Journal of Intelligent & Fuzzy Systems - Volume 46, issue 2

Authors: Lakshmana Kumar, R. | Jayanthi, S. | Muthu, BalaAnand | Sivaparthipan, C.B.

Article Type: Research Article

Abstract: The proliferation of mobile technology has given rise to a multitude of applications, among them those designed with malicious intent, aimed at compromising the integrity of mobile devices (MDs). To combat this issue, this study introduces an innovative anomaly application detection system leveraging Federated Learning in conjunction with a Hyperbolic Tangent Radial-Deep Belief Network (FL-HTR-DBN). This system operates through two distinct phases: training and testing. During the training phase, the system first extracts log files and transforms them into a structured format, harnessing the power of the Hadoop System. Subsequently, these structured logs are converted into vector representations using the …Updating Gate-BERT (UG-BERT) technique, thereby facilitating feature extraction. These features are then annotated utilizing the Symmetric Kullback Leibler Divergence squared Euclidean distance-based K Means (SKLD-SED K Means) algorithm. The FL-HTR-DBN model is subsequently trained using these labelled features. The detected anomalies are hashed and securely stored within an index tree, alongside their corresponding hashed Media Access Control (MAC) addresses. In the testing phase, log files are cross-referenced with the hashed index tree to identify potential anomalies. Notably, this novel approach outperforms many valuable outcomes in comparison with the existing approaches ConAnomaly, QLLog and LogCAD in terms of precision 97.5, recall 97.1, accuracy 95.9, F-measure 93.9, sensitivity 94.8 and specificity 95.9. Show more

Keywords: Updating gate –BERT (UG-BERT), symmetric kullback leibler divergence squared euclidean distance-based K means (SKLD-SED K Means), federated learning based hyperbolic tangent radial - deep belief network (FL-HTR-DBN)

DOI: 10.3233/JIFS-233361

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3245-3258, 2024

Authors: Yuvashri, Prakash | Saraswathi, Appasamy

Article Type: Research Article

Abstract: Every decision-making process particularly those involving real-life issues is disproportionately plagued by uncertainty. It is also unavoidable and obvious. Since its conception are several ways for representing uncertainty have been proposed by numerous academics to cope with uncertainty. Fuzzy sets and hierarchical such as picture fuzzy sets stand out among them as excellent representation techniques for modeling uncertainty. However, there are several significant drawbacks to the current uncertainty modeling techniques. Due to its vast versatility and benefits we here embrace the idea of the spherical fuzzy set, an extension of the picture fuzzy set. On the other hand amid uncertainty …in real life the multi-objective plays a critical role. In this research paper determining a Multi-Objective Linear Programming Problem of Spherical fuzzy sets serves to stimulate nous. The score function corresponding to the degree positive, negative and neutral is the foundation upon which the suggested approach is developed. Additionally we apply the suggested strategy to the solution of the multi-objective linear programming problem to demonstrate its superiority through certain numerical examples. Maximization or Minimizing of the cost is the primary goal of the multi-objective linear programming problem. Using an explicitly defined score function the suggested solution transformed the Spherical Fuzzy Multi-Objective Linear Programming Problem into a Crisp Multi-Objective Linear Programming Problem (CMOLPP). We establish some theorems to show that the efficient solution of CMOLPP is likewise an efficient solution of SFMOLPP. The CMOLPP is then further simplified into a single-objective Linear Programming Problem (LPP) thus we revamp the modified Zimmermann’s approach in the environment of a nonlinear membership function with the aid of the suggested technique. It is possible to simply solve this single-objective LPP using any software or standard LPP algorithm. The suggested approach achieves the fuzzy optimum result without altering the nature of the issue. An application of the suggested approach has been used to illustrate it and its results have been distinguished from those of other preexisting methods found in the literature. To determine the importance of the suggested technique which adjudicate thorough theorem and result analysis is conducted. Show more

Keywords: Crisp solution, spherical fuzzy number, spherical fuzzy multi-objective linear programming problem, spherical fuzzy optimal solution

DOI: 10.3233/JIFS-233441

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3259-3280, 2024

Authors: Zekrifa, Djabeur Mohamed Seifeddine | Lamani, Dharmanna | Chaitanya, Gogineni Krishna | Kanimozhi, K.V. | Saraswat, Akash | Sugumar, D. | Vetrithangam, D. | Koshariya, Ashok Kumar | Manjunath, Manthur Sreeramulu | Rajaram, A.

Article Type: Research Article

Abstract: Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the …Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future. Show more

Keywords: Autoencoder-generative adversarial network (AE-GAN)

DOI: 10.3233/JIFS-235582

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3281-3294, 2024

Authors: Sakthipriya, R. | Suja, K.

Article Type: Research Article

Abstract: The purpose of this article is to study the notion of statistical limit superior(SLS) and statistical limit inferior(SLI) in non-Archimedean(NA) L -fuzzy normed spaces( L -FNS). The concept of SLS and SLI is examined and extended to SLS and SLI in NA L -FNS. Moreover, the analogue of some results between SLS and SLI over NA L -FNS have been discussed. And also, it is proved that a bounded sequence is statistically convergent over NA L -FNS. Throughout this article, K …denotes a complete, non-trivially valued, non-Archimedean fields(NAF). Show more

Keywords: Statistical limit superior and statistical limit inferior, 𝔏-fuzzy normed space, non-Archimedean fields

DOI: 10.3233/JIFS-224359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3295-3306, 2024

Authors: Wu, Yuhang | Jiao, Xu | Hao, Qingbo | Xiao, Yingyuan | Zheng, Wenguang

Article Type: Research Article

Abstract: The next Point-of-Interest (POI) recommendation, in recent years, has attracted an extensive amount of attention from the academic community. RNN-based methods cannot establish effective long-term dependencies among the input sequences when capturing the user’s motion patterns, resulting in inadequate exploitation of user preferences. Besides, the majority of prior studies often neglect high-order neighborhood information in users’ check-in trajectory and their social relationships, yielding suboptimal recommendation efficacy. To address these issues, this paper proposes a novel Double-Layer Attention Network model, named DLAN. Firstly, DLAN incorporates a multi-head attention module that can combine first-order and high-order neighborhood information in user check-in trajectories, …thereby effectively and parallelly capturing both long- and short-term preferences of users and overcoming the problem that RNN-based methods cannot establish long-term dependencies between sequences. Secondly, this paper designs a user similarity weighting layer to measure the influence of other users on the target users leverage the social relationships among them. Finally, comprehensive experiments are conducted on user check-in data from two cities, New York (NYC) and Tokyo (TKY), and the results demonstrate that DLAN achieves a performance in Accuracy and Mean Reverse Rank enhancement by 8.07% -36.67% compared to the state-of-the-art method. Moreover, to investigate the effect of dimensionality and the number of heads of the multi-head attention mechanism on the performance of the DLAN model, we have done sufficient sensitivity experiments. Show more

Keywords: Point-of-interest recommendation, user preferences, attention network, social information

DOI: 10.3233/JIFS-232491

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3307-3321, 2024

Authors: Li, Aiguo | Feng, Rongrong

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-233967

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3323-3338, 2024

Authors: Ma, Qianxia | Zhu, Xiaomin | Bai, Kaiyuan | Pu, Qian | Zhang, Runtong

Article Type: Research Article

Abstract: Multi-attribute group decision-making (MAGDM) is one of the research hotspots in human cognitive and decision-making theory. However, there are still challenges to the existing MAGDM methods in modeling uncertain linguistics of decision-makers’ (DMs’) cognitive information and objectively obtaining weights. Therefore, this paper aims to develop a new MAGDM method considering incomplete known weight information under spherical uncertain linguistic sets (SULSs) to model uncertain information in MAGDM problems. The method mainly includes the following aspects. Firstly, a new concept, which enables an intuitive evaluation of neutral membership and hesitancy degrees at the linguistic evaluation, has been is first developed for capturing …the more uncertain information. Secondly, the cosine similarity measure (CSM) and cross-entropy measure (CEM) are widely used to measure ambiguous information because of their robustness of measurement results. The CSM and CEM are extended to SULSs to calculate the DMs’ and attributes weights quantitively, respectively. Thirdly, in terms of effective integration of fuzzy information to obtain more accurate decision results, the Hamy mean (HM) and dual Hamy mean (DHM) operators are valued due to their consideration of the interrelationships between inputs. Two extension operators, named spherical fuzzy uncertain linguistic weight HM and DHM, are proposed to integrate spherical fuzzy uncertain linguistic information in the third stage. In the experiment, a decision case is presented to illustrate the applicability of the proposed method, and results show the effectiveness, flexibility and advantages of the proposed method are demonstrated by numerical examples and comparative analysis. Show more

Keywords: Multi-attribute group decision-making, spherical uncertain linguistic set, Hamy mean, cosine similarity measure, cross-entropy measure

DOI: 10.3233/JIFS-235044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3339-3361, 2024

Authors: Songhua, Huan

Article Type: Research Article

Abstract: The development of an accurate electricity demand forecasting model is of paramount importance for promoting global energy efficiency and sustainability. Nonetheless, the presence of outliers and inappropriate model training can result in suboptimal performance. To tackle these challenges, this study explores the potential of Convolutional Neural Network (CNN) and active learning theory as forecasting solutions, offering high efficiency and advantages for long time series. In this study, a hybrid model that combines Isolation Forest (IF), Outlier Reconstruction (OR), CNN and Random Forest (RF) is conducted to mitigate computational complexity and enhance the accuracy of electricity demand forecasting in the presence …of outliers. IF is employed to detect outliers in electricity demand time series, while OR is used to reconstruct subsequences based on calendrical heterogeneity for training. CNN is applied for both training and forecasting, and the final output is combined using RF. The effectiveness of the proposed IF-OR-CNN-RF model is validated using electricity data collected from recent sources in Australia at different sampling frequency. The experimental results demonstrate that, in comparison with other popular CNN-based electricity demand forecasting models, IF-OR-CNN-RF model outperforms with significantly improved performance metrics. Specifically, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R-squared values are 77.92, 179.18 and 0.9769 in 5-minute frequency; 162.67, 353.96 and 0.9775 in 10-minute frequency; 841.27, 1374.79 and 0.9622 in 30-minute frequency; 2746.01, 3824.00 and 0.9262 in 60-minute frequency; 9106.08, 12269.04 and 0.8044 in 120-minute frequency. IF-OR-CNN-RF model represents a valuable framework for future electricity demand forecasting, particularly in scenarios involving outliers. Show more

Keywords: Outlier reconstruction, deep learning, electricity demand, forecasting model, calendrical heterogeneity

DOI: 10.3233/JIFS-235218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3363-3394, 2024

Authors: Sucharitha, G. | sankardass, Veeramalai | Rani, R. | Bhat, Nagaraj | Rajaram, A.

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-235744

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3395-3409, 2024

Authors: Sagar, Maloth | Vanmathi, C.

Article Type: Research Article

Abstract: Machine learning techniques commonly used for intrusion detection systems (IDSs face challenges due to inappropriate features and class imbalance. A novel IDS comprises four stages: Pre-processing, Feature Extraction, Feature Selection, and Detection. Initial pre-processing balances input data using an improved technique. Features (statistical, entropy, correlation, information gain) are extracted, and optimal ones selected using Improved chi-square. Intrusion detection is performed by a hybrid model combining Bi-GRU and CNN classifiers, with optimized weight parameters using SI-BMO. The outputs from both classifiers are averaged for the result. The SI-BMO-based IDS is compared with conventional techniques Blue Monkey Optimization (BMO), Grasshopper Optimization Algorithm …(GOA), Deer Hunting Optimization (DHO), Poor Rich Optimization (PRO), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) for performance evaluation. Show more

Keywords: Intrusion detection system, Improved class imbalance processing, bi-directional gated recurrent unit (Bi-GRU), convolutional neural network (CNN), self-improved blue monkey optimization (SI-BMO), cyber-physical systems (CPS)

DOI: 10.3233/JIFS-236400

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3411-3427, 2024