Dr AYATHU SRI LAKSHMI

Teaching is my passion. In Teaching profession from 1997. Taught for UG, PG, Engineering, MCA, MBA. Written 5 books. Published 17 research articles. Published 5 patents.

EDUCATION

MCA from Regional Engineering College, Warangal, Telangana, India
M.Tech (Computer Science Engineering) from Acharya Nagarjuna University, Guntur, Andhra Pradesh, India
PhD from Sri Padmavathi Viswavidyalayam, Tirupathi, Andhra Pradesh

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Computer Science Applications, Computer Engineering

Scopus Publications

Cloud based cognitive disaster response supply chain framework for effective relief operations management using fog and context-aware IOT
Mortha Sharmila, Ayathu Sri Lakshmi, Ramkumar Ramkumar, Raja Rajeswari Nachiappan, Nelahonne Mohan Jyothi, Avula Pavani
Aip Conference Proceedings, 2024
Blockchain integrated optimized e-commerce business process model
Ayathu Sri Lakshmi, Savitha Nadig Jayadevappa, Cheryl D’Souza, Sushil Lekhi, Nelahonne Mohan Jyothi, Avula Pavani
Aip Conference Proceedings, 2024
Integration of block chain in e-commerce optimizes every process of the e-commerce.It boosts the benefits to end customers, merchants, and suppliers.In this research paper, study is conducted to know how e-commerce process can be optimized by integrating with blockchain platform.Blockchain offers streamlined processes and seamless experience to its stakeholders.The use of hyper ledger keeps the record more authentic and secure.Blockchain upholds the value system like trust and loyalty.All participants have equal rights.Blockchain eliminates unnecessary processes and makes it much faster.Payment and refund will be faster, and counterfeit goods will be eliminated.The information stored in blockchain cannot be manipulated or deleted.The information provided by the merchant on the product description is highly authentic.Cryptocurrency removes the barriers of foreign exchange.The study also focuses on the challenges of implementing blockchain.
Automated Worms Monitoring System
S Veena, A Sri Lakshmi, S Tayiba Fathima, S Thanushree
2024 International Conference on Knowledge Engineering and Communication Systems Ickecs 2024, 2024
The core of raising silkworms for silk products is sericulture. India is the world’s second-biggest buyer of silk goods. India’s social, economic, cultural, and political advancements are rooted in sericulture. A healthy silkworm’s development depends on temperature and moisture at all stages, but particularly during development. One of the most important factors to take into account for the successful and healthy rearing of silkworms is disinfection. In order to create a real-time sericulture monitoring and disinfection actuation system, we propose in this study an Arduino enabled Internet of Things grounded method. Additionally, we apply image processing technology to determine the stages of the silkworm life cycle. Using arising Arduino, our prototype facilitates realtime data collecting. Arduino is used in the system’s design and implementation.
Comparative Performance Analysis of Fine Tuned Optimized Deep Transfer Learning Techniques for Fruit Quality Assessment
A. Sri Lakshmi, Mortha Sharmila, N J Savitha, Cheryl D Souza, Rajani Thota, N M Jyothi
2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques Easct 2023, 2023
Automatic and accurate detection of quality of fruits is critical need of the food industry to save manpower and time. The Transfer learning models are state-of-the-art deep learning techniques which offer high performance for classification chores. They make use of the knowledge from pre-training and require a smaller number of labelled training datasets. This reduces the learning time and cost of preparing large sized labelled dataset. The objective of the current research is to study, apply and evaluate the performance of various contemporary transfer learning models in classification of the quality of five different types of fruits into fresh unspoiled and rotten spoiled classes. Optimize the result using fine tuning and to investigate the most suitable model with highest accuracy, precision, and recall. The outcome of the research exhibited that MobileNet transfer learning model performed with highest classification of accuracy of 99.76%, with 99% precision and recall. The EfficientNet model achieved second highest accuracy of 99.73% and AlexNet exhibited third highest accuracy of 99.56%. The experiment is bench marked with the open-source dataset of fruits. The developed model is suitable for real time use.