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Centring African indigenous knowledge: Afro-feminist perspectives on women's empowerment
This chapter explores the Afro-feminist perspective of the significance of African indigenous knowledge in the context of women's emancipation. The recognition of gender inequities in Africa prompts a need for the incorporation of intersectionality in feminist discussions that include a wide range of cultural contexts. The chapter emphasizes the significance of intergenerational learning in preserving knowledge and empowering older women via examining power relations, colonial legacies, and the integration of Western-traditional medicine. This chapter examines the impact of indigenous community and feminist organization involvement on legislative progress, focusing on protecting indigenous women. Global connections, cross-cultural discussions, and unity facilitate the empowerment of Afro-feminism. These elements surpass geographical boundaries and incorporate indigenous traditions. 2024, IGI Global. All rights reserved. -
Centrality-based graph entropy and sensitivity analysis of n-inordinate invariant intersection graphs
Centrality is a real-valued function on the vertex set of a graph that helps in determining the vitality of its vertices. Graph entropy gives the structural information of complex networks, based on some information of the network entities. An algebraic intersection graph called the n-inordinate invariant intersection graph has been constructed from the symmetric group and its structural properties are being studied, in the literature. In this paper, we discuss the centrality measures and the graph entropy of the n-inordinate invariant intersection graphs and their complements, and analyze the sensitivity of these networks, based on the centrality measures of their vertices. 2020 World Scientific Publishing Company. -
Centrality measures-based sensitivity analysis and entropy of nonzero component graphs
The nonzero component graph of a finite-dimensional vector space over a finite field is a graph whose vertices are the nonzero vectors in the vector space, and any two vertices are adjacent if the corresponding linear combination contains a common basis vector. In this paper, we discuss the centrality measures and entropy of the nonzero component graph and also analyze the sensitivity of the graph using the centrality measures. 2025 World Scientific Publishing Company. -
Centrality measures-based sensitivity analysis and entropy of nonzero component graphs
The nonzero component graph of a finite-dimensional vector space over a finite field is a graph whose vertices are the nonzero vectors in the vector space, and any two vertices are adjacent if the corresponding linear combination contains a common basis vector. In this paper, we discuss the centrality measures and entropy of the nonzero component graph and also analyze the sensitivity of the graph using the centrality measures. 2024 World Scientific Publishing Company. -
Central Bank Digital Currencies (CBDC) as Catalysts for Financial Inclusion and Economic Expansion in Emerging Asian Economies
This chapter examines the potential of Central Bank Digital Currencies (CBDCs) to enhance financial inclusion and economic growth in emerging economies. Unlike decentralized cryptocurrencies, CBDCs are government- backed digital currencies that offer secure and regulated alternatives to traditional money. The chapter explores how CBDCs can improve access to financial services, reduce transaction costs, and promote financial stability, particularly in countries with limited banking infrastructure. It also discusses the challenges and risks associated with CBDCs, such as financial stability concerns and the displacement of commercial banks. Through case studies from China and India, the chapter highlights how well- designed CBDCs can act as a catalyst for inclusive economic development, while emphasising the need for robust regulatory frameworks for successful implementation. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Centering Ground Water Scarcity: Ensuring Futuristic Sustainable Farming With Blockchain and IoT
Depletion of groundwater is a major threat to agricultural sustainability, especially in and around irrigated areas. This chapter discusses monitoring water usage, soil moisture and environmental using sensors powered by IoT and how such technologies offers real-time insights into water usage for efficient management of water. It also examines decentralized smart contracts that can promote fair water use rights and incentive farmers' conservation behaviours. Combining the adaptation of technological innovation and sustainable water management, the paper offers policy prescriptions to climate proof agriculture. With means of interdisciplinary views, practical applications and policy suggestions, this book snapshots the agro-environmental management policy and the smart irrigation systems towards the protection of water resources, and future generations of smart farming applications worldwide. 2026, IGI Global Scientific Publishing. All rights reserved. -
Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier For Hate Speech Detection in Online Social Network
Social networking uses internet-based platforms to facilitate users to make connections with others and share various forms of content, including text, images, videos, and links. Social networking services are mainly used for non-social interpersonal communication. Many approaches have been developed for hate speech detection, but they still face significant challenges, particularly in classifying text into multiple labels accurately and in a timely manner. For accurate hate speech detection in social networks, a Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier (CRCOCDBC) model is developed. The objective of the developed CRCOCDBC is to detect multi-class hate speech with minimal time and error rate. Comparative analysis shows improved performance in terms of minimum error and higher authentication accuracy and precision than other well-known methods. 2026 Seventh Sense Research Group. -
Cementing the future from gray to green
This chapter explores the potential of green structures and nature-based solutions in the context of Bangalore, a city that is rapidly urbanizing and struggling with the environmental impacts of traditional building materials such as concrete. Concrete, while being a mainstay of modern construction, contributes significantly to global CO2 emissions (Fischetti etal., 2023). The problem is how we can maintain Bangalore's rapid growth without concrete's ill effects. What are the alternatives to concrete, how do the different materials compare with each other, and how will it affect Bangalore? In Bangalore, using materials like clay and mud blocks instead of bricks, simple Kota tiles and clay tiles for flooring, or Hempcrete would have a significant environmental benefit. This chapter also discusses nature solutions, which are nature-based strategies for creating sustainable and climate-resilient solutions to address infrastructure needs for Bangalore. These solutions often offer higher quality, lower cost, and more excellent resiliency than traditional infrastructure. Composition and Properties of Hempcrete is a biocomposite building material that combines the internal woody core of the industrial hemp plant with a lime-based binder. The result is a lightweight, insulating, and flame-retardant material with excellent thermal properties. The porous cellulosic structure of the hemp core provides exceptional bonding strength with lime, making foam concrete a versatile material that can be used in both new construction and renovation of existing buildings. One of the main thermal properties and advantages is its high thermal mass. This means they can store heat and keep buildings warmer in the winter and cooler in the summer (Hemp). This property reduces the need for energy-intensive heating and cooling systems, making buildings made from Hempcrete more energy-efficient and cost-effective in the long run. 2026 Elsevier Inc. All rights reserved. -
Cellular agriculture research progress and prospects: Insights from bibliometric analysis
World agriculture is facing a daunting task to feed the burgeoning population against multiple production and environmental threats. The alarming growth in population vis-vis current food production is expected to increase the global food insecurity levels. Inter alia, cellular agriculture an incipient technology is being considered as a potential alternative to cater for the growing demand for food and nutrition. The technology aims to develop edible agricultural products including meat with reduced environmental footprint against conventional farm production. In this context, an attempt has been made to review the progress of cellular agriculture research in four decades (19812020) through a bibliometric analysis and to suggest a roadmap for future research. The study sourced data from the Web of Science during October 2020. Using keywords, the database showed 212 searches pertaining to cellular agriculture from 135 journals worldwide. Of the journals, seven had at least five published articles and 33 had two articles each. Subsequently, the bibliographic coupling among the identified journals was carried out. It is found that the Journals: Appetite, Meat Science, and Journal of Agricultural and Environmental Ethics had the largest circles corresponding to their respective number of publications coupled with notable linkages with other journals. Also, a detailed analysis was performed on categories, growth trend, keywords, institutions, regions and leading researchers of cellular agriculture. The findings indicate that the Appetite Journal followed by the Journal of Agricultural and Environmental Ethics had published a significant percentage of articles on cellular agriculture, and Environmental Science and Technology was identified as the highly cited journal. The USA, England and the Netherlands were identified as the progressive regions in cellular agriculture research. The bibliometric analysis points to sluggish progress in cellular agriculture research and production despite its potential benefits. Future research should focus on the cost-effectiveness of the technology, consumer willingness to buy, development of food safety protocols on its merit and regional policy governance coupled with popularising its paybacks in the context of ensuring food security. 2021 The Author(s) -
Celestial Image Classification using Ensemble Learning and Vision Transformers
Astronomical image classification plays a crucial role in understanding the universe, but deep learning models often stumble when faced with scarce labeled data. In our work, we address this gap in two key ways: first, by building a richly varied dataset from just 600 Hubble Space Telescope images and, through targeted augmentation, expanding it to 4,500 distinct training examples; and second, by introducing a hybrid learning strategy that marries transformer-driven feature extraction with gradient-boosted decision trees. We used benchmark standard convolutional architectures (ResNet-50, DenseNet-121) alongside the Data-Efficient Image Transformer (DeiT) and two novel hybrids-DeiT-RF and DeiT-XGBoost (DXg). In DXg, DeiT captures complex spatial patterns, an adaptive dimensionality reduction layer hones in on the most informative features, and XGBoost delivers the final classification. This fusion not only boosts accuracy across nebulae, galaxies, and star clusters but also enhances interpretability by revealing which transformer-derived features most influence the model's decisions. 2025 IEEE. -
Celestial Image Classification Using Attention And Boosting Mechanism
Astronomical image classification is vital in the comprehension of celestial objects, but deep learning models are severely challenged by the lack of labeled datasets. The novelty of the study is two-fold - the development of the dataset and a hybrid learning method that combines both transformer-based feature extraction and gradient-boosted decision trees to improve classification performance for celestial image classification. This study is a comparison of CNNs, transformers, and hybrid models in nebulae, galaxy, and star cluster classification using the dataset collected from the Hubble Space Telescope image archive. Through progressive data augmentation, the dataset was augmented from 603 images to 4,500 high-diversity training samples to enhance model generalization. This research explores various architectures, including ResNet-50, DenseNet-121, EfficientNetV2-S, DeiT (Data-Efficient Image Transformer), and hybrid models like DeiT-RF (Data-Efficient Image Transformer - Random Forest) and DeiT-XGBoost (DXg). DXg brings a novel fusion mechanism in which DeiT learns high-level spatial representations, adaptive dimensionality reduction fine-tunes feature selection, and XGBoost best classifies celestial objects. Such a unique combination of transformers and gradient boosting improves interpretability without sacrificing state-of-the-art performance. 2025 IEEE. -
CELEBRITY MEN AS ENDORSERS OF JEWELLERY IN MALAYALAM ADVERTISEMENTS
Jewellery being mainly a womans product is largely being sold by celebrity men now in Malayalam advertisements, indicating a shift in pattern from using female models. Mohanlal, Dileep, Suresh Gopi, Vikram and Vijay are some of the celebrities who have been endorsing jewellery. The objectives of this study were to analyse the different elements of the jewellery advertisements using celebrity men, study the reasons for the changing trends (from female models to celebrity men) ?? audience perspective and study any patterns across the advertisements. A content analysis of some of the advertisements were done based on the following parameters: the celebrity, attire of the celebrity, dialogues, main message in the advertisements, advertising appeal, background setting and objects, use of text, kind of background score or music, and jewellery presentation. A survey was conducted on a sample size of 50 to help answer the research questions from an audience perspective. It was found that all the celebrities were those who have had long standing careers. It was also found that all the advertisements were in accordance with the Match Up theory. It was found that in terms of dialogue delivery there was a mix of both the spokesperson and presenter styles that were adopted in the advertisements. The format of the advertisement and the way in which content is presented seems to be chosen based on the celebrity who is endorsing. It was found that both emotional messages as well as messages that require more logical or rational thinking are dealt with almost in equal numbers by the celebrities, hence breaking this stereotypical notion. From the survey analysis it was found that the on-film personalities of all the celebrities are kept intact in the ads as well; glittering generality is the most frequently used persuasion technique in the ads. -
Celebrity Endorsements in Fashion Purchases
This study investigates the impact of celebrity endorsements on consumer purchase intentions in the fashion apparel sector, focusing on three key variables: celebrity likeability, which is often aligned to cultural norms, and the celebrity familiarity. Information was obtained from 100 participants across India, and chi-square analysis was applied to the hypotheses. The analysis shows that all of these factors are significantly related to attitudes toward purchasing at a less than 0.05 level of significance. Four factors were determined to have significant impact with celebrity likeability coming out strongly to support the notion that consumers buy endorsed products to emulate the celebrity. Cultural fit adds consistency to trust and identity, and familiarity enhances recall, and confidence on the brands. In view of these observations, marketers ought to look at strategic celebrity selection more intensely. The endorser choice is highly recommended to be selected in accordance with the values and preferences of the target market to have the most influence on the buying decision. This paper reveals the need to adopt targeted and culturally appropriate appeals in influencing purchase behaviour in the Indian fashion domain. 2026 selection and editorial matter, Dr. Harold Andrew Patrick and Dr. Ravichandran Krishnamoorthy; individual chapters, the contributors. -
Celebrity endorsements the interplay between intellectual property law and the consumer protection act, 2019
The ambit of Copyright law has expanded over time, leading to development of newer concepts such as, Personality Rights. These rights are vested in individuals who have acquired an identifiable persona in the eyes of the public. There are two important facets to personality rights-Right to Publicity &Right to Privacy. When such identifiable identities use their acquired celebrity status to promote goods and services of a company to attract more consumers, it can be understood as Celebrity Endorsements. This is the most common source of marketing used by major companies to increase sales and garner goodwill and reputation. However, this source of communicating necessary information to the public becomes dangerous when celebrities promote false or misleading advertisements. To counter such issues, the Consumer Protection Act, 2019 introduced provisions tohold celebrities endorsing such products or services to be liable for injury suffered by consumers. The Law mandates that in order to ensure that such misleading advertisements arent promoted, the celebrities must conduct due diligence of the products before endorsing them. However, the question remains that to what extent can celebrities, who are not directly involved in production or manufacturing, be held liable for exploiting their personality rights? This paper aims at addressing the newly created legal interlink between personality rights via celebrity endorsements and protection of consumer interests. 2020, National Institute of Science Communication and Information Resources. All rights reserved. -
CELEBRITY ENDORSEMENT AS A PART OF PR STRATEGY OF NGOS
Celebrity endorsement in India is a big market and has been growing continuously. Brands often use celebrities to make an impact on people but today even the non-governmental organizations have been taking help of celebrities to endorse their cause and the NGO. Moreover, this has become an inevitable part of their public relations strategy. This paper analysis the public relations strategies of three NGOs and tries to find out why there is a requirement of celebrities in the non-profit sector and also how the celebrities have contributed to its development. -
Celebration of christmas as a symphony of interfaith in ?tm?nut?pam of St Chavara
This article is an attempt to reflect on the interfaith consciousness of St Kuriakose Elias Chavara, by making an Indian reading of his classical work ?tm?nut?pam, specifically focusing on how the incarnation of Christ is presented and celebrated with an open and inclusive approach. In ?tm?nut?pam, while explaining the episode of the Infancy Narrative, St Chavara addresses Child Jesus with the significant Indian name, Brahman?than, and Jesus is being worshipped by Brahmac?rinis with unique Indian offerings. The addition of an Indian character called S?nti as an aged shepherdess making conversation with Mother Mary makes the narrative Indian. Because of his deep and affective knowledge of Indian culture and religion, and having a moving openness and a dialogical approach to them, St. Chavara could develop a relevant cultural modification of his faith, which will have its unique stamp in the Indian Christian Theology. 2017 Journal of Dharma: Dharmaram Journal of Religions and Philosophies. -
CeLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer
Cervical cancer is a leading cause of mortality in third world countries. Although there are multiple ways of screening cervical cancer, colposcope image analysis is considered to be standard routine method of diagnosis. Due to factors like lack of skilled personnel and interobserver variability, there is a need for automated diagnostic support for cervical cancer. However, artificial intelligence solutions for medical image analysis done through deep and machine learning models require high quality, non-erroneous and sufficient amount of data. Owing to the lack of such established benchmark datasets for the colposcope images, this work aims at establishing a standard benchmark multi state colposcope image dataset that also contains clinical findings pertaining to each case. In order to establish the quality of the images, mask R-CNN method is used for segmenting the images. Subsequently, a series of IMAGENet pretrained deep learning models are deployed on the dataset to evaluate the performance. The dataset will be made available upon request for strictly research purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
CDADITagger: An Approach Towards Content Based Annotations Driven Image Tagging Integrating Hybrid Semantics
Considering the rapid growth of multimedia data, especially images, image tagging is considered the most efficient way to organize or retrieve images. The significance of image tagging is growing extensively but the frameworks employed for tagging these images aren't sophisticated. These images aren't properly tagged because of a lack of resources for tagging or manual tagging is a challenging task considering such voluminous data. Already existing frameworks take both the image data and tag-related textual data but ultimately resulted in mediocre or unpalatable performance as they are dataset centered. To overcome these limitations in existing frameworks we proposed an image tagging mechanism, CDADITagger capable of automatically tagging images efficiently and much more reliable compared to existing frameworks. This framework can tackle real-world applications like tagging a new unknown image as the framework isn't powered by dataset alone but is designed to inculcate images from search engines like Google, Bing, etc. to have comprehensive knowledge of real-time data. These images are classified using CNN and tag-related textual data is classified using decision trees for enhanced performance. While tagging images from the classified tags, are sorted based on the semantic computation values, only the top 50% of the instances classified are selected. The tags which are more correlated to the image are ranked and finalized. The proposed semantically inclined framework CDADITagger outshined the well-established frameworks with an accuracy of 96.60% and a precision of 95.84% making it a more reliable approach. 2022 IEEE. -
CCIR: The Next Frontier in Mobile Network Evolution - Integrating Communication and Computing for Enhanced Services
The mobile RAN is the bridge and enabler between end users and application services, and fortunately, the computational capacity of base stations in RANs[1] has experienced tremendous growth accompanied by increasingly stringent service requirements from emerging applications such as AR/VR. These two trends imply an unprecedented opportunity that the abundant computing resources in base stations could be leveraged to host latency-sensitive applications if being managed properly, thereby giving rise to a new vision named Communication and Computing Integrated RAN (CCIR), where not only communication but also computing services are delivered by RANs in a coherent way[2]. In fact, CCIR implies an even more radical departure of designing future mobile networks-going beyond regarding the role of RANs as connecting links. This article provides an elaboration on the fundamental design philosophies and principles of CCIR, the logical architecture. We will explore further on key tech-nologies toward realizing our vision. Specifically, we provide a thorough view on what CCIR really means - it involves different integration granularities between communication and computing functions; meanwhile it keeps evolving until approaching real-time joint scheduling and holistic resource management based on one unified infrastructure. To assess the feasibility and advantages brought by CCIR, several field experiments were carried out where computing resources are migrated within different base stations. In conclusion, CCIR is a prospective evolution direction for future mobile networks dealing with communication-computation-converged requirements in new service use scenarios[3]. Using current infrastructure more intelligently and efficiently will facilitate better user experience, greener mobile networks as well as serve as better platform for future advancements in wireless technology. Grenze Scientific Society, 2025. -
CBMIR: Content Based Medical Image Retrieval Using Hybrid Texture Feature Extraction Method
Due to the revolution of digital era in the medical domain at various hospitals across the world, the online users on the internet access have been increased. So the amount of collections of digitized medical images has grown rapidly and continuously. As well it is ratting significant to mention that the images are globally used by radiologists, professors in medical colleges and Lab technicians, etc. These Images are increasingly applied to communicate information about patient history. In this context, there is a necessity to develop appropriate systems to manage these medical images in storage and retrieval for diagnosis of the patient information. Another big issue is the convolution of image data and that can be interpreted in different ways. In order to manipulate these data and establish policies to its content is very tedious job. This will raise another big question. These issues motivated the researchers to give more focus on the image retrieval area whose goal is trying to solve those problems to provide an efficient retrieval system to the user community. In this perspective, this work has been proposed to facilitate radiologists, professors in medical colleges, lab technicians, and all other medical image user communities for their purpose for easy access from the remote location. 2022 IEEE.