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

Authors: Xu, Dongsheng | Sun, Yuhuan | He, Xinyang

Article Type: Research Article

Abstract: This paper provides a novel target threat assessment model that utilizes TOPSIS and three-way decision-making under a single-valued neutrosophic environment. The presented model provides theoretical support for combat decision-making in complex battlefield environments with uncertain information. The model employs single-valued neutrosophic sets to handle uncertain data, which enhances the descriptive ability of information. The maximum deviation method is used to calculate attribute weight factors, which highlights the importance of each attribute. The final target threat ranking is obtained based on the relative closeness coefficient of each target. Furthermore, the proposed model constructs a multi-attribute aggregation loss function matrix for each …target, sets the risk avoidance coefficient under the knowledge of the battlefield condition, and calculates the decision threshold of each target using three-way decision theory. This method produces the classification of the target choice. The numerical examples and comparison analysis demonstrate that the suggested model can handle ambiguous scenario information effectively and reasonably, transform traditional decision-making ranking results into three-way classification findings, and provide a rationale for choosing an attacking target. Show more

Keywords: Threat assessment, three-way decision, TOPSIS, single-valued neutrosophic

DOI: 10.3233/JIFS-232267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9669-9680, 2023

Authors: Ramyasree, Kummari | Kumar, Chennupati Sumanth

Article Type: Research Article

Abstract: At present, the local appearance-based texture descriptors in Facial Expression Recognition have limited accuracy due to the inability to encode the discriminative edges. The major cause is the presence of distorted and weak edges due to noise. Hence, this paper proposes new Expression Descriptor called as Weighted Edge Local Directional Pattern (WELDP) which can discriminate the weak and strong edges. Unlike the conventional local descriptors, WELDP searches for the support of neighbor pixels in the determination of Facial expression attributes such as Edges, Corners, Lines, and Curved Edges. WELDP encodes only Strong edge responses and discards weaker edge responses after …extracting them through edge detection masks. This work adapted two masks for edge detection: they are Robinson Compass Mask and Kirsch Compass Mask. Moreover, the WELDP aims at code redundancy and encode each pixel only with seven bits (one sign bit and six directional bits). Then the WELDP image is described by a histogram and then processed through SVM (Support Vector Machine) for expression identification. From the simulation experiments, the proposed WELDP is found as better than several existing methods. Show more

Keywords: Face expression recognition, edge detection, gaussian weight, compass mask, directional encoding, and accuracy

DOI: 10.3233/JIFS-232985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9681-9696, 2023

Authors: Priya Varshini, A.G. | Anitha Kumari, K.

Article Type: Research Article

Abstract: As the size and complexity of projects grows, estimates are increasingly used, especially in the agile community. Software development cannot begin without first conducting thorough planning and estimation. Estimating how much work a project will take is a common first step in the software development life cycle. By employing ensemble techniques, we integrate multiple learning algorithms to build a more accurate predictive model. The core elements of our proposed stacked ensemble strategy include Decision Tree, Principal Components Regression, Random Forest, NeuralNet, GLMNET, XGBoost, Earth, and Support Vector Machine. Moreover, we augment the model’s performance by incorporating a blend of these …foundational algorithms with other ensemble regression methods. Extensive testing in the suggested research work with a number of Super Learners demonstrates that Regression is the best technique for judging effort. The evaluation of the different estimators involved the use of various metrics, including Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Percentage of Close Approximations within 25% of the True Values (PRED (25)), R-Squared Coefficients, Precision, Recall, and F1-Score. The proposed method yields more trustworthy predicted performance than either single-model approaches or stacked ensembles. Effort estimation serves as the foundation for the rest of the project management process. Show more

Keywords: Software effort estimations, stacked ensemble method, super learner, principal components regression

DOI: 10.3233/JIFS-230676

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9697-9713, 2023

Authors: Patidar, Neelam | Makrariya, Akshara

Article Type: Research Article

Abstract: The human body is a complex system that can be disrupted by various types of infections and viruses, and body temperature is a major contributor to these problems. To prevent this, doctors recommend comfortable clothing made from good fabric. This paper proposes a model that can be used to analyze how different types of fabric impact the thermal profile of skin layers during and after physical activity. The information gained from this model could be useful in designing exercise apparel for different climates and in generating thermal stress protocols for treating infections and providing physical activity guidelines for healthy living. …The model uses Pennes’ bio-heat equation and finite difference method to examine the temperature distribution in skin layers while accounting for both physiological and clothing parameters. The numerical findings were compared to existing studies, and the model’s accuracy was found to be in good agreement with previous research. The proposed model can be used to predict how much rest and acclimation are needed to cope with thermal stress and could also be modified to obtain thermal information for patients with skin diseases. Additionally, the thermal profile obtained from this model could be helpful in designing exercise clothes for patients with skin diseases. Show more

Keywords: Finite difference method (FDM), exercise, skin layers, clothing, temperature distribution, mathematical modeling, one dimensional (1D)

DOI: 10.3233/JIFS-231524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9715-9728, 2023

Authors: Naveen Kmuar, M. | Godfrey Winster, S.

Article Type: Research Article

Abstract: Investigation of human face images forms an important facet in affective analysis. The work, a DL-based ensemble is proposed for this purpose. Seven pre-trained models namely Facenet, Facenet2018, VGG16, Resnet-50, Senet-50, Arcface and Openface that have been developed for face verification have been exploited and customized for emotion identification. To each of these models, each all over interaction with softmax method to classification groups are augmented and entire network is then trained completely for emotion recognition. After training all the models individually, the probabilities for each of the class by each of the model are summed to derive at the …final value. The class that holds the highest of this value is finalized as the predicted emotion. Thus, the proposed methodology involves image collection, image pre-processing comprising of contrast enhancement, face detection and extraction, face alignment, image augmentation facilitating rotation, shifting, flipping and zooming transformations and appropriate resizing and rescaling, feature extraction and classification through ensemble of customized afore-mentioned pre-trained convolutional neural networks, evaluation and evolving of best weights for emotion recognition from face images with enhanced accuracy. The proposed methodology is evaluated on the well-established FER-2013 dataset. The methodology achieves a validation accuracy of 74.67% and test accuracy of 76.23%. Further, similar images of another dataset (Face Expression Recogniton dataset) are included for training the models and the impact of extra training is assessed to see if there is improvement in performance. The experiments reveal marked improvement in face emotion identification performance reaching values of 94.98% for both validation and test set of FER-2013 dataset and 94.99% on validation set of Face Expression Recognition dataset. Show more

Keywords: Emotion identification, transfer learning, ensemble, pre-trained models, CNN, DNN, DL, multi-class classification, image classification, human faces

DOI: 10.3233/JIFS-231199

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9729-9752, 2023

Authors: Ge, Yilin | Sun, Liping | Wang, Di

Article Type: Research Article

Abstract: Veneer is the critical raw material for manufacturing man-made board products, therefore the quality of the veneer determines the level of the man-made board. However, defects in the veneer may significantly lower its grade. Currently, identifying veneer defects requires manual inspection and subsequent inpainting using a veneer digging machine. Unfortunately, this method only removes the defects of the veneer but ignore the consistency of its texture. To address this issue, we propose a feasible veneer defect reconstruction method that utilizes a texture-aware-multiscale-GAN architecture. Our method performs texture reconstruction of veneer defects to increase the texture information of the reconstructed image …while improving the model efficiency, so that generates natural-looking textures in the reconstructed defect areas. The model is trained by end-to-end updating of four cascades of efficient generators and discriminators. We also employed a loss function based on local binary patterns (LBP) to ensure that the restored images contain sufficient texture information. Finally, region normalization is used in the model to enhance the accuracy of the model. Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) are used to evaluate the effectiveness of image restoration, the results show that PSNR of the method reacheds 35.32 and SSIM reaches 0.971. By minimizing the difference between the generated texture and that of the original image, our model produces high-quality results. Show more

Keywords: Image reconstruction, deep learning, veneer defect, LBP, texture aware multiscale

DOI: 10.3233/JIFS-231692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9753-9769, 2023

Authors: Chen, Wei | Tang, Hong | Yan, Tingting

Article Type: Research Article

Abstract: The energy consumption of mechanical products in China is enormous, and the energy utilization rate is low, which is increasingly receiving people’s attention. Conducting product design for energy optimization is of great significance for improving energy utilization efficiency. The scheme design of a product is the key to achieving innovation in product design, and the evaluation and decision-making of the design scheme directly affect the results of the later stage of the design. Therefore, correctly evaluating and making reliable decisions on product design schemes that are oriented towards fuzzy decision optimization is an important aspect of product innovation conceptual design. …The product modeling design quality evaluation is a multiple attribute group decision making (MAGDM) problems. Recently, the Combined Compromise Solution (CoCoSo) method and information entropy method has been employed to cope with MAGDM issues. The interval neutrosophic sets (INSs) are employed as a tool for portraying uncertain information during the product modeling design quality evaluation. In this paper, the CoCoSo method is designed for MAGDM under INSs. Then, the interval neutrosophic numbers CoCoSo (INN-CoCoSo) method based on the Hamming distance and Euclidean distance is built for MAGDM. The information Entropy method is employed to produce the weight information based on the Hamming distance and Euclidean distance under INNSs. Finally, a practical numerical example for product modeling design quality evaluation is supplied to show the INN-CoCoSo method. The main contributions of this paper are constructed: (1) This paper builds the novel MAGDM based on CoCoSo model under INSs; (2) The information Entropy method is employed to produce the weight information based on the Hamming distance and Euclidean distance under INNSs; (3) The new MAGDM method is proposed for product modeling design quality evaluation based on INN-CoCoSo. Show more

Keywords: Multiple attribute group decision making (MAGDM), interval neutrosophic sets (INSs), CoCoSo method, information entropy, informationization teaching ability evaluation

DOI: 10.3233/JIFS-233825

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9771-9783, 2023

Authors: Gupta, Shivani | Patel, Nileshkumar | Kumar, Ajay | Jain, Neelesh Kumar | Dass, Pranav | Hegde, Rajalaxmi | Rajaram, A.

Article Type: Research Article

Abstract: Due to resource constraints and the diverse nature of the devices involved, energy efficiency and scalability enhancement are important challenges in the Internet of Things (IoT) ecosystem. It is difficult to manage the edge resources in a consistent way that promotes cooperation and sharing of resources across the devices because of the heterogeneity of the Internet of Things devices and the dynamic nature of the surroundings in which edge computing takes place. In this research, we offer Intelligent techniques for resource optimization for Internet of Things devices. This is a full-stack system architecture to support across heterogeneous Internet of Things …devices that have limited resources. The paradigm that is being suggested is made up of several edge servers, and Internet of Things (IoT) devices have the qualities of being heterogeneity-compatible, high performing, and intelligently adaptable. In order to do this, a clustered environment is generated in heterogeneous Internet of Things devices, and a routing method called Search and Rescue Optimization is used to set up connections between the CH nodes. After that, the edge nodes that are closest to the source of the data are chosen for transmission. Overall, what was suggested Multi-Edge-IoT accomplishes superior efficiency in terms of consumption of energy, latency, communication overhead, and packet loss rate than existing approaches to attaining energy efficiency in the Internet of Things. Show more

Keywords: Multi-edge-IoT, EDGE load balancing, heterogeneous network, Bi-fuzzy vikor, search & rescue optimization algorithm

DOI: 10.3233/JIFS-233819

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9785-9801, 2023

Authors: Xia, Jing | Zhang, Shiya

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-234976

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9803-9813, 2023

Authors: Niu, Guocheng | Hu, Dongmei | Zhao, Yang | Eladdad, M.E.

Article Type: Research Article

Abstract: To solve the problem that the operation state of transformer is difficult to quantify, a method of quantitative evaluation and prediction of transformer operating state is proposed, which combines the information entropy of matter element and Support Vector Machine. In the evaluation, various hydrogen gases in the transformer operation are taken as the evaluation indexes and the three-dimensional cross compound element is constructed. The analytic hierarchy process (AHP) is used to determine the theoretical weight of the evaluation index, and the entropy method is used to determine the objective weight of the evaluation index, and the final weight is the …joint weight of the theoretical weight and the objective weight. Transformer Health index is calculated by using complex element correlation entropy. In prediction, the grid search, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize the parameters of Support Vector Machine. and the prediction model of Health index is established by SVM. Experiment results show that the Support Vector Machine based on Gauss kernel function and genetic algorithm has a prominent effect on the prediction of health index. Show more

Keywords: Transformer, health index, analytic hierarchy process (AHP), matter element information entropy, support vector machine (SVM)

DOI: 10.3233/JIFS-182785

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9815-9825, 2023

Authors: Majumder, Saibal | Kutum, Rintu | Khatua, Debnarayan | Sekh, Arif Ahmed | Kar, Samarjit | Mukerji, Mitali | Prasher, Bhavana

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-220990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9827-9844, 2023

Authors: Cheng, Yafei | Zhao, Bin

Article Type: Research Article

Abstract: In recent years, there are many works about conditional distributivity for aggregation functions, which is closely related to integration theory and utility theory. In this paper, our main idea is to solve conditional distributivity equations from left and right for semi-t-operators over uninorms. One part focuses on these equations involving semi-t-operators over t-norms and obtains some complete characterizations. The other part gives the necessary and sufficient conditions of conditional distributivity for semi-t-operators over uninorms in U max and U min under the condition 0 < U  (x , y ) <1, …which transforms it into the conditional distributivity between t-norms and t-conorms (semi-t-norms and t-conorms, semi-t-conorms and t-norms). Show more

Keywords: Semi-t-operators, t-norms, uninorms, conditional distributivity

DOI: 10.3233/JIFS-230966

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9845-9860, 2023

Authors: Ragu, G. | Ramamoorthy, S.

Article Type: Research Article

Abstract: When a digital adversary or an insider compromised a framework, cloud Forensic examiners can simply lay out the scene of the crime and reconstruct how the event took place using scientific evidence to determine when, why, and how it happened. Be that as it may, computerized proof procurement in a cloud environment is confounded and demonstrated troublesome, Despite modern scientific securing tool compartments. Multi-occupancy, Geo-area, and Administration Level Understanding have added another layer of complexity to obtaining computerized proof from a cloud environment. To moderate these intricacies of proof procurement in the cloud environment, we want a system that can …forensically keep up with the reliability and respectability of proof. In this review, we plan and execute a Blockchain-based Forensic in Cloud (BBFC) structure, utilizing a Cloud Forensic methodology (CFA). The outcomes from our single contextual analysis will exhibit that BBFC will alleviate the difficulties and intricacies looked at by computerized forensic specialists in getting acceptable advanced proof from the cloud biological system. Moreover, a quick exhibition observing the proposed Blockchain based measurable in cloud structure was assessed. BBFC will guarantee dependability, respectability, validness, and non-renouncement of the proof in the cloud. The proposed BBFC framework was also subjected to performance evaluation, considering factors such as latency, bandwidth, energy and resource utilization, and failure points. This evaluation provides insights into the efficiency and effectiveness of the framework in real-world cloud forensic scenarios. Show more

Keywords: Blockchain, cloud computing, forensic data

DOI: 10.3233/JIFS-231072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9861-9874, 2023

Authors: Chen, Xinquan | Ma, Jianbo | Qiu, Yirou | Liu, Sanming | Xu, Xiaofeng | Bao, Xianglin

Article Type: Research Article

Abstract: The purpose of clustering is to identify distributions and patterns within unlabelled datasets. Since the proposal of the original synchronization clustering (SynC) algorithm in 2010, synchronization clustering has become a significant research direction. This paper proposes a shrinking synchronization clustering (SSynC) algorithm utilizing a linear weighted Vicsek model. SSynC algorithm is developed from SynC algorithm and a more effective synchronization clustering (ESynC) algorithm. Through analysis and comparison, we find that SSynC algorithm demonstrates superior synchronization effect compared to SynC algorithm, which is based on an extensive Kuramoto model. Additionally, it exhibits similar effect to ESynC algorithm, based on a linear …version of Vicsek model. In the simulations, a comparison is conducted between several synchronization clustering algorithms and classical clustering algorithms. Through experiments using some artificial datasets, eight real datasets and three picture datasets, we observe that compared to SynC algorithm, SSynC algorithm not only achieves a better local synchronization effect but also requires fewer iterations and incurs lower time costs. Furthermore, when compared to ESynC algorithm, SSynC algorithm obtains reduced time costs while achieving nearly the same local synchronization effect and the same number of iterations. Extensive comparison experiments with some class clustering algorithms demonstrate the effectiveness of SSynC algorithm. Show more

Keywords: SynC algorithm, Kuramoto model, shrinking synchronization, a linear weighted Vicsek model, near neighbor points

DOI: 10.3233/JIFS-231817

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9875-9897, 2023

Authors: Rida, Zakaria | Boukachour, Hadhoum | Ennaji, Mourad | Machkour, Mustapha

Article Type: Research Article

Abstract: The articulation between machine tutoring and human tutoring remains a productive research within in the context of Intelligent Tutoring Systems (ITS), particularly in the context of e-learning where the dropout rate is high. We explore an innovative approach, the automation of tutoring as it is done in the classroom to respond to the difficulties of the learner. We propose a generic Intelligent Multi-Tutoring System (IMTS) architecture composed of two modules COMES and MAT. The Communication Entry Service (COMES) module manages communications between the IMTS and a Learning Management System (LMS). The module Multi-Agent Tutoring (MAT) is the multi-agent system developed …with JADE, which allows the dynamic adaptation of tutoring (Machine, Peer, Teacher) according to the profile of the learner. We offer a configurable system to customize tutoring to the individual needs of each learner. It can be grafted onto any learning platform, making it multidisciplinary and easy to integrate into existing learning environments. The teacher will be able to devote more time to learners which need greater his intervention.The peer will develop human and relational qualities linked to their know-how, transversal skills sought by recruiters. To validate this architecture, we provide an application and results that integrate the elements of the described model. The results of the experiment prove the feasibility and reliability of our approach. Show more

Keywords: Intelligent tutoring system, multi-agent system, adaptive system, markov, complex system

DOI: 10.3233/JIFS-232319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9899-9913, 2023

Authors: Liu, Yiyang | Li, Changxian | Cui, Yunxian | Song, Xudong

Article Type: Research Article

Abstract: Intelligent bearing fault diagnosis plays an important role in improving equipment safety and reducing equipment maintenance costs. Noise in the signal can seriously reduce the accuracy of fault diagnosis. To improve the accuracy of fault diagnosis, a novel noise reduction method based on weighted multi-scale morphological filter (WMMF) is proposed. Firstly, Teager energy operator (TEO) is used to amplify the morphological information of the signal. Then, a scale filtering operator using envelope entropy (SFOEE) is proposed to select appropriate scales. At these scales, the noise in the signal can be adequately suppressed. A new weighting method is proposed to integrate …the selected scales to construct the WMMF. Finally, multi-headed self-attention capsule restricted boltzmann network (MSCRBN) is proposed to diagnose bearing faults.The performance of the TEO-SFOEE-WMMF-MSCRBN fault diagnosis method is verified on the CWRU dataset. Compared with existing fault diagnosis methods, this approach achieves 100% identification accuracy. The experimental results indicate that the proposed diagnosis method can effectively resist noise and precisely diagnose bearing faults. Show more

Keywords: Bearing fault diagnosis, mathematical morphological filter, restricted boltzmann machine, capsule network

DOI: 10.3233/JIFS-232737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9915-9928, 2023

Authors: Cheng, Gang | You, Qinliang | Shi, Lei | Wang, Zhenxue | Luo, Jia | Li, Tianbin

Article Type: Research Article

Abstract: With the rapid development of information science and social networks, the Internet has accumulated various data containing valuable information and topics. The topic model has become one of the primary semantic modeling and classification methods. It has been widely studied in academia and industry. However, most topic models only focus on long texts and often suffer from semantic sparsity problems. The sparse, short text content and irregular data have brought major challenges to the application of topic models in semantic modeling and topic discovery. To overcome these challenges, researchers have explored topic models and achieved excellent results. However, most of …the current topic models are applicable to a specific model task. The majority of current reviews ignore the whole-cycle perspective and framework. It brings great challenges for novices to learn topic models. To deal with the above challenges, we investigate more than a hundred papers on topic models and summarize the research progress on the entire topic model process, including theory, method, datasets, and evaluation indicator. In addition, we also analyzed the statistical data results of the topic model through experiments and introduced its applications in different fields. The paper provides a whole-cycle learning path for novices. It encourages researchers to give more attention to the topic model algorithm and the theory itself without paying extra attention to understanding the relevant datasets, evaluation methods and latest progress. Show more

Keywords: Topic model, text mining, semantic understanding, whole-cycle, topic detection

DOI: 10.3233/JIFS-233551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9929-9953, 2023

Authors: Lalitha, K. | Murugavalli, S. | Roseline, A. Ameelia

Article Type: Research Article

Abstract: For retrieving the relevant images from the internet, CBIRs (content based image retrievals) techniques are most globally utilized. However, the traditional image retrieval techniques are unable to represent the image features semantically. The CNNs (convolutional neural networks) and DL has made the retrieval task simpler. But, it is not adequate to consider only the finalized aspect vectors from the completely linked layers to fill the semantic gap. In order to alleviate this problem, a novel Hash Based Feature Descriptors (HBFD) method is proposed. In this method, the most significant feature vectors from each block are considered. To reduce the number …of descriptors, pyramid pooling is used. To improve the performance in huge databases, the hash code like function is introduced in each block to represent the descriptors. The proposed method has been evaluated in Oxford 5k, Paris 6k, and UKBench datasets with the accuracy level of 80.6%, 83.9% and 92.14% respectively and demonstrated better recall value than the existing methods. Show more

Keywords: Content-based image retrieval, CNNs, hash based feature descriptor (HBFD), pyramid pooling and hash code

DOI: 10.3233/JIFS-233891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9955-9964, 2023

Authors: Liao, Tao | Sun, Haojie | Zhang, Shunxiang

Article Type: Research Article

Abstract: The entity-relationship extraction model has a significant influence in relation extraction. The existing model cannot effectively identify the entity-relationship triples in overlapping relationships. It also has the problem of long-distance dependencies between entities. In this paper, an inter span learning for document-level relation extraction model is proposed. Firstly, the model converts input of the BERT pre-training model into word vectors. Secondly, it divides the word vectors to form span sequences by random initial span and uses convolutional neural networks to extract entity information in the span sequences. Dividing the word vector into span sequences can divide the entity pairs that …may have overlapping relationships into the same span sequence, partially solving the overlapping relationship problem. Thirdly, the model uses inter span learning to obtain entity information in different span sequences. It fuses entity type features and uses Softmax regression to achieve entity recognition. Aiming at solving the problem of long-distance dependence between entities, inter span learning can fuse the information in different span sequences. Finally, it fuses text information and relationship type features, and uses Linear Layer to classify relationships. Experiments demonstrate that the model improves the F1-score of the DocRED dataset by 2.74% when compared to the baseline model. Show more

Keywords: Joint extraction, entity relation extraction, span, document-level extraction, neural network

DOI: 10.3233/JIFS-234202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9965-9977, 2023

Authors: Bai, Shi | Li, Pujie

Article Type: Research Article

Abstract: This paper presents an in-depth study and analysis of oil painting classification and simulation using an improved embedded learning fusion vision perception algorithm. This paper analyzes and models the image quality evaluation problem by simulating the human visual system and extracting quality perception features as the main entry point to improve the prediction accuracy of the overall algorithm. This paper proposes a multi-classification method of CCNN, which uses the similarity measure based on information first to achieve multi-classification of artwork styles and artists, and this part is the main part of this paper. This paper uses the wiki art repository …to construct a dataset of oil paintings, including over 2000 works by 20 artists in 13 styles. CNN achieves an accuracy of 85.75% on the artist classification task, which is far more effective than traditional deep learning networks such as Resnet. Finally, we use the network model of this paper and other network models to train the classification of 3, 4, and 6 categories of art images. The accuracy of art image classification by this paper’s algorithm is higher than that of the current mainstream convolutional neural network models, and the extracted features are more comprehensive and more accurate than traditional art image feature extraction methods, which do not rely on researchers to extract image features. Experiments show that the proposed method can achieve excellent prediction accuracy for both synthetic distorted images and distorted images. Show more

Keywords: Visual perception, embedded learning, oil painting classification, algorithm simulation

DOI: 10.3233/JIFS-234545

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9979-9989, 2023

Authors: Srivastava, Stuti | Bansal, Richa | Thapar, Antika

Article Type: Research Article

Abstract: Bandwidth consecutive multicoloring problem is also known as a b-coloring problem. Let G  = (V , E ) be a graph where V is the set of vertices and E is the set of edges. Let each vertex v of V has a positive integer weight b  (v ) and each edge (v , w ) of E has a non-negative integer weight b  (v , w ). A bandwidth consecutive multicoloring of a graph is a problem of assigning b  (v ) consecutive positive integers to each vertex v of V in such …a manner that the difference between all the integers of vertex v and all the integers of vertex w is greater than b  (v , w ). The maximum integer assigned in this coloring is called the span of the coloring. The b-coloring is the problem of minimizing this span. No metaheuristic is proposed for general graphs so far for this problem till date as it is strongly NP-hard. In this paper, we proposed three heuristics for the problem including a greedy randomized adaptive search procedure (GRASP). The efficiency of these algorithms is tested on benchmark graphs and their performance is compared among themselves. Experimental results showed that among all three proposed heuristics, GRASP performed well for the given problem. Show more

Keywords: Graph theory, bandwidth coloring, greedy coloring, GRASP

DOI: 10.3233/JIFS-224242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9991-10002, 2023

Authors: Weng, Zhi | Liu, Ke | Zheng, Zhiqiang

Article Type: Research Article

Abstract: The detection and identification of individual cattle plays an integral role in precision feeding and insurance claims, among others. Most current research is based on high-performance computing devices, which limits the application of deep learning techniques to mobile terminals. To this end, in this paper, we propose a channel-pruned YOLOv5 network-based method for cattle face detection on mobile terminals, referred to as NS-YOLO. First, the original model is sparsely trained and a sparse regularization penalty term is applied to the BN layers, then the corresponding mask values are labeled according to different weight thresholds, and the channels are pruned with …global thresholds. Second, the detection precision is recovered by fine-tuning the model. Finally, the NCNN forward inference framework is used to quantize the model and an Android-based cattle face detection application is developed. Experimental results show that the model size, number of parameters and FLOPs are reduced by 86.10%, 88.19% and 63.25%, respectively, and the inference time is reduced by 35.53% compared to the original model, while mAP0.5 is reduced by only 1.6%. In particular, the 16-bit quantized model reduces the model size by 93.97% and the inference time by 34.97% compared to the original model on the mobile side. The results show that the proposed method can be easily deployed in resource-constrained mobile devices and has great advantages in practical engineering applications. Show more

Keywords: Cattle face detection, channel pruning, YOLOv5, model quantization, mobile deployment

DOI: 10.3233/JIFS-232213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10003-10020, 2023

Authors: Tang, Bin | Xie, Kai | Tao, Wenbin | Liu, Zhenyu | Zhu, Chuanqi | Zhao, Neng | Wang, Yiyang

Article Type: Research Article

Abstract: In order to improve bearing capacity of rockbolts in deep-buried coal mine roadways, orthogonal tests were conducted to study influencing factors of rockbolt anchoring effect. Wavelet neural network model was introduced to predict the pull-out force of rockbolt. The activation and output functions of the wavelet neural network were improved, and the scaling and translation parameters were also modified by using the gradient descent method. These improvements enhanced the approximation rate of the wavelet neural network model, and solve the problem that the wavelet transform method is monotonous and difficult to adapt to the complex and variable engineering conditions. Research …results illustrated that The value of the ultimate pull-out force is positively correlated with the strength of the specimen and pre-tension value of the specimen. According to the test results, the coal mine roadway support scheme was optimized, and the high prestress full-length anchoring rockbolt support technology was proposed. The effectiveness of research was verified through the engineering applications and in-situ monitoring results. Show more

Keywords: Rockbolt, improved Wavelet Neural Network, high prestress, pull-out test, field performance

DOI: 10.3233/JIFS-232435

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10021-10032, 2023

Authors: Li, Jing | Li, Chengyu | Meng, Lusha

Article Type: Research Article

Abstract: Global warming caused by excessive emissions of carbon dioxide (CO2 ) has become a hot topic globally in today’s society, and optimizing carbon emission performance (CEP) is an effective way to alleviate CO2 emissions. Many studies have explored CEP at the global, national, provincial and sector levels. However, due to the difficulty in obtaining energy consumption data, there is a lack of studies at the urban agglomeration and city levels. Taking the urban agglomeration dimension as the starting point, this paper constructs an improved epsilon-based measure (EBM) model to measure the CEP of the Yellow River Basin. A spatial …data analysis model was introduced to explore the regional spatial characteristics of CEP. The newly developed spatial statistical model was used to study the driving factors of CEP. The results showed that: (1) The overall CEP of the Yellow River Basin was relatively high, showing an upward trend of volatility. There were significant differences between the seven urban agglomerations and 69 cities. (2) The CEP of the Yellow River Basin showed a trend of spatial agglomeration. The urban agglomerations of the eastern region showed a low-value agglomeration phenomenon, and the urban agglomerations of the central and western regions showed a trend of high-value agglomeration. (3) Economic development level (PGGDP), technological progress (TP), industrialization level (IND) and human capital (HC) can play a positive role in promoting the improvement in CEP, and population structure (PD) and energy structure (ES) can play a negative role in promoting the improvement in CEP. Industrial agglomeration (IA) and CEP show a “U"-shaped relationship that first inhibits and then promotes. In addition, foreign direct investment (FDI), IND, and HC have significant spatial spillover effects on neighboring cities. Show more

Keywords: Yellow River Basin, urban agglomeration, carbon emission performance, spatial Durbin model, spatial agglomeration

DOI: 10.3233/JIFS-233246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10033-10052, 2023

Authors: Shu, Mingming | Liu, Xiaoyu | Zhou, Hongming

Article Type: Research Article

Abstract: In order to better realize the effective display of painting art, this paper puts forward an interactive modeling method of structural sense of painting art communication from the perspective of media integration. From the perspective of comprehensive media, the painting art is spread and displayed, and the interactive evaluation index of painting art communication structure sense is constructed, and the interactive behavior evaluation model of painting art communication structure sense is constructed to realize the interactive modeling of communication structure sense. The experimental results show that from the perspective of integrating media, the somatosensory interaction mode of the communication structure …of painting art is highly practical in the practical application process, which meets the research requirements and can realize the effective display of painting art in a modified way. Show more

Keywords: Integrated media perspective, painting art, somatosensory interaction

DOI: 10.3233/JIFS-234284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10053-10062, 2023

Authors: Zhang, Hu | Bai, Ping | Li, Ru

Article Type: Research Article

Abstract: Short text classification task is a special kind of text classification task in that the text to be classified is generally short, typically generating a sparse text representation that lacks rich semantic information. Given this shortcoming, scholars worldwide have explored improved short text classification methods based on deep learning. However, existing methods cannot effectively use concept knowledge and long-distance word dependencies. Therefore, based on graph neural networks from the perspective of text composition, we propose concept and dependencies enhanced graph convolutional networks for short text classification. First, the co-occurrence relationship between words is obtained by sliding window, the inclusion relationship …between documents and words is obtained by TF-IDF, long-distance word dependencies is obtained by Stanford CoreNLP, and the association relationship between concepts in the concept graph with entities in the text is obtained through Microsoft Concept Graph. Then, a text graph is constructed for an entire text corpus based on the four relationships. Finally, the text graph is input into graph convolutional neural networks, and the category of each document node is predicted after two layers of convolution. Experimental results demonstrate that our proposed method overall best on multiple classical English text classification datasets. Show more

Keywords: Short text classification, Knowledge graph, Graph convolutional neural networks, Long-distance dependency, Building graph for text

DOI: 10.3233/JIFS-222407

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10063-10075, 2023

Authors: Guo, Peng | Wang, Xiaonan | Zhang, Duo

Article Type: Research Article

Abstract: Punishment promotes cooperation among selfish agents. Unlike previous studies, we propose a new supervision (heterogeneous preference supervision, HPS) mechanism based on the original random supervision (ORS) mechanism considering regulators’ limited supervision ability and agents’ heterogeneous preferences. The concepts of exemption list capacity, observation period, and removal time are introduced as the variables under the HPS mechanism. A public goods game model is built to verify the supervision effects under the ORS and HPS mechanisms. The simulation results show that the HPS mechanism can promote cooperation more than the ORS mechanism. Increasing the exemption list capacity can make regulators pay more …attention to defectors and improve the cooperation level. Setting a relatively moderate observation period benefits a better supervision effect, while a too-small or too-large observation period leads to the collapse of cooperation. Shortening the removal time can increase the updating speed of the exemption list and enhance the role of the exemption list, resulting in improving the fraction of cooperators. Show more

Keywords: Public goods game, supervision mechanism, supervision ability, heterogeneous preference, exemption list

DOI: 10.3233/JIFS-230775

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10077-10088, 2023

Authors: Bharathi, J. | Nandhini, S.

Article Type: Research Article

Abstract: This paper explores the behaviour of a Bulk Arrival Retrial Queue Model (BARQ) with two phases of service under the Bernoulli Vacation schedule and Breakdown (BVSB). Each batch of customers arriving the system finds if the server is available, instantly utilizes the service. If the server is busy, under breakdown, or taking a vacation, then the customers enter into the orbit. After completing both service stages, the server will either take a vacation with probability p or wait until the next customer arrives with probability 1 - p or q . Our approach considers the nature of the customer as …balking and also takes into account the breakdown of server, which may occur instantaneously during any stage of service. Significant performance measures have been derived and presented. A numerical study of the proposed model is carried out using MATLAB and results were reported. Show more

Keywords: Retrial Queues, two types of service, Bernoulli Vacation, steady-state, Fortuitous Breakdown, impatient customers

DOI: 10.3233/JIFS-231195

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10089-10098, 2023

Authors: Thilagavathy, A. | Mohanaselvi, S.

Article Type: Research Article

Abstract: Consolidating cubical fuzzy numbers (CFNs) is essential in an uncertain decision-making process. This study focuses on creating innovative cubical fuzzy aggregation operators based on the newly proposed Einstein operational laws, utilizing the Bonferroni mean function to capture the interrelationships among the aggregated CFNs. The first contribution of this paper is introducing a novel cubical fuzzy Einstein Bonferroni mean averaging operator. Building upon this operator, we extend our research to develop cubical fuzzy Einstein Bonferroni mean weighted, ordered weighted, and hybrid averaging operators, taking into account the weights of the aggregated CFNs. To ensure their effectiveness, we thoroughly investigate the desirable …properties of these proposed operators. Furthermore, we leverage the introduced operators to establish a new approach known as the cubical fuzzy linear assignment method, which proves valuable in resolving multiple criteria group decision-making problems. As a practical demonstration of the method’s utility, we apply it to address a real-life challenge: identifying the optimal location for constructing a wind power plant under a cubical fuzzy environment. To validate the effectiveness of our approach, we compare its results with those obtained using existing methods from the literature. Additionally, we conduct a statistical analysis to visualize the correlative conjunction between the ranking outcomes obtained by different operators Show more

Keywords: Cubical fuzzy set, Einstein operational laws, Bonferroni mean, averaging aggregation operators, linear assignment method, wind power plant location selection

DOI: 10.3233/JIFS-232252

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10099-10125, 2023

Authors: Guo, Yijian | Sun, Kaiqiong | Luo, Gang | Wang, Meng

Article Type: Research Article

Abstract: Leaf segmentation is crucial for plant recognition, especially for tree species identification. In natural environments, leaf segmentation can be very challenging due to the lack of prior information about leaves and the variability of backgrounds. In typical applications, supervised algorithms often require pixel-level annotation of regions, which can be labour-intensive and limited to identifying plant species using pre-labelled samples. On the other hand, traditional unsupervised image segmentation algorithms require specialised parameter tuning for leaf images to achieve optimal results. Therefore, this paper proposes an unsupervised leaf segmentation method that combines mutual information with neural networks to better generalise to unknown …samples and adapt to variations in leaf shape and appearance to distinguish and identify different tree species. First, a model combining a Variational Autoencoder (VAE) and a segmentation network is used as a pre-segmenter to obtain dynamic masks. Secondly, the dynamic masks are combined with the segmentation masks generated by the mask generator module to construct the initial mask. Then, the patcher module uses the Mutual Information Minimum (MIM) loss as an optimisation objective to reconstruct independent regions based on this initial mask. The process of obtaining dynamic masks through pre-segmentation is unsupervised, and the entire experimental process does not involve any label information. The experimental method was performed on tree leaf images with a naturally complex background using the publicly available Pl@ntLeaves dataset. The results of the experiment showed that compared to existing excellent methods on this dataset, the IoU (Intersection over Union) index increased by 3.9%. Show more

Keywords: Leaf extraction, deep learning, unsupervised image segmentation, mutual information, VAE

DOI: 10.3233/JIFS-232696

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10127-10139, 2023

Authors: Umamageswari, A. | Deepa, S. | Bhagyalakshmi, A. | Sangari, A. | Raja, K.

Article Type: Research Article

Abstract: To assess non-verbal reactions to commodities, services, or products, sentiment analysis is the technique of identifying exhibited human emotions utilizing artificial intelligence-based technology. The facial muscles flex and contract differently in response to each facial expression that a person makes, which facilitates the deep learning AI algorithms’ ability to identify an emotion. Facial emotion analysis has numerous applications across various industries and domains, leveraging the understanding of human emotions conveyed through facial expressions, so it is very much required in healthcare, security and survelliance, Forensics, Autism and cultural studies etc,.. In this study, facially expressed sentiments in real-time photographs as …well as in an existing dataset are classified using object detection techniques based on deep learning. Fast Region-based Convolution Neural Network (R-CNN) is an object detection system that uses suggested areas to categorize facial expressions of emotion in real-time. Using a high-quality video collection made up of 24 actors who were photographed facially expressing eight distinct emotions (Happy, Sad, Disgust, Anger, Surprise, Fear, Contempt and Neutral). The Fast R-CNN and Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method used for classification and feature extraction respectively. In order to assess the deep network’s performance, the proposed work builds a confusion matrix. The network generalizes to new images rather well, as seen by the average recognition rate of 97.6% for eight emotions. The suggested deep network approach may deliver superior recognition performance when compared to CNN and SVM methods, and it can be applied to a variety of applications including online classrooms, video game testing, healthcare sectors, and automated industry. Show more

Keywords: Deep learning, R-CNN, emotion recognition, facial expression, computer vision

DOI: 10.3233/JIFS-233842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10141-10155, 2023

Authors: Liu, Jun

Article Type: Research Article

Abstract: Composite cylindrical shells play a crucial role in aerospace and marine structures. This study investigates the optimal structure for cylindrical multilayer composite shells under the effect of axial pressure using the finite element method and NSGA-II genetic algorithm to determine the maximum buckling load capacity. The critical buckling load of multilayer composite shells depends on various parameters, such as fiber angle, the number of layers, the material of the layers, and their thickness. The objective functions are used to increase the structure load capacity and reduce its weight. ABAQUS software was used to perform finite element analysis on the composite …cylindrical shell for determining the buckling load. Using the response surface model, the relationship between variables and objective functions has been determined. Results of the proposed response surface model for the training stages are evaluated using various statistical indices and the root mean square error for buckling load and shell weight variables is 0.065 and 0.140, respectively. In the next step, the NSGA-II genetic optimization algorithm was used to modify the layout and thickness of the composite layers to optimize the buckling strength and weight of the structure. A genetic algorithm based on NSGA-II was used to optimize the geometric characteristics. Show more

Keywords: Multi-objective optimization, NSGA-II, buckling load, genetic algorithm, cylindrical shell

DOI: 10.3233/JIFS-230826

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10157-10165, 2023

Authors: Sun, Tiantian

Article Type: Research Article

Abstract: A scientific financial transfer payment system is an important guarantee for promoting the modernization of the national governance system and governance capacity. The new development concept puts forward new requirements for fiscal governance at a new historical stage. The reform and improvement of the special transfer payment system match the responsibility and responsibility of fiscal governance, and better serve the national strategic pattern of rural revitalization and common prosperity. The performance evaluation of financial special poverty alleviation (SPA) development funds is conducive to improving the efficiency of fund utilization, achieving the radiation effect and multiple effect of financial SPA development …funds, improving the ability of financial transfer payments, and enhancing the modernization of national financial governance capabilities. The performance evaluation of financial SPA development funds under the background of rural revitalization is a multiple attribute group decision making (MAGDM). Based on the existing MABAC model, the MABAC model is extended to 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). Firstly, the definitions of 2TLPFSs, 2TLPFWA operator and 2TLPFWG operator is introduced. Then, the existing MABAC method is also introduced. The 2-tuple linguistic Pythagorean fuzzy number MABAC method (2TLPFN-MABAC) is constructed to cope with the MAGDM under 2TLPFSs. Finally, a case study for performance evaluation of financial SPA development funds under the background of rural revitalization is constructed and some comparative analyses is employed to verify the 2TLPFN-MABAC method. Show more

Keywords: Multiple attribute group decision making (MAGDM), MABAC method, 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs), Financial SPA development funds

DOI: 10.3233/JIFS-232168

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10167-10181, 2023

Authors: Sun, Yin-Kun | Hua, Jun | Li, Yan-Na | Chen, Guang-Wei

Article Type: Research Article

Abstract: With the rapid development of science and technology, automatic control systems have been applied in more and more fields. At the same time, the requirements for the stability, accuracy, response speed, and self-regulation ability of the system are also increasing. In the wood processing industry, the heating system of factories is an important link to ensure normal production. Therefore, in order to further improve the production efficiency of the wood processing industry and enhance the stability and controllability of the heating system in wood processing production, this article takes into account the delay and coupling effects in the rapid heating …process, and combines fuzzy control with temperature control to study and establish a woodworking thermal mechanical coupling model based on fuzzy control algorithm. The results show that compared with traditional PID control, fuzzy control has advantages such as short response time, small overshoot, high steady-state accuracy, fast steady-state recovery, and good dynamic and static performance. Under the condition of rapid heating, the existence of delay effect weakens the effect of Thermal shock, while the coupling effect not only affects the propagation of thermoelastic waves in the elastic body, but also weakens the weakening effect of delay effect on Thermal shock to a certain extent. Show more

Keywords: Woodworking, fuzzy control, thermal-mechanical coupling, thermodynamic coupling, delay effect

DOI: 10.3233/JIFS-232242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10183-10192, 2023

Authors: Sritha, P. | Valarmathi, R.S. | Poongodi, C.

Article Type: Research Article

Abstract: One of the best methods for assessing a baby’s health is foetal electrocardiography (FECG). Previously restricted to more widespread global disorders such as common ischemia, it is new way to investigating foetal heart rate irregularities. Current prenatal monitoring practices ignore critical FECG waveform elements that are the foundation of both pediatric, adult cardiac assessment, and instead of focusing solely on the foetal heart rate. In this paper we proposed Double Multiply-and-Accumulate (MAC) approach used for package operators into a single DSP block of commercial FPGAs, theoretically doubling the calculation speed for FECG monitoring. For a variety of technical reasons, they …were using the Space-Time Block Code (STBC) monitoring mode of operation. To strengthen the security of FECG monitoring, the Advanced Encryption Standard (AES) method may be used with the double MAC operators using STBC based FECG monitoring that has been developed. The solution was then assessed using state-of-the-art the Space-Time Block Code (STBC) based FECG techniques, and its validity was confirmed using Verilog simulation and FPGA synthesis. The calculation throughput of an STBC-based FECG monitoring system was found to be doubled using the Double MAC approach. Our implementation result demonstrates that keys are necessary for 128-bit AES encoding and decoding operations via VHDL-coded transformations. It is now more vital than ever to do a feasibility analysis of any hardware design due to the increase in the number of ways presented for minimizing noise. The efficiency increased (92%), and the delay was decreased to 19.35 ns by employing this double MAC architecture. The simulation results demonstrate that transformations for coding on an FPGA are implemented using the Xilinx VIVADO tool. Show more

Keywords: FECG monitoring, Double MAC, STBC based FECG, Verilog, AES, Xilinx

DOI: 10.3233/JIFS-234164

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10193-10211, 2023

Authors: Wang, Hui

Article Type: Research Article

Abstract: In Visual Communication Design (VCD), noise data is easy to appear, which reduces image quality and affects the effect of VCD. The non local mean image denoising algorithm is a good filtering denoising algorithm, but there are still issues of information interference and missing. To improve the performance of noise recognition and image denoising technology, this study proposes a non local mean image denoising algorithm based on machine learning technology. The whale optimization algorithm, as a machine learning technique, has good performance in seeking optimal solutions. Therefore, it is applied to optimize the filtering parameters of non local mean image …denoising algorithms to improve the perforGAmance of non local mean image denoising algorithms. To address the shortcomings of the whale optimization algorithm, BP neural network is introduced for optimization. Finally, the experiment uses the improved particle swarm optimization algorithm to optimize the BPNN and applies it to the recognition and classification of noise data. Combining the above contents, the IBINLM image denoising algorithm is constructed experimentally. It is verified that the IPSO-BPNN model’s loss value is 0.12; The recognition accuracy of the model for noise pixels is 98.64%; F1 value reaches 96.32%; The fitting degree reaches 0.983. The PSNR of IBINLM algorithm is 35.86 dB; MSE is 0.29; AUC value reaches 0.903. The results show that the IPSO-BPNN model and IBINLM image denoising algorithm have better performance compared to other models, which can improve the quality of visual communication works, playing an essential role in image transmission and storage in visual communication design. Show more

Keywords: Machine learning, non-local mean, image denoising, whale optimization algorithm, visual communication design

DOI: 10.3233/JIFS-234632

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10213-10225, 2023

Authors: Guo, Ying | Peng, Jinzhu | Ding, Shuai | Liang, Jing | Wang, Yaonan

Article Type: Research Article

Abstract: In this paper, a variable impedance control method is proposed for uncertain robotic systems based on a nonlinear force contact-based flexible environmental model. First, a nonlinear force contact model between the rigid manipulator and flexible environment is applied to the compliant control of the manipulator, which can avoid excessive force overshoot that usually exists in the traditional spring-damping environmental model. Then, to achieve better force/position tracking performances, a fuzzy-based adaptive variable impedance controller is designed based on the force contact-based flexible environmental model, where the impedance parameters are adjusted online through the force and position feedback of the robotic system, …and the fuzzy logic system is used to compensate the uncertainties. Moreover, the stability of the adaptive variable impedance control scheme is proved by the Routh stability criterion, and the boundness of all the signals in the closed-loop control system is guaranteed by the Lyapunov stability theorem. Finally, the effectiveness of the proposed method is verified by the simulation of a two-link manipulator, and the results demonstrate that the performances of position tracking are improved, while the force overshoot and oscillation time are reduced. Show more

Keywords: Variable impedance control, flexible environment, fuzzy logic system, force contact model, robotic system

DOI: 10.3233/JIFS-224250

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10227-10241, 2023

Authors: Kayyidavazhiyil, Abhilash

Article Type: Research Article

Abstract: Prediction of malicious attacks and monitoring of network behaviour is significant for providing security and mitigating the loss of credential information. In order to monitor network traffic and identify different types of attacks in the network, numerous existing algorithms have been provided for classifying unauthorized access from the authorized access. However, the traditional techniques have faced complications in satisfying the accuracy while making predictions of malicious activities. Detection accuracy have been addressed as a drawback which hinders in making appropriate identification of threats. In order to overcome such challenges, the proposed work is designed with effective IDS mechanism for detecting …and classifying the attacks taken from the UNSW-NB15 and NSL-KDD dataset. IDS (Intrusion Detection System) implementation is accomplished with three stages such as pre-processing is the initial phase in which scaling re-sizing of all images to similar width and height. Process of checking missing values reduces the computational complexities and enhances accuracy. Second stage is the novel feature-selection process accomplished by E-GSS (Enhanced Genetic Sine Swarm Intelligence) for selecting significant and optimal features. Finally, classification is the final phase in which intrusion is classified using novel DMH-ANN (Deep Meta-Heuristics Artificial Neural Network) which is internally being compared to three classifiers such as RF (Random Forest), NB (Naïve Bayes) and XG-Boost (Extreme Gradient). Experimental evaluation is carried out with the performance metrics such as accuracy, precision and recall and compared with existing algorithms for exhibiting the effectiveness of the proposed model. The research outcome reveals its efficiency in detecting and classifying attacks with greater accuracy. Show more

Keywords: Intrusion detection, UNSW-NB15 dataset, NSL-KDD, Genetic Sine Swarm, Metaheuristic ANN, Naïve Bayes, XG-Boost, random forest

DOI: 10.3233/JIFS-224283

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10243-10265, 2023

Authors: Vaishnavi, D. | Balaji, G.N.

Article Type: Research Article

Abstract: Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising …performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters. Show more

Keywords: Copy Move technique, image forgery, deep learning, hyperparameter tuning, metaheuristics

DOI: 10.3233/JIFS-230291

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10267-10280, 2023

Authors: Sri Geetha, M. | Grace Selvarani, A.

Article Type: Research Article

Abstract: Breast cancer is responsible for the deaths of hundreds of women every year. The manual identification of breast cancer has more difficulties, and have the possibility of error. Many imaging approaches are being researched for their potential to identify breast cancer (BC). Incorrect identification might sometimes result in unneeded therapy and diagnosis. Because of this, accurate identification of breast cancer may save a great number of patients from needing unneeded surgery and biopsies. Deep learning’s (DL) performance in the processing of medical images has substantially increased as a result of recent breakthroughs in the sector. Because of their improved capacity …to anticipate outcomes, deep learning algorithms are able to reliably detect BC from ultrasound pictures. Transfer learning is a kind of machine learning that reuses knowledge representations from public models that were built with the use of large-scale datasets. Transfer learning has been shown to often result in overfitting. The primary purpose of this research is to develop and provide suggestions for a deep learning model that is effective and reliable in the detection and classification of breast cancer. A tissue biopsy is obtained from the suspicious region in order to ascertain the nature of a breast tumor and whether or not it is cancerous. Tumors may take any of these forms. When the images have been reconstructed with the help of a variational autoencoder (VAE) and a denoising variational autoencoder (DVAE), a convolutional neural network (CNN) model is used. This will be the case because it opens up a new area of the field to be investigated. The histological subtypes of breast cancer are used in conjunction with the degree of differentiation to execute the task of breast cancer categorization. Show more

Keywords: Medical image classification, disease detection, deep learning, breast cancer, convolutional neural network (CNN), variationalautoencoder, histopathology image

DOI: 10.3233/JIFS-231345

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10281-10294, 2023