Strategic approaches to enhancing data security in intelligent power networks through technology and policy Cholaraja Mudimannan, Sonam Singh Bhati, Atmaram Shelke, Priyadarshan Dhabe, Kommineni Ravi Kumar, Cherukuri Krishna Veni Journal of Discrete Mathematical Sciences and Cryptography, 2026 Fast-changing smart grids have transformed the energy business by enabling real-time tracking, automation, and electricity sharing. However, digitisation has caused data security challenges including hacking, leaks, unauthorised access, and privacy concerns. This study examines smart ways to improve data security in smart power networks using contemporary technology and strong legislative frameworks. The study also examines policy-driven measures such obeying rules, global safety standards, and multi-stakeholder governance structures to assure responsibility and resilience. Technology and politics must collaborate for smart, flexible safety. The results show how crucial it is to keep an eye on dangers, collaborate with all stakeholders, and invest in cybersecurity to protect critical power resources.
GestureX: A Real-Time Hand Gesture Recognition System for Intuitive Human-Computer Interaction Priyadarshan Dhabe, Rutuja Kulkarni, Anchit Chedge, Hana Khan, Sanyog Jadhav, Shreya Dighe 2025 International Conference on Computing Technologies and Data Communication Icctdc 2025, 2025 Hand gesture recognition is a promising field, which gives new ways for human-computer interaction. In this paper, a hand gesture recognition system implemented using MediaPipe and OpenCV is developed. The system includes three functional modules such as Virtual Mouse, Magic Canvas, and MediaPlayer Control. It applies real-time hand tracking and gesture detection capabilities for intuitive, hardware-free interactions. The Virtual Mouse module essentially replaces traditional mouse hardware, while end users use defined hand gestures to control cursor movement and perform mouse operations in a very intuitive way, such as clicking, dragging, and scrolling. The Magic Canvas module forms a creative workspace, including drawing by hand and writing by hand, changing colors, and saving these creations with simple hand gestures as input. The MediaPlayer Control module provides hand-gesture-based controls for enhancing multimedia experiences, including volume and brightness adjustment and even navigation, where it could skip videos forward or backward by a specific time. The integration of modules demonstrates flexibility of hand gesture recognition for practical applications. This goes as far as direct input of a seamless and efficient alternative to other devices. High accuracy and quick response built this system accessible for a wide use case, ranging from accessibility tools to creative applications and multimedia management. This research here emphasizes that gesture-based interaction systems make way for developments that potentially improve user experience.
Modified Cosine Similarity Measure for Enhanced Document Comparison in Plagiarism Detection 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Dual Vector Features for Rotation-Invariant Handwritten Character Recognition 15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
Medi-Compass: Symptom-Based Ayurvedic Medication Recommendation System Priyadarshan Dhabe, Rutuja Garje, Shivam Ghodake, Harsh Dhoot, Pradnya Ingle, Ishaaq Shaikh Proceeding of 2024 International Conference on Communication Computing and Energy Efficient Technologies I3ceet 2024, 2024 The significance of medication in modern healthcare is undeniable, driven by continuous advancements in pharmaceutical research and development. This research introduces an innovative website employing innovative and advanced machine learning algorithms to analyze patient symptoms and recommend optimal Ayurvedic treatments. Despite modern medical advancements, Ayurvedic medication remains a vital and trusted alternative for its minimal side effects and cost-effectiveness. This study examines the integration of machine learning with Ayurvedic practices, emphasizing its ability to provide personalized, holistic treatment plans. Ayurveda, with its roots in ancient Indian tradition, addresses multiple ailments simultaneously through natural formulations, offering a comprehensive and safer approach to wellness. The machine learning model enhances the precision and accessibility of these treatments, promoting broader acceptance and trust. This research underscores the potential of combining machine learning with Ayurveda, advocating for its integration into mainstream healthcare to provide balanced and sustainable medical solutions. The machine learning algorithms employed are designed to continuously learn and improve from user data, ensuring increasingly accurate and personalized treatment recommendations over time. This innovative approach not only modernizes Ayurvedic practice but also bridges the gap between traditional and contemporary medicine, fostering a more inclusive and effective healthcare ecosystem.
Enhancing K-Nearest Neighbors with Dimensionality Reduction: An Impact on Recognition Time and Accuracy 15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
Deep Learning-LSTM based Football Commentary Generation and PCFG based Event Generation Dependent on User Input 15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
SynthoTranslate: Multimodal Knowledge Assimilation Tool for Overcoming Auditory Barriers and Bridging Language Barriers Priyadarshan Dhabe, Ojas Joshi, Jignesh Barhate, Basun Kundu, Arman Nandeshwar, Manthan Gujar Proceedings International Conference on Computational Intelligence and Networks, 2024 This paper introduces an automated system to improve educational accessibility through multilingual video translation and dubbing. It uses advanced speech-to-text (STT) technologies, natural language processing (NLP), and voice conversion models for accurate subtitles, translations, and dubbed audio in Indian languages. Key components include Whisper and Wav2vec for subtitle generation, GoogleTrans for translation, and Silero models for natural-sounding voice cloning. Indian Sign Language (ISL) transcription is integrated to support hearing-impaired students, with Wav2Lip ensuring precise lip-syncing of dubbed audio. Evaluation shows a Word Error Rate (WER) of 1.9% and cosine similarities over $\\mathbf{9 5 \\%}$. The system also offers content summarization and Q&A to enhance engagement. Future work will focus on optimizing speech recognition and improving ISL transcription for greater accessibility.
Real-Time Driving License Verification System Using Face Recognition Priyadarshan Dhabe, Sneha Bhat, Ishan Shivankar, Tanishk Shrivastava, Prathmesh Sonawane, Rohit Sutrave, Shivam Mattoo 2024 International Conference on Innovations and Challenges in Emerging Technologies Icicet 2024, 2024
Fingerprint verification using Haar Wavelet Priti S. Sanjekar, Priyadarshan S. Dhabe Iccet 2010 2010 International Conference on Computer Engineering and Technology Proceedings, 2010
Design of drodeasys (drowsy detection and alarming system) Hrishikesh B. Juvale, Anant S. Mahajan, Ashwin A. Bhagwat, Vishal T. Badiger, Ganesh D. Bhutkar, Priyadarshan S. Dhabe, Manikrao L. Dhore Lecture Notes in Electrical Engineering, 2009
