Volume 13 | Issue 5 |

 Abstract

This research paper introduces an interactive e-commerce dashboard developed using Microsoft Power BI. The system aims to support strategic business decision-making in the online retail sector. Leveraging dynamic data visualization tools, the dashboard enables comprehensive insights into product trends, customer behavior, regional sales, and seasonal variations. The integration of customer segmentation, real-time key performance indicators (KPIs), and pricing analytics highlights the increasing importance of business intelligence tools in enhancing operational efficiency and market responsiveness in digital commerce.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

E-commerce, Power BI, Dashboard, Sales Analysis, Business Intelligence, Data Visualization, Customer Behavior, Retail Analytics

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

An IoT-enabled smart retrofit meter-based framework for real-time behavioral analysis of water consumption is presented in this paper. This paper explores the integration of IoT-based smart retrofit meters with deep learning algorithms for analyzing water consumption in educational campuses. The system captures meter images, processes them locally using ResNet-18 for digit recognition, and refines this data through a Hamming distance-based approach before cloud transmission for real-time analysis. The study focuses on water usage in both student hostels and faculty quarters within an Intermittent Water Supply (IWS) system, capturing variations influenced by factors like academic schedules, holidays, and lifestyle differences. Results demonstrate that student hostels exhibit more dynamic usage patterns, while faculty quarters show relatively consistent demand. These findings underscore the potential for scalable, data-driven water management solutions tailored to diverse residential settings.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep learning, IoT, ResNet-18, Hamming distance, Real-time analysis, Digit detection algorithm.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The ever- growing need of Artificial Intelligence (AI) in today's time is making it a part of everyone's life. AI can be found out in every aspect, be it music, visual arts, texts or any other creative works. It has helped many industries revolutionize in their own manner like healthcare, finance, entertainment, etc. But with these advancements a common problem which is also arising is the applicability of AI in the realm of Intellectual Property Rights (IPR). The complex question of copyright laws comes into picture. There is a strong debate which is still going on among various groups of people whether AI should be given Intellectual Property Rights and should it be considered a creator or inventor for the works it generates or not. The prevalent nature of the AI generated creations along with the current IP laws which only acknowledge a human creator and author, leads to confusion in terms of authorship, ownership and protections. Legal cases like Naruto v. Slater and Thaler v. Commissioner of Patents emphasize the dominant view that non-human entities are unable to possess IP rights. The supporters of the same contend that there is a need for revised policies for the involvement of AI in promoting innovation and research, in order to avoid the obstruction of technological progress. This document highlights the necessity of a balanced legal structure that encourages innovation while safeguarding the integrity of intellectual property rights. As AI reshapes creative and technological fields, modifying IP regulations to align with its growing influence will be crucial for sustaining a fair and progressive legal system. The significance and effects of these changes on creators, legal frameworks, and society at large are examined, alongside various suggested approaches to align innovation with intellectual property rights.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Intellectual property rights; artificial intelligence; copyright; patent; trademark; legal systems; AI generated content

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Customer satisfaction measures level of satisfaction with a product,service,or experience. In the evolving automobile market, understanding customer satisfaction has become crucial for sustaining business growth and building long-term loyalty. This study focuses on analyzing the level of customer satisfaction at Morris Garage (MG), Tirupati. Through a structured questionnaire distributed among MG vehicle users, various dimensions of customer satisfaction--such as product quality, service experience, after-sales support, pricing, and dealership interaction--were assessed. The study findings reveal that while a significant number of customers are satisfied with aspects like vehicle performance, safety features, and service responsiveness, there remain areas such as comfort and documentation transparency that need improvement. The insights drawn from this research aim to help MG Tirupati refine its customer engagement strategies and enhance overall satisfaction.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

: Customer Satisfaction, After-sales Service, purchase behavior ,customer feedback.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The aim of this present study to examine the impact of anxiety on well-being among young adult and explore the role of coping levels . A total of 200 students (100 from engineering and 100 from non-engineering streams) from the Tricity. region (Chandigarh, Mohali, Panchkula) participated in this study. Standardised tools used State-Trait Anxiety Inventory (Spielberger, 1972), the WHO-5 Well-being Index, and the Coping Scale (Hamby, Grych, & Banyard, 2013). Data were analyzed using Python's pandas library, employing descriptive statistics, Pearson correlation, and t-tests revealed a significant negative correlation between anxiety and well-being. Students who went through higher levels of anxiety often showed to have lower well-being scores. However ,this wasn't the case across the board. Those who had strong coping skills managed their anxiety more effectively--their sense of well-being remained relatively stable, during challenging times. These findings underscore the importance of strong coping for mental health and highlight the value of integrating coping skills training into university mental health services to support students' holistic well-being.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Anxiety, Coping , Well-being , engineering, State-Trait Anxiety Inventory.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The continuous deterioration of the natural environment has raised the concern for ecological preservation, which in turn, has resulted in green consumption. This has brought radical changes in consumer buying preferences for green products along with their preferences for purchasing from eco-conscious organizations that contribute to the environment sustainability. Due to ecological concerns, green products have gained more consideration in recent years, & their accessibility is rising. In addition, the presence of an eco-label encourages people to purchase environmentally friendly products. This paper focus to ascertain customer attitudes regarding green food products and compile data on the subject. This study serves as the foundation for the literature review of this research study. This research focuses on green marketing, more precisely on customers' attitude & purchasing practices when it comes to green food products. It has become a global priority to preserve the environment from human-caused pollution. With this critical knowledge offered by various researchers and experts, businesses have recognized the necessity & value of green marketing.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Consumer Attitude, Green Food Products, Purchase Behavior Intention, Environment.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As more people use automated client support systems and call centers, there is an increasing need for improved methods of measuring client satisfaction and improving the quality of support. We confess multitudinous calls in our daily lives -- several of which are promotional or related to loans and finance. This layout focuses on sentiment analysis to approximate arriving helpdesk calls with the intent to determine client emotion and serve their businesses better. With the application of natural language processing(NLP) methods, we analyze both the tone and content of verbal interactions and their abstracts. The suggested method identifies spam calls based on the conversation and shares the news with other druggies to assist them in evading similar unwanted calls.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sentiment Classification, Speech Recognition ,Text Classification , Emotion Detection

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A state-of-the-art educational web tool, the AI-Powered Learning Assistant with Concept Visualization enhances student learning through intelligent support and visual aids. It has an AI Virtual Assistant that uses the Gemini API. A professor who can provide students with concise, conversational answers to their academic queries. The Concept Visualizer also transforms difficult textual data into interactive flowcharts and mind maps to enhance understanding of abstract topics. The application supports multimodal input methods, including voice, image, and document uploads, to satisfy a range of learning preferences. Built using React.js and TailwindCSS on the frontend, Python and FastAPI on the backend, and MongoDB for data management, the platform offers a scalable and responsive solution. By combining AI-driven explanations with visual aids, thisinitiative aims to support students with varying learning styles by providing tailored and organized experiences for learning. calability and ease of maintenance are guaranteed by the modular architecture. This solution uses state-of-the-art AI algorithms and graphics libraries to improve user engagement and make learning easier and customized. This project lays the groundwork for the future development of intelligent educational technology by utilizing gamification, adaptive learning pathways, and advanced analytics.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI,Virtual Professor,Concept Visualizer

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the rapidly evolving and demanding realm of technology, individuals frequently face significant stress due to substantial workloads, stringent deadlines, and ever-changing work settings. Extended exposure to such stress can adversely affect both personal health and professional efficiency. Traditional stress detection methods, like self-reported surveys and physiological evaluations, tend to be intrusive and lack real-time functionality. Consequently, there is an increasing demand for more efficient and less invasive stress monitoring solutions. This research seeks to tackle this issue by creating an innovative stress detection system specifically designed for individuals. By utilizing advancements in image processing and machine learning, the system examines facial expressions and visual indicators that suggest stress. Through the use of advanced algorithms, the system can accurately assess and interpret stress levels in real-time. Additionally, the system incorporates continuous feedback mechanisms by consistently gathering employee surveys to monitor stress levels over time and assess the success of interventions. With its real-time capabilities and non-intrusive nature, this system represents a significant advancement over traditional stress detection methods, offering employees and organizations valuable insights to promote healthier work environments.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

machine learning, Stress detection, image processing, Real time monitoring

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper proposes a groundbreaking approach to the Sarcasm Detection in Text, focusing mainly on addressing the limitations of detecting objects in varied text types. Accurate sarcasm detection is crucial for improving the performance of sentiment analysis systems and other natural language processing (NLP) applications, such as chatbots, social media analysis, and content moderation. This paper explores various methodologies for detecting sarcasm in text, ranging from traditional machine learning techniques to state-of-the-art deep learning models. I also examine the role of contextual and semantic features, such as user behavior, conversational history, and linguistic patterns, in enhancing sarcasm detection accuracy. My findings highlight the importance of integrating domain-specific knowledge with advanced computational approaches to address the inherent complexities of sarcasm in text effectively.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Picture 6064, PictureKeywords-Sarcasm detection, Sentiment analysis, Natural language processing (NLP), Machine learning, Deep learnigs

  License

Creative Commons Attribution 4.0 and The Open Definition