Air Quality Index Analysis Using Single-Valued Neutrosophic Plithogenic Graph for Multi-Decision Process

Prem Kumar

doi:https://doi.org/10.54216/IJNS.160103

Fusion: Practice and Applications | 2023

Benchmarking Machine Learning for Sentimental Analysis of Climate Change Tweets in Social Internet of Things

Climate change has become one of the most critical problems threatening our world, gaining increased attention in either academia or industry. Climate change has been demonstrated as the major barrier in the way of sustainable development strategy in the 2030 Agenda. Nowadays, the Social Internet of Things (SIoT) has paved new ways for public deliberations and has transformed the communication of global issues such as climate change. Thus, sentiment analysis of SIoT media streams can offer great help in improving the mitigation and adaptation to climate change. Machine learning (ML) is demonstrating great success in a wide range of SIoT applications. However, training ML algorithms for sentimental analysis of climate change is notoriously hard as it suffers from feature engineering issues, information squashing, unbalancing, and curse-of-dimensionality, which bounds their possible power for modeling social awareness of climate change. Besides, the absence of a standard benchmark with reasonable and dependable experimentations brings a practically intractable difficulty to the evaluation of the efficiency of new solutions. In this regard, this study introduces the first reasonable and reproducible benchmark devoted to evaluating the potential of ML algorithms in identifying users’ opinions about climate change. Moreover, a novel taxonomy is presented for categorizing the existing ML algorithms, exploring their optimal hyperparameter, and unifying their elementary settings. Inclusive experiments are then performed on real Twitter data with different families of ML algorithms. To promote further study, a detailed analysis is provided for the state of the field to uncover the open research challenges and promising future directions.

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Irina V. Pustokhina mail -

Denis A. Pustokhin mail

link https://doi.org/10.54216/FPA.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Machine learning for False Information Detection in Social Internet of Things

By capitalizing on object relationships and local navigability, the social internet of things (SIoT) is one of the burgeoning paradigms that could solve the technical challenges of conventional IoT. Because of this paradigm's capacity to combine conventional IoT with social media, it is possible to create smart objects and services with greater utility than those created using conventional IoT infrastructures. In recent years, scholars have become interested in SIoT, leading to a plethora of works examining various mechanisms for providing services and technologies within this context. In this vein, we present a comprehensive review of recent research covering important aspects of SIoT. In this research, we give a detailed justification for the function of several machine learning paradigms and provide a practical application of it to unexamined concerns relating to erroneous data and other social IoT. First, we give a classification of false news detection approaches and an analysis of these techniques. Second, the potential uses for detecting fake news are examined at length, including how it might be applied to the areas of fake profile detection, traffic management, bullying detection, etc . We also suggested a detailed review of the possibilities of machine learning algorithms for detecting bogus news and intervening in social networks. In our paper, we introduce categories of fake news detection methods providing a comparison between these methods. After that, the promising applications for false news detection are extensively discussed in terms of fake account detection, bot detection, bullying detection, and the security and privacy of SIoT. After all, A thorough discussion of the potential of machine learning approaches for fake news detection and interventions in SIoT networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks for aiding the readers and researchers in explaining the motive and role of the different machine learning paradigms to offer them a comprehensive realization of so far unexplored issues related to false information and other scenarios of SIoT networks.

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Mahmoud M. Ismail mail -

Nihal N. Mostafa mail -

Esmeralda Kazia mail -

Ibrahim Elhenawy mail

link https://doi.org/10.54216/FPA.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2022

An Optimization Model for Stock Market Direction Prediction

Stock market direction prediction becomes an essential task in the business sector. The inherent volatile behavior of stock markets worldwide makes the prediction process difficult. The improvement in the prediction accuracy of the stock market direction prediction helps to avoid the risks involved in the investment process. In this aspect, this study designs a swallow swarm optimization (SSO) with a fuzzy support vector machine (FSVM) model for stock market direction prediction. The proposed SSO-FSVM model encompasses preprocessing, feature extraction, FSVM, and SSO based parameter tuning. The usage of the SSO algorithm to fine-tune the parameters involved in the FSVM model helps to significantly improve the overall predictive performance. To validate the improved performance of the SSO-FSVM model, a wide range of experiments were carried out using two benchmark datasets. The experimental outcomes reported the betterment of the SSO-FSVM model over the recent approaches in terms of several evaluation metrics. 

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Mingzhong Liu mail -

N Metawa mail

link https://doi.org/10.54216/AJBOR.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2021

More on Single Valued Neutrosophic R-dynamic Vertex Coloring and R-dynamic Edge Coloring of graphs

The notion of neutrosophic sets facilitates the analysis of values that are unclear or indeterminate. In thispaper, we discuss the single valued neutrosophic R-dynamic vertex coloring of Cartesian product of SVNG’sand join of SVNG’s. Further we have described the concept of single valued neutrosophic R-dynamic edgecoloring and provided some examples and theorems.

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Aparna V mail -

Mohanapriya N mail -

broumi said mail

link https://doi.org/10.54216/IJNS.160102

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2020

Chaotic Butterfly Optimization with Optimal Multi-key Image Encryption Technique for Wireless Sensor Networks

Wireless sensor network (WSN) comprises a set of sensor nodes, mainly used for data collection and tracking process. The imaging sensors in WSN captures the images from the target environment, which needs to be securely transmitted to the base station (BS). Since data transmission in WSN takes place through wireless links, security is a major challenging issue involved in the design of WSN. Image encryption is a commonly available solution to securely transmit the images to destination without comprising security. Therefore, this study designs a novel Chaotic Butterfly Optimization with Optimal Multi-key Image Encryption (CBO-OMKIE) technique for WSN. The goal of the CBO-OMKIE technique is to securely encrypt the images in WSN. The proposed CBO-OMKIE technique involves the design of multi-key based image encryption technique to accomplish security in WSN. In addition, the CBO algorithm is applied to determine the optimal keys involved in the encryption process and it helps for improving the security level to a maximum extent. The performance validation of the CBO-OMKIE technique takes place using benchmark test images and the outcomes were examined under several aspects. The simulation outcome pointed out the enhanced security analysis of the CBO-OMKIE technique over the other techniques.

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Disheng Zheng mail -

Kai Liang mail

link https://doi.org/10.54216/JISIoT.010203

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2019

Intelligent Differential Evolution based Feature Selection with Deep Neural Network for Intrusion Detection in Wireless Sensor Networks

Wireless sensor network (WSN) is mainly utilized for data gathering and surveillance applications. As WSN is majorly deployed in harsh and hostile environments, security remains a critical issue which needs to be resolved. An intrusion detection system (IDS) is one of the proficient ways used to determine the presence of abnormal behaviors (i.e. intrusions) in the network. Earlier studies have focused on the design of machine learning (ML) and deep learning (ML) models to design IDS. With this motivation, this paper presents an intelligent differential evolution based feature selection with deep neural network (IDEFS-DNN) for intrusion detection in WSN. The proposed IDEFS-DNN model aims to select optimum set of features and classify the intrusions in the network. In addition, the IDEFS-DNN technique involves the design of IDEFS technique to choose a subset of optimum features. Moreover, the chosen features are fed into the DNN technique for classification purposes. The usage of IDEFS technique helps to reduce the complexity and increase the classifier outcome. In order to portray the improved performance of the IDEFS-DNN technique, wide ranging experiments take place on benchmark datasets and the results are inspected under varying aspects. The simulation results ensured the enhanced intrusion detection performance of the IDEFS-DNN technique over the other IDS models.

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Ibrahim M. EL-Hasnony mail

link https://doi.org/10.54216/JISIoT.000204

Volume & Issue

Vol. Volume 0 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2019

Dragonfly Algorithm with Gated Recurrent Unit for Cybersecurity in Social Networking

The advancements of information technologies and wireless networks have created open online communication channels. Inappropriately, trolls have abused the technologies to impose cyberattacks and threats. Automated cybersecurity solutions are essential to avoid the threats and security issues in social media. This paper presents an efficient dragonfly algorithm (DFA) with gated recurrent unit (GRU) for cybersecurity in social networking. The proposed DFA-GRU model aims to determine the social networking data into neural statements or insult (cyberbullying) statements. Besides, the DFA-GRU model primarily undergoes preprocessing to get rid of unwanted data and TF-IDF vectorizer is used. In addition, the GRU model is employed for the classification process in which the hyperparameters are optimally adjusted by the use of DFA, and thereby the overall classification results get improved. The performance validation of the DFA-GRU model is carried out using benchmark dataset and the results are examined under varying aspects. The experimental outcome highlighted the enhanced performance of the DFA-GRU model interms of distinct measures.

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Yutao Han mail -

Ibrahim M. EL-Hasnony mail -

Wenbo Cai mail

link https://doi.org/10.54216/JCIM.000107

Volume & Issue

Vol. Volume 0 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Hybrid Heuristic AI Technique for Enhancing Intrusion Detection Systems in IoT Environments

In the evolving landscape of the Internet of Things (IoT), effective intrusion detection is paramount for maintaining security and data integrity. This study introduces a hybrid heuristic technique utilizing artificial intelligence for enhancing intrusion detection systems (IDS) in IoT environments. By integrating various machine learning models, the research focuses on training, tuning, and validating a sequential neural network to predict intrusion occurrences based on extensive data analysis. The methodology involves modelling, which starts with training machine learning algorithms to predict labels from features, tuning the models to meet organizational requirements, and validating them using holdout data. Key machine learning techniques explored include logistic regression, k-nearest neighbors (KNN), naive Bayes, support vector machines (SVM), decision trees, random forests, and neural networks. Each technique's applicability to classification tasks, particularly binary and multivariate scenarios, is discussed in the context of enhancing IDS capabilities. A sequential neural network model, comprising multiple dense and dropout layers, was developed and trained with 148,033 parameters to achieve high accuracy and robustness. The architecture's effectiveness in learning intricate patterns associated with malicious activities while avoiding overfitting is emphasized. The study demonstrates the model's proficiency in binary classification tasks, which is critical for distinguishing between normal and anomalous behaviors in IoT systems. The results indicate that the neural network, optimized using the hybrid heuristic approach, shows a significant reduction in validation loss and a steady improvement in accuracy over multiple epochs. Despite initial overfitting signs, the model maintains high performance on unseen data, underscoring the importance of ongoing model assessment and tuning.

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Yousra Abdul Alsahib S. Aldeen mail -

Fadhel K. Jabor mail -

Ghufran A. Omran mail -

Mohammed Hamid Kassem mail -

Raghad Hamid Kassem mail -

Ali Naseer Abood mail

link https://doi.org/10.54216/JISIoT.140101

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Digital Automatic of Clothing Design Cad Based on Intelligent Sensing Technology

Clothing design plays an important role in personal image expression and social and cultural transmission. The traditional fashion design method has many problems, such as low efficiency and large design error, and it is difficult to bring users better wearing experience. In order to meet different users’ Design needs, reduce design errors, and improve users’ satisfaction with design results, this paper combined with intelligent sensing technology, conducted in-depth research on digital automation analysis of clothing design CAD (Computer Aided Design). Aiming at the clothing design process, this paper first constructed a brand-new clothing design CAD system, using the depth transducer to solve the 3D information of the relevant feature points, and realized the accurate acquisition of the human body feature size information. Through the registration of adjacent frame point data, the 3D human body modeling was carried out. Then, according to the user’s physical characteristics and related information collected by the sensor, the paper compared the user’s characteristic information to filter out the user’s preferences, and used the recommendation algorithm to calculate the corresponding parameters to realize the intelligent choice of clothing styles. Finally, through the measurement of each index by the sensor, the size adjustment of the garment and the specific design of the garment were realized. In order to verify the effect of clothing design CAD system based on intelligent sensing technology, this paper conducted system tests. The results showed that in terms of clothing comfort, clothing quality and clothing functionality, the number of users satisfied and very satisfied reached 50.4%, 47.9% and 51.3%, respectively. From the overall survey results, the system has a high degree of user satisfaction. The research conclusion of this paper shows that the digital automatic analysis of clothing design CAD based on intelligent sensing technology can effectively meet the needs of users, improve their wearing experience, and promote the intelligent development of clothing design.

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Hai Liu mail -

Lei Hu mail

link https://doi.org/10.54216/JISIoT.140102

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2021

Air Quality Index Analysis Using Single-Valued Neutrosophic Plithogenic Graph for Multi-Decision Process

Recently, Plithogenic set and its graphical structure visualization is stuide for dealing with multi-attribute data set. In this pocess a problem arises while characterization of uncertainty in tue, false and uncertain regions, independently. One of the suitable examples is Air Quality Index and its impact on human health contains multi-valued attributes. In this case the conflict may arise among two experts about acceptation, rejection and uncertain impact of AQI for human health. To resolve this issue a single-valued neutrosophic Plithogenic set and its graphical strcutue visualization is discussed in this paper with an illustrative example.

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Prem Kumar Singh mail

link https://doi.org/10.54216/IJNS.160103

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new