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Ship Detection Based on Faster R-CNN Using Range-Compressed Airborne Radar Data

This paper introduces a novel approach to ship monitoring for enhanced maritime safety and security. Traditional methods rely on Automatic Identification Systems (AIS) and marine radar, but their effectiveness is hindered by the absence of AIS on some vessels. To overcome this limitation, Faster R-CNN, trained on Range-Compressed Airborne Radar Data, is proposed. By utilizing airborne radar signals, the need for AIS installations is eliminated. The Faster R-CNN algorithm is trained on both Time Domain and Doppler Domain data types for object detection and classification, respectively. Leveraging Resnet50 as the backbone model, the system achieves efficient ship detection by analyzing specific regions, thus reducing false detections. This innovative approach presents a significant advancement in sea monitoring capabilities, ensuring enhanced safety and security at sea.

Published by: K. Vinay Kumar, Dr. Y. Md. Riyazuddin

Author: K. Vinay Kumar

Paper ID: V10I2-1144

Paper Status: published

Published: April 22, 2024

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Research Paper

Bidirectional sign to audio converter

The goal of this paper is to create a helpful system for people who have trouble hearing and those who use sign language. This system can change sign language into spoken words and vice versa. It uses a motion capture system to change sign language and a voice recognition system to change spoken words. It shows the signs as writing on the screen and also displays the meaning of spoken words as moving images or videos.

Published by: Satyam K. Singh, Aishwarya Shrivastava, Priti R. Navik, Sonali Padalkar

Author: Satyam K. Singh

Paper ID: V10I2-1161

Paper Status: published

Published: April 22, 2024

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Research Paper

Keystroke Dynamics: A Machine Learning Approach to Behavioural Biometric Authentication

With the ever-increasing dependence on digital services, ensuring the security of user accounts has become a paramount concern. Traditional authentication methods, such as passwords and PINs, have demonstrated vulnerabilities to various attacks. Keystroke dynamics, a behavioral biometric, offers a promising solution for adaptive authentication by analyzing typing patterns unique to everyone. This project explores the implementation of keystroke dynamics in adaptive authentication systems using machine learning algorithms. The primary objective is to create a robust, secure, and user-friendly authentication mechanism that continuously adapts to the changing typing behavior of users while maintaining a high level of accuracy. The proposed system employs a diverse dataset collected from users performing various typing tasks to train machine learning models. Features such as keystroke latency, flight time, and typing rhythm are extracted and used as inputs to the algorithms. Several popular machines learning techniques, including support vector machines, neural networks, and random forests, are employed to build classification models capable of distinguishing between legitimate users and unauthorized intruders. This project advocates for the adoption of keystroke dynamics in adaptive authentication systems, utilizing machine learning algorithms to create a secure and user-friendly experience. By combining behavioral biometrics with cutting-edge technology, the proposed approach offers a robust defense against unauthorized access, paving the way for more secure and convenient authentication methods in the digital era.

Published by: Swarangi Anant Sawant, Sakshi Vasant Kalambe, Rupali Pashte

Author: Swarangi Anant Sawant

Paper ID: V10I2-1164

Paper Status: published

Published: April 22, 2024

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Research Paper

Build an Amazon Connect call center

The purpose of this paper is to investigate how Amazon Connect and Amazon Lex may be integrated to create a state-of-the-art customer contact center system that will enhance customer service operations. The study entails a thorough analysis of the advantages, disadvantages, and best practices related to establishing a customer contact center with Amazon Connect and Amazon Lex in terms of technology. It contains case studies, a summary of pertinent research, and helpful implementation advice. Significant advantages of the Amazon Connect and Amazon Lex connection include enhanced productivity, Scalability, cost-effectiveness, and personalized customer experiences. Nevertheless, there are obstacles including complicated chatbot training and regulatory compliance. To solve these issues, solutions and practical implementation insights are given. The conclusions are also supported by prior research and real-world experiences. Businesses may use the information in this paper to improve customer service operations by putting Amazon Connect and Amazon Lex into practice as part of a contemporary contact center solution. The useful advice and best practices provided can aid in resolving issues and maximizing the advantages of this integration, eventually enhancing general client happiness and loyalty

Published by: Ismail Emad Sakerde, Khan Mohammed Umer, Rathod Mihir Visabhai, Reena Kothari

Author: Ismail Emad Sakerde

Paper ID: V10I2-1159

Paper Status: published

Published: April 19, 2024

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Research Paper

Multi-scale Deep Learning for Histopathological Image Analysis: The Deep-Hipo Approach

The digitization of whole-slide imaging within digital pathology has propelled the advancement of computer-assisted tissue examination utilizing machine learning methodologies, particularly convolutional neural networks (CNNs). Numerous CNN-based approaches have been proposed to effectively analyze histopathological images for tasks such as cancer detection, risk prediction, and cancer subtype classification. While many existing methods have relied on patch-based examination due to the immense size of histopathological images, such small window patches often lack sufficient information or patterns for the tasks at hand. Pathologists routinely inspect tissues at various magnification levels to scrutinize complex morphological patterns through microscopes. In response to these challenges, we propose a novel deep-learning model for histopathology, named Deep-Hipo, which concurrently utilizes multi-scale patches for precise histopathological image analysis. Deep-Hipo simultaneously extracts two patches of identical size from both high and low magnification levels, enabling the capture of intricate morphological patterns within both large and small receptive fields of a whole-slide image.

Published by: Keerthana M., Dr. Y. Md. Riyazuddin

Author: Keerthana M.

Paper ID: V10I2-1146

Paper Status: published

Published: April 19, 2024

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Research Paper

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 Sagar

Author: Ishaan Sagar

Paper ID: V10I2-1157

Paper Status: published

Published: April 19, 2024

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