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

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