Journal of Intelligent & Fuzzy Systems - Volume 45, issue 1

Authors: Dey, Aniruddha | Ghosh, Manas | Chowdhury, Shiladitya | Kahali, Sayan

Article Type: Research Article

Abstract: This paper presents a novel decision-making method for face recognition where the features were extracted from the original image fused with its corresponding true and partial diagonal images. To extract features, we adopted the generalized two-dimensional FLD (G2DFLD) feature extraction technique. The feature vectors from a test image are given as input to neural network-based classifier. It is trained with the feature vectors of original image and diagonally fused images and thereby the merit weights with respect to different classes were generated. To address the factors that affect the face recognition accuracy and uncertainty related to raw biometric data, a …fuzzy score for each of the classes is generated by treating a type-2 fuzzy set. This type-2 fuzzy set is formed by the feature vectors of both the diagonally fused training samples and the test image of the respective classes. A concluding score for each of the classes under consideration is computed by fusing complemented merit weight with the complemented fuzzy score. These class-wise concluding scores are considered in the face recognition process. In this study, the well-known face databases (AT&T, UMIST and CMU-PIE) are used to evaluate the performance of the proposed method. The experimental results illustrate the fact that the proposed method has exhibited superior classification precision as compared with other state-of-art methods. Our T2FMFImg F method achieves highest face recognition accuracies of 99.41%, 98.36% and 89.80% in case of AT&T, UMIST and CMU-PIE (with expression), respectively while for CMU-PIE (with Light) the highest recognition accuracy is 97.957%. In addition to it, the presented method is quite successful in fusing and classifying textural information from the original and partial diagonal images by integrating them with type-2 fuzzy set-based treatment. Show more

Keywords: Image-level fusion, confidence factor, face recognition, fuzzy type-2

DOI: 10.3233/JIFS-224288

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 743-761, 2023

Authors: Badshah, Noor | Arif, Muhammad | Khan, Tufail Ahmad | Ullah, Asmat | Rabbani, Hena | Atta, Hadia | Begum, Nasra

Article Type: Research Article

Abstract: Segmenting outdoor images in the presence of haze, fog or smog (which fades the colors and diminishes the contrast of the observed objects) has been a challenging task in image processing with several important applications. In this paper, we propose a new fractional-order variational model that will be able to de-haze and segment a given image simultaneously. The proposed method incorporates the atmospheric veil estimation based on the dark channel prior (DCP). This transmission map can reduce significantly the edge artifacts and enhance estimation precision in the resulting image. The transmission map is then changed over to the high-quality depth …map, with which the new fractional-order variational model can be framed to look for the haze free segmenting image for both grey and color outdoor images. An explicit gradient descent scheme is employed to find efficiently the minimizer of the proposed energy functional. Experimental tests on real world scenes show that the proposed method can jointly de-haze and segment hazy or foggy images effectively and efficiently. Show more

Keywords: Foggy or hazy images, fractional-order total variation, image de-hazing, image segmentation, inhomogeneous intensity, object detection

DOI: 10.3233/JIFS-230385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 763-781, 2023

Authors: Li, Dongping | Shen, Shikai | Yang, Yingchun | He, Jun | Shen, Haoru

Article Type: Research Article

Abstract: In order to solve the problems of inaccurate trajectory time prediction and poor privacy protection of dataset publishing mechanism, the study adds deep learning models into the trajectory time prediction model and designs the SLDeep model. Its performance is compared with LRD, STTM and DeepTTE models for experiments, and the results show that the SLDeep model. The lowest mean absolute error value was 116.357, indicating that it outperformed the other models. The study designed the Travelet publishing mechanism by incorporating differential privacy methods into the publishing mechanism, and compared it with Li’s and Hua’s publishing mechanisms for experiments. The results …show that the mutual information index value of Travelet publishing mechanism is 0.06, which is better than Li’s and Hua’s publishing mechanisms. The experimental results show that the performance of the trajectory time prediction model incorporating deep learning and the dataset publishing mechanism incorporating differential privacy methods has been greatly improved, which can provide new ideas to obtain a more accurate and all-round trajectory big data management system. Show more

Keywords: Deep learning, differential privacy, trajectory time prediction, release mechanism

DOI: 10.3233/JIFS-231210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 783-795, 2023

Authors: Chola Raja, K. | Kannimuthu, S.

Article Type: Research Article

Abstract: Autism Spectrum Disorder (ASD) is a complicated neurodevelopment disorder that is becoming more common day by day around the world. The literature that uses machine learning (ML) and deep learning (DL) approaches gained interest due to their ability to increase the accuracy of diagnosing disorders and reduce the physician’s workload. These artificial intelligence-based applications can learn and detect patterns automatically through the collection of data. ML approaches are used in various applications where the traditional algorithms have failed to obtain better results. The major advantage of the ML algorithm is its ability to produce consistent and better performance predictions with …the help of non-linear and complex relationships among the features. In this paper, deep learning with a meta-heuristic (MH) approach is proposed to perform the feature extraction and feature selection processes. The proposed feature selection phase has two sub-phases, such as DL-based feature extraction and MH-based feature selection. The effective convolutional neural network (CNN) model is implemented to extract the core features that will learn the relevant data representation in a lower-dimensional space. The hybrid meta-heuristic algorithm called Seagull-Elephant Herding Optimization Algorithm (SEHOA) is used to select the most relevant and important features from the CNN extracted features. Autism disorder patients are identified using long-term short-term memory as a classifier. This will detect the ASD using the fMRI image dataset ABIDE (Autism Brain Imaging Data Exchange) and obtain promising results. There are five evaluation metrics such as accuracy, precision, recall, f1-score, and area under the curve (AUC) used. The validated results show that the proposed model performed better, with an accuracy of 98.6%. Show more

Keywords: Autism spectrum disorder, Meta-Heuristic, Deep learning, Convolution neural network, seagull and elephant herding optimization, LSTM, fMRI.

DOI: 10.3233/JIFS-223694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 797-807, 2023

Authors: Wu, Chong | Mao, Zengli | Zhan, Baoqiang | Wu, Yahui

Article Type: Research Article

Abstract: The ocean plays a crucial role in human society’s survival and development. While China’s marine economy has grown rapidly in recent years, it has also led to serious problems inhibiting ecosystem sustainability. This paper proposes high-quality development of the marine economy and combines the improved entropy value method, fuzzy hierarchical analysis method (FAHP), and data envelopment analysis (DEA) method to establish a quadratic relative evaluation model. A two-layer comprehensive index framework with 19 indicators is built to measure various aspects of the marine economy, including innovation, coordination, green, openness, and sharing. Empirical analysis conducted on 11 coastal provinces in China …using data mainly collected from the Chinese Statistical Yearbook reveals significant spatial patchiness in the high-quality development level of the marine economy. This discrepancy is largely due to differences in geographical locations, resources, and government policies. The study analyzes four benchmark provinces of high-quality development and summarizes their experiences. The paper concludes by providing suggestions and implications to support government decision-making. Show more

Keywords: Marine economy, high-quality, DEA, quadratic relative evaluation

DOI: 10.3233/JIFS-224173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 809-830, 2023

Authors: Sathish Kumar, P.J. | Ponnusamy, Muruganantham | Radhika, R. | Dhurgadevi, M.

Article Type: Research Article

Abstract: Underwater wireless sensor networks (UWSNs) are designed to perform cooperative monitoring and data collection tasks by combining several elements, such as automobiles and sensors located in a particular acoustic area. Several studies have been carried out to improve energy efficiency and routing reliability. However, UWSN faces several challenges, such as high ocean interference and noise, long transmission delays, limited bandwidth, and low sensor node battery energy. In this work, a novel underwater clustering-based hybrid routing protocol (UC-HRP) has been proposed to address these issues. The overall process is carried out in three phases. In the first phase, the fuzzy-ELM approach …is used to initialize the cluster based on parameters such as Doppler spread, path loss, noise, and multipath. In the second phase, the cluster head is selected using Cluster Centre Cluster Head Selection (C3HS) based on Link quality, distance, node degree, and residual energy. In the third phase, Hybrid Artificial Bee Colony (HABC) algorithm is used for selecting an optimal route based on the parameters such as reliability, bandwidth effectiveness, average path loss, and average transmission latency. The performance of the proposed UC-HRP method is evaluated using a variety of parameters, including the network lifetime, packet delivery ratio, alive nodes, and energy consumption. The proposed technique improves the network lifetime by 14.03%, 16.25%, and 18.34% better than ACUN, ANC-UWSNS, and MERP respectively. Show more

Keywords: Underwater wireless sensor networks, fuzzy extreme learning machine, cluster centre cluster head selection, hybrid artificial bee colony algorithm

DOI: 10.3233/JIFS-230172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 831-843, 2023

Authors: Chen, Guomin | Jin, Yingwei | Cheng, Shili | Jiao, Huihua

Article Type: Research Article

Abstract: Fuel Cells are novel devices that have been proposed as new power generation systems. The advantages of solid oxide fuel cells are higher efficiency, higher stability, fuel flexibility, lower emissions, and generally lower cost. In the present study, the fuzzy model is employed to build the model of the solid oxide fuel cell considering various sputtering power, thickness of electrolyte, and temperatures of cell. The maximum iterations for the adaptive neuro-fuzzy inference model was considered 50 iterations. About 3500 samples were applied for the training process, and almost 900 samples were utilized for the testing. After modeling process, the genetic …algorithm, particle swarm, simulated annealing, and hybrid firefly-particle swarm optimizers are applied to achieve the optimum value of current and power densities. The results showed that proposed fuzzy model could approximate the model the system with a good agreement with experimental data. Additionally, the obtained data confirm the accuracy, high convergence speed, and robustness of the proposed hybrid optimizer compared to three efficient optimization algorithms. Accordingly, the correlation factor for the proposed fuzzy model for the training and testing dataset was obtained to be 0.9298 and 0.9289, correspondingly. Show more

Keywords: Performance improvement of SOFC, adaptive neuro-fuzzy inference model, various optimization algorithms, experimental dataset accuracy, comparative study

DOI: 10.3233/JIFS-221125

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 845-862, 2023

Authors: Li, Lin

Article Type: Research Article

Abstract: In recent years, the use of Gas Turbines (GTs) to generate electricity has grown exponentially. Therefore, for the optimal performance of gas power plants, a lot of research has been done on modeling different parts of GTs, estimating model parameters, and controlling them. But most of the available methods are not accurate enough, like most linear methods, or are model-based, which require an accurate model of the system (like most nonlinear methods), or there is a constant need to adjust the controller parameters. To address these shortcomings, this study uses a new hybrid method including the brain emotional learning-based intelligent …controller, the nonlinear multivariate method in the form of feedback linearization, and an adaptive control method of mode predictive reference model used to quickly control the GT. The Rowen model is used to simulate the nonlinear model of the GT. Owing to the influence of exhaust temperature on the speed of GT and the multivariate system model, nonlinear multivariate controller design is considered. First, the adaptive control method of the state-predictive reference model for a multi-output multi-input system, in general, is presented, and then, the proposed method for a GT with real dynamic values is implemented. The simulation results show the ability of the proposed controller to control the GT. In order to prove the efficiency of the proposed method, the obtained results are compared with the PID industrial controller method and the classical reference model method. Show more

Keywords: GTs, speed control, brain emotional learning based intelligent controller, feedback linearization, dynamic simulation

DOI: 10.3233/JIFS-221408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 863-876, 2023

Authors: Ammasaikutti, Pradeep | Palanisamy, Kannan

Article Type: Research Article

Abstract: A single phase Soft Switching-Solid State Transformer (SS-SST) design is proposed with H-bridge topology as an alternative solution to fulfil the demand of low (or) medium grid power applications. A medium/low frequency transformers fed with H-bridge circuit are incorporate without DC-voltage link, and it’s provided sinusoidal output voltage into the grid. An optimization of Cuckoo Search Firefly (CSF) algorithm was proposed in this research to find optimum switching angle and duty cycle in bridge circuit unit. At present optimum grid power is achieved a maximum efficiency of medium/low power frequency with the help of proposed SS-SST (MS4T) model. For proposed …design is used to electric aircraft, ship power systems, battery energy storage systems (BESS) and fast charging electric vehicles (EV). Which are appealing the networks of medium-voltage DC (MVDC). Proposed MS4T design is based on soft-switching transformer with low conduction loss, low EMI and high efficiency via H-bridge converter circuit. The capacitor voltage balancing control between cascade module and design of the component including a medium level voltage frequency transformer that is implement a 1 kV to 0.25 kV MS4T described. Therefore, the efficacy of the present investigations are established with MATLAB platform. The medium voltage Micro Grid (MG) output is estimated under different operation load conditions. A simulation result of the grid power is measured minimum harmonics level by using optimum switching angle, switching frequency and duty cycle arrangements. Show more

Keywords: Soft switching-solid state transformer, cuckoo search firefly algorithm, H-bridge circuit, medium level voltage, grid

DOI: 10.3233/JIFS-224393

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 877-890, 2023

Authors: Yang, Wendong | Wang, Jingyi | Yang, Sibo | Zhang, Kai

Article Type: Research Article

Abstract: Short-term load prediction has always played an increasingly important part in power system administration, load dispatch, and energy transfer scheduling. However, how to build a novel model to improve the accuracy of load forecasts is not only an extremely challenging problem but also a concerning problem for the power market. Specifically, the individual model pays no attention to the significance of data selection, data preprocessing, and model optimization. So these models cannot always satisfy the time series forecasting’s requirements. With these above-mentioned ignored factors considered, to enhance prediction accuracy and reduce computation complexity, in this study, a novel and robust …method were proposed for multi-step forecasting, which combines the power of data selection, data preprocessing, artificial neural network, rolling mechanism, and artificial intelligence optimization algorithm. Case studies of electricity power data from New South Wales, Australia, are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed model has significantly increased the accuracy of load prediction in all quarters. As a result, the proposed method not only is simple, but also capable of achieving significant improvement as compared with the other forecasting models, and can be an effective tool for power load forecasting. Show more

Keywords: Short-term load prediction, data selection, data preprocessing, optimization, forecasting

DOI: 10.3233/JIFS-224567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 891-909, 2023