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Decolonising the Gateway of India
This article interrogates how a colonial monument, the Gateway of India in Mumbai, former Bombay, continues to carry and be endowed with a title that is a misplaced embodiment of Indian social histories. Built in the 1920s, this monument, definitely a work of architectural grandeur, continues to carry its erroneous rendition and confines Indias vast social histories to the colonial moment, with an anglo-centric focus. As the monument symbolises the memory of the colonial regime, it also signifies its oppression as well as its exit from the subcontinent, rather than witnessing anyone coming to India, except King George in 1911, as the monuments title seems to suggest. A mnemonic device of colonialism, this misleading label needs to be seriously revisited, for it not only romanticises the colonial past but also fails to lead our memories back to certain crucial episodes in earlier social histories, from which the monument and its specific place, Mumbai, are more or less fully absent. 2023 The Author(s). -
Decolonizing Open Science: Southern Interventions
Hegemonic Open Science, emergent from the circuits of knowledge production in the Global North and serving the economic interests of platform capitalism, systematically erase the voices of the subaltern margins from the Global South and the Southern margins inhabiting the North. Framed within an overarching emancipatory narrative of creating access for and empowering the margins through data exchanged on the global free market, hegemonic Open Science processes co-opt and erase Southern epistemologies, working to create and reproduce new enclosures of extraction that serve data colonialism-capitalism. In this essay, drawing on our ongoing negotiations of community-led culture-centered advocacy and activist strategies that resist the racist, gendered, and classed structures of neocolonial knowledge production in the metropole in the North, we attend to Southern practices of Openness that radically disrupt the whiteness of hegemonic Open Science. These decolonizing practices foreground data sovereignty, community ownership, and public ownership of knowledge resources as the bases of resistance to the colonial-capitalist interests of hegemonic Open Science. The Author(s) 2021. -
Decolonizing social psychology in India: Exploring its role as emancipatory social science /
Psychology & Society, Vol.8, Issue 1, pp.57-74, ISSN: 2041-5893. -
Decolonizing the Home at Home in the Pandemic: Articulating Women's Experience
Feminism bears the promise of liberation of and equality for women. Reading and teaching feminist texts, within the academia and in activist spaces, has provided the opportunity to explore what it means to become and be a woman. This article explores the experience of teaching a course on women's writing at the undergraduate level during the COVID-19 pandemic. Normally, a course on feminist writings is an occasion for self-reflection, thereby providing an opportunity to establish a dialogue between the domestic and the public. Such dialogues took place in secure institutional spaces such as classrooms or conference halls, without the intrusion of the domestic. However, as the teacher-student interaction shifted to an online mode during the pandemic, all the participants in this dialogue, including the instructor and the students, found themselves in domestic spaces, with family members listening. The article chronicles the anxieties of a woman instructor, as she teaches feminist texts from home to learners who are sitting behind computer screen in their homes and the possible impact of feminist ideas on the domestic spaces of all participants. 2022 The Author(s). Published by Oxford University Press on behalf of the English Association. All rights reserved. -
Decolonizing the Mind: Invoking the Vernacular Experience in a Postcolonial Language Classroom
This chapter attempts to understand the teaching-learning practices, programmes, courses, and pedagogies of an English department that recently co-opted cultural studies as a means of decolonisation in a private university in India to understand how cultural diversity, learner diversity, teacher experiences, and learner interests became considered factors in language learning pedagogies and selection of learning content. The research will employ mixed methods of qualitative and quantitative techniques of course content analysis, student interviews to gauge the impact of the learning on the decolonisation process, teacher interviews to understand approaches to task design, and the intended outcome and the strategies and perception changes in material production and task development when the learning shifted to the online mode as a result of the pandemic disruption. 2023 by IGI Global. All rights reserved. -
Decomposition of graphs into induced paths and cycles
A decomposition of a graph G is a collection ? of subgraphs H1,H2,..., Hr of G such that every edge of G belongs to exactly one Hi. If each Hi is either an induced path or an induced cycle in G, then ? is called an induced path decomposition of G. The minimum cardinality of an induced path decomposition of G is called the induced path decomposition number of G and is denoted by ?i(G). In this paper we initiate a study of this parameter. -
Decomposition of graphs into induced paths and cycles
A decomposition of a graph G is a collection ? of subgraphs H1,H2,...,Hr of G such that every edge of G belongs to exactly one Hi. If each Hi is either an induced path or an induced cycle in G, then ? is called an induced path decomposition of G. The minimum cardinality of an induced path decomposition of G is called the induced path decomposition number of G and is denoted by ?i(G). In this paper we initiate a study of this parameter. -
Decomposition of Graphs into Paths and Cycles
Journal of Discrete Mathematics Vol.2013 Article ID 721051 ISSN No. 2090-9845 -
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Deconstruction of representation of working women in Indian femvertisements /
Femvertisements are advertisements wherein brands use the concepts of feminism, women empowerment etc. These advertisements talk about breaking the stereotypes that women are confined to in our society. The irony comes when these empowering advertisements themselves have hidden stereotypes that invariably end up doing more harm than good. -
Decoupling Identification Method of Continuous Working Conditions of Diesel Engines Based on a Graph Self-Attention Network
For diesel engine malfunction detection, machine learning-based intelligent detection approaches have made great strides, but some performance deterioration is also observed due to the significant ambient noise and the change in operating circumstances in the actual application situations. Diesel engine fault diagnostic models can be negatively impacted by complex and erratic working circumstances. Identifying the working condition can provide as a baseline for the current unit operating state, which is crucial information when trying to pinpoint the source of an issue. Many existing techniques for identifying operational states use power as an identifier, segmenting it into discrete intervals from which the current state's power may be derived using a classification model. However, the working condition characteristics should be constant, and defining it exclusively in terms of power would lead to the connection of speed and load elements. In this study, we offer a regular working situation model that is independent of speed and load characteristics, and we use a graph self-attention network to construct a model for identifying the working condition. On a diesel engine research bench, a vast amount of experimental data is acquired for training and testing models, including 32 different operating situations under normal and typical fault scenarios. The R2 adj values of 99.70% and 99.27% for normal and typical defect experimental data, correspondingly, demonstrate the efficacy of the suggested technique under the circumstance of uninformed nnerating situations. 2023 IEEE. -
Decrypting Free Expression: AMMA-WCC Conflict and Comment Culture Rattling the Malayalam Film Industry
The chapter examines the gender-power dynamics in the Malayalam film industry through an analysis of a skit, a YouTube video and trolls related to a recent controversy involving the Association of Malayalam Movies Artistes (AMMA) and the Women in Cinema Collective (WCC). This analysis is supported by an exploration of the historical roots of sexism in the industry and a discussion about how it continues to perpetuate sexism in the industry. The study also investigates the emergence of WCC as a response to the actresss molestation case and the subsequent division within the industry. The research focuses on the Sthree Shaktheekaranam skit performed at AMMAs cultural show, a YouTube video, Oru Feminichi Kadha and a sample of trolls which targeted the WCC and women who refuse to comply with AMMAs patriarchal bias. The chapter analyses the content of these representations, highlighting the power play structuring them. The study sheds light on the contradictions and hypocrisy within the industry and its portrayal of progressive values while perpetuating regressive gender norms. 2024 selection and editorial matter, Francis Philip Barclay and Kaifia Ancer Laskar; individual chapters, the contributors. -
Deducing Water Quality Index (WQI) by Comparative Supervised Machine Learning Regression Techniques for India Region
Water quality is of paramount importance for the wellbeing of the society at large. It plays avery important role in maintaining the health of the living being. Several attributes like biological oxygen demand (BOD), power of hydrogen (pH), dissolved oxygen (DO) content, nitrate content (NC) and so on help to identify the appropriateness of the water to be used for different purposes. In this research study, the focus is to deduce the Water Quality Index (WQI) by means of artificial intelligence (AI)-based machine learning (ML) models. Six parameters, namely BOD, DO, pH, NC, total coliform (CO) and electrical conductivity (EC) are used to measure, analyze and predict WQI using nine supervised regression machine learning techniques. Bayesian Ridge regression (BRR) and automatic relevance determination regression (ARD regression) yielded a low mean squared error (MSE) value when compared to other regression techniques. ARD regression model parameters as independent a priori so that non-zero coefficients do not exploit vectors that are not just sparse, but they are dependent. In the estimation process, BRR contains regularization parameters; regularization parameters are not set hard but are adjusted to the relevant data. Due to these reasons, ARD regression and BRR models performed better. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications
Machine learning (ML) is currently a crucial tool in the field of cyber security. Through the identification of patterns, the mapping of cybercrime in real time, and the execution of in-depth penetration tests, ML is able to counter cyber threats and strengthen security infrastructure. Security in any organization depends on monitoring and analyzing user actions and behaviors. Due to the fact that it frequently avoids security precautions and does not trigger any alerts or flags, it is much more challenging to detect than traditional malicious network activity. ML is an important and rapidly developing anomaly detection field in order to protect user security and privacy, a wide range of applications, including various social media platforms, have incorporated cutting-edge techniques to detect anomalies. A social network is a platform where various social groups can interact, express themselves, and share pertinent content. By spreading propaganda, unwelcome messages, false information, fake news, and rumours, as well as by posting harmful links, this social network also encourages deviant behavior. In this research, we introduce Deep Belief Network (DBN) with Triple DES, a hybrid approach to anomaly detection in unbalanced classification. The results show that the DBN-TDES model can typically detect anomalous user behaviors that other models in anomaly detection cannot. 2024 World Scientific Publishing Company. -
Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT
The diabetes is a critical disease from the small children to old age people. Due to improper diet and physical activities of the living population, obesity becomes prevalent in young generation. If we analyze self care of individual life, no man or women ready to spend their time for health care. It leads to problem like diabetes, blood pressure etc. Today is a busy world were robots and artificial machines ready to take care of human personal needs. Automatic systems help humans to manage their busy schedule. It motivates us to develop a diabetes motoring system for patients using IoT device in their body which monitors their blood sugar level, blood pressure, sport activities, diet plan, oxygen level, ECG data. The data are processed using feature selection algorithm called as particle swarm optimization and transmitted to nearest edge node for processing in 5G networks. Secondly, data are processed using DBN Layer. Thirdly, we share the diagnosed data output through the wireless communication such as LTE/5G to the patients connected through the edge nodes for further medical assistance. The patient wearable devices are connected to the social network. The Result of our proposed system is evaluated with some existing system. Time and Performance outperform than other techniques. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Deep CNN Based Interpolation Filter for High Efficiency Video Coding
Video coding is a current focus in research area as the world focus more on multimedia transfer. High Efficiency Video Coding (HECV) is prominent among existing one. The interpolation in HEVC with fixed half-pel interpolation filter uses fixed interpolation filter derived from traditional signal processing methods. Some research came up with CNN based interpolation filter too, here we are proposing a deep learning-based interpolation filter to perform interpolation in inter prediction in HEVC. The network extracts the low-resolution image and extract the patch and feature in that to predict a high-resolution image. The network is trained to predict the HR image for the given patch, it can be repeated to generate the full frame in the HEVC. The system uses cleave approach to reduce the computational complexity. The trained network is validated and tested for different inputs. The results show an improvement of 2.38% in BD-bitrate saving for low delay configuration. 2024 IEEE. -
Deep Convolution Neural Network for RBC Images
The suggested study's objectives are to develop an unique criterion-based method for classifying RBC pictures and to increase classification accuracy by utilizing Deep Convolutional Neural Networks instead of Conventional CNN Algorithm. Materials and Procedures A dataset-master image dataset of 790 pictures is used to apply Deep Convolutional Neural Network. Convolutional Neural Network and Deep Convolutional Neural Network comparison using deep learning has been suggested and developed to improve classification accuracy of RBC pictures. Using Gpower, the sample size was calculated to be 27 for each group. Results: When compared to Convolutional Neural Network, Deep Convolutional Neural Network had the highest accuracy in classifying blood cell pictures (95.2%) and the lowest mean error (85.8 percent). Between the classifiers, there is a statistically significant difference of p=0.005. The study demonstrates that Deep Convolutional Neural Networks perform more accurately than Conventional Neural Networks while classifying photos of blood cells[1]. 2022 IEEE. -
Deep Convolutional Neural Network Driven Interpolation Filter for High Efficiency Video Coding
Research in video coding has gained significant importance in recent years, driven by the increasing demand for multimedia transmission. High Efficiency Video Coding (HEVC) has emerged as a prominent standard in this field. Interpolation is a crucial aspect of HEVC, particularly when using fixed half-pel interpolation filters derived from traditional signal processing techniques. In recent times, there has been an exploration of interpolation filters that are based on Convolutional Neural Networks (CNNs). Conventional signal processing techniques are used in traditional HEVC methods to employ fixed half-pel interpolation filters. Recent advancements have delved into the application of Convolutional Neural Networks (CNNs) to enhance interpolation performance. Our proposed method utilises a sophisticated CNN architecture specifically crafted to extract valuable features from low-resolution image patches and accurately predict high-resolution images. The network consists of multiple layers of CNN blocks, which utilise 1 and 3 convolutional kernels to enable efficient and thorough feature extraction through parallel processing. This architecture improves computational efficiency and greatly enhances prediction accuracy The suggested interpolation filter shows a 2.38% enhancement in bitrate savings, as evaluated by the BD-rate metric, specifically in the low delay P configuration. This highlights the potential of deep learning techniques in improving video coding efficiency. 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). -
Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis
The complexity of classifying malware is high since it may take many forms and is constantly changing. With the help of transfer learning and easy access to massive data, neural networks may be able to easily manage this problem. This exploratory work aspires to swiftly and precisely classify malware shown as grayscale images into their various families. The VGG-16 model, which had already been trained, was used together with a learning algorithm, and the resulting accuracy was 88.40%. Additionally, the Inception-V3 algorithm for classifying malicious images into family members did significantly improve their unique approach when compared with the ResNet-50. The proposed model developed using a convolution neural network outperformed the others and correctly identified malware classification 94.7% of the time. Obtaining an F1-score of 0.93, our model outperformed the industry-standard VGG-16, ResNet-50, and Inception-V3. When VGG-16 was tuned incorrectly, however, it lost many of its parameters and performed poorly. Overall, the malware classification problem is eased by the approach of converting it to images and then classifying the generated images. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection
In the landscape of diabetes-related ocular complications, diabetic retinopathy stands as a formidable challenge, reigning as the leading cause of vision impairment worldwide. Despite extensive research, the quest for effective treatments remains an ongoing pursuit. This study explores the burgeoning domain of AI-driven approaches in ocular research, particularly focusing on diabetic retinopathy detection. It delves into various diagnostic methodologies, encompassing the detection of microaneurysms, identification of hemorrhages, and segmentation of blood vessels, primarily utilizing retinal fundus photographs. Our findings juxtapose conventional machine learning techniques against deep neural networks, showcasing the remarkable efficacy of Convolutional neural network (CNN) and Random Forest (RF) in segmenting blood vessels and the robustness of deep learning in lesion identification. As we navigate the quest for clearer vision, artificial intelligence takes center stage, promising a transformative leap forward in the realm of vision care. 2024 River Publishers.