An Interactive Visualization and Data Analysis of Supermarket Store
Breast Cancer Diagnosis Model Based on Convolutional Neural Networks’ Multiple Architectures.
A Generative AI Model for Forest Fire Prediction and Detection
Impact of Artificial Intelligence on Cyber Shopping in Kanniyakumari District
Role of Artificial Intelligence in Investment Management
A Study on Spending Patterns in the Digital Era with Special Reference to Tamilnadu
Artificial Intelligence in Investment Management, Asset Management and Warehouse Management
Influence of Digital Transformation and Artificial Intelligence in Business
A Study on Employee Perception towards Digital Marketing Services
Video Analytics for Optimizing Bank Services
A Comparative Analysis for Identifying the Polarity of People Based on Emotional Pulse in a Smart City
Video Analytics for Optimizing Bank Services
An Ensemble Technique to Predict Mental Illness using Data Mining Techniques
Role of Artificial Intelligence in Investment Management
Enhancing Donor Acquisition and Retention in Blood Banks via AI-Powered Decision Support Framework
In the ever-evolving landscape of retail, the utilization of data-driven insights plays a pivotal role in enhancing decision-making processes. This research paper delves into the development and implementation of an interactive visualization system tailored specifically for the analysis of data within a supermarket store environment. In order to predict the sales of a business, an intelligent model was built using Linear-Regression, LASSO-regression and XG-Boost techniques which has been shown to be more effective than existing models.
In year 2020, the World Health Organization (WHO) estimates that 2.3 million women worldwide were diagnosed of breast cancer, which resulted in 685,000 deaths. According to projections, the number of women who have been diagnosed with breast cancer over the last five years before and by the end of 2020 was expected to reach 7.8 million, making it the most common type of cancer worldwide. Early diagnosis could prevent the ailment however, lack of availability of health facilities, cost of accessing treatment especially in developing nations are among the challenges confronting the solution. With the advent of cutting edge technologies, such as artificial intelligence, and machine learning models, access to solution in terms of early diagnosis are becoming possible. Based on literature, Convolutional Neural Networks (CNNs) considering its multiple architectures is promising in bringing the solution to bear. Therefore, this research aims to proposed an architecture of CNNs that gives the best accuracy, F1 score, and Cohen Kappa score among Custom Optimized CNN, ResNet, EfficientNet architectures being considered in this work. From the results based on the datasets, ResNet’s performance across the five metrics outweigh the other two architectures. For example, while ResNet reported an accuracy, precision, and F1 score of 0.9987, 0.9934, and 0.9950 respectively, EfficientNet, which has the second performance reported 0.9977, 0.9914, and 0.9939 as accuracy, precision, and F1 score respectively.
Forest fires pose significant threats to forest ecosystems, impacting humans, animals, and plants reliant on these environments. Traditional detection methods rely on handcrafted features like color, motion, and texture, yet achieving accuracy remains challenging. Our project introduces a novel approach using a lightweight fire detection method employing Deep Convolution Neural Networks (DCNN), considering temporal aspects for enhanced accuracy. By leveraging DCNN, we aim to improve forest fire detection capabilities, mitigating the devastating effects of wildfires on both natural habitats and communities. This method represents a promising advancement in the field, offering potential solutions to the ongoing challenge of timely and accurate forest fire detection.
Cyber shopping across the globe has developed to a great extent, especially after the COVID-19 pandemic. As technology is changing rapidly, Artificial Intelligence (AI) plays a dynamic role. The customers’ wants and preferences are fulfilled at the present times with the aid of AI in cyber shopping. The introduction of AI in cyber shopping observes the customers’ selection, buying patterns, likings, regularity of the purchase, and the sum spent on the products. Therefore AI aids in the accomplishment of all the needs of cyber shoppers (Jangra, G and Jangra, M) . The research objective is to find out how AI has created an impact on the cyber shoppers in Kanniyakumari District. The primary data collection is through convenience sampling, and the sample size is 200. The secondary data collection was through journals, books, websites, and databases. Amazon is the most used AI-based cyber website by the customer while Indiamart is the least used. The findings suggest that demographic factors play a crucial role in shaping customer preferences and engagement with online shopping platforms.
AI is transforming the field of investment management by enhancing decision-making, improving operational efficiency, and optimizing portfolio management. As the technology evolves, it is expected that AI will play an even more integral role in driving the future of investment management, offering both opportunities and challenges for traditional asset managers. This research aims to know the most preferred artificial intelligence investing apps in investing management and to study the awareness among investors in artificial intelligence investing management. For this study, 120 investors were selected from the Kanniyakumari district using a convenience sampling method. Primary and secondary data were collected for this study and SPSS tools were used to analyze the data. The research found that there is no significant difference between gender and awareness among investors of AI in investing management. The study concludes that AI plays a transformative role in investment management by enhancing decision-making, optimizing portfolios, and enabling personalized strategies, ultimately driving more efficient and effective investment outcomes in an increasingly complex financial landscape.
