Journal of Intelligent & Fuzzy Systems - Volume 45, issue 2

Authors: Li, Zhao | Sun, Weiyi | Li, Dongze

Article Type: Research Article

Abstract: With the implementation of China’s “two-carbon” target strategy, the status of renewable energy in the power system is gradually improving. In order to improve the carbon emission control level of power grid, it is necessary to predict the peak carbon emission of power grid. A prediction method of grid carbon emission peak based on energy elasticity coefficient is proposed. A time series model of grid carbon emission samples was constructed, and a combination of combinatorial sorting and machine learning was used to reconstruct the time series of grid carbon emission peaks. Based on the results of time series reconstruction, feature …extraction and classification training of grid carbon emission peak are carried out, and the measurement error correction of grid carbon emission under the constraint of energy consumption elasticity is realized. Combined with the association rule mining method, the fusion processing of grid carbon emission peak samples under the elastic constraint of energy consumption is realized. Through characteristic detection and fusion processing of energy consumption elasticity coefficient, the reconstituted carbon emission samples of power grid constrained by energy consumption elasticity were reconstructed, and the peak carbon emission of power grid was predicted. The simulation results show that this method has high accuracy and convergence in predicting the peak carbon emission of power grid, and improves the error correction ability in the prediction process. Show more

Keywords: Energy consumption, elastic coefficient, power grid, carbon emissions, peak value prediction, association rule mining

DOI: 10.3233/JIFS-224599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2919-2930, 2023

Authors: Lei, Chang

Article Type: Research Article

Abstract: Cloud computing has emerged as one of the most promising technologies for meeting rising computing needs. However, high-performance computing systems are more likely to fail due to the proliferation of components and servers. If a sub-system fails, the entire system may not be functional. In this regard, the occurrence of faults is tolerable using an efficient fault-tolerant method. Since cloud computing involves storing data on a remote network, system failures and congestion are the most common causes of faults. The paper presents a new approach to adopting a fault-tolerant mechanism that adaptively monitors health to detect faults, handles faults using …a migration technique, and avoids network congestion. With the advantage of the Ant Colony Optimization (ACO) algorithm and active clustering, the load is distributed evenly in data centers. Simulation results indicate that our algorithm outperforms previous algorithms regarding total execution time and imbalance degree up to 10% and 17%, respectively. Show more

Keywords: Cloud computing, fault tolerance, load balancing, energy efficiency

DOI: 10.3233/JIFS-230102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2931-2948, 2023

Authors: Long, Xiaoqing | Gao, Fei

Article Type: Research Article

Abstract:  Cooperative attack with unmanned aerial vehicles (UAVs) plays a critical role in modern military warfare. To achieve multi-swarm cooperative attack with obstacle avoidance of formation, this paper proposes a cooperative control strategy that integrates flight control and autonomous marshaling. Firstly, an improved dynamics model with virtual leader-following mode is constructed to achieve obstacle avoidance of the formation. And an improved interference fluid dynamic system (IIFDS) is applied to improve path selectivity during multi-swarm attack. Secondly, a two-layer attack framework based on distributed swarm coordinated trajectory tracking with heading angle constraints is designed to achieve autonomous clustering of the UAVs and …target striking. Finally, the proposed improved dynamics model is compared with the particle swarm optimization (PSO) algorithm and artificial potential field (APF) method in terms of obstacle avoidance of formation to demonstrate its superiority, which can obtain better benefits. Furthermore, two simulations of multi-swarm cooperative attack are conducted to validate the effectiveness of the control strategy. The proposed method expands the application of UAVs attack with obstacle avoidance of formation and provides a valuable reference for modern military operations. Show more

Keywords: Multi-swarm cooperative attack, autonomous marshalling, coordinated trajectory tracking, IIFDS

DOI: 10.3233/JIFS-231180

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2949-2965, 2023

Authors: Wang, Chu | Wang, Jian

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-231449

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2967-2977, 2023

Authors: Kala, R. | Deepa, P.

Article Type: Research Article

Abstract: Brain tumor is an anomalous growth of brain cells. Segmentation of brain tumors is currently the most important surgical and pharmaceutical procedure. However, manually segmenting the brain tumor is a challenging task due to the complex structure of brain. In recent years, artificial intelligence techniques with the fuzzy logic have shown better results in the field of medicine. In this work, a novel deep learning classification network with fuzzy hexagonal membership function (DLC-FHMF) model has been proposed for accurately segmenting brain tumors. The different MRI modalities namely T1, T1-c, T2 and Flair images are preprocessed using a fuzzy hexagonal trilateral …and median filter to eliminate the Rician noise. Afterwards, the DLC-FHMF model is used for segmenting the tumor portion by using the multimodal composition of MRI as input. The fuzzy weights are determined with hexagonal membership functions and convoluted with the corresponding MRI images. The quantitative examination is carried out using the performance metrics namely accuracy, specificity, precision, sensitivity, incorrect segmentation, under-segmentation, and over-segmentation. In addition to the above metrics, the pre-processing metrics include PSNR, RMSE, and SSIM. The experimental fallout portrayals that the proposed DLC-FHMF approach attains a better accuracy range of 99% for detecting brain tumors using the BRATS 2013 dataset. The proposed DLC-FHMF model improves the overall accuracy by 15.1%, 11.1%, 3.0%, 21.2% and 0.5% better than ANN, SVM, NB, DNN and DAE respectively. Show more

Keywords: Brain tumor, magnetic resonance image, fuzzy logic, deep learning, segmentation

DOI: 10.3233/JIFS-221990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2979-2992, 2023

Authors: Zhang, Yongliang | Lu, Yang | Zhu, Wuqiang | Wei, Xing | Wei, Zhen

Article Type: Research Article

Abstract: Deep learning has dominated the research field of traffic sign detection, but the traffic sign detection algorithms based on deep learning have difficulty in solving the two tasks of localization and classification simultaneously when performing traffic sign detection on realistic and complex traffic scene images, and the images or the types of traffic signs provided by the public dataset used by the relevant algorithm cannot meet the situations encountered in realistic traffic scenes.To solve the above problems, this paper creates a new road traffic sign dataset, and based on the YOLOv4 algorithm, designs a multi-size feature extraction module and an …enhanced feature fusion module to improve the algorithm’s ability to locate and classify traffic signs simultaneously, in view of the complexity of realistic traffic scene images and the large variation of traffic sign sizes in the images. The experimental results on the newly created dataset show that the improved algorithm achieves 83.63% mean Average Precision (mAP), which is higher than several major object detection algorithms based on deep learning for the same type of task at present. The newly created dataset in this paper is publicly available at https://github.com/zhang1018/Traffic-sign-dataset-for-public . Show more

Keywords: Traffic sign detection and recognition, traffic sign datasets, autonomous driving, convolutional neural networks, intelligent traffic system

DOI: 10.3233/JIFS-210838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2993-3004, 2023

Authors: Jayashree, P. | Laila, K. | Amuthan, Aara

Article Type: Research Article

Abstract: The large flux of online products in today’s world makes business reviews a valuable source for consumers for making sound decisions before making online purchases. Reviews are useful for readers in learning more about the product and gauge its quality. Fake reviews and reviewers form the bulk of the review corpus, making review spamming an open research challenge. These spam reviews require detection to nullify their contribution to product recommendations. In the past, researchers and communities have taken spam detection problems as a matter of serious concern. Yet, for all that, there is space for the performance of exploration on …large-scale complex datasets. The work contributes towards robust feature selection with derived features that provide more details on malicious reviews and spammers. Ensemble and other standard machine learning techniques are trained and evaluated over optimal feature sets. In addition, the Metapath-based Graph Convolution Network (M-GCN) framework is proposed, which is an implicit knowledge extraction method to automatically capture the complex semantic meaning of reviews from the heterogeneous network. It makes analysis of triplet (users, reviews, and products) relationships in e-commerce sites through examination of Top-n feature sets in a mutually reinforcing manner. The proposed model is demonstrated on Yelp and Amazon benchmark datasets for evaluation of efficacy and it is shown outperforming state-of-the-art techniques with and without graph-utilization, providing an accuracy of 96% in the prediction task. Show more

Keywords: Spam review detection, feature sets derivation, machine learning, Metapath, graph convolution network

DOI: 10.3233/JIFS-223136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3005-3023, 2023

Authors: Jadhav, Ranjana S. | Dhore, Manikrao Laxmanrao

Article Type: Research Article

Abstract: Transliteration is phonetically translating a language’s words into an international or non-native screenplay. The machine translation process now plays an essential role in scholarly research. The most crucial complement criterion of the English translation system is preserving the phonetic qualities of the language specification after English translation in the chosen language. However, a suitable bilingual text corpus is necessary for statistical models to attain improved transliteration accuracy. Marathi-to-English direct machine translation is done through a cross-language information retrieval system using the CNN classifier model in this proposed research. The proposed method considers a sequence labelling issue brought on by the …split transliteration units used in the process. All half-consonant clusters in the Devanagari script are effectively mapped as half-consonant “a” s and labelled using the Modified Intermediate Phonetic Code (MIPC). After generating the phonetic units for each feature in the base and aim languages, the weight is assigned to a phonetic unit in both languages, and individual phonetic unit probabilities are computed. If the probability is zero, then segments are established and recalculated for each segment based on the target phonetic unit location in the word. Therefore, the proposed approach classifies the required phonetic unit with a high accuracy rate. Show more

Keywords: Machine Transliteration, phonetic unit, Devanagari, syllabification, N-grams, CNN classifier model

DOI: 10.3233/JIFS-223591

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3025-3037, 2023

Authors: Tang, Bingsong | Li, Nan

Article Type: Research Article

Abstract: Disputes are inevitable in construction. Tremendous losses caused by dispute are so amazing that professionals try to figure out how to manage it. It is a practical way to study the dispute problem from the perspective of governance theory. In this study, the paper intends to investigate the characteristics of contractual governance for disputes. Based on governance theory, the framework of contractual governance for dispute is constituted of governance structure (GS) and governance mechanism (GM). The flexibility of GS and GMs are all explored so as to better draft the contracts. By a multiple cases study, a new conceptual model …instructing governance picture for construction disputes was proposed which was mainly inspired from literature. The cases study shows that the GS determination is rigidly drafted and executed while the mostly GMs are flexibly designed. The rigid GS has an advantage to a stable foundation and the flexible GMs are apt to coordinate the disputes. Show more

Keywords: Contract flexibility, dispute resolution, governance theory, rigid governance, flexible governance

DOI: 10.3233/JIFS-224227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3039-3051, 2023

Authors: Hu, Shan | Jiang, Weitao | Rong, Lingda | Hu, Shixuan | Zhong, Xiaoying | Wei, Yaxin

Article Type: Research Article

Abstract: Accessible products play an essential role in the lives of people with disabilities. This paper aims to identify key user satisfaction with accessible products factors affecting the use of accessible products by people with disabilities that influence user satisfaction. The extended model incorporates the essential elements of the TAM, TPB, and PR models and user satisfaction as an external variable. Data were collected from 339 users of accessible products. Structural equation modeling was used to identify significant variables in this study. SEM considered “behavioral intention” to be the most important among them. This study generated design strategies based on significant …factors analyzed in the findings and validated the design cases using the PSSUQ questionnaire, which showed that users had better user satisfaction when using accessible products with the new design strategies. Show more

Keywords: Accessibility, user satisfaction, structural equation modeling (SEM), technology acceptance model (TAM), theory of planned behavior (TPB), theory of perceived risk (PR)

DOI: 10.3233/JIFS-231121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3053-3075, 2023