publications

2021

1. Elsevier

   Explainability using decision trees and monte carlo simulations

   Siddavatam, Irfan, Dalvi, Ashwini, Thakkar, Viraj, Vedpathak, Aditya, Moradiya, Smit, and Jain, Apoorva

   May 2021

   Abs Bib

   The prominence of AI algorithms has reached new heights in todays world. Algorithms are implemented in each and every aspect of our life with the primary intention of improving it. However, the working of most of these AIs is not entirely understood, this causes the problem. Since the models are not understood, the researchers do not know how to manually improve on it, creating an artificial ceiling. Understanding these algorithms is the key to realize how these machines think and not only learn from them but also teach it to understand better. The primary idea put forward in this paper is to explain a black-box model using a mimicking simulation, rather than the usual calculative approaches. The primary idea put forward is to understand how an AI works using a decision tree that will be designed to mimic the AI. It proposes the use of a Decision Tree based approach along with randomization using Monte Carlo simulations for a more precise simulation of the black-box.

   @techreport{siddavatam_explainability_2021,
     bibtex_show = {true},
     abbr = {Elsevier},
     address = {Rochester, NY},
     type = {{SSRN} {Scholarly} {Paper}},
     title = {Explainability using decision trees and monte carlo simulations},
     url = {https://papers.ssrn.com/abstract=3868707},
     language = {en},
     number = {ID 3868707},
     urldate = {2021-11-17},
     institution = {Social Science Research Network},
     author = {Siddavatam, Irfan and Dalvi, Ashwini and Thakkar, Viraj and Vedpathak, Aditya and Moradiya, Smit and Jain, Apoorva},
     month = may,
     year = {2021},
     keywords = {Artificial Intelligence, Decision Tree, XAI, Monte Carlo Simulation}
   }

2. IEEE

   Link Harvesting on the Dark Web

   Dalvi, Ashwini, Siddavatam, Irfan, Thakkar, Viraj, Jain, Apoorva, Kazi, Faruk, and Bhirud, Sunil

   In 2021 IEEE Bombay Section Signature Conference (IBSSC) Nov 2021

   Abs Bib

   In this information age, web crawling on the internet is a prime source for data collection. And with the surface web already being dominated by giants like Google and Microsoft, much attention has been on the Dark Web. While research on crawling approaches is generally available, a considerable gap is present for URL extraction on the dark web. With most literature using the regular expressions methodology or built-in parsers, the problem with these methods is the higher number of false positives generated with the Dark Web, which makes the crawler less efficient. This paper proposes the dedicated parsers methodology for extracting URLs from the dark web, which when compared proves to be better than the regular expression methodology. Factors that make link harvesting on the Dark Web a challenge are discussed in the paper.

   @inproceedings{dalvi_link_2021,
     bibtex_show = {true},
     abbr = {IEEE},
     title = {Link {Harvesting} on the {Dark} {Web}},
     doi = {10.1109/IBSSC53889.2021.9673428},
     booktitle = {2021 {IEEE} {Bombay} {Section} {Signature} {Conference} ({IBSSC})},
     author = {Dalvi, Ashwini and Siddavatam, Irfan and Thakkar, Viraj and Jain, Apoorva and Kazi, Faruk and Bhirud, Sunil},
     month = nov,
     year = {2021},
     keywords = {Uniform resource locators, Text recognition, IEEE Sections, Crawlers, Web pages, Data collection, Information age, Hyperlink Extraction, Dark Web, Web Scraping, Link Harvesting},
     pages = {1--5}
   }

2022

1. IGI

   ML-enabled informed intervention for crowdsourcing-based optimization of medical resources

   Siddavatam, Irfan, Dalvi, Ashwini, Patel, Abhishek, Panchal, Aditya, Vedpathak, Aditya S., and Thakkar, Viraj

   2022

   Abs Bib

   It is said that every adversity presents the opportunity to grow. The current pandemic is a lesson to all healthcare infrastructure stakeholders to look at existing setups with an open mind. This chapter’s proposed solution offers technology assistance to manage patient data effectively and extends the hospital data management system’s capability to predict the upcoming need for healthcare resources. Further, the authors intend to supplement the proposed solution with crowdsourcing to meet hospital demand and supply for unprecedented medical emergencies. The proposed approach would demonstrate its need in the current pandemic scenario and prepare the healthcare infrastructure with a more streamlined and cooperative approach than before.

   @incollection{siddavatam_ml-enabled_2022,
     bibtex_show = {true},
     abbr = {IGI},
     type = {chapter},
     title = {ML-enabled informed intervention for crowdsourcing-based optimization of medical resources},
     copyright = {Access limited to members},
     url = {https://www.igi-global.com/chapter/ml-enabled-informed-intervention-for-crowdsourcing-based-optimization-of-medical-resources/www.igi-global.com/chapter/ml-enabled-informed-intervention-for-crowdsourcing-based-optimization-of-medical-resources/288821},
     language = {en},
     urldate = {2021-11-17},
     journal = {Handbook of Research on Applied Intelligence for Health and Clinical Informatics},
     author = {Siddavatam, Irfan and Dalvi, Ashwini and Patel, Abhishek and Panchal, Aditya and Vedpathak, Aditya S. and Thakkar, Viraj},
     year = {2022},
     doi = {10.4018/978-1-7998-7709-7.ch017}
   }

2. Springer

   Recognizing child unsafe apps through user reviews on the google play store

   Dalvi, Ashwini, Siddavatam, Irfan, Thakkar, Viraj, Vedpathak, Aditya, and Patel, Abhishek

   In Advanced Computing and Intelligent Technologies 2022

   Abs Bib

   Google Play Store serves as a platform to host, download, and review android applications. Many researchers have explored the user review section and worked on approaches and solutions that would prove a more effective pipeline to enable developer feedback on application issues and praised features proving the section’s abundance of information. This work uses this same data to attempt a novel use case of determining child unsafe apps on Google Play Store. User reviews are collected using a crawler and categorized for selected keywords relating to child, media, and India. Since Google Play Store does not provide a definitive number of downloads, this work attempts to mitigate this challenge by instead calculating the user density for an application. The user density helps establish the engagement users have with an application and is calculated by the difference in the timestamps of the most and least recent reviews divided by the sum of total reviews and its upvotes for an application. 60,620 reviews from 1,600 applications were extracted to validate the proposed concept. This concept has proved effective in recognizing applications that present child unsafe content while also offering a novel concept of calculating user density.

   @inproceedings{dalvi_recognizing_2022,
     bibtex_show = {true},
     abbr = {Springer},
     address = {Singapore},
     series = {Lecture {Notes} in {Networks} and {Systems}},
     title = {Recognizing child unsafe apps through user reviews on the google play store},
     isbn = {9789811621642},
     doi = {10.1007/978-981-16-2164-2_9},
     language = {en},
     booktitle = {Advanced {Computing} and {Intelligent} {Technologies}},
     publisher = {Springer},
     author = {Dalvi, Ashwini and Siddavatam, Irfan and Thakkar, Viraj and Vedpathak, Aditya and Patel, Abhishek},
     editor = {Bianchini, Monica and Piuri, Vincenzo and Das, Sanjoy and Shaw, Rabindra Nath},
     year = {2022},
     keywords = {User review ,  Google play store ,  Content classification },
     pages = {111--120}
   }