C109805030425

IJAINN Journal — Mon, 14 Apr 2025 04:02:52 +0000

The Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) has ISSN 2582-7626 (online), open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in February, April, June, August, October and December.The aim of the journal is to publish high quality peer–reviewed original articles in the area of Artificial Intelligence and Neural Networking that covers Neural Networks, Fuzzy Logic, Simulated Biological Evolution Algorithms (Like Genetic Algorithm), Ant Colony Optimization, Reasoning and Evolution, Intelligence Applications, Computer Vision and Speech Understanding, Multimedia and Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and Ai Planning Strategies and Tools, Computational Theories of Learning, Technology and Computing (Like Particle Swarm Optimization), Intelligent System Architectures, Knowledge Representation, Bioinformatics, Natural Language Processing, Multiagent Systems, Supervised Learning, Unsupervised Learning, Deep Learning, Big Data and AI Approaches, Reinforcement Learning, Learning with Generative Adversarial Networks, Regression and Prediction, Problem Solving and Planning, Clustering, Classification, Neural Information Processing, Vision and Speech Perception, Heterogeneous and Streaming Data, Probabilistic Models and Methods, Reasoning and Inference, Marketing and Social Sciences, Knowledge Discovery, Web Mining, Information Retrieval, Design and Diagnosis, Game Playing, Streaming Data, Music Modelling and Analysis, Robotics and Control, Multi-Agent Systems. #Neural Networks #Fuzzy Logic #Simulated Biological Evolution Algorithms (Like Genetic Algorithm) #Ant Colony Optimization #Reasoning and Evolution #Intelligence Applications #Computer Vision and Speech Understanding #Multimedia and Cognitive Informatics #Data Mining and Machine Learning Tools #Heuristic and AI Planning Strategies and Tools #Computational Theories of Learning #Technology and Computing (Like Particle Swarm Optimization) #Intelligent System Architectures #Knowledge Representation #Bioinformatics #Natural Language Processing #Multiagent Systems #Supervised Learning #Unsupervised Learning #Deep Learning #Big Data and AI Approaches #Reinforcement Learning #Learning with Generative Adversarial Networks #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Probabilistic Models and Methods #Reasoning and Inference #Marketing and Social Sciences #Knowledge Discovery #Web Mining, Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent systems #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

AI-Powered Anomaly Detection in Air Pollution for Smart Environmental Monitoring
Raghav Abrol

Raghav Abrol, Researcher, Department of CSAI, NSUT, New Delhi, India.

Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Air pollution is a growing concern due to its adverse effects on human health and the environment [1]. Traditional air quality monitoring stations provide accurate data but are expensive to maintain and limited in coverage [2]. This research explores an AI-based anomaly detection framework to enhance air quality assessment and support the development of virtual monitoring stations [3]. The study utilizes four machine learning techniques—Z-score, Isolation Forest, Autoencoders, and Long Short-Term Memory (LSTM) networks—to analyse pollution data [4]. The Z-score method detects extreme pollution values by measuring statistical deviations [5], while Isolation Forest identifies outliers by isolating anomalies in the dataset [6]. Autoencoders, a deep learning approach, learn typical pollution patterns and highlight deviations [7], and LSTM networks forecast air quality trends while identifying unexpected pollution spikes [8]. By integrating these techniques, the proposed system improves pollution monitoring, allowing for real-time detection of anomalies and better forecasting of pollution levels [9]. The findings suggest that AI-driven virtual monitoring stations can provide a scalable, cost-effective alternative to traditional sensorbased systems [10]. This approach has the potential to enhance environmental monitoring, support proactive pollution control measures, and contribute to data-driven policymaking for air quality management [11].

Keywords: LSTM, Isolation Forest, Carbon Monoxide, Long Short-Term Memory.
Scope of the Article: AI Applications

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Volume-5 Issue-3, April 2025 – Indian Journal of Artificial Intelligence and Neural Networking (IJAINN)

C109805030425

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