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اولین همایش بین المللی هوش مصنوعی
Hybrid ANN and Ant Colony Algorithm for IoT Data Classification
نویسندگان :
Khadejeh Nemati
1
Safouro Ashoori
2
Moohamad hadi Amini
3
1- دانشکده ملی مهارت میرزا کوچک صومعه سرا
2- دانشکده ملی مهارت میرزا کوچک صومعه سرا
3- دانشکده ملی مهارت میرزا کوچک صومعه سرا
کلمات کلیدی :
ant colony optimization algorithm،artificial neural network،artificial neural network،data classification
چکیده :
In this study, a hybrid approach based on the Ant Colony Optimization (ACO) algorithm and a Multilayer Perceptron (MLP) neural network is proposed for classifying Internet of Things (IoT) data. Managing and analyzing the large volume of data in IoT networks with multiple sensors presents a significant challenge. This research employs the ACO algorithm to identify and select the most relevant sensors and features from the collected data. By performing a parallel search through the feature space, ACO selects the best feature combinations from the vast amount of data, resulting in more accurate classification. These optimized features are then used to train the MLP neural network. The combination of ACO and MLP improves both classification accuracy and the learning speed of the model. ACO enhances model performance by efficiently optimizing the features and avoiding local optima. Meanwhile, the MLP’s multilayer structure strengthens its ability to learn complex patterns and recognize non-linear relationships in IoT data. Experimental results show that ACO alone achieves an accuracy of 0.8 and an F1-Score of 0.839, while the combination with MLP increases the accuracy to 0.81 and the F1-Score to 0.849. This improvement indicates that integrating ACO with MLP enhances classification precision and strengthens the model’s ability to learn complex patterns. The hybrid approach also reduces processing time and adapts better to dynamic, large-scale IoT data, demonstrating that combining swarm intelligence methods like ACO with neural networks provides an effective solution for IoT data processing and classification.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.5.4