There are a lot of online sources which aggregate and provide news from various outlets but due to the abundance of content there is an overload to the user. Personalization is therefore necessary to deliver interesting content to the user and alleviate excessive information. In this paper, we propose a novel semantically inclined knowledge driven approach for multi-source aggregation and recommendation of news with a focus on personalization to address the aforementioned issues. The proposed approach surpasses the existing work and yields an accuracy of 96.62% 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
PE-v-SVR based Architecture to Predict and Prevent Low and Slow-Rate DDoS Attacks using Machine Learning
Distributed Denial of Service (DDoS) attacks continue to emerge; low and slow attacks pose a serious threat. These small-scale attacks often evade traditional security protections and increase the risk of long-term outages and loss of service. Our research aims to develop effective predictive models and strategic defences to detect and mitigate slow DDoS attacks. The proposed model combines Power Spectral entropy and V-Support Vector Regression. More importantly, the version achieves the first-class error price in the variety of zero to at least one, demonstrating its effectiveness in detecting and predicting DDoS attacks. Research results show the effectiveness of the proposed design using PSD (power spectral density) entropy and V-SVR. The best mean square error obtained further confirms the ability of the model in this context. V-SVR in low and sluggish DDoS assaults. 2024 Bharati Vidyapeeth, New Delhi. -
Regression Analysis on Macroeconomic Factors and Dividend Yield on Bank Nifty Index Returns
The study has examined an impact of macroeconomic variables and dividend yield on Bank NIFTY Index. It analyses the relationship amongst macroeconomic variables and dividend yield. The study used quarterly data from 1 January 2010 to 31 December 2019. It employed statistical measures like regression analysis to analyse the impact of independent variables (macroeconomic factors and dividend yield) on the dependent variable (Bank NIFTY returns) and multicollinearity tests to understand the relationship amongst the independent variables. The observations concluded that GDP, government bond yield and dividend yield have a significant impact on Bank NIFTY returns but CPI does not have a significant impact on Bank NIFTY returns. We can also conclude that all the independent variables are not correlated to each other. The study suggested to policy makers, in India, that they should maintain economic stability through policies of growth that will eventually boost the banking sector and the economy. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Managing with Machines: A Comprehensive Assessment on the Use of Artificial Intelligence in Organizational Perspectives
This complete study, delves into the multifaceted impacts of artificial Intelligence (AI) inside organizational settings, highlighting its ability and demanding situations. The investigation spans numerous aspects along with AI-driven customer relationship management (CRM), employee productivity, and overall performance enhancement thru AI. By analyzing distinct AI applications and methodologies across different organizational functions, this studies presents insights into how AI can transform industries, decorate CRM, improve employee productiveness, and foster sustainable development. Despite the promising programs, the study also addresses the pitfalls and enormous hesitancy in AI adoption due to disasters in some high-profile AI projects. The paper underscores the significance of strategic AI integration, context-consciousness, and the want for organizational readiness to leverage AI's full capability whilst aligning with the Sustainable improvement goals (SDGs). 2024 IEEE. -
Beyond Automation: Understanding the Transformational Capabilities of AI in Management
The investigation explores at the various ways that artificial intelligence (AI) is affecting management techniques. The study highlights the dichotomy between automation and augmentation, highlighting how artificial intelligence (AI) can replace human work through automation, but its ultimate use in augmenting human capabilities (augmentation) leads to better organisational performance. This analysis reveals how AI-driven tactics enhance operational efficiency, decision-making, and productivity by synthesising research findings from a variety of domains, including manufacturing, banking, municipal sectors, and remote work environments. It also looks at how AI may change management through big data and data analytics, recommending a shift to an integrated strategy that combines automation and human understanding to promote creativity and long-term growth. 2024 IEEE. -
Achieving Organisational Achievement via the Use of Al in Machine Management
Among the most effective ways of increasing the outcomes of the company, there is the application of Artificial Intelligence ( Al) in machine management. This study article focuses on the role of Al in improving the work of machines and decision-making as well as increasing the effectiveness of organisations. The focus of the study is to enhance the machine management efficiency in order to reduce the downtime, decrease the costs of operation and increase the productivity through the application of Al technologies such as automated systems, predictive maintenance and real-time data analysis. This study finds that the indicators of operational efficiency, product quality and labour safety are determined by a case study of various industries. This study has proposed that the integration of Al into machine management goes beyond being a technical adjustment; it is a shift in the culture and approach of organisations, human resource management (HRM) practices and operations. The real-time insights and predictive analysis of the market that is provided by Al can help in meeting the needs of the market that are ever-evolving while at the same time being efficient and innovative. This makes enterprises to have an edge over the competitors. The findings of the study are therefore a clear indication that in the ever evolving business environment, there is the need to embrace the use of Al to foster growth and development of organisations. 2025 Shiney Chib, Falguni Pawar, Shantanu S. Bose, Thirulogasundaram V.P., Prasanna H.N. and Lakshmi S.R. All rights reserved. -
Optimal Feature Selection for the Classification of Hyperspectral Imagery Using Adaptive SpectralSpatial Clustering
Hyperspectral images captured through the hyperspectral sensors play an imperative part in remote sensing applications in the present context. Unlike traditional images sensed with few bands in the visible spectrum, the hyperspectral (HS) images are obtained with hundreds of spectral band ranges from infrared to ultraviolet regions. Because of its vast spatial and spectral data, it requires an extensive computational system for processing and its hidden features are needed to be unveiled in an effective manner specifically for the classification of HS imagery. This approach exploits the high spectral band correlation and rich spatial information of the HS images for the generation of feature vectors. To attain optimal feature space for the best probable classification, an adaptive approach is incorporated to adaptively choose spectralspatial features for feature selection to classify the pixels effectively. Furthermore, the HS image encompasses several bands including noisy bands. To categorize the images with great accuracy, it is suggested to eradicate the noisy bands whilst retaining the informative bands. In this research, an adaptive spectralspatial feature selection scheme is proposed for HS images where the extremely correlated representative bands are considered for analysis with uncorrelated and noisy spectral bands are judiciously discarded during its classification process. This hybrid approach not merely diminishes the computational time and also improves the general classification accuracy significantly. The empirical result displays that the proposed work surpasses the conventional approach of HS image classification systems. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Unveiling Sentiment Trends: An Approach to Utilize Machine Learning in Studying User Activities on New Social Applications
Sentiment analysis is the examination of textual data to determine the writer's attitude, which can be positive, negative, or neutral. In the context of social media analysis, sentiment analysis is peculiar as it helps to identify trends in large amounts of data that are posted by social media users. In the case of sentiment analysis algorithms, the text is categorized into positive, negative, and neutral. Classification of sentiments involves the use of several algorithms such as the decision tree, support vectors, and neural networks. In other words, the paper intends to determine the users sentiment using the decision tree model. Some of the common data sets that have been utilized in this study include the COVID-19 pandemic data, movie reviews, and product ratings. What is tried to be accomplished in this type of case is to determine the efficiency and stability of the decision trees, as well as their optimum success region. Based on the results, it can be pointed out that the accuracy is the highest for the COVID-19 Tweets dataset when referring to the simulation model, which is 98%; hence, the decision tree is best used in the context of the health sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
AI Applications Computer Vision and Natural Language Processing
Artificial intelligence (AI) applications in computer vision and natural language processing (NLP) have made major advances in recent years, challenging a number of sectors and areas. This multidisciplinary topic combines NLP, which examines the study of human language, and computer vision, which concentrates on the understanding of visual data. This study examines the wide range of applications that are included within this convergence, highlighting the revolutionary potential of AI technology. AI has made it possible to make significant advances in autonomous systems, object identification, and image recognition in the field of computer vision. These developments have stimulated innovation and increased efficiency, revolutionizing sectors including healthcare, autonomous vehicles, and security. Meanwhile, AI-driven advances in NLP have produced strong language models that can produce, comprehend, and translate text. These approaches have been utilized to improve accessibility and efficiency of communication in chatbots, sentiment analysis, and language translation services. This chapter explores the basic ideas and advancements in these two fields, emphasizing the opportunities and novel challenges that arise from integrating computer vision and NLP. Additionally covered are data privacy, ethical issues, and the possibility of prejudice in AI applications. The study also highlights the ongoing need for these fields' advancement and investigation in order to solve real-world problems and fully utilize AI's potential in the computer vision and NLP industries. 2025 The Institute of Electrical and Electronics Engineers, Inc. -
Deep Learning-Based Optimised CNN Model for Early Detection and Classification of Potato Leaf Disease
After rice and wheat, potatoes are the third-largest crop grown for human use worldwide. The different illnesses that can harm a potato plant and lower the quality and quantity of the yield cause potato growers to suffer significant financial losses every year. While determining the presence of illnesses in potato plants, consider the state of the leaves. Early blight and late blight are two prevalent illnesses. A certain fungus causes early blight, while a specific bacterium causes late blight. Farmers can avoid waste and financial loss if they can identify these diseases early and treat them successfully. Three different types of data were used in this study's identification technique: healthy leaves, early blight, and late blight. In this study, I created a convolutional neural network (CNN) architecture-based system that employs deep learning to categorise the two illnesses in potato plants based on leaf conditions. The results of this experiment demonstrate that CNN outperforms every task currently being performed in the potato processing facility, which needed 32 batch sizes and 50 epochs to obtain an accuracy of about 98%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Lattice thermal conduction in cadmium arsenide
Lattice thermal conductivity (LTC) of cadmium arsenide (Cd3As2) is studied over a wide temperature range (1-400 K) by employing the Callaway model. The acoustic phonons are considered to be the major carriers of heat and to be scattered by the sample boundaries, disorder, impurities, and other phonons via both Umklapp and normal phonon processes. Numerical calculations of LTC of Cd3As2 bring out the relative importance of the scattering mechanisms. Our systematic analysis of recent experimental data on thermal conductivity (TC) of Cd3As2 samples of different groups, presented in terms of LTC, ? L, using a nonlinear regression method, reveals good fits to the TC data of the samples considered for T < ? 50 K, and suggests a value of 0.2 for the Gruneisen parameter. It is, however, found that for T > 100 K the inclusion of the electronic component of TC, ? e, incorporating contributions from relevant electron scattering mechanisms, is needed to obtain good agreement with the TC data over the wide temperature range. More detailed investigations of TC of Cd3As2 are required to better understand its suitability in thermoelectric and thermal management devices. 2022 Chinese Physical Society and IOP Publishing Ltd. -
Energy-Aware Multilevel Clustering Scheme for Underwater Wireless Sensor Networks
The expansion of wireless sensor networks in the underwater environment resulted in underwater wireless sensor networks. It has dramatically impacted the research arena because of its widespread and real-time applications. But successful implementation of underwater wireless sensor networks faces many issues. The primary concern in the underwater sensor network is sensor nodes' energy depletion problem. In this paper, to improve the lifetime of the underwater wireless sensor network, an Energy-Aware Multi-level Clustering Scheme is proposed. The underwater network region is considered 3D concentric cylinders with multiple levels. Further, each level is divided into various blocks, representing one cluster. The proposed algorithm follows vertical communication mode from the sea bed to the surface area in a bottom-up fashion. Multiple levels with varying heights overcome the communication issues due to high water pressure towards the sea bed. Simulations are carried out to show the efficiency of the proposed algorithm, which performs better in terms of a prolonged network lifetime and average residual energy. The simulation result shows significant improvement in the network lifetime compared with current algorithms. 2013 IEEE. -
NAFLD Detection Using Natural Gradient Boosting: A Probabilistic Ensemble Approach for Improved Accuracy and Calibration
A growing global health concern, non-alcoholic fatty liver disease (NAFLD) must be accurately and promptly detected to avoid serious complications. This study suggests a model based on Natural Gradient Boosting (NGBoost) for accurate clinical feature-based NAFLD prediction. In contrast to traditional gradient boosting algorithms, NGBoost uses natural gradients to estimate the entire conditional probability distribution of outcomes, which enhances uncertainty quantification and calibration. Using a publicly accessible Kaggle dataset, the models performance was compared to KNN, SVM, and Decision Tree classifiers. According to experimental results, NGBoost outperformed conventional classifiers in terms of precision, recall, and F1 score, achieving the highest accuracy of 92.8%. Excellent discriminative ability was indicated by the ROC curve, and strong generalization ability with minimal overfitting was confirmed by the trainingvalidation loss analysis. These findings demonstrate how NGBoost may be used to support clinical decisions, allowing for earlier detection and treatment. Subsequent research endeavors will investigate the validation of the model on more extensive real-world datasets and broaden its relevance to additional liver-related conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Bridging the Gap: Exploring Blockchains Role in Enhancing Financial Inclusion in the Indian Context
In recent years, the concept of financial inclusion has gained prominence as a crucial element in both economic and societal progress. This study investigates how blockchain technology could enhance financial accessibility in the unique socioeconomic context of India. Through an extensive analysis of a variety of blockchain applications, such as decentralized banking, digital identity verification, and transparent transaction procedures, this paper investigates the feasibility of utilizing blockchain technology to enhance inclusive financial systems. It closely examines the obstacles to blockchain adoption in India, such as governmental regulations, public sentiment, and the limitations of the countrys technology infrastructure. In order to raise awareness and encourage adoption, it also examines how to include blockchain education initiatives. This study adds to the discussion on financial inclusion and blockchain technology by fusing theoretical knowledge with real-world applications. Additionally, it offers a way to apply blockchain technology to advance inclusive economic growth in India. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Future of Work in Creative Industries
Digital technologies, platform- based labour and AI are causing a significant transformation in the future of work in creative industries. This chapter looks at how the changes are transforming creative professions, work models and career expectations. In the digital transition, numerous cultural barriers to entry have been eliminated and individuals can tap into international markets without institutional intermediaries. With creative employees becoming more freelance and web based entrepreneurs, artists have to manage an increased pressure to balance between creativity and commercial and technological concerns. Lastly, it also gives future research and policy implications such as the need to provide structural systems that safeguard mental health, equitable pay and inclusiveness to opportunities. This chapter contends that creators, platforms, educators and policymakers must work together in order to achieve a healthy and equitable creative economy and sustainable creative futures. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Post Covid Scenario Effective E-Mentoring System in Higher Education
During Covid-19 pandemic many people and institutions preferred online coaching instead of in person education. The problem with online is that it will be difficult to carry on interconnections between students and professors in that environment. The main constraint for conducting online session is that the people in remote areas may find a difficulty to connect to online sessions having network issues. Electronic mentoring (e-mentoring) is implemented like a website in which the mentor and mentee can communicate with each other. With the help of this mentoring the project can provide a best solution for both the mentor and mentee. They can communicate with each other with the help of online platform and even with the help of emails.This proposed method will help them to keep the track of their academic progress and achievements of students. This article mainly focus on the mentoring through physical and virtual environment in which the mentee will be interacting with the mentor to know the progress of their academics. This article discusses about the website which is developed to fulfill the needs of the student and it discusses about the various stages of development that helped in building the website. Students can share their difficulties and their achievements with the mentor who are assigned for them particularly. In future planning to implement artificial intelligence technique to online mentoring process, this is for the betterment of student's growth. 2023 IEEE. -
Total Global Dominator Coloring of Trees and Unicyclic Graphs
A total global dominator coloring of a graph G is a proper vertex coloring of G with respect to which every vertex v in V dominates a color class, not containing v and does not dominate another color class. The minimum number of colors required in such a coloring of G is called the total global dominator chromatic number, denoted by Xtgd (G). In this paper, the total global dominator chromatic number of trees and unicyclic graphs are explored. 2023 University of Baghdad. All rights reserved.