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                <text>Social media has created a buzz ever since its inception. It has always created the curiosity factor amongst the people. There has been numerous literatures and information regarding social media and its influence on people, but the most important aspect is the bond that people seem to have developed with social media today. Considering the dependability of the audience on social media, the credibility factor has to be strictly monitored.</text>
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                <text> MANIVAL RAJU   SHOBHA</text>
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                <text>Fitness is possibly a new found scientific understanding of health and nutrition in people today. Off late, people have realised the importance of fitness. Each medium sells the concept of fitness differently to its audience. The manner in which it projects the same may have a positive or negative understanding of the same. Therefore, this study attempts to answer whether there is any credibility in media content on fitness. Content analysis on a print medium and two social media pages have been conducted followed by a focus group discussion to understand the audience perception of the said credibility if any. Conclusion has been drawn based on the content analysis of the medium considered along with opinions of the respondents of the focus group discussion. </text>
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                <text>Word  of mouth which is synonymous to organic marketing, buzz marketing and viral  marketing is one of the  most popular and a non expensive  form  of  advertising  which  is   undertaken  by   almost anyone and  everyone. The  changing times are  seeing a changing use  of this   form  of advertising. The  study  revolves  around  this medium of mass communication and mass consumption. The researcher thus  seeks to understand the  use of word  of mouth advertising in case of movies and fast moving consumer goods  and to  find out  its  credibility in  terms of usage.  A sample size  of 100 was selected using  snowball sampling technique. The method of primary data  collection comprised of surveys in the  form of questionnaire which was distributed online. The study revealed that word    of   mouth   advertising   is   more    preferred,   trusted   and considered credible in case  of movies which is the  greatest source of  entertainment  for  people  even   today  and   not  fast  moving consumer goods  which are  tried and  tested by  consumers themselves.</text>
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                <text>Fraud on its own was and is devastating a lot of businesses, be them small or large. Particularly in the field of finance where we can see constant attacks on both individuals and enterprises alike. As such, credit cards are the most targeted as they are linked to both personal information and accounts. It is also evident to say that credit card fraud detection research is very much needed to deter and mitigate the impact of fraud on the financial field in general. It is important to identify frauds before it is too late so that the stolen credit card cannot be used for fraudulent transactions. To effectively detect these fraud transactions, we use a data consisting of fraudulent and non-fraudulent transactions to create a model that classifies these transactions with a high accuracy based on a machine learning technique. We used Artificial Neural Network with Logistic Regression to measure and in order to achieve high accuracy, we refined the parameters using the algorithms Back-propagation which has proved to have a high accuracy rate giving the model the ability to distinguish a fraudulent transaction from a normal one.  BEIESP.</text>
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                <text>International Journal of Innovative Technology and Exploring Engineering, Vol-8, No. 7, pp. 313-316.</text>
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                <text>Oumar A.W., CHRIST (Deemed to be University), Bengaluru, India; Peter A.D., CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>Patent Number: 202221047470. Applicant: Pankaj Shambunath Mishra.&#13;
Credit Card Fraud Detection Using Python and Machine Learning Abstract: As we are heading towards the digital world cyber security is becoming an essential element of our life. When we talk about security in digital life then the major challenge is to discover the anomalous activities. When we make any transaction while purchasing any product online a big proportion of consumers choose credit cards. The credit limit in credit cards often helps us to making purchases even if we don’t have the funds at that time. But, on the other hand, these features are utilised by cyber attackers.</text>
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                <text>Social media is a platform where people communicate, interact, share ideas, interest in careers, photos, videos, etc. The study says that social media provides an opportunity to observe human behavioral traits, spatial and temporal relationships. Based on study Crime analysis using social media data such as Facebook, Newsfeed articles, Twitter, etc. is becoming one of the emerging areas of research across the world. Using spatial and temporal relationships of social media data, it is possible to extract useful data to analyse criminal activities. The research focuses on implementing textual data analytics by collecting the data from different news feeds and provides visualization. This researchs motivation was identified based on relevant work from different social media crime and Indian government crime statistics. This article focuses on 68 types of different crime keywords for identifying the type of crime. Nae Bayes classification algorithm is used to classify the crime into subcategories of classes with geographical factors, and temporal factors from RSS feeds. Mallet package is used for extracting the keywords from the news-feeds. K-means algorithm is used to identify the hotspots in the crime locations. KDE algorithm is used to identify the density of crime, and also our approach has overcome the challenges in the existing KDE algorithm. The outcome of research validated the proposed crime prediction model with that of the ARIMA model and found equivalent prediction performance.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.</text>
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                <text>Prathap B.R., Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Krishna A.V.N., Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Balachandran K., Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>Since the late 1990s, social media has added more features and users. Due to the rise of social media, blogs and posts by common people are now a part of mainstream journalism. Twitter is a place where people can share their ideas about culture, society, the economy, and politics. India's large population and rising crime rate make it hard for law enforcement to find and stop illegal activities. This article shows the use of Twitter data to analyse, forecast, and visualise criminal activity using statistical and machine learning models and geospatial visualisation techniques. This helps law enforcement agencies make the best use of their limited resources and put them in the right places. The research aims to present a spatial and temporal picture of crime in India and is split into three parts: Classification, Visualisation, and Forecasting. Crime tweets are identified using a hashtag query argument in the tweepy python package's search_tweets function, followed by substring-keyword classification. The visualisation uses gmaps and bokeh python packages for geospatial and matplotlib for analytical applications. The forecasting portion compares AR, ARIMA, and LSTM to determine the best model for time series forecasting of crime tweet count.   2023 IEEE.</text>
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                <text>The United Nations established the goal of achieving global food security by 2030 as one of its top sustainable development goals in 2015. The current agricultural harvest is insufficient to accomplish the zero-hunger objective and feed the world's growing population. It would require more extensive and consistent crop production. Gene-editing technologies have recently emerged as viable alternatives to permit precise modifications in genomes with increased efficiency and yield higher agricultural productivity. Due to their simplicity, adaptability, and reproducibility across a diverse variety of species, genetic engineering techniques like CRISPR-Cas9 have become quite prominent. CRISPR-Cas9 gene-editing technology can improve crop yields, quality, stress resistance, food safety, nutritional security, and shelf-life, reduce antibiotic resistance, and hasten plant domestication. Cutting-edge techniques like genome editing (GE) allow for the precise introduction or mutation of specified genes into plant genomes. The advent of programmable nucleases like CRISPR-Cas9 has improved gene editing and potentially improved food production and nutritional security. Knock-out, knock-in, gene activation, gene repression, nuclear rearrangements, base editing, molecular breeding, and epigenome engineering are just a few ways that CRISPR systems can target and change genes. For novel applications in plant genetic engineering, CRISPR-Cas systems can be repurposed for GE toward de-novo speciation; mitochondrial and plastid genome engineering toward enhancing photosynthesis, submergence, and drought tolerance. The versatility of CRISPR-associated systems broadens the scope of crop development applications that they can be used for, especially in improving food and nutritional security, which is the focus of this chapter.  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>Critical Analysis Of Computer Application At Secondary Schools of Bangalore District  </text>
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                <text>Victor, Samson R.</text>
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                <text>Christ(Deemed to be University)</text>
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                <text>Swamy, Srikanta S.</text>
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                <text>&lt;a href="http://hdl.handle.net/10603/365043" target="_blank" rel="noreferrer noopener"&gt;http://hdl.handle.net/10603/365043&lt;/a&gt;</text>
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                <text>Critical analysis of inventory management with reference to garments in retailing</text>
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                <text>. C Om Prakash</text>
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                <text>The Major focus of this empirical research is to find out the relationships between various factors of inventory management in medium format garment retail shops that affect the sales. Samples of 100 retail shops were surveyed and the data collected was analyzed.

  Garment retailing is broadly classified under Men??s garment, women??s garments and Kids garments. The garment retail segment has seen good growth in recent years, thanks to the good economic environment in the country.

  This study is concentrated on medium format garment retailers who are facing lot of problems. Some of the problems are tremendous competitive pressure from large format stores &amp;amp; international brands, increasing overheads and high rentals.  To counter these pressures, the medium format retailers must try to consolidate their operations, reduce their overheads like high inventory, faster inventory turnover

  The empirical results shows that the medium format garment retailers are having a positive outlook even though they are having tough competition. This may be because of the good economy and surge in disposable incomes of the urban population. The study also shows that by managing inventory in a tight manner and using latest technology to manage the store will ensure minimal losses and maximize customer satisfaction leading to higher sales realization.

  Majority of the retailers placed extra orders to take care of demand uncertainties and errors in forecasting. The average number of customers lost due to non availability of right garment was three customers per day. The study found that there is a mismatch between order frequency and supply frequency leading to higher safety stocks. This must be bridged and suppliers must increase their response time to match retailers order cycles.

  Shrinkage data was not available with majority of retailers. Medium format retailers are not tracking shrinkage with frequent audits and analysis. Some branded retailers who had some data attributed shrinkage on employee theft and shoplifting as the major cause of shrinkage.

  Most of the sourcing was done locally since Bangalore is a big manufacturing hub for garments. The mode of transport used was by road using LCVs because of traffic restrictions within the city for HCVs.

  Almost all retailers had bar code and scanner with offline point of sale software for billing and tracking sales. Latest technologies like radio frequency identification, online software with features like online ordering &amp;amp; tracking were not being used by retailers.

  The average turnover of medium format retailers was good and their main overhead was the high rentals. Rents in prime locations were exorbitant but unavoidable because these locations were the main shopping areas.

  Garment retailers must use technology to keep track of their customers, their needs and match tem with right inventory to succeed in this competitive era.

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                <text>Critical Analysis of MoS2-Based Systems for Textile Wastewater Treatment</text>
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                <text>Indiscriminate discharge of toxic organic contaminant-laden wastewater into water bodies is one of the major issues posing a risk to the environment in general and aquatic living systems in particular. Widely used textile dyes are ubiquitous in the effluents emanating from industries. Photocatalysts, due to their efficiency and eco-friendliness, can be effectively used to remove pollutant dyes from the water bodies. Molybdenum disulfide (MoS2), an emerging co-catalyst, has high photocatalytic activity, strong absorptivity, non-toxicity, and low cost; with a graphene-like structure, it offers functional features similar to graphene: high charge carrier transfer, strong wear resistance, and good mechanical strength. However, in aspects such as cost, abundance, versatile morphologies, and tunable band gap with efficient visible light absorption properties, MoS2 scores over graphene. The present chapter discusses the recent advances in nanostructured MoS2 materials for applications in environmental remediation. Special emphasis has been paid to MoS2 and MoS2-based systems for the photocatalytic degradation of various organic contaminants such as malachite green, methyl orange, rhodamine B, and methylene blue that find extensive use in the textile industry. As a result, MoS2 systems play an essential role in nanocomposites, especially in speeding up photo-induced electron transport and lowering electron recombination rates, making them desirable photocatalysts for the degradation of pollutants. The chapter focuses on addressing SDG 3 (Good Health and Wellbeing), SDG 6 (Clean Water and Sanitation), SDG 7 (Clean and Affordable Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), SDG 14 (Life Below Water), and SDG 15 (Life on Land).  2025 Moharana Choudhury, Ankur Rajpal, Srijan Goswami, Arghya Chakravorty and Vimala Raghavan.</text>
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                <text>Madhushree R.; Jose A.; Pinheiro D.; Sunaja Devi K.R.</text>
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                <text>Analytical Case Studies on Municipal and Biomedical Waste Management: Perspectives on Sustainable Development Goals, pp. 55-82.</text>
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                <text>CRC Press</text>
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                <text>2024-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1201/9781003499695-5" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781003499695-5&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207928118&amp;amp;doi=10.1201%2F9781003499695-5&amp;amp;partnerID=40&amp;amp;md5=7358c83ba110de1df240cc0e70726e63" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207928118&amp;amp;doi=10.1201%2f9781003499695-5&amp;amp;partnerID=40&amp;amp;md5=7358c83ba110de1df240cc0e70726e63&lt;/a&gt;</text>
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                <text>ISBN: 978-104016491-4; 978-103279691-8</text>
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                <text>Madhushree R., Department of Chemistry, Christ (Deemed to be University), Karnataka, Bangalore, 560029, India; Jose A., Molecular Medicine and Pathology, University of Auckland, Auckland, 1023, New Zealand; Pinheiro D., Department of Chemistry, Christ (Deemed to be University), Karnataka, Bangalore, 560029, India; Sunaja Devi K.R., Department of Chemistry, Christ (Deemed to be University), Karnataka, Bangalore, 560029, India</text>
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