首页|New Machine Learning Study Findings Have Been Reported from Department of Comput er Sciences (Anomaly Detection Framework for Iot-enabled Appliances Using Machin e Learning)
New Machine Learning Study Findings Have Been Reported from Department of Comput er Sciences (Anomaly Detection Framework for Iot-enabled Appliances Using Machin e Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting out of Chandigarh, India, by NewsRx edit ors, research stated, "Addressing the contemporary complexity inherent in anomal y detection within heterogeneous systems is paramount. This paper presents a nov el methodology tailored to pinpoint appliance anomalies within the framework of Internet of Things (IoT) technology." Our news journalists obtained a quote from the research from the Department of C omputer Sciences, "By amalgamating the capabilities of IoT and Machine Learning (ML), this approach not only heightens the precision and dependability of anomal y detection but also serves as a practical solution for industrial applications. To better align with the current industrial landscape, we emphasize the practic al implications of our work. Our methodology is designed to cater specifically t o industrial needs, offering a solution that can be seamlessly integrated into e xisting systems, thereby enhancing operational efficiency and reliability. The c ore of our approach lies in employing a hybrid method, utilizing both the Facebo ok Prophet and Isolation Forest ML algorithms for robust and intelligent anomaly detection. This duallayered strategy, integrating forecasting and classificati on objectives, ensures a comprehensive approach to anomaly detection tailored fo r industrial settings. Evaluation of our methodology involves rigorous testing a gainst real-time and emulated datasets, as well as comparison with existing meth ods. MSE has also been calculated using DT, RF, SVM, NB and Logistic supervised regressor. The Facebook Prophet model's accuracy, assessed using Root Mean Squar ed Error (RMSE), demonstrates its proficiency in forecasting values closely alig ned with reference data points. Meanwhile, the IForest Unsupervised ML model exc els in identifying anomalies, achieving high accuracy rates across various conta mination levels. Through meticulous cross-validation, our proposed method exhibi ts significant accuracy, with rates of 93.60% and 95.72% on real-time and emulated datasets, respectively. The hybrid model (Fbprophet + iforest) has an average accuracy of 96.35% on (real-time + emulate d). These results underscore the efficacy and reliability of our approach in ind ustrial anomaly detection scenarios."
ChandigarhIndiaAsiaCyborgsEmergi ng TechnologiesMachine LearningDepartment of Computer Sciences
NETL
NSTL
