Ankita Singh

PhD Candidate

Ankita Singh

PhD Candidate

Ankita began her journey at KLU in September 2023 as a PhD candidate under the primary supervision of Prof. Dr. Alexander Himme and secondary supervision of Prof. Dr. Henrik Leopold. Her research primarily focuses on developing and applying systems that utilize state-of-the art machine learning algorithms to forecast the influence of non-financial information from earnings calls on firm value. This research topic showcases her dedication to exploring the intersection of machine learning, accounting, and marketing. It has the potential to provide valuable implications for managers, analysts, and investors that want to better understand the influence of non-financial information on firm value.

Ankita holds a Bachelor of Engineering in Information Technology from Amaravati University, India, and a Master's degree in Computer Science & Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya, India. During her Master's program, she achieved a significant milestone by publishing her first article in a top-tier journal, showcasing her expertise and research capabilities. Her main areas of interest revolve around machine learning and data science, which highlight her passion for these fields.

Ankita had the opportunity to work at Accenture Solutions Pvt Ltd as a senior software engineer and team lead after completing her Bachelor's degree. During her time there, she gained valuable industry experience and had the chance to work in different locations such as London, United Kingdom, and Düsseldorf, Germany. Ankita has made significant contributions to various initiatives for clients in the financial and telecommunications sectors. Her expertise and experience have allowed her to provide valuable insights and solutions to these industries. Her contributions have helped clients in these sectors achieve their goals and improve their operations. Her participation in Corporate Social Responsibility initiatives was evident.




Since 2023       PhD candidate, Kühne Logistics University, Hamburg, Germany
2016 - 2018 Master of Science in Computer Science and Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India
2006 - 2010 Bachelor of Engineering in Information Technology, Amaravati University (SGBAU), Amaravati, India

Professional Experience

Since 2023 Research Associate, Kühne Logistics University, Hamburg, Germany
2022 - 2023   Internship, Pantech Solutions Pvt Ltd, Chennai, India
2011 - 2019 Team Lead, Accenture Solutions Pvt Ltd, India

2015 - 2015

Senior Software Engineer, Accenture Services Pvt Ltd, Düsseldorf, Germany

2012 - 2012 Software Engineer, Accenture Services Pvt Ltd, London, United Kingdom



Abstract: Facial expression is a gesture developed with the facial muscles that communicates the individual's emotional expression. As Artificial Inteligence(AI) and Computer Vision technology advances, machines must learn to recognize human emotions in order to communicate effectively. Using the Deep Convolutional Neural Network (DCNN) technique, face expression are examined to detect emotion using deep learning. The proposed system is trained and evaluated in real time using the ADFES-BIV dataset. In the training phase, transfer learning technique is used to extract features from pictures as well as live video and a haar-cascade classifier is employed to detect the multiple faces in a frame. The Facial Action Coding System detects five universal emotions: happy, sad, neutral, surprise and anger and some micro emotions such as contempt, embarrass, fear, disgust and pride also can be detected by a person's face. It can recognize many facial emotions at the same time using deep learning. The system achieved a training accuracy of 81.67%.

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