Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Prabu, Saranya | Padmanabhan, Jayashree

Article Type: Research Article

Abstract: Software-Defined Networking (SDN) is a strategy that leads the network via software by separating its control plane from the underlying forwarding plane. In support of a global digital network, multi-domain SDN architecture emerges as a viable solution. However, the complex and ever-evolving nature of network threats in a multi-domain environment presents a significant security challenge for controllers in detecting abnormalities. Moreover, multi-domain anomaly detection poses a daunting problem due to the need to process vast amounts of data from diverse domains. Deep learning models have gained popularity for extracting high-level feature representations from massive datasets. In this work, a novel …deep neural network architecture, supervised learning based LD-BiHGA (Low Dimensional Bi-channel Hybrid GAN Attention) system is designed to learn class-specific features for accurate anomaly detection. Two asymmetric GANs are employed for learning the normal and abnormal network flows separately. Then, to extract more relevant features, a bi-channel attention mechanism is added. This is the first study to introduce an innovative hybrid architecture that merges bi-channel hybrid GANs with attention models for the purpose of anomaly detection in a multi-domain SDN environment that effectively handles real-time unbalanced data. The suggested architecture demonstrates its effectiveness on three benchmark datasets, achieving an average accuracy improvement of 7.225% on balanced datasets and 3.335% on imbalanced datasets compared to previous intrusion detection system (IDS) architectures in the literature. Show more

Keywords: Hybrid GAN, intrusion detection, deep learning, attention model, dimensionality reduction, denoising autoencoder

DOI: 10.3233/JIFS-233668

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 457-478, 2024

Authors: Ren, Jianji | Yang, Donghao | Yuan, Yongliang | Liu, Haiqing | Hao, Bin | Zhang, Longlie

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-233990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 479-492, 2024

Authors: Yuan, Hao | Yang, Hao | Li, Ruiqi | Wang, Jun | Tian, Lin

Article Type: Research Article

Abstract: For the purpose of real-time monitoring the hazard information on the electric power construction site, a personal safety monitoring system based on Artificial intelligence internet of things (AIoT) technology is designed. After the system sensing layer collects the gas information of the construction site through the gas sensor, limit current oxygen sensor and DS1820B temperature sensor, the edge computing device of the edge layer directly stores its calculation in the database of the platform layer through the data gateway. The Artificial Intelligence (AI) analysis module of this layer invokes the monitoring data of the power construction site of the database, …and uses the personal safety identification method of the power construction site based on artificial intelligence technology, to complete the abnormal identification of monitoring data and realize personal safety monitoring. In addition, the system is also equipped with a power-fail detection module, which can collect the working voltage through the voltage transformer and compare it with the mains power standard to judge whether there is a power-fail risk, so as to prevent the problem of threatening personal safety due to the power-fail of the energized equipment. After testing, the system can monitor the operation status of the construction site in real time to protect personal safety. Show more

Keywords: AIoT technology, power construction, operation site, personal safety, monitoring system

DOI: 10.3233/JIFS-235087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 493-504, 2024

Authors: Rahim, Muhammad | Amin, Fazli | Tag Eldin, ElSayed M. | Abd El-Wahed Khalifa, Hamiden | Ahmad, Sadique

Article Type: Research Article

Abstract: The selection of an appropriate third-party logistics (3PL) provider has become an inescapable option for shippers in today’s business landscape, as the outsourcing of logistics activities continues to increase. Choosing the 3PL supplier that best meets their requirements is one of the most difficult difficulties that logistics consumers face. Effective decision-making (DM) is critical in dealing with such scenarios, allowing shippers to make well-informed decisions within a restricted timeframe. The importance of DM arises from the possible financial repercussions of poor decisions, which can result in significant financial losses. In this regard, we introduce p, q-spherical fuzzy set (p, q …-SFS), a novel concept that extends the concept of T-spherical fuzzy sets (T-SFSs). p, q- SFS is a comprehensive representation tool for capturing imprecise information. The main contribution of this article is to define the basic operations and a series of averaging and geometric AOs under p, q -spherical fuzzy (p, q -SF) environment. In addition, we establish several fundamental properties of the proposed aggregation operators (AOs). Based on these AOs, we propose a stepwise algorithm for multi-criteria DM (MCDM) problems. Finally, a real-life case study involving the selection of a 3PL provider is shown to validate the applicability of the proposed approach. Show more

Keywords: T-spherical fuzzy set, aggregation operators, decision-making, p, q-spherical fuzzy set, multi-criteria decision-making

DOI: 10.3233/JIFS-235297

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 505-528, 2024

Authors: Sánchez-DelaCruz, Eddy | Abdul-Kareem, Sameem | Pozos-Parra, Pilar

Article Type: Research Article

Abstract: Background: Many neurodegenerative diseases affect human gait. Gait analysis is an example of a non-invasive manner to diagnose these diseases. Nevertheless, gait analysis is difficult to do because patients with different neurodegenerative diseases may have similar human gaits. Machine learning algorithms may improve the correct identification of these pathologies. However, the problem with many classification algorithms is a lack of transparency and interpretability for the final user. Methods: In this study, we implemented the PS -Merge operator for the classification, employing gait biomarkers of a public dataset. Results: The highest classification percentage was 83.77%, which means …an acceptable degree of reliability. Conclusions: Our results show that PS -Merge has the ability to explain how the algorithm chooses an option, i.e., the operator can be seen as a first step to obtaining an eXplainable Artificial Intelligence (XAI). Show more

Keywords: PS-Merge, Classification, Neurodegenerative diseases, XAI

DOI: 10.3233/JIFS-235053

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 529-541, 2024

Authors: Vidya, S. | Jagannathan, Veeraraghavan | Guhan, T. | Kumar, Jogendra

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-235798

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 543-561, 2024

Authors: He, Jialin

Article Type: Research Article

Abstract: With the rapid development of information technology, software products are playing an increasingly important role in people’s production and life, and have penetrated into many industries. Software quality is the degree to which the software meets the specified requirements, and is an important indicator to evaluate the quality of the products used. At present, the scale of software is increasing, and the complexity is increasing. It is an urgent problem to reasonably grasp and ensure the product quality. The measurement and evaluation of Software quality characteristics is an effective means to improve Software quality. Faced with the complex system of …software, there are many factors that affect product quality. Current research mainly measures software product quality from a qualitative perspective. The computer software quality evaluation is a classical multi-attribute group decision making (MAGDM). Type-2 Neutrosophic Numbers (T2NNs) is a popular set in the field of MAGDM and many scholars have expanded the traditional MAGDM to this T2NNs in recent years. In this paper, two new similarity measures based on sine function for T2NN is proposed under T2NNs. These two new methods are built for MAGDM based on the sine similarity measures for T2NN (SST) and sine similarity weighted measures for T2NN (SSWT). At the end of this paper, Finally, a practical case study for computer software quality evaluation is constructed to validate the proposed method and some comparative studies are constructed to verify the applicability. Thus, the main research contribution of this work is constructed: (1) two new similarity measures based on sine function for T2NN is proposed under T2NNs; (2) These two new methods are built for MAGDM based on the sine similarity measures for T2NN (SST) and sine similarity weighted measures for T2NN (SSWT); (3) an example for computer software quality evaluation is employed to verify the constructed techniques and several decision comparative analysis are employed to verify the constructed techniques. Show more

Keywords: Multi-attribute decision making (MAGDM), Type-2 neutrosophic numbers (T2NNs), similarity measure, sine function, computer software quality evaluation

DOI: 10.3233/JIFS-233407

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 563-578, 2024

Authors: Sharma, Shamneesh | Mishra, Nidhi

Article Type: Research Article

Abstract: The expeditious advancement and widespread implementation of intelligent urban infrastructure have yielded manifold advantages, albeit concurrently engendering novel security predicaments. Examining current patterns in the security of smart cities is paramount in comprehending nascent risks and formulating efficacious preventative measures. The present study suggests the utilization of Latent Semantic Analysis (LSA) as a means to scrutinize and reveal implicit semantic associations within a collection of textual materials pertaining to the security of smart cities. Through the process of gathering and pre-processing pertinent textual data, constructing a matrix that represents the frequency of terms within documents, and utilizing techniques that reduce …the number of dimensions, Latent Semantic Analysis (LSA) has the ability to uncover concealed patterns and associations among concepts related to security. This study proposes five recommendations for future research that employ a topic modeling technique to investigate the often-explored subjects related to smart city security. This discovery provides additional evidence in favor of the proposition that a robust blockchain-driven framework is vital for the advancement of smart cities. Latent Semantic Analysis (LSA) offers important insights into the dynamic landscape of smart city security by employing several techniques such as pattern recognition, document or phrase clustering, and result visualization. Through the examination of patterns and developments, individuals in positions of political authority, urban planning, and security knowledge possess the ability to uphold their proficiency, render judicious choices substantiated by empirical data, and establish proactive strategies aimed at preserving the security, privacy, and sustainability of intelligent urban environments. Show more

Keywords: Smart cities, security in smart cities, Latent Semantic Analysis (LSA), trends in smart cities, natural language processing

DOI: 10.3233/JIFS-235210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 579-596, 2024

Authors: Aruna Jasmine, J. | Heltin Genitha, C.

Article Type: Research Article

Abstract: Predicting the landslide-prone area is critical for various applications, including emergency response, land planning, and disaster mitigation. There needs to be a thorough landslide inventory in current studies and appropriate sampling uncertainty issues. Landslide risk mapping has expanded significantly as machine learning techniques have developed. However, one of the primary issues in Landslide Prediction is data imbalance (DI). This is problematic since it is challenging or expensive to generate an accurate inventory map of landslides based on previous data. This study proposes a novel landslide prediction method using Generative Adversarial Networks (GAN) for generating the synthetic data, Synthetic Minority Oversampling …Technique (SMOTE) for overcoming the data imbalance problem, and Bee Collecting Pollen Algorithm (BCPA) for feature extraction. Combining 184 landslides and ten criteria, including topographic wetness index (TWI), aspect, distance from the road, total curvature, sediment transport index (STI), height, slope, stream, lithology, and slope length, a geographical database was produced. The data was generated using GAN, a Deep Convolutional Neural Network (DCNN) technique to populate the dataset. The proposed DCNN-BCPA approach findings were merged with current machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM), logistic regression (LR). The model’s accuracy, precision, recall, f-score, and RMSE were measured using the following metrics: 92.675%, 96.298%, 90.536%, 96.637%, and 45.623%. This study suggests that harmonizing landslide data may have a substantial impact on the predictive capabilities of machine learning models. Show more

Keywords: Bee collecting pollen algorithm, data balancing, generative adversarial network, landslide susceptibility, synthetic data

DOI: 10.3233/JIFS-234924

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 597-617, 2024

Authors: Choe, Kwang-Il | Huang, Xiaoxia | Ma, Di

Article Type: Research Article

Abstract: To achieve the carbon neutrality goal, enterprises should consider not only the development of new low-carbon emission projects but also the adjustment of the existing high-carbon emission projects. This paper discusses a multi-period project adjustment and selection (MPPAS) problem under the carbon tax and carbon quota policies. First, we propose an uncertain mean-chance MPPAS model for maximizing the profit of the project portfolio under the carbon tax and carbon quota policies. Then, we provide the deterministic equivalent of the proposed model and conduct the theoretical analysis of the impact of carbon tax and carbon quota policies. Next, we propose an …improved adaptive genetic algorithm to solve the proposed model. Finally, we give numerical experiments to verify the proposed algorithm’s performance and show the proposed model’s applicability. Research has shown that the government can achieve the carbon neutrality goal by determining reasonable carbon tax and carbon quota policies, and companies can make the optimal investment decisions for the project portfolio by the proposed model. In addition, the proposed algorithm has good performances in robustness, convergence speed, and global convergence. Show more

Keywords: Project portfolio, uncertainty theory, carbon emission reduction, adaptive genetic algorithm

DOI: 10.3233/JIFS-231970

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 619-637, 2024