Departments

Admin

Ms. Athira K. P.

Assistant Professor

Areas of Specialization

Computer Science
Areas of Interest

Machine Learning
Data mining
Artificial intelligence

Educational Summary


Course University Year
M.Tech Computer Science and engineering NSS College of engineering Palakkad KTU 2024
B.Tech Computer Science and engineering Jyothi engineering college KTU 2022

Experience


From Date To Date Institution Name Designation
2025-06-16 0000-00-00 Jyothi engineering college Assistant professor
2024-06-10 2025-06-11 Sreepathy engineering college Assistant professor
0000-00-00 0000-00-00

Achievements


Achievement Year
Published and Granted a patent on title " AI Based Device for Agricultural Disease Detection" on June 20.26. 2026
Participated & completed AICTE Training And Learning (ATAL) Academy Faculty Development Program on NEXT-GENERATION AUTOMATED SYSTEMS: MULTIMODAL LEARNING, MLOPS, RESPONSIBLE DATA ANALYTICS AND QUANTUM PROCESSING at AHALIA SCHOOL OF ENGINEERING & TECHNOLOGY 2026
Completed 14 days internship in " Artificial Intelligence & Machine Learning. During this tenure successfully completed the project tile : " A Machine Learning-Based Intrusion Detection System for Network Security". 2026
Participated in the Six-Day Faculty Development Programme entitled “Next-Generation Artificial Intelligence: Agentic, Responsible and Sustainable AI Systems”. 2026
Successfully completing the course " Artificial Intelligence for All" in Infosys springboard 2025
Participated & completed AICTE Training And Learning (ATAL) Academy Faculty Development Program on Future-Ready Educators: Harnessing Intelligent Computing Systems for Teaching and Research at Jain Deemed to be University Off Campus at Kochi 2025
Successfully completing the course "Hands-on Supervised Machine Learning with Python" in Infosys springboard 2025
Participated in the 2-Week Online Winter Course on National Capacity Building Program on Research and Publications organized by the Indian Institute of Technology, Dharwad, Karnataka. 2025
Successfully completing the course " Step-by-Step Machine Learning with Python" in Infosys springboard 2025
Participated & completed AICTE Training And Learning (ATAL) Academy Faculty Development Program on Recent Advances & Emerging Trends & Application in Robotics and Automation at NITTE MEENAKSHI INSTITUTE OF TECHNOLOGY 2025
Participated in the All-India Women Start-a-thon (AIWS) 2025-26, demonstrating enthusiasm, commitment, and entrepreneurial spirit. 2025

Papers Published


IEEE Paper published : title: Multi disease detection and diagnosis through ensemble learning:- Athira K P, "Multi Disease Detection And Diagnosis Through Ensemble Learning," 2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI), KANNUR, India, 2024, pp. 1-6, doi: 10.1109/APCI61480.2024.10616480. keywords: {Heart;Deep learning;Machine learning algorithms;Heart beat;User interfaces;Sensors;Medical diagnosis;Machine Learning;Deep Learning;Ensemble Learning Random Forest;CNN;XGBoost;Max30100 heart beat sensor},
J. Paulson, J. Jacob, L. Vincent, R. P. Cherian, A. K. P and S. Xavier, "Home Companion Robo: An Affordable Voice-Assisted Healthcare Service Robot for Enhanced Patient Independence," 2026 International Conference on AI-Driven Solutions for Sustainable Smart Cities: Challenges and Opportunities (ADSSSC), Thrissur, India, 2026, pp. 683-687, doi: 10.1109/ADSSSC67751.2026.11581981.(IEEE) keywords: {Robots;Medical services;Design methodology;Printing;Arm;Wearable devices;Grippers;Modules (abstract algebra);Costing;Costs;Healthcare robotics;assistive technology;voice interface;Raspberry Pi;service robot},
J. T, A. K. P, A. V. A and A. S. A, "A Novel Federated Big Data Analytics Framework Using Latent Energy Interaction Modeling and Rule-Embedded Anomaly Intelligence for Smart Grids," 2026 10th International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 2026, pp. 247-253, doi: 10.1109/ICISC69558.2026.11681368.(IEEE) keywords: {Modeling;Smart grids;Timing;Energy;Loading;Federated learning;Equations;Anomaly detection;Learning (artificial intelligence);Accuracy;Federated Learning;Smart Grid;Load Forecasting;Anomaly Detection;Latent Energy Interaction Modeling;Cross-Attention;Temporal Transformer;Energy Rule-Embedded Neural Validator;Data Privacy;Distributed Energy Systems},

Projects Guided


Projects Name Details Year
AI Powered Multi - Hazard Disaster Prediction Emergency Alert and Survivor Detection System Natural disasters such as floods, landslides, earthquakes, cyclones, and wildfires result in significant loss of human lives, infrastructure, and economic resources, while existing disaster management systems often suffer from limited prediction accuracy, delayed emergency response, and poor integration of monitoring technologies. This project proposes an AI-powered Multi-Hazard Disaster Prediction, Early Warning, and Survivor Detection System that integrates Machine Learning algorithms (Random Forest and LSTM) with an IoT–Cloud architecture to provide accurate disaster prediction and rapid emergency response. Real-time data collected from IoT sensors, GIS information, satellite imagery, and weather services are analyzed to predict potential disasters, while a Flask-based backend processes the data and issues instant alerts to citizens and disaster management authorities through web and mobile notifications. 2026
Home Companion Robo: An Affordable Voice-Assisted Healthcare Service Robot for Enhanced Patient Independence Abstract—In healthcare facilities, particularly elder care and rehabilitation centres, mobility limitations significantly hinder patients’ ability to perform daily activities. Simple tasks such as retrieving water or medication often require caregiver assistance, leading to reduced patient independence and increased caregiver workload. To address this challenge, we present Chitti, an af- fordable, voice-assisted indoor service robot designed to support individuals with limited mobility. Built on a wheeled base with an integrated gripper arm, the robot leverages lightweight on- device object detection powered by a Raspberry Pi 4, a camera module, and a servo-driven arm to identify and retrieve requested items. Users interact with the robot through a smartphone or wearable application using voice commands, enabling seamless hands-free operation. By automating item-fetching tasks, Chitti reduces caregiver burden while enhancing patient autonomy. Its cost-effective and modular design m 2026