Journal of Intelligent & Fuzzy Systems - Volume 44, issue 4

Authors: Zhang, Yang | Zhou, Wentao | Ma, Lina

Article Type: Research Article

Abstract: The success of technological innovation is related to the future and destiny of enterprises, but because of its uncertainty and high risk, the risk of failure of technological innovation exists objectively. This paper uses grounded theory to code the typical cases of technological innovation failure at home and abroad and explores the causes of technological innovation failure. It is found that policies and regulations, institutional environment, and market environment are the important external factors that cause the failure of enterprise technological innovation, while the defects of enterprise technological innovation products, enterprise system, internal management, technological resources, and managers are the …important internal factors that cause the failure of enterprise technological innovation. By constructing the evolution model of enterprise technological innovation failure, it is found that the failure of enterprise technological innovation is the result of the joint action of enterprise management operation mechanism, technology, capital, and other restraint mechanisms, as well as market and policy system guidance mechanism. Show more

Keywords: Failure of technological innovation, influence factors, formation mechanism, grounded theory, multicase

DOI: 10.3233/JIFS-221756

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6277-6291, 2023

Authors: Cui, Chunsheng | Che, Libin | Wei, Meng

Article Type: Research Article

Abstract: The steady development of commercial banks plays a key role in maintaining the healthy development of the economy. The ability to judge financial risks is a reflection of the comprehensive risk management level of a commercial bank, and it is also an important criterion for measuring its competitiveness and operational stability. Based on the analysis of economic development laws and reference to relevant literature, this paper screened out the eight most representative risk evaluation measurement indicators of commercial banks, ranked these indicators in preference according to expert opinions, established a group decision-making model, and then obtained the consensus ranking by …using the least divergence method. The PCbHA method was used to check the consistency of the results, modify the expert opinions, iteratively calculate, and finally construct the importance ranking of commercial bank risk indicators. This paper discusses the construction of an evaluation system based on the perspective of risk management to enrich and improve the risk management content of commercial banks, enhance the risk prevention and control ability, and provide suggestions for the prevention and management of risks in commercial banks. Show more

Keywords: Iteration, group decision-making, PCbHA, commercial bank, risk monitoring

DOI: 10.3233/JIFS-222508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6293-6302, 2023

Authors: Shukla, Poorva | Patel, Ravindra | Varma, Sunita

Article Type: Research Article

Abstract: Recently, Vehicular Ad-hoc Network (VANET) has been one of the emerging fields of research. Many researchers are doing their research on various challenges of VANET. Congestion or blockage has become a critical issue in intelligent transportation systems, and this problem may arise daily due to the usage of smart technology in VANET. So we need some mechanism which controlscongestion. This paper present the trustworthy, long-lasting and consistent block chain congestion control mechanism using the heterogeneity of Dullening Nural Network (DNN), Q-Learning, and Software Define Network (SDN) model for an accurate result, fixed infrastructure, together with a correct prediction of congestion …when it occurs at the edge of the network and give the fast and correct decision of congestion w.r.t VANET trust, Quality of service (QOS) and other vehicles current request. The focus of our research is on distributed SDN Technology and block chain technology for the development of smart cities and linked vehicles. So we proposed an inexpensive mechanism with low latency and a low bandwidth block chain system. Based on the Simulation result, our proposed architecture gives 82% and 98% reliability and efficiency gain in a congestion environment compared to traditional approaches. This paper aims to increase throughput, Packet Delivery Ratio (PDR), energy consumption time, and less end-to-end delay and routing overhead during communication. Show more

Keywords: Edge computing, blockchain system, DNN, Q-learning, SDN

DOI: 10.3233/JIFS-223073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6303-6326, 2023

Authors: Yang, Hong | Wang, Fan | Wang, Lina

Article Type: Research Article

Abstract: In this paper, the second-order fuzzy homogeneous differential equation is transformed into a more special simplest form under the condition that the solution of the boundary value problem of the equation exists and is unique. Then the eigenvalues of the boundary value problem of the second-order simplest fuzzy homogeneous differential equation are studied and the theorems that make the eigenvalues exist are proposed and then illustrated with examples. Finally, it is proved that when the second-order fuzzy coefficient p ˜ ( t ) in the second-order fuzzy homogeneous differential equation is a fuzzy number, …the solution set of its corresponding second-order granular homogeneous differential equation becomes larger, that is, the solution set of fuzzy differential equations with real numbers is a subset of the solution set with fuzzy coefficients as fuzzy numbers. Show more

Keywords: Fuzzy numbers, fuzzy differential equations(FDEs), granular differentiability, the horizontal membership function, fuzzy eigenvalues

DOI: 10.3233/JIFS-223003

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6327-6340, 2023

Authors: Cao, Yunrui | Ma, Jinlin | Hao, Chaohua | Yan, Qi

Article Type: Research Article

Abstract: Tangut characters were created by the Tangut of the Western Xia (Xi Xia) Dynasty in ancient China and are over 1000 years old. In deep-learning-based recognition studies on Tangut characters, the lack of category-complete datasets has been problematic. Data augmentation cannot augment the character categories of unknown styles, whereas the use of image generation can effectively solve the problem. In this study, we consider the generation of antique book calligraphy styles of Tangut characters as a problem of learning to map from existing printed styles to personalized antique book calligraphy styles. We present M-ResNet, a multi-scale feature extraction residual unit, …and Tangut-CycleGAN, a model for generation Tangut characters that combine M-ResNet and a cycle-consistent adversarial network (CycleGAN). This method uses unpaired data to generate Tangut character images in the calligraphy style of ancient books. To enhance the response of the model to significant channels, a squeezing-and-excitation (SE) module is introduced based on Tangut-CycleGAN to design the Tangut-CycleGAN+SE method for generating images of Tangut characters. This method is not only suitable for Tangut character image generation, but also can effectively generate calligraphy with aesthetic value. In addition, we propose an overall quality discrepancy evaluation metric, FA (Fréchet inception distance + Accuracy), to evaluate the quality of character image generation, which combines style discrepancy and content accuracy metrics. Show more

Keywords: Tangut character, CycleGAN, unpaired data, image generation, evaluation metric

DOI: 10.3233/JIFS-221892

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6341-6358, 2023

Authors: Zhao, Dongfang | Chen, Yesheng | Liu, Shulin | Shen, Jiayi | Miao, Zhonghua

Article Type: Research Article

Abstract: Fault diagnosis is of great significance for industrial equipment maintenance, and feature extraction is a key step of the entire diagnosis scheme. The symbolic aggregate approximation (SAX) is a popular feature extraction approach with great potential recently. In spite of the achievements the SAX has made, the adverse information aliasing still exists in its calculation procedure, and it may make the SAX fail to guarantee the information correctness. This work focuses on analyzing the information aliasing phenomenon of the SAX, followed by developing a novel alternative method, i.e. parallel symbolic aggregate approximation (PSAX). In the proposed PSAX, the information aliasing …is suppressed by designing anti-aliasing procedure, and the average of the symbolic results of several intermediate sequence is adopted to replace the final symbolic result. The Case Western Reserve University (CWRU) rolling bearing data together with the gas valve data of an actual reciprocating compressor assist in verifying the superiority exhibited by the suggested method. The experimental results show that, compared with other methods, the accuracy advantage of the PSAX on the 2 datasets can reach 1% –5%, indicating it is capable of providing high-quality feature vector for intelligent fault diagnosis. Show more

Keywords: Fault diagnosis, feature extraction, symbolic aggregate approximation, parallel symbolic aggregate approximation

DOI: 10.3233/JIFS-223575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6359-6374, 2023

Authors: Pichaimani, Venkateswari | Kalava, Manjula Ramakrishama

Article Type: Research Article

Abstract: Wireless localization or positioning is essential for delivering location-based services for designing location tracking systems. Traditional indoor floor planning system employs wireless signals for accurate position estimation. But these positioning schemes failed to perform position estimation effectively and accurately through many obstacles or objects. The novel technique called Linear Features Projective Geometric Damped Convolutional Deep Belief Network (LFPGDCDBN) is introduced to improve the position estimation accuracy with minimum error. The proposed LFPGDCDBN technique includes two major processes namely dimensionality reduction and position estimation. First, the dimensionality reduction process is performed by projecting the principle features using Linear Helmert–Wolf blocked Sammon …projection. After the feature selection, Geometric Levenberg–Marquardt Convolutional deep belief network is employed to estimate the position of the devices with higher accuracy and minimum error. The Convolutional deep belief network uses the triangulation geometric method to identify the position of the device in an indoor positioning system. Then the Levenberg–Marquardt function is a Damped least square method to minimize the squares of the deviations between the expected and observed results at the output unit. As a result, the LFPGDCDBN increases the positioning accuracy and minimizes the error rate. Experimental MATLAB assessment is carried out with various factors such as computational time, Computational space, positioning accuracy, and positing error. The experimental results and discussion indicate that the proposed LFPGDCDBN provides improved performance in terms of achieving higher positioning accuracy and minimum error as well as computational time when compared to the existing methods. The experimental results and discussion indicate that the proposed LFPGDCDBN increases the positioning accuracy by 47% and computational time, computational complexity, and reduces the positioning error by 45%, 29%, and 74% as compared to state-of-the-art works. Show more

Keywords: Indoor floor planning, linear Helmert–Wolf blocked, Sammon projection based feature selection, Geometric Levenberg–Marquardt Convolutional deep belief network, damped least square method

DOI: 10.3233/JIFS-223618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6375-6386, 2023

Authors: Sandhu, Muhammad Abdullah | Amin, Asjad

Article Type: Research Article

Abstract:  During the last decade, dengue fever has emerged as a life-threatening disease. Dengue fever is caused by the bite of the dengue mosquito, and it spreads rapidly especially in the rainy season due to the availability of water carriers inside and outside the living vicinity. In this work, we propose an automated model for dengue larvae detection and tracking using Convolutional Neural Network (CNN) and Kalman filters. Despite substantial literature available on object tracking, no model has been proposed for dengue larvae. We started our work by collecting water areas and dengue larvae datasets as no public datasets were available. …Our water areas dataset has 30 videos of different containers and environments. The dengue larvae dataset has 50 short videos of dengue larvae having different locations, backgrounds, and textures. In the first step, we used CNN to detect water areas, and the detected water area is then processed for the detection and tracking of larvae. Next, we propose a Kalman filter-based workflow for dengue larvae detection and tracking. A Gaussian Mixer Model with background subtraction is applied for foreground and object detection. Then we used Kalman filters to track the moving larvae in the experimental videos. The proposed model shows excellent results considering the small size of larvae and the challenging dataset. Subjective and objective experimental results clearly show the superior performance of the proposed model. The feedback received from the health authorities has been encouraging and the work is expected to facilitate the health department in eliminating the dengue. Show more

Keywords: Dengue larvae, Detection, Tracking, CNN, Kalman Filtering

DOI: 10.3233/JIFS-223660

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6387-6401, 2023

Authors: Li, Ze | Liu, Xiaoze | Ji, Lin | He, Guanglong | Sun, Liang

Article Type: Research Article

Abstract: The diversity of attribute categories brings certain difficulties to data feature detection. In order to improve the accuracy and efficiency of feature detection, a hybrid attribute feature detection method for power system intelligent operation and maintenance big data based on improved random forest algorithm is proposed. Clustering processing power system intelligent operation and maintenance big data, based on data clustering results to reduce the characteristics of data mixed attributes, reduce the scale of data processing, and discretize the data mixed attributes; BP neural network is used to preprocess the results. Make corrections to improve the accuracy of feature detection, use …the improved random forest algorithm to classify the data, and improve the convergence speed of the method. Finally, the support vector machine method is used to realize the feature detection of data mixed attributes. The experimental results show that the feature detection accuracy and efficiency of the method designed in this paper are high, and more features can be detected, which verifies its effectiveness. The method designed in this paper has the minimum RMSE value and the maximum value is only 0.12, which is far lower than the RMSE value of the improved spectral clustering algorithm and multi-domain feature extraction method, and has high detection accuracy. Show more

Keywords: Improved random forest algorithm, power system, operation and maintenance big data, mixed attributes, BP neural network, support vector machine

DOI: 10.3233/JIFS-223852

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6403-6412, 2023

Authors: Gobinath, C. | Gopinath, M.P.

Article Type: Research Article

Abstract: Recent reports indicate a rise in retinal issues, and automatic artery vein categorization offers data that is particularly instructive for the medical evaluation of serious retinal disorders including glaucoma and diabetic retinopathy. This work presents a competent and precise deep-learning model designed for vessel segmentation in retinal fundus imaging. This article aims to segment the retinal images using an attention-based dense fully convolutional neural network (A-DFCNN) after removing uncertainty. The artery extraction layers encompass vessel-specific convolutional blocks to focus the tiny blood vessels and dense layers with skip connections for feature propagation. Segmentation is associated with artery extraction layers via …individual loss function. Blood vessel maps produced from individual loss functions are authenticated for performance. The proposed technique attains improved outcomes in terms of Accuracy (0.9834), Sensitivity (0.8553), and Specificity (0.9835) from DRIVE, STARE, and CHASE-DB1 datasets. The result demonstrates that the proposed A-DFCNN is capable of segmenting minute vessel bifurcation breakdowns during the training and testing phases. Show more

Keywords: Deep learning, fundus image, fully-convolutional neural networks, blood vessel segmentation, artery vein classification

DOI: 10.3233/JIFS-224229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6413-6423, 2023