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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Wang, Qi | Lu, TongWei
Article Type: Research Article
Abstract: Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of …images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack. Show more
Keywords: Convolutional neural network, image splicing forgery detection, non-natural regions
DOI: 10.3233/JIFS-232025
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7447-7459, 2024
Authors: Xu, Zhedong | Su, Yongbo | Guo, Fei
Article Type: Research Article
Abstract: In the process of digital transformation and development in various industries, there are more and more large-scale optimization problems. Currently, swarm intelligence optimization algorithms are the best method to solve such problems. However, previous experimental research has found that there is still room for improvement in the performance of using existing swarm intelligence optimization algorithms to solve such problems. To obtain the high-precision optimal value of whale optimization algorithm (WOA) for solving large-scale optimization problems, the optimization problem knowledge model is studied to guide the iterative process of WOA algorithm, and a novel whale optimization algorithm based on knowledge model …guidance (KMGWOA) is proposed. First, a population update strategy based on multiple elite individuals is proposed to reduce the impact of the local optimal values, and the knowledge model to guide population update is constructed by combining the proposed population update strategy with the population update strategy based on global optimal individual. Second, a collaborative reverse learning knowledge model with multiple elite and poor individuals in the solution space is proposed to prevent long-term non-ideal region search. The above two knowledge models guide the iterative process of WOA algorithm in solving large-scale optimization problems. The performance of the KMGWOA algorithm guided by the proposed knowledge models is tested through the well-known classical test functions. The results demonstrate that the proposed KMGWOA algorithm not only has good search ability for the theoretical optimal value, but also achieves higher accuracy in obtaining the optimal value when it is difficult to obtain the theoretical optimal value. Moreover, KMGWOA algorithm has fast convergence speed and high effective iteration percentage. Show more
Keywords: Knowledge model, whale optimization algorithm, large-scale problem, population update strategy, collaborative reverse learning
DOI: 10.3233/JIFS-236930
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7461-7478, 2024
Authors: Sangeetha, M. | Nimala, K.
Article Type: Research Article
Abstract: NLP, or natural language processing, is a subfield of AI that aims to equip computers with the ability to understand and analyze human language. Sentiment analysis is a widely used application of NLP, particularly for examining attitudes expressed in online conversations. Nevertheless, many social media comments are written in languages that are not native to the authors, making sentiment analysis more difficult, especially for languages with limited resources, such as Tamil. To tackle this issue, a code-mixed and sentiment-annotated corpus in Tamil and English was created. This article will explain how the corpus was established, including the process of data …collection and the assignment of polarities. The article will also explore the agreement between annotators and the results of sentiment analysis performed on the corpus. This work signifies various performance metrics such as precision, recall, support, and F1-score for the transformer-based model such as BERT, RoBerta, and XLM-RoBerta. Among the various models, XLM-Robert shows slightly significant positive results on the code-mixed corpus when compared to the state of art models. Show more
Keywords: Sentiment analysis, Tamil-English Code-mix, natural language processing, corpus, grammar rule
DOI: 10.3233/JIFS-236971
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7479-7493, 2024
Authors: Zhou, Mi | Xiong, Xue-Di | Pei, Feng
Article Type: Research Article
Abstract: Marine high-end equipment reflects a country’s comprehensive national strength. The safety assessment of it is very important to avoid accident either from human or facility factors. Attribute structure and assessment approach are two key points in the safety assessment of marine high-end equipment. In this paper, we construct a hierarchical attribute structure based on literature review and text mining of reports and news. The hierarchical attribute structure includes human, equipment, environment and management level. The correlations among these attributes are analyzed. The assessment standards of attributes are described in details. Different evaluation grades associated with attributes are transformed to a …unified one by the given rules. As for the assessment approach, the evidential reasoning approach is applied for uncertain information fusion. Group analytical hierarchical process is used to generate attribute weights from a group of experts, where process aggregation method and result aggregation method are combined in a comprehensive way. The importance of expert is computed by the uncertainty measure of expert’s subjective judgment. A drilling platform is finally assessed by the proposed attribute structure and assessment approach to illustrate the effectiveness of the assessment framework. Show more
Keywords: Safety assessment, marine high-end equipment, evidential reasoning, uncertainty, group analytical hierarchical process
DOI: 10.3233/JIFS-237750
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7495-7520, 2024
Article Type: Research Article
Abstract: Compared with large enterprises, the development scale and organizational structure of small and medium-sized enterprises are insufficient, which brings certain limitations to the development of small and medium-sized enterprises in China. In order to promote the long-term development of small and medium-sized enterprises in the new era, it is necessary to require enterprise leaders to innovate marketing plans, strengthen risk management of enterprises, and enhance their strength in market competition. The market risk evaluation of small and medium sized enterprises (SMSEs) in the new era is a multiple-attribute decision-making (MADM). The IVIFSs are employed as the tool for portraying uncertain …information during the market risk evaluation of SMSEs in the new era. In this paper, the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive power geometric (IVIFHIPG) technique is addressed based on IVIF Hamacher interactive weighted geometric (IVIFHIWG) technique and power geometric (PG) technique. Some properties of IVIFHIPG technique were addressed. Then, the IVIFHIPG technique is employed to manage MADM under IVIFSs. Finally, an example for market risk evaluation of SMSEs in the new era is employed to verify the IVIFHIPG technique. Thus, the main contributions of this paper are addressed: (1) the IVIFHIPG technique is addressed based on IVIFHIWG technique and PG technique; (2) the IVIFHIPG technique is came up with to manage the MADM under IVIFSs; (3) a numerical example for market risk evaluation of SMSEs in the new era has been came up with to show the IVIFHIPG technique; and (4) some comparative analysis is addressed to verify the I IVIFHIPG technique. Show more
Keywords: Multiple-attribute decision-making (MADM), IVIF sets (IVIFSs), IVIFHIPG technique, market risk evaluation
DOI: 10.3233/JIFS-238763
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7521-7537, 2024
Authors: Mathavan, N. | Ramesh, G.
Article Type: Research Article
Abstract: A groundbreaking study employs interval arithmetic to address the challenging multi-objective interval traveling salesperson problem. Customizing methods like a nearest neighbor, branch and bound, two-way heuristics, and dynamic programming effectively resolve this complex problem. Preserving interval values without the need for classical form conversion is a significant advantage. Researchers validated this approach through extensive experiments, consistently demonstrating superior outcomes compared to existing methods. These algorithmic approaches were optimized for Python 3.11 64-bit to enhance processing speed and efficiency.
Keywords: Multi-objective interval traveling salesperson problem, new interval arithmetic, weighted sum method, Python program
DOI: 10.3233/JIFS-235966
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7539-7553, 2024
Authors: Wang, Yuansen | Lv, Guibin | He, Jialin | Cheng, Feng | Li, Dongke
Article Type: Research Article
Abstract: To comprehensively and objectively evaluate the actual safety condition in road and bridge engineering construction, the road and bridge engineering construction safety risk evaluation index system is constructed combined with the factors induced by emergencies in the road and bridge engineering construction process. Aiming at the dynamic uncertainty of road and bridge construction safety risk, using Fuzzy Set Theory and an improved similar aggregation method to determine the prior probabilities and conditional probabilities of network nodes, and then selecting the transition probabilities of nodes through expert opinions and incident reports, leading to the development of a dynamic evaluation model for …safety risks in road and bridge engineering construction based on Fuzzy Dynamic Bayesian Network, this model can make the construction safety risk prediction result accurately. Taking the Hebi City Provincial Highway 304 reconstruction project as an example for analysis, the results indicate that the model can accurately predict the probability of changes in safety risks in road and bridge engineering construction. Additionally, it can identify critical risk factors and provide crucial supporting information for decision-makers to optimize risk management strategies. Show more
Keywords: Road and bridge engineering, similar aggregation method, Dynamic Bayesian Network, risk analysis
DOI: 10.3233/JIFS-236301
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7555-7566, 2024
Authors: Gao, Zhihui | Han, Meng | Liu, Shujuan | Li, Ang | Mu, Dongliang
Article Type: Research Article
Abstract: The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization …problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively. Show more
Keywords: Beluga whale optimization algorithm, harris hawk optimization algorithm, high utility itemsets mining, good point set, intelligent optimization algorithm
DOI: 10.3233/JIFS-236793
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7567-7602, 2024
Authors: Wu, Yanqiu | Liu, Min | Sun, Dehong
Article Type: Research Article
Abstract: Person re-identification relies on discriminative features. However, most researches focus on extracting features from the high-layer of network while ignoring the middle-layer features, some important details are overlooked frequently. To address this issue, we propose a Multi-Scale and Multi-Patch Feature Fusion Network(MSPF). We employ modified OSFA to extract, align, and fuse the feature maps in the middle-layer of network, which can compensate for the lack of detailed information in the high-level network features. To obtain richer detailed global features of pedestrian, we construct a multi-patch feature fusion module(MPF). We concatenate the global features extracted from modified OSFA and MPF to …obtain global features with richer detailed representations. Cross-entropy loss, triplet loss and center loss are combined to constrain our model. We evaluate the performance of our model on Market-1501, CUHK03_labeled and DukeMTMC. The results prove that our method is superior to the state-of-the-art approaches. Show more
Keywords: Person re-identification, multi-scale, multi-patch, feature fusion
DOI: 10.3233/JIFS-237113
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7603-7612, 2024
Authors: Ketepalli, Gayatri | Bulla, Premamayudu
Article Type: Research Article
Abstract: In intrusion detection, the curse of dimensionality and the trade-off between maintaining a low false alarm rate and achieving a high detection rate are significant challenges. This research suggests a unique strategy based on dimensionality reduction methods to improve the performance of network intrusion detection systems (NIDS). Compressing high-dimensional network traffic data using a Long Short-Term Memory Autoencoder (LSTMAE) allows the reduced characteristics to be submitted to a classifier to identify anomalies that may indicate an attack. Using standard datasets, including Network Security Laboratory - Knowledge Discovery in Datasets (NSL-KDD), UNSW-NB15, and Canadian Institute for Cyber Security - Intrusion Detection …Systems (CICIDS2017), the proposed model is tested with classifiers like Random Forest (RF) and LightGBM (Light Gradient Boosting Machines). It is hoped that by adopting this method, NIDS response times may be improved while costs associated with storing and processing data are minimized. Precision, recall, F-score, accuracy, detection rate (DR), and false alarm rate (FAR) are only a few of the performance measures used to assess the quality of the suggested models. The experimental findings show that the proposed LSTMAE model reduces prediction errors more effectively than classic machine learning techniques such as Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVM), Deep Belief Networks (DBN), Deep Neural Networks (DNN), Autoencoder (AE), and Long Short-Term Memory (LSTM). The results also show that the proposed solution outperforms the state-of-the-art methods of detection accuracy and computing complexity using accuracy, precision, recall, F1_Score, detection rate, and FAR. Show more
Keywords: Network intrusion detection system, dimensionality reduction, LSTMAE, RF classifier, NSL-KDD, CICIDS2017, UNSW-NB15
DOI: 10.3233/JIFS-232228
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7613-7626, 2024
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