Journal of Intelligent & Fuzzy Systems - Volume 45, issue 4

Authors: Thao, Le Quang | Diep, Nguyen Thi Bich | Bach, Ngo Chi | Cuong, Duong Duc | Linh, Le Khanh | Linh, Nguyen Viet | Linh, Tran Ngoc Bao

Article Type: Research Article

Abstract: Vietnamese students are facing significant academic pressure due to societal and familial expectations, which leads to an unfavorable learning environment. We aim to employ a temporary spatial-temporal stress monitoring system. Using Wireless Sensor Network (WSN) technology, it collects data on students’ emotional states and incorporates a prediction model, “Reduce Students’ Stress in School” (R3 S), to detect students’ emotional states across school premises. The integration of R3 S and WSN is conducted in three stages. Initially, sensor nodes are deployed in schools to collect emotional data. Subsequently, we introduce a novel hybrid model combining a one-dimensional Convolutional Neural Network with Long Short-Term …Memory networks (1D-CNN-LSTM) to generate a predictive emotional map. This model’s performance, evaluated using RMSE and MAE metrics, shows exceptional precision compared to other LSTM models. When predicting the “stress” condition, the R3 S model achieved a Mean Absolute Error (MAE) of 10.30 and a Root Mean Square Error (RMSE) of 0.041. Lastly, we generate a comprehensive map of cumulative emotional conditions, serving as a guide for school counselors. This map aids in fostering a healthy, conducive learning environment. Show more

Keywords: Monitor student emotion, wireless sensor network, LSTM, 1DCNN, prediction stress

DOI: 10.3233/JIFS-232256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6735-6749, 2023

Authors: Arun Kumar, A. | Manikandan, B.V. | Kannan, S. | Bhuvanesh, A.

Article Type: Research Article

Abstract: This paper proposed a multi-objective-based Generation Expansion Planning (GEP) for the real-word power generation system of Tamil Nadu, an Indian state. GEP aims to solve numerous conflicting problems for constructing new power plants. The proposed approaches are Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) and Circle Search algorithm. The key objectives of the proposed method is to reduce budget, to maximize reliability and to minimize the pollutant discharge. Therefore, the apt formulations are modeled and solved to establish the conflicting facets of GEP problem. This paper implements MOCLPSO algorithm to solve Multi-Objective GEP (MOGEP) problem for 7-year and 14-year planning …horizon. By then, the proposed model is implemented at MATLAB/Simulink platform and the implementation is calculated. The proposed method shows better results in all approaches like Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO) and Cuckoo Search algorithm. The outcomes establish the competence of MOCLPSO and Circle Search Algorithm to offer good-ranged Pareto optimal non-dominated solutions. Show more

Keywords: CLPSO, recuperation, GEP, Tamil Nadu, power station, utility

DOI: 10.3233/JIFS-232909

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6751-6766, 2023

Authors: Nisha, A.S. | Siva Rani, T.S.

Article Type: Research Article

Abstract: The process of fusing different images from various imaging modalities into a single, fused image that contains a wealth of information and improves the usability of medical images in real-world applications is known as medical image fusion. The most useful features from data can be automatically extracted by deep learning models. In the recent past, the field of image fusion has been preparing to introduce a deep learning model. In this work we can achieve the multi-Focus medical image fusion by hybrid deep learning models. Here the relevant health care data are collected from database (CT & MRI brain images). …Following the input images are pre-processed using sliding window and the abnormal data is eliminated using distribution map method. Further the proposed work comprises 3 steps, 1) the proposed method is used to extract the features from the input image using the modified Tetrolet transform (MMT), which uses a brain image as an input image. This model is capable of identifying anomalous trends in time series data and automatically deriving from the input data characteristics that characterise the system state.2) Propose a novel hybrid model based on CNN with Bi-LSTM (Bi-directional Short Term Memory) multi-focus image fusion method to overcome the difficulty faced by the existing fusion methods. 3) This hybrid model are used to predict the brain tumor present in the fused image. Finally, experimental results are evaluated using a variety of performance measures. From the results, we can see that our suggested model contributes to an increase in predictive performance while also lowering the complexity in terms of storage and processing time. Show more

Keywords: CNN with Bi-LSTM, hierarchical data fusion, deep learning, health care applications, sliding window, modified tetrolet transform, multi-focus image fusion

DOI: 10.3233/JIFS-224439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6767-6783, 2023

Authors: Thomas, Julia T. | Kumar, Mahesh

Article Type: Research Article

Abstract: In industry, for the quality inspection processes, acceptance sampling plans proved to be economically viable, but the unpredictability of the plan’s characteristics made the use of conventional acceptance sampling plans less reliable. The generalized fuzzy multiple deferred state sampling plan (GFMDSSP) is suggested in this study for qualities that consider the difficulty in calculating the precise value of the percentage of defectives in a batch. The strategy is created with a minimal average sample size in mind and the performance measures have already been determined. An analysis of the current fuzzy acceptance sampling plans for characteristics is conducted, and an …important conclusion is drawn regarding the effectiveness of the proposed scheme. Analysis of the impact of inspection errors on the sampling process reveals a decline in plan acceptance standards that is correlated with escalating inspection errors. Finally, some numerical examples are provided to support the findings. Show more

Keywords: Fuzzy acceptance sampling plans, average sample number, acceptable quality level, limiting quality level, inspection errors

DOI: 10.3233/JIFS-224487

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6785-6796, 2023

Authors: Samy, V.S. | Thenkanidiyoor, Veena

Article Type: Research Article

Abstract: Due to the unpredictable nature of the weather and the complexity of atmospheric movement, extreme weather has always been a significant and challenging meteorological concern. Meteorological problems and the complexity of how the atmosphere moves have made it necessary to find a technological solution. Deep learning techniques can automatically learn and train from vast quantities of data to provide enhanced feature expression. This is frequently used in computer vision, natural language processing, and other domains to enhance the performance of numerous real-time problems. The purpose of this research is to propose a deep learning-based approach for effectively predicting extreme weather …events such as blizzards. To recognize weather patterns and forecast blizzards, the proposed deep learning-based method primarily employs RNN with LSTM. Real-time datasets from the Polar Regions were used to test the proposed approach’s accuracy, and tests were conducted to compare it to existing weather forecasting models. The accuracy of the model is 49.60% (univariate) and 55.19% (bivariate) using bivariate attributes of wind speed and air pressure based on the calculated RMSE values such as 0.0023 and 0.0021. Show more

Keywords: Weather patterns analytics, machine learning, deep learning, extreme prediction and weather forecasting

DOI: 10.3233/JIFS-224543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6797-6812, 2023

Authors: Thao, Le Quang | Bach, Ngo Chi | Cuong, Duong Duc | Linh, Le Khanh

Article Type: Research Article

Abstract: Babies who can’t communicate through language use crying as a way to express themselves. By identifying the unique characteristics of their cries, parents can quickly meet their needs and ensure their health. This study aimed to create a lightweight deep learning model called Bbcry to classify the cries of babies and determine their needs, such as hunger, pain, normal, deafness, or asphyxia. The model was trained using the Chillanto dataset and underwent three stages of development. Initially, the Wav2Vec 2.0 model was utilized as a teacher for the Knowledge Distillation (KD) method and applied to the transformer and prediction layers …to reduce the number of required parameters. Then, a projection head layer was added and linked to the transformer layers to control their impact on the Wav2Vec 2.0 model. This resulted in the first version of the Bbcry model with an accuracy of 93.39% and an F1-score of 87.60%. Finally, the number of transformer layers was reduced to create the Bbcry-v4 model with only 9.23 million parameters, which used only 10% of the parameters of Wav2Vec 2.0 while only slightly reducing accuracy and F1-score. The study concludes with a software demonstration that shows the proposed model’s ability to accurately recognize and determine the needs of infants based on their cries. Show more

Keywords: Dunstan baby language, infant cry classification, knowledge distillation, Wav2Vec

DOI: 10.3233/JIFS-232118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6813-6824, 2023

Authors: Fan, Jianping | Yuan, Jiu | Wu, Meiqin

Article Type: Research Article

Abstract: This paper studies a large-scale group decision-making method (LSGMD) based on incomplete hesitant fuzzy linguistic preference relations (IHFLPRs) and proposes an improved model for additive consistency of hesitant fuzzy linguistic preference relations (HFLPRs). Additionally, consistency control and fuzzy C-means (FCM) clustering are utilized to enhance efficiency and reliability. Firstly, a model is proposed to address the issues of missing elements in IHFLPRs and insufficient additive consistency in HFLPRs, aiming to more accurately reflect decision makers’ preference relationships towards candidate alternatives. Subsequently, the FCM method is employed to cluster decision experts’ preference information and obtain the overall preference information. Finally, the …rationality and accuracy of our proposed method are demonstrated through a case study and comparative analysis. Show more

Keywords: Incomplete hesitant fuzzy linguistic preference relations, consistency control, large-scale group decision making, Fuzzy C-means clustering

DOI: 10.3233/JIFS-232615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6825-6836, 2023

Authors: Lin, Pao-Ching | Huang, Jui-Chan | Ho, Ping-Tsan

Article Type: Research Article

Abstract: In recent years, tourism has developed rapidly and made great contributions to the economic enhancement of various regions; While tourism environment carrying ability assessment is the key to tourism sustainable development. The randomness and fuzziness of the traditional multi-level fuzzy comprehensive tourism environmental carrying ability assessment model cannot be combined effectively. In view of this, to construct a reasonable and objective assessment model, this study improves the multi-level fuzzy comprehensive tourism environmental carrying ability assessment model based on cloud model. The results indicate that the unimproved model judges that this tourism environment carrying ability level corresponds to level 2 for …selecting tourism destination. And it is in a suitable load state. The evaluation results on the foundation of cloud model improved multi-level fuzzy comprehensive tourism environmental carrying ability assessment show that its Ex is 5.748, En is 1,296 and He is 0.1, which is between moderate to slightly overloaded, and the overall state is moderate, but there is a tendency to develop towards slightly overloaded. The evaluation results of the improved model are more intuitive in showing the carrying capacity of the tourism environment, and these evaluation results are more objective and reliable, which verifies the applicability of the research model. This research model provides a theoretical basis and data support for the study of tourism environment carrying capacity. Show more

Keywords: Tourism, environmental carrying ability, cloud model, fuzzy integrated assessment, assessment model

DOI: 10.3233/JIFS-232982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6837-6847, 2023

Authors: Li, Kunpeng | Xu, Junjie | Zhao, Huimin | Deng, Wu

Article Type: Research Article

Abstract: Most of the flight accident data have uneven distribution of categories. When the traditional classifier is applied to this data, it will pay less attention to the minority class data. Synthetic Minority Over-sampling Technique (SMOTE), and its improvements are well-known methods to address this imbalance problem at the data level. However, traditional algorithms still have the problems in blurring the boundary of positive and negative classes and changing the distribution of original data. In order to overcome these problems and accurately predict flight accidents, a new Clustered Biased Borderline SMOTE(CBB-SMOTE) is proposed for Quick Access Recorder (QAR) Go-Around data. It …generates more obvious positive and negative class boundaries by using K-means for boundary minority class data and safety minority class data respectively, and maintains the original data distribution to the greatest extent through a biased oversampling method. Experiments were carried out on a group of QAR Go-Around data. The data set is balanced by CBB-SMOTE, SMOTE, Cluster-SMOTE algorithm respectively, and the random forest algorithm is used to predict the new data set. The experimental results show that CBB-SMOTE outperforms the SMOTE in terms of G-means value, Recall and AUC. Show more

Keywords: Imbalanced learning, oversampling, SMOTE, QAR Go-Around data, data generation

DOI: 10.3233/JIFS-233548

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6849-6862, 2023

Authors: Suresh Kumar, P. | Barkathulla, A.

Article Type: Research Article

Abstract: A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed …in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively. Show more

Keywords: Wireless sensor network, cluster head selection, energy efficiency, clustering, network lifetime

DOI: 10.3233/JIFS-221370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6863-6873, 2023