Journal of Intelligent & Fuzzy Systems - Volume 47, issue 5-6

Authors: Shukla, Shiv Shankar Prasad | Singh, Maheshwari Prasad

Article Type: Research Article

Abstract: Detecting behavioral changes associated with suicidal ideation on social media is essential yet complex. While machine learning and deep learning hold promise in this regard, current studies often lack generalizability due to single dataset reliance. Traditional embedding techniques struggle with semantic analysis,leading to challenges in achieving high accuracy models and conventional validation methods have data drift limitations. To address these challenges, this study proposes a novel evaluation approach using natural language processing across diverse platforms like Twitter and Reddit. By integrating BERT embedding, adept at handling semantic nuances, with an optimized Stacked Classifier combining different base classifiers and XGBoost as …the meta-classifier, the model excels in swiftly detecting signs of suicidal ideation compared to the Voting Classifier, i.e., the combination of Decision Tree, Random Forest, Gradient Boost and XGBoost and several machine learning models. Additionally, the study explores advanced embedding techniques like MUSE and LLM, and deep learning models including Bi-LSTM, Bi-GRU, and Text-CNN for comparison.This ensemble approach aims to create a model that is not only interpretable but also robust, reducing computational complexity and enhancing resilience against noisy data—common challenges faced in text classification tasks. Through K-fold validation, which involves partitioning the dataset into k equal-sized subsets or "folds" and training the model k times, using k-1 folds for training and one-fold for testing each time, the proposed model achieves impressive accuracy rates of 97% on Reddit and 96% on Twitter datasets, underscoring its effectiveness in identifying suicidal ideation across social media platforms. Show more

Keywords: Stacked Classifier, Voting Classifier, MUSE, BERT, Bi-GRU, Bi-LSTM, suicidal ideation

DOI: 10.3233/JIFS-234506

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 335-349, 2024

Authors: Liu, Yu | Zhu, Ye | Chong, Haoze | Yu, Ming

Article Type: Research Article

Abstract: Deep learning-based image semantic segmentation approaches heavily rely on large-scale training datasets with dense annotations and often suffer from scarce semantic labels for unseen categories. This limitation has spurred a research trend in Few-shot image Semantic Segmentation (FSS), which makes it possible to segment objects of new categories using only a few labeled samples. Although more and more FSS methods are emerging and gradually integrated into practical applications, a deep understanding of its achievements and issues is still missing. In this survey, we focus on the recent developments of FSS, specifically on FSS methods based on meta-learning. According to different …network architectures, we summarize the related research into three classes, that are Convolutional Neural Network-based (CNN-based) models, Graph Neural Network-based (GNN-based) models, and Transformer-based models. Then, we explore the specific implementations of these models, including parameter-based methods, metric-based methods, attention-based methods, and optimization-based methods. Furthermore, we illustrate datasets and analyze the experimental results of various kinds of methods. Toward the end of the paper, we discuss the limitations of FSS and present its applications and challenges to provide further research directions. Show more

Keywords: Deep learning, few-shot learning, image semantic segmentation, meta-learning

DOI: 10.3233/JIFS-235220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 351-367, 2024

Authors: Zhang, Li | Chen, Xiaobo

Article Type: Research Article

Abstract: Aiming at the shortcomings of the traditional butterfly optimization algorithm in solving the high-dimensional classification feature selection problem, which has low convergence and is prone to fall into local optimal solutions, a new hybrid butterfly optimization algorithm is proposed, i.e., HBOA-SCV (A novel hybrid butterfly optimization algorithm with sine cosine velocity). The algorithm is applied to solve a high-dimensional classification feature selection problem. Firstly, the algorithm’s global exploration and local exploitation ability can be dynamically balanced by introducing inertia weight coefficients w based on multiple learning strategies. Secondly, using the updated speed position formula of the sine-cosine acceleration strategy, …individual butterflies’ autonomous search ability and convergence speed can be further improved. Finally, according to the fitness value of each butterfly individual, the moving step length and direction of the butterfly individual are automatically adjusted better to fit the actual search process of the butterfly individual, increase the search ability in the global range, and avoid the algorithm from falling into the local optimum. To verify the algorithm’s effectiveness, 18 high-dimensional classification numbers are selected to carry out simulation and comparison experiments between HBOA-SCV and traditional BOA algorithm, five improved BOA algorithms and other comparative algorithms for high-dimensional classification data successively. The experimental results show that the average fitness value and classification accuracy of the HBOA-SCV algorithm are better than the comparison algorithm, thus verifying the superiority of the HBOA-SCV algorithm. Show more

Keywords: Butterfly optimization algorithm, Feature Selection, inertia weights, Sine-cosine acceleration strategy, global exploration and local exploitation

DOI: 10.3233/JIFS-236372

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 369-391, 2024

Authors: Yi, Lingzhi | Cheng, Siyue | Wang, Yahui | Ma, Hao | Luo, Bote | Hu, Yao

Article Type: Research Article

Abstract: Shading and array fault can cause a significant impact on the output power of rural rooftop PV array (RRPVA) and result in power efficiency losses. One of the most popular methods to attenuate the adverse effects of these is reconfiguration in RRPVA. However, the conventional reconfiguration only aims to maximize power output. Hence, this paper proposes a multi-objective pelican optimization algorithm (MOPOA) to improve efficiency and extend the switching life for RRPVA. Comparing the reconfiguration results of the particle swarm algorithm (PSO) and genetic algorithm (GA), the mismatch loss, power loss, performance ratio, and power enhancement percentage of RRPVA under …different shading situations are calculated for each of the three algorithms. This paper simulates and analyzes 4×4 symmetric RRPVA and 4×3 asymmetric RRPVA. The results show that MOPOA is 8.4%, 8.5%, 11.2%, 11.5% better than PSO; and 3.8%, 3.5%, 7.6%, 5.6% better than GA in terms of percentage power enhancement (P en ) in 4×4 symmetric RRPVA. In the 4×3 asymmetric RRPVA, the P en of MOPOA is 5.6%, 9.0%, 10.5%, 9.4% better than PSO, and 4.2%, 2.6%, 3.6%, 2.8% better than GA, respectively. In the case of array fault, the power enhancements were 19.4% and 18.3%, respectively. Show more

Keywords: Dynamic reconfiguration in RRPVA, multi-objective pelican optimization algorithm, power enhancement, multi-type RRPVA

DOI: 10.3233/JIFS-236528

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 393-409, 2024

Authors: Li, Jie | Hong, Chaoqun | Huang, Pengcheng | Wang, Xiaodong | Ran, Lang

Article Type: Research Article

Abstract: Deep Hashing is a technique used for retrieving images on a large-scale, encoding the latent code of images into binary codes, which significantly reduces computational and storage costs in image retrieval. This enables fast similarity comparison and search. However, this technique encounters two significant challenges: the extraction of discriminating category-specific image features and the conflict between metric learning and quantization learning. The latter challenge often results in the binary representation of latent codes being considerably ambiguous. To tackle these challenges, this paper proposes a novel Cross-Scale Fusion Deep Hash Network. The model is built upon a dual-branch framework, aiming to …capture the most representative retrieval features. One branch employs Spatial Pyramid Pooling layers and a self-attention mechanism for local information extraction, whereas the other branch uses a sliding window methodology for capturing global information. Upon obtaining the local and global information, the Cross Feature Synergy Module proposed in this paper integrates these data points to form a comprehensive feature vector, ultimately generating a complete representation of the image. In order to address the conflict between metric learning and quantization learning, as well as improve the binary codes further, this paper introduces a meticulously designed, threshold-dependent Hash-Guided Metric Loss (HGM-Loss). The novel network proposed in this paper demonstrates superior retrieval performance in standard benchmark tests on multiple datasets, including CIFAR-10, CIFAR-100, ImageNet, and MS-COCO, outperforming the existing hash methods. Show more

Keywords: Deep hashing, binary encoding, image retrieval

DOI: 10.3233/JIFS-237502

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 411-426, 2024

Authors: Jiménez, Robinson | Castillo, Ricardo | Jaramillo, Jorge

Article Type: Research Article

Abstract: This article delves into the integration between CNN-based artificial vision and robotic navigation algorithms with the aim of efficient autonomous driving of a tracked mobile robot in residential environments. The development is based on a machine vision system, through a camera mounted on the robot, capturing scenes from different environments within a residential home to identify its current location. PROBLEM: Robotic navigation’s kinematics are usually implemented in spatial coordinates of an unknown environment, thus limiting the human-robot interaction to a naive completion of commands by ignoring the potential behind the environmental context in which the robot behaves. The integration …of artificial vision into robotic navigation is expected to enhance a robot’s performance in supporting domestic environment tasks. METHODOLOGY: To achieve the identification of the robot’s location and its direction of movement, a convolutional neural network is employed, which has two branches that identify different aspects of the environment from the robot’s perspective. Once a destination is set within the environment, a branched exploration algorithm is implemented, allowing the robot to navigate while knowing its location. RESULTS: Mobile robotic algorithms for path planning and obstacle avoidance were implemented along with a 98.33% accuracy CNN measured on its capacity to identify residential rooms from the robot’s first-person perspective. These algorithms’ incorporation resulted in the successful guidance of a tracked differential mobile robot through the rooms of a virtual residential environment, avoiding obstacles in the process and identifying locations through which the robot crosses. Show more

Keywords: Autonomous driving, computer vision, obstacle avoidance, path planning, residential environments

DOI: 10.3233/JIFS-238028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 427-437, 2024

Authors: Mollashahi, Hossein | Fakhrzad, Mohammad Bagher | Hosseini Nasab, Hasan | Khademi Zare, Hasan

Article Type: Research Article

Abstract: In recent years, the establishment of competitive markets has led researchers to pay more attention to the subject of supply chain design and competition in their studies. In this research, a multi-objective mathematical model is proposed for the design of a dynamic, integrated network in a competitive, sustainable, and resilient closed-loop supply chain for perishable goods under disruptions. In this model, competition between two chains is examined with a focus on economic, environmental, social, and resilience considerations. To solve this competitive model, a two-stage approach is used. In the first stage, game theory is employed to determine equilibrium values in …competitive decisions, and considering the complexities of the model, the Multi Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II) metaheuristic algorithms are used to solve the supply chain design problem. To evaluate the efficiency of the model and the proposed solution approach, the performance of each algorithm is analyzed based on five criteria: computation time, distance, average distance from the ideal solution, diversity, and the number of solutions examined, using random numerical examples. The results are analyzed graphically and statistically. In comparison to the NSGA-II algorithm, the MOPSO algorithm demonstrates better performance in terms of all criteria, with average improvements of 36.5% in distance, 33.9% in average distance from the ideal solution, 20.8% in diversity, and 79.6% in the number of solutions examined. The results indicate the effectiveness of the proposed approach and model in designing sustainable and resilient supply chain networks under competition for perishable goods. Show more

Keywords: Supply chain network design, resilience, sustainability, inter-chain competition, disruption

DOI: 10.3233/JIFS-238397

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 439-455, 2024

Authors: Xu, Tongzhao | Tohti, Turdi | Liang, Yi | Zuo, Zicheng | Hamdulla, Askar

Article Type: Research Article

Abstract: Existing multi-hop knowledge graph question answering (KGQA) methods, which attempt to mitigate knowledge graph (KG) sparsity by introducing external text repositories instead of leveraging the question-answer information itself, ignore the semantic gap between the question modality and the knowledge graph modality as well as the role played by neighboring entities in the best answer selection. To address the above problems, we propose a Joint Reasoning-based Embedded Multi-hop KGQA (JREM-KGQA) method, which addresses these issues through three key innovations: 1) Early Joint Embedding. We construct a Question Answering-Knowledge Graph-Collaborative Work Diagram (QA-KG-CWD) and train the diagram using a knowledge graph embedding …(KGE) model. This not only alleviates the knowledge graph sparsity but also effectively enhances the model’s long-path reasoning ability. 2) Semantic Fusion Module. We narrowed the semantic gap between the question modality and the knowledge graph modality through the semantic fusion module to achieve more effective reasoning. 3) Node Relevance Scoring. We employ three node relevance scoring strategies to ensure that the best answer is selected from the huge knowledge graph. We evaluated our model on MetaQA as well as PQL datasets and compared it with other methods. The results demonstrate that our proposed model outperforms existing methods in terms of long-path reasoning ability, effective mitigation of knowledge graph sparsity, and overall performance. We have made our models source code available at github: https://github.com/feixiongfeixiong/JREM-KGQA Show more

Keywords: Knowledge graph, multi-hop knowledge graph question answering, knowledge graph embedding

DOI: 10.3233/JIFS-239090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 457-469, 2024

Authors: Zhang, Xingpeng | Pan, Yunfeng | Wang, Qiuli | Yang, Mo | Xiao, Bin

Article Type: Research Article

Abstract:  Cell nucleus segmentation plays a significant role in Computer-Aided systems for cancer diagnosis. However, the nuclear images are characterized by different sizes, overlap, adhesion, and similarities between nuclei and other structures, making this task challenging. Aiming to adjust and enhance the feature learning ability of the network, this paper proposes a FourierFilter Irregular Attention U-Net (FFIA-UNet), which contains FourierFilter Irregular Attention (FFIA) and multi-receptive filed fusion (MRF) module. FFIA module seeks to learn deeper characteristics by taking advantage of frequency information and deformable convolution. MRF module improve the learning capacity of fuzzy edges and irregular forms via multiple dilated convolution. …Experiments on three datasets show that the proposed FFIA-UNet achieves state-of-the-art. Dice-Score and mIoU reached 0.929 and 0.885 respectively on DSB2018. Furthermore, numerous ablation experiments have demonstrated the module’s efficacy. Show more

Keywords: Nucleus segmentation, FourierFilter Irregular Attention, deformable convolution, multi-receptive filed

DOI: 10.3233/JIFS-239115

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 471-485, 2024

Authors: Li, Xin | Wang, Jia | Sun, Lixu | Li, Wei

Article Type: Research Article

Abstract: A number of devices in Industrial Internet are various types in recent years. The monitored traffic data from different devices always unlabeled and contain various types of attack traffic. In other words, misjudgments occurring by the ambiguity with these various unlabeled traffic in situation assessment of Industrial Internet need to solve urgently for above complex network scenario. In this paper, a new self-supervised situation assessment method FCVnet (FCM-CNN-ViT Net) is proposed to reduce the misjudgement probability. An enhanced fuzzy c-means clustering method EFCM (Enhanced Fuzzy C-means Clustering), is designed for the unlabelled traffic data. Meanwhile the self-supervised pre-training is carried …out by improving initial cluster centre selection to obtain more accurate labels. In order to capture more global features for better feature representation, MCFV (Multi-Convolutional Fusion and Vision Transformer) module combining Multi-Convolutional Neural Network and Vision Transformer (ViT) is designed to capture and fuse features from local details to broader context. Experimental results show that the precision and recall of the proposed FCVnet are improved by 7.51% and 15.16% on average with two data sets. Show more

Keywords: Situation assessment, self-supervised learning, fuzzy c-means clustering, feature fusion, situation awareness

DOI: 10.3233/JIFS-241030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 5-6, pp. 487-497, 2024