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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Saral, G. Baby Subasri, S. Ramya Shree, R. Bose, K. Regin Rajalakshmi Institute of Technology Dept. Artificial Intelligence & Data Science Chennai India Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Student Rajalakshmi Institute of Technology Chennai India Rajalakshmi Institute of Technology Dept. AI&DS & Cloud Computing Chennai India

ISBN: (纸本)9798331512965

Dance movement accuracy prediction is a critical component of modern dance training, yet traditional methods often lack objectivity due to the subjective nature of evaluations and the complexity of human movement. To address these challenges this paper introduces MoveMatch, which is a web-based platform built for real-time dance movement prediction. By leveraging deep learning techniques, the platform performs posture estimation in real-time and detects key points. The system compares the detected poses using Euclidean distance to calculate the alignment between corresponding key points which provides a quantitative measure of the user's accuracy. Based on this comparison, MoveMatch delivers real-time feedback, offering an accuracy score and personalized suggestions for improvement. This immediate feedback fosters a more efficient and engaging learning experience for dancers. Extensive testing of MoveMatch in various dance styles demonstrates its effectiveness in objectively assessing performance, enhancing both training quality and accessibility. The integration of pose estimation, deep learning and real-time comparison offers a novel, user-friendly solution for dancers seeking to improve their skills, transforming traditional dance education with AI-driven insights. © 2025 IEEE.

关键词： Deep learning

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Move Match: Live Dance Motion Monitoring and Feedback System

Move Match: Live Dance Motion Monitoring and Feedback System

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： G Baby Saral Subasri. S Ramya Shree R K. Regin Bose Dept. Artificial Intelligence & Data Science Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Student Rajalakshmi Institute of Technology Chennai India Dept. AI&DS & Cloud Computing Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

关键词： Training Deep learning Humanities Accuracy Three-dimensional displays Tracking Pose estimation Euclidean distance Streaming media Real-time systems

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RF-Based UAV Classification and Identification Using Machine Learning 10

RF-Based UAV Classification and Identification Using Machine...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Dubey, Nidhish Sai Nithin, Nanduri Mahathi Tripathi, Shrivishal Dr. Spm Iiit Student of Data Science and Artificial Intelligence Naya Raipur India Dr. Spm Iiit Faculty of Electronics and Communications Engineering Naya Raipur India

ISBN: (纸本)9798350382471

In today's market, the position of Unmanned Aerial Vehicles (UAVs) are considered ubiquitous. They are used for hobby recreation and are also customized to meet clients' unique industrial needs, including in fields like the construction and agriculture sectors. Nonetheless, UAVs' involvement in criminal and illegal activities is also increasing. Hence, effective detection and identification of UAVs have become an essential field in research. In order to effectively recognize and identify UAVs, a new machine learning-based technique is proposed in this study. To be exact, radio frequency (RF) data pretreatment and feature extraction phases are described together with an enhanced approach for UAV identification and detection utilizing ensemble learning that utilizes hierarchical notions. The RF signals are filtered to increase the efficiency of the output. Raw signals from numerous UAVs in various operating states, spanning the modes of on, off, video taking, flying, connected, and hovering, were comprehensively researched using publicly accessible datasets. Additionally, we developed an algorithmic solution to find and identify intrusive drones using RF datasets. The algorithm has been trained to detect if a UAV is in range, immediately identify the UAV type, and accurately determine the mode of flight of said discovered UAV. © 2023 IEEE.

关键词： detection identification machine learning radio frequency unmanned aerial vehicles

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SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees

SourcePlag - Source Code Plagiarism Detection Based on Abstr...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Nakul Sharma Siddharth Shinde Swarup Bhosale Suyog Patil Department of Artificial Intelligence and Data Science Faculty of Engineering Vishwakarma Institute of Information Technology Pune India Department of Artificial Intelligence and Data Science Student of Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities.

关键词： Java Codes Plagiarism Source coding Syntactics Programming Security Reliability Python Testing

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data-driven Model Predictive Control of Nanoparticle Production in Modular Reactors

Data-driven Model Predictive Control of Nanoparticle Product...

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European Control Conference (ECC)

作者： Rohan Saswade Ganapavarapu Sai Tarun Nirav Bhatt Sridharakumar Narasimhan Department of Biotechnology and is a student of Interdisciplinary Dual Degree Programme in Cyber-Physical Systems Indian Institute of Technology Madras Chennai India Department of Chemical Engineering’ Indian Institute of Technology Madras Chennai India Department of Data Science and Artificial Intelligence and Biotechnology and Robert Bosch Centre for Data Science and Artificial Intelligence Indian Institute of Technology Madras Chennai India Department of Chemical Engineering and Robert Bosch Centre for Data Science and Artificial Intelligence Indian Institute of Technology Madras Chennai India

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Microreactors are an essential part of modular chemical systems involved in the on-demand production of chemicals such as nanomaterials, pharmaceuticals, specialty chemicals, etc. Model-based nonlinear predictive control of microreactors is a challenging task due to the high online computational cost associated with developing and maintaining high-order first-principles nonlinear models. In this work, we propose a nonlinear data-driven model predictive control (NMPC) scheme for nanoparticle production in microreactors. In this paper, a non-linear Auto Regressive Exogenous Neural Network model (NARX-NN) is developed with the flow rates of the reactants as inputs and the peak value of the absorbance spectra (an indirect measure of the average size of nanoparticles) as output by performing a set of experiments in Corning Advanced-Flow TM Reactors (AFR). Typically, producing a new desired average size nanoparticle on-demand is done by manual changes in the flow rates of reactants. In this work, a nonlinear model predictive controller using the identified NARX-NN model is formulated to track a change in the set point, the peak value of the spectra. The formulated controller with the identified NARX-NN model is demonstrated via the simulation studies. It is shown that the proposed NMPC with the NARX-NN model performs well in different scenarios of silver nanoparticle production.

关键词： Silver Computational modeling Production Predictive models Size measurement Inductors Task analysis

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RF-Based UAV Classification and Identification Using Machine Learning

RF-Based UAV Classification and Identification Using Machine...

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IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

作者： Nidhish Dubey Nanduri Mahathi Sai Nithin Shrivishal Tripathi Student of Data Science and Artificial Intelligence Dr. SPM IIIT Naya Raipur India Faculty of Electronics and Communications Engineering Dr. SPM IIIT Naya Raipur India

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Developing Eye Tracking Exercise System for Treatment of Lazy Eye

Developing Eye Tracking Exercise System for Treatment of Laz...

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Medical Technologies National Conference (TIPTEKNO)

作者： Kaan Kurtel Melahat Kizil Umuthan Ercan Suzan E. Ozsimsek Hasan Baris Mihci Department of Software Engineering Izmir University of Economics Balcova Izmir Turkey Izmir University of Economics Paramedic Program Vocational School of Health Services Luleå University of Technology MSc. Student in Applied Artificial Intelligence E-commerce Project Manager Macquarie University MSc. Student in Data Science

The aim of this study is to develop a fun and effective computer application game for children with amblyopia and to prevent eye strain in people whose work or lifestyle involves constantly looking at screens. The main motivation is to use children’s enthusiasm for playing video games for the treatment of amblyopia. For this, hardware and software products were brought together, and a software was developed according to software engineering principles. The system was tested with a group of volunteers and necessary alterations were made.

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Voice Activated Face Recognition based Smart Support System

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Journal of Physics: Conference Series 2021年第1期1916卷

作者： N Yuvaraj M Sanjeev G Jenifa K R Sri Preethaa Student Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India

Voice assistants usually respond to voice commands and provide the user with specific details about his question. Currently, voice assistants can process product orders, answer questions, perform tasks such as playing music, or initiate a quick phone call with a friend. For voice assistants, the long-term goal is to serve as a smart bridge between humans and the immense information and capabilities delivered by the Internet. Taking away the need to use some gadget or screen to communicate in various locations with the internet, technology or other humans. This paper deals with such a voice assistance for schedule maintenance for individual which starts with the face recognition. Once a person enters the classroom / office, their face is captured and the face identification is done. If the identified person is the authorised one, then the system responds with a greeting message. When the person starts to ask about the schedule to the system, it responds with the schedule of the day of that identified person. In case of emergency schedules like meetings, the intimation is sent to the user as a message to alert him/her.

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From the Field: Using Automated Digital Recording Systems as Effective Tools for the Monitoring of Birds and Amphibians

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Wildlife Society Bulletin 2010年第1期34卷

作者： MIGUEL A. ACEVEDO LUIS J. VILLANUEVA-RIVERA University of Puerto Rico Department of Biology San Juan Puerto Rico 00931-3360 USA Miguel A. Acevedo(left) is a Masters student of the Tropical Ecology of the Department of Biology at the University of Puerto Rico Rio Piedras Campus. His professional interests include bird and amphibian ecology and conservation land use exotic species bioacoustics and informatics. He is currently working in conjunction with Villanueva-Rivera and others in the use of artificial intelligence and data mining to develop an automated remote biodiversity monitoring network in Puerto Rico. Luis J. Villanueva-Rivera(right) is a Masters student of the Tropical Ecology of the Department of Biology at the University of Puerto Rico Rio Piedras Campus. His professional interests include vertebrate ecology and conservation evolution and public's understanding of science. Luis has developed a website for the general public about the Puerto Rican frogs of the genusEleutherodactylus(http:***) as well as co-authored with the senior author an online database of the Puerto Rican birds as well as sightings in the island (http:***). He also has an interest in the adaptation and use of new technologies in ecological research like the automated recorders used in this study and online databases for data dissemination.

There is a need to improve the quantity and quality of data in biodiversity monitoring projects. We compared an automated digital recording system (ADRS) with traditional methods (point-counts and transects) for the assessment of birds and amphibians. The ADRS proved to produce better quantity and quality of data. This new method has 3 additional advantages: permanent record of a census, 24 h/d data collection and the possibility of automated species identification. (WILDLIFE SOCIETY BULLETIN 34(1):211–214; 2006)

关键词： amphibians ADRS Automated Digital Recording System birds monitoring methods Puerto Rico

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