Volume-10, Issue-2

March-April, 2024

Research Paper

1. Indoor navigation using augmented reality

The majority of contemporary competitive commercial navigation programs rely on GPS-based navigation technology. However, it is the interior navigation performance is lower than that in an outdoor situation. Much of the research and development on indoor navigational systems entails the installation of additional equipment, which often comes with a substantial setup charge. A study and comparison were undertaken to identify the best indoor localization, pathfinding, and path navigation systems for an indoor navigation strategy. The goal of this project is to demonstrate a user-friendly and cost-effective indoor navigation system. The recommended solution combines augmented reality technology with the built-in sensors included in the majority of mobile devices to determine the user's location and give an immersive navigation experience. In this project, a smartphone app for indoor navigation was developed and tested. AR Core will use the predicted route to display AR guidance. Surveys were done to assess the methodology's effectiveness and gather input from participants. The method's architecture, an example, and applications are described.

Published by: Mahalakshmi Padam, Dr. Y. Md. RiyazuddinResearch Area: Data Science

Organisation: Gitam Deemed to Be University, Rudraram, TelanganaKeywords: Indoor Navigation, Indoor Localization, Erroneous Orientation, Heuristic, Image Recognition, Latency Reduction, Path Navigation, Augmented Reality,

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Research Paper

2. Crime analysis in India using machine and deep learning techniques

Crime analysis is a critical aspect of law enforcement, aiding in the understanding, prediction, and prevention of criminal activities. In a vast and diverse country like India, with its complex socio-economic landscape, traditional methods of crime analysis often fall short in capturing the intricacies and patterns of criminal behavior. In recent years, machine learning (ML) and deep learning (DL) techniques have emerged as powerful tools to analyze crime data, offering the potential to uncover hidden patterns and trends that can enhance law enforcement strategies. This paper presents a comprehensive overview of crime analysis in India utilizing machine learning and deep learning methodologies. We begin by discussing the challenges inherent in traditional crime analysis methods, highlighting the need for more sophisticated approaches to address the complexities of crime dynamics in India. Subsequently, we delve into the theoretical foundations of machine learning and deep learning, providing insights into various algorithms and techniques commonly employed in crime analysis. Drawing upon real-world datasets from Indian cities, we demonstrate the application of machine learning and deep learning techniques in crime prediction, hotspot identification, and criminal profiling.

Published by: Thatikonda Shanmukham, Dr.Md. RiyazuddinResearch Area: Computer Science

Organisation: GITAM (Deemed To Be University), Rudraram, TelanganaKeywords: Machine Learning (ML), Deep Learning (DL), Analysis Methods, Theoretical foundations, Crime Dynamics, Crime Prediction, Hotspot Identification, Criminal Profiling, Convolutional Neural Networks (CNN), Crime Computational Techniques, Crime Incident Reports, Demographic Information, socioeconomic indicators, Geospatial Data Predictive Accuracy, Scalability, Spatial Dynamics, Temporal Dynamics

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Research Paper

3. Buyer’s Perception of Starbucks

This document provides an analysis of the buyer's perception of Starbucks, a leading global coffeehouse chain. The study aims to understand the factors influencing consumers' perceptions of Starbucks and their preferences regarding its products and services. Utilizing a mixed-methods approach, including surveys and interviews, data was collected from a diverse sample of Starbucks customers across different demographics and locations. Key findings reveal that Starbucks customers perceive the brand positively, associating it with quality, convenience, and a welcoming atmosphere. The analysis delves into the factors driving these perceptions, such as product quality, customer service, brand image, and social responsibility initiatives. Additionally, the study explores the impact of factors like pricing, competition, and cultural influences on consumer perceptions and purchasing behavior. Implications of the findings suggest opportunities for Starbucks to further enhance customer satisfaction and loyalty through targeted marketing strategies, product innovation, and community engagement initiatives. The document concludes with recommendations for Starbucks and other businesses in the coffee industry to leverage consumer perceptions effectively and maintain competitive advantage in the market

Published by: Yerravelli Nikhil Moses, Dr. Y. Md. RiyazuddinResearch Area: Data Science

Organisation: Gitam Deemed University, Visakhapatnam, Andhra PradeshKeywords: Starbucks, Perceptions, Utilizing, Customer Service

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Research Paper

4. 3D Dense CNN for Hyperspectral Imaging-Based Bloodstain Classification

Blood is a crucial piece of evidence in forensic science for reconstructing and solving crimes. Although numerous chemical procedures are utilized to recognize the blood at a crime scene, these various chemical-based methods might affect DNA analysis. One potential application of bloodstain detection and classification using hyperspectral imaging (HSI) is in forensic science for crime scene investigation. In this paper, we developed a deep learning classifier 2D CNN, 3D CNN and Dense for blood stain detection in the field of forensic science. We conduct experiments using a publicly available Hyperspectral-based Bloodstain dataset for experimental and validation purposes. This dataset contains a variety of chemicals, including blood and blood-like compounds such as ketchup, artificial blood, beetroot juice, poster paint, tomato concentrate, acrylic paint, and questionable blood. With the initial training/testing ratio set to 90/10 of the data samples, we compare the results with state-of-the-art three different CNN architecture with PCA, as preprocessing techniques. The result demonstrates that the 3D Dense CNN can offer improved classification accuracies, smoother classification maps, and more discriminable features for hyperspectral image classification.

Published by: Tejaskumar B. Sheth, Dr. Milind S.ShahResearch Area: Deep Learning

Organisation: Gujarat Technological University, Gandhinagar, GujaratKeywords: Deep Learning, Convolution Neural Networks, Dense CNN, Hyperspectral Image Classification, Forensic Science, Blood Detection.

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Research Paper

5. Role of Consumer Psychology in Sales and Marketing

This paper examines the role of consumer psychology in sales and marketing in the contemporary context. It is essential to understand the hows and whys of consumer decisions, and factors that influence their buying behavior and their purchasing patterns. This is critical knowledge to understand the target audience to develop effective marketing strategies for optimum sales and revenue. The existing market is highly competitive and the key players need to know how to create the right product for the right consumers. Insights into and perceptions of consumer psychology enable the designing of an effective marketing strategy to attract consumers, designing, and producing new products. This is a crucial skill and knowledge for successful sales and marketing.

Published by: Ishaan SagarResearch Area: Consumer Psychology

Organisation: American Embassy School, New Delhi, DelhiKeywords: Consumer Psychology, Consumer Behavior, Marketing Strategy Perception

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Online paper publication is ongoing for the current issue and authors can submit their paper for this issue until Ongoing Submissions.