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Framework to analyze customer's feedback in smartphone industry using opinion mining
In the present age cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact 2018 Institute of Advanced Engineering and Science. All rights resented. -
Assessment of ML techniques and suitability to predict the compressive strength of high-performance concrete (HPC)
Using industrial soil waste or secondary materials for making cement and concrete has encouraged the construction industry because it uses fewer natural resources. High-performance concrete (HPC) is recognized for its exceptional strength and sturdiness compared to conventional concrete. Accurate prediction of the compressive concentration of HPC is vital for optimizing the concrete mix design and ensuring structural integrity. Machine learning (ML) techniques have shown promise in predicting concrete properties, including compressive strength. This research focuses on various ML techniques for their suitability in predicting the compressive dilution of HPC. In this research, the Extended Deep Neural Network (EDNN) technique is used to analyze the strengths, limitations, and performance of different ML algorithms and identify the most effective methods for this specific prediction task. However, there is a problem with accuracy. Therefore, our research approach is the EDNN-centred strength characteristics prediction of HPC. In the suggested approach, data is initially acquired. Afterward, the data is pre-processed through normalization and removing missing data. Thus, the data are fed into the EDNN algorithm, which forecasts the strength characteristics of the particular mixed input designs. With the Multi-Objective Jellyfish Optimization (MOJO) technique, the value of weight is initialized in the EDNN. The activation function is the Gaussian radial function. In the experimental analysis, the implementation of the suggested EDNN is evaluated to the performance of the prevailing algorithms. When compared to current research methodologies, the proposed method performs better in this regard. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Detection and localization for watermarking technique using LSB encryption for DICOM Image
Watermarking is an effective way of transferring hidden data from one place to another, or proving ownership of digital content. The hidden data can be text, audio, images GIF etc., the data is embedded in a cover object usually an image or a video sequence. Usually the watermarking system(s) rely on their hidden aspect, as their primary security measure, once this is established that the cover object is counting some hidden data, then it is generally possible to recover the hidden information. The author proposed an in-genuine technique for DICOM color image water marking by combining Multi Quadrant LSB with truly random mixed key cryptography. This system provides a high level of security by just the water marking technique, as it breaks the cover image into up to four quadrants, & does LSB replacement of two bytes each quadrant. The bit sequence as the quadrant sequence can be randomized to increase the randomness, use of truly random mixed key cryptography, by using a pre shared, variable length, truly random, private key, turns hidden data into noise, which can only be recovered by having the private key. Thus, the proposed technique truly diminishes the probability of recovering hidden data, even if it is detected that something is hidden in cover object. 2022 Taru Publications. -
Design of Machine Learning Model for Health Care Index during COVID- 19
Predicting stock prices and index movement in the field of finance is always challenging. The events in the macro-economic framework affect the trends of the market and the COVID-19 pandemic was a major reason for the slowdown of the global economies in the short run. It was assumed that the healthcare industry has completely been transformed due to changing behavioral habits of individuals. The study presents the time series approach with the help of historical prices on the Bombay Stock Exchanges (BSE) Health Care Index, both in the long and short run, using the ARIMA model. The period of the study is from February 1999 to August 2020. The ARIMA equations are used to forecast the future price movement of the Health Care Index till December 2020. The findings reveal that the market will continue with the same volatility, and investors should give due attention to analysis and logical reasoning rather than following their feeling of overconfidence. 2024 Taylor & Francis Group, LLC. -
Sustainability of Indian tourism in backdrop of COVID-19
The Indian tourism and travel industry is one of the fastest growing industry. According to WTTC (2019), India ranked 10th among 185 countries in terms of travel & tourism's having a total contribution to GDP of 6.8% of the total economy, Rs. 13,68,100 crores (US$ 194.30 billion) (www.ibef.org). In the year 2017, The United Nations World Tourism Organization (UNWTO) has declared 2017 as the 'International Year of Sustainable Tourism for Development', which underscores tourism's critical role in fostering inclusive growth. Hence, the efforts to achieve sustainability got an impetus and gained much wanted attention. However, everything came to standstill with the onset of Corona Virus Pandemic in November 2019, questioning the survival of the industry itself. The present crisis caused tremendous losses which have resulted in large scale job losses bringing the sustainability in question. This study aims to investigate the state of sustainability of Indian tourism through infrastructure development, environmental degradation, social, economic and cultural impacts on destinations due to this growth in the backdrop of the present COVID pandemic. It is an empirical study of perceptions of tourists to Indian destinations. The data was collected through self-administered questionnaires and interviews. A total of 520 valid responses were analyzed and results revealed a different scenario. The study concludes with a discussion of the findings and providing a few recommendations to rectify the situation for a sustainable industry and future. 2021 Ecological Society of India. All rights reserved. -
Feature extraction and diagnosis of dementia using magnetic resonance imaging
Dementia is a state of mind in which the sufferer tends to forget important data like memories, language, etc.. This is caused due to the brain cells that are damaged. The damaged brain cells and the intensity of the damage can be detected by using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix (GLRM), are used for the clear extraction of data from the image of the brain. Then the data obtained from the extraction techniques are further analyzed using four machine learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN). The results are further analyzed using a confusion matrix to find accuracy, precision, TPR/FPR - True and False Positive Rate, and TNR/FNR - True and False Negative Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature Extraction (FE) technique with the combination of the SVM and KNN algorithm. 2023, Bentham Books imprint. All rights reserved. -
Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study
In the realm of scientific investigation, traditional survival studies have historically focused on mitigating failures over time. However, when both observed and unobserved variables remain enigmatic, adverse consequences can arise. Frailty models offer a promising approach to understanding the effects of these latent factors. In this scholarly work, we hypothesize that frailty has a lasting impact on the reversed hazard rate. Notably, our research highlights the reliability of generalized Lindley frailty models, rooted in the generalized log-logistic type II distribution, as a robust framework for capturing the widespread influence of inherent variability. To estimate the associated parameters, we employ diverse loss functions such as SELF, MQSELF, and PLF within a Bayesian framework, forming the foundation for Markov Chain Monte Carlo methodology. We subsequently utilize Bayesian assessment strategies to assess the effectiveness of our proposed models. To illustrate their superiority, we employ data from renowned Australian twins as a demonstrative case study, establishing the innovative models advantages over those relying on inverse Gaussian and gamma frailty distributions. This study delves into the impact of hidden and obscure factors on left-censored data, utilizing Bayesian methodologies, with a specific emphasis on the application of generalized Lindley frailty models. Our findings contribute to a deeper understanding of survival analysis, particularly when dealing with complex and unobservable covariates. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Pure and suggestive impulse buying in mobile shopping app: shopping pattern of young consumers
Purpose: This study differentiates pure impulse buying behaviour from suggestive impulse buying behaviour in using mobile shopping applications (apps). This study aims to assess the moderating effects of instant discount and cashback promotional offers along with the mediating effects of impulse buying intention (IBI) and user satisfaction (US), using the app stimuli (performance expectancy, effort expectancy, layout, atmosphere, privacy and security). Design/methodology/approach: The study was done in three stages: analysis of variance, followed by structural equation modelling (SEM) and paired t-tests. Findings: The results showed that instant discounts and cashback offers are different from each other for the mediating variable IBI. The SEM results for pure impulse buying showed that, except for layout, the remaining variables have a positive relationship with IBI. For suggestive impulse buying, effort expectancy and layout were significantly related to both the mediating variables. Finally, pure and suggestive impulse buying behaviour showed significant differences. Originality/value: Previous studies have looked into impulse buying in its generic sense and not through the types of impulse buying they were measuring. As impulse buying behaviour is a predominant theme for discussion today, marketing professionals and researchers must comprehend the impact of app stimuli in the context of select types of impulse buying behaviour. 2024, Emerald Publishing Limited. -
Impact of video product presentation and scarcity claim on mobile-based impulse buying
The introduction of mobile shopping apps has resulted in the growth of impulse buying or excessive compulsion, especially in the fashion industry. This paper aims to establish a relationship between app stimuli (including performance expectancy, effort expectancy, atmosphere, layout, and privacy and security) and impulse buying behaviour. Besides this, it also examines the moderating effects of the video product presentation and product scarcity claim and the mediating effects of impulse buying intention and user satisfaction. The study was carried out in two phases. In the first phase, a paired t-test analysis was carried out to compare the mean of each set. This was followed by multi-group structural equation modelling to check the models validity. The results show that while video product presentation produced a significant difference between impulse buying intention and impulse buying behaviour for both the male and female respondents, scarcity claims achieved positive results only for male participants. The SEM results, meanwhile, demonstrated that both the mediating variables bear a relationship to performance expectancy, privacy and security, and impulse buying behaviour. However, effort expectancy was only related to impulse buying intention, while atmosphere and layout were exclusively associated with user satisfaction. Based on the findings of the study, theoretical and managerial implications are presented. 2023 Korean Scholars of Marketing Science. -
Convergence of retail banking interest rates to households in euro area: time-varying measurement and determinants
This study measures time-varying progress of retail banking (to households) interest rates convergence and examines its determinants for twelve countries of the euro area, between 2003 and 2014. First, we measure convergence of interest rates using five different time-varying indicators, namely asymmetric dynamic conditional correlation (ADCC), beta convergence, sigma convergence, variance ratio, and dynamic cointegration. We then estimate panel regressions for each type of interest rate to identify the determinants of convergence over pre-crisis and crisis periods. The estimated ADCC is employed as the dependent variable and explanatory variables measure potential macroeconomic, external linkages, industry-specific, institutional and sociological determinants. The results reveal that convergence is weak and heterogeneous across sub-periods (pre-crisis and crisis), economic groups (core and periphery), product type (savings and credit) and products maturities (short, medium and long). Among the fundamental determinants, inflation, output correlation, and sociological factors strongly impact convergence, however, the explanatory power of determinants weakens during the crisis period. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Asymmetric dynamic conditional copula correlation and fundamental determinants of interest rate comovement
We study time-varying interest rate comovement and its determinants for the retail banking sector in the euro area over pre-crisis and during crisis, between 2003 and 2018. The analysis is conducted for 11 euro area countries, each classified as either core or periphery. Copula Asymmetric DCC-GARCH is estimated for each country-pair to measure the dynamic interest rate correlations for deposits and loans to households. We then examine the determinants by regressing quarterly correlations on macroeconomic and cross-border linkages, banking, and sociological variables. We also assess the impact of the two crises and of policy initiatives, including negative interest rate, Single Supervisory Mechanism, and Single Regulatory Mechanism. Different panel regressions reveal limited, although varied, influence of determinants on correlations across different products, maturities, and country groups. We conclude that the intrinsic features of the retail banking industry, such as customers' trust, information asymmetry, and political influence, hinder strong interest rate convergence in the euro area. 2019-Center for Economic Integration, Sejong Institution, Sejong University, All Rights Reserved. -
Exploring the Plausibility of Pre-Purchase Decision Process in User Acceptance of Smart Wearable Technology Devices
The market for smart wearable technology products is growing rapidly. Although wearable technology is still in its early stages, a longer-term outlook is required. This study inspected the existence of consumers pre-purchase stage for smart wearable technology devices. It further analyzed the factors that influenced customers decisions in the pre-purchase phase. The methodology adopted was quantitative, using which 240 users of smart wearables were given a structured questionnaire to fill up. The Smart PLS 3.0 software was used for structural equation modeling and path analysis. The results indicated that customers go through a pre-purchase decision journey. Their decisions are influenced by individual characteristics, product description, information source utility, data usefulness, trust, visibility of the product, and demonstrability. Together, these factors resulted in the customers successful transition from the pre-purchase stage to the purchase decision stage. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
AI Diagnosis: Rise of AI-Powered Assessments in Modern Education Systems
The literature on the limitations on the current archaic education system is limitless, the consequences of which have only been exacerbated in the current lockdown scenario. The timed evaluations have not only failed as an assessment tool during these times but research has shown there are increased rates of using unfair means and proctoring as a result. Not only was the system faulty to begin with, it is failing miserably under current lockdown situations. Simultaneously the current literature keeps positing that since technology has become an integral part of our life already, it would not be long before technology integrates with education and assessments. Taking into consideration the need and potential of an integrative system, this paper aims to explore how artificial intelligence can be effectively introduced into education and improve learning outcomes. The paper performs a Comprehensive Literature Review (CLR), and analyses data based on the framework developed by Onwuegbuzie and Frels (2015). The paper thus reviews literature with the aim to explore current models of AIEd and relevant psychological concepts relating to learning and career outcomes. The evidence is consistence with the rationale for research problem: current AI methodologies in education focus only on delivering learning material, using AI as a means, instead of taking into other factors improving learning and education outcomes. The subsequent literature review on the factors influencing learning outcomes establish that there are two main thematic influences on students learning and behavioral outcomes: inside-school and out of school factors, which have been further implored in context of technological advancements. 2021. Transnational Press London. All Rights Reserved. -
A study of factors affecting consumer behavioural intentions towards adoption of gamification
Advanced technology with a high degree of accessibility of the Internet across countries has led to the emergence of e-commerce and m-commerce. The global brands and the start-ups are equally attracted to this change in the way the goods and services can be marketed, making market access through the Internet a core part of the strategy for marketers. The same has led to various digital promotional techniques for customer engagement. Among various digital promotional techniques, gamification is one technique where customers are expected to play online games to win the reward points for getting discounts for their purchases. An attempt was made to identify various factors influencing the consumer behavioural intentions towards adoption of gamification as one of the means to get products at discounted prices. In this study, factor analysis was used to find the major factors that influence the consumer behavioural intentions to adopt gamification as one of the digital promotional techniques. The study found eight major factors that influence the adoption of gamification, they are Personal Perspective, Usefulness, Easy to Use, Price Consciousness, Perceived Critical Mass, Flow Experience, Awareness, and Personal Innovativeness. This study brought in a different perspective by exploring the role of possible factors influencing gamification adoption in the Indian market, helping marketers, the major factors, and their influence on the consumer's behavioural intentions of adoption of gamification as one of the digital promotion techniques. -
After-sale service experiences and customer satisfaction: An empirical study from the Indian automobile industry
For the growth of any industry, services play an essential role. Customers are more aware of the type of services they receive, and the expectations from the service providers are very high. Twenty-two percent total Gross Domestic Product (GDP) of the country is generated through the automotive industry. Global automotive majors have entered India and have dramatically changed the country's car production scenario. Changes to international technology design and adaptation have helped Indian car manufacturing compete globally, facing worldwide challenges. Considering services' high significance and essential role in the automobile industry, this study examined customer satisfaction with after-sales service experiences in the automobile sectorthis paper analyses customer satisfaction concerning automotive service interactions. The conceptual framework explains the impact on customer satisfaction in various car industries from various experiences, including employee behaviour, service lead time, service quality, service processes, and service costs. The respondents from Bangalore were selected. The data collection sampling approach used was convenience sampling. In a standardized questionnaire, data is collected from 400 respondents. The results demonstrate the substantial influence of service interactions on customer satisfaction. 2022 Elsevier Ltd -
Sustainable after sale services: The effect of perceived value on customers behavioural intention
There is an increasing beleive that the biggest show stopper in any industry will no longer be technology or capital, but the environment (Sheth & Sinha, 2015). It evidently defines the main problem of our world today, and the concern, regarding our future generations. Ensuring that our action today do not limit the range of economic, social, and environmental options to future generation, the fundamental principle of sustainability has emerged (Trevena, Kaldor & Downs, 2014).In light of the same concern, Recently, sustainability management has developed in the service industry. Green/sustainable after sale services in customer durables have been progressively joining the service industry. Customersupport determinesthe sustainable development of the consumer durable industry for their services. This paper aims to explore relationships among perceived values viz. hedonic and utilitarian values, and behaviour intentions of the customer. A total of 360valid questionnaires were collected, and regression method was used to measure and test the research hypotheses. Thestudy presents empirical evidence of impact of hedonic and utilitarian values on customersbehavioural intentions. Finally, theoretical and practical implications are discussed and suggestions for future research are provided. 2019 SERSC. -
Software development with UML modelling and software testing techniques
This chapter focuses on software development principles and discusses each principle thoroughly with diagrammatic representation. It also includes the definition of UML (unified modeling language) modelling with an explanation regarding how UML modelling takes place and a detailed example. It also focuses on software testing methods, with each method definition and diagrams well explained. A simple case study situation is taken to discuss the example of UML model. This chapter's main objective is to focus on all key points of software development testing and model design techniques precisely. 2023, IGI Global. All rights reserved. -
Indias roadmap of convergence to international financial reporting standards (IFRS)
[No abstract available] -
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The Roadmap Implementation for Smart Cities via High Level Communication Technology
This paper explores the integration of smart city technologies to enhance urban living standards and optimize city services. Leveraging modern 6 g technologies such as the Internet of Things (IoT), fog computing, robotics, and predictive analytics, smart cities aim to improve efficiency across various sectors including healthcare, transportation energy, and education. Through real-time monitoring enabled by Wireless Sensor Networks (WSNs), IoT devices, and unmanned aerial vehicles (UAVs), smart cities can efficiently manage resources and infrastructure. In this paper proposes an architectural design to improve urban security using 6G technology and present an extensively light weighted secured mechanism for easing intricacy in medium channel. This study validate and test arithmetical framework with respect to extensively light weighted secured mechanism. The instant study explores the background of defined urban security framework focusing on Internet of Things technology and its application in urban development. This study also introduces a lightweight edge fogging algorithm to optimize general computer network topologies. The proposed framework is thoroughly analyzed and evaluated through computational analysis, simulation, and comparison with existing models. The results show that the proposed framework with 6 G technology and lightweight security model shows better performance, less service downtime, and higher connectivity with current models. 2024 IEEE.