Journal of Intelligent & Fuzzy Systems - Volume 46, issue 3

Authors: Liu, Yang | Yi, Ran | Ma, Ding | Wang, Yongfu

Article Type: Research Article

Abstract: Due to the complexity of the maritime environment and the diversity of the volume and shape of monitored objects in the maritime, existing object detection algorithms based on Convolutional Neural Networks (CNN) are challenging to balance the requirements of high accuracy and high real-time simultaneously in the field of maritime object detection. In response to the characteristics of complex backgrounds, significant differences in object size between categories, and the characteristic of having a large number of small objects in maritime surveillance videos and images, the Maritime dataset with rich scenes and object categories was self-made, and the OS-YOLOv7 algorithm was …proposed based on the YOLOv7 algorithm. Firstly, a feature enhancement module named the TC-ELAN module based on the self-attention mechanism was designed, which enables the feature map used for detection to obtain enhanced semantic information fused from multiple scale features. Secondly, in order to enhance the attention to the area of dense small objects and further improve the positioning accuracy of occluded small objects, this study redesigned the SPPCSPC structure. Then, the network structure was improved to alleviate the problem of decreased object detection accuracy caused by the loss of semantic feature information. Finally, experimental results on self-made datasets and mainstream maritime object detection datasets show that OS-YOLOv7 has a better object detection effect compared to other state-of-the-art (SOTA) object detection algorithms at the cost of reasonable inference time and parameter quantity and can achieve good object detection accuracy on mainstream datasets with high real-time performance. Show more

Keywords: Object detection, real-time, maritime, object recognition, multi-scale

DOI: 10.3233/JIFS-237263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7259-7271, 2024

Authors: Feng, Yue | Zhu, Yuanguo | He, Liu

Article Type: Research Article

Abstract: In recent years, there has been a great development in parameter estimation methods for uncertain differential equations (UDEs). However, the observations we can obtain in real life are limited, in which case the form of function in a UDE is unknown. When dealing with such UDEs, we may use observational data to make nonparametric estimates. There are many nonautonomous systems in real life, and nonautonomous UDEs can simulate some uncertain nonautonomous dynamical systems well. In this paper, a nonparametric estimation method based on the nonautonomous UDEs of the binary Legendre polynomial is proposed. Then, three numerical examples are given to …verify the reliability of nonparametric estimation. As an application, a real data example of global average monthly temperatures is used to illustrate the effectiveness of our method. Show more

Keywords: Uncertainty theory, uncertain differential equations, nonparametric estimation, global average monthly temperature

DOI: 10.3233/JIFS-235022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7273-7281, 2024

Authors: Devulapalli, Praveen Kumar | Boppidi, Srikanth | Sake, Pothalaiah | Matta, Jagadeesh Chandra Prasad | Gopal, Dhanalakshmi | Maganti, Sushanth Babu

Article Type: Research Article

Abstract: High bit-error rates and high transmission rates are required for the Multimedia Wireless Sensor Networks (MWSN) to transmit high-quality pictures through smart devices. To fully utilize the advantages of the technology known as Multiple-Input Multiple-Output, MWSN heavily relies on cooperative communication. Large-scale wireless networks use multi-radio-multi-channel to improve performance by simultaneous broadcasts across symmetrical channels to reduce interference. Expanding cooperative communication in vast networks is subject to severe interference. And, as each node in the network is mobile, routing and transmission delay pose significant problems for cooperative multimedia wireless sensor networks. Mobility increases the MWSNs’ dynamic nature, which reflects in …the overhead control traffic. To address above issues, a Cluster-based Delay Aware Cooperative Relay Selection (CDACRS) was proposed by employing mobility and distance metrics and channel assignment (CA) using dynamic Global Table (GT). To minimize the end-to-end transmission latency without compromising aggregate throughput, our approach chooses a relay-node depending on the mobility and the maximum available channel capacity. Further, to improve the end-to-end energy consumption, Power Aware Transmission (PAT) protocol is developed by calculating maximum transmission power required to meet target bit error rate (BER). The proposed method’s performance is evaluated against the Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS); energy efficient and quality aware multi-hop cooperative image transmission; and Energy Aware Cooperative Image Transmission (EACIT) algorithms and observed that our approach improves the transmission delay by 37.5% (approx.) and end-to-end energy consumption by 48.8%. Show more

Keywords: Wireless multimedia sensor networks, Delay aware routing, Cooperative image transmission, Energy efficiency, Optimal relay selection

DOI: 10.3233/JIFS-234312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7283-7293, 2024

Authors: Srivastava, Jyoti | Srivastava, Ashish Kumar | Muthu Kumar, B. | Anandaraj, S.P.

Article Type: Research Article

Abstract: Text summarizing (TS) takes key information from a source text and condenses it for the user while retaining the primary material. When it comes to text summaries, the most difficult problem is to provide broad topic coverage and diversity in a single summary. Overall, text summarization addresses the fundamental need to distill large volumes of information into more manageable and digestible forms, making it a crucial technology in the era of information abundance. It benefits individuals, businesses, researchers, and various other stakeholders by enhancing efficiency and comprehension in dealing with textual data. In this paper, proposed a novel Modified Generative …adversarial network (MGAN) for summarize the text. The proposed model involves three stages namely pre-processing, Extractive summarization, and summary generation. In the first Phase, the Text similarity dataset is pre-processed using Lowering Casing, Tokenization, Lemmatization, and, Stop Word Removal. In the second Phase, the Extractive summarization is done in three steps Generating similarity metrics, Sentence Ranking, and Sentence Extractive. In the third stage, a generative adversarial network (GAN) employs summary generation to jointly train the discriminative model D and the generative model G. To classify texts and annotate their syntax, Generative Model G employs a convolutional neural network called Bidirectional Gated Recursive Unit (CNN-BiGRU). The performance analysis of the proposed MGAN is calculated based on the parameters like accuracy, specificity, Recall, and Precision metrics. The proposed MGAN achieves an accuracy range of 99%. The result shows that the proposed MGAN improves the overall accuracy better than 9%, 6.5% and 5.4% is DRM, LSTM, and CNN respectively. Show more

Keywords: Text summarization, convolutional neural network, bidirectional gated recurrent unit

DOI: 10.3233/JIFS-236813

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7295-7306, 2024

Authors: Shu, Wenhao | Li, Shipeng | Qian, Wenbin

Article Type: Research Article

Abstract: In real-world scenarios, datasets generally exhibit containing mixed-type of attributes and imbalanced classes distribution, and the minority classes in the data are the primary research focus. Attribute reduction is a key step in the data preprocessing process, but traditional attribute reduction methods commonly overlook the significance of minority class samples, causing the critical information possessed in minority class samples to damage and decrease the performance of classification. In order to address this issue, we develop an attribute reduction algorithm based on a composite entropy-based uncertainty measure to handle imbalanced mixed-type data. To begin with, we design a novel oversampling method …based on the three-way decisions boundary region to synthesize the samples of minority class, for the boundary region to contain more high-quality samples. Then, we propose an attribute measure to select candidate attributes, which considers the boundary entropy, degree of dependency and weight of classes. On this basis, a composite entropy-based uncertainty measure guided attribute reduction algorithm is developed to select the attribute subset for the imbalanced mixed-type data. Experimental on UCI imbalanced datasets, as well as the results indicate that the developed attribute reduction algorithm is significantly outperforms compared to other attribute reduction algorithms, especially in total AUC, F1-Score and G-Mean. Show more

Keywords: imbalanced data, three-way decisions, neighborhood rough set, uncertainty measure, attribute reduction

DOI: 10.3233/JIFS-237211

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7307-7325, 2024

Authors: Li, Jingyan | Mo, Yuanbin | Hong, Lila | Gong, Rong

Article Type: Research Article

Abstract: Dynamic optimization problems exist widely in chemical industry, and its operational variables change with the evolution of both space and time. Therefore, dynamic optimization problems have important research significance and challenges. To solve this problem, a multi-strategy mayfly optimization algorithm (MMOA) combined with control variable parameterization method(CVP) is proposed in this paper. MMOA introduces three improvements on the basis of the original algorithm, namely, circle chaos crossover strategy, center wandering strategy and boundary correction strategy. The hybrid strategy can better balance the exploration and exploitation ability of the algorithm. Based on MATLAB simulation environment, MMOA was evaluated. The experimental results …show that MMOA has excellent performance in solving precision, convergence speed and stability for the benchmark function. For the six classical chemical dynamic optimization problems, MMOA obtained the performance indexes of 0.61071, 0.4776, 0.57486, 0.73768, 0.11861 and 0.13307, respectively. Compared with the data in the previous literature, MMOA can obtain more accurate control trajectory and better performance indicators. It provides an effective way to solve the dynamic optimization problem. Show more

Keywords: Chemical dynamic system, process control, dynamic optimization, mayfly optimization algorithm, control vector parameterization

DOI: 10.3233/JIFS-237786

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7327-7352, 2024

Authors: Zhang, Hong | Liu, Shaojie

Article Type: Research Article

Abstract: The amount of used new energy vehicle transactions is increasing quickly as the social economy matures, yet prices are typically low, making it increasingly difficult to select a fair trading system. Enhancing the score function is crucial in order to account for how different people’s attitudes affect the outcome of decisions and to choose an acceptable trading strategy that is applicable to other scenarios and has a favorable impact on transaction flow. The choice of a trading scheme for new energy-using vehicles is usually regarded as a multi-attribute decision problem. In this paper, the Intuitionistic Fuzzy Hybrid Averaging (IFHA) operator …integration operator with an improved score function is proposed based on the influence of herd mentality on decision-makers. In order to examine the correlation between the score function and the decision outcome using the Spearman rank correlation coefficient, an application to a real situation and some comparative analyses are provided. The outcomes demonstrate that the decision-making process for used car trading schemes can make use of the proposed improved score function. Show more

Keywords: Intuitionistic fuzzy set, multi-characteristic decision making, used new energy car, improved score function, spearman rank correlation coefficient

DOI: 10.3233/JIFS-231358

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7353-7365, 2024

Authors: Xiong, Yu | Cai, Ting | Zhong, Xin | Zhou, Song | Cai, Linqin

Article Type: Research Article

Abstract: Speech emotion recognition is of great significance in the industry such as social robots, health care, and intelligent education. Due to the obscurity of emotional expression in speech, most works on speech emotion recognition (SER) ignore the consistency of speech emotion recognition, leading to fuzzy expression and low accuracy in emotional recognition. In this paper, we propose a semantic aware speech emotion recognition model to alleviate this issue. Specifically, a speech feature extraction module based on CNN and Transformer is designed to extract local and global information from the speech. Moreover, a semantic embedding support module is proposed to use …text semantic information as auxiliary information to assist the model in extracting emotional features of speech, and can effectively overcome the problem of low recognition rate caused by emotional ambiguity. In addition, the model uses a key-value pair attention mechanism to fuse the features, which makes the fusion of speech and text features preferable. In experiments on two benchmark corpora IEMOCAP and EMO-DB, the recognition rates of 74.3% and 72.5% were obtained under respectively, which show that the proposed model can significantly improve the accuracy of emotion recognition. Show more

Keywords: Speech emotion recognition, obscure emotion, semantic awareness, deep learning, pattern recognition

DOI: 10.3233/JIFS-232280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7367-7377, 2024

Authors: Song, Kun | Yi, Huai’an | Song, Xinru | Shu, Aihua | Huang, Jiefeng

Article Type: Research Article

Abstract: The surface roughness of the workpiece is one of the important indicators to measure the quality of the workpiece. Vision-based detection methods are mainly based on human-designed image feature indicators for detection, while the self-extraction method of milling surface features based on deep learning has problems such as poor perception of details, and will be affected by surface rust. In order to solve these problems, this paper proposes a visual inspection method for surface roughness of milling rusted workpieces combined with local equilibrium histogram and CBB-yolo network. Experimental results show that local equilibrium histogram can enhance the milling texture and …improve the accuracy of model detection when different degrees of rust appear on the surface of the milled workpiece. The detection accuracy of the model can reach 97.9%, and the Map can reach 99.3. The inference speed can reach 29.04 frames per second. And the inspection of workpieces without rust, this method also has high detection accuracy, can provide automatic visual online measurement of milling surface roughness Theoretical basis. Show more

Keywords: Surface roughness detection, CBB-yolo, milling workpieces, local equilibrium histosquare

DOI: 10.3233/JIFS-233590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7379-7388, 2024

Authors: Zhou, Yancong | Ma, Yumei | Sun, Xiaochen | Peng, Aihuan | Zhang, Bo | Gu, Xiaoying | Wang, Yan | He, Xingxing | Guo, Zhen

Article Type: Research Article

Abstract: Kiwifruit has a high decay rate, in part because quality changes during storage cannot be easily monitored in real time. In order to better monitor the shelf life of kiwifruit and understand the quality changing process during storage, internal quality indexes such as hardness, respiratory intensity and TSS(Total Soluble Solid) were considered into the prediction models. The prediction models were constructed based on BPNN (Back Propagation Neural Network), Random Forest (RF) and XGBoost (eXtreme Gradient Boosting) respectively. And transfer learning algorithm was used to construct the quality prediction models with BPNN, RF, and XGBoost algorithms as the base learner. In …the experiments, sample data were augmented by adding Gaussian noise, which effectively prevented the model from over-fitting. The experimental results showed that the prediction accuracy of each index based on transfer learning was better than that of individual BPNN, RF and XGBoost. Moreover, the average prediction accuracy of the models based on transfer learning was 96.2%, and that of respiratory intensity was as high as 99.4%. Therefore transfer learning can be used to effectively analyze and predict changes of kiwifruit quality indexes during storage. Show more

Keywords: BPNN, RF, XGBoost, transfer learning, kiwifruit, quality prediction

DOI: 10.3233/JIFS-233718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7389-7400, 2024