S. V. Akram, R. Singh, A. Gehlot, A. K. Thakur,
Volume 17, Issue 4 (12-2021)
Abstract
Waste management is crucial for maintaining the hygienic environment in urban cities. The establishment of a reliable and efficient IoT system for waste management is based on integrating low power and long-range transmission protocol. Low Power Wide Area Network (LPWAN) is specially designed for the aforementioned requirement of IoT. LoRa (Long Range) is an LPWAN transmission protocol that consumes low power for long-range transmission. In this study, we are implementing long-range (LoRa) communication and cloud applications for real-time monitoring of the bins. The customized sensor node and gateway node are specifically designed for sensing the level of bins using ultrasonic sensor and communicating it to the cloud via long-range and internet protocol connectivity. Blynk and cayenne are the two cloud-based applications for storing and monitoring the sensory data receiving from the gateway node over internet protocol (IP). The customization of nodes6 and utilization of two cloud-based apps are the unique features in this study. In the future, we will implement blockchain technology in the study for enabling a waste-to-model platform.
M.e. S.m Mehzabeen , Phd R Gayathri, Pattunnarajam Paramasaivam , Phd Ramya A,
Volume 21, Issue 4 (11-2025)
Abstract
Hepatitis C virus (HCV) detection is a critical aspect of early intervention and effective management of the disease. This paper presents a comprehensive study focused on enhancing the detection accuracy of HCV through the integration of advanced techniques - SMOTE, Optuna, and SHAP - alongside extensive exploratory data analysis (EDA). The study addresses class imbalance using Synthetic Minority Over-sampling Technique (SMOTE), optimizes model performance with Optuna for hyperparameter tuning, and provides model interpretability using SHAP (SHapley Additive exPlanations). EDA is leveraged to gain valuable insights into the dataset's characteristics, ensuring robust data preprocessing and feature engineering. The results show 97% improved HCV detection performance, highlighting the efficacy of the proposed methodology in medical diagnostics and aiding healthcare professionals in making informed clinical decisions.
