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Analysing Collaborative Contributions and Sentiments in the Quantum Computing Ecosystem
Quantum computing, a revolutionary paradigm leveraging the principles of quantum mechanics, has emerged as a transformative technology with the potential to solve complex problems at unparalleled speeds. Within the quantum computing ecosystem, companies and research institutes play pivotal roles in advancing hardware, algorithms, and applications. This research explores the transformative landscape of quantum computing, focusing on key contributors such as Google, IBM, D-Wave, Azure, Amazon, Intel, EeroQ, and IonQ. Through sentiment analysis, topic modelling, and thematic analysis, the study aims to comprehensively understand the current state and trends within the quantum computing ecosystem. The findings unveil an overall positive sentiment and identified topics ranging from cloud computing services to quantum computing advancements. Thematic analysis provides actionable insights, emphasizing collaboration within the ecosystem. Rooted in the analysis of secondary data from key companies' articles, the methodology establishes a robust framework for discerning contributions, collaborations, and strategic orientations in quantum computing. 2024 IEEE. -
Analysing Crypto Trends: Unveiling Ethereum and Bitcoin Price Forecasts Through Analytics-Driven Weighted Moving Averages
This research meticulously analyses the performance dynamics of two paramount cryptocurrencies, Bitcoin and Ethereum, over 2,682 observations. Preliminary findings indicate a near alignment in the mean returns of both assets, with Ethereum marginally outperforming Bitcoin. Interestingly, Ethereums superior returns are accompanied by heightened volatility, underlined by its more significant standard deviation. Both cryptocurrencies manifest negative skewness, hinting at a proclivity for negative returns, with Bitcoin showing a sharper skew. Their pronounced kurtosis values attest to the potential for extreme price swings. Regarding forecasting efficacy, the Weighted Moving Average (WMA) method emerges as superior for both assets, yielding the most accurate predictions. At the same time, the Exponential Moving Average (EMA) demonstrates the highest forecast errors. Further, the Relative Strength Index (RSI) evaluation suggests Ethereum may be oversold, alluding to potential investment opportunities. In contrast, Bitcoin, with its mid-range RSI, resides in a neutral zone devoid of clear market signals. The findings shed light on the nuanced performance and forecasting landscape of these leading cryptocurrencies, offering pivotal insights for potential investors. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Analysing Customer Profile, Expectations and Satisfaction with Airport Retail in Coimbatore, Insights into the Airport Environment and Decision Making Dynamics
Airport retailing has become a crucial income source and a fundamental aspect of improving traveler experiences. Nevertheless, the intricate relationship between traveler characteristics, anticipations, shopping environments, promotional tactics, purchasing choices, and contentment remains insufficiently examined. This research fills this knowledge gap by exploring the structural associations among six key factors: Customer Profile (CP), Customer Expectation (CE), Airport Retailing Environment (ARE), Retail Marketing Strategy (RMS), Customer Preference Decision (CPD), and Customer Satisfaction (CS) in the context of airport retail environments A descriptive study framework was implemented, concentrating on travelers participating in retail purchases at Coimbatore airport. Firsthand information was obtained from 203 participants using a structured survey. The collected information was assessed using Structural Equation Modeling (SEM) to determine the interconnections among these elements and their overall effect on passenger contentment. The findings reveal that Customer Expectation is significantly influenced by Customer Profile, while Airport Retailing Environment is directly shaped by Customer Expectation but not by Customer Profile. Retail Marketing Strategy is strongly impacted by Airport Retailing Environment but shows no significant relationship with Customer Expectation. These findings emphasize the significance of analyzing traveler behavior and tailoring retail strategies to enhance contentment in airport shopping environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Analysing Employee Management Using Machine Learning Techniques and Solutions in Human Resource Management
In the contemporary landscape of Human Resource Management (HRM), organizations are increasingly turning to advanced technologies to streamline employee management processes. This study explores the integration of machine learning (ML) techniques as a transformative solution for optimizing HRM practices, with a specific focus on employee management. By leveraging the power of ML algorithms, this research aims to enhance decision-making, efficiency, and overall effectiveness in HRM. The study encompasses a comprehensive analysis of existing HRM challenges, such as talent acquisition, performance evaluation, and employee retention, and proposes ML-based solutions to address these issues. By applying natural language processing, pattern identification, and predictive analytics, businesses may learn a great deal about employee behavior, performance patterns, and possible areas for development. HR professionals are more equipped to make well-informed choices, customize employee experiences, and put proactive talent development initiatives into action thanks to this data-driven approach. Additionally, the study examines the moral issues and difficulties surrounding the use of ML in HRM, stressing the significance of openness, justice, and privacy. By understanding and mitigating these concerns, organizations can successfully harness the transformative potential of ML in employee management, fostering a more dynamic and adaptive HRM framework. The study's conclusions add to the growing body of knowledge on the relationship between technology and HRM and offer useful advice to businesses looking to use cutting-edge approaches to improve labor management procedures. 2024 IEEE. -
Analysing Enhanced EEG Based Brain Computer Interface for Motor Imagery Tasks Using Statistical Analysis
BCIs have emerged as a useful tool for helping people with neurological disorders like epilepsy, ALS, and cerebral palsy, who have severely limited communication, by analyzing EEG signals and turning them into actionable signals. This paper aims to investigate the application of BCIs in analyzing EEG signals and designing them to give meaningful signals to the users. Five healthy, right-handed university students (18-21 years) were selected for the study and were asked to spell the words mentally without vocalizing for four cognitive tasks; forward, stop, left, and right. At 100 Hz, EEG signals were sampled and pre-processed with a notch filter and feature extraction was done using DWT to extract important features and KNN was used for classification. All the subjects showed high accuracy with more than 90% and maximum accuracy of 95.89% was obtained by Subject S3. Standard deviations between 1.32 to 1.41, which indicate low variability in performance among all the subjects. These results showed that the DWT with KNN combination can be used for real-time BCI applications and can offer a reliable communication method for people with motor impairment and disabilities. 2025 IEEE. -
Analysing Gendered Microaggressions in Bengalurus IT Sector: A Sentiment-Based Inquiry
Gender microaggressions experienced by women in Bengalurus IT sector forms the subject of this paper employing sentiment analysis tools such as VADER and BERT to process survey responses, interview transcripts, and publicly available reviews of employees from the platform Glassdoor. A mixed-methods design is chosen so that computational sentimental analysis can be integrated along with qualitative insights such that patterns of emotional response and disparities across hierarchical levels can be identified. The results obtained highlight that sentiments vary significantly across organisational roles and underscores the significant psychological impact these microaggressions have on job satisfaction and career growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Analysing grief on twitter: A study of digital expressions on Om Puri's death /
Funes Journal of Narratives And Social Sciences, Vol.2, pp. 136-152, ISSN No. 2532-6732. -
Analysing the Ascendant Trend of Veganism: A Comprehensive Study on the Shift towards Sustainable Dietary Choices
Background: Veganism has become a prominent social and culinary movement due to concerns about animal welfare, environmental sustainability, and ones own health. Vegans strive to consume only plant-based meals in order to lessen the suffering of animals, stop the environmental damage caused by the animal agriculture sector, and enhance their own health. Objective: This chapter aspires to understand various dynamics of consumer consciousness towards veganism through social media analysis (Twitter) and research opinions. Materials and Methods: This chapter used a qualitative approach and a three-part methodology. Firstly, a literature review examines the impact of veganism on human health, ethical needs and sustainable food choices. Secondly, the authors extracted tweets and analysed them using data visualisation software- NVivo with the essential parameters being themes, sentiment, world map, and word cloud. Results: Sentiment analysis explained consumer perception towards veganism as a storming blackball result of 36.1 present positive insights. Word map analysis describes veganism as a global phenomenon. The third part analysed the Scopus research data and identified food, diet and meat as major themes in veganism. The Scopus database sentimental analysis also re-emphasised the growing positive insights towards it. Conclusion: This study highlighted the significance of veganism as a sustainable dietary choice for addressing urgent global issues while promoting a thoughtful and compassionate approach to eating. It is also emerging as a powerful tool for positive change in preserving and promoting biodiversity. 2024 selection and editorial matter, Mourade Azrour, Jamal Mabrouki, Azidine Guezzaz, Sultan Ahmad, Shakir Khan and Said Benkirane; individual chapters, the contributors. -
Analysing the Effectiveness of Solana Blockchain Platform and PoH Consensus Algorithm in Providing a Solution for Blockchain Scalability Problem
Solana started its journey in April 2018 and is now a public blockchain - based platform which aspires better scalability than other existing blockchains while providing security and decentralization. It backs the development of decentralized applications and smart contracts (DApps). The goal of the study is to confirm several of its characteristics, like its transaction throughput, or the pace in which legitimate transactions are committed to a Solana network block over the course of a one-second period (TPS). A secondary dataset that was gathered over the course of 60 days and made available on GitHub was utilized. Our data analysis findings demonstrate that the transaction throughput on an average is about 3006 TPS at a much lower transaction fees than the fees users pay for many other blockchains that facilitate the same operations, such as use of smart contracts and the development of DApps. The document explains the workings of the Solana blockchain, which, in the words of its creators, claims to address the scalability issue without compromising security and decentralization. Grenze Scientific Society, 2025. -
Analysing the Impact of CSR Spending by Big 4 Firms on their Financial Profitability
This study delves into this ongoing debate whether socially responsible companies perform better which leads to financial profit or instead have no impact. This study focuses on leading accounting companies i.e., PricewaterhouseCoopers (PwC), Deloitte, Ernst & Young (EY), and KPMG and whether CSR Spending impacts their financial profitability or goes unnoticed. Grenze Scientific Society, 2024. -
Analysing the impact of oil prices, economic activity, and trade policy uncertainty on CO2 emissions in the US context: A wavelet approach
This study examines the simultaneous co-movements between oil prices, economic activity, trade policy uncertainty, and CO2 emissions in the United States using a series of wavelet methodologies. Unlike traditional approaches, the wavelet approach is appropriate for understanding the time-varying associations at different frequencies and is designed to efficiently handle the non-stationary nature of economic and environmental time series data. The empirical results highlight the potential of a leading relationship where economic activities and trade policy uncertainties drive CO2 emissions in the US during the period from January 1990 to January 2022. Contrarily, the link between oil prices and CO2 emissions is characterized by intricate dynamics, exhibiting both lagging and leading co-movements at different frequencies. Moreover, economic activities have a positive impact on CO2 emissions, while in the high quantile tails, trade policy uncertainty decreases CO2 emissions. This means economic activity is slowing down during the period of high trade policy uncertainty. Our findings highlight the necessity of specific policies that reconcile economic growth with environmental sustainability, manage the effect of oil price changes on CO2 emissions, and match trade policies with emission-minimizing goals. Based on the results, this research offers important implications for policymakers to ensure the equilibrium between economic activity and environmental management within the scope of sustainable development goals. 2025 International Association for Gondwana Research -
Analysing the Impact of Perceived Risk, Trust and Past Purchase Satisfaction on Repurchase Intentions in Case of Online Grocery Shopping in India
The Indian online grocery market has been propelling since last few years. The size of online grocery market in 2020 was estimated as $2.9 billion and it is further anticipated to reach at the compound annual growth rate (CAGR) of 37.1% during 2021 to 2028. Companies such as Amazon, Flipkart grocery, BigBasket, Grofers and Jiomart have been coming up with new attractions for consumers such as providing timely no contact delivery, accepting various digital modes of payment and offering several discounts which have fascinated consumers towards buying their regular grocery from various online platforms. Corona virus has also fuelled up the safety concerns of people; due to which a large section of the citizens are working from home and are dependent on the online platform for various purposes including grocery shopping. This has provided several growth opportunities to the online grocery market. This research investigates about the purchase behaviour of customers towards online grocery shopping. The study aims to understand the purchase behaviour of e-grocery shoppers of India and to examine the association between satisfactions with online purchase, trust on online grocers, perceived risk and online repurchase intention of grocery items. The study uses primary data collected from 555 online grocery buyers. The findings of the study indicate that online customer satisfaction is a significant factor that influences repurchase intentions of online grocery shopping. Perceived risk negatively influence trust as well as repurchase intentions. Trust is found to be a mediating factor between shopping satisfaction and repurchase intentions. The study also builds and tests an online customer behavioural model with actual purchasing behaviour and identifies the continued presence of perceived risk, shopping satisfaction and trust in grocery e-retailing. 2023 IMI. -
Analysing the impact of the taxation law amendment of 2019 on corporate taxation in India
The Taxation Law (Amendment) Act, 2019 in India has brought major changes in the taxation revenue as well as in legal provisions. The actual ground reality of the Amendment on a microeconomic level is unknown, but a correlation analysis on macroeconomic indicators show that there is a high positive correlation between the corporate tax revenue and the GDP growth. The author also interlinks the effects of tax cuts on the economy with privatization and how it can mitigate the risks of tax evasion. There is a generalized misconception with privatization that it leads to a significant loss in taxation revenue. The study shows that in fact, privatization helps to expand the earnings of the Government by widening the taxation structure and slab, which the author has found through statistics. It is high time to have strong regulatory measures to prevent tax evasion by encouraging more corporate entities to become a part of the tax base. Indian Institute of Finance. -
Analysing the Influence of Activation Functions in CNN models for Effective Malware Classification
With the advancement of information technology, malware has become a persistent cyber security concern that targets computer systems, smart devices, and wide networks. Due to flaws in performance accuracy, analysis type, and malware classification methodologies that miss unsuspected malware attacks, malware classification has thus always been a significant concern and a challenging subject. Using the Malimg dataset, which has 9349 samples from 25 different families, this study classifies malware using a deep learning algorithm called a convolution neural network and evaluating the accuracy using a number of activation functions in this study. The proposed CNN model for malware classification achieves a high accuracy rate without the need for complex feature engineering. The model achieved the highest accuracy of 96.93% when using the Rectified Linear Unit (ReLU) activation functions whereas Leaky Relu gives accuracy of 96.76%, Pre relu gives 96.36%, ELU gives 95.72% and tanh gives accuracy of 95.58%. 2024 IEEE. -
Analysing the market for digital payments in India using the predator-prey model
Technology has revolutionized the way transactions are carried out in economies across the world. India too has witnessed the introduction of numerous modes of electronic payment in the past couple of decades, including e-banking services, National Electronic Fund Transfer (NEFT), Real Time Gross Settlement (RTGS) and most recently the Unified Payments Interface (UPI). While other payment mechanisms have witnessed a gradual and consistent increase in the volume of transactions, UPI has witnessed an exponential increase in usage and is almost on par with pre-existing technologies in the volume of transactions. This study aims to employ a modified Lotka-Volterra (LV) equations (also known as the Predator-Prey Model) to study the competition among different payment mechanisms. The market share of each platform is estimated using the LV equations and combined with the estimates of the total market size obtained using the Auto-Regressive Integrated Moving Average (ARIMA) technique. The result of the model predicts that UPI will eventually overtake the conventional digital payment mechanism in terms of market share as well as volume. Thus, the model indicates a scenario where both payment mechanisms would coexist with UPI being the dominant (or more preferred) mode of payment. 2023 Balikesir University. All rights reserved. -
Analysing the Relationship Between FDI and the Dimensions of Sustainable Development in India
After dominating international development policy for more than 20years, sustainability and sustainable development have attracted more and more attention in policy and scholarly discourse. Significantly, recent events have emphasised the significance of pursuing sustainability and sustainable development even more. These include the adoption of a circular economy strategy, the move to renewable energy sources, the fight against climate change, and attempts to reduce emissions from fossil fuels. Foreign direct investment is one method of achieving sustainable development. Therefore, a sustainable relationship between FDI and sustainable development in India has been established in the subsequent pages of this paper and this causal relationship has been analysed. This causal relationship has been established via the vector error correction model. Through the findings, this paper doubles down on the idea that FDI is a great source for the introduction of sustainable development. However, it is also found that policymakers should focus on the implementation of those policies that utilise the benefits of FDI while simultaneously working against the negative environmental impacts and promoting the need for human development. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
ANALYSING THE SAFETY OF A CAMPUS USING SPATIAL SYNTAX
Everybody has been in campus environments and academic buildings at some point in their lives. The layout of these structures is crucial because it influences how a person behaves and presents themselves. The use of space syntax enables us to examine how individuals behave in relation to their surroundings and how places are used. The nature of the space and the way people move through it have improved because of the application of space syntax in campus planning.A primary concern is safety, this paper is devoted to comprehending how various user groups navigate across a university. Here, we'll be looking at how students move around and behave in relation to how safe they feel on campus. Each user group's paths, nodes and gathering places will be recorded and we'll confirm both the original puiposes and the current uses of the spaces. Additionally, several maps will be created to support the study that the campus is a safe place to be, including axial mapping and analysis mapping, convex mapping and grid analysis mapping. This with a combination of survey shall be used to understand safety with respect to space syntax. ZEMCH Network. -
Analysing Twitter User Behaviour with Process Mining: A Study on Activity Patterns
Social media sites provide a platform to share the information. People share their views and interests. Social media data provides information on user, activity, network, and content. Researchers anticipate a lot of information from social media data. It covers the activities of user, people connected to them, and their likes and dislikes. If users data is processed keenly, one can easily understand a users behaviour with his actions and predicts the next action of the user. It also helps in describing the relations among the users. This study illustrated the process mining algorithms to uncover the insights of Twitter users data. The model depicts the overall process flow of Twitter user activities. Behavioural patterns like common sequences, repeated user actions, direct relations, and rare interactions are analysed. The models performance is assessed with the metrics like fitness, precision, and simplicity to choose the best model for the dataset. Inductive miner outperformed well with other algorithms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Analysing Young Adults Preferences for AI-Generated and Human-Created Art in India: A Comparative Study Using the Mixed Method Approach
Artificial intelligence (AI) has emerged as a transformative tool in creating art, blending computational precision with creative processes. This study explores the appeal of AI-generated art compared to human-created physical and digital art among young adults in India, particularly focusing on visual art students. Additionally, the research addresses critical questions regarding the aesthetic appreciation and criticism of AI-generated art, its impact on human creativity, and its challenges to traditional art and its future. The research employed a mixed-method approach to understand preferences, motivations, and perceptions regarding these two art forms. The Art Reception Survey (ARS) was utilised to measure individuals engagement with visual aesthetics and their preferences. The qualitative approach using Multimodal Critical Discourse Analysis (MCDA) enabled deeper analysis, which helped examine how meaning, perceptions, and visual cues must have shaped their responses. The findings indicate a strong preference for original works involving creative thought processes and artistic skills-factors that lean towards a preference for traditional artwork. The findings suggest that despite rapid advancements in AI, people still significantly value human effort and creativity. The participants also acknowledged that blending both art forms can open new avenues of opportunity for the artists. The study suggests that traditional art will likely remain highly valued and argues that AI should not be seen in opposition to conventional art but as complementary tools for artistic innovation. While human-created art remains strongly appreciated, embracing AI would be the way forward, as outright rejection may not always be feasible or beneficial. 2025, Iquz Galaxy Publisher. All rights reserved. -
Analysis and Actions Planned for Programme Outcomes in Outcome Based Education for a Particular Course
In India many of the technical institutions are NBA (National Board of Accreditation) accredited and the accreditation is a way to maintain quality of education. The outcome-based education (OBE) plays an important role in technical education across the world. So, in this research we will show how we can implement the attainment process related to OBE for a particular course. In this paper we will discuss how the course outcome and mapping of course outcome with program outcome can be defined. Then we will discuss the process to calculate the attainment. Finally, the program gaps were identified for that course and actions were suggested. 2024 IEEE.