Volume 13 | Issue 3 |

 Abstract

The increasing penetration of smartphones in rural India has led to a significant shift in consumer behaviour, particularly in the Barwani district. This study investigates the impact of after-sales services on repeat purchase intentions for smartphones in rural areas. The research employs structured surveys and interviews to analyze key service factors such as service accessibility, repair quality, response time, and customer satisfaction. Findings suggest that efficient after-sales services significantly enhance brand loyalty and repeat purchase behavior. This study also highlights major challenges faced by rural consumers in accessing after-sales service, such as long distances to service centers and inconsistent customer support. The research concludes with recommendations for optimizing after-sales service models to foster sustainable consumer retention in rural markets.

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 Keywords

After-sales service, customer retention, rural markets, smartphone industry, brand loyalty, repeat purchase intentions, consumer satisfaction, service accessibility, repair quality

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this study QCA based encryption standard is implemented in which and gates and xor gates are designed using majority logic gates and not gates. QCA technology has a great advent in the upcoming years. In AES the key operation is done by xor gates. In this 128 bit AES encryption and decryption is implemented using verilog coding and the same is synthesized and simulated using the Xilinx vivado tools.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AES, FPGA, QCA

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Creative Commons Attribution 4.0 and The Open Definition

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Iot The Aadhar-based fingerprint voting machine is designed to provide a secure and efficient method of voter authentication and election participation. The system utilizes an Arduino microcontroller, along with a fingerprint sensor to authenticate voters through their Aadhar- linked fingerprints, ensuring the integrity of the voting process. The I2C LCD is used for displaying relevant information and guiding the voter through the process. A keypad is incorporated to allow voters to enter their Aadhar card number and select their preferred party. In case of abnormal conditions, such as multiple attempts or an invalid fingerprint, a buzzer provides an audible alert. This system enhances the voting process by adding an extra layer of security, eliminating fraudulent votes, and ensuring a smooth, user-friendly experience.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

IoT, Aadhar-based fingerprint, authentication, voting process, web application

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Kenaf (Hibiscus cannabinus L.) leaf powder, recognized for its rich nutritional profile, has been successfully integrated into millet flakes muesli, presenting an innovative approach in functional food and nutritional therapy. The nutrient composition of kenaf leaf powder-enriched muesli was rigorously calculated per 100g, revealing it to be nutrient-dense: 398 kcal, 10.3g protein, 78.9g carbohydrates, 25.1g total sugars, 4.6g total fat, 23.5mg sodium, 0.9mg vitamin C, 60.8mg calcium, and 2.9mg iron. The combination of kenaf leaf powder, millet flakes, nuts, edible seeds, and 70% dark chocolate results in a balanced and nutritious diet. This innovative muesli formulation leverages the health benefits of kenaf leaf powder to enhance nutritional intake and support overall health, particularly focusing on personalized nutrition and breast cancer management. Incorporating this product into millet flakes muesli provides the advantages of a natural, sustainable food while also offering therapeutic benefits. This study highlights the necessity for further research into the health benefits and applications of kenaf leaf powder in functional foods, potentially leading to new dietary strategies and interventions aimed at improving public health outcomes. The integration of kenaf leaf powder into millet flakes muesli represents a promising advancement in functional foods, with significant relevance to therapeutic and personalized nutrition. The findings provide a foundation for further research and validation of kenaf leaf powder as a valuable ingredient in health-promoting food products.

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 Keywords

Kenaf, muesli, functional food, public health, personalized nutrition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Kashmir is a product of partition that has all the years determined the relationship of India and Pakistan. Every move of both the countries on the world political scene is by one way or the other related with it. It has kept India and Pakistan divided and has largely influenced the international outlook of the two countries. Many a times, it has caused embarrassing situation for the friends and allies of the two countries. Because of this dispute the two countries are spending huge sums of money on arms and this phenomenon, has greatly affected the economic development of the two countries

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Creative Commons Attribution 4.0 and The Open Definition

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Kashmir, Pakistan, UN

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: Tuberculosis (TB) remains one of the deadliest infectious diseases worldwide, disproportionately affecting low- and middle-income countries. The disease, caused by Mycobacterium tuberculosis, primarily affects the lungs but can spread to other organs. TB transmission occurs through airborne droplets from individuals with active pulmonary TB. Despite global efforts, drug-resistant TB poses a significant challenge, necessitating continuous research and novel treatment strategies. This paper provides a comprehensive overview of TB, including its etiology, transmission, epidemiology, pathophysiology, diagnostic methods, and treatment regimens. It also highlights adverse drug reactions associated with first-line and second-line anti-tubercular drugs, emphasizing the need for effective pharmacovigilance to ensure patient safety. Furthermore, advancements in TB treatment, emerging drug-resistant strains, and future directions in TB management are discussed. The study underscores the importance of early diagnosis, adherence to treatment protocols, and ongoing research to achieve global TB elimination goals.

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 Keywords

TB, Treatment , Symptoms, Precaution

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Creative Commons Attribution 4.0 and The Open Definition

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E-learning, which includes the integration of modern education and information technology, plays an important role in promoting educational approval. As the field of user groups and e-learning applications expands further, it is guaranteed that the quality of online training is becoming increasingly important. Coursera is a remarkable online learning platform, offering a variety of courses and tutorials in many subjects. Working with over 300 major universities and companies, including Stanford, Duke, Google and IBM, Coursera offers lessons in a variety of fields. His offers include Coursera's Google courses to prepare individuals for careers in digital marketing, data analytics, IT support and user experience design. These courses aim to use Google's tools and technologies to acquire the skills that learners need to thrive in advanced industries. The case study focuses on analyzing participant exams for Google courses from Coursera. Our goal is to use natural language processing (NLP) and machine learning technology processing to predict feelings in these reviews as positive or negative. Using these approaches, we would like to gain insight into participants' opinions and experiences in Coursera and Google courses.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

E-Leaning , Coursera, Opinion mining, Sentiment analysis, Machine Learning, NLP

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The COVID-19 pandemic has significantly influenced global health, leading to noticeable BMI fluctuations due to lifestyle changes such as altered dietary habits, reduced physical activity, and increased sedentary behaviour. System analyzes BMI variations before and after the pandemic and employs a Stacking Ensemble Model integrating Random Forest, Gradient Boosting, Decision Trees, SVR, KNN, AdaBoost, and Linear Regression to improve BMI prediction accuracy. The system categorizes individuals into Underweight, Normal, Overweight, and Obese groups and provides personalized health recommendations based on predicted BMI trends. A Flask-based web application is developed to allow users to input their gender, height, and weight for real-time BMI assessment. The findings aim to assist individuals and policymakers in obesity prevention and health management. Future enhancements include real- time monitoring, reinforcement learning, and cloud- based deployment for adaptive health tracking.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

BMI Prediction, COVID-19, Machine Learning, Stacking Ensemble Model, Health Informatics, Obesity Management, Predictive Analytics, Public Health Monitoring, Reinforcement Learning, Flask Web Application.

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Creative Commons Attribution 4.0 and The Open Definition

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Air pollution arises from the release of harmful substances into Earth's atmosphere, stemming from diverse sources such as industrial emissions, vehicle exhaust, agricultural practices, and the combustion of fossil fuels. The goal of this study is to forecast air quality levels using a dataset of different contaminants that were measured in various Indian cities. A wide range of air pollution measurements, including PM2.5, PM10, NOx, and others, are included in the dataset. Pre-processing is applied to the dataset in order to manage missing and categorical data and provide reliable input for machine learning models. To identify intricate patterns and forecast AQI categories, we utilize a blend of DBSCAN clustering and Logistic Regression techniques. By include underlying clusters as part of the feature set, this hybrid technique allows us to investigate the temporal and spatial dynamics in the data, perhaps leading to an improvement in prediction accuracy. The partition data, train the model, and evaluate the results using our technique, evaluating efficacy based on accuracy and other performance measures. The research findings have the potential to significantly impact environmental monitoring and public health policies. Specifically, they can offer valuable insights into trends in air quality and improve the predictive accuracy of AQI categorization, which is essential for making timely decisions and implementing preventive measures in urban planning and public health initiatives. The comparative result shown that DBSCAN with LR is more accurate to predict air quality than LSTM.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Air quality prediction, DBSCAN algorithm, Logistic Regression technique, Long Short-Term Memory

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Creative Commons Attribution 4.0 and The Open Definition