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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Mahendran, S. | Gomathy, B.
Article Type: Research Article
Abstract: This study addresses the escalating energy demands faced by global industries, exerting pressure on power grids to maintain equilibrium between supply and demand. Smart grids play a pivotal role in achieving this balance by facilitating bidirectional energy flow between end users and utilities. Unlike traditional grids, smart grids incorporate advanced sensors and controls to mitigate peak loads and balance overall energy consumption. The proposed work introduces an innovative deep learning strategy utilizing bi-directional Long Short Term Memory (b-LSTM) and advanced decomposition algorithms for processing and analyzing smart grid sensor data. The application of b-LSTM and higher-order decomposition in the analysis …of time-series data results in a reduction of Mean Absolute Percentage Error (MAPE) and Minimal Root Mean Square (RMSE). Experimental outcomes, compared with current methodologies, demonstrate the model’s superior performance, particularly evident in a case study focusing on hourly PV cell energy patterns. The findings underscore the efficacy of the proposed model in providing more accurate predictions, thereby contributing to enhanced management of power grid challenges. Show more
Keywords: Smart grids, deep learning, PV cells, error rate and absolute error, prediction
DOI: 10.3233/JIFS-238850
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Ning, Yi | Liu, Meiyu | Guo, Xifeng | Liu, Zhiyong | Wang, Xinlu
Article Type: Research Article
Abstract: Accurate load forecasting is an important issue for safe and economic operation of power system. However, load data often has strong non-stationarity, nonlinearity and randomness, which increases the difficulty of load forecasting. To improve the prediction accuracy, a hybrid short-term load forecasting method using load feature extraction based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and refined composite multi-scale entropy (RCMSE) and improved bidirectional long short time memory (BiLSTM) error correction is proposed. Firstly, CEEMDAN is used to separate the detailed information and trend information of the original load series, RCMSE is used to reconstruct the feature …information, and Spearman is used to screen the features. Secondly, an improved butterfly optimization algorithm (IBOA) is proposed to optimize BiLSTM, and the reconstructed components are predicted respectively. Finally, an error correction model is constructed to mine the hidden information contained in error sequence. The experimental results show that the MAE, MAPE and RMSE of the proposed method are 645 kW, 0.96% and 827.3 kW respectively, and MAPE is improved by about 10% compared with other hybrid models. Therefore, the proposed method can overcome the problem of inaccurate prediction caused by data and inherent defects of models and improve the prediction accuracy. Show more
Keywords: Short-term load forecasting, complete ensemble empirical mode decomposition with adaptivenoise, refined composite multi-scale entropy, improved butterfly optimization algorithm, bidirectional long short time memory neural network
DOI: 10.3233/JIFS-237993
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Arulmurugan, A. | Jose Moses, G. | Gandhi, Ongole | Sheshikala, M. | Arthie, A.
Article Type: Research Article
Abstract: In the current scenario, feature selection (FS) remains one of the very important functions in machine learning. Decreasing the feature set (FSt) assists in enhancing the classifier’s accuracy. Because of the existence of a huge quantity of data within the dataset (DS), it remains a colossal procedure for choosing the requisite features out of the DS. Hence, for resolving this issue, a new Chaos Quasi-Oppositional-based Flamingo Search Algorithm with Simulated Annealing Algorithm (CQOFSASAA) has been proffered for FS and for choosing the optimum FSt out of the DSs, and, hence, this lessens the DS’ dimension. The FSA technique can be …employed for selecting the optimal feature subset out of the DS. Generalized Ring Crossover has been as well embraced for selecting the very pertinent features out of the DS. Lastly, the Kernel Extreme Learning Machine (KELM) classifier authenticates the chosen features. This proffered paradigm’s execution has been tested by standard DSs and the results have been correlated with the rest of the paradigms. From the experimental results, it has been confirmed that this proffered CQOFSASAA attains 93.74% of accuracy, 92% of sensitivity, and 92.1% of specificity. Show more
Keywords: Quasi-oppositional, feature selection, Flamingo Search Algorithm, Simulated Annealing, convergence rate
DOI: 10.3233/JIFS-233557
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023
Authors: Harikumar, Yedhu | Muthumeenakshi, M.
Article Type: Research Article
Abstract: The Indian stock market is a dynamic, complicated system that is impacted by many different variables, making it difficult to anticipate its future. The utilization of deep learning and optimization techniques to forecast stock market movements has gained popularity in recent years. To foresee the Indian stock market, an innovative approach is presented in this study that combines the Grey Wolf Optimization algorithm with a hybrid Convolutional Neural Network (CNN) and Bi-Directional Long-Short Term Memory (Bi-LSTM) framework. The stock market information is first pre-processed utilizing a CNN to extract pertinent features. The Bi-LSTM system, that is intended to capture the …long-term dependencies and temporal correlations of the stock market statistics, is then fed the CNN’s outcome. The model parameters are then optimized utilizing the Grey Wolf Optimization (GWO) technique, which also increases forecasting accuracy. The findings demonstrate that, in terms of forecasting accuracy, the suggested method outperforms a number of contemporary methods, including conventional time series models, neural networks, and evolutionary algorithms. Thus, the suggested methodology provides an effective way to forecast the Indian stock market by combining deep learning and optimization approaches. Show more
Keywords: Indian stock market, grey wolf optimization, deep learning approach, bi-directional long-short term memory, convolutional neural network
DOI: 10.3233/JIFS-233716
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Li, Xiaoli | Du, Linhui | Yu, Xiaowei | Wang, Kang | Hu, Yongkang
Article Type: Research Article
Abstract: During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict …HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R ) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods. Show more
Keywords: HVAC, energy consumption, weighted similarity measure, deep neural network, Just-in-Time learning
DOI: 10.3233/JIFS-233544
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Maleki, Monavareh | Ebrahimi, Mohamad | Davvaz, B.
Article Type: Research Article
Abstract: The concept of entropy and information gain of BE-algebras in scientific disciplines such as information theory, data science, supply chain and machine learning assists us to calculate the uncertanity of the scientific processes of phenomena. In this respect the notion of filter entropy for a transitive BE-algebra is introduced and its properties are investigated. The notion of a dynamical system on a transitive BE-algebra is introduced. The concept of the entropy for a transitive BE-algebra dynamical system is developed and, its characteristics are considered. The notion of equivalent transitive BE-algebra dynamical systems is defined, and it is proved the fact …that two equivalent BE-algebra dynamical systems have the same entropy. Theorems to help calculate the entropy are given. Specifically, a new version of Kolmogorov– Sinai Theorem has been proved. The study introduces the concept of information gain of a transitive BE-algebra with respect to its filters and investigates its properties. This study proposes the use of filter entropy to approximate the level of risk introduced by a BE-algebra dynamical system. This aim is reached by defining the information gain with respect to the filters of a BE-algebra. This methodology is well developed for use in engineering, especially in industrial networks. This paper proposes a novel approach to assess the quantity of uncertainty, and the impact of information gain of a BE-algebra dynamical system. Show more
Keywords: Generator, transitive BE-algebra, dynamical system, entropy, information gain
DOI: 10.3233/JIFS-232363
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Yang, Jiyun | Gui, Can
Article Type: Research Article
Abstract: Malware attack is a growing problem on the Android mobile platform due to its popularity and openness. Although numerous malware detection approaches have been proposed, it still remains challenging for malware detection due to a large amount of constantly mutating apps. The opcode, as the most fundamental part of Android app, possesses good resistance against obfuscation and Android version updates. Due to the large number of opcodes, most opcode-based methods employ statistical-based feature selection, which disrupts the correlation and semantic information among opcodes. In this paper, we propose an Android malware detection framework based on sensitive opcodes and deep reinforcement …learning. Firstly, we extract sensitive opcode fragments based on sensitive elements and then encode the features using n -gram. Next, we use deep reinforcement learning to select the optimal subset of features. During the process of handling opcodes, we focus on preserving semantic information and the correlation among opcodes. Finally, our experimental results show an accuracy of 0.9670 by using the 25 opcode features we obtained. Show more
Keywords: Android malware, deep reinforcement learning, feature selection, machine learning
DOI: 10.3233/JIFS-235767
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Badshah, Noor | Begum, Nasra | Rada, Lavdie | Ashfaq, Muniba | Atta, Hadia
Article Type: Research Article
Abstract: Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a …new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively. Show more
Keywords: Image segmentation, image registration, linear curvature (LC), conditional mutual information (CMI), Jaccard similarity index (JSI)
DOI: 10.3233/JIFS-233306
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Wang, Tianxiong | Xu, Mengmeng | Yang, Liu | Zhou, Meiyu | Sun, Xin
Article Type: Research Article
Abstract: Kansei Engineering (KE) is a product design method that aims to develop products to meet users’ emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using tradition methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet …are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user’s multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user’s multiple emotional images. Using the example of electric bicycle form design show that this proposed method can effectively complete multi-objective product solutions innovation design. Show more
Keywords: Text mining, Back propagation neural network (BPNN), Multi-objective evolutionary algorithm (MOEA), Non-dominated sorting genetic algorithm-II (NSGA-II), Kansei engineering
DOI: 10.3233/JIFS-230668
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Jianping, Liu | Yingfei, Wang | Jian, Wang | Meng, Wang | Xintao, Chu
Article Type: Research Article
Abstract: To better understand users’ behavior patterns in web search, numerous click models are proposed to extract the implicit interaction feedback. Most existing click models are heavily based on the implicit information to model user behaviors, ignoring the impact of explicit information between queries and documents in search sessions. In this paper, we fully consider the topic relevance between queries and documents in search sessions and propose a novel topic relevance-aware click model (TRA-CM) for web search. TRA-CM consists of a relevance estimator and an examination predictor. The relevance estimator consists of a topic relevance predictor and a click context encoder. …In the topic relevance predictor, we utilize the pre-trained BERT model to model the content information of queries and documents in search sessions. Meanwhile, we use transformer to encode users’ click behaviors in the click context encoder. We further apply a two-stage fusion strategy to obtain the final relevance scores. The examination predictor estimates the examination probability of each document. We further utilize learnable filters to attenuate log noise and obtain purer input features in both relevance estimator and examination predictor, and investigate different combination functions to integrate relevance scores and examination probabilities into click prediction. Extensive experiment results on two real-world session datasets prove that TRA-CM outperforms existing click models in both click prediction and relevance estimation tasks. Show more
Keywords: BERT, click model, click prediction, deep learning, web search
DOI: 10.3233/JIFS-236894
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
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