Journal of Advanced Research in Data Structures Innovations and Computer Science

Development of a 3-node LoRa-based hopping network for ambient Carbon monoxide monitoring with MQ-7 and Carbon dioxide with MQ-135 gas sensors

2025-10-03T11:53:07+00:00

IoT in Healthcare Sector and Smart Home Appliances Applications

2025-10-03T12:24:09+00:00

The Internet of Things (IoT) is rapidly transforming the digital ecosystem by enabling smart, connected environments across various domains. Among its most impactful applications are IoT in healthcare and smart homes, where real-time data exchange and automation are improving quality of life and service delivery. However, as IoT systems scale, they also face critical challenges—notably in terms of security, data integrity, interoperability, and privacy. In the healthcare domain, IoT facilitates innovations such as remote patient monitoring, wearable health devices, and AI-driven diagnostics, allowing for continuous care and early detection of anomalies. In smart homes, IoT enables automation, energy efficiency, voice-activated controls, and enhanced safety. While these advancements offer immense potential, they also require robust frameworks to ensure data protection and system reliability.

Effective Machine Learning Techniques for Handling missing Data

2026-01-22T04:46:45+00:00

Missing data in machine learning is a significant challenge, impacting predictive models’ performance. It can be caused by errors in data collection, incomplete responses, or system failures. Incorrect handling can lead to biased or inaccurate predictions. This paper explores various imputation methods, including mean imputation, median imputation, random imputation, and end-of-distribution imputation. Each method has specific applications based on the dataset’s nature and missing information. Mean imputation, which replaces missing values with the average of available data, is most effective when the data follows a normal distribution. The mean is a reliable measure of central tendency in symmetric distributions, but it may not be suitable for skewed data due to its less sensitive nature to outliers. Median imputation, the middle value in a sorted dataset, is ideal for skewed
data distributions. Random imputation, a more flexible technique, replaces missing values with randomly selected values but may require more computational resources, especially in large datasets. End-of- distribution imputation fills missing values with the lowest or highest value. This paper emphasises the significance of hyperparameter tuning in machine learning models, specifically GridSearchCV. This tool systematically explores various model parameter combinations to find the best-performing set, preventing overfitting and ensuring model generalisation to unseen data. It is particularly useful for complex models requiring fine-tuning. The paper emphasises the importance of combining robust imputation techniques with hyperparameter optimisation methods for reliable machine learning models, enhancing predictive power and reliability.