Browse Items (11807 total)
Sort by:
-
Impact of climate adaption and resilience on mental and social wellbeing
According to United Nations Climate Change, adaptation refers to adjustments in ecological, social, or economic systems in response to actual or expected climatic stimuli and their effects. In contrast, resilience is all about being able to cope with unexpected or difficult circumstances and being able to persevere in the face of challenges, overcoming barriers and bouncing back after setbacks. While adaptability involves changing to manage under new conditions, resilience, through bouncing back, implies the ability to revert to a previous, more positive state after experiencing some difficulty or challenge. Marianne Hrabok (2020) The pathways through which extreme climate events affect mental health are numerous and include direct (e.g., exposure to trauma) and indirect (social, economic disruptions) routes (Ramadan and Ataallah, 2021). Climate-related catastrophes have significant impacts on the mental well-being of the populations involved, causing surges in cases of depression, anxiety, and posttraumatic stress disorder (PTSD) primarily (Gina Martin, 2022). Studies suggest that the mental well-being impacts and negative emotions that stem from climate change awareness may be shared among child populations (Doherty & Clayton, 2011). In addition to direct and indirect psychological impacts, climate change is likely to impact social and community relationships. Some of these impacts may result directly from changes in climate, but most are likely to be indirect results of shifts in how people use and occupy territory. The response to climatic change by any living organism or system is to adapt or be resilient. This chapter discusses the different types of adaptation and resilience strategies theoretically and successfully adopted by various countries in the world. These strategies have a remarkable impact on the mental and social well-being of its stakeholders. With the discussion on the impacts, this chapter will also suggest strategies to be adopted at an individual level to either adapt or resilience toward climatic change to enhance mental and social well-being at the same time. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
A study on the role of media in the promotion of the konkani language /
Traditions and language have become crucial aspects in keeping up the culture of a particular place. A language is one of the most important means through which a culture can be sustained and even prevented from dying in the light of the westernization and globalization of the society that we are living in today. One such language that has been facing immense threat against the growing strengths and forces of the Westernised world is that of Konkani, a language most typically spoken on the Western coast of India, also known as the Konkan coast. -
Impacts of Cloud Computing in Digital Marketing
In modern day of digital marketing the cloud computing is proving extremely beneficial links for businesses. Moreover, it's characteristic to access the stored data from anywhere makes it more popular among the entrepreneurs. The present paper is an exploration of the cloud computing in respect of digital marketing. The paper defines and correlates the term cloud computing, digital marketing, as well as also elaborates about benefits that can be harvested by the integration of cloud computing in digital marketing strategy. 2021 IEEE. -
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. -
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. -
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. -
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. -
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). -
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. -
Can bot be my mental health therapist? A pandemic panorama
[No abstract available] -
Analysis of MRI Images to Discover Brain Tumor Detection Using CNN and VGG-16
Brain tumor is a malignant illness where irregular cells, excess cells and uncontrollable cells are grown inside the brain. Now-a-days Image processing plays a main role in discovery of breast cancer, lung cancer and brain tumor in initial stage. In Image processing even the smallest part of tumor is sensed and can be cured in early stage for giving the suitable treatment. Bio-medical Image processing is a rising arena it consists of many types of imaging approaches like CT scans, X-Ray and MRI. Medical image processing may be the challenging and complex field which is rising nowadays. CNN is known as convolutional neural network it used for image recognition and that is exactly intended for progression pixel data. The performance of model is measured using two different datasets which is merged as one. In this paper two models are used CNN and VGG-16 and finding the best model using their accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Internet of Things Enhancing Sustainability of Business
When one assumes that the current era is the era for digital revolution then the Internet of Things (IoT) is supposed to be one of the most significant among all. It is the IoT which is assisting the bussinesses. Current IoT applications, on the other hand, are still in their early stages, and the true capacity of viable business opportunities has yet to be realised. However, IoT adoption may need considerable integration and experienced personnel. It also frequently generates new requirements in terms of security and interoperability, or the ability for different computer hardware systems as well as software applications to "speak"to one another. 2022 IEEE. -
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. -
Production of bioactive compounds from cell and organ cultures of Centella asiatica
Centella asiatica, commonly known as mandukaparni, has garnered recognition for its efficacy in addressing a spectrum of health concerns. Its diverse pharmacological properties encompass roles in treating neuro-related issues, gastrointestinal problems, and cardiovascular conditions. Furthermore, it exhibits multifaceted therapeutic effects, including antioxidant, antidiabetic, wound healing, skin protective, and anti-osteoporotic properties. This herbaceous plant is rich in bioactive compounds such as centellosides (triterpene saponins) including madecassoside, madecassic acid, asiatic acid, and asiaticoside. These compounds, crucial for their pharmacological potential, are biosynthetically produced through the mevalonate and methylerythritol phosphate pathways. However, the challenge lies in the production of these important secondary metabolites, given the adverse impact on the availability of mandukaparni due to increasing demand. To address this concern, this chapter emphasizes the biotechnological interventions for the production of bioactive phytochemicals. These include plant tissue culture techniques, such as cell and organ cultures, along with elicitation strategies, genetic engineering approaches, and bioreactor-scale production. These methods aim to enhance the sustainable production of centellosides, providing valuable insights for researchers and paving the way for future opportunities in the field of plant-based therapeutics. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
A Review on Synchronization and Localization of Devices in WSN
Wireless sensor networks are communication networks that deal with sensor devices that are wirelessly interconnected in order to collect and forward data between different environments. Network scaling of small sensor devices with all its limitations has a foolproof scope for future applications. The advantage of minimal infrastructural cost and applicability within challenging environments make it an attractive choice. Statistics have been shown to prove the demand for research for synchronization and localization as a research problem. WSNs are capable of dynamically building virtual infrastructure and getting synchronized with the rhythm of communication setup. Limitations in the amount of energy that can be utilized make it a necessity for the networks to be more optimal in terms of energy consumption. These challenges necessitate the need to study and analyze the recent advancements implemented in approaching synchronization and localization problems. This paper reviews recent research proposals and methodologies to identify related attributes and their relation to the system. A detailed comparative study is conducted to identify relevant patterns that influence the performance of the networks in terms of energy consumption. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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. -
Negative consumer engagement via food delivery applications: A real concern for generation Z?
The traits of Generation Z are notable for how they define and reshape themselves in the 21st century. The current study investigates the factors that Generation Z users consider when deciding on a food application. The respondents in the current study were divided into working and nonworking groups, and the results imply that since college students are still supported financially by their parents or guardians, their main concerns are higher discounts, prices, and quantities. An in-depth and semistructured questionnaire was addressed to both groups of 15 respondents belonging to Generation Z. The stakeholders who would benefit from this research would primarily be online delivery restaurants, food application developers, academic researchers, and Generation Z cohorts. 2024, IGI Global. All rights reserved. -
Design, Synthesis, and Applications of Carbon Dots with Controlled Physicochemical Properties
Modification of carbon dots (CDs) is essential to enhance their photophysical newlineproperties and applicability. Physical (e.g., composite material blending, coreshell architecture) and chemical (e.g., doping, surface passivation) methods exist to modify CDs. Different precursors can impart varied functionalities and heteroatomic dopants on CDs. Despite several modification strategies, the reproducibility and scalability of CDs still need to be improved. Newer approaches for modifying CDs are thus essential to ensure lab-to-lab and batchto-batch consistency. Our study focused on developing novel strategies for the physicochemical modifications of CDs. The theoretical simulation we performed for surface-functionalised CDs with the aid of density functional theory and time-dependent density functional theory helped to predict the mechanism of photoluminescence (PL) and to analyse the effect of hydrogen bonding on the newlineproperties of CDs (Chapter 3). We have developed a novel and general method for preparing amine functionalized CDs from modified paper precursors (Chapter 4). This strategy allows us to prepare CDs with customized functionalities, alleviating the post-synthesis modification. A novel ionimprinting strategy involving CDs synthesised from modified paper precursors newlinewas also developed through our research (Chapter 5). In another work, we utilized silk fibers as a matrix for immobilising CDs (Chapter 6). CDs prepared from mulberry leaves were fed to silkworms to produce CD-embedded silk fibres, which could be used to detect dopamine. In addition, we prepared CDs newlinefrom an unreported natural source (frankincense), which were used to detect lead ions (Chapter 7). We demonstrated the heavy metal sensing application of these newlineCDs in combination with a UV-light LED chip and a smartphone, which is relevant in resource-limited areas. The research results presented in the thesis are expected to inspire further investigations and applications related to CDs. -
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)