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Unveiling the Role of Psychological Pain within Informal Institutions in Addressing Intimate Partner Violence
This study redefines the exploration of Intimate Partner Violence (IPV) by emphasizing psychological pain as the pivotal element of trauma, shifting away from focusing solely on aftermath experiences. Psychological pain has been considered as a core area for this research through the lens of biopsychosocial model and unbearable psychache. These theoretical approaches examine psychological pain as the foundational factor in subsequent victim experiences and reactions involved in intimate partner violences (IPV). Utilizing an in-depth case study method, it rigorously analyzes a victim's narrative within the IPV realm, detailing the intricate connection between psychological pain and resulting trauma. The participant of this study is visually impaired and the perceived pain and its intensity in the context of disability have also been analyzed. This pain significantly influences victimization and exacerbates physical suffering. IPV, trauma, and visual impairment intersect, creating complex challenges for individuals and communities. The paper discusses pain and IPV in the context of informal institutions and their complementary or challenging roles. By emphasizing psychological pain as the core of trauma dynamics, this research redefines the understanding of pain involved in IPV. The insights gained can contribute to the crucial implications for interventions among survivors in the realm of intimate partner violence. 2024 by authors, all rights reserved. -
Electrocatalytic oxidation and determination of morin at a poly (2,5-dimercapto-1,3,4-thiadiazole) modified carbon fiber paper electrode /
Journal Of The Electrochemical Society, Vol.163, Issue 8, ISSN:0013-4651 (print) 1945-7111 (web). -
A strategic evaluation on competency of Karanataka destinations through destination management organizations /
American Journal Of Industrial and Business Management, Vol.6, pp.102-108, ISSN: 2164-5175. -
Destination governance and a strategic approach to crisis management in tourism /
Journal Of Investment And Management, Vol.5, Issue 1, pp.1-5, ISSN: 2328-7721 (Online), 2328-7713 (Print). -
Socioeconomic determinants of COVID-19 in Asian countries: An empirical analysis
The spread of coronavirus disease, 2019, has affected several countries in the world including Asian countries. The occurrences of COVID infections are uneven across countries and the same is determined by socioeconomic situations prevailing in the countries besides the preparedness and management. The paper is an attempt to empirically examine the socioeconomic determinants of the occurrence of COVID in Asian countries considering the data as of June 18, 2020, for 42 Asian countries. A multiple regression analysis in a cross-sectional framework is specified and ordinary least square (OLS) technique with heteroscedasticity corrected robust standard error is employed to obtain regression coefficients. Explanatory variables that are highly collinear have been dropped from the analysis. The findings of the study show a positive significant association of per capita gross national income and net migration with the incidence of total COVID-19 cases and daily new cases. The size of net migration emerged to be a potential factor and positive in determining the total and new cases of COVID. Social capital as measured by voters' turnout ratio (VTR) in order to indicate the people's participation is found to be significant and negative for daily new cases per million population. People's participation has played a very important role in checking the incidence of COVID cases and its spread. In alternate models, countries having high incidence of poverty are also having higher cases of COVID. Though the countries having higher percentage of aged populations are more prone to be affected by the spread of virus, but the sign of the coefficient of this variable for Asian country is not in the expected line. Previous year health expenditure and diabetic prevalence rate are not significant in the analysis. Therefore, people-centric plan and making people more participatory and responsive in adhering to the social distancing norms in public and workplace and adopting preventive measures need to be focused on COVID management strategies. The countries having larger net migration and poverty ratio need to evolve comprehensive and inclusive strategies for testing, tracing, and massive awareness for sanitary practices, social distancing, and following government regulation for management of COVID-19, besides appropriate food security measures and free provision of sanitary kits for vulnerable section. 2020 John Wiley & Sons Ltd -
Quality of work life and work motivation among garment sector executive employees /
The International journal Of Indian Psychology, Vol.3, Issue 1, pp.111-119, ISSN No: 2349-3429. -
Optimized handwritten character recognition using artificial neural network
Handwritten character recognition (HCR) plays important role in the modern world and is one of the focused area of research in the field of image processing and pattern recognition. Handwritten character recognition refers to the process of conversion of hand-written character into printed/word file character which can immensely improve the interface between man and machine in numerous application. It is difficult to process with great variations in writing styles, different size and orientation angle of the character that are existing. Also segmentation of cursive handwritten text is difficult as the edges cant be detected easily. There are numerous approaches to recognize handwritten data. The images are acquired using a digital camera or scanner and stored in standard format like JPG, PNG etc. The second stages include pre-processing techniques like Binarization, Skeletonization, thinning, resizing the image and segmentation. In our work we mainly concentrated on extracting statistical features of alphabets like mean, variance, standard deviation, Skewness and kurtosis, which differentiates a character from another. We used feed forward algorithm to train Artificial Neural Network (ANN). The features of input character after pre-processing are fed into ANN. A database of 650 samples is created to test input samples for recognition of character by neural net-work. The Experimental results that we have achieved show 88.46 % accuracy rate with minimum time taken for training. IJSTR 2020. -
A New Economics Awaits Us
This article attempts to look into the concept of othering in the context of urban development. The major motivation for the initiation of this article came after reading Dipankar Guptas book review titled A New Sociology Awaits Us (EPW, 26 December 2020), which mainly concentrated on urban affairs. 2022 Economic and Political Weekly. All rights reserved. -
A space for the space theorist remembering henri lefebvre
Despite being somewhat ignored in Indian academia, Henri Lefebvre comes to our rescue every time, helping us understand and respond to spacetime challenges. 2020 Economic and Political Weekly. All rights reserved. -
Food and communities in post-COVID-19 cities: Case of India
While Covid-19 pandemic has affected countries across the world, the burden has been shared disproportionately by urban poor from the cities in Global South. In much of Global South, while cities have emerged as growth centers, they are mostly driven by informalities, belying the image of cities, visualized in the mainstream development economics literature as a place of secured formal jobs that free one from the drudgery of rural life. Covid-19 pandemic has exposed these fault-lines in the cities. India serves as a typical case of such urban-centric growth, with informal workers, predominated by disadvantaged social and religious categories, accounting for 81% of workers in urban space. In cities, migrant in general and seasonal migrants increasingly account for bulk of informal workforce. The lockdown imposed in the wake of Covid-19 pandemic left the community of households reliant on informal works for livelihoods, without any rights and entitlements, which affect their access to food. The review of evidence collected in both primary surveys and macro level data points towards sluggishness in recovery of jobs, which coupled with high food inflation, suggests that access to food continues to be an issue in urban governance. The paper calls for a roadmap entailing both short-term and long-term measures to build sustainable urban livelihoods for ensuring food secure urban space in India. 2023 The Author(s) -
Spectral and type I X-ray burst studies of 4U 1702?429 using AstroSat observations
4U 1702?429, an atoll-type neutron star low-mass X-ray binary, was observed twice by the AstroSat/Soft X-ray Telescope (SXT) and Large Area X-ray Proportional Counters (LAXPC-20) on 2018 April 27 and 2019 August 8. Persistent emission spectra of the source were well fitted with the model combination - constant tbabs (thcomp diskbb+powerlaw). The parameters obtained from the spectral analysis revealed the source to be in a hard spectral state during the observations. Time-resolved spectral analyses were performed on the three type I X-ray bursts detected from the source. Burst analysis showed that the source underwent a photospheric radius expansion. Consequently, the radius of the neutron star and distance to the source (with isotropic and anisotropic burst emission) were obtained as 12.65+?008690 km and 6.92+?000916 and 8.43+?001020 kpc, respectively. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Automated Brain Imaging Diagnosis and Classification Model using Rat Swarm Optimization with Deep Learning based Capsule Network
Earlier identification of brain tumor (BT) is essential to increase the survival rate of the patients. The commonly used imaging technique for BT diagnosis is magnetic resonance imaging (MRI). Automated BT classification model is required for assisting the radiologists to save time and enhance efficiency. The classification of BT is difficult owing to the non-uniform shapes of tumors and location of tumors in the brain. Therefore, deep learning (DL) models can be employed for the effective identification, prediction, and diagnosis of diseases. In this view, this paper presents an automated BT diagnosis using rat swarm optimization (RSO) with deep learning based capsule network (DLCN) model, named RSO-DLCN model. The presented RSO-DLCN model involves bilateral filtering (BF) based preprocessing to enhance the quality of the MRI. Besides, non-iterative grabcut based segmentation (NIGCS) technique is applied to detect the affected tumor regions. In addition, DLCN model based feature extractor with RSO algorithm based parameter optimization processes takes place. Finally, extreme learning machine with stacked autoencoder (ELM-SA) based classifier is employed for the effective classification of BT. For validating the BT diagnostic performance of the presented RSO-DLCN model, an extensive set of simulations were carried out and the results are inspected under diverse dimensions. The simulation outcome demonstrated the promising results of the RSO-DLCN model on BT diagnosis with the sensitivity of 98.4%, specificity of 99%, and accuracy of 98.7%. 2023 World Scientific Publishing Company. -
Brand awareness of 'generation y' customers towards doughnut retail outlets in India
The Research is all about knowing the customers acquiring top of mind recall about doughnut retail outlets in Bangalore city, India through various methods. Once the brand is established in the minds of the consumers, it occupies a unique position and special meaning and value is generated. Brand awareness is the consumer's conscious or unconscious decision, expressed through intention or behavior, to repurchase a brand continually. In order to create brand loyalty, advertisers must break consumer habits, help them to acquire new habits and reinforce those habits by reminding consumers of their purchase and encourage them to continue purchasing those products in the future. 'Generation Y' refers to customers millennial, the generation of people born during the 1980s and early 2000s. 'Generation Y' consumer's access social media on daily basis but they often ignore advertisements that are targeted to them. The previous research works on' Generation Y' customers emphasize that marketers must focus on social media marketing to draw the attention of these customers. Determining the brand awareness of 'Generation Y' customers was considered, in order to know the present level of awareness about the doughnut brands, increase the customer traffic and sales as 'Generation Y' customers are the target customers for doughnut retail outlets. -
Brand awareness of 'generation y' customers towards doughnut retail outlets in India /
The Journal Of Business And Retail Management Research, Vol.11, Issue 4, pp.108-113, ISSN: 2056-6271 (Online) 1751-8202 (Print). -
Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN
Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection. 2023, Modern Education and Computer Science Press. All rights reserved. -
Twitter sentiment analysis on online food services based on elephant herd optimization with hybrid deep learning technique
Twitter is a social media stage, making it a valuable resource for learning about peoples opinions, feelings, and thoughts. For this reason, experts came up with methods to analyse the tone of tweets and determine whether they were favourable or negative. This article aims to assist businesses, and especially app-based meal delivery businesses, in conducting competitive research on social broadcasting and transforming social broadcasting data into data production for decision-makers. In this analysis, we compared Swiggy, Zomato, and UberEats. Customers tweets about all these brands are obtained using R-Studio, and a deep learning-based sentiment examination approach is functional on the retrieved tweets. The pseudo-inverse learning autoencoder is able to provide feature extraction in the form of an analytic solution after pre-processing, without resorting to many iterations. In this research, we suggest framework for combining the Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) models. ConvBiLSTM is used, which is a word embedding model that uses numerical values to represent tweets. The CNN layer takes the feature implanting as input and outputs lower features. In this instance, elephant herd optimization is used to fine-tune the Bi-LSTM weights. Among the three firms, the results indicate that Zomato got the most positive feedback (29%), followed by Swiggy (26%), and UberEats (25%). Zomato also had fewer bad reviews than Swiggy and UberEats, with only 11% of users having a poor experience. In addition, tweets were evaluated for unfavourable views against all three meal delivery services, and suggestions for improvement were offered. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
An efficient clustering approach for optimized path selection and route maintenance in mobile ad hoc networks
Mobile ad hoc network (MANET) is arranged with multiple nodes that communicate wirelessly. However, MANET communication suffers from various issues such as inadequate security, low stability, high power consumption, and a lack of specific infrastructure of the network. Moreover, the route failure happened in the network due to the unrestricted node movement, which has increased energy utilization, delay, and reduced lifetime of the nodes. To overcome these issues, the novel Eagle Based Density Clustering (EBDC) approach is developed in this research that predicts the link failure and increased the lifetime of the nodes. Here, the developed EBDC approach is utilized for clustering and route maintenance in MANET for that it creates the nodes using the star topology. Initially, the developed approach selects the Cluster Head and transmits the message through the created path. Subsequently, the link failure is detected by the EBDC model, and it creates a new reference layer to replace the exhausted layer. Hence, the developed EBDC model has enhanced the network lifetime and reduced energy utilization. Furthermore, this model is implemented using Network Simulator 2, and the parameters like accuracy, energy consumption, Packet Delivery Ratio, network lifetime, end-to-end delay, and throughput are calculated. Additionally, the attained outcomes are compared with prevailing methods for evaluating the efficiency of the developed approach. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet
Integrating cutting-edge technology with conventional farming practices has been dubbed smart agriculture or the agricultural internet of things. Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks. 2023, Springer Nature Limited. -
The Factors Impacting Parental Choice in Picking Non-public Schools for Their Children
The purpose of the study was to investigate the school related factors influencing parental choice of private schools in the city of Bangalore. The study intended to analyze factors affecting parents choice of private schools in Bangalore, to discuss the extent to which various factors influence parents choice of private schools. The study used descriptive survey design. The target population of this study consisted of all parents of students studying in private primary schools in the city of Bangalore. A total sample of 180 parents was drawn purposively from Bangalore. The tool used for collecting the data was a self-constructed questionnaire which included 32 statements were prepared on the basis of a 5-point Likert scale. The study identified seven distinct factors affecting the parents decision of choosing a private school. Among these the factor that was seen to have most significant influence on parents decision to choose a private was school environment. The second most important factor that parents considered was the School quality. Third, parents considered curricular activities offered by school. Next, parents considered Quality of instruction while choosing a school. However, student welfare, parental involvement and proximity to the area of residence were considered less important by parents when choosing a school. The Author(s) 2021. -
Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain
Purpose: This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain. Design/methodology/approach: A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups. Findings: The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups. Practical implications: Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience. Originality/value: Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work. 2024, Emerald Publishing Limited.