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A COMPARATIVE ANALYSIS OF THE INVESTIGATIVE STYLES OF REPORTING IN MEDIA TODAY - A case study on India today group.
Investigative journalism has become the call of the day as too many scams are being reported. It all started with the ??Watergate scam that was reported by Washington post. In India it was the 1980s period when investigative journalism arose, there have been a few milestone cases. First being Bofors gun scandal, which is considered one of the most influential works of the Indian investigative journalism. Then the other which shook the Indian Sensex, the Harshad Mehta scam. Since 2007 there have been a number of scams which have been investigated by the media. The UPA government has been under the scanner since then. Due to the growing number of scams in the country, investigative journalism has grown in the past few years. This study compares the investigative reporting approach of general magazine and the business magazine using content analysis. This study aims to understand how professional and reader friendly are the approaches of investigative reports today in media. -
A Comparative Analysis of Traditional and Machine Learning Forecasting Techniques
Forecasting is the process of making predictions or estimates about future events or conditions based on historical data, trends, and patterns. It involves analyzing past data and using statistical or other quantitative methods to project future outcomes, such as sales figures, market trends, weather patterns, or financial performance. Forecasting can be used in a wide range of fields, including economics, finance, business, weather forecasting, and sports. The accuracy of a forecast depends on the quality of the data, the methods used, and the assumptions made about the future. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
A Comparative Analysis On Machine Learning Algorithm for Score Prediction and Proposal of Enhanced Nae Bayes
Sports attracted a lot of people to watch various games all over the world. India is not an exception. Among various games, cricket has special attention. Cricket in India contributes to the Indian economy on a large scale. Cricket is also known for the broad amount of data gathered for each team, season, and player. Hence, cricket is a perfect domain to work on various data analysis and machine learning approaches to acquire useful insights and information. In this paper, algorithms were used to enhance the output of the team in a sports league, particularly, IPL (cricket). It reflects the performance of the team on a deeper analysis of the requirements of T20 cricket. 2022 IEEE. -
A Comparative Assessment of Cascaded Double Voltage Lift Boost Converter
In several power conversion applications, dc-dc boost converters with voltage boost techniques are extensively used in order to meet the growing power demand. The main drawback of conventional dc-dc boost converter is obtaining high DC voltages, when operated at high duty ratio which causes switching losses and decreases overall efficiency because of the switch being used to be in 'ON' state for long time and voltage stresses across switch increases. The main objective of proposed converter is to obtain high voltage without extreme duty ratio. When input voltage of 15V DC is given, 201.1V DC output voltage is attained at duty ratio of 0.4 by the cascaded double voltage lift boost converter. To validate the performance of proposed converter, simulation is carried out in LTspice XVII and a comparative assessment of proposed converter with other converters at different duty ratio are realized. 2020 IEEE. -
A Comparative Investigation on the use of Machine Learning Techniques for Currency Authentication
In the present banking sector, identifying the real and the fake note is a very challenging task because if we do it manually, it takes a long time to check which is real and which is fake. This research study article aims to authenticate the money between real and fake by using different machine algorithms facilitating learning, such as K-means Clustering, Random Forest Classification, Support Vector Machines, and logistics Regression. Specifically, we consider the banknote dataset. The data of money is extracted from various banknote images by using the wavelet transform tool, which is primarily used to remove elements from the images. However, we are mainly concerned with the different machine learning algorithms, so we take the two variables, where the first variable indicates image variance and the second indicates image skewness. We use these two variables to train our machine learning algorithms. So, majorly, by applying the different machine learning algorithms, which are supervised and unsupervised, we find the accuracy for the respective machine learning algorithms and then visualize and classify the real and fake notes separately. Finally, the prediction is based on integrity, which means the efficiency value is based on how much the mechanism system can uncover the fake notes. Then, after calculating the accuracy of currency authentication, there is a high possibility that the accuracy of the particular algorithm is the best algorithm, so the application of currency authentication will be very useful for the bank to easily find duplicate notes. 2022 IEEE. -
A Comparative Performance Analysis of Convolution W/O OpenCL on a Standalone System
Initial approach of this paper is to provide a deep understanding of OpenCL architecture. Secondly, it proposes an implementation of a matrix and image convolution implemented in C (Serial Programming) and OpenCL (Parallel Programming), to describe detailed OpenCL programming flow and to provide a comparative performance analysis. The implementation is being carried on AMD A10 APU and various algebraic scenarios are created, to observe the performance improvement achieved on a single system when using Parallel Programming. In the related works authors have worked on AMDAPPSDK samples such as N-body & SimpleGL to understand the concept of vector data types in OpenCL and OpenCL-GL interoperability, have also implemented 3-D particle bouncing concept in OpenCL & 3D-Mesh rendering using OpenCL. Lastly, authors have also illuminated about their future work, where they intend to implement a novel algorithm for mesh segmentation using OpenCL, for which they have tried to form a strong knowledge base through this work. 2015 IEEE. -
A Comparative Study in Predictive Analytic Frameworks in Big Data
Every information processing sector uses predictive analytic framework in terms of distributed datasets through a variety of applications. These analytic frameworks are effectively used for various analyses of data, parameter, and attributes. Leveraging data to make insightful decisions for maximizing the effectiveness requires the determination of the best predictive framework for any organization. Even a retail unit which wants to scale up its production rely on multiple parameters. These parameters must be analyzed for effective quality control in any domain. Since there are diversities in every domain the data will be in varied form, and these are accumulated as Big Data. These analyses are done using machine learning frameworks. The strategy involved would differ from one domain to another such as in the health care sector the framework might predict the magnitude of patients admitted to the urgent care facility over the upcoming days whereas in the production industry the framework would align quality control measures. This article analyses a few domains and their deployed machine learning impacts in a strategic way. 2023 American Institute of Physics Inc.. All rights reserved. -
A Comparative Study of Collaborative Movie Recommendation System
The number of movies available has expanded, making it challenging to select a film that uses current technology to meet users' needs. Following the widespread use of internet services, recommendation systems have become commonplace. The objective for all recommendation systems now is to employ filtering and clustering algorithms to recommend content users are interested in. Suggestions for a media commodity like movies are offered to consumers by locating user profiles of people with comparable likes which makes users' preferences initially determined to allow them to rate movies of their choosing. After a period of use, the recommender system understands the user and offers films that are more likely to receive higher ratings. A comparison study on the existing models helps to understand future scope and improvements for more personalized models for movie recommendation. In comparison to previous models, the MovieLens dataset gives a dependable model that is exact and delivers more customized movie suggestions. In this paper, an approach to do a detailed study and review the user preferences based on item and content of movies has been made to understand the filtering techniques of the collaborative recommendation system to increase accuracy and give highly rated movies as recommendations to the user is carried and based on the results the recommendation system is built with a content-based filtering technique. 2022 IEEE. -
A COMPARATIVE STUDY OF DOMESTIC VIOLENCE IN BRICS NATIONS PRE AND POST COVID-19
The most common kind of sexual or physical abuse suffered by women is that by a partner. Human rights are violated when males or boys use violence against women or girls. When it comes to domestic abuse, it is estimated that one in three women will experience some kind of gender-based violence at some point in their lives. The number of women who have been abused by a romantic partner or a non-relationship sexual partner is estimated to reach 736 million. For years, the worlds leaders have recognised its seriousness. In 1995, the Beijing Declaration and Platform for Action said that violence against women must be eliminated. Within the UNs 2030 Agenda for Sustainable Development, aworldwide goal to abolish all kinds of violence against women and girls in public and private spaces was added. Global action was called for in 2016 by the World Health Assemblys Resolution 69.5, which urged anational multisector approach to combating violence against women and young girls. In spite of all of these responsibilities, 49 countries still dont have a clear policy on domestic abuse. Lower and lower-middle-income women nations are particularly vulnerable to this violence, which has long-term effects on their health and well-being. In the worlds poorest nations, women aged 15 to 49 have a lifetime frequency of domestic abuse of 37 percent. One in every four women who have ever been in a relationship has been a victim of domestic abuse at some point in their lives. 2023, University of Tyumen. All rights reserved. -
A Comparative Study of Effectiveness of Option Forecasting Models: Black Sholes Vs Simple Hybrid Neural Networks.
Many studies have shown that Artificial Neural Networks has the capacity to learn the underlying mechanics of stock markets. In fact, Artificial Neural Networks has been widely used for forecasting financial markets. However, such applications to Indian Stock Markets are scarce. This paper applies neural network models to predict the option prices which are traded in National Stock Exchange. Multilayer perceptron network is used to build the option forecasting model and the network is trained using Back Propagation algorithm. It is found that the predictive power of the network model is not influenced by the neural network using realised volatility. The study shows that satisfactory results can be achieved when applying Hybrid Neural Networks to forecast for the next 30 days. The result shows Black Scholes model outperforms the Hybrid Neural Network models and also when we compared the Hybrid Neural Networks results with the econometric Models such as OLS and EGARCH we saw that the Econometric models give the good results. -
A Comparative Study of Factors Influencing Consumers' Preference for Store Brands and National Brands - A Case Study of Big Bazaar in Bangalore
The recent wave of reforms by the Government to introduce Foreign Direct Investment (FDI) in various sectors is bringing a new zeal to the investment climate in India. One of the most debated reforms is the policy for allowing 51 percent FDI in multi-brand retail. The Government has now approved of 51 percent FDI in multi-brand retail. According to Deloitte, organized retail, which constitutes 8 percent of the total retail market today, will grow much faster than unorganized retail and is expected to be 20 percent by 2020. With the emergence of organized retail, a new set of brands ?? store brands, have evolved. Store brands are slowly gaining popularity across the organized retail sector. While elaborate research on the emergence of store brands have been undertaken in the developed economies of North America and Western Europe, research in the context of the Indian market is still at the nascent stage. This study intends to identify the factors influencing the consumers preference towards national brands and store brands across Bangalore. As Big Bazaar is the largest retail store chain in India and also stocks a large variety of store brands, the study has been confined to the Big Bazaar outlets across Bangalore, with specific focus on the food category. Survey research method was followed in this study. Questionnaire was used for collecting primary data while the secondary data was collected from selective sources of data like journals, websites, research reports, magazines and newspapers. The convenience sampling technique was used. A sample of 250 consumers was selected from the Bangalore city. The statistical techniques which are used in the study include descriptive statistics, frequencies and percentages, reliability test and one- way ANOVA. Some of the key findings of the study are:- There is no significant difference between national and store brands for factors like product innovation, repeat purchase, product variety across category, shelf placement, brand equity, taste, shelf life and nutritional benefit. The factors perceived quality, packaging, price rise, trust, TV/Newspaper promotions, shelf search, social acceptance, freshness and cleanliness influences a consumers preference towards national brands. Four factors primarily influence consumers preference towards store brands. They are perceived risk, value for money, copycats and in-store promotion. vi It is intended that the findings of this research, about the factors influencing consumers preference towards national and store brands, will be useful to retailers, food product manufacturers etc. It will help them to assess their current strategies revolving around their respective national or store brands. The findings will also help them to target the right audience to gain maximum mileage as extensive detail has been provided based on a number of demographic variables. The content in this report details out the research conducted in this regard followed by a conclusion. Keywords: Organized retail, Store brands, National brands, Foreign Direct Investment, Demography -
A Comparative Study of LGMB-SVR Hybrid Machine Learning Model for Rainfall Prediction
Weather forecasting is a critical factor in deter mining the crop production and harvest of any geographical location. Among various other factors, rainfall is a crucial determining component in the sowing and harvesting of crops. The aim and intent of this paper is to analyze various machine learning algorithms like LightGBM and SVR, and develop a hybrid model using LightGBM and SVR to accurately predict rainfall The hybrid model implements both LightGBM and SVR on a preprocessed dataset and then combines the predicted values of the results through an ensemble model which considers the average of these values based on a predefined weight. The weight of the model is determined by considering various combinations, and the result with the least error is taken into consideration for that particular dataset. The study shows that the hybrid model performed better than LightGBM and SVR individually, and produced the least root mean square error yielding a more accurate prediction of rainfall. 2021 IEEE. -
A Comparative Study of Machine Learning and Deep Learning Algorithms to Predict Crop Production
Agriculture is a field that plays an essential part in strengthening a country's economy, especially in agrarian countries like India, where agriculture and crop productivity play a large role in the economy. The research focuses on comparing machine learning and Deep learning algorithms in predicting total crop yield production. The parameters considered for the study are State name, District name, Year, Season, Crop, Area and Production. The dataset is resourced from the data.gov.in website. Random forest from Machine Learning and Sequential model from Deep learning are compared, and the performance metric considered for the study is R2 score. The objective is to assess how well the independent variable predicts the variance in the dependent variable. Random Forest algorithm achieved an R2 score of 0.89, whereas Deep Learning Sequential algorithm gave an R2 score of 0.29. 2023 American Institute of Physics Inc.. All rights reserved. -
A Comparative Study of Machine Learning Techniques for Credit Card Customer Churn Prediction
A customer is a churner when a customer moves from one service provider to another. Nowadays, with an increasing number of severe competition with inside the market, essential banks pay extra interest on customer courting management. A robust and real-time credit card holders churn evaluation is vital and valuable for bankers to preserve credit cardholders. Much research has been observed that retaining an old customer is more than five times easier compared to gaining a new customer. Hence, this paper proposes a method to predict churns based on a bank dataset. In this work, Synthetic Minority Oversampling Technique (SMOTE) has been used for handling the imbalanced dataset. Credit card customer churn is predicted using random forest, k-nearest neighbor, and two boosting algorithms, XGBoost and CatBoost. Hyperparameter tuning using grid search has been used to increase the accuracy. The experimental result shows Catboost has achieved an accuracy of 97.85% and tends to do better than the other models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A comparative study of magnetite and MnZn ferrite nanoliquids flow inspired by nonlinear thermal radiation
The characteristics of the magnetohydrodynamic (MHD) stagnation point flow of ferrofluids are investigated. The effects of nonlinear thermal radiation, heat generation and viscous dissipation are considered. Two different nanoparticles (Fe3O4 and MnZnFe2O4) are comprised in the base fluid (water). The ordinary differential equations are formed using suitable similarity transformations from the governing partial differential equations. The subsequent nonlinear ordinary differential equations are solved numerically using RKF-45 method. The influence of governing parameters on the results are analysed. It is found that the thermal boundary layer thickens due to the influence of nonlinear radiation and heat generation for both the fluids. The rate of heat transfer is higher for MnZn ferrite-nanofluid in comparison with magnetite nanofluid. 2017 by American Scientific Publishers All rights reserved. -
A Comparative Study of ML and DL Approaches for Twitter Sentiment Classification
This research made use of various machine learning (ML) and deep learning (DL) methods - such as support vector machines, random forests, logistic regression, naive Bayes, and XGBoost, convolutional neural networks (CNNs), and feedforward neural networks (FNNs) - for tweet analysis to investigate public sentiment towards Ola and Uber. The objective is to determine the most effective method for distinguishing between good and negative tweets. Feature engineering techniques improve the algorithms interpretation of tweet content. To balance out the disparity between positive and negative tweets. The project aims to uncover customer wants and concerns on Twitter to help Ola and Uber, in addition to improving Algorithms Accuracy. The study intends to help these ride-hailing businesses make educated modifications to boost customer happiness by closely examining tweets. Essentially, the study assesses how well various ML and DL algorithms comprehend user feedback on Uber and Ola. The overarching goal is to not only enhance computational methods but also contribute to the improvement of these ride-hailing services, ultimately fostering a more positive online environment for Ola and Uber enthusiasts. In summary, the study investigates sentiment analysis techniques on Twitter to optimize understanding of Ola and Uber-related tweets, aiming to facilitate positive changes for the ride-hailing services and their customers, promoting a friendlier Twitter community. 2024 IEEE. -
A Comparative Study of Nutrient Composition, Proteolytic Activity, Phytochemical Profiles, Vitamin C Content, and Antioxidant Properties in the Peels of Selected Perennial Fruits
The escalating global demand for fruits has led to a surge in fruit production, resulting in significant fruit waste, particularly peels. The present study aims to investigate the nutrient content, proteolytic activity, phytochemical levels, vitamin C and antioxidant properties of five perennial fruits, namely Carica papaya (papaya), Selenicereus costaricensis (Red dragon fruit), Ananas comosus (Pineapple), Musa acuminata (Cavendish banana), Punica granatum (Pomegranate) peels of varying ripening stages. Accordingly, two ripening stages for pomegranate, papaya and dragon fruit (PoR1 and PoR2; PaR1 and PaR2; DR1 and DR2, respectively) and three stages for banana and pineapple (BR1, BR2 and BR3; PiR1, PiR2 and PiR3, respectively) were identified based on ethylene gas emission. The elemental analysis showed that fruit peels of Pineapple (PiR3), Banana (BR2), Papaya (PaR2), and Dragon fruit (DR2) showed significantly higher content of macro and micro-elements compared to the other ripening stages. Pomegranate peels exhibited the highest proteolytic activity (5.09 0.98unitsg?1), total phenolics (246.09 0.25mgg?1), total flavonoids (158.27 1.72mgg?1), tannins (103.94 0.09mgg?1), DPPH scavenging activity (129.43 1.34%), and antioxidant activity (127.14 1.35mgg?1 by phosphomolybdate assay). A. comosus peels had the greatest vitamin C levels (95.53 3.52mgg?1). Anti-nutrient analysis revealed safe levels of oxalates, phytates, and alkaloids, except for high oxalate levels in pomegranate peels. Notably, all parameters exhibited an increasing trend with ripening stages, with a decline during senescence in Banana (BR3) and Pomegranate peel (PoR2). This knowledge of fruit peel composition can enhance their utilization by humans, pharmaceutical and food industries, while also contributing to more effective waste management. Our study addresses the pressing need for sustainable fruit peel utilization in the context of escalating fruit production and waste. The Author(s), under exclusive licence to National Academy of Agricultural Sciences 2024. -
A Comparative Study of Pollution Levels in Major Cities of India During Covid-19 in India
This paper aims to study the major pollutants of the four metro cities of India before and after covid 19 first wave. The cities considered for the study are Bangalore, Delhi, Mumbai, and Kolkata. The major pollutants considered for the study are PM2.5, PM10, NO, NO2, NOx, SO2, CO, and Ozone. The basic aim of the study is to find the effect of lockdown and covid restrictions on the level of pollutants across the four major cities of India. We used both parametric and non-parametric tests for the analysis using SPSS. From the study, it is clear that there is a significant decrease in all the major pollutants across India's major cities.6. 2023, University of Wollongong. All rights reserved. -
A comparative study of Ravi Chopra's Mahabharata(1988) on Doordarshan and Siddharth Anand Kumar's Mahabharat (2014) on Star Plus /
Portrayal of characters on both Ravi Chopra’s Mahabharata and Siddharth Anand Kumar’s Mahabharata on Star Plus. Identifying the costumes of characters of old Mahabharata and new Mahabharata. Identifying the visualization and special effects in both old and new Mahabharata. -
A Comparative Study of Spectral Indices for Surface Water Delineation Using Landsat 8 Images
Surface water delineation is an important step in performing change detection studies on water bodies with the help of multispectral images. Commonly used techniques for surface water delineation from multispectral images are single band density slicing, spectral index based, machine learning based classification and spectral unmixing based methods. This paper presents a comparative study of commonly used spectral indices Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index (WRI), Normalized Difference Forest Index (NDFI), Enhanced water Index (EWI), Weighted Normalized Difference Water Index (WNDWI), Automated Water Extraction Index (AWEI), Tasseled Cap Water Index (TCW), Global Water Index (GWI)and Sum457 that were developed for water detection for their suitability and effectiveness when applied on Landsat 8 images. While all the above mentioned indices showed their usefulness in water detection, simpler and faster indices like GWI and Sum457 yielded comparable results to that of more complex ratios like EWI and WNDWI. 2019 IEEE.