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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: Zhang, Zhi-Hao | Wang, Jie-Sheng | Chen, Lin
Article Type: Research Article
Abstract: The colony is one of the important research objects in microbial technology, which can realize the evaluation of food safety level, environmental pollution degree, therapeutic effect of medical drugs, and characteristics of agricultural fungicides. Traditional colony image research requires human visual observation and statistics, which will result in low work efficiency and high work intensity. Colony image edge detection is an important basis for colony image research. Traditional edge detection operators cannot meet the accuracy requirements of the detection results. This paper proposes a Mediocrity Ant Colony Algorithm (MACA) to achieve edge detection of colony images. MACA combines the mediocrity …rule, uses empirical functions to establish a pheromone database that can be used as a pheromone update reference table, adopts the Chebyshev distance as a weight that affects pheromone update, and combines heuristic information acquisition with maximum variance classification method and local path weights. The method that jointly affects the ant transition probability incorporates feedback rules for obtaining path weights to improve the edge detection effect. By performing edge detection simulation experiments on six colonies of three types of bacteria, and comparing with the classic edge detection operators and two classic ant colony edge detection algorithms, the detection performance, detection results and running time are proposed. The stability and accuracy of MACA algorithm is better than other methods, and the ideal results of the colony image edge detection by the ant colony algorithm are obtained. Show more
Keywords: Colony image, mediocrity ant colony algorithm, edge detection
DOI: 10.3233/JIFS-233769
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2665-2691, 2024
Authors: Wang, Bing | Yue, Wei | Zhang, Lu
Article Type: Research Article
Abstract: The California Bearing Ratio (CBR) holds significant importance in the design of flexible pavements and airport runways, serving as a critical soil parameter. Moreover, it offers a means to gauge the soil response of subgrades through correlation, an aspect pivotal in soil engineering, particularly in shaping subgrade design for rural road networks. The CBR value of soil is influenced by numerous factors, encompassing variables like maximum dry density (MDD), optimum moisture content (OMC), liquid limit (LL), plastic limit (PL), plasticity index (PI), soil type, and soil permeability. The condition of the soil, whether soaked or unsoaked, also contributes to this …value. It is worth noting that determining CBR is time-consuming and extensive. Acknowledging the gravity of this determination, the study introduces a pioneering approach employing machine learning. This innovative technique uses a foundational multi-layer perceptron model, harnessing the algorithm’s robust capabilities in addressing regression challenges. A hybridization approach enhances the multi-layer perceptron’s performance and achieves optimal results. This approach integrates the Bonobo Optimizer (BO), Smell Agent Optimization (SAO), Prairie Dog Optimization (PDO), and Gold Rush Optimizer (GRO). The hybrid models proposed in this study exhibit promising results in predicting CBR values. The MLAO3 hybrid model is particularly noteworthy, emerging as the most accurate predictor among the range of models, with an impressive R2 value of 0.994 and an RMSE value of 2.80. Show more
Keywords: California bearing ratio, multi-layer perceptron, meta-heuristic algorithms, hybrid machine learning
DOI: 10.3233/JIFS-233794
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2693-2711, 2024
Authors: Zhang, Benfei | Huang, Lijun | Wang, Jie | Zhang, Li | Wu, Yue | Jiang, Yizhang | Xia, Kaijian
Article Type: Research Article
Abstract: In this paper, a novel semi-supervised fuzzy clustering algorithm, MFM-SFCM, based on a membership fusion mechanism is proposed for Diffusion-weighted imaging (DWI) brain infarction lesion segmentation. The proposed MFM-SFCM algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process that arises in the semi-supervised fuzzy C-means clustering (SFCM) when emphasizing supervised information. By using a new membership fusion mechanism, MFM-SFCM eliminates this issue, greatly improving the accuracy of clustering results and accelerating convergence speed. This allows fuzzy clustering to achieve good results in the segmentation of DWI brain infarction lesions using a small …amount of labeled information. The effectiveness of the MFM-SFCM algorithm is demonstrated through experiments conducted on a real-world dataset of DWI brain images. Show more
Keywords: Semi-supervised clustering, supervised information, FCM, membership fusion mechanism, medical image segmentation
DOI: 10.3233/JIFS-234148
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2713-2726, 2024
Authors: Zhang, Ju | Zhang, Tao | Xiang, Yanpeng | Liu, Jiahao | Zhang, Yu
Article Type: Research Article
Abstract: Information hiding is a crucial technology in the field of information security. Embedding capacity and stego-image quality are two key performance metrics in information hiding. In recent years, many information-hiding methods have been proposed to enhance embedding capacity and stego-image quality. However, through the study of these methods, we found that there is still room for improvement in terms of performance. This paper proposes a high-capacity information-hiding method based on a chunking matrix (CM). CM divides a 256×256 matrix into blocks, where each block contains k ×k corresponding secret numbers. A pair of pixels is extracted from the original …image and used as the coordinates for the matrix. In the search domain at that coordinate position, the corresponding secret number is found, and the matrix coordinates of the secret information are used as the pixel value for the stego-image. This paper evaluates the security and effectiveness of CM through measures such as embedding capacity, peak signal-to-noise ratio (PSNR), and bit-plane analysis. CM achieves a maximum embedding capacity of 4.806 bits per pixel (bpp ) and maintains a PSNR value of more than 30 dB. Furthermore, the bit-plane analysis fails to detect the presence of the information hidden using CM method. Show more
Keywords: Information hiding, security, chunking matrix, block, stego-image
DOI: 10.3233/JIFS-234236
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2727-2741, 2024
Authors: Wang, Ling | Ni, Zhiyun
Article Type: Research Article
Abstract: In recent years, the smart city concept has become popular due to its ability to improve the quality of life for urban residents. Smart community, smart transportation, and smart healthcare are among the several fields the idea covers. Integrating cloud computing technology into the healthcare industry has revolutionized healthcare delivery, enabling efficient data storage, analysis, and remote access to critical medical resources. However, choosing high-quality healthcare services from many cloud service providers remains challenging. This study presents the Quality of Service-driven Cloud Healthcare Services Selection (QCHSS) framework, underpinned by deep reinforcement learning, to tackle the intricate challenge of optimizing cloud-based …healthcare services. QCHSS prioritizes Quality of Service (QoS) criteria, elevating patient experiences and outcomes. Leveraging Deep Reinforcement Learning (DRL), particularly the Deep Q-network (DQN) technique, we intelligently select cloud healthcare services, resulting in substantial improvements in availability, reliability, energy efficiency, and throughput. This research not only advances cloud-based healthcare service selection but also underscores the transformative potential of DRL in complex decision-making processes, offering a significant contribution to the field and enhancing healthcare service quality. Show more
Keywords: Healthcare services, cloud computing, reinforcement learning, neural network
DOI: 10.3233/JIFS-234582
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2743-2757, 2024
Authors: Sun, Haibin | Li, Zheng
Article Type: Research Article
Abstract: Millions of traffic accidents occur worldwide each year, resulting in tens of thousands of deaths. The primary cause is the distracted behavior of drivers during the driving process. If the distracted behaviors of drivers during driving can be detected and recognized in time, drivers can regulate their driving and the goal of reducing the number of traffic fatalities can be achieved. A deep learning model is proposed to detect driver distractions in this paper. The model can identify ten behaviors including one normal driving behavior and nine distracted driving behaviors. The proposed model consists of two modules. In the first …module, the cross-domain complementary learning (CDCL) algorithm is used to detect driver body parts in the input images, which reduces the impact of environmental factors in vehicles on the convolutional neural network. Then the output images of the first module are sent to the second module. The Resnet50 and Vanilla networks are ensembled in the second module, and then the driver behavior can be classified. The ensemble architecture used in the second module can reduce the sensitivity of only a single network on the data, and then the detection accuracy can be improved. Through the experiments, it can be seen that the proposed model in this paper can achieve an average accuracy of 99.0%. Show more
Keywords: Deep learning, neural networks, distracted behavior, ensemble learning, semantic segmentation
DOI: 10.3233/JIFS-234593
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2759-2773, 2024
Authors: Wang, Fang
Article Type: Research Article
Abstract: The rapid development of cultural tourism in recent years refers to a process of cultural experience of tourist objects with cultural characteristics. It can not only vigorously carry forward the rich and colorful history and cultural deposits, but also combine the huge economic and cultural benefits generated by tourism, and promote the rapid development of cultural construction. Cultural tourism is a kind of way that all kinds of social groups enjoy, and it is a deep and lasting way of communication, which can promote the communication between people of different social strata. The existing literature has explored the influence of …tourists’ psychological carrying capacity, but failed to explain the process and degree of influence. Based on behavioral and experience theories, this paper proposes that culture has a positive impact on tourists’ psychological carrying capacity through tourist experience, and tests relevant hypotheses. The primary psychological traits of historical and cultural tourists include curiosity about historical mysteries, the desire for historical knowledge, motivation to collect spiritual enrichment, academic interest in cultural heritage exploration, and an aesthetic appreciation for classical history. Key determinants include the scale and conservation of historical and cultural resources, their combination with natural attractions, and the personal qualities of tourists and the cultural competence of tour guides. The mental health care model combines tourism and psychology to facilitate both physical and mental well-being through professional psychological counseling services, aiding tourists in their recovery and self-healing. This integrated approach offers a broad scope and potential as an effective tool for addressing negative emotions, with demonstrated therapeutic effects focusing on psychological and social factors. Show more
Keywords: Role of psycho-occupational therapy, cultural tourism, tourists, mode of physical and mental recovery
DOI: 10.3233/JIFS-235010
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2775-2788, 2024
Authors: Zhaoxian, Ren | Min, Qu
Article Type: Research Article
Abstract: People’s demands for a higher quality of life are increasing, and furniture remains an essential part of daily life. In traditional furniture design methods, designers typically rely on their experience, leading to significant disparities between design solutions and user expectations. A comprehensive model is proposed with combination of Fuzzy KANO (FKANO) method, the Criteria Importance through Intercriteria Correlation (CRITIC) method, and the Coupling Coordination Degree (CCD) method for furniture design and evaluation, using desk design as an example. Firstly, FKANO model is applied to classify and filter user requirements, identifying crucial user needs as the basis for subsequent design. Secondly, …three desk design proposals that align with user requirements are formulated. Thirdly, the CRITIC method is introduced, using the filtered user requirements to construct an evaluation system and calculate the weights of various indicators. Lastly, the CCD method is applied to select the optimal desk design from five samples, including three designed by this study and two existing on the market. This comprehensive approach contains critical stages such as requirement identification, weight determination, and solution selection, achieving comprehensive research objectives. Besides, sensitivity analysis was conducted to validate the effectiveness of this integrated model, demonstrating its ability to balance different user requirements under different weight settings. The results indicate that the proposed approach enhances the scientific rigor, systematization, and user satisfaction of the furniture design and decision-making process. It offers valuable guidance for furniture manufacturers and designers, allowing furniture products to more effectively align with market demands, thus enhancing their competitiveness. Show more
Keywords: FKANO model, CRITIC method, CCD technique, design and evaluation, furniture design
DOI: 10.3233/JIFS-235272
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2789-2810, 2024
Authors: Tran-Anh, Dat | Nguyen Huu, Quynh | Nguyen Thi Phuong, Thao | Dao Thi Thuy, Quynh
Article Type: Research Article
Abstract: The wilting of leaves caused by disease poses risks to both harvest yield and the environment. Therefore, the timely detection of disease signs on leaves is crucial to enable farmers to prevent disease outbreaks and safeguard their crops. However, manually observing all diseased leaves on a large scale demands substantial time and human effort. In this study, we propose an effective method for automated disease detection on leaves. Specifically, this method utilizes images captured from mobile phones. The proposed technique combines four models (ensemble of models) with distinct features: (1) ResNeXt50 model with a high-quality image processing, (2) ViT model …with a low-quality image processing, (3) Efficientnet B5 model combines a self-learning with noisy input, and (4) Mobilenet V3 model with image segmentation. Experimental results demonstrate that the proposed method outperforms some of the state-of-the-art methods on TLU-Leaf dataset (ours) with F1-score of 90% and Cassava Leaf Disease dataset with F1-score of 87%. Show more
Keywords: Convolutional neural network, deep learning, multiple-model, leaf disease classification
DOI: 10.3233/JIFS-235940
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2811-2823, 2024
Authors: Hu, Huixian | Wang, Xiu | Li, Tian
Article Type: Research Article
Abstract: In the IP sector, the combination of visible image fusion (VIF) with infrared (IR) gives a more comprehensive and accurate description of a target image. To get over the problems of detail and energy loss during the fusion process caused by current deep learning fusion approaches, it is proposed to use a fusion strategy of IR and visible pictures based on full convolutional network (FCN) applying transfer learning. FCN model can take any size of the input and generate constant size of the output with desired rules. Through effective inference and learning procedure, the ability of features extraction and energy …conservation can be enhanced a lot. Experimental results demonstrate that the suggested method succeeds in improving IF quality over the other two comparable methods by preserving high light intensity and retrieving detail information. This also confirms its dominance across five different objective assessment indices: mutual information (MI), entropy (EN), edge-based similarity measure (Qabf), sum of correlations of differences (SCD), and multi-scale structural similarity for image (MS-SSIM). Show more
Keywords: Image fusion, full convolutional network, transfer learning, zero-phase component analysis
DOI: 10.3233/JIFS-236094
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2825-2834, 2024
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