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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: Adar-Yazar, Elanur | Karatop, Buket | Karatop, Selim Gökcan
Article Type: Research Article
Abstract: Many factors such as population growth, development of industry/technology, and increase in production-consumption disrupt the ecological balance and cause climate change, which is a global problem. Determining the criteria that cause climate change is very important in finding effective solutions to the problem. In the study, the criteria were determined, weighted with a new method, Step-wise Weight Assessment Ratio Analysis (SWARA), and ranked according to their priorities with two-layer fuzzy logic model. The Fuzzy SWARA method allows the evaluation process, which becomes complicated due to the difficulties and factors experienced in decision-making, to be carried out more effectively and realistically. …The risk and effect of climate change in Turkiye were evaluated regionally. However, the developed model also has a wide application area. Research findings revealed that the highest risk/effect of climate change have the Marmara and Central Anatolia regions. The lowest risk region is the Eastern Anatolia. Air pollution, population growth and deforestation have the highest weights. Important suggestions have presented especially for priority criteria. In this way, the factors that should be prioritized in climate change environmental problem solutions have been revealed and will make it easier for researchers and managers to provide more effective management. Show more
Keywords: Climate change, two-layer, fuzzy SWARA, Turkiye, risk
DOI: 10.3233/JIFS-236298
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10695-10711, 2024
Authors: Ramkumar, N. | Karthika Renuka, D.
Article Type: Research Article
Abstract: In BCI (brain-computer interface) applications, it is difficult to obtain enough well-labeled EEG data because of the expensive annotation and time-consuming data capture procedure. Conventional classification techniques that repurpose EEG data across domains and subjects lead to significant decreases in silent speech recognition classification accuracy. This research provides a supervised domain adaptation using Convolutional Neural Network framework (SDA-CNN) to tackle this problem. The objective is to provide a solution for the distribution divergence issue in the categorization of speech recognition across domains. The suggested framework involves taking raw EEG data and deriving deep features from it and the proposed feature …selection method also retrieves the statistical features from the corresponding channels. Moreover, it attempts to minimize the distribution divergence caused by variations in people and settings by aligning the correlation of both the source and destination EEG characteristic dissemination. In order to obtain minimal feature distribution divergence and discriminative classification performance, the last stage entails simultaneously optimizing the loss of classification and adaption loss. The usefulness of the suggested strategy in reducing distributed divergence among the source and target Electroencephalography (EEG) data is demonstrated by extensive experiments carried out on KaraOne datasets. The suggested method achieves an average accuracy for classification of 87.4% for single-subject classification and a noteworthy average class accuracy of 88.6% for cross-subject situations, which shows that it surpasses existing cutting-edge techniques in thinking tasks. Regarding the speaking task, the model’s median classification accuracy for single-subject categorization is 86.8%, while its average classification accuracy for cross-subject classification is 87.8%. These results underscore the innovative approach of SDA-CNN to mitigating distribution discrepancies while optimizing classification performance, offering a promising avenue to enhance accuracy and adaptability in brain-computer interface applications. Show more
Keywords: Brain-computer interface, supervised domain adaptation, Convolutional Neural Network, Electroencephalography, distribution divergence
DOI: 10.3233/JIFS-237890
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10713-10726, 2024
Authors: Mohana, M. | Subashini, P. | Shukla, Diksha
Article Type: Research Article
Abstract: In recent years, face detection has emerged as a prominent research field within Computer Vision (CV) and Deep Learning. Detecting faces in images and video sequences remains a challenging task due to various factors such as pose variation, varying illumination, occlusion, and scale differences. Despite the development of numerous face detection algorithms in deep learning, the Viola-Jones algorithm, with its simple yet effective approach, continues to be widely used in real-time camera applications. The conventional Viola-Jones algorithm employs AdaBoost for classifying faces in images and videos. The challenge lies in working with cluttered real-time facial images. AdaBoost needs to search …through all possible thresholds for all samples to find the minimum training error when receiving features from Haar-like detectors. Therefore, this exhaustive search consumes significant time to discover the best threshold values and optimize feature selection to build an efficient classifier for face detection. In this paper, we propose enhancing the conventional Viola-Jones algorithm by incorporating Particle Swarm Optimization (PSO) to improve its predictive accuracy, particularly in complex face images. We leverage PSO in two key areas within the Viola-Jones framework. Firstly, PSO is employed to dynamically select optimal threshold values for feature selection, thereby improving computational efficiency. Secondly, we adapt the feature selection process using AdaBoost within the Viola-Jones algorithm, integrating PSO to identify the most discriminative features for constructing a robust classifier. Our approach significantly reduces the feature selection process time and search complexity compared to the traditional algorithm, particularly in challenging environments. We evaluated our proposed method on a comprehensive face detection benchmark dataset, achieving impressive results, including an average true positive rate of 98.73% and a 2.1% higher average prediction accuracy when compared against both the conventional Viola-Jones approach and contemporary state-of-the-art methods. Show more
Keywords: AdaBoost, Computer Vision (CV), face detection algorithm, particle swarm optimization, Viola-Jones
DOI: 10.3233/JIFS-238947
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10727-10741, 2024
Authors: Dhivya, S. | Rajeswari, A.
Article Type: Research Article
Abstract: The utilization of the spectrum is optimized through which primary users of modern wireless communication technologies might obtain a higher chance of detection. The research aims to study how the NI-USRP hardware platform can be used to set up greedy cooperative spectrum sensing for cognitive radio networks. Research primarily deals with energy detection and eigenvalue-based detection approaches, both of which are highly recognized for their capacity to sense the spectrum without having prior knowledge of the primary user signals. In the hardware arrangement, there is one transmitter and two cognitive radio receivers. LABVIEW makes it simple to deploy and maximizes …the detection probability across a large sample. Here, it was demonstrated that cooperative spectrum sensing is superior to non-cooperative spectrum sensing, which results in a reduction in the risk of errors occurring during detection. The research discovered that the OR combination rule has a higher detection probability than the AND rule at the same time. The research emphasizes the significance of expanding cooperative spectrum sensing to improve overall detection capabilities. SNRs that are more than 10 dB allow the energy detector to operate, and the eigenvalue detector continues to work when the SNR drops to –9 dB. Show more
Keywords: Cognitive radio, cooperative spectrum sensing, NI-USRP hardware implementation, energy detection, eigenvalue-based detection
DOI: 10.3233/JIFS-239871
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10743-10755, 2024
Authors: Ding, Xiaoting | Jiang, Jiuchuan | Wei, Mengting | Leng, Yue | Wang, Haixian
Article Type: Research Article
Abstract: Analyzing physiological signals in the brain under outdoor conditions, like observing animal behavior, forms the normative basis for the outdoor task and provides new insights into the cognitive neuronal mechanisms of children’s functional brain systems. Here we investigated EEG data from a cohort of seventeen children (6–7 years old, 30-channel EEG) in the resting state and animal-observation state, using the microstate method combined with source-localization analysis to identify the changes in network-level functional interactions. Our study suggested that: while observing animal behavior, the parameters (global explained variance, occurrence, coverage, and duration) of microstates showed a regular trend, and the dynamic …reorganization patterns of children’s brains were associated with verbal input networks and higher-order cognitive networks; the activity of the brain network in the frontal and temporal lobes of children increased, while the activity of the insula brain area decreased after observing the behavioral activities of animals. This study may be essential to understand the effects of animal behavior on changes in healthy children’s emotions and have important implications for education. Show more
Keywords: Naturalistic observation task, healthy children, EEG microstates, brain development
DOI: 10.3233/JIFS-235533
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10757-10771, 2024
Authors: Yu, Jiamao | Yu, Ying | Qian, Jin | Han, Xing | Zhu, Feng | Zhu, Zhiliang
Article Type: Research Article
Abstract: Efficient feature representation is the key to improving crowd counting performance. CNN and Transformer are the two commonly used feature extraction frameworks in the field of crowd counting. CNN excels at hierarchically extracting local features to obtain a multi-scale feature representation of the image, but it struggles with capturing global features. Transformer, on the other hand, could capture global feature representation by utilizing cascaded self-attention to capture remote dependency relationships, but it often overlooks local detail information. Therefore, relying solely on CNN or Transformer for crowd counting has certain limitations. In this paper, we propose the TCHNet crowd counting model …by combining the CNN and Transformer frameworks. The model employs the CMT (CNNs Meet Vision Transformers) backbone network as the Feature Extraction Module (FEM) to hierarchically extract local and global features of the crowd using a combination of convolution and self-attention mechanisms. To obtain more comprehensive spatial local information, an improved Progressive Multi-scale Learning Process (PMLP) is introduced into the FEM, guiding the network to learn at different granularity levels. The features from these three different granularity levels are then fed into the Multi-scale Feature Aggregation Module (MFAM) for fusion. Finally, a Multi-Scale Regression Module (MSRM) is designed to handle the multi-scale fused features, resulting in crowd features rich in high-level semantics and low-level detail. Experimental results on five benchmark datasets demonstrate that TCHNet achieves highly competitive performance compared to some popular crowd counting methods. Show more
Keywords: Crowd counting, Transformer, CNN, multi-granularity, progressive learning
DOI: 10.3233/JIFS-236370
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10773-10785, 2024
Authors: Huang, Cheng | Hou, Shuyu
Article Type: Research Article
Abstract: To address the issue of target detection in the planar grasping task, a position and attitude estimation method based on YOLO-Pose is proposed. The aim is to detect the three-dimensional position of the spacecraft’s center point and the planar two-dimensional attitude in real time. First, the weight is trained through transfer learning, and the number of key points is optimized by analyzing the shape characteristics of the spacecraft to improve the representation of pose information. Second, the CBAM dual-channel attention mechanism is integrated into the C3 module of the backbone network to improve the accuracy of pose estimation. Furthermore, the …Wing Loss function is used to mitigate the problem of random offset in key points. The incorporation of the bi-directional feature pyramid network (BiFPN) structure into the neck network further improves the accuracy of target detection. The experimental results show that the average accuracy value of the optimized algorithm has increased. The average detection speed can meet the speed and accuracy requirements of the actual capture task and has practical application value. Show more
Keywords: Pose estimation, planar grasp, convolutional neural network, attention mechanism, feature fusion
DOI: 10.3233/JIFS-234351
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10787-10803, 2024
Authors: Hajiloei, Mehdi | Jahromi, Alireza Fakharzadeh | Zolmani, Somayeh
Article Type: Research Article
Abstract: Density based methods are significant approaches in outlier detection for high dimensional datasets and Local correlation integral (LOCI) is one of the best of them. To extend LOCI for fuzzy datasets, we should employ suitable metrics to measure the distance between two fuzzy numbers. Euclidean distance measure is a classic one in metric learning, but to overcome curse of dimensionality, we apply fractional distance metric too. Then, after introducing the FLOCI outlier detection algorithm for identifying the fuzzy outliers, we study the efficiency of the proposed method by doing some numerical experiments, in which the obtained results were completely successfull. …We also compared the results with Fuzzy versions of Distance based ABOD and SOD methods to prove robustness of this approache. More than the above, one of the main advantages of the new approach is the determination of outlierness factor for each data which is not presented in classical LOCI method. Show more
Keywords: Outlier data, Multi-granularity deviation factor, Triangular fuzzy number, LOCI method, Fractional distance metric
DOI: 10.3233/JIFS-234448
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10805-10812, 2024
Authors: Liang, Yonghong | Ge, Xianlong | Jin, Yuanzhi | Zheng, Zhong | Zhang, Yating | Jiang, Yunyun
Article Type: Research Article
Abstract: The rapid development of modern cold chain logistics technology has greatly expanded the sales market of agricultural products in rural areas. However, due to the uncertainty of agricultural product harvesting, relying on the experience values provided by farmers for vehicle scheduling can easily lead to low utilization of vehicle capacity during the pickup process and generate more transportation cost. Therefore, this article adopts a non-linear improved grey prediction method based on data transformation to estimate the pickup demand of fresh agricultural products, and then establishes a mathematical model that considers the fixed vehicle usage cost, the damage cost caused by …non-linear fresh fruit and vegetable transportation damage and decay rate, the cooling cost generated by refrigerated transportation, and the time window penalty cost. In order to solve the model, a hybrid simulated annealing algorithm integrating genetic operators was designed to solve this problem. This hybrid algorithm combines local search strategies such as the selection operator without repeated strings and the crossover operator that preserves the best substring to improve the algorithm’s solving performance. Numerical experiments were conducted through a set of benchmark examples, and the results showed that the proposed algorithm can adapt to problem instances of different scales. In 50 customer examples, the difference between the algorithm and the standard value in this paper is 2.30%, which is 7.29% higher than C&S. Finally, the effectiveness of the grey prediction freight path optimization model was verified through a practical case simulation analysis, achieving a logistics cost savings of 9.73%. Show more
Keywords: Pick-up routing problems, fresh logistics, gray prediction, hybrid simulated annealing
DOI: 10.3233/JIFS-235260
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10813-10832, 2024
Authors: Faheem Nikhat, H. | Sait, Saad Yunus
Article Type: Research Article
Abstract: To ensure a safe and pleasant user experience while watching content on YouTube, it is necessary to identify and classify inappropriate content, especially content that is inappropriate for children. In this work, we have concentrated on establishing an efficient system for detecting inappropriate content on YouTube. Most of the work focuses on manual pre-processing; however, it takes too much time, requires manpower support, and is not ideal for solving real-time problems. To address this challenge, we have proposed an automatic preprocessing scheme for selecting appropriate frames and removing unwanted frames such as noise and duplicate frames. For this purpose, we …have utilized the proposed novel auto-determined k-means (PADK-means) algorithm. Our PADK-means algorithm automatically determines the optimal cluster count instead of manual specifications. By doing this, we have solved the manual cluster count specification problem in the traditional k-means clustering algorithm. On the other hand, to improve the system’s performance, we utilized the Proposed Feature Extraction (PFE) method, which includes two pre-trained models DenseNet121 and Inception V3 are utilized to extract local and global features from the frame. Finally, we employ a proposed double-branch recurrent network (PDBRNN) architecture, which includes bi-LSTM and GRU, to classify the video as appropriate or inappropriate. Our proposed automatic preprocessing mechanism, proposed feature extraction method, and proposed double-branch RNN classifier yielded an impressive accuracy of 97.9%. Show more
Keywords: DenseNet121, inappropriate YouTube content detection, InceptionV3, PADK-means, PFE, PDBRNN
DOI: 10.3233/JIFS-236871
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10833-10845, 2024
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