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                <text>Production of bioactive compounds from cell and organ cultures of Centella asiatica </text>
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                <text>bioreactor; Centella asiatica; centellosides; elicitation; plant tissue culture</text>
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                <text>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.</text>
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                <text>Biotechnological Production of Bioactive Phytochemicals of Medicinal Value: a Comprehensive Treatise, pp. 585-603.</text>
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                <text>&lt;a href="https://doi.org/10.1016/B978-0-443-21818-7.00015-0" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/B978-0-443-21818-7.00015-0&lt;/a&gt;
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                <text>Vasantha V.L., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Sudheer W.N., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Al-Khayri J.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Ahsa, Saudi Arabia; Jain S.M., Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland; Nagella P., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India</text>
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                <text>Automated Brain Imaging Diagnosis and Classification Model using Rat Swarm Optimization with Deep Learning based Capsule Network</text>
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                <text>Brain tumor; capsule network; Deep learning; image segmentation; medical imaging; Rat swarm optimizer</text>
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                <text>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.</text>
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                <text>Vasantharaj A.; Rani P.S.; Huque S.; Raghuram K.S.; Ganeshkumar R.; Shafi S.N.</text>
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                <text>International Journal of Image and Graphics, Vol-23, No. 3</text>
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                <text>World Scientific</text>
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                <text>&lt;a href="https://doi.org/10.1142/S0219467822400010" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1142/S0219467822400010&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110292625&amp;amp;doi=10.1142%2FS0219467822400010&amp;amp;partnerID=40&amp;amp;md5=288247278eeda665816d07b5d3f84619" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110292625&amp;amp;doi=10.1142%2fS0219467822400010&amp;amp;partnerID=40&amp;amp;md5=288247278eeda665816d07b5d3f84619&lt;/a&gt;</text>
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                <text>ISSN: 2194678</text>
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                <text>Vasantharaj A., Department of ECE, Excel Engineering College (Autonomous), Namakkal Tamil Nadu, India; Rani P.S., Department of CSE, R.M.D Engineering College, Tamil Nadu, Chennai, India; Huque S., Department of CSE, Guru Nanak Institutions Technical Campus, Telangana, Hyderabad, India; Raghuram K.S., Department of Mechanical Engineering, Vignan's Institute of Information Technology(A), Andhra Pradesh, Visakhapatnam, India; Ganeshkumar R., Department of CSE, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Shafi S.N., Department of Computer Science, Institute of Technology, Woldia University, Ethiopia</text>
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                <text>A comparative study of structureless data aggregation protocols in wireless sensor networks (WSNs)</text>
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                <text>Vasaya F.N.; Sandeep J.; Thomas L.</text>
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                <text>Industry 4.0 Key Technological Advances and Design Principles in Engineering, Education, Business, and Social Applications, pp. 118-134.</text>
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                <text>&lt;a href="https://doi.org/10.1201/9781003343332-6" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781003343332-6&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200911089&amp;amp;doi=10.1201%2F9781003343332-6&amp;amp;partnerID=40&amp;amp;md5=fa9093822059e0bbd0cc1987f07753f6" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200911089&amp;amp;doi=10.1201%2f9781003343332-6&amp;amp;partnerID=40&amp;amp;md5=fa9093822059e0bbd0cc1987f07753f6&lt;/a&gt;</text>
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                <text>ISBN: 978-104004898-6; 978-103237687-5</text>
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                <text>Vasaya F.N., CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Sandeep J., CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Thomas L., CHRIST (Deemed to be University), Bengaluru, Karnataka, India</text>
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                <text>Internet of Things Enhancing Sustainability of Business</text>
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                <text>Business sustainability; Competitive advantage; Digital revolution; IoT; Smart devices</text>
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                <text>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.</text>
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                <text>Journal of Psychiatric Research, Vol-134, pp. 30-31.</text>
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                <text>Vasudevan M., School of Business and Management, Christ University, Bangalore, India; Alagarsamy S., School of Business and Management, Christ University, Bangalore, India; Mehrolia S., School of Business and Management, Christ University, Bangalore, India; Jeevananda S., School of Business and Management, Christ University, Bangalore, India</text>
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                <text>Brand awareness of 'generation y' customers towards doughnut retail outlets in India</text>
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            <description>The topic of the resource</description>
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                <text>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.</text>
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                <text>Journal of Business and Retail Management Research, Vol-11, No. 4, pp. 108-113.</text>
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                <text>All Open Access; Bronze Open Access</text>
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                <text>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.</text>
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                <text>Vatambeti R.; Damera V.K.; Karthikeyan H.; Manohar M.; Sharon Roji Priya C.; Mekala M.S.</text>
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                <text>International Journal of Computer Network and Information Security, Vol-15, No. 6, pp. 1-15.</text>
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                <text>Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India; Damera V.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, India; Karthikeyan H., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Manohar M., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Sharon Roji Priya C., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Mekala M.S., School of Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea</text>
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                <text>Deep learning; Elephant herd optimization; Online food service; Pseudoinverse learning autoencoder; Swiggy; Twitter sentiment analysis; Zomato</text>
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                <text>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.</text>
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                <text>Vatambeti R.; Mantena S.V.; Kiran K.V.D.; Manohar M.; Manjunath C.</text>
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                <text>Cluster Computing, Vol-27, No. 1, pp. 655-671.</text>
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                <text>Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, India; Mantena S.V., Department of Computer Science and Engineering, SRKR Engineering College, Bhimavaram, 534204, India; Kiran K.V.D., Department of CSE, Koneru Lakshmaiah Education Foundation, AP, Vaddeswaram, India; Manohar M., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Manjunath C., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>An efficient clustering approach for optimized path selection and route maintenance in mobile ad hoc networks</text>
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                <text>Clustering; Mobile ad hoc network; Network lifetime; Route maintenance; Routing protocol; Star topology</text>
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                <text>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.</text>
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                <text>Vatambeti R.; Sanshi S.; Krishna D.P.</text>
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                <text>Journal of Ambient Intelligence and Humanized Computing, Vol-14, No. 1, pp. 305-319.</text>
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                <text>Vatambeti R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Sanshi S., Department of Information Science and Engineering, BMS Institute of Technology &amp;amp; Management, Bangalore, India; Krishna D.P., Department of Computer Science and Engineering, Narayana Engineering College, Andhra Pradesh, Guduru, India</text>
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                <text>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.</text>
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                <text>Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, India; Venkatesh D., Department of Computer Science and Engineering, GITAM School of Technology, GITAM University-Bengaluru Campus, Bengaluru, India; Mamidisetti G., Department of Computer Science and Engineering, Malla Reddy University, Hyderabad, India; Damera V.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India; Manohar M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Yadav N.S., Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, 500075, India</text>
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                <text>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.</text>
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                <text>Proceedings of the 5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021, pp. 1170-1174.</text>
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                <text>Vatsa M., School of Business Studies, Sharda University, Greater Noida, India; Rastogi S., CHRIST (Deemed to Be University), Department of Commerce, India; Tiwari A., CHRIST (Deemed to Be University), Department of Commerce, India; Jain S., Information Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, Chennai, 603203, India</text>
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                <text>Master of Arts in Media and Communication Studies</text>
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                <text>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.</text>
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                <text>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.</text>
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                <text>Ved A.S., School of Education, CHRIST (Deemed to be University), Bangalore, Karnataka, India; M.P.M P.K., School of Education, CHRIST (Deemed to be University), Bangalore, Karnataka, India</text>
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                <text>The sensor-based breadboard is rapidly covering almost every application from human health monitoring to prediction of diseases in accordance with the weather change. This paper presents a sensor based precision crop monitoring system for agriculture application and estimates the energy consumption of the sensor nodes. This high accuracy energy efficient system drastically reduces the damages to the crops and investment made to it. The main focus of the proposed research work is to reduce the energy consumption and minimize the traffic between the nodes of the sensor during the transmission of sensor information. The qualitative metrics has been carried to evaluate the performance of the proposed system which outperform the existing scenario.   2022 IEEE.</text>
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                <text>Vedaiyan R., Jain Deemed to Be University, School of Sciences, Department of Computer Science and It, Bangalore, India; Raj R.J.S., Cmr Institute of Technology, Department of Ise, Bengaluru, India; V L H.J., School of Business and Management, Christ University, Business Analytics, Bengaluru, India</text>
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                <text>Talent acquisition-artificial intelligence to manage recruitment</text>
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                <text>The research aims to examine the awareness of Artificial Intelligence among the HR managers and Talent Acquisition managers in the process of Talent Acquisition, Investigating the factors influencing the adoption and usage of Assisted Intelligence, and evaluating the impact of Artificial Intelligence on Talent Management. Multi-Stage sampling method was adopted to collect the responses from the 384 customers across the HR and TA managers working across the IT companies situated in Bangalore, Mysore, Pune, and Chennai &amp;amp; Hyderabad. SAS was applied to perform the Simple Percentage Analysis, Correlation Analysis, Multiple Linear Regression Analysis to validate the hypothesis. The demographic &amp;amp; construct variables considered were Adoption, Actual usage, Perceived usefulness, Perceived Ease of Use, &amp;amp; Talent Management. Awareness of the Artificial Intelligence technology and its adoption in managing Talent Acquisition has the positive and high correlation and followed by its actual usage. Candidate experience is the most influencing variable from the first factor, Competency and Easy to use is the most influencing variable from the second factor, Effectiveness in the adoption and actual usage of Artificial Intelligence in Talent Acquisition. Talent Management is the highest predictor of using the technology and its adoption is the most influencing predictor in the effective implementation of the technology among the Information Technology Companies.   The Authors, published by EDP Sciences.</text>
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                <text>Vedapradha R.; Hariharan R.; David Winster Praveenraj D.; Sudha E.; Ashok J.</text>
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                <text>E3S Web of Conferences, Vol-376</text>
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                <text>Vedapradha R., Department of Commerce, Mount Carmel College, Autonomous, Bangalore, India; Hariharan R., CHRSIT (Deemed to Be University), Yeshwanthpur Campus, Bangalore, India; David Winster Praveenraj D., CHRSIT (Deemed to Be University), Yeshwanthpur Campus, Bangalore, India; Sudha E., CHRSIT (Deemed to Be University), Yeshwanthpur Campus, Bangalore, India; Ashok J., CHRSIT (Deemed to Be University), Yeshwanthpur Campus, Bangalore, India</text>
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                <text>Purpose: Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct. The aim of the study is to examine the level of awareness among the brokers when integrated with technology for the purpose of executing the trades. Design/Methodology: A self-administered and structured 350 questionnaires were designed and circulated to collect the preliminary information from the stock brokers operating in NSE and BSE within the geographical limits of Bangalore district using the Systematic Sampling method to obtain a sample size of 235. Awareness, Automated trading, Elimination of human error, portfolio management, tracking order, order placement were the critical variables observed to validate the hypothesis using Simple Percentage Analysis &amp;amp; Chi-Square Analysis using Statistical Analysis Software (SAS). Findings: It was found that there is robust association between the level of awareness of the mentioned technology in its application by the stock brokers of NSE and BSE operating in Bangalore. Portfolio management and automated trading are the highly associated application of Algorithmic trading among the stock brokerage services. Originality: Algorithmic trading makes use of complex formulas, combined with mathematical models and human oversight, to make decisions to buy or sell financial securities on an exchange. It can be used in a wide variety of situations including order execution, arbitrage, and trend trading strategies. Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second.   The Authors, published by EDP Sciences.</text>
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                <text>Vedapradha R., Department of Commerce, Mount Carmel College, Vasanth Nagar, Bangalore, 560052, India; Hariharan R., Department of Commerce, Mount Carmel College, Vasanth Nagar, Bangalore, 560052, India, School of Business &amp;amp; Management, CHRIST (Deemed to Be University), Yeshwanthpur Campus, Bangalore, 560073, India; Winster Praveenraj D.D., School of Business &amp;amp; Management, CHRIST (Deemed to Be University), Yeshwanthpur Campus, Bangalore, 560073, India; Sudha E., School of Commerce, CHRIST (Deemed to Be University), Yeshwanthpur Campus, Bangalore, 560073, India; Pandey M., School of Business &amp;amp; Management, CHRIST (Deemed to Be University), Yeshwanthpur Campus, Bangalore, 560073, India; Ambrose S., School of Commerce, CHRIST (Deemed to Be University), Yeshwanthpur Campus, Bangalore, 560073, India</text>
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                <text>Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain</text>
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                <text>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.</text>
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                <text>Vedapradha R.; Joshi D.; Hariharan R.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195580377&amp;amp;doi=10.1108%2FIJLM-12-2023-0509&amp;amp;partnerID=40&amp;amp;md5=287b586104bff54a5959c2c04451c726" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195580377&amp;amp;doi=10.1108%2fIJLM-12-2023-0509&amp;amp;partnerID=40&amp;amp;md5=287b586104bff54a5959c2c04451c726&lt;/a&gt;</text>
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                <text>ISSN: 9574093</text>
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                <text>Vedapradha R., School of Commerce, Mount Carmel College Autonomous, Bangalore, India; Joshi D., Department of Operations and Supply Chain Management, St. Josephs Institute of Management, Bangalore, India; Hariharan R., School of Business and Management, Christ (Deemed to be University), Yeshwanthpur Campus, Bengaluru, India</text>
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