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Colonialism and Communalism: Religion and Changing Identities in Modern India
Christhu Doss examines how the colonial construct of communalism through the fault lines of the supposed religious neutrality, the hunger for the bread of life, the establishment of exclusive village settlements for the proselytes, the rhetoric of Victorian morality, the booby-traps of modernity, and the subversion of Indian cultural heritage resulted in a radical reorientation of religious allegiance that eventually created a perpetual detachment between proselytes and the others. Exploring the trajectories of communalism, Doss demonstrates how the multicultural Indian society, known widely for its composite culture, and secular convictions were categorized, compartmentalized, and communalized by the racialized religious pretensions. A vital read for historians, political scientists, sociologists, anthropologists, and all those who are interested in religions, cultures, identity politics, and decolonization in modern India. 2024 M. Christhu Doss. -
Colonoscopy contrast-enhanced by intuitionistic fuzzy soft sets for polyp cancer localization
Medical images often suffer from low contrast, irregular gray-level spacing and contain a lot of uncertainties due to constraints of imaging devices and environment (various lighting conditions) when capturing images. In order to achieve any clinical-diagnosis method for medical imaging with better comprehensibility, image contrast enhancement algorithms would be appropriate to improve the visual quality of medical images. In this paper, an automated image enhancement method is presented for colonoscopy images based on the intuitionistic fuzzy soft set. The fuzzy soft set is used to model the intuitionistic fuzzy soft image matrix based on a set of soft features of the colonoscopy images. The technique decomposes the fuzzy image into multiple blocks and estimates a soft-score based on an adaptive soft parametric hesitancy map by using the hesitant entropy for each block to quantify the uncertainties. In the processing stage, an adaptive intensity modification process is done for each block according to its soft-score. These scores are accurately addressed the gray-level ambiguities in colonoscopy images that lead to better results. Finally, the enhanced image achieved by performing a defuzzification together with all unprocessed blocks. Qualitative and quantitative assessments demonstrate that the proposed method improves image contrast and region-of-interest of polyps in colonogram. Experimental results on enhancing a large CVC-Clinic-DB and ASU-Mayo clinic colonoscopy benchmark datasets show that the proposed method outperforms the state-of-the-art medical image enhancement methods. 2020 Elsevier B.V. -
Color Connections inn-Inordinate Invariant Intersection Graphs
Investigating different properties of algebraic graphs is a curious avenue of research in algebraic graph theory, as these algebraic graphs are well-structured classes of graphs. In these lines, various structural properties of the algebraic intersection graph, called the n-inordinate invariant intersection graphs, have been studied in the literature. Color connections in graphs is the assignment of colors or equivalently, labels to the entities of a non-trivial connected graph, whose protocols are defined based on the connectivity aspects. In this article, we study the different color connections in the n-inordinate invariant intersection graphs and their complements, called the n-inordinate invariant non-intersection graphs. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Color image segmentation based on improved sine cosine optimization algorithm
Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signalnoise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Colorimetric and theoretical investigation of coumarin based chemosensor for selective detection of fluoride
Coumarin based Sensor 1 has been designed and synthesized to recognize fluoride ion visually with high selectivity and sensitivity over other anionic analytes through color change from very faint yellow to pink in acetonitrile. The probable binding phenomenon in solution phase has been explained by 1H NMR study of sensor 1 with different concentration of fluoride ions. The binding constant of the sensor 1 with fluoride has been determined as 3.9 104 M?1 and the lower detection limit 6.5 M of the sensor 1 towards fluoride, which has made the sensor 1 as a promising backbone for selective detection of fluoride. For the practical application, test strips based on sensor 1 were fabricated, which could act as a convenient and efficient naked eye F?test kits. The experimentally observed absorption maxima along with its binding nature with fluoride ions also have been supported through theoretical calculations using density functional theory (DFT) calculations. 2022 -
Colorimetric Indicator Solution from Sappan Heartwood (Caesalpinia sappan L.) Extract for Milk Quality Monitoring
This study utilizes Caesalpinia sappan L., traditionally valued for its culinary and medicinal uses, to develop a colorimetric indicator solution for monitoring milk spoilage. The indicator provides real-time updates on milk freshness through color changes induced by biochemical alterations during spoilage. The color of the indicator solution transitions distinctly from orange-red to orange to yellow as the pH shifts from 7.00 to 5.50 to 3.50, correlating with progressive stages of spoilage. An orange-red color was observed for the fresh stage, orange color for about to be spoilt, and yellow color for the spoilt stage of milk samples. The colorimetric changes are attributed to the presence of Brazelin in Caesalpinia sappan L. Digital images of the indicator solution treated with milk samples were analyzed using RGB (red, green, and blue) indices, with the green chromatic shift serving as a reliable parameter for quantifying color changes, providing reliable assessment of milk spoilage. Findings of this study highlight a simple, accessible, and accurate method for milk quality monitoring that requires no specialized equipment or trained personnel, making it suitable for food safety practices in resource-limited settings. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Coloring of n-inordinate invariant intersection graphs
In the literature of algebraic graph theory, an algebraic intersection graph called the invariant intersection graph of a graph has been constructed from the automorphism group of a graph. A specific class of these invariant intersection graphs was identified as the n-inordinate invariant intersection graphs, and its structural properties has been studied. In this article, we study the different types of proper vertex coloring schemes of these n-inordinate invariant intersection graphs and their complements, by obtaining the coloring pattern and the chromatic number associated. 2024 The Author(s) -
Coloring of Non-zero Component Graphs
The non-zero component graph of a finite dimensional vector space V over a finite field F is the graph G(V?) = (V, E), where vertices of G(V?) are the non-zero vectors in V, two of which are adjacent if they share at least one basis vector with non-zero coefficient in their basic representation. In this paper, we study the various types of colorings of non-zero component graph. (2024), (Universidad Catolica del Norte). All rights reserved. -
Colouring of (P3? P2) -free graphs
The class of 2 K2-free graphs and its various subclasses have been studied in a variety of contexts. In this paper, we are concerned with the colouring of (P3? P2) -free graphs, a super class of 2 K2-free graphs. We derive a O(?3) upper bound for the chromatic number of (P3? P2) -free graphs, and sharper bounds for (P3? P2, diamond)-free graphs and for (2 K2, diamond)-free graphs, where ? denotes the clique number. The last two classes are perfect if ?? 5 and ? 4 respectively. 2017, Springer Japan KK, part of Springer Nature. -
COLPOUSIT: A Hybrid Model for Tourist Place Recommendation based on Machine Learning Algorithms
Tourism is an important sector for a country's economic growth. The travel recommendations should be made focused on better growth and attract more travelers. There is a huge amount of travel information and ideas available on the web that allows the users to make poor travel decisions. This paper focuses on building a hybrid travel recommender system by implementing collaborative-based, popularity-based, and nearby place weighted recommender system. The proposed system recommends the travel spots to the users based upon their interests and other criteria specified. In order to implement these methods, we applied a comparative study on different machine learning algorithms for collaborative-based approach and have performed weighted hybridization. These methods provide a personalized and customized list of similar places with respect to places of interest to the users. Thus, a hybrid system built using these methods provides a better recommendation of places with the advantages of these methods. The obtained results confirm that the hybrid method better than other recommender approaches when used separately. 2021 IEEE. -
Combating Evolving Threats: A Systematic Review of Online Ad Fraud Detection
This systematic review investigates online ad fraud detection and brand safety research from 2011 to 2024. The analysis reveals a continuous battle against click fraud and its evolving forms. Machine learning has become a cornerstone of detection efforts, offering superior capabilities compared to traditional methods. The rise of mobile advertising necessitated the development of specialized solutions to address distinct user behavior and data patterns on this platform. However, research highlights an expanding threat landscape beyond click fraud, encompassing impression fraud and placement fraud. Brand safety concerns have also gained prominence, emphasizing the importance of protecting brand reputation. The review underscores the need 2025 by IGI Global Scientific Publishing. All rights reserved. -
Combatting Phishing Threats: An NLP-Based Programming Approach for Detection of Malicious Emails and Texts
Attackers are employing more advanced strategies to trick people into divulging private information or carrying out harmful deeds, and phishing is still a serious cybersecurity risk. We provide a new method in this study that combines algorithms based on AI-based expert systems and deep learning (ML) with the use of NLP-based programming (NLP) approaches to identify fraudulent emails and messages. Using a variety of datasets that include samples of both authentic and phishing messages, our approach preprocesses textual data, extracts relevant characteristics, and trains AI-based expert systems and deep learning models. Metrics including accuracy, precision, recall, and F1-score are used to assess the effectiveness of different AI-based expert systems and deep learning methods, such as logistic regression, random forests, decision trees, and neural networks, among others. To collect semantic information and increase detection accuracy, we also investigate the integration of sophisticated NLP-based techniques, such as word embeddings. The efficacy of our suggested strategy in reducing this common cybersecurity issue is highlighted by our results, which show promising performance in correctly recognizing phishing attempts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Combinational edge detection using multiple color channels and GrabCut
Identification of edges is very important in feature extraction and pattern recognition. An edge of an image detected by converting it from RGB format to a Grayscale image and can sometimes be inefficient and inaccurate. This inefficiency is caused due to various color differences that get erased or rewritten during the process of grayscale conversion. Edge locations in a colored image are derived by analysing the variations in the multiple color channels and merging the gradients in these channels to compute a single edge snapping vector field, which is derived using the Euclidean distances between two distinct pixels in an image. This lets to retain the various multidimensional characterizations of color channels of an image as the color differences can be closely calculated using human perceptions. Hence this method is proved to provide more accuracy in edge detection. Furthermore, the results are improved when combined with a finer edge detection method GrabCut, which allows the user to detect the edges in an image using reduced iterations. The proposed paper uses a combinational approach to efficiently acquire edges in the image by enhancing the color properties of an image and then using the GrabCut method to retrieve the edges present in an image. IAEME Publication. -
Combined Antimicrobial Activity of Chromolaena odorata, Azadirachta indica Leaf Extract, against Streptomyces scabiei (Plant pathogen) and Paenibacillus polymyxa, and their interaction
Streptomyces scabiei is a gram-positive soil dwelling actinobacteria, that causes the scab disease in many plants, especially in potatoes. This causes black blotches on the tuber that negatively impacts the economic and market worth of potatoes. Plant species like Chromolaena odorata and neem are known for their antibacterial potential against common human microflora and agricultural pathogens. Aiming to find natural solutions to plant pathogens, the current study analyzes, the antimicrobial effect of these plant extracts at different concentrations using well diffusion, MIC and MBC. Combination study was also performed to test the synergistic antimicrobial effect against Streptomyces scabiei. In addition, another soil dwelling bacteria, Paenibacillus polymyxa, known for its plant growth promoting potential was also tested against the plant pathogen to understand microbe-microbe interactions through cross streak assay. Both plant extracts showed promising antimicrobial effect against Streptomyces scabiei, however, they showed very less antimicrobial potential against the plant growth promoting bacterium. Moreover, cross streak assay showed that both the bacterium coexisted together. Hence, further studies can be conducted to formulate a biofertilizer containing the two-plant extract in optimum concentrations and Paenibacillus polymyxa to prevent scab disease and also enhance plant growth. 2025, Crop Protection Research Centre. All rights reserved. -
Combined effect of channel to rib width ratio and gas diffusion layer deformation on high temperature Polymer electrolyte membrane fuel cell performance
The present study investigates the combined influence of Channel to Rib Width (CRW) ratio and clamping pressure on the structure and performance of High Temperature-Polymer Electrolyte Membrane Fuel Cell (HT-PEMFC) using a three-dimensional numerical model developed previously. It also considers the impact of interfacial contact resistance between the Gas Diffusion Layer (GDL) and Bipolar Plate (BPP). The structural analysis of the single straight channel HT-PEMFC geometry shows that the von-Mises stress greatly increases in the GDL under the ribs as the CRW ratio increases resulting in considerably high deformation. The cell performance analysis depicts the significance of ohmic resistance and concentration polarization for different CRW ratios, particularly at higher operating current densities. However, in low to medium current density regions, the CRW ratio has little influence on cell performance. A substantial impact on the species, overpotential, and current distributions is observed. The findings also reveal that the CRW ratio significantly affects the temperature distribution in the cell. 2022 Hydrogen Energy Publications LLC -
Combined Enzymatic Action of Pseudomonas psedoalcaligenes Protease and Stutzerimonas xanthomarina Amylase in Textile Blood Stain Removal
Blood is a body fluid. When it contacts oxygen, it can clot, forming a dark solid substance. In accidents involving wounds, blood is released and can stain clothing when absorbed by fabrics. Removing blood stains from fabrics is challenging with various commercial detergents. This study aimed to determine the efficacy of commercially available detergent powder supplemented with amylase and protease enzymes in removing bloodstains from textiles. Stutzerimonas xanthomarina and Pseudomonas pseudoalcaligenes were isolated from soil and examined for their enzymatic activity. The results demonstrated that these enzymes can effectively remove bloodstains from cotton fabric, with maximum activity observed at 50% saturation with ammonium sulfate and specific activity of protease at 93.45 2.17 U/mg and amylase at 36.28 1.36 U/mg. This study suggests that the addition of enzymes to chemical detergents can enhance their surface impact and improve their effectiveness. These findings indicate a potential for the development of more efficient detergents. The Author(s) 2025. -
Combined weighted feature extraction and deep learning approach for chronic obstructive pulmonary disease classification using electromyography
The COVID-19 outbreak has led to a rise in respiratory disease-related deaths, including Chronic Obstructive Pulmonary Disease (COPD). Early diagnosis of COPD is crucial, but it can be challenging to distinguish between different chronic pulmonary diseases due to their similar symptoms, leading to misdiagnosis and time-consuming manual inspections. To address this issue, this paper explores the use of a deep learning model to differentiate COPD from other lung diseases using lung sound captured during Electromyography (EMG). The model includes steps such as noise removal, data augmentation, combined weighted feature extraction, and learning. The model's efficacy was evaluated using various metrics, including accuracy, precision, recall, F1-score, kappa coefficient, and Matthews correlation coefficient (MCC), with and without augmentation. The results show that the model achieved 93% accuracy and outperformed other existing state-of-the-art deep learning models, increasing the robustness of clinical decision-making. The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. -
Combining Text Information and Sentiment Dictionary for Sentiment Analysis on Twitter During Covid
Presence of heterogenous huge data leads towards the 'big data' era. Technique's proliferation is rapidly increasing data and making dynamic changes that results in 'big data' world. Progressive transition in technologies and adoption of social media in the society also stepped into the 'big data' epoch. Social media popularity is uprising attention in the community. This platform reduces the communication gap among people. Recently, tweeter use increased with unprecedented rate. Presence of social media like tweeter has broken the boundaries and touches the mountain in generating the unstructured data. It opened research gate with great opportunities for analyzing data and mining 'valuable information'. Sentiment analysis is the most demanding, versatile research to know user viewpoint. Society current trend can be easily observed through social network websites. These opportunities bring challenges that leads to proliferation of tools. This research works to analyze sentiments using tweeter data using Hadoop technology. This study explores the big data arduous tool called Hadoop. Further, it explains the need of Hadoop in present scenario and role of Hadoop in storing ample of data and analyzing it. Hadoop cluster, HDFS, and Hive are also discussed in detail. Researchers enthusiastic work is deeply studied and presented here. Dataset used in performing the experiment is explained briefly. Moreover, this research explains thoroughly the implementation work and provide workflow. Next session provides the experimental results and analyzes of result. Finally, last session concludes the paper, its purpose, and how it can be used in upcoming research. 2024 IEEE. -
Comic Memes and Sexist Humor in India: Tools for Reinforcement of Female Body-Image Stereotypes
Memes have been described as communicative and aesthetic practices that serve cultural, social, political purpose on a digital platform. Several studies, in the last decade, have attempted to study this digital aesthetic knowledge production as a powerful tool for political, racial, and gender-related discourses. Most often this knowledge is produced through comic multi-media texts. Many theorists believe that, digital media reinforces inequality, marginalization and such other social issues through the audio-visual-textual medium as much as it establishes the counter-discourses for equality, body activism, racial activism and the like. Speed and lack of censorship can be the cardinal reasons for the popularity of these memes. Among the mass-influencing gender-related memes are those encouraging fat-talk and body-image stereotypes. In the Indian context, 'Tag a Friend' memes is one such widely circulated meme which communicates body-shaming messages through sexist humor. It mainly targets the fat/colored/transgender women. The current study examines these memes using multimodal discourse analysis methodology. The paper attempts to investigate the revival/reproduction potential of color-shaming and body-shaming stereotypes via comic memes through Shiffman's memetic dimensions. The analysis establishes that memes can be a prominent site for the re-production of the problematic ideology of body/color shaming even in the 21st century. AesthetixMS 2021 -
Coming out of the desi closet: disclosure of same-sex sexuality in metropolitan-India
Coming out of the closet is a psychosocial process that entails the disclosure of ones non- heteronormative sexual orientation to family, peers, and the wider publica phenomenon that is necessitated by the prevalence of societal heteronormativity. With the recent legal decriminalization of consensual same-sex sexual relationships in India, there is renewed interest in and emergent necessity to expand upon the existing academic discourse on the lives, rights, health and well-being of same-sex attracted individuals in India. The present study accumulates detailed narratives of disclosure of sexual orientation of five male and five female young-adults of same-sex sexuality from ages 18 to 25 in metropolitan cities of India. Thematic narrative analysis is used to gain insight into the factors of being in the closet, those underlying coming out of the closet, and the expectations from and impact of coming out to ones family. Five major themes have emergedthree restraint factors and two propulsion factors influencing sexual identity disclosure. Restraint factors are those that reduce the probability of coming out and these arean incessant pressure to conceal, perceived lack of stability and support, and anticipated disintegration of long-standing familial tradition. Propulsion factors act as catalysts of disclosure and these are target congeniality i.e. approachability of the target of disclosure, and parental validationwhich, when attained, enables the individual to come out more easily to others. The findings have been critically compared and contrasted with the existing body of literature in the domain, which sets the agenda for further inquiry. 2021 Taylor & Francis Group, LLC.