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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: Chang, Te-Min | Lin, Sin-Jin | Hsu, Ming-Fu | Yang, Min-Lang
Article Type: Research Article
Abstract: Because the nature of numerical information is intuitive and comprehensible, it has been widely used to form a basis for decision making, yet numerical information based on historical principle does not reflect messages about future corporate performance. To confront this issue, one may consider textual information that can transmit future corporate potential without any hysteresis. The key point is how to digest an extensive amount of textual information and identify those topics most likely to precede changes in operation status. Topic modeling can categorize these textual disclosures based on their underlying content and help examine which topics have a strong …relevance to corporate operations. To extract decisive words from textual information, we set up a statistical-based approach with objectivity as opposed to frequently used heuristics (i.e., dictionary-based approaches with human involvement). Joint utilization of topic modelling and a statistical-based approach can compress an excessive amount of textual information into a manageable size in a timely manner and further realize a discrepancy among various topics in terms of relevance and influence on corporate operations. Our results benefit managers and current and future investors in how to structure regulatory filings and how word choices are decisive to them in their decision judgments. Show more
Keywords: Management decision, textual information, financial news media, risk management
DOI: 10.3233/JIFS-211732
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4947-4960, 2022
Authors: Baskaran, P. | Karuppasamy, K.
Article Type: Research Article
Abstract: The advancement of the Internet of Things (IoT) technologies will play a significant role in the evolution of the smart city, smart healthcare, and smart grid applications. The key objective of IoT is to allow the autonomous exchange of valuable data between invisibly embedded devices with the help of some prominent technologies. Wireless Sensor Network (WSN) is one of the emerged technologies used for sensing and data exchange processes in IoT-based applications. Network sustainability and energy stability are the most significant multi-objectives to attain an energy-efficient IoT-based WSN (IWSN). Consequently, in order to handle these multi-objectives, a novel Adaptive Regional …Clustering (ARC) scheme has been proposed in this paper by exploiting two appropriate methodologies. Primarily, location-based modelling is employed to gather the location information from each sensor node in the IWSN environment. Thereafter, an effective hierarchical clustering can be carried out with the assist of the ARC algorithm. The cluster head will be chosen based on node capacity and node trust value by implementing the Enhanced Monkey Inspired Optimization (EMIO) algorithm. Finally, the optimal cluster head node acts as an energy-efficient local director for conducting inter-cluster connectivity, data transmission, and other duties. The effectiveness of the proposed ARC-EMIO scheme has been assessed using the NS-3 simulator and the results evident that the proposed scheme guarantees better performance with an improved network lifetime of 35% and energy efficiency of 22% when compared with the existing state-of-the-art clustering techniques. Show more
Keywords: Internet of things, wireless sensor networks, multi-objective, regional clustering, monkey inspired optimization
DOI: 10.3233/JIFS-213017
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4961-4974, 2022
Authors: Chithra, K. | Shunmughanaathan, V.K. | Karthik, S. | Srihari, K.
Article Type: Research Article
Abstract: Mobile Ad hoc Networks (MANETs) are independent of central administration or any infrastructure, hence it is flexible in nature. Though, the network is self organizing, the mobile nodes have some resource constraints. There is always a requirement forefficient routing protocol to manage the energy consumption and reduce the energy wastages in MANETWith that in mind, this article proposes Ant Colony Optimization for Enhanced Energy Efficient Routing (EEER-ACO). Furthermore, the network design balances the Transition Probability Standard (TPS)offset Coefficient to maximize navigation processing effectiveness and decrease path finding packets. Furthermore, the Surviving route lifespan is calculated based upon that node’s position …and speed rate. Through incorporating the Residual Node Lifetime (RNL) and the Residual Link Lifetime (RLL), the Residual Node Lifetime (RNL), the ACO based pheromone has been designed. Further, the algorithm involves in choosing an optimal quality route for assuring continuous and efficient data packet transmission over the defined MANET. The investigation took into account the energy consumption of nodes as well as associated motility. Furthermore, the results indicate that the EEER-ACO algorithm improves network durability by reducing end-to-end delay, data packet loss, and path discovery rate. In comparison to previous algorithms, the proposed study has demonstrated that it achieves a 35 percent better performance than traditional protocols. Show more
Keywords: Network lifetime, path discovery, Ant Colony Optimization (ACO), energy utilization, routing
DOI: 10.3233/JIFS-212913
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4975-4985, 2022
Authors: Phatai, Gawalee | Chiewchanwattana, Sirapat | Sunat, Khamron
Article Type: Research Article
Abstract: In the business sector, predicting the movement of the Stock Exchange of Thailand (SET) index is challenging. Due to worldwide stock market fluctuations, investors commonly invest in price-changing businesses solely in the long term. Therefore, an accurate SET index movement prediction method is significant for investment purposes and has been the goal of many previous studies. Some studies have indicated that neural network (NN) models perform more effectively and accurately than traditional statistical models; accordingly, NNs employing backpropagation (BP) with sigmoid and smooth adaptive activation functions (SAAFs) and 10 metaheuristic algorithms to determine the initial prediction weights were developed in …this study. An experiment was conducted using a Thailand SET50 index dataset, and the results revealed that the model utilizing SAAFs with a cultural algorithm (CA) for weight initialization yielded more precise and efficient predictions than those of other competing models. This finding indicated the possibility of applying the proposed method for SET index movement prediction in the future. Show more
Keywords: Smooth adaptive neural network, weight initialization, cultural algorithm, SET index movement prediction
DOI: 10.3233/JIFS-213233
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4987-5000, 2022
Authors: Liu, Jianhua | Wang, Zhiheng
Article Type: Research Article
Abstract: In order to solve the problem that the population diversity of sparrow search algorithm (SSA) decreases and easily falls into the local optimal solution when it approaches the global optimal, an artificial immune algorithm-sparrow search algorithm (AIA-SSA) is proposed in this paper by combining artificial immune algorithm and sparrow search algorithm. This paper uses 10 benchmark functions for experimental simulation of AIA-SSA algorithm, and compares it with five widely used intelligent algorithms and SSA. Experimental results show that AIA-SSA overcomes the deficiency of SSA and improves the search accuracy, convergence speed and stability of the algorithm. Meanwhile, this paper applies …AIA-SSA to network intrusion detection and constructs a network intrusion detection model based on support vector machine (SVM). After testing, the accuracy of AIA-SSA-SVM prediction for various network attacks has been greatly improved. It not only shows that AIA-SSA-SVM has a broad application prospect in the field of network security, but also verifies the feasibility and advanced nature of AIA-SSA in solving practical engineering problems. Show more
Keywords: Intrusion detection, SSA, intelligence algorithm, adaptive search, differential evolution algorithm
DOI: 10.3233/JIFS-210813
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5001-5011, 2022
Authors: Wang, Chuantao | Feng, Fan
Article Type: Research Article
Abstract: With the development of Internet+medicine, online medical treatment has gradually become the new development direction of medical industry. Many hospitals provide online registration services to the public, and due to the lack of professional medical knowledge of patients, the problem of wrong registration often occurs. How to use deep learning technology to provide professional help to patients and reduce the waste of medical resources has become an urgent problem. To address the above problems, this paper proposes an ERNIE-based text classification model for intelligent triage. The model consists of two parts, ERNIE and BiGRU. The pre-training model ERNIE is used …to extract the feature representation of the text, and then input to the BiGRU neural network to get the text classification results. Compared with different models on 2 datasets, the experimental results show that the model proposed in this paper has better accuracy and recall than other models. Show more
Keywords: Text classification, ERNIE, deep learning, intelligent triage
DOI: 10.3233/JIFS-212140
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5013-5022, 2022
Authors: Idrees, Ammara | Gilani, S.A.M. | Younas, Irfan
Article Type: Research Article
Abstract: Coronary artery disease (CAD) is a common heart disease that causes the blockage of coronary arteries. To reduce fatality, an accurate diagnosis of this disease is very important. Angiography is one of the most trustworthy and conventional methods for CAD diagnosis however, it is risky, expensive, and time-consuming. Therefore in this study, we proposed a differential evolution-based support vector machine (SVM) for early and accurate detection of CAD. To improve the accuracy, different data preprocessing techniques such as one-hot encoding and normalization are also used with differential evolution for feature selection before performing classification. The proposed approach is benchmarked with …the Z-Alizadeh Sani and Cleveland datasets against four state-of-the-art machine learning algorithms, and a highly cited genetic algorithm-based SVM (N2GC-nuSVM). The experimental results show that our proposed differential evolution-based SVM outperforms all the compared algorithms. The proposed method provides accuracies of 95±1% and 86.22% for predicting CAD on the benchmark datasets. Show more
Keywords: Coronary Artery Disease (CAD), Machine Learning (ML), Differential Evolution (DE), Genetic Algorithm (GA), Support Vector Machine (SVM), Naïve Bayes (NB), Multilayer perceptron (MLP), Classification, True positive rate (TRP), False positive rate (FPR)
DOI: 10.3233/JIFS-213130
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5023-5034, 2022
Authors: Lee, Dohyun | Kim, Kyoungok
Article Type: Research Article
Abstract: Boosting methods are known to increase performance outcomes by using multiple learners connected sequentially. In particular, Adaptive boosting (AdaBoost) has been widely used owing to its comparatively improved predictive results for hard-to-learn samples based on misclassification costs. Each weak learner minimizes the expected risk by assigning high misclassification costs to suspect samples. The performance of AdaBoost depends on the distribution of noise samples because the algorithm tends to overfit noisy samples. Various studies have been conducted to address the noise sensitivity issue. Noise-filtering methods used in AdaBoost remove samples defined as noise based on the degree of misclassification to prevent …overfitting to noisy samples. However, if the difference in the classification difficulty between classes is considerable, it is easy for samples from classes that are difficult to classify to be defined as noise. This situation is common with imbalanced datasets and can adversely affect performance outcomes. To solve this problem, this study proposes a new noise detection algorithm for AdaBoost that considers differences in the classification difficulty of classes and the characteristics of iteratively recalculated sample weight distributions. Experimental results on ten imbalanced datasets with various degrees of imbalanced ratios demonstrate that the proposed method defines noisy samples properly and improves the overall performance of AdaBoost. Show more
Keywords: AdaBoost, noise-robust learning, noise-filtering, class imbalance, class separation
DOI: 10.3233/JIFS-213244
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5035-5051, 2022
Authors: Banitalebi, Sadegh | Ahn, Sun Shin | Jun, Young Bae | Borzooei, Rajab Ali
Article Type: Research Article
Abstract: In this paper, the notions of normal m -dominating set, normal m -domination number, inverse normal domination set (number) and inverse normal m -domination number are introduced, and some the related results are investigated. Finally, a utilization relevant to decision-making based on influencing factors the company’s efficiency is presented.
Keywords: Pythagorean fuzzy graph, normal m-dominating set, normal m-domination number, inverse normal domination set (number), inverse normal m-domination number
DOI: 10.3233/JIFS-220319
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5053-5062, 2022
Authors: Poongodi, J. | Kavitha, K. | Sathish, S.
Article Type: Research Article
Abstract: In recent years, the Internet of Things (IoT) has attracted more attention after the integration of IoT devices with the cloud for data management. IoT is used for sharing data in healthcare services. However, security and privacy vulnerabilities still exist in data transfers to and from cloud environments. Due to memory limitations, it is difficult to implement security protocols in IoT devices. So an intermediate secure node is established to enhance data security and privacy while handling healthcare data. Blockchain is a renowned technology that is adopted in various data management and security applications, but its potential is not unleashed …effectively in healthcare data management. Therefore in this research work, firstly, the data is pre-processed and then the Ant colony optimization method is used to find the shortest path for efficient data delivery. And, a blockchain based data security approach for healthcare data management is presented. The main objective is to enhance security against healthcare data threats. In the IoTs fog layer, a public-permissioned blockchain security process with elliptical curve cryptography and digital signature is presented as a distributed ledger database that provides immutable security, transparency in transactions, and prevents data tampering. The performance of the proposed approach is verified through various parameters like certificate generation time and size, data retrieval time, and size for better validation. Show more
Keywords: Blockchain, healthcare IoT, data security, cryptography
DOI: 10.3233/JIFS-220797
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 5063-5073, 2022
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