Journal of Intelligent & Fuzzy Systems - Volume 44, issue 3

Authors: Zhang, Zhaojun | Sun, Rui | Xu, Tao | Lu, Jiawei

Article Type: Research Article

Abstract: When the shuffled frog leaping algorithm (SFLA) is used to solve the robot path planning problem in obstacle environment, the quality of the initial solution is not high, and the algorithm is easy to fall into local optimization. Herein, an improved SFLA named ISFLA combined with genetic algorithm is proposed. By introducing selection, crossover and mutation operators in genetic algorithm, the ISFLA not only improves the solution quality of the SFLA, but also accelerates its convergence speed. Moreover, the ISFLA also proposes a location update strategy based on the central frog, which makes full use of the global information to …avoid the algorithm falling into local optimization. By comparing ISFLA with other algorithms including SFLA in the map environment of different obstacles, it is confirmed that ISFLA can effectively improve the minimum path optimization and robustness in the simulation experiments of mobile robots. Show more

Keywords: Robot path planning, shuffled frog leaping algorithm, genetic algorithm, location update strategy

DOI: 10.3233/JIFS-222213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5217-5229, 2023

Authors: Sundarakumar, M.R. | Mahadevan, G. | Natchadalingam, R. | Karthikeyan, G. | Ashok, J. | Manoharan, J. Samuel | Sathya, V. | Velmurugadass, P.

Article Type: Research Article

Abstract: In the modern era, digital data processing with a huge volume of data from the repository is challenging due to various data formats and the extraction techniques available. The accuracy levels and speed of the data processing on larger networks using modern tools have limitations for getting quick results. The major problem of data extraction on the repository is finding the data location and the dynamic changes in the existing data. Even though many researchers created different tools with algorithms for processing those data from the warehouse, it has not given accurate results and gives low latency. This output is …due to a larger network of batch processing. The performance of the database scalability has to be tuned with the powerful distributed framework and programming languages for the latest real-time applications to process the huge datasets over the network. Data processing has been done in big data analytics using the modern tools HADOOP and SPARK effectively. Moreover, a recent programming language such as Python will provide solutions with the concepts of map reduction and erasure coding. But it has some challenges and limitations on a huge dataset at network clusters. This review paper deals with Hadoop and Spark features also their challenges and limitations over different criteria such as file size, file formats, and scheduling techniques. In this paper, a detailed survey of the challenges and limitations that occurred during the processing phase in big data analytics was discussed and provided solutions to that by selecting the languages and techniques using modern tools. This paper gives solutions to the research people who are working in big data analytics, for improving the speed of data processing with a proper algorithm over digital data in huge repositories. Show more

Keywords: HADOOP, SPARK, scalability, batch processing, big-data

DOI: 10.3233/JIFS-223295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5231-5255, 2023

Authors: Saranya, N. | Srinivasan, K. | Pravin Kumar, S.K.

Article Type: Research Article

Abstract: Ripeness of the fruit is significant in agriculture since it affects the fruit’s quality and sales. Manually determining the fruit’s ripeness has various drawbacks, including the fact that it consumes time, needs a lot of work, and occasionally results in errors. One of the crucial areas of the economies of nations is the agricultural sector. However, the manual approach is still occasionally used to assess the maturity of fruit. Fruit ripeness could be automatically categorized by the advancement of computer vision and machine learning technology. The Convolutional Neural Network (CNN) is used in this work is to classify the different …ripeness stages of banana fruit. The four stages of banana ripeness are unripe, mid-ripe, ripe, and overripe. Proposed method uses a fuzzy-based convolutional neural network with tunicate swarm algorithm. The proposed model outperforms cutting-edge computer vision-based algorithms in both coarse and perfectly acceptable classification of maturation phases. The experimental results using images of bananas at various stages of ripening, achieves overall accuracy of 96.9%. Show more

Keywords: Banana, ripening stages, convolutional neural network, fuzzy logic, and tunicate swarm algorithm

DOI: 10.3233/JIFS-221841

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5257-5273, 2023

Authors: Wang, Peng | Lu, Shaojun | Cheng, Hao | Liu, Lin | Pei, Feng

Article Type: Research Article

Abstract: The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the development and national security of countries. The maintenance of large vessels is a complex management engineering project that presents a challenge in lowering maintenance time and enhancing maintenance efficiency during task scheduling. This paper investigates a preemptive multi-skill resource-constrained project scheduling problem and a task-oriented scheduling model for marine power equipment maintenance to address this challenge. Each task has a minimum capability level restriction during the scheduling process and can be preempted at discrete time instants. …Each resource is multi-skilled, and only those who meet the required skill level can be assigned tasks. Based on the structural properties of the studied problem, we propose an improved Moth-flame optimization algorithm that integrates the opposition-based learning strategy and the mixed mutation operators. The Taguchi design of experiments (DOE) approach is used to calibrate the algorithm parameters. A series of computational experiments are carried out to validate the performance of the proposed algorithm. The experimental results demonstrate the effectiveness and validity of the proposed algorithm. Show more

Keywords: Project scheduling, multi-skill, preemption, moth-flame optimization algorithm, ship maintenance

DOI: 10.3233/JIFS-221994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5275-5294, 2023

Authors: Wu, Jiali | Sheng, Yuhong

Article Type: Research Article

Abstract: Uncertain data envelopment analysis (DEA) model make an estimate of the efficiency of decision making unit (DMU) under data uncertainty. The current research on uncertain DEA model is only based on sectional data to calculate DMU’s static efficiency for the DMU’s set in the same period. From this article, we attempt to combine Malmquist productivity index and uncertain DEA model (the uncertain DEA-Malmquist productivity index model) to calculate the dynamic change of DMU’s efficiency over time. Additionally, the impact of technical factors and scale factors on DMU’s efficiency can be further explored and the Malmquist productivity index will be decomposed …into pure technical efficiency change, scale efficiency change and technical change. Finally, the article uses the model to analyze the provincial environmental efficiency from 2014 to 2016 in China. Show more

Keywords: Uncertainty theory, uncertain DEA model, malmquist productivity index, decision making unit

DOI: 10.3233/JIFS-222109

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5295-5308, 2023

Authors: Wang, Encheng | Mao, Zichen | Wang, Jie | Lin, Daming

Article Type: Research Article

Abstract: Wind power is widely used in industry, meteorology, shipping and so on. Accurate measurement of wind parameters is the key to improve the efficiency of wind power application. But at present, wind parameters are largely measured by different devices based on time difference method, which is easily influnced by enviromental noise. Beam-forming algorithm can improve the ability to resist environmental noise and the accuracy of hardware itself. Therefore, the beam-forming algorithm can be used to measure wind parameters in the high noise environment. However, the efficiency of the algorithm depends on how to search for spectral peak. In this paper, …a three-dimensional wind measurement method with chaotic-sequence improved genetic-particle swarm optimization algorithm is proposed to improve the waveform searching efficiency of beamforming algorithm. It first searches for rough target wind parameters globally, and then searches for precise target wind parameters locally. Through simulation verification, the proposed algorithm can measure the wind parameters after 0.087s under the condition of system error of 50dB and environmental noise of 20dB, the accuracy of wind speed is 0.5%, the accuracy of wind direction is 1%, and the accuracy of pitch angle is 0.5%. Compared with the wind measurement by traversal method, the proposed algorithm can improve the wind measurement efficiency by about 20 times, and has similar or even better measurement results.. And by comparing with other algorithms, the advantages of this algorithm are verified. Show more

Keywords: Three-dimensional wind measurement, beam-forming algorithm, chaotic sequence, genetic algorithm, particle swarm algorithm

DOI: 10.3233/JIFS-223378

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5309-5320, 2023

Authors: Gang, Wang | Ling, Song Jin | Yin, Feng Jia | Yan, Jia Dong | Yan, Zhao

Article Type: Research Article

Abstract: In this study, a novel hybrid metaheuristic model was developed to forecast the undrained soil shear (USS ) property from cone penetration test (CPT ) data (data from bore log sample from 70 different sites in Louisiana). This algorithm produced with the integration of grey wolf optimization (GWO ) and multilayer perceptron neural network (MLP ), named GWO  - MLP , where different numbers of hidden layers were tested (1 to 4). The duty of optimization algorithm was to determine the optimal number of neurons in each hidden layer. To this objective, the system comprised five inputs entitled sleeve friction, cone …tip persistence, liquid limit, plastic limitation, too much weight, and USS as outcome. The developed models for forecasting the USS of soil show the proposed best models have R2 at 0.9134 and 0.9236 in the training and predicting stage. Although the total ranking score of GWO-MLP2 and GWO-MLP4 is equal, the OBJ value shows that GWO-MLP4 has better performance than GWO-MLP2. In this case, considering the time of model running and a greater number of hidden layers suggests that GWO-MLP2 could be most appropriate. Therefore, the GWO-MLP3 model outperforms other GWO-MLP networks in the training and testing phase. Show more

Keywords: CPT, undrained shear strength of soil, estimation, grey wolf optimization, multilayer perceptron neural network

DOI: 10.3233/JIFS-221058

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5321-5332, 2023

Authors: Huang, Yuexin | Yu, Suihuai | Chu, Jianjie | Su, Zhaojing | Zhu, Yaokang | Wang, Hanyu | Wang, Mengcheng | Fan, Hao

Article Type: Research Article

Abstract: Design knowledge is critical to creating ideas in the conceptual design stage of product development for innovation. Fragmentary design data, massive multidisciplinary knowledge call for the development of a novel knowledge acquisition approach for conceptual product design. This study proposes a Design Knowledge Graph-aided (DKG-aided) conceptual product design approach for knowledge acquisition and design process improvement. The DKG framework uses a deep-learning algorithm to discover design-related knowledge from massive fragmentary data and constructs a knowledge graph for conceptual product design. The joint entity and relation extraction model is proposed to automatically extract design knowledge from massive unstructured data. The feasibility …and high accuracy of the proposed design knowledge extraction model were demonstrated with experimental comparisons and the validation of the DKG in the case study of conceptual product design inspired by massive real data of porcelain. Show more

Keywords: Conceptual product design, design knowledge graph, deep learning, knowledge acquisition, joint entity and relation extraction

DOI: 10.3233/JIFS-223100

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5333-5355, 2023

Authors: Yi, Tian | Li, Mingbo | Lei, Deming

Article Type: Research Article

Abstract: Unrelated parallel machine scheduling problem (UPMSP) with additional resources and UPMSP with learning effect have attracted some attention; however, UPMSP with additional resources and learning effect is seldom studied and meta-heuristics for UPMSP hardly possess reinforcement learning as new optimization mechanism. In this study, a shuffled frog-leaping algorithm with Q-learning (QSFLA) is presented to solve UPMSP with one additional resource and learning effect. A new solution presentation is presented. Two populations are obtained by division. A Q-learning algorithm is constructed to dynamically decide search operator and search times. It has 12 states depicted by population quality evaluation, four actions defined …as search operators, a new reward function and a new action selection. Extensive experiments are conducted. Computational results demonstrate that QSFLA has promising advantages for the considered UPMSP. Show more

Keywords: parallel machine scheduling, additional resource, learning effect, shuffled frog-leaping algorithm, reinforcement learning

DOI: 10.3233/JIFS-213473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5357-5375, 2023

Authors: Zhang, Min | Wang, Jie-Sheng | Liu, Yu | Wang, Min | Li, Xu-Dong | Guo, Fu-Jun

Article Type: Research Article

Abstract: In most data mining tasks, feature selection is an essential preprocessing stage. Henry’s Gas Solubility Optimization (HGSO) algorithm is a physical heuristic algorithm based on Henry’s law, which simulates the process of gas solubility in liquid with temperature. In this paper, an improved Henry’s Gas Solubility Optimization based on stochastic fractal search (SFS-HGSO) is proposed for feature selection and engineering optimization. Three stochastic fractal strategies based on Gaussian walk, Lévy flight and Brownian motion are adopted respectively, and the diffusion is based on the high-quality solutions obtained by the original algorithm. Individuals with different fitness are assigned different energies, and …the number of diffusing individuals is determined according to individual energy. This strategy increases the diversity of search strategies and enhances the ability of local search. It greatly improves the shortcomings of the original HGSO position updating method is single and the convergence speed is slow. This algorithm is used to solve the problem of feature selection, and KNN classifier is used to evaluate the effectiveness of selected features. In order to verify the performance of the proposed feature selection method, 20 standard UCI benchmark datasets are used, and the performance is compared with other swarm intelligence optimization algorithms, such as WOA, HHO and HBA. The algorithm is also applied to the solution of benchmark function. Experimental results show that these three improved strategies can effectively improve the performance of HGSO algorithm, and achieve excellent results in feature selection and engineering optimization problems. Show more

Keywords: Henry’s gas solubility optimization, stochastic fractal search, feature selection, benchmark function

DOI: 10.3233/JIFS-221036

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5377-5406, 2023

Authors: Sebastin Suresh, S. | Prabhu, V. | Parthasarathy, V.

Article Type: Research Article

Abstract: The Internet of Things (IoT) enabled wireless sensor network (WSN) is now widely employed in various sectors like smart city and vehicle transportation for their expanded capabilities such as data storage, access, and monitoring. The use of smart sensors that continuously collect data from the smart environment makes these possible. Furthermore, these facilitate the easy access of stored data over a secure IoT-gateway for mobile users. This device mobility that allows shifting to multiple locations, makes it challenging to route data across many access points. In this regard, it induces packet loss and improper node selection, which could result in …connection failure and network unreliability. This study proposes a new data routing protocol called as Fuzzy Logic Nodes Distributed Clustering for Energy-Efficient Fault Tolerance (F-NDC-EEFT). It can be deployed on any network platform, including mobile and non-mobile nodes. It considers performance metrics such as delivery rate, withstand node aliveness, communication delay, and energy efficiency to find an optimized path for the better performance of IoT enabled WSNs. The clustering approach is applied to the instant data load, which divides it into the distinct node groups. When proposed algorithm is tested alongside existing routing protocols for performance, it is found to save energy, minimize the number of connection failures, boost the throughput, and increase the network’s lifetime. Show more

Keywords: CH eligibility, energy efficiency, fuzzy modules, energy aware routing protocol, IoT enabled WSN

DOI: 10.3233/JIFS-221733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5407-5423, 2023

Authors: Tang, Chen | Yu, Qiancheng | Li, Xiaoning | Lu, Zekun | Yang, Yufan

Article Type: Research Article

Abstract: The stock market is a chaotic system, and stock forecasting has been the research focus. This paper proposes a multi-factor model based on DeepForest-CQP to make it more applicable to the stock domain. A t -test is used for selecting factors, and orthogonalization and heteroskedasticity tests are performed for the combined factors, which are particularly important in stock forecasting. DeepForest-CQP was combined with the multi-factor model to construct a stock selection model that can achieve higher returns. The obtained multi-factor quantitative stock selection model is used to study stock selection strategies, and simulated trading is used to evaluate the multi-factor …model and stock selection strategies and compare them with various machine learning multi-factor models. The experimental results show that the DeepForest-CQP-based multi-factor stock selection model achieves significant performance advantages in all backtesting metrics. Show more

Keywords: Multi-factor model, quantitative stock selection, machine learning, stock prediction, heteroskedasticity

DOI: 10.3233/JIFS-222328

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5425-5436, 2023

Authors: Chen, Zhixiang

Article Type: Research Article

Abstract: This paper modifies the original Teaching-Learning-based Optimization (TLBO) algorithm to present a novel Group-Individual Multi-Mode Cooperative Teaching-Learning-based Optimization (CTLBO) algorithm. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher-learner cooperation strategies in teaching and learning processes. In the preparation phase, teacher-learner interaction and teacher self-learning mechanism are applied. In the teaching phase, class-teaching and performance-based group-teaching operators are applied. In the learning phase, neighbor learning, student self-learning and team-learning strategies are mixed together to form three operators. Experiments indicate that CTLBO has significant improvement in accuracy and convergence ability compared with original …TLBO in solving large scale problems and outperforms other compared variants of TLBO in literature and other 9 meta-heuristic algorithms. A large-scale industrial engineering problem—warehouse materials inventory optimization problem is taken as application case, comparison results show that CTLBO can effectively solve the large-scale real problem with 1000 decision variables, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO, revealing that CTLBO can far outperform other algorithms. CTLBO is an excellent algorithm for solving large scale complex optimization issues. Show more

Keywords: Teaching-learning-based optimization, group-individual multi-mode cooperation, performance-based group teaching, teacher self-learning, team learning

DOI: 10.3233/JIFS-222516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5437-5465, 2023

Authors: Naresh Patel, K.M. | Ashoka, K. | Park, Choonkil | Shanmukha, M.C. | Azeem, Muhammad

Article Type: Research Article

Abstract: Diagnosis of human disease is a more difficult and complex process since it requires the consideration of various factors and symptoms to make a decision. Generally, the classification of diseases with fuzzy values is the most interesting topic because of accurate results. In this paper, we design a Bat-based Random Forest (BbRF) framework to enhance the performance of categorizing diseases with fuzzy values which also protect the privacy of the developed scheme. It involves pre-processing, attributes selection, fuzzy value generation, and classification. Additionally, the developed framework is implemented in Python tool and patient disease datasets are used for implementation. Moreover, …pre-processing remove the error and noise, attributes are selected based on the duration of diseases. Finally, classify the patient disease based on the generated fuzzy value. To prove the efficiency of the developed framework, attained results are compared with other existing techniques in terms of accuracy, sensitivity, specificity, F-measure, and precision. Show more

Keywords: Bat-based random forest, fuzzy value, optimization

DOI: 10.3233/JIFS-222749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5467-5479, 2023

Authors: Aramuthakannan, S. | Ramya Devi, M. | Lokesh, S. | Manimegalai, R.

Article Type: Research Article

Abstract: The internet and social networks produce an increasing amount of data. There is a serious necessity for a recommendation system because exploring through the huge collection is time-consuming and difficult. In this study, a multi-modal classifier is introduced which makes use of the output from dual deep neural networks: GRU for text analysis and Faster R-CNN for image analysis. These two networks reduce overall complexity with minimal computational time while retaining accuracy. More precisely, the GRU network is utilized to process movie reviews and the Faster RCNN is used to recognize each frames of the movie trailers. Gated Recurrent Unit …(GRU) is a well-known variety of RNN that computes sequential data across recurrent structures. Faster RCNN is an enhanced version of Fast RCNN, it combines with the rectangular region proposals and with the features is extract by the ResNet-101. Initially, the trailer of the movie is manually splitted into frames and these frames are pre-processed using fuzzy elliptical filter for image analysis and the movie reviews are also tokenized for text analysis. The pre-processed text is taken as an input for GRU to classify offensive and non-offensive movies and the pre-processed images are taken as an input for Faster R-CNN to classify violence and non- violence movies based on the extracted features from the movie trailer. Afterwards, the four classified outputs are given as input for fuzzy decision-making unit for recommending best movies based on the Mamdani fuzzy inference system with gauss membership functions. The performance of the dual deep neural networks was evaluated using the specific parameters like specificity, precision, recall, accuracy and F1 score measures. The proposed GRU yields accuracy range of 97.73% for reviews and FRCNN yields the accuracy range of 98.42% for movie trailer. Show more

Keywords: Movie recommendation, deep learning, Mamdani fuzzy inference system, Gated Recurrent Unit, Faster R-CNN

DOI: 10.3233/JIFS-222970

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5481-5494, 2023

Authors: Sumo, Peter Davis | Ji, Xiaofen | Cai, Liling

Article Type: Research Article

Abstract: Due to the growing call to embrace environmentally responsible and sustainable business practices, textile reverse logistics (TRL) and recovery practices, such as reusing, remanufacturing, or recycling, are gaining prominence. Textile recycling companies can simultaneously obtain economic and environmental benefits via more efficient RL practices. However, a system for measuring these efficiencies is paramount, as it is impossible to run a reverse logistics system efficiently without the ability to measure its performance. Studies on performance measurement of TRL firms are completely lacking, and those of the general RL literature use manual systems that require longer time and participation of many workers …to complete. In this study, we develop a performance prediction model based on DEA and ANFIS. Data for the ANFIS were derived from the DEA computation. To enhance the model, PSO, GA, and Jaya algorithms were introduced to tweak the ANFIS parameters. Results from the ANFIS hybrid models reveal ANFIS-Jaya to have a better prediction accuracy with R2 of 0.9832 and 0.9851 in training and testing datasets, respectively. This study contributes to the RL performance management literature and the limited research on used clothing collection, textile recycling, and RL performance management measurement. Show more

Keywords: Textile, reverse logistics, DEA-ANFIS, recycling, Jaya algorithm

DOI: 10.3233/JIFS-223418

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5495-5505, 2023

Authors: Wu, Qiyue | Yuan, Yinlong | Cheng, Yun | Ye, Tangdi

Article Type: Research Article

Abstract: Emotion recognition based on EEG (electroencephalogram) is one of the keys to improve communication between doctors and patients, which has attracted much more attention in recent years. While the traditional algorithms are generally based on using the original EEG sequence signal as input, they neglect the bad influence of noise that is difficult to remove and the great importance of shallow features for the recognition process. As a result, there is a difficulty in recognizing and analyzing emotions, as well as a stability error in traditional algorithms. To solve this problem, in this paper, a new method of EEG emotion …recognition based on 1D-DenseNet is proposed. Firstly, we extract the band energy and sample entropy of EEG signal to form a 1D vector instead of the original sequence signal to reduce noise interference. Secondly, we construct a 1D-Densenet model, which takes the above-mentioned 1D vector as the input, and then connects the shallow manual features of the input layer and the output of each convolution layer as the input of the next convolution layer. This model increases the influence proportion of shallow features and has good performance. To verify the effectiveness of this method, the MAHNOB-HCI and DEAP datasets are used for analysis and the average accuracy of emotion recognition reaches 90.02% and 93.51% respectively. To compare with the current research results, the new method proposed in this paper has better classification effect. Simple preprocessing and high recognition accuracy make it easy to be applied to real medical research. Show more

Keywords: EEG signals, emotion recognition, DenseNet, shallow features, feature fusion

DOI: 10.3233/JIFS-223456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5507-5518, 2023

Article Type: Correction

DOI: 10.3233/JIFS-219325

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5519-5519, 2023