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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Srinivasan, Manohar | Senthilkumar, N.C.
Article Type: Research Article
Abstract: The Internet of Things (IoT) has many potential uses in the day-to-day operations of individuals, companies, and governments. It makes linking all devices to the internet a realistic possibility. Convincing IoT devices to work together to implement several real-world applications is a challenging feat. Security issues impact innovative platform applications due to the current security state in IoT-based operations. As a result, intrusion detection systems (IDSs) tailored to IoT platforms are essential for protecting against security breaches caused by the Internet of Things (IoT) that exploit its vulnerabilities. Issues with data loss, dangers, service interruption, and external hostile assaults are …all part of the IoT security landscape. Designing and implementing appropriate security solutions for IoT environments is the main emphasis of this research. Within the Internet of Things (IoT) context, this research creates a Spotted Hyena Optimizer (SHO-EDLID) method for intrusion detection using ensemble deep learning. The main goal of the demonstrated SHO-EDLID method was to detect and categorize intrusions in an Internet of Things setting. It comprises many subprocesses, including feature selection, categorization, and pre-processing. The SHO-EDLID method uses a SHO-based feature selection strategy to identify the best feature subsets. It then used an ensemble of three DL models— a deep belief network (DBN), a stacked autoencoder (SAE), and a bidirectional recurrent neural network (BiRNN)— to detect and name cyberattacks. Finally, the DL models’ parameters are tuned using the Adabelief optimizer. A comprehensive simulation was run to illustrate that the offered model performed better. According to a thorough comparative analysis, the suggested method outperformed other recent approaches. Purpose of the Manuscript : To identify the best feature subsets, the SHO-EDLID method used the SHO-based feature selection method... Afterward, cyberattack identification and tracking were carried out using an ensemble of three DL models: DBN, SAE, and BiRNN. The final step in optimizing the DL models’ parameters is the Adabelief optimizer. The main comparative results : The proposed model present the Comparative analysis of SHO-EDLID algorithm with other existing systems and its outperform the performance in precision 97.50, accuracy 99.56, Recall 98.42, F-Measure.97.95. Show more
Keywords: Security, internet of things, deep learning, ensemble learning, spotted hyena optimizer
DOI: 10.3233/JIFS-240571
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Yang, Cheng | Xu, Xinrui
Article Type: Research Article
Abstract: The quality of building materials will affect the implementation effect of construction projects. To ensure the service capacity of building materials, it is necessary to do a good job in selecting suppliers. In the specific evaluation of building material suppliers, after evaluation, suppliers with poor quality are excluded to ensure the quality of material supply, reasonably improve the construction effect of the building project, meet the construction needs of the building project, and improve the quality of the building project. The selection and application of building material suppliers (BMSs) is a multiple-attribute group decision-making (MAGDM) technique. In this study, the …2-tuple linguistic neutrosophic number combined grey relational analysis (2TLNN-CGRA) technique is constructed based on the classical grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Finally, a numerical example for building material supplier selection was constructed and some comparisons is constructed to illustrate the 2TLNN-CGRA technique. The main contribution of this study is constructed: (1) the 2TLNN-CGRA technique is implemented to cope with the MAGDM under 2TLNSs; (2) the 2TLNN-CGRA technique is implemented in line with the 2TLNN Hamming distance (2TLNNHD) and 2TLNN Euclidean distance (2TLNNED) simultaneously under 2TLNSs; (3) the numerical example for building material supplier selection is implemented to show the 2TLNN-CGRA technique; and (4) some efficient comparative studies are constructed with several existing decision techniques. Show more
Keywords: Multiple-attribute group decision-making (MAGDM), 2TLNSs, 2TLNN-CGRA technique, building material suppliers
DOI: 10.3233/JIFS-221334
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Liu, Dapeng
Article Type: Research Article
Abstract: In order to improve the remanufacturing efficiency of scrap mechanical parts and comprehensively detect their surface fault status, this paper proposes a color three-dimensional reconstruction method of scrap mechanical parts based on an improved semi-global matching (SGM) algorithm. In experiments, this method demonstrated significant performance advantages in dealing with complex mechanical component structures and large illumination interference environments. Experimental results show that the three-dimensional color model reconstructed by this method has clear texture and small dimensional error, and is suitable for online analysis of surface fault information of scrap mechanical parts in actual production lines. Through quantitative analysis, compared with …the traditional SGM method, the method in this paper improves the structural similarity index (SSIM) by an average of 19.8% and reduces the mean square error (MSE) by an average of 33.1%. Show more
Keywords: Waste mechanical parts, binocular vision, SGM, Color 3D reconstruction
DOI: 10.3233/JIFS-237214
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Jansi Rani, J. | Manivannan, A.
Article Type: Research Article
Abstract: This paper focuses on solving the fully fuzzy transportation problem in which the parameters are triangular Type-2 fuzzy numbers due to the instinctive of human imprecision. To deal with uncertainty more precisely, a triangular Type-1 fuzzy transportation problem is reformed as a transportation problem with triangular Type-2 fuzzy parameters in this paper. In order to compare triangular Type-2 fuzzy numbers, a new ranking(ordering) technique is proposed by extending the Yager’s function. However, two efficient algorithmic approaches namely, triangular Type-2 fuzzy zero suffix method (TT2FZSM) and triangular Type-2 fuzzy zero average method (TT2FZAM) are proposed to generate the initial transportation cost …of the fully triangular Type-2 fuzzy transportation problem. Both TT2FZSM and TT2FZAM are converging towards an optimal solution. In addition to TT2FZSM and TT2FZAM, the modified distribution method is applied to ensure optimality. Subsequently, we carry out a comprehensive discussion of the obtained results to establish the validation of the proposed approach. Show more
Keywords: Transportation problem, triangular type-2 fuzzy number, ranking function, optimal solution
DOI: 10.3233/JIFS-237652
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Yan, Huiming | Yan, Zilin | Wang, Weiling | Liu, Shuyue
Article Type: Research Article
Abstract: In recent years, the burgeoning imperative of energy-efficient building management practices has surged dramatically, underscoring an urgent mandate for comprehensive studies that integrate cutting-edge optimization algorithms with precise heating load forecasting techniques. These studies are not merely endeavors; they represent concerted efforts to increase building energy efficiency and address mounting concerns regarding sustainability and resource utilization. In the intricate domain of heating, ventilation, and air conditioning (HVAC) systems, energy optimization challenges are being meticulously confronted through rigorous exploration and the application of innovative problem-solving methodologies. This pioneering study introduces groundbreaking methodologies by seamlessly integrating two state-of-the-art optimization algorithms— the Red …Fox Optimization and the Golden Eagle Optimizer— with the Decision Tree model. This fusion is aimed at enhancing the accuracy of heating load predictions and streamlining HVAC system optimization processes, marking a significant leap toward achieving heightened energy efficiency and operational efficacy in building management practices. The study emphasizes the significance of precise heating load prediction in advancing energy efficiency, realizing cost savings, and fostering environmental sustainability in building management. Furthermore, it delves into the multifaceted impact of various building features on heating load, encompassing variables such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. These insights furnish actionable intelligence for refined decision-making processes in both building design and operation. Based on the results, the DT single model experienced the weakest performance among the three models, with R 2 = 0.975 and RMSE = 1.608. The model DTFO (DT + FOX) achieves an extraordinary R 2 value of 0.996 and RMSE value of 0.961 for heating load prediction, surpassing the performance benchmarks set by other models. This achievement holds considerable promise for aiding engineers in crafting energy-efficient buildings, particularly within the swiftly evolving landscape of smart home technologies. Show more
Keywords: Decision tree, heating load, red fox optimization, golden eagle optimizer
DOI: 10.3233/JIFS-240283
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Sriraam, Natarajan | Chinta, Babu | Suresh, Seshadhri | Sudharshan, Suresh
Article Type: Research Article
Abstract: Assessing fetal growth and development requires accurate identification of the fetal area contour and measurement of the Crown-Rump Length (CRL). In this paper, we presented a unique method for autonomously segmenting the fetal region in ultrasound images and calculating the CRL based on the U-Net architecture. Because of its capacity to capture both global and local information, the U-Net model is a popular choice for image segmentation tasks. Our method employs the U-Net model to extract the fetal region contour and measure the CRL, resulting in a dependable and efficient prenatal evaluation solution.
Keywords: Fetal, segmentation, U-Net, ultrasound image
DOI: 10.3233/JIFS-219403
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024
Authors: Macias, Cesar | Soto, Miguel | Cardoso-Moreno, Marco A. | Calvo, Hiram
Article Type: Research Article
Abstract: Mental and cognitive well-being is of paramount significance for human beings. Consequently, the early detection of issues that may culminate in conditions such as depression holds great importance in averting adverse outcomes for individuals. Depression, a prevalent mental health disorder, can severely impact an individual’s quality of life. Timely identification and intervention are critical to prevent its progression. Our research delves into the application of Machine Learning (ML) and Deep Learning (DL) techniques to potentially facilitate the early recognition of depressive tendencies. By leveraging the cognitive triad theory, which encapsulates negative self-perception, a pessimistic outlook on the world, and a …bleak vision of the future, we aim to develop predictive models that can assist in identifying individuals at risk. In this regard, we selected The Cognitive Triad Dataset, which takes into account six different categories that encapsulate negative and positive postures about three different contexts: self context, future context and world context. Our proposal achieved great performance, by relying on a strict preprocessing analysis, which led to the models obtaining an accuracy value of 0.97 when classifying aspect contexts; 0.95 when classifying sentiment-aspects; and a value of 0.93 in accuracy was achieved under the aspect-sentiment paradigm. Our models outperformed those reported in the literature. Show more
Keywords: Cognitive triad inventory, depression detection, machine learning, deep learning, natural language processing
DOI: 10.3233/JIFS-219333
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Mundada, Shyamal | Jain, Pooja | Kumar, Nirmal
Article Type: Research Article
Abstract: Sustainable agriculture revolves around soil organic carbon (SOC), which is essential for numerous soil functions and ecological attributes. Farmers are interested in conserving and adding additional soil organic carbon to certain fields in order to improve soil health and productivity. The relationship between soil and environment that has been discovered and standardized throughout time has enhanced the progress of digital soil-mapping techniques; therefore, a variety of machine learning techniques are used to predict soil properties. Studies are thriving at how effectively each machine learning method maps and predicts SOC, especially at high spatial resolutions. To predict SOC of soil at …a 30 m resolution, four machine learning models—Random Forest, Support Vector Machine, Adaptive Boosting, and k-Nearest Neighbour were used. For model evaluation, two error metrics, namely R2 and RMSE have been used. The findings demonstrated that the calibration and validation sets’ descriptive statistics sufficiently resembled the entire set of data. The range of the calculated SOC content was 0.06 to 1.76 %. According to the findings of the study, Random Forest showed good results for both cases, i.e. evaluation using cross validation and without cross validation. Using cross validation, RF confirmed highest R2 as 0.5278 and lowest RMSE as 0.1683 for calibration dataset while without cross validation it showed R2 as 0.8612 and lowest RMSE as 0.0912 for calibration dataset. The generated soil maps will help farmers adopt precise knowledge for decisions that will increase farm productivity and provide food security through the sustainable use of nutrients and the agricultural environment. Show more
Keywords: Machine learning, remote sensing data, digital soil mapping, spatial predictions, precision farming
DOI: 10.3233/JIFS-240493
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Zheng, Danjing | Song, Xiaona | Song, Shuai | Peng, Zenglong
Article Type: Research Article
Abstract: This paper investigates an observer-based boundary controller design for interconnected nonlinear partial differential equation (PDE) systems. First, the Takagi–Sugeno (T–S) fuzzy model is adopted to accurately describe the target systems. Then, boundary measurements are employed to reduce the number of sensors. Next, considering the phenomenon of abnormal interference that may lead to measurement outliers and observer parameters’ uncertainties, an outlier-resistant non-fragile observer expressed by a saturation function is designed to guarantee the desired control objectives. Moreover, the boundary control approach is employed to trade-off the cost of system design and system performance. Furthermore, utilizing the membership function-dependent Lyapunov functions and …free-weight matrixes, sufficient conditions ensuring the closed-loop systems’ exponential stability are obtained while decreasing the conservativeness of the system stability analysis. Finally, the proposed method’s feasibility and effectiveness are validated by an example. Show more
Keywords: Boundary measurements, boundary control, interconnected nonlinear partial differential equation systems, membership function-dependent Lyapunov functions, outlier-resistant non-fragile observer
DOI: 10.3233/JIFS-238858
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Hayel, Rafa | El Hindi, Khalil | Hosny, Manar | Alharbi, Rawan
Article Type: Research Article
Abstract: Instance-Based Learning, such as the k Nearest Neighbor (kNN), offers a straightforward and effective solution for text classification. However, as a lazy learner, kNN’s performance heavily relies on the quality and quantity of training instances, often leading to time and space inefficiencies. This challenge has spurred the development of instance-reduction techniques aimed at retaining essential instances and discarding redundant ones. While such trimming optimizes computational demands, it might adversely affect classification accuracy. This study introduces the novel Selective Learning Vector Quantization (SLVQ) algorithm, specifically designed to enhance the performance of datasets reduced through such techniques. Unlike traditional LVQ algorithms that …employ random vector weights (codebook vectors), SLVQ utilizes instances selected by the reduction algorithm as the initial weight vectors. Importantly, as these instances often contain nominal values, SLVQ modifies the distances between these nominal values, rather than modifying the values themselves, aiming to improve their representation of the training set. This approach is crucial because nominal attributes are common in real-world datasets and require effective distance measures, such as the Value Difference Measure (VDM), to handle them properly. Therefore, SLVQ adjusts the VDM distances between nominal values, instead of altering the attribute values of the codebook vectors. Hence, the innovation of the SLVQ approach lies in its integration of instance reduction techniques for selecting initial codebook vectors and its effective handling of nominal attributes. Our experiments, conducted on 17 text classification datasets with four different instance reduction algorithms, confirm SLVQ’s effectiveness. It significantly enhances the kNN’s classification accuracy of reduced datasets. In our empirical study, the SLVQ method improved the performance of these datasets, achieving average classification accuracies of 82.55%, 84.07%, 78.54%, and 83.18%, compared to the average accuracies of 76.25%, 79.62%, 66.54%, and 78.19% achieved by non-fine-tuned datasets, respectively. Show more
Keywords: Machine learning, instance based learning, learning vector quantization, k-nearest neighbor, value difference metric (VDM)
DOI: 10.3233/JIFS-235290
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
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