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All Journal International Journal of Advances in Applied Sciences
Deepak Dharrao
Symbiosis International (Deemed University)
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Detecting facial image forgeries with transfer learning techniques Nishigandha N. Zanje; Anupkumar M Bongale; Deepak Dharrao
International Journal of Advances in Applied Sciences Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i1.pp93-105

Abstract

Digital images have become ubiquitous in our daily lives, appearing on our smartphone screens and online websites. They are widely used in numerous industries, including media, forensic and criminal investigations, medicine, and more. The ease of access to consumer photo editing tools has made it simple to manipulate images. However, such altered images pose a serious risk in fields where image authenticity is crucial, making it challenging to confirm the reliability of digital images. Digital image fraud involves altering an image's meaning without leaving any obvious signs. In this study, we present three convolutional neural network-based transfer learning techniques “CNN” classification of facial image forgeries, using VGG-19, InceptionV3, and DenseNet201. Among these methods, DenseNet201 achieved the highest accuracy of 99%, followed by InceptionV3 at 94% and VGG-19 at 84%.
TherapyBot: a chatbot for mental well-being using transformers Deepak Dharrao; Shilpa Gite
International Journal of Advances in Applied Sciences Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i1.pp1-12

Abstract

The field of natural language processing (NLP) and conversational artificial intelligence (AI) has one ingenious application in the psychological space. Depression and anxiety are two major issues that the world is facing, with close to 41% of adults reporting these symptoms in the United States alone, as of December 2020. It has also been observed that most of the people are not open about it. As a result, it is critical to address this issue on a global scale. Developed countries reportedly have 9 psychiatrists per 100,000 people. One way to mitigate this is the use of chatbots. We propose a transformer-based methodology to build a therapy bot that has been trained on a combination of open-domain conversations from a publicly available dataset and therapist-client conversations from a self-constructed dataset. This end-to-end data-driven model shows quality performance in conversations and adds value by aiding in the case of mental health issues. The proposed architecture is proven to be effective in its usability in the psychological space for both single-turn and multi-turn dialogue. The performance of the proposed system shows loss is 0.29 and perplexity is 1.34, both metrics keeps gradually decreasing and it means an improvement in performance of chatbots system.
Co-Authors Anupkumar M Bongale Nishigandha N. Zanje Shilpa Gite