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Synthesis, Structural and Optical Characterization of Pure And Cobalt Doped Zinc Oxide Nanoparticles By Sol-Gel Method

This study uses the sol-gel method to synthesize and characterize pure zinc oxide (ZnO) and cobalt-doped zinc oxide (Co-ZnO) nanoparticles. ZnO, a wide-bandgap semiconductor, is known for its optical transparency, high exciton binding energy, and antimicrobial properties, making it valuable for various applications in optoelectronics, sensors, and biomedical fields. Incorporating cobalt as a dopant modifies ZnO's structural and optical characteristics, enhancing its functionality in photocatalysis, spintronics, and magnetic storage devices. X-ray diffraction (XRD) analysis confirmed the hexagonal wurtzite structure for ZnO and Co-ZnO. However, Co doping led to decreased crystallite size, increased strain, and higher dislocation density, indicating lattice distortions. UV-Vis spectroscopy showed a shift in the absorption edge for Co-ZnO, suggesting bandgap narrowing, improving its visible-light absorption capabilities. This modification enhances the photocatalytic efficiency of Co-ZnO, making it highly suitable for environmental remediation and solar energy applications. The findings demonstrate that while pure ZnO is preferable for UV-blocking, optoelectronic applications, and sensors, Co-ZnO exhibits superior performance in photocatalysis and magnetic-based technologies. The study concludes that cobalt doping enhances ZnO’s properties, expanding its potential applications in energy, environmental, and electronic industries.

Published by: Dhivya R, Poonguzhali V, Sahana Fathima A, Abinaya B

Author: Dhivya R

Paper ID: V11I2-1159

Paper Status: published

Published: April 25, 2025

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Research Paper

Heart Disease Prediction

Heart disease remains one of the leading causes of mortality globally. new diagnosis gets importantly better endurance rates and cuts discourse costs. In the research, we explore Machine learning techniques to predict heart disease based on clinical Information. exploitation associate in nursing open-source dataset we apply and value respective sorting Procedures, including logistical regression, decision trees, support vector machines (SVM). Our results demonstrate that machine learning can effectively identify potential heart disease cases, providing a promising tool for healthcare Uses.

Published by: Aniruddha Ambre

Author: Aniruddha Ambre

Paper ID: V11I2-1364

Paper Status: published

Published: April 24, 2025

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Research Paper

Design and Implementation of IoT-Based War Spying Robot with Wireless Night Vision Camera

The goal of this project is to create a robot that uses a wireless night vision camera and an Android app to monitor human activity over Wi-Fi. The user receives real-time visual data transmissions from the robot. It ensures constant surveillance by transmitting live video even in dimly lit areas. The robot can move in the following directions: forward, left, right, backward, and stop when controlled by Wi-Fi. Operation from distant locations is made possible by sending data to a remote IoT cloud database. The system offers a reasonably priced alternative for remote surveillance because it is constructed with a low-cost microprocessor. Since the robot's video can be broadcast to a PC, it can be used for remote surveillance applications like inspections, reconnaissance, and monitoring.

Published by: Yathish M, Kavana M S, Ananya V R, Dr. Prakash Kuravatti

Author: Yathish M

Paper ID: V11I2-1367

Paper Status: published

Published: April 24, 2025

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Review Paper

The Transformative Role of Artificial Intelligence in Healthcare: Current Applications, Challenges, and Future Directions

Artificial intelligence (AI) revolutionizes healthcare by enhancing diagnostic accuracy, optimizing treatment protocols, also streamlining administrative workflows. This review synthesizes recent advancements in AI applications across disease prediction, medical imaging, robotic surgery, personalized medicine, and healthcare analytics, while critically evaluating ethical, legal, and technical challenges. A systematic literature review of 75 peer-reviewed articles (2013–2023) from IEEE Xplore, PubMed, and ScienceDirect reveals that convolutional neural networks (CNNs) and natural language processing (NLP) are driving innovations in medical imaging and clinical decision support. Case studies, like Google DeepMind’s retinal disease detection and IBM Watson for Oncology, underscore AI’s potential to reduce diagnostic errors by 30–50%. However, challenges persist in algorithmic bias, data privacy, and model interpretability. Federated learning and explainable AI (XAI) are emerging solutions that fill these gaps. Finally, future directions include the integration of AI with wearable devices to provide proactive care with aid from genomic data. Multidisciplinary collaboration for creating the standard frameworks of AI governance is argued in this paper to ensure equitable and ethical AI deployment in the global healthcare systems. Medical applications of AI can increase the efficiency of decision-making and management of care operations. We serve to review recent literature on AI applications in healthcare to deal with their potential ethical, legal, and technical challenges. The literature review was conducted systematically, using peer-reviewed articles (2019-2023) published from IEEE Xplore, PubMed, and ScienceDirect. Studies related to the use of AI in healthcare were included in the criteria. Studies about cybersecurity, AI in business, or non-English languages were excluded from the criteria. Studies that did not provide enough information to be included in the analysis were excluded. The review focused on applications of AI in predicting diseases, aiding medical imaging, assisting robotic surgery, enabling personalized medicine, and supporting healthcare analytics. We also explored the ethical, legal, and technical challenges of AI in healthcare. CNN and NLP are driving innovations in medical imaging and clinical decision support.

Published by: Ashish Nakhate, Gaurav Saini, Jaipal singh, Jayprakash Asati, Yaman Singh

Author: Ashish Nakhate

Paper ID: V11I2-1274

Paper Status: published

Published: April 23, 2025

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Research Paper

Power in Every Step: A Piezoelectric Staircase Solution

Coal, along with other fossil fuels, is still the major source of power generation. However, with a rapid consumption speed, these fossil fuels will be exhausted in the future. Also, the use of fossil fuels for power generation causes air pollution and the greenhouse effect. So, to deal with these problems, we need a greener solution. This project mainly aims to build a power generator using the piezoelectric sensor. The sensor converts the pressure into an electrical charge. When pressure is applied to piezoelectric sensors, an electric charge is generated. This generated output in the form of alternating current (AC) is then converted into Direct current (DC) to store for further use. This generation technique can be used in more vibration-producing areas, mainly staircases in public places (like railway stations), for lower power applications.

Published by: Lakshya S

Author: Lakshya S

Paper ID: V11I2-1277

Paper Status: published

Published: April 23, 2025

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Research Paper

Obstacle Avoiding Arduino Controlled Bipedal Robot

This paper presents the design and development of an Arduino-controlled bipedal robot with integrated obstacle avoidance. The robot simulates human walking using servo motors and executes real-time path adjustments based on ultrasonic sensor input. Inverse kinematics principles guide the movement, and the structure is fabricated using PLA-based 3D printing for lightweight and modular design. The system demonstrates efficient coordination of mechanical, electronic, and software elements for autonomous walking and navigation in dynamic environments.

Published by: Kalyani Anumalla, Bhoomi Gupta, Mohd. Ajmal Javed, Prof. Nikhil VS

Author: Kalyani Anumalla

Paper ID: V11I2-1286

Paper Status: published

Published: April 23, 2025

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