Journal of Intelligent & Fuzzy Systems - Volume 44, issue 1

Authors: Kaladevi, P. | Janakiraman, Sengathir | Ramalingam, Praveen | Muthusankar, D.

Article Type: Research Article

Abstract: The advent of machine learning in the recent decade has excelled in determining new potential features and non-linear relationships existing between the data derived from the Electronic Health Records (EHR). Machine learning also enhances the process of handling data with maximum predictor variables compared to observations during the data mining process of prediction. The EHR data is often confronted with quality issues that are related to misclassification, missingness and measurement errors. In this context, ensemble classification schemes are determined to be essential for preventing the quality issues of EHR data. Moreover, the data sources like EHR include sensitive information that …needs to be protected from disclosure before it is forwarded to the mining process. Further, the sensitive data of EHR must be hidden without modifying the dataset such that it does not influence the prediction accuracy of the incorporated ensemble classification mechanism. In this paper, the process of hiding EHR data is facilitated through Improved Sensitivity Drift based k-Anonymized Data Perturbation Scheme (ISD-k-ADP) that randomly perturbs the data in the dataset by including restricted amount of noise. This controlled amount of included noise is derived carefully from the Sensitivity Drift based depending on the expected privacy level before it is sent to the process of classification. This ISD-k-ADP scheme is reliable such that, it prevents the impact induced by the hidden data during the process of Two Stage Bagging Pruning based Ensemble Classification (TSBP-EC). Furthermore, the TSBP-EC uses the methods of distance and accuracy based pruning that aids in minimizing the size of the ensemble for ensuring effective and efficient classification using machine learning. The simulation results of the proposed ISD-k-ADP-TSBP-EC scheme is determined to be predominant based on Classification Accuracy, Precision, Recall and Kappa Statistic in contrast to the standard schemes. Show more

Keywords: Ensemble classification, two stage bagging pruning, sensitivity drift, heuristic-based data perturbation, electronic health records, machine learning

DOI: 10.3233/JIFS-221615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 149-166, 2023

Authors: Uma Maheswari, K. | Valarmathi, A.

Article Type: Research Article

Abstract: A heart attack is a common cause of death globally. It can be treated successfully through a simple and accurate diagnosis. Getting the right diagnosis at the right time is very important for the treatment of heart failure. Currently, the conventional method of diagnosing heart disease is not reliable. Machine learning is a type of artificial intelligence that can be used to analyze the data collected by sensors. Data mining is another type of technology that can be utilized in the healthcare industry. These techniques help predict heart disease based on various factors. We developed a prediction and recommendation model …aimed at predicting heart disease using the Optimized Deep Belief Network. It does so by taking into account the various features of the heart disease UCI and Stalog database. Finally, the proposed method classifies healthy people and people with heart illness with an accuracy of 97.91%. Show more

Keywords: Heart disease, diagnosis, machine learning, deep learning

DOI: 10.3233/JIFS-221272

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 167-184, 2023

Authors: Zhenlin, Wei | Chuantao, Wang | Xuexin, Yang

Article Type: Research Article

Abstract: Sentiment classification aims to complete the automatic judgment task of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning models require a large number of manually annotated samples of sentiment tendency for supervised training. Faced with massive online review data, the feasibility of manual tagging is worrisome. In addition, the traditional deep learning model ignores the imbalanced distribution of the number of classification samples, which will lead to a decline in classification performance in the practical application of the model. Considering that the online review data contains weak tagging information such as scores and labels, …and the distribution is imbalanced, a weak tagging and imbalanced networks for online review sentiment classification is constructed. The experimental results show that the model significantly outperforms the traditional deep learning model in the sentiment classification task of hotel review data. Show more

Keywords: Sentiment classification, imbalanced classification, weak tagging, deep learning

DOI: 10.3233/JIFS-221565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 185-194, 2023

Authors: Balasubramanian, Kishore | Prabu, A.V. | Shaik, Mahammad Firose | Naik, R. Anjaneyulu | Suguna, S. Kanimozhi

Article Type: Research Article

Abstract: Today’s healthcare sectors are driven and work to rescue patients as soon as possible by giving them the right care and treatment. A healthcare monitoring system works in two ways: by keeping track of the patient’s activities and overall health. For prompt treatment, such as giving the right and suitable medication, administering an injection, and providing additional medical help, nursing supervision is required. Wearable sensors are fixed or connected to the patient’s body and can follow their health. These IoT medical gadgets let clinicians diagnose patients and comprehend the processes from remote. However, the amount of data produced by IoT …devices is so large that it cannot be handled manually. A model for automated analysis is required. Convolution Neural Network with Long-Short Term Memory (CNN-LSTM) was therefore suggested in this study as a Hybrid Deep Learning Framework (HDLF) for a Patient Activity Monitoring System (PAMS) that brings all healthcare activities with its classes. To incorporate medical specialists from all over the world and enhance treatment outcomes, the framework offers an advanced model where patient activities, health conditions, medications, and other activities are distributed in the cloud. An effective architecture for Wearable Sensor Network-based Human Action Recognition that combines neural network Simple Recurrent Units (SRUs) and Gated Recurrent Units (GRUs). For assessing the multimodal data input sequence, deep SRUs and a variety of internal memory states is utilized in this research. Furthermore, for addressing the concerns about accuracy oscillations or instability with decreasing gradients, a deep GRUs to store and learn the knowledge is conveyed to the future state. The analysis suggests that CNN-LSTM is then contrasted with some of the currently used algorithms, and it is found that the new system has a 99.53% accuracy rate. The difference between this accuracy result and the current value is at least 4.73%. Show more

Keywords: Sensor network, Body Wearable Sensors, surveillance monitoring, Healthcare Monitoring System (HMS), Physiological Parameter Analyzation

DOI: 10.3233/JIFS-212958

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 195-211, 2023

Authors: Zhao, Shuping | Wang, Dong | Lei, Ting | Wang, Yifan

Article Type: Research Article

Abstract: The selection of a waste-to-energy (WTE) plant site is the core issue that determines whether the WTE project can effectively treat municipal solid waste, reduce environmental pollution, and promote the development of a circular economy, and is often determined through group decision-making. The complexity of this group decision problem makes the opinions of decision makers often with uncertainty. The single-valued neutrosophic set (SVNS) can reduce the loss of information that contains uncertainty by quantitatively describing the information through three functions. In addition, existing studies on group decision-making for WTE plant siting suffer from the problem that decision maker weights do …not change in concert with those decision makers’ decision information. Therefore, this study proposes a group decision-making method based on SVNSs. First, a group consensus strategy is proposed to improve group consensus by removing the evaluation value of the corresponding solution for decision makers who do not reach consensus and are unwilling to modify their preferences. Second, a decision maker weight determination and adjustment method is proposed to readjust the decision maker weights from the solution level according to their respective consensus degree when the decision makers’ preference information changes. This method enables the decision makers’ preferences and weights to be changed jointly. An illustrative example and a comparative analysis of WTE plant siting decisions demonstrate the feasibility and superiority of the method. The experimental results show that the method is effective in helping decision makers to select the optimal WTE plant site more accurately. Show more

Keywords: Waste-to-energy, site selection, single-valued neutrosophic sets, group consensus

DOI: 10.3233/JIFS-220124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 213-224, 2023

Authors: Abu Talha, Muhammad | Zafar, Adeel

Article Type: Research Article

Abstract: False information is becoming more frequent in distributing disinformation by distorting people’s awareness and decision-making by altering their views or knowledge. The propagation of disinformation has been aided by the proliferation of social media and online forums. Allowing it to readily blend in with true information. Parody news and rumors are the most common types of misleading and unverified information, and they should be caught as soon as possible to avoid their disastrous consequences. As a result, in recent years, there has been a surge in interest in effective detection approaches. For this study, a customized dataset was built that …included both real and parody tweets from Pakistan and India. This study proposes a two-step strategy for detecting parody tweets. In the first stage of the approach the unstructured data is converted into structured data set. In the second step, multiple supervised artificial intelligence algorithms were employed. An experimental assessment of the different classification methods inside a customized dataset was undertaken in this study, and these classification models were compared using evaluation metrics. Our results showed accuracy of 92%. Show more

Keywords: Social media, parody tweets, binary classification, machine learning, deep learning, word embedding

DOI: 10.3233/JIFS-221200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 225-236, 2023

Authors: Liu, Xuning | Zhang, Zixian | Zhang, Guoying

Article Type: Research Article

Abstract: Accurate and rapid prediction of the coal and gas outburst is very significant for preventing accident and protecting environment, the paper presents a novel feature selection and outburst classifier framework which can identify effective candidate features and improve the classification accuracy. First, Apriori is applied for preliminarily extracting the association rules from sample data and attribute features in coal and outburst, and it can present the effective sample data and features for outburst prediction. Second, in order to reduce the redundancy of the strong association rules obtained from Apriori, Boruta is applied for selecting all highly relevant optimal features based …on the obtained strong association rules. Third, Random Forest(RF) is used to assign different weights to different features in optimal candidate features considering the importance of different features to outburst, based on the above obtained high-quality sample data and optimal features, the parameters of KNN model optimized by Bayesian Optimization(BO) is used to predict the coal and gas outburst. The experimental results show that the proposed feature selection model Apriori-Boruta can obtain significant sample data, and the proposed RF- KNN optimized classifier model can achieve higher performance in terms of the number of optimal features and prediction accuracy compared with traditional prediction models. Show more

Keywords: Coal and gas outburst, Apriori, Boruta, RF, KNN

DOI: 10.3233/JIFS-213457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 237-250, 2023

Authors: Arslan, Serdar | Yazici, Adnan

Article Type: Research Article

Abstract: The semantic query problem is commonly called the semantic gap and is one of the significant problems in multimedia data retrieval. In this study, we focus on multimedia data retrieval by combining semantic information with data content to solve the semantic gap problem effectively. The main idea behind the combination of low-level content descriptors and the concept of multimedia data is to represent the content information with the semantic information by adding a low-level content descriptor as a new dimension to the index structure. This new dimension is represented by constructing an array index structure that uses a fuzzy clustering …algorithm. Thus, a new high-dimensional index structure, named MM-FOOD, supporting querying of multimedia data, including fuzzy querying, is presented in this paper. This proposed index structure’s construction and query algorithms are explained throughout this paper. Our experiments show that our indexing mechanism is considerably efficient compared to the basic indexing approach, which stores low-level content and semantic concept descriptors in separate structures when the data size is large. Show more

Keywords: High-dimensional indexing, multimedia data retrieval, fuzzy querying, multidimensional scaling

DOI: 10.3233/JIFS-220673

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 251-282, 2023

Authors: Wang, Jinyan | Wu, Fangjing

Article Type: Research Article

Abstract: Most of the published literature on concrete containing fly ash was limited to predicting the hardened properties of concrete. It is understood that exist so restricted studies focusing on forecasting both hardened and fresh features of self-compacting concrete (SCC). Hence, it is goaled for developing models for predicting the fresh and hardened properties of SCC by the support vector regression method (SVR). This study aims to specify SVR method key parameters using Ant lion optimization (ALO) and Biogeography-based optimization (BBO) algorithms. The considered properties of SCC in the fresh phase are the L-box test, V-funnel test, slump flow, and in …the hardened phase is CS. Results demonstrate powerful potential in the learning section for all considered properties as well as approximating in the testing phase. It can be seen that the proposed models have R2 incredible value in the learning and testing phase. It means that the correlation between observed and predicted properties of SCC from hybrid models is acceptable so that it represents high accuracy in the training and approximating process. All in all, in most of the cases, the SVR model developed by ALO outperforms BBO-SVR, which depicts the capability of the ALO algorithm for determining the optimal parameters of the considered method. Show more

Keywords: Fly ash, self-compacting concrete, rheological properties, support vector regression, ALO, BBO, compressive strength

DOI: 10.3233/JIFS-220744

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 283-297, 2023

Authors: Merbah, Amal | Makrizi, Abdelilah | Essoufi, El Hassan

Article Type: Research Article

Abstract: One of the pertinent concerns in traffic management is to optimize the waiting time at the traffic light junctions. We have has already developed an integrated nonlinear model which heavily relies on the genetic algorithm (GA). Indeed, GA proves efficient in terms of the computational time given the environmental constraints and the various variables inherent to the types of users and the degree of priority allotted to each of them. However, it was revealed that some issues having to do with instability require further adjustments. In the present article the aforementioned model is revisited with the aim of addressing …the high standard deviations attributed to the objective function. More specifically, the present work considers the side effects of GA in sweeping the entire space of eligible solutions. In this respect, fuzzy Logic (FL) is integrated as a major component in order to orient the GA research. At the computational level, GA places the solution found by FL at the center of the solution space around which the initial population can be built. The implementation of this hybrid method reduces both the waiting time at traffic lights and the standard deviation of the results, showing a significant improvement in the management system. Show more

Keywords: Traffic control, nonlinear model, fuzzy logic, genetic algorithms

DOI: 10.3233/JIFS-221535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 299-307, 2023