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@article{207575,
        author = {Harshitha R and Varsha R D and C Hema Prabha},
        title = {Neurocloud:An AI-Driven Framework for Intelligent Anamoly Detection and Performance Monitoring in a Cloud Environment},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1758-1766},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207575},
        abstract = {The exponential rise in cloud computing infrastructure presents numerous obstacles in maintaining its reliability, performance, and security. Classical approaches for monitoring that depend on hardcoded heuristics and rules are challenging to keep pace with today's data volume, velocity, and variability. This paper presents a novel approach for anomaly detection powered by artificial intelligence algorithms. It employs unsupervised machine learning algorithms, deep learning time-series forecasting, and reinforcement-based reaction strategies to promptly recognise and rectify cloud workload anomalies nearly instantly. The solution integrates Isolation Forest for processing high-dimensional metrics, LSTM Autoencoder models for multiple time-series streams, and One-Class SVM for validation. Experimental evaluation in a multi-tenant cloud environment ensemble voting demonstrates a detection accuracy of 94.1%. The average detection latency decreased from 18.2 to 3.1 minutes over six months of testing. The approach scales linearly up to sixteen worker nodes. Furthermore, the paper discusses challenges associated with false positives and drifts. This research includes suggestions for a federated continual learning strategy.},
        keywords = {Anomaly Detection, Cloud Computing, Machine Learning, Isolation Forest, LSTM Autoencoder, One-Class SVM.},
        month = {August},
        }