STM Journals Peer-reviewed research from Consortium e-Learning Network

3 publications

  • Published Subscription Review Article

    AI Chatbot for Expressing Visual Content

    By Ashwini Garole, Sneha Nath, Dhruv Patil, Hindavi Mhatre, Shivtej Kadam

    Abstract: Recently, the artificial intelligence (AI) chatbot for expressing visual content has shown remarkable multi-modal capabilities. It can recognize funny features in photos and create webpages straight from handwritten text. These characteristics are uncommon in earlier vision language models. We think the use of a more sophisticated large language model (LLM) is the main factor behind vision verbalizer's superior multi-modal generating capabilities. We introduce vision verbalizer, which employs a single projection …

    Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 2, 2024 · pp. 11–19 Read article

  • Published Subscription Review Article

    ExploroBot: A Versatile Robotic Explorer

    By Ashwini Garole, Aditya Kale, Abhinav Patil, Siddhesh Tatkare, Shravani Waghmare

    Abstract: ExploroBot: A versatile robotic explorer is a cutting-edge robotic platform designed for exploration and surveillance applications. Equipped with six battery-operated motors for mobility and an Arduino UNO microcontroller as its brain, the rover integrates ultrasonic sensors and two ESP32 CAM modules for obstacle avoidance and live footage streaming. With precise control mechanisms and advanced sensor technologies, ExploroBot: A versatile robotic explorer offers a versatile solution for navigating complex environments while …

    Published in Journal of Advancements in Robotics · Vol. 11, Issue 2, 2024 · pp. 1–8 Read article

  • Published Subscription Review Article

    Network Intrusion Detection System Using Decision Tree

    By Yash Jadhav, Harsh Deshpande, Ashwini Garole, Komal Sawant, Aman Patil

    Abstract: This paper presents a novel approach to network intrusion detection systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making …

    Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 22–33 Read article

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