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Detection of high-frequency pulsation in WR135: Investigation of stellar wind dynamics
We report the detection of high-frequency pulsations in WR 135 from short-cadence (10 minute) optical photometric and spectroscopic time series surveys. The harmonics up to the sixth order are detected from the integrated photometric flux variations, while the comparatively weaker eighth harmonic is detected from the strengths of the emission lines. We investigate the driving source of the stratified winds of WR 135 using the radiative transfer modeling code, CMFGEN, and find the physical conditions that can explain the propagation of such pulsations. From our study, we find that the optically thick subsonic layers of the atmosphere are close to the Eddington limit and are launched by the Fe opacity. The outer optically thin supersonic winds (Tross = 0.1 0.01) are launched by the He II and C IV opacities. The stratified winds above the sonic point undergo velocity perturbation that can lead to clumps. In the optically thin supersonic winds, dense clumps of smaller size (fVFF = 0.27 0.3, where fVFF is the volume filling factor) pulsate with higher-order harmonics. The larger clumps (fVFF = 0.2) oscillate with lower-order harmonics of the pulsation and affect the overall wind variability. 2024. The Author(s). -
A Predictive Modelling of Factors Influencing Job Satisfaction Through a CNN-BiGRU Algorithm
The fields of humanities, psychology, and sociology are where the word 'job satisfaction' originated. According to psychology, it is a condition in which a worker experiences his circumstances emotionally and responds by experiencing either pleasure or suffering. It is regarded as a variable in various sociological categories pertaining to how each employee assesses and thinks about his work. Because a satisfied employee contributes to and builds upon an organization's success, job satisfaction is intimately tied to an employee's performance and the quality of the work they do. As a result, job satisfaction directly correlates to an organization's success. The proposed strategy incorporates data preprocessing, feature selection, and model training. The missing value is a common feature of data preparation. Feature selection is chosen using the ANOVA F-Test Filter, the Chi-Square Filter, and the full Data Set Construction procedure. The model's efficacy can be evaluated with the help of CNN-BiGRU. The proposed technique is compared to two more models: BiGRU and CNN. It has been shown that our proposed technique outperforms two other models. 2023 IEEE. -
An Expected Model of Management Program in India
Pravara Management Review, Vol 15, Issue 2, pp. 17-23, ISSN No. 0975-7201 -
Predicting Consumer's Brand Switching Behaviour for Cell Phones
The IUP Journal of Marketing Management, ICFAI, Vol. XV, Issue 4, ISSN No. 0972-6845 -
Unsupervised Feature Selection Approach for Smartwatches
Traditional feature selection methods can be time-consuming and labor-intensive, especially with large datasets. This studys unsupervised feature selection approach can automate the process and help identify important features preferred by a particular segment of users. The unsupervised feature selection method is applied for smartwatches. Smartwatches continue to gain popularity. It is important to understand which features are most important to users to design and develop smartwatches that are more engaging, user-friendly, and meet the needs and preferences of their target audience. The rapid pace of technological innovation in the smartwatch industry means that new features and functionalities are constantly being developed. Multi-cluster feature selection, Laplacian score, and unsupervised spectral feature are used. Conjoint analysis is done on the most common features in all three selection methods. The unsupervised feature selection technique is used for identifying the relevant and important features of new smartwatch users.The practical implication of the research is in the application of the technique in the new product design of smartwatches. The result of the study also informs smartwatch manufacturers and developers on the features they need to prioritize and invest in. This can ultimately result in better and more user-friendly smartwatches and a good overall experience for the user. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Using Academic Performance Indicator to Evaluate the Cost to Company of Management Graduates
As the placement season hits CBS Business School, India, the pressure to get placed is at its peak. As the placement season draws to a close, the unplaced students storm the Directors office complaining about unfair treatment in the process. They lay blame on the random shortlisting followed by the Placement co-ordinator. Concerned with these allegations, the Director calls on faculty to investigate the situation. During the conversation one of the students, Rachit, expresses regret in not focusing solely on academics and instead on developing a more well-rounded profile. He feels that that is the reason for his failure to get placed. A fundamental question arises of how closely academic performance and Cost to Company (CTC) are related. Data is collected to examine the validity of the long-held belief that higher academic performance leads to higher paying job placement. 2022 NeilsonJournals Publishing. -
Classifying voice-based customer query using machine learning technique
Timely attention to issues raised by customers is critical. It is imperative that the average handling time is lesser, which in turn contributes to productivity. It was found from the data from the banking industry in the US that, on average, a customer service call last for seven minutes. The first two minutes are for the call to get redirected to the respective team. This study investigates a method using machine learning to classify and redirect the customers into the respective department directly based on their initial voice response or voice message. It will substantially reduce the service time. CRISP-DM methodology is being used to design the process of the study. The most frequently occurring issues and the department to which they are associated are created through machine learning from the dataset that contained product reviews and metadata of different issues. The programming languages that are used in this study are Python, HTML and Java. An interface is created by using HTML, which makes it quite user-friendly. The study tests the effectiveness of converting voice to text and interprets which department the call should be transferred to address the issue. A support vector machine and a logistic regression model were used for the prediction, and it was found that the models provided an accuracy of 83 and 84 percent, respectively. The study proves that using ML and voice recognition reduces the average handling time. 2021 Ecological Society of India. All rights reserved. -
A Comparative Analysis of Machine Learning Algorithms for Image Classification: Evaluating Performance
Image classification plays a crucial role in various applications, and selecting the most effective machine learning algorithm is essential for achieving accurate results. In this study, we conducted a comparative analysis of several well-known supervised machine learning techniques, including logistic regression, support vector machine (SVM), k-nearest neighbours (kNN), nae Bayes, decision trees, random forest, AdaBoost, and artificial neural networks (ANN). To assess the performance of these algorithms, we utilised different fonts of the English alphabet as our dataset and performed the analysis using the R programming language. We evaluated the algorithms based on standard performance criteria, such as the area under the Receiver Operating Characteristic curve (ROC), accuracy, F1 score, precision, and recall. Our research findings demonstrated that the classification performance varied depending on the training size of the dataset. Notably, as the training size increased, neural networks exhibited superior performance compared to other machine learning techniques. Consequently, we conclude that neural networks and SVM are the most effective algorithms for image classification based on our study. By conducting this comprehensive analysis, we contribute valuable insights into selecting appropriate machine learning algorithms for image classification tasks. Our findings emphasise the significance of considering the training dataset size and highlight the advantages of neural networks and SVM in achieving high classification accuracy. This study provides valuable guidance for practitioners and researchers in choosing the most suitable machine learning algorithm for image classification, considering their specific requirements and dataset characteristics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Using Analytics to Measure the Impact of Pollution Parameters in Major Cities of India
Coronavirus is airborne and can spread easily. Air pollution may have an impact on breathing and also keep the virus airborne. The levels of air pollution were impacted by the lockdown measures, restricting the vehicular and industrial pollutants. Therefore, there is a need to understand the relation between air pollution levels and the Coronavirus infection rate. The study aims to find the effect of various pollutants across major cities of India on the R-value. The pollution data was collected from the Governments official portal. The major pollutants on which the data was collected are PM2.5, PM10, NO, NO2, NOx, SO2, CO, and Ozone. The data on air pollution levels were also collected for the selected cities from April 2020 to April 2021. The spread is measured as the reproduction number at time t (Rt), which is an estimate of infectious disease transmissibility throughout an outbreak, or it is the rating of Coronavirus or any diseases ability to spread. The data is analysed using MS Excel and R Programming. Descriptive statistics and regularisation are performed on the data. The study results reveal that some pollutants positively and negatively affect the infection rate. However, the effect is very low, and it concluded that the pollution might not directly affect infection rates. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Cultural quotient: Evolving culturally intelligent business scholar-practitioners
Analytical competency is an essential skill when it comes to the present-day business scenario of the world. However, these days we see a shift in the business needs when it comes to working in a globalized environment. Not only is the intelligence quotient (IQ) looked at but organizations these days are in pursuit of individuals who have another side to their profile - the culturally intelligent side (assessed using the cultural quotient). The need of such a skill can be attributed to the fact that organizations are now churning out their human side of addressing the employees when it comes to ensuring that they blend in the organization with ease. Acquiring a workforce which possesses high cultural intelligence can be a tough task; however, training employees to become culturally competent can be a doable task. Like any other personality trait which can be imbibed over time through constant analysis and observation, cultural competency is one such area which may be cultivated through various methodologies and practices. 2018, IGI Global. -
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. -
Depth Wise Separable Convolutional Neural Network with Context Axial Reverse Attention Based Sentiment Analysis on Movie Reviews
Sentiment Analysis (SA) in movie reviews involves using natural language processing techniques to determine the sentiment expressed in reviews. This analysis helps in understanding the overall audience sentiment towards a movie, categorizing reviews as positive, negative, or neutral. It's useful for filmmakers, marketers, and audiences. The existing methods does not provide sufficient accuracy, error rate and complexity was increased. To overcome the aforementioned problem, Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN) is proposed for accurately classifying SA in movie reviews. In this input image is taken from two datasets such as IMDB dataset and Polarity dataset. The pre-processing is done using six steps namely, Cleaning, Tokenization, Case Folding, Normalization, Stop Word Elimination, and Stemming for the purpose of removing noises. Following that feature extraction are done using Bag-Of-Words and Term Frequency-Inverse Document Frequency (BOW-TF-IDF). After that classification are done using Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN)for classifying the AS in movie reviews. The efficiency of the proposed DWSCNN-CARAN-BOA is analyzed using a dataset and attains 99.94% accuracy, 98.76% recall and attains better results compared with the existing methods. In the future, this approach will use the adversarial instances it generated to conduct adversarial training and assess the potential improvement in classification performance. It also looks into the possibilities of creating adversarial examples at the word and sentence levels by combining structured knowledge from high-quality knowledge bases. 2024 IEEE. -
Efficiency study of coconut producer companies in India-A DEA approach
The concept of the Farmer Producer Company (FPC) model has been a hot issue, especially during the 2020-21 Indian farmers' protest. Considering the pioneering initiatives of the Coconut Development Board (CDB) in setting up CPCs, we compare the technical efficiencies of CPCs that focus on coconut and its byproducts in Rural India for two consecutive financial years (2018-19 and 2019-20). Coconut Producer Companies' efficiency scores are estimated using Data Envelopment Analysis (DEA), a mathematical technique to assess technical efficiencies across homogeneous units. The results reveal that 35.11 percent of the sampled CPCs for FY 2018-19 are overall technical efficient, and approximately 76 percent are purely technical efficient. It is found that technical inefficiency is reported for a few CPCs due to scale inefficiency. The overall technical and pure technical efficiency have improved in FY 2019-20 compared to the previous period. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Relative Efficiencies of Farmer Producer Companies in India- Slack-Based Model Approach
The concept of the farmer producer company (FPC) model has received a large momentum especially during the 20202021 farmers' protest in India. This paper examines the relative efficiencies of 46 FPCs in Kerala using non-radial data envelopment analysis (DEA) for the financial year 2018-19. We use a non-oriented slack-based model (SBM) under assumptions of constant and variable returns to scale. The results reveal that 36.96 per cent of the sample FPCs are overall technical efficient and 50 per cent of the FPCs are pure technical efficient. It is found that technical inefficiency is reported for a few FPCs due to scale inefficiency. Among the input and output targets suggested for inefficient FPCs, reduction in the 'number of shareholders' and augmentation of 'profits' reported in most cases to improve their efficiency scores. Based on the findings, we suggest the concerned stakeholders to provide additional financial and non-financial supports to the needy rather than focusing on establishing new FPCs. 2022 IEEE. -
Developing a global sustainable electricity use index using the pressure-state-response framework
This study analyse and compare the sustainable electricity usage in 60 countries listed on the official websites of World Energy Consumption Statistics and Climate Bond Initiative. The study also analyses the impact of increased usage of sustainable electricity on the economies' dependence on non-renewable energy sources in the evaluation system. We used a standard index system based on the Pressure-State Response (PSR) model to measure global sustainable electricity usage. Model results convey that Norway is the best performer in sustainable electricity usage, while several European countries display commendable scores, confirming their commitment to sustainable electricity practices. On the other hand, despite being the leading economies in terms of GDP, major economies such as the United States, China, Japan, and India have underperformed compared to others in the evaluation system. The study employs regression techniques to explain the relationship between sustainable electricity usage and non-renewable energy dependence. Results confirm a negative relationship between the variables, indicating the role of sustainable energy practices in reducing fossil fuel consumption. It emphasizes the urgency of a balanced approach to economic growth and natural resource usage to support a green future. 2024 Elsevier Ltd -
Augmented Reality-Enabled Education for Middle Schools
Augmented reality acts as an add-on to teachers while teaching students, and this helps the teachers and students to have an interactive session. Augmented realitys usage in education is cited as one of the major changes in the educational sector. Thus, the work carried out makes a positive impact in the educational industry. Augmented reality provides features like image recogntion, motion tracking, facial recognition, plane detection, etc., to provide interactive sessions. Simultaneous localization and mapping and concurrent odometry and mapping have proved to be efficient algorithms for augmented reality on mobile devices. The work carried out allows students to view interactive newspapers while reading a specific article. It also allows them to view a dynamic three-dimensional model of the solar system on their smartphone using augmented reality. 2020, Springer Nature Singapore Pte Ltd. -
Measuring Financial Inclusion in India: An Approach
In light of the COVID-19-induced financial crisis, the need for robust financial services and networks has become more apparent than ever, which necessitated the accurate measurement of the breadth of financial inclusion in India. First, the study conducted a detailed critical review of the current indices and their construction methodology. Then, we created a financial inclusion index for India by accounting for the flaws existing in the current indices. The primary contribution of this study to the existing literature is the new approach it proposed for the assignment of weights in the financial inclusion index. Based on this new financial inclusion index, the study concluded that Indias Southern states and union territories showed better financial inclusion. In contrast, the traditionally backward BIMARU states of Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh, and a few of the North Eastern states of India, lagged. The study also provided a refined and inclusive definition of financial inclusion based on its new approach to index creation. 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Catalyzing Security and Efficiency: Blockchains Integration with IoT and Cloud Computing
Blockchain technology is a system that combines a number of computer technologies, encryption, shared storage, namely intelligent contracts, consensus processes, and peer-to-peer (P2P) networks. This research project begins with a description of the architecture of blockchains, followed by a comparison of the various consensus techniques used across various blockchain implementations. This studys scope includes a thorough analysis of the entire blockchain ecosystem. Our investigation also explores the complexity of the consensus models built into different blockchain platforms. This research painstakingly dissects these elements to pinpoint crucial elements that are essential for propelling the adoption and development of blockchain technology. In conclusion, our research corrects misconceptions about blockchains expansive potential and helps to direct the development of the technology across a wide range of industries. These results are significant for determining the future direction of blockchains enduring influence. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
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Crafting an experiencescape for sustainable cultural tourism: A case study of udaipur's craft village "Shilpgram"
India, known for its rich and varied cultural heritage, hosts numerous fairs and festivals as vibrant platforms to showcase its diversity. Events like the Kala Ghoda Arts Festival, Ellora-Ajanta International Festival, Surajkund Crafts Mela, Pushkar Fair, and many others celebrate the country's artistic and cultural wealth. This book chapter explores sustainable culture and heritage development in Udaipur's craft village, 'Shilpgram.' The village hosts an annual craft fair during the peak tourist season, attracting artists across India to showcase their unique crafts, folk dances, and paintings. This chapter assesses Shilpgram's feasibility as an experiencescape model using a case study method. Interviews with key stakeholders provided insight into the destination's difficulties, strengths, and best practices. The study adds to our understanding of cultural locations' responsibilities in cultural tourism by balancing historical preservation with current expectations. 2023, IGI Global. All rights reserved.