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Decoding boomerang hiring: A suggestive framework to improve organizational efficiency
In an ever changing, volatile and dynamic business environment, efforts put by the human resources reflect the organizational efficiency. Organizations should always focus on maintaining smooth relations with the Alumni and Boomerangs as they play a crucial role in the expanding horizons of business. A positive word of mouth also helps in improving the goodwill and image of the company. It will encourage the prospective employees to view the organization in a positive light. Rehiring former employees is one of the mechanisms for recruitment used by a large number of corporations primarily because of the inherent advantage of added experience as well as savings in terms of cost of recruitment and training. The present study attempts to give an overview of Boomerang Hiring, the possible value additions being made in terms of Human Capital and Social Capital on basis of the type of respective organizations they are returning from. Additionally, the perspective of the rehired employee is also presented. The study is further enriched by quoting a few instances from the corporate world. The Rehiring Strategies tailored as per organizational requirements will lead towards holistic growth and development of the entity. 2020 SERSC. -
Decoding Cognitive Control and Cognitive Flexibility as Concomitants for Experiential Avoidance in Social Anxiety
Background and objectives: Avoidance is regarded as a central hallmark of social anxiety. Experiential avoidance is perilous for social anxiety, specifically among university students (young adults). Additionally, cognitive control and cognitive flexibility are crucial components of executive functions for a fulfilling and healthy lifestyle. The current research is a modest attempt to understand how cognitive flexibility and cognitive control affect the emergence of experiential avoidance in social anxiety in young adults. Methods: Using an ex-post facto design, the Social Phobia Inventory was employed to screen university students with social anxiety based on which one hundred and ninety-five were identified. Thereafter, participants completed the standardized measures on experiential avoidance, cognitive control and cognitive flexibility. Results: A stepwise multiple regression analysis was computed wherein the cognitive control predicts an amount of 5% of variance towards experiential avoidance, whereas a 10% of additional variance has been contributed by cognitive flexibility. Interpretation and Conclusions: The statistical outcome indicated that cognitive control is positively associated with experiential avoidance which is a negative correlate to cognitive flexibility among university students. Both also emerged as significant predictors of experiential avoidance and add a cumulative variance of 15% towards the same. This conclusion supports the need for improved and efficient management techniques in counseling and clinical settings. The Author(s) 2024. -
Decoding HERO: Predicting psychological capital with subjective well-being
The positive psychology movement has gained momentum in recent years and organizations have ascribed great importance to employee well-being in light of the favorable outcomes associated with it. The widely researched Psychological Capital (PsyCap) has been consistently linked to well-being across a variety of contexts but a gap still exists in literature about what lies to the 'left' of psychological capital. The present study attempts to fill this gap by examining subjective well being components- positive and negative affect and life satisfaction, as potential antecedents of PsyCap. The Academic PsyCap questionnaire, the Positive and Negative Affect Schedule (PANAS) and the Satisfaction with Life Scale (SWLS) were administered to participants. Results confirmed the expected associations between affect and PsyCap-positive affect positively predicted PsyCap and its four constituents whereas negative affect emerged as a negative predictor of PsyCap and its dimensions. Life satisfaction positively predicted only individuals' total hope scores. Thus, highlighting the role of subjective well-being components as antecedents of PsyCap, these findings suggest that promoting higher positive affect and lower negative affect can do more than just make individuals feel good, rather, it can bolster their reservoirs of crucial psychological resources as well. 2021 Ecological Society of India. All rights reserved. -
DECODING INTENTIONS TO PURCHASE ORGANIC FOOD PRODUCTS IN AN EMERGING ECONOMY VIA ARTIFICIAL NEURAL NETWORKS
Purpose. This study investigates the factors influencing consumers intentions to purchase organic food products in an emerging economy. It addresses the knowledge gap regarding the slower growth of the organic food market in these regions despite the global trend toward environmental sustainability. Methodology / approach. A survey approach involving 350 participants was used. Structural equation modeling (SEM) with SmartPLS 4 and Artificial Neural Network (ANN) with IBM SPSS 28 were used to analyse the impact of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products. Results. The study found significant positive influences of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products, explaining 63.1 % of the variance. Both the analysis approaches (PLS-SEM & ANN) revealed that, health consciousness, followed by awareness of need, emerged as the most important factor related to the intention to purchase organic food products. The results highlight the importance of awareness and personal values in driving pro-environmental behaviour. Originality / scientific novelty. This research offers essential insights into the determinants of organic food purchase intentions in an emerging economy. It emphasises the significance of awareness and personal values in fostering sustainable consumption behaviour, addressing a less explored area in existing literature. Practical value / implications. The findings have important implications for policymakers and marketers. Strategies focused on consumer education about the benefits of organic food can enhance awareness and appeal. Understanding core psychological needs and beliefs that shape consumer motivations can guide the development of effective marketing strategies. The study highlights the strong environmental consciousness among consumers and their desire to protect the environment. 2024, Institute of Eastern European Research and Consulting. All rights reserved. -
Decoding Retail Realities: Traditional Retailers' Outlook on Sales Erosion to Modern Retail Economy
The traditional retail landscape in Indian metropolises has changed significantly in the last several decades, mostly due to the modern retail economy's growth, including corporate chain stores and e-commerce sites. Small merchants have been gradually displaced as a result of this paradigm shift, which has been exacerbated by changed Foreign Direct Investment (FDI) laws that have brought significant money into the Indian market, as well as a rise in consumer disposable income and the wave of digitalization. This study explores small merchants' consequences as they contend with the growing power of organized retail and e-commerce behemoths. Despite earlier research studies mostly focusing on the organized trade's exponential rise due to changing customer behavior, this paper fills this gap by illuminating the traditional retailers perspective towards the contemporary retail landscape and highlighting the threats to small businesses with a traditional focus. The study uses empirical analysis using tools like SPSS and SEM models to examine the initial troubles faced by small retailers of fast-moving consumer goods (FMCG), highlighting the difficulties they face in competing with the powerful forces of deep discounting, massive sales events, and evolving consumer tastes. This exploratory research analyzed the undermining factors like utilitarian and hedonic, purchasing patterns, menaces, hindrances, pecuniary and location as reasons for the retail paradigm from traditional to modern trade. The outcome emphasized that utilitarian factors like ambience, experience, status, variety, payment modes, single-store distribution and assortment are the drivers behind the explosion of traditional trade by the modern trade in retail economy. 2024 The Author(s). -
Decoding the X-Ray Flare from MAXI J0709-159 Using Optical Spectroscopy and Multiepoch Photometry
We present a follow-up study on the recent detection of two X-ray flaring events by MAXI/Gas Slit Camera observations in soft and hard X-rays from MAXI J0709-159 in the direction of HD 54786 (LY CMa), on 2022 January 25. The X-ray luminosity during the flare was around 1037 erg s-1 (MAXI), which got reduced to 1032 erg s-1 (NuSTAR) after the flare. We took low-resolution spectra of HD 54786 from the 2.01 m Himalayan Chandra Telescope and the 2.34 m Vainu Bappu Telescope (VBT) facilities in India, on 2022 February 1 and 2. In addition to H? emission, we found emission lines of He i in the optical spectrum of this star. By comparing our spectrum of the object with those from the literature we found that He i lines show variability. Using photometric studies we estimate that the star has an effective temperature of 20,000 K. Although HD 54786 is reported as a supergiant in previous studies, our analysis favors it to be evolving off the main sequence in the color-magnitude diagram. We could not detect any infrared excess, ruling out the possibility of IR emission from a dusty circumstellar disk. Our present study suggests that HD 54786 is a Be/X-ray binary system with a compact object companion, possibly a neutron star. 2022. The Author(s). Published by the American Astronomical Society. -
Decolonising Caste in the Indian Context: The Psyche of the Oppressor
Caste is a social construct as well as a psychological phenomenon. So far, it has been predominantly viewed, understood and researched through the lens of anthropology, sociology, economics and political science. However, very little understanding has been gained in the domain of psychological science with respect to caste in the Indian context. The population of the Global South (includes the regions of Asia, Africa, Latin America and Oceania) cannot be understood with the frameworks and research undertaken by the Global North (Europe and North America, known as the West, the industrialised world) because the knowledge production centres of psychology have predominantly been Euro-American centric, as many critics have pointed out. Although research has been scarce in relation to caste and psychology, it has mostly revolved around the oppressed. Therefore, this article aims to shift the focus from the oppressed to the oppressor. To understand Indian human behaviour and thought, it is essential to view it through the lens of the colonial past, the caste system and religion, which are intertwined with each other. This article aims to look at the psychology of the oppressor in the Indian context through the psychological frameworks of purity and pollution. It also stems from the premise that casteism is inculcated through modelling and intergenerational learning. Hence, the above-mentioned factors help to understand unequal power relations and discrimination, which facilitate the decolonisation of the Indian psyche. It also highlights the influence of colonisation on the mind and behaviour with respect to caste. 2023 Department of Psychology, University of Allahabad. -
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. -
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 -
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 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 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. -
Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection
In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of the digital content. The computer vision features based on the frame change are extracted using a proposed deep learning model called the Cascaded Deep Sparse Auto Encoder (CDSAE) trained by temporal CNN. The detection process is performed using a Deep Neural Network (DNN) to classify the deep fake image/video from the real image/video. The proposed model is implemented using Face2Face, FaceSwap, and DFDC datasets which have secured an improved detection rate when compared to the traditional deep fake detection approaches. 2022. Balasubramanian et al. -
Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset
Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset. 2023 Institute of Advanced Engineering and Science. All rights reserved.