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Dahou, A.; Chelloug, S.A.; Alduailij, M.; Elaziz, M.A. Improved Feature Selection for Social Internet of Things. Encyclopedia. Available online: (accessed on 16 April 2024).
Dahou A, Chelloug SA, Alduailij M, Elaziz MA. Improved Feature Selection for Social Internet of Things. Encyclopedia. Available at: Accessed April 16, 2024.
Dahou, Abdelghani, Samia Allaoua Chelloug, Mai Alduailij, Mohamed Abd Elaziz. "Improved Feature Selection for Social Internet of Things" Encyclopedia, (accessed April 16, 2024).
Dahou, A., Chelloug, S.A., Alduailij, M., & Elaziz, M.A. (2024, March 20). Improved Feature Selection for Social Internet of Things. In Encyclopedia.
Dahou, Abdelghani, et al. "Improved Feature Selection for Social Internet of Things." Encyclopedia. Web. 20 March, 2024.
Improved Feature Selection for Social Internet of Things

The Social Internet of Things (SIoT) ecosystem tends to process and analyze extensive data generated by users from both social networks and Internet of Things (IoT) systems and derives knowledge and diagnoses from all connected objects. To overcome many challenges in the SIoT system, such as big data management, analysis, and reporting, robust algorithms should be proposed and validated. 

Social Internet of Things (SIoT) Deep Learning (DL) Chaos Game Optimization (CGO) feature selection

1. Introduction

The IoT concept was proposed by Kevin Ashton in 1999, and there are many sectors in developed and developing countries that have investigated IoT-based projects [1,2,3,4]. Thus, an IoT application relies on uniquely identifiable objects with sensing, connectivity, and interoperation capabilities [5]. It is worth mentioning that a certain number of IoT-based standards have been developed, while there are still some research challenges for designing IoT middleware and providing security [6]. Then, the SIoT was proposed as an extension of IoT technology. The idea of the SIoT consists of establishing social relationships between IoT devices. Moreover, the SIoT aims to provide decentralized intelligence by allowing IoT objects to become social and smart. Along with decentralizing the intelligence, an SIoT object can request support from its social IoT objects to complete a specific request. More importantly, the Quality of Experience (QoE) is high in the SIoT, which may also lead to creating a business model by monetizing information and connectivity sharing [7]. Upon receiving the client’s request, the SIoT object checks if it can handle the received request. Elsewhere, the request will be forwarded to its friends. Therefore, selecting social friends is a crucial step that impacts the reliability of the SIoT application [8]. As stated in [7,9], five important social relationships can be established between SIoT objects, as summarized in Table 1.
Table 1. SIoT relationships.
There needs to be standard architecture for the SIoT. Nevertheless, there are four available SIoT architectures: device, global connection, platform, and application layers [10]. As reported in [11], the SIoT has been successfully applied to a smart home for home safety and energy efficiency. One of the big challenges of the SIoT is related to identifying and communicating relevant data, given that the SIoT data may be structured/instructed [9]. Additionally, various types of SIoT data, including audio, video, and text, can be accessed and communicated in an SIoT network. In this regard, the authors of [12] developed a realistic SIoT dataset extracted from a smart city scenario. The considered dataset allows the incorporation of static and mobile devices. Besides, the data model allows the creation of a profile for each object to define the potential set of offered services and applications that can be deployed. More specifically, an analysis of the impact of each social relationship on network navigability was presented in [12]. The topic of the data analysis of the SIoT has been addressed in many recent papers [13,14].
Lakshmanaprabu et al. [15] introduced a framework for effectively classifying SIoT data. The developed framework was based on map-reduce and a supervised classifier model. In particular, the SIoT was investigated for analyzing the trajectories of many users. Therefore, a recommendation system was developed in [15,16] for service discovery using the knowledge–desire–intention (KDI) model. Another topic that has attracted researchers concerns sentiment analysis in the SIoT. Notably, three levels of sentiment analysis exist, and they embrace the document level, sentence level, and aspect level. The first level categorizes sentiments from the entire document, while the second predicts the sentiment popularities expressed in each sentence. The aspect level is more efficient than the first and the second levels, as it classifies sentiments expressed in opinions [17]. Despite the amount of SIoT data, the multimodality and accuracy of sentiment analysis are the main challenges.

2. Improved Feature Selection Based on Chaos Game Optimization for Social Internet of Things with a Novel Deep Learning Model

The authors of [24] presented a framework for mining Twitter and analyzing users’ perceptions of the SIoT. The proposed framework allows to obtain a Twitter feed. The data cleaning and pre-processing detects slang, applies lemmatization, and removes stop words. After that, extensive sentiment analysis was conducted based on an Improved Popularity Classifier (IPC), SentiWordNet (SWNC), Fragment Vector Model (FVM), and hybrid classifier that combines the IPC and SWNC. The experimental results discussed in [24] demonstrated that the FVM, which is a semi-supervised algorithm, achieved the best accuracy of 94.88%. The approach presented in [24] is simple to apply. However, it has yet to be compared to other benchmark techniques. Further, it is limited to unimodal text.
The work presented in [25] targeted the classification of sentiments in Twitter real-time data, where multiple-sentence tweets and multi-tweet threads were considered. Thus, Reference [25] explored a Hierarchical Attention Network (HAN) that was developed based on a Recurrent Neural Network (RNN) composed of GRU/LSTMs and attention mechanisms. In particular, the main motivation for the approach described in [25] consists of analyzing sentiments in real-time Twitter data, including multiple sentences, as well as multiple tweets. Moreover, the HAN allows one to read a full sentence, and then, the attention mechanism selects the most-significant words. Next, the HAN outputs a sentence that incorporates the semantic content of the input sentence. Additionally, the HAN includes a sentence hierarchy process for creating document embedding. Two English tweet datasets, including the Standard Twitter sentiment Gold standard (STC-Gold) and the SemEval-2017 datasets, were used for evaluating the proposed HAN, which achieved an accuracy of 71.7% and 94.6%, respectively. The evaluation of the results of the approach introduced in [25] is limited to two datasets. Additionally, the authors did not exploit multimodal text.
So far, the problem of multimodal sentiment analysis has been studied in many research papers. The model explained in [26] integrated interactive Transformer and Softmax mapping. The former can detect the current interactive information between modalities, while the latter projects each modality in a new space for further fusion. The Multimodal Opinion Sentiment and Emotion Intensity (CMU-Mosei) (, accessed on 2 January 2020) and Multimodal EmotionLines (Meld) (, accessed on 2 January 2020) datasets were selected for testing the proposed approach, which demonstrated good results compared to the benchmark techniques. In particular, the best accuracy achieved by the proposed approach was 82.47% for binary classification.
The contribution presented in [27] considered two levels of multimodal fusion for sentiment analysis. The first level combines text with audio and combines text with video features. The Softmax fusion was applied to combine the prediction results. The Multimodal Corpus of Sentiment Intensity (CMU-Mosi), CMU-Mosei, and Interactive Emotional Dyadic Motion Capture (Iemocap) (, accessed on 2 January 2020) datasets were evaluated, and the proposed approach outperformed the benchmark techniques for binary and multi-classification, where the best-achieved accuracy attained a value of 97.86%. The effectiveness of the approach presented in [27] is mainly related to fusion at the data and decision levels.
The framework published in [28] allows a dynamic fusion of various modalities for sentiment analysis. Besides, the authors of [28] suggested and validated a new loss function that supported finding the suitable target sub-space. Considering the CMU-Mosi and CMU-Mosei datasets, the approach described in [28] achieved the best accuracy among the benchmark techniques for the two evaluated datasets, and the best accuracy attained a value of 87.5%. Notably, the framework designed in [28] performs the fusion of audio, visual, and language data. Unfortunately, the validation of the results was limited to two datasets.
The idea presented in [29] focused on human multimodal language based on a network that extracts multimodal sequence features. Thus, the model proposed in [29] considers language, vision, and acoustics. More specifically, the Gated Recurrent Unit (GRU) network [30] was explored to generate internal modal information. Then, the Softmax function was used to calculate the correlation between two timestamps. Finally, the ReLU function and Sigmoid layer were used for sentiment analysis. The proposed method was validated using the CMU-Mosei dataset, where the proposed approach demonstrated the best F1-score for binary sentiment classification. It achieved good results for six label classifications for emotion classification.
The model’s objective presented in [31] is to handle the problem of the dynamic weights of multimodal data. To this end, a Bidirectional Encoder Representation Transformer (BERT) [32] and a Transformer encoder [33] were adopted. Hence, the CMU-Mosei and CMU-Mosi datasets were used, and the results discussed in [32] were evaluated in terms of the mean absolute error, Pearson correlation, and accuracy. It is worth mentioning that the approach proposed in [31] provided the best results for all performance metrics for the two datasets. It is worth mentioning that the framework presented in [31] is based on different encoding techniques for dealing with multimodal data. Hence, BERT was adopted to provide lexical embedding, while the Transformer’s encoder was proven to be effectivefor visual and acoustic data. Another advantage of the framework described in [31] is that it was tested for aligned and non-aligned data.
The authors of [34] proposed an Integrating Consistency and Difference Network (ICDN) that relies on mapping transfer between different modalities. The mapping transfer was also investigated to extract multimodal features. The CMU-Mosi and CMU-Mosei datasets were explored to validate the proposed approach for multi-classification and regression tasks. More specifically, the approach presented in [34] attained the best results regarding the accuracy, F1-score, mean absolute error, and correlation compared to the baseline techniques. The best-achieved accuracy for binary and multi-classification was 83.8% and 52.0%, respectively. The major advantage of the ICDN over related works concerns the reduction of interference between irrelevant modalities. The model presented in [35] can support inter- and intra-modality dynamics. Further, the asymmetric window is used to represent the asymmetric weights of context. The approach presented in [35] was tested on the CMU-Mosi dataset, and it achieved the best accuracy and the best F1-score of 80% and 79.9%, respectively. The model introduced in [35] is limited to analyzing sentiments in user-generated videos.
The authors of [36] recently developed a self-attention fusion framework that considers text, audio, and visual features. Hence, the proposed framework allows the detection of internal and external features’ correlation. It is built based on an attention network, which takes the three stated features and outputs the attention scores to indicate the importance of each feature. More specifically, the self-attention framework is hierarchical and based on a read–write mechanism to capture the correlation of different modalities. The experimental results shown in [36] were conducted using the CMU-Mosi dataset and showed the effectiveness of the self-attention mechanism for increasing the accuracy compared to the benchmark techniques.
With the high-quality results obtained using the previously discussed method, they still had some limitations with respect to their quality. For example, the ability to balance between global and local search still requires more improvements. Since this will influence the quality of the selected features that will reflect the classification accuracy, this motivated to propose an alternative FS method based on the integration between the CGO and TransCNN as a DL model.
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