Journal of Intelligent & Fuzzy Systems - Volume 44, issue 6

Authors: Li, Feng

Article Type: Research Article

Abstract: With the advent of the information age, the development direction of automobiles has gradually changed, both from the domestic and foreign policy support attitude, or from the actual actions of the automotive industry and scientific research institutes’ continuous efforts, it is not difficult to see that driverless vehicle. At this time, the testing and evaluation of the intelligent behavior of driverless vehicles is particularly important. It is particularly important not only to regulate the intelligent behavior of unmanned vehicles, but also to promote the key It can not only regulate the intelligent behavior of unmanned vehicles, but also promote the …improvement of key technologies of unmanned vehicles and the research and development of driver assistance systems. The evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is often considered as a multi-attribute group decision making (MAGDM) problem. In this paper, the EDAS method is extended to the interval neutrosophic sets (INSs) setting to deal with MAGDM and the computational steps for all designs are listed. Then, the criteria importance through intercriteria correlation (CRITIC) is defined to obtain the attribute’s weight. Finally, the evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is given to demonstrate the interval neutrosophic number EDAS (INN-EDAS) model and some good comparative analysis is done to demonstrate the advantages of INN-EDAS. Show more

Keywords: Multi-attribute group decision making (MAGDM), interval neutrosophic sets (INSs), EDAS method, comprehensive obstacle-avoiding behavior, unmanned vehicles

DOI: 10.3233/JIFS-223370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10721-10732, 2023

Authors: Sridevi, A. | Preethi, M.

Article Type: Research Article

Abstract: The technologically adapted agricultural procedures convert conventional farming practices and introduce smart farming or smart agriculture. Manual interventions in farming are unavoidable, however, it was reduced due to the Internet of Things (IoT). Sensors are used to monitor the farms which reduce the manpower requirements as well the cost. In this research work, a smart monitoring and prediction system was developed using IoT along with Fog computing. The physical data from farms are collected through IoT sensors and processed using a novel correlation-based ensemble classifier. Fog computing is adopted in the proposed work to reduce the data transmission delay and …computation complexities. Simulation analysis using benchmark datasets demonstrates the proposed model performance in terms of precision, recall, F1-score, and accuracy. Comparative analysis with conventional techniques like neural networks, extreme learning machine, and hybrid particle swarm optimization algorithm, validates the superior performance of the proposed model. With maximum accuracy of 96.67% proposed model outperforms conventional approaches. Show more

Keywords: Internet of Things (IoT), fog computing, latency, monitoring, feature extraction, prediction, correlation-based approach, ensemble classifier

DOI: 10.3233/JIFS-224225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10733-10746, 2023

Authors: Diao, Xiu-Li | Zheng, Cheng-Hao | Zeng, Qing-Tian | Duan, Hua | Song, Zheng-guo | Zhao, Hua

Article Type: Research Article

Abstract: With the increase in needs for personalized learning of online students, knowledge tracing (KT), a technique aimed at tracing the state of a student’s knowledge mastery and predicting performance in future exercises, has become a hot topic in personalized learning research. The behavioral features exhibited during students’ learning process bear information that impacts the state of a student’s knowledge mastery. To study the influence of learning behaviors on students’ knowledge mastery state in the learning process, we propose a Precise Modeling of Learning Process based on M ultiple B ehavioral F eatures for K nowledge T racing model (MBFKT), which …models a student’s learning process by making use of these behavioral features. MBFKT initially processes these features through multi-head attention networks, memory networks, and recurrent neural networks to model students’ learning process into three memory links: memory decline link, memory enhancement link, and memory update link. Various update strategies are designed for each memory link, and the performance of numerous possible combinations of behavioral features in the memory links is compared, for the rules of learning and forgetting to be explained. Furthermore, we also study the contribution and degree of influence of different behavioral features on a student’s knowledge mastery state, by which MBFKT is improved, thus enhancing the accuracy of prediction. Through experiments on real online education datasets and comparison with existing benchmark methods, it is observed that MBFKT has evident advantages in predicting performance with good interpretability. Show more

Keywords: personalized learning, knowledge tracing, multiple behavioral features, memory links, educational data mining

DOI: 10.3233/JIFS-224351

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10747-10764, 2023

Authors: Sivaranjani, S. | Vivek, C.

Article Type: Research Article

Abstract: Spectrum sensing will be an essential component in developing cognitive radio networks, which will be an essential component of the subsequent generation of wireless communication systems. Over the course of several decades, a great deal of different strategies, including cyclo-stationary, energy detectors, and matching filters, have been put up as potential solutions. Obviously, each of these methods comes with a few of negatives that you have to take into consideration. When the Signal-to-Noise Ratio (SNR) changes, energy detectors work poorly; cyclo-stationary detectors are technically sophisticated; and employing matching filters needs experience with Primary User (PU) signals. Researchers have recently been …devoting a great deal of attention to Machine Learning (ML) and Deep Learning (DL) algorithms as a result of the potential uses that these algorithms may have in the development of exceptionally accurate spectrum sensing models. The capacity to learn from data in a way that traditional learning algorithms are unable to has led to the rise in prominence of these types of algorithms. The Hybrid Model of Improved Long Short Term Memory with Improved Extreme Learning Models (HILSTM-IELM), to be more specific, is what is being suggested since it reduces the amount of energy that is used during data transmission as well as the range and the duty cycle. Because of this, the disadvantage in existing methodology, proposed technique reduced to a certain level in energy consumption. In the last step of this analysis, the performance of the HILSTM-IELM-based spectrum sensing is compared to that of a variety of different methods that are currently in use. According to the findings of recent studies, the spectrum sensing method that was created provides superior performance to that of technologies in terms of the accuracy, sensitivity, and specificity of data transmission systems. Show more

Keywords: Improved long short-term memory, improved extreme learning machines, energy detectors, cyclo-stationary features, machine learning, deep learning algorithms

DOI: 10.3233/JIFS-224376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10765-10779, 2023

Authors: Wang, Zhiyong

Article Type: Research Article

Abstract: Assessment of energy needed for crack growth in concrete structures has been an interesting topic since the use of fracture mechanics to concrete. However, experimental procedures need time, cost and efforts. Based on historical data, regression approaches were created using mechanical characteristics and mixed design factors to quantify the concrete preliminary (Gf ) and whole (GF ) fracture energy. This work combined support vector regression (SVR) analysis with antlion optimization (ALO) and Harris Hawks optimization (HHO) approaches to build a hybridized SVR evaluation to fully comprehend Gf and GF . Evaluation metrics demonstrate that both optimized ALO-SVR and HHO-SVR …assessments could perform wonderfully throughout the estimation mechanism. Whenever the superior SVR investigation was contrasted to the literature, it was observed that the uniquely developed ALO-SVR regression also provides a reasonable boost in effectiveness, with benefits across the board. Finally, although the HHO-SVR technique has its particular capabilities in the simulating procedure, the ALO-SVR analysis seems to be highly reliable for determining Gf and GF . Show more

Keywords: Preliminary and entire fracture energy, concrete, SVR analysis, metaheuristic optimization algorithms

DOI: 10.3233/JIFS-224464

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10781-10798, 2023

Authors: Kasture, Neha | Jain, Pooja

Article Type: Research Article

Abstract:  Speech Recognition and its potential applications in terms of “talking devices” have become indispensable in today’s world. Technological advances like mobiles, smart home assistants or tablets extensively use the techniques of automatic speech recognition that works good for adults but cannot always follow and understand children’s speech. The primary goal of this paper is to bridge the gap of communication between voice assistants and Indian children speaking English as secondary language. The issue of lack of children’s speech corpora with English as non-native language, is addressed by creating a dataset of children in the age group of 5-15 years, speaking …Hindi or Marathi as their mother tongue and English as their second language. The analysis and implementation of the proposed work shows the accuracy of approximately 96% and potential for further scope by increasing the size of dataset in lower age group. The key contributions of our work are (i) creating speech dataset of Indian children whose mother-tongue is Hindi or Marathi, (ii) employing and evaluating hybrid Convolutional Neural Network (CNN) as an age classifier, (iii) language modeling to customize children vocabulary, (iv) checking accuracy and performance of the system. Show more

Keywords: Analysis of Children’s Speech, Automatic Speech Recognition, Child-Machine Interaction, Children’s Speech Recognition, Convolutional Neural Network

DOI: 10.3233/JIFS-224472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10799-10813, 2023