Mendeley
TY  _  JOUR
ID  -  2024080647570
TI  -  Enhancing IoT Device Behavior Prediction through Machine Learning Models
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Minhass Shubham
AU  -  Chauhan Ritu
AU  -  Kaur Harleen
AD  -  Artificial Intelligence and IoT Lab, Center for Computational Biology and Bioinformatics, Amity university, Noida, Uttar Pradesh, India.
AD  -  Jamia Hamdard, department of Computer Science
AD  -  Jamia Hamdard, department of Computer Science
AD  -  Amity Institute of Information Technology, Amity university, Noida, UP
Y1  -  2025
PY  -  2025
VL  -  49
IS  -  13
SP  -  63
EP  -  76
KW  -  Machine learning
KW  -   predictive model
KW  -   Smart Devices
KW  -   Google Home Mini
KW  -   Alexa
KW  -   IoT
KW  -   KNN
DO  -  
N2  -  <p>There is an urgent need for precise and trustworthy models to forecast device behavior and evaluate vulnerabilities as a result of the Internet of Things' (IoT) explosive growth. By assessing the effectiveness of several machine learning algorithms logistic regression, decision trees, random forests, Na&iuml;ve Bayes, and KNN on two popular IoT devices Alexa and Google Home Mini this study seeks to enhance IoT device behavior forecasting. Our results show that Na&iuml;ve Bayes and random forest models are more accurate and efficient than other algorithms at predicting device behavior. These findings demonstrate how important algorithm selection is for maximizing the performance of IoT systems. The study also emphasizes the usefulness of precise device behavior prediction for practical uses such as industrial control systems, home automation, and medical monitoring. For example, accurate forecasts can improve decision-making in crucial situations, facilitate more seamless automation, and stop system failures. In addition to adding to the expanding corpus of research on IoT data analysis, this study establishes the foundation for the creation of increasingly sophisticated machine learning models that can manage the intricate and ever-changing nature of IoT ecosystems. Future studies should concentrate on increasing the dataset's diversity to encompass a wider range of IoT environments and devices and enhancing the model's adaptability to changing IoT environments.</p>
UR  - rimag.ir/fa/Article/47570
L1  - rimag.ir/fa/Article/Download/47570
ER  -  
