Full list also on Google scholar
2022
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Women Assault Detection and Providing Help Using Pitch
Rachakonda Hrithik Sagar, L. Krishna Sai Raj Goud, Aastha Sharma, Tuiba Ashraf & Arun Prakash Agrawal
In 2022 International Conference on Advancements in Interdisciplinary Research (AIR 2022) - Springer , 2023
Paper Abstract Project pageWomen’s safety has ever been a serious concern. Even after several initiatives taken by governments all over the world, viz installation of CCTV cameras, panic buttons on mobile phones, deployment of security personnel, etc., there are still some challenges to be addressed. Women are still susceptible to acid attacks, molestation, eve-teasing, and sexual harassment. Solutions offered so far are constrained by the cost and feasibility of application depending upon the geographical region. For example, CCTV cameras seem to be a good solution but are costly and at times cannot be installed in rural areas due to electricity constraints and privacy constraints. Keeping this in view, the authors in this paper propose an economically viable solution for women’s safety by intercepting the pitch of female voices using sensors. Sensors are economic, affordable, portable, and easy to install even on trees in rural areas. They also do not require too much electricity to keep them operational. Motivated by the advantages offered by sensors, authors have proposed a novel economic solution for women’s safety. The proposed solution was implemented using sound frequency sensors, GSM GPRS Module, Arduino, and raspberry pi.
2021
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MALIGNANT SKIN CANCER DETECTION USING CONVOLUTIONAL NEURAL NETWORKING
Rachakonda Hrithik Sagar, Abhishek Bingi, Aashray Pola,Krishna Sai Raj Goud, Tuiba Ashraf, Subrata Sahana
In International Journal of Technical Research and Science (IJTRS) , 2021
Paper Abstract Project pageThe incidence of skin cancer is increasing by epidemic proportions. According to WHO, Skin Cancer is the world’s 6th most common cancer. It can be classified into Basal cell carcinoma, Squamous cell carcinoma and Melanoma among which Melanoma is more difficult to predict. By using this method we can assist Dermatologists to detect at an early stage as Computer Vision plays a vital role in diagnosis. In this paper, to detect skin cancer we are using machine learning-based algorithms. Traditionally classification algorithms are Convolutional neural networking which Consists of initialization, adding a convolutional layer, summing pooling layer, summing flattening layer, summing a dense layer, then compiling Convolutional neural networks and fitting the CNN model to a dataset. We used machine learning model architecture to determine if the skin images of the patients are harmful or harmless via using machine learning libraries provided in python. We have chosen this approach to be more precise and specific in recognizing about cancer and ultimately declining the mortality rate caused by it.
2020
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Revolution of AI-Enabled Health Care Chat-Bot System for Patient Assistance [ORAL]
Rachakonda Hrithik Sagar, Tuiba Ashraf, Aastha Sharma, Krishna Sai Raj Goud, Subrata Sahana & Anil Kumar Sagar
In Lecture Notes in Electrical Engineering book series , 2020
We show how Large Language Models such as GPT can be used to enable better quality and generalized human action generation
Paper Abstract Project pageChat-Bot is like our personal virtual assistants which can conduct a conversation through textual methods or auditory methods. One of the important tools of AI is Chabot’s which can interact directly with humans and provide them with a considerate solution to their problem. These kinds of AI programs are designed to simulate how a human behaves as a conversational partner, after passing the Turing test. Chabot’s are generally accessed via public virtual assistants such as Google Assistant, Microsoft Cortana, Apple Siri, or via various individual organizations’ apps and websites. The process of building a Chat-Bot involves two tasks: understanding the user’s intent and predicting the correct solution. In the development of Chat-Bot, the first task is to understand the input entered by the user. The prophecy is made depending on the first task. The main feature one can use of Dialog Flow API is follow-up intent. By using follow-up intent, one can create a decision tree that will help in the prediction of a disease by the Chat-Bot. Through this project, one can get insights into the actual understanding of Chat-Bot, explore various NLP techniques, and understand how to harness the power of NLP tools (Bennet Praba et al in Int J Innov Technol Explor Eng 9:3470–3473, (2019) [1]). One can thoroughly enjoy building their Chat-Bot from scratch and learning the advancements in the domain of machine learning and general AI.
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