Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Jiang, Xianliang | Yang, Ze | Huang, Junkai | Jin, Guang | Yu, Guitao | Zhang, Xi | Qin, Zhen

Article Type: Research Article

Abstract: Rivers serve as vital water sources, maintain ecological equilibrium, and enhance landscapes. However, the looming issue of floating debris stemming from improper waste disposal and illegal discharge, poses an imminent threat to river ecosystems and their aesthetic appeal. Conventional human-led inspections prove labor-intensive, inefficient, and prone to errors. This study introduces an innovative approach for river debris detection, employing Unmanned Aerial Vehicles (UAVs) imagery in conjunction with a refined YOLOv5n model. This approach offers three key contributions. Primarily, the YOLOv5n model is bolstered by integrating the Efficient Channel Attention (ECA) module and reshaping the MobileNetV3 backbone to align with MobileNetV3S, …thereby significantly streamlining computational demands and model intricacy. Additionally, precision and speed are augmented by eliminating the detection head for larger targets, while decreasing computational requirements. Subsequently, to counter dataset scarcity, we curate a UAV-derived river debris dataset, encompassing five prevalent debris types, serving as an indispensable resource for method refinement and assessment. Lastly, the upgraded model’s evaluation on Jetson Nano yields an mAP of 87.2%, merely 0.7% lower than the original YOLOv5n model. Remarkably, the refined model achieves substantial reductions of 57.1% in parameters, 52.6% in volume, and 54.8% in GFLOPs. Additionally, inference time is abbreviated to 57.3ms per Jetson Nano image, 13.4ms faster than the original. These findings underscore edge computing’s potential in river restoration. In conclusion, the fusion of deep learning object detection and UAV imagery empowers adept river debris detection. Show more

Keywords: Rivers, floating debris, UAV Imagery, YOLOv5n model, edge computing

DOI: 10.3233/JIFS-234222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2507-2520, 2024

Authors: Sruthi, S. | Anuradha, B.

Article Type: Research Article

Abstract: Fire poses a significant threat to both lives and property, necessitating effective early detection measures. Despite challenges in identifying smoke and fire in their initial stages, we have devised a cost-efficient visual detection system. Early fire detection enhances its potential effectiveness. CCTV surveillance systems are now commonplace in developed countries, serving as tools for periodic monitoring of various locations. However, fluctuating ambient light conditions, camera angles, and seasonal variations can introduce data distortions, occlusions, and impact model accuracy. To address these issues, we’ve implemented a method combining deep learning networks and machine learning strategies for flame detection and direction classification. …Our innovative QuickDenseNet extracts dense features from segmented flame video frames. We introduce the Ensemble Score Voted SVM (ESV-SVM), employing SVM as the primary learner and score voting as the auxiliary learner. Our approach is rigorously evaluated through simulations, measuring accuracy and various Key Performance Indices (KPIs), including Precision, F1-score, Recall, Correlation, Error, FPR, and Correlation Coefficients. Remarkably, our proposed method achieves an impressive precision rate of approximately 99.5%. Show more

Keywords: Fire detection, ensemble learning, deep feature, CNN, video surveillance, color segmentation, dense network

DOI: 10.3233/JIFS-236387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2521-2535, 2024

Authors: Kaur, Ranjeet | Tripathi, Alka

Article Type: Research Article

Abstract: The present work is an effort to support the typographical errors of keywords that are not supported by existing compilers and integrated development environment(IDE) in ’C’ language. The fuzzy automata modelling approximate string matching is proposed for error handling during lexical analysis. By introducing fuzziness to lexemes the typographical errors can be rectified at the time of compilation and flexibility of lexical analyser can be greatly improved. The recognition of fuzzy tokens during lexical analysis is described in order to correct errors caused by sticking key, deletion, typing and swapping key in keywords during C programming. Algorithms and pseudo code …are being developed to measure the degree of membership of crisp and fuzzy lexemes. Accuracy is tested and examined once the fuzzy lexemes are trained using a neural network. The proposed method is an add on feature that can be incorporated in existing compilers and IDEs to increase their flexibility. Show more

Keywords: Fuzzy lexemes, fuzzy automata, error handling, approximate string matching, fuzzy lexical analysis

DOI: 10.3233/JIFS-223021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2537-2546, 2024

Authors: Konduru, Ashok Kumar | Mazher Iqbal, J.L.

Article Type: Research Article

Abstract: Emotion recognition from speech signals serves a crucial role in human-computer interaction and behavioral studies. The task, however, presents significant challenges due to the high dimensionality and noisy nature of speech data. This article presents a comprehensive study and analysis of a novel approach, “Digital Features Optimization by Diversity Measure Fusion (DFOFDM)”, aimed at addressing these challenges. The paper begins by elucidating the necessity for improved emotion recognition methods, followed by a detailed introduction to DFOFDM. This approach employs acoustic and spectral features from speech signals, coupled with an optimized feature selection process using a fusion of diversity measures. The …study’s central method involves a Cuckoo Search-based classification strategy, which is tailored for this multi-label problem. The performance of the proposed DFOFDM approach is evaluated extensively. Emotion labels such as ‘Angry’, ‘Happy’, and ‘Neutral’ showed a precision rate over 92%, while other emotions fell within the range of 87% to 90%. Similar performance was observed in terms of recall, with most emotions falling within the 90% to 95% range. The F-Score, another crucial metric, also reflected comparable statistics for each label. Notably, the DFOFDM model showed resilience to label imbalances and noise in speech data, crucial for real-world applications. When compared with a contemporary model, “Transfer Subspace Learning by Least Square Loss (TSLSL)”, DFOFDM displayed superior results across various evaluation metrics, indicating a promising improvement in the field of speech emotion recognition. In terms of computational complexity, DFOFDM demonstrated effective scalability, providing a feasible solution for large-scale applications. Despite its effectiveness, the study acknowledges the potential limitations of the DFOFDM, which might influence its performance on certain types of real-world data. The findings underline the potential of DFOFDM in advancing emotion recognition techniques, indicating the necessity for further research. Show more

Keywords: Hidden markov model, emotion detection, speech signal, artificial intelligence, cuckoo search, distributed diversity measures

DOI: 10.3233/JIFS-231263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2547-2572, 2024

Authors: Gao, Lijun | Zhu, Jialong | Zhang, Xuedong | Wu, Jiehong | Yin, Hang

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-231653

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2573-2584, 2024

Authors: Liu, Cong | She, Wenhao

Article Type: Research Article

Abstract: Defect detection in mobile phone cameras constitutes a critical aspect of the manufacturing process. Nonetheless, this task remains challenging due to the complexities introduced by intricate backgrounds and low-contrast defects, such as minor scratches and subtle dust particles. To address these issues, a Bilateral Feature Fusion Network (BFFN) has been proposed. This network incorporates a bilateral feature fusion module, engineered to enrich feature representation by fusing feature maps from multiple scales. Such fusion allows the capture of both fine and coarse-grained details inherent in the images. Additionally, a Self-Attention Mechanism is deployed to garner more comprehensive contextual information, thereby enhancing …feature discriminability. The proposed Bilateral Feature Fusion Network has been rigorously evaluated on a dataset of 12,018 mobile camera images. Our network surpasses existing state-of-the-art methods, such as U-Net and Deeplab V3+, particularly in mitigating false positive detection caused by complex backgrounds and false negative detection caused by slight defects. It achieves an F1-score of 97.59%, which is 1.16% better than Deeplab V3+ and 0.99% better than U-Net. This high level of accuracy is evidenced by an outstanding precision of 96.93% and recall of 98.26%. Furthermore, our approach realizes a detection speed of 63.8 frames per second (FPS), notably faster than Deeplab V3+ at 57.1 FPS and U-Net at 50.3 FPS. This enhanced computational efficiency makes our network particularly well-suited for real-time defect detection applications within the realm of mobile camera manufacturing. Show more

Keywords: Defect detection, image segmentation, feature fusion, deep learning, mobile camera

DOI: 10.3233/JIFS-232664

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2585-2594, 2024

Authors: Jiang, Li | Yang, Lu | Zang, Xiaoning | Dong, Junfeng | Lu, Wenxing

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-233045

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2595-2614, 2024

Authors: Zheng, Lingfei | Hu, Zhubing | Yao, Meiling | Xu, Pengwei | Ma, Jing

Article Type: Research Article

Abstract: Hand gesture recognition is important in human-computer interaction with wide applications in many fields. Different from common hand gesture recognition based on 2D images acquired from RGB camera, the utilization of 3D images provides additional spatial information about the target and attracts more and more attention in hand gesture recognition. However, most 3D images for hand gesture recognition are based on depth maps, which only take the distance information as a channel of 2D images, without taking full use of the 3D information. Besides, greater data volume of 3D images brings challenges to the arithmetic facility of hand gesture recognition. …Here, we proposed a point cloud based method for hand gesture recognition. To fully use the 3D information, plane points for template matching were extracted based on their normal distributions, which leads to the average recognition rate over 97%. Pre-classification was implemented to ensure a high-efficient recognition without additional requirements for the computer. The proposed method may provide approach for accurate and efficient hand gesture recognition based on 3D images. Show more

Keywords: Hand gesture recognition, point cloud, 3D images, template matching

DOI: 10.3233/JIFS-233120

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2615-2627, 2024

Authors: Hameed, Saira | Ahmad, Uzma | Ullah, Samee | Shah, Abdul Ghafar

Article Type: Research Article

Abstract: Fuzzy graphs are of great significance in the modeling and analysis of complex systems characterized by uncertain and imprecise information. Among various types of fuzzy graphs, cubic fuzzy graphs stand out due to their ability to represent the membership degree of both vertices and edges using intervals and fuzzy numbers, respectively. The study of connectivity in fuzzy graphs depends on understanding key concepts such as fuzzy bridges, cutnodes and trees, which are essential for analyzing and interpreting intricate networks. Mastery of these concepts enhances decision-making, optimization and analysis in diverse fields including transportation, social networks and communication systems. This paper …introduces the concepts of partial cubic fuzzy bridges and partial cubic fuzzy cutnodes and presents their relevant findings. The necessary and sufficient conditions for an edge to be a partial cubic fuzzy bridge and cubic fuzzy bridge are derived. Furthermore, it introduces the notion of cubic fuzzy trees, provides illustrative examples and discusses results relevant to cubic fuzzy trees. The upper bonds for the number of partial cubic fuzzy bridges in a complete CFG is calculated. As an application, the concept of partial cubic fuzzy bridges is used to identify cities most severely affected by traffic congestion resulting from accidents. Show more

Keywords: Fuzzy graph, connectivity, bridges, trees, cubic fuzzy graph

DOI: 10.3233/JIFS-233142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2629-2647, 2024

Authors: Mohamed Nusaf, A. | Kumaravel, R.

Article Type: Research Article

Abstract: Air pollution exerts a profound impact on both public health and the natural environment. In India, festivals like Diwali also contaminate the air by releasing pollutants into the atmosphere. It is essential to identify the most polluted region by estimating these pollutants. Since air quality assessment involves multiple air pollutants, there may be inherent uncertainty associated with data. This study employs a fuzzy Multi Attribute Decision Making (MADM) framework fuzzy Analytical Hierarchy Process-Entropy-fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FAHP-Entropy-FVIKOR) to model the impact of air pollution as a decision-making problem to address the uncertainty and assess the air quality during …the Diwali festival from 2019 to 2021 in Tamil Nadu, India. An integrated weighting approach is utilised to determine the weights of the air pollutants using a fuzzy Analytical Hierarchy Process and Entropy methods. Mainly, the fuzzy VIKOR approach is employed to rank the polluted regions. The validation of the proposed model is established through a comparative analysis using Spearman’s rank correlation with two other existing fuzzy MADM methods. Furthermore, a sensitivity analysis is conducted to evaluate the influence of priority weights and the interdependence of pollutants in determining regional rankings. The results conclude that a strong positive correlation is attained between the proposed and existing methods and the highest levels of air pollution during the festival period are observed in Gandhi Nagar (2019), Rayapuram (2020), T. Nagar, Sowcarpet and Triplicane (2021) in their respective years. These findings substantiate the consistency and effectiveness of the proposed approach. Show more

Keywords: Air pollution, entropy, fuzzy MADM, fuzzy VIKOR, fuzzy AHP

DOI: 10.3233/JIFS-233593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2649-2663, 2024