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A comparative study of bayesian and classical methods for the weighted Lindley distribution under unified hybrid censoring with survival data applications
In survival analysis and reliability engineering, censoring schemes play a crucial role in efficient data collection and analysis. This study investigated the unified hybrid censoring scheme (UHCS), a versatile framework that integrates multiple censoring strategies, to evaluate the suitability of the Weighted Lindley (WL) distribution for modeling lifetime data. Maximum likelihood estimates (MLEs) and their corresponding asymptotic confidence intervals are derived for the parameters of the WL distribution. In the Bayesian framework, parameter estimation was performed under a squared error loss function. A detailed Monte Carlo simulation study was conducted to compare the performance of classical and Bayesian estimators across various sample sizes and censoring schemes. The simulation results revealed that Bayesian estimators consistently yielded lower mean squared errors (MSEs) than their classical counterparts, and the associated credible intervals were generally narrower than the frequentist confidence intervals. To demonstrate the practical applicability of the proposed methods, the analysis was applied to real-world survival datasets. The results highlighted the effectiveness of the WL distribution under UHCS, offering valuable insights for researchers and practitioners in reliability and survival analysis. 2025 the Author(s), licensee AIMS Press. -
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 Gender and Age-Based Differences in Organisational Culture: Evidence from an Empirical Analysis
Organisational culture (OC) plays a significant role in shaping employee attitudes, engagement, and overall effectiveness. However, limited empirical evidence explores how demographic factors, such as age and gender, influence employees perceptions of organisational culture. This study reveals age and gender-based differences in organisational culture among employees from leading Indian-origin IT services companies in Bengaluru. Grounded in the Denison Organizational Culture Model, the study examines four key dimensions: involvement, consistency, adaptability, and mission. Data were collected from employees using a structured questionnaire, and statistical analyses, including ANOVA and Z-tests, were applied to examine differences in cultural perceptions. The results indicated that overall organisational culture scores did not differ significantly among age or gender groups. Specific dimensions, such as capability development, core values, agreement, and vision, exhibited significant age-related differences, with younger employees (2030 years) perceiving a stronger culture than those in the 3140 age group. No significant gender-based differences were observed across any dimension. These findings demonstrate the importance of demographic responsiveness in shaping inclusive organisational practices. The study contributes to organisational behaviour literature and offers practical implications for HR managers and leaders seeking to develop employee engagement and cultural alignment in the IT services sector. Keywords:. 2026, Iquz Galaxy Publisher. All rights reserved. -
A comparative study of leadership styles between public and private sector
This dissertation project is a descriptive research, it focuses on the differences in leadership styles between public and private sector, also examining whether leaders learn more towards being people oriented or task oriented. Task-oriented leaders prioritize towards goal achievement and tend to adopt more hands off approach when it comes to managing people. In contrast, people oriented leaders demonstrate concern for subordinates exhibiting warmth and support, but are less involved in task management. The project starts with the study of three banks of each public and private sector and taking interview given by 5 leaders from each bank to know about what kind of leadership style they possess based on which it could be determined how close they are with people and how much importance they give to the achievement of task. 2025, IGI Global Scientific Publishing. -
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 Algorithms for Recommendation Systems
This research explores recommendation algorithms for e-commerce efficacy. From e-commerce giants like Amazon to streaming services like Netflix, recommendation algorithms are integral in giving personalized experiences to attract and retain customers. It tests KNN, K-Means, Decision Tree (Gini, Entropy), and Naive Bayes on the Amazon review dataset 2018Electronics category. Decision Trees emerged as the most accurate predictor of user preferences, suggesting the trees ability to capture complex data relationships is key for relevant product recommendations. To get a better understanding, this research also examines each algorithms power and weakness in the context of recommendation systems. It offers valuable information on how to approach the optimization of their recommendation strategies in e-commerce businesses, highlighting not only the most effective approach (Decision Trees) but also the considerations for choosing an algorithm based on its strengths and weaknesses (e.g., interpretability vs. accuracy). Ultimately, this research contributes to informing data-driven decision-making for personalized recommendations in e-commerce, paving the way for a more user-centric shopping experience. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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 Models for Predicting Earthquake Magnitudes
This study evaluates the effectiveness of machine learning models in predicting earthquake magnitudes, aiming to address the challenges posed by traditional seismological methods. By leveraging geospatial and temporal data, the research compares the performance of Random Forest (RF), Artificial Neural Network (ANN), and K-Nearest Neighbours (KNN) using Mean Absolute Error (MAE) as the primary metric. Random Forest demonstrated the lowest MAE of 0.3291, showcasing its ability to handle complex, non-linear patterns better than ANN (0.3660) and KNN (0.3758). The analysis highlights the importance of geospatial and temporal factors in improving prediction accuracy, offering insights into their predictive significance. While traditional methods struggle with high-dimensional data, this study eliminates these limitations by employing machine learning models capable of extracting meaningful patterns. These findings underscore the potential of ensemble methods like Random Forest for enhancing earthquake prediction systems. Future research will explore hybrid approaches and real-time data integration to further advance predictive accuracy in seismology. 2025 IEEE. -
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 machine learning: Models for web tracker detection
Web trackers, used by websites, collect user data and monitor online activity, often with or without explicit consent. With concerns for online privacy, there is a growing need to detect these web trackers. This study evaluates several machine learning (ML) techniques for detecting web trackers, focusing on evaluating their performance from the key metrics such as accuracy, precision, and recall. We analyzed supervised methods, such as random forest, support vector machines (SVM), neural networks, gradient boosting, and unsupervised methods, including DBSCAN and isolation forest. Models were trained on a comprehensive dataset extracted from URLs with feature engineering, and data preprocessing techniques were applied to enhance model performance and detect both known and unknown trackers and normal traffic. Our results indicate that supervised models outperform unsupervised methods, demonstrating their superior ability in distinguishing web trackers from normal traffic. This work highlights the effectiveness of ML-based tracker detection and outlines opportunities for improving privacy protection through adaptive supervised learning methods. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
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 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.