Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Thirugnanam, G. | Sahul Hameed, Jennathu Beevi | Bharathidasan, B.

Article Type: Research Article

Abstract: In addition to existing cryptographic systems, watermarking technologies have been developed to add extra security. Digital watermarking utilizes embedding or hiding techniques to protect multimedia files from copyright violations. Fundamental procedures of digital watermarking techniques are embedding and extraction. Singular value decomposition (SVD) based Image watermarking schemes become popular owing to its better trade-off among robustness and imperceptibility. Nevertheless, false positive problem (FPP) is a major issue of SVD-based watermarking schemes. The singular value that is a fixed value and does not contain structural information about image is the primary cause of FPP problem. Therefore, Message Digest algorithm image watermarking …scheme based on Funk Singular Value Decomposition and Fractional-Order Polar Harmonic Transform (FSVD-FOPHT) is proposed in this paper to address this problem. The MD-5 algorithm is used to extract data from the host and watermark imageries and then create secret key. The FSVD-FOPHT method is utilized to hide watermark information in host image. The secret keys are extracted from hided image using inverse process of Fractional-Order Polar Harmonic Transforms with Funk Singular Value Decomposition algorithm. By using the extraction procedure, watermark image is extracted, and then reconstructs original watermarked image. During extraction procedure, the secret key is used for authentication to address FPP. Then, the proposed method is implemented in MATLAB and performance is analyzed with evaluation metrics, such as Embedding capacity, MSE, PSNR, and NC. The proposed method provide 14.6%, 17.34%, 19.53%, 21.46% and 23.89% high PSNR for cold-snow-landscape-water test image, 14.29%, 16.47%, 18.39%, 20.16% and 21.93% high PSNR for landscape-nature-sky-blue Test image, 16.85%, 19.99%, 22.70%, 27.22% and 29.16% high Embedding Capacity for cold-snow-landscape-water test image 22.83%, 24.64%, 27.92%, 29.60% and 31.77% high Embedding Capacity for landscape-nature-sky-blue Test image 35.38%, 32.63%, 30.95%, 28.61% and 26.08% low extraction time compared with existing methods SVD-CMSF-SIW, FE-IWS-DNN, AR-IWS-DNN, BBET-SHA1-SIW and LSB-DWT-SIW respectively. Show more

Keywords: Fractional-order polar harmonic transforms funk singular value decomposition embedding and extraction, message digest algorithm, secure image watermarking

DOI: 10.3233/JIFS-222182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9499-9521, 2023

Authors: Liu, Hong | Wang, Gaihua | Li, Qi | Wang, Nengyuan

Article Type: Research Article

Abstract: The detection of magnetic tile quality is an essential link before the assembly of permanent magnet motor. In order to meet the high standard of magnetic tile surface defect detection and realize the rapid and automatic segmentation of magnetic tile defects, a magnetic tile surface defect segmentation algorithm based on cross self-attention model (CSAM) is proposed. It adopts high-low level semantic feature fusion method to build the dependency relationship between the deep and shallow features. Multiple auxiliary loss functions are used to constrain the network and reduce the noise in the deep features. In addition, an image enhancement method is …also designed to solve the problem of insufficient annotated data. The experimental results show that the network can achieve 79.6% mIoU and 98.5% PA, which can meet the high standard requirements of magnetic tile manufacturing. Show more

Keywords: Defect detection, data enhancement, cross self-attention, multiple auxiliary loss, semantic segmentation

DOI: 10.3233/JIFS-232366

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9523-9532, 2023

Authors: Xiao, Yanjun | Zhao, Yue | Li, ShiFang | Song, Weihan | Wan, Feng

Article Type: Research Article

Abstract: The foundation of textile machinery digitization and intelligence is condition monitoring and identification. Online condition monitoring of looms is of great significance to ensure their long-term stable operation and improve their digital management level. However, the existing loom condition monitoring methods have problems such as insufficient depth of information mining, low condition recognition rate and poor system versatility. As a result, the loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory is studied. To begin, we examine the loom operation mechanism, loom state characterization, and loom state feature data composition. Then, using the fuzzy rough …set method, we analyze and make decisions on the loom state feature data, apply the theory of uncertainty and importance improvement DSmT fusion to solve the uncertainty problem of the rough set method’s decision rules, and build the loom state feature decision network on the embedded terminal using the decision rules. Meanwhile, to collect, communicate, display, and alarm loom characteristic data, this paper employs the STM32F407ZET6 microcontroller and designs a loom system status data collection platform with the AD7730 as the core, as well as tests loom status monitoring data collection and loom status data analysis and decision method based on this platform. The experimental findings show the usefulness of attribute data gathering as well as data analysis and decision-making processes. The technology enhances the precision of loom condition identification and decision making, as well as the safety and quality of manufacturing. It is critical for carrying out applications like as problem detection, remote monitoring, efficiency optimization, and intelligent weaving machine management. Show more

Keywords: Keywords: Rapier loom, dezert-Smarandache, fuzzy rough sets, condition monitoring, attribute reduction

DOI: 10.3233/JIFS-230950

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9533-9553, 2023

Authors: Dong, Yingda | He, Chunguang | Qin, Yanping | Yuan, Yunmei | Gao, Fan | Duo, Huaqiong | Wang, Ximing

Article Type: Research Article

Abstract: A novel enhancement method to improve resolution and contrast has been proposed to address the issues of blurring and distortion commonly encountered in traditional patterns. Initially, a discrete wavelet transform, a stationary wavelet transform, and an interpolation algorithm are used to obtain high-resolution images of traditional patterns. Subsequently, improved singular value matrix coefficients and reconstructed gamma function are used to enhance the image contrast to obtain high-resolution and contrast-enhanced patterns. Experimental results demonstrate the efficacy of this method, as evidenced by improved evaluation indexes, such as mean square error, peak signal-to-noise ratio, and structural similarity, in comparison to other existing …methods. The proposed method effectively improves the quality of traditional patterns and offers significant contributions to research on the restoration and protection of traditional patterns. Show more

Keywords: Traditional pattern enhancement, stationary wavelet transform, discrete wavelet transform, singular value matrix, gamma function

DOI: 10.3233/JIFS-232169

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9555-9569, 2023

Authors: Shenbagavalli, S.T. | Shanthi, D.

Article Type: Research Article

Abstract: Due to the vast amount of patient health data, automated healthcare systems still struggle to classify and diagnose various ailments. Learning redundant data also reduces categorization accuracy. A Deep Belief Network (DBN) has been used to precisely extract the most important aspects from clinical data by ignoring irrelevant/redundant features. Due of many learning variables, training is complicated. Similarly, the hybrid model has been employed by ensemble Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to categorize diseases. But, its efficiency depends on the proper choice of kernels and hyper-parameters. Therefore, this paper develops an efficient …feature extraction and classification model for healthcare systems. First, several medical data related to the patient’s health are collected. Then, an Optimized DBN (ODBN) model is presented for maximizing the accurateness of DBN by optimizing the learning variables depends on the Ant Lion Optimization (ALO) algorithm. With learning ODBN, the most relevant features are extracted with reduced computational complexity. After that, the CNN-LSTM with Unsupervised Fine-tuned Deep Self-Organizing Map (UFDSOM)-based classifier model is designed to categorize the extracted features into categories of illnesses. In this novel classifier, dropout normalization and parameter tuning processes are applied to avoid overfitting and optimize the hyper-parameters, which results in a less training period. In the end, studies utilizing publically accessible datasets show that the ODBN with CNN-LSTM-UFDSOM system outperforms classical models by 98.23%. Show more

Keywords: Medical data classification, DBN, CNN-LSTM, SVM, Ant lion optimizer

DOI: 10.3233/JIFS-224370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9571-9589, 2023

Authors: Su, Jiafu | Wang, Dan | Xu, Baojian | Zhang, Fengting | Ling, Xu

Article Type: Research Article

Abstract: A crucial step for agricultural product merchants to achieve profitable and sustainable development in the live-streaming e-commerce age is evaluating the risk of the agricultural products live-streaming e-commerce platform. However, there isn’t much reliable research available right now on the risk evaluation of platforms. Therefore, this study suggests an improved risk evaluation method based on interval-valued intuitionistic fuzzy multi-criteria group decision-making (MCGDM). This method determines the decision-maker weight for the risk criterion according to the levels of professionalism of the decision-makers in the risk criterion and uses the VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rate the risk …of the alternative agricultural products live-streaming e-commerce platforms. The viability and dependability of the approach described in this work are demonstrated using a case study. The strengths and weaknesses of this approach are illustrated by a comparative analysis. With the help of this paper, agricultural product merchants will be able to identify the live-streaming e-commerce platform that carries the least amount of overall risk and work toward the paper’s stated objectives of sustainable development in addition to developing and enhancing theoretical research findings in the field. Show more

Keywords: Live-streaming e-commerce platform, risk assessment, MCGDM, interval-valued intuitionistic fuzzy number, professionalism of decision makers

DOI: 10.3233/JIFS-231403

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9591-9604, 2023

Authors: Chen, Liang-Ching | Chang, Kuei-Hu

Article Type: Research Article

Abstract: Within the new era of artificial intelligence (AI), education industry should develop in the direction of intelligence and digitalization. For evaluating learners’ academic performances, English high-stakes test is not only a mere means for measuring what English as a Foreign Language (EFL) stakeholders know or do not know but also likely to bring life-changing consequences. Hence, effective test preparation for English high-stakes test is crucial for those who futures depend on attaining a particular score. However, traditional corpus-based approaches cannot simultaneously take words’ frequency and range variables into consideration when evaluating their importance level, which makes the word sorting results …inaccurate. Thus, to effectively and accurately extract critical words among English high-stakes test for enhancing EFL stakeholders’ test performance, this paper integrates a corpus-based approach and a revised Importance-Performance Analysis (IPA) method to develop a novel frequency-range analysis (FRA) method. Taiwan College Entrance Exam of English Subject (TCEEES) from the year of 2001 to 2022 are adopted as an empirical case of English high stake test and the target corpus for verification. Results indicate that the critical words evaluated by FRA method are concentrated on Quadrant I including 1,576 word types that account for over 60% running words of TCEEES corpus. After compared with the three traditional corpus-based approaches and the Term Frequency-Inverse Document Frequency (TF-IDF) method, the significant contributions include: (1) the FRA method can use a machine-based function words elimination technique to enhance the efficiency; (2) the FRA method can simultaneously take words’ frequency and range variables into consideration; (3) the FRA method can effectively conduct cluster analysis by categorizing the words into the four quadrants that based on their relative importance level. The results will give EFL stakeholders a clearer picture of how to allocate their learning time and education resources into critical words acquisition. Show more

Keywords: Artificial intelligence (AI), English high-stakes test, corpus-based approach, Importance-Performance Analysis (IPA) method, Term Frequency-Inverse Document Frequency (TF-IDF) method, frequency-range analysis (FRA) method

DOI: 10.3233/JIFS-231539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9605-9620, 2023

Authors: Hussain, Abrar | Ullah, Kifayat | Al-Quran, Ashraf | Garg, Harish

Article Type: Research Article

Abstract: Renewable energy sources play an influential role in the world’s climate and reduce the rate of harmful gasses such as carbon dioxide, methane, nitrous oxide, and many other greenhouse gasses that contribute to global warming. The theoretical concept of the T-spherical fuzzy (T-SF) set (T-SFS) is the most suitable model to evaluate energy resources under uncertainty. This article illustrates appropriate operations based on Dombi triangular norm and t-conorm. We derived a series of new aggregation approaches, such as T-SF Dombi Hamy mean (T-SFDHM) and T-SF weighted Dombi Hamy Mean (T-SFDWHM) operators. Further authors illustrated a list of new approaches such …as T-SF Dual Dombi Hamy mean (T-SFDDHM), and T-SF Dombi weighted Dual Hamy mean (T-SFDWDHM) operators. Some exceptional cases and desirable properties of our derived approaches are also studied. We illustrate an application of renewable energy resources to be evaluated using a multi-attribute group decision-making (MAGDM) method. A case study was also studied to choose appropriate energy resources using our proposed methodology of the T-SFDWHM and T-SFDWDHM operators. To show the effectiveness and validity of our current methods, we compared the existing results with currently developed aggregation operators (AOs). Show more

Keywords: T-Spherical fuzzy values, aggregation operators, Dombi aggregation models, and multi-attribute decision-making method

DOI: 10.3233/JIFS-232505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9621-9641, 2023

Authors: Wang, Qingling | Zheng, Jian | Zhang, Wenjing

Article Type: Research Article

Abstract: Majority classes are easily to be found in imbalance datasets, instead, minority classes are hard to be paid attention to due to the number of is rare. However, most existing classifiers are better at exploring majority classes, resulting in that classification results are unfair. To address this issue of binary classification for imbalance data, this paper proposes a novel fuzzy support vector machine. The thought is that we trained two support vector machines to learn the majority class and the minority class, respectively. Then, the proposed fuzzy is used to estimate the assistance provided by instance points for the training …of the support vector machines. Finally, it can be judged for unknown instance points through evaluating that they provided the assistance to the training of the support vector machines. Results on the ten UCI datasets show that the class accuracy of the proposed method is 0.747 when the imbalanced ratio between the classes reaches 87.8. Compare with the competitors, the proposed method wins over them in classification performance. We find that aiming at the classification of imbalanced data, the complexity of data distribution has negative effects on classification results, while fuzzy can resist these negative effects. Moreover, fuzzy can assist those classifiers to gain superior classification boundaries. Show more

Keywords: Binary classification, fuzzy, imbalanced data, support vector machines

DOI: 10.3233/JIFS-232414

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9643-9653, 2023

Authors: Moghaddam Teymourlu, Sohrab Abdollahzadeh | Amini, Amir

Article Type: Research Article

Abstract: In the current study, a new approach to assess and select food suppliers in hospitals is presented using integrated group evaluation method of fuzzy best- worst method (FBWM) and fuzzy gray relational analysis (FGRA). Evaluation criteria are selected by experts and weighed by the fuzzy best-worst method. After that, suppliers are rated using FGRA method. The proposed approach was implemented with seven criteria in one of the Iranian hospitals, and the results showed that quality, delivery time and trust criteria had the highest and skilled manpower and lack of surplus production criteria had the lowest score. Using FGRA, existing suppliers …were ranked and the appropriate supplier was identified. In order to evaluate the reliability of the results, sensitivity analysis was performed on the criteria changes. The results showed that the supplier’s selection is greatly influenced by the criteria estimation values by the experts. Show more

Keywords: Food supply chain, supplier evaluation and selection, fuzzy best-worst method, fuzzy gray relational analysis, health and medical centers

DOI: 10.3233/JIFS-231845

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9655-9668, 2023