Publications

Abstract : The social internet of things (SIoT) facilitates numerous networking services and novel applications for the IoT, making it more productive and powerful. The state-of-the-art machine learning architectures often face different challenges when processing big data such as increased memory volume, increased training time, and higher computational costs. To tackle these challenges, this paper presents a novel framework for big data classification. Initially, an adaptive filter removes unwanted data and noises. The dimensionality of the filter data is then reduced via the Hadoop map reducer. After that, the battle royale optimization (BRO) algorithm is applied for feature selection. The optimal feature selection process performed by the BRO algorithm improves the accuracy and performance of the proposed fuzzy optimal deep convolutional neural network (FO-DCNN). The FO-DCNN model is formed by integrating the fuzzy-based remora optimization (F-RO) algorithm and deep CNN architecture for robust SIoT data classification. When evaluated using the Twitter, Rotten tomato, skin disease, diabetes, and hepatitis datasets, the proposed model offers improvements in terms of classification accuracy, memory consumption, and computational time.