Waste Management Optimization Using Reinforcement Learning Algorithm

Neeta Lokhande, Aneesh Raskar — Fri, 17 May 2024 00:00:00 +0000

Urbanization and population growth have increased waste, creating an excellent challenge for waste management systems. In response to these challenges, this study investigates using Reinforcement Learning (RL) algorithms to optimize waste management in urban environments. The primary purpose of this study is to solve the problem of changing the waste collection process, which is essential in reducing operating costs and increasing overall profit, with the effects of waste management. The use of Q-learning, a reinforcement learning algorithm, forms the basis of our approach. Q-learning was chosen for its performance in handling arbitrary decisions and its ability to make weak decisions, perfectly adapting to the complexities and differences in the garbage collection period. Extensive testing and analysis demonstrate the effectiveness of the proposed support learning-based waste management optimization model. This research aims to use innovative technology to improve how we plan waste collection on the fly, making waste management more efficient and cost-effective.

House Price Prediction through Data Mining and Machine Learning Algorithms

Fri, 17 May 2024 00:00:00 +0000

Data mining plays a pivotal role in the real estate sector, where it is extensively utilized to derive valuable insights from raw data, thereby facilitating the prediction of house prices and identification of crucial housing features. Given the profound impact of housing price fluctuations on homeowners and the market, considerable research is directed towards analyzing various factors and developing predictive models. Among the array of models investigated, Random Forest, Naive Bayes, and Multiple Linear Regression emerge as the most efficient. Moreover, spatial factors and real estate agent's involvement are pivotal in inaccurate price predictions. This study benefits developers and researchers alike, providing insights into the criteria that drive housing prices and identifying the most effective machine learning models for analysis. By understanding these factors and utilizing advanced modelling techniques, stakeholders in the real estate industry can make informed decisions that benefit both buyers and sellers.

Journal of Innovations in Data Science and Big Data Management

Waste Management Optimization Using Reinforcement Learning Algorithm

House Price Prediction through Data Mining and Machine Learning Algorithms