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

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