Journal of Intelligent & Fuzzy Systems - Volume 44, issue 3

Authors: Annie Nancy, G. | Ramakrishnan, Kalpana | Senthil Nathan, J.

Article Type: Research Article

Abstract: Pressure injury usually develop in the bony prominence of immobile bedridden subjects. Predicting pressure injuries based on the subjects’ physiological information will reduce the burden of the caretakers in adjusting the frequency of repositioning such subjects. Visual assessment, diagnostic, and prognostic approaches only provide pressure injury information after onset. Therefore, the objective of this unique modeling technique is to predict the internal alterations that take place in human tissues before the onset of pressure injuries. In this approach the bio-mechanical and bio-thermal properties was integrated to predict the internal changes of skin, fat, and muscle layers when subjects were self-loaded …continuously for one hour in the sacrum region. A change in temperature of all the layers, as well as the distribution of Von-Mises stress in these layers, was observed. The inflammation caused by the changes in the temperature and the stress was measured from the simulation model. Ultrasound measurements was also taken for the same subjects in the supine position in the sacral region, before and after one hour by applying a self-load. An identical change in the thickness of the above-mentioned layers due to thermal expansion was noticed. Hence this computational model is hypothesized to give identical thermal expansion in comparison with the ultrasound measurements. There was an agreement between the thermal expansion using the simulation technique and the ultrasound technique which was assessed through Bland-Altman analysis, with a 96% confidence interval. Show more

Keywords: Bio-thermal model, bio-mechanical model, sacrum, pressure injury, multi-physics coupling

DOI: 10.3233/JIFS-222485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5045-5057, 2023

Authors: Linhares, Luís Fernando | da Silva, Alisson Marques | Meireles, Magali Resende Gouvêa

Article Type: Research Article

Abstract: Private transport has become a viable and increasingly popular alternative to urban transportation. However, with this growth, an old and recurring problem becomes more latent: the relationship between passenger demands and taxi supply. This problem suggests the creation and use of techniques which make it possible to reduce the gap between the demand for taxi passengers and the effective contingent of vehicles needed to meet this demand. This work introduces a new approach to forecasting and classifying taxi passengers’ demands. The proposed approach uses historical data from taxi rides and meteorological data. The Kruskal-Wallis method identifies the most relevant variables, …and an evolving fuzzy system performs demand forecasting/classification. Five evolving systems are evaluated with our approach: Autonomous Learning Multi-Model (ALMMo), evolving Multivariable Gaussian Fuzzy System (eMG), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy (eFCE), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Multi-Innovations Recursive Weighted Least Squares (eFMI), and evolving Neo-Fuzzy Neuron (eNFN). In addition, computational experiments using real-world data were conducted to evaluate and compare the performance of the proposed approach. The results revealed that it obtained performance superior or comparable to state-of-the-art ones. Therefore, the experimental results suggest that the proposed approach is promising as an alternative for forecasting and classifying taxi passenger demand. Show more

Keywords: Taxi demand, forecasting, classification, evolving systems, fuzzy systems

DOI: 10.3233/JIFS-222115

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5059-5084, 2023

Authors: Naqvi, Deeba R. | Sachdev, Geeta | Ahmad, Izhar

Article Type: Research Article

Abstract: Game theory has been successfully applied in a variety of domains to deal with competitive environments between individuals or groups. The matrix games involving fuzzy, interval fuzzy, and intuitionistic fuzzy numbers exclusively examine the numeric components of an issue. However, several researchers have also examined various extensions of conventional game theory, considering the ambiguous situations for payoffs and goals. In many real-life scenarios, qualitative information is often critical in expressing the payoffs of a matrix game. Thus, the present work contributes to the field of matrix games where the payoffs have been quantified via qualitative variables, termed interval-valued hesitant fuzzy …linguistic sets. The mathematical formulation and solution concept for matrix games involving interval-valued hesitant fuzzy linguistic numbers is designed by utilizing an aggregation operator supported by linguistic scale function and solving them by employing score function. Finally, the proposed approach is validated by applying it to electric vehicle sales. Show more

Keywords: Interval-valued, linguistic set, hesitant fuzzy set, matrix games, average aggregation operator, linguistic scale function, electric vehicles

DOI: 10.3233/JIFS-222466

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5085-5105, 2023

Authors: Gullapelly, Aparna | Banik, Barnali Gupta

Article Type: Research Article

Abstract: Multi-object tracking (MOT) is essential for solving the majority of computer vision issues related to crowd analytics. In an MOT system designing object detection and association are the two main steps. Every frame of the video stream is examined to find the desired objects in the first step. Their trajectories are determined in the second step by comparing the detected objects in the current frame to those in the previous frame. Less missing detections are made possible by an object detection system with high accuracy, which results in fewer segmented tracks. We propose a new deep learning-based model for improving …the performance of object detection and object tracking in this research. First, object detection is performed by using the adaptive Mask-RCNN model. After that, the ResNet-50 model is used to extract more reliable and significant features of the objects. Then the effective adaptive feature channel selection method is employed for selecting feature channels to determine the final response map. Finally, an adaptive combination kernel correlation filter is used for multiple object tracking. Extensive experiments were conducted on large object-tracking databases like MOT-20 and KITTI-MOTS. According to the experimental results, the proposed tracker performs better than other cutting-edge trackers when faced with various problems. The experimental simulation is carried out in python. The overall success rate and precision of the proposed algorithm are 95.36% and 93.27%. Show more

Keywords: Computer vision, surveillance, tracking, correlation filters, holistic samples

DOI: 10.3233/JIFS-223516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5107-5121, 2023

Authors: Nalini Joseph, L. | Anand, R.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219330 .

DOI: 10.3233/JIFS-223018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5123-5135, 2023

Authors: Sudhagar, D. | ArokiaRenjit, J.

Article Type: Research Article

Abstract: Many real-time applications, including some emerging ones, rely on high-dimensional feature datasets. For simplifying the high-dimensional data, the various models are available by using the different feature optimization techniques, clustering and classification techniques. Even though the high-dimensional data is not handled effectively due to the increase in the number of features and the huge volume of data availability. In particular, the high-dimensional medical data needs to be handled effectively to predict diseases quickly. For this purpose, we propose a new Internet of Things and Fuzzy-aware e-healthcare system for predicting various diseases such as heart, diabetes, and cancer diseases effectively. The …proposed system uses a newly proposed Intelligent Mahalanobis distance aware Fuzzy Weighted K-Means Clustering Algorithm (IMFWKCA) for grouping the high dimensional data and also applies a newly proposed Moth-Flame Optimization Tuned Temporal Convolutional Neural Network (MFO-TCNN) for predicting the diseases effectively. The experiments have been done by using the UCI Repository Machine Learning datasets and live streaming patient records for evaluating the proposed e-healthcare system and have proved as better than others by achieving better performance in terms of precision, recall, f-measure, and prediction accuracy. Show more

Keywords: Feature optimization, clustering, e-healthcare system, high dimensional data, internet of things

DOI: 10.3233/JIFS-220629

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5137-5150, 2023

Authors: Saini, Munish | Adebayo, Sulaimon Oyeniyi | Singh, Harnoor | Singh, Harpreet | Sharma, Suchita

Article Type: Research Article

Abstract: The United Nations prescribed the Sustainable Development Goals (SDGs) to various nations to provide enduring answers to widespread problems and to give long-lasting solutions to common issues being faced across the globe. SDG 5 in particular was aimed at minimizing gender inequality by employing 9 targets and 14 indicators. The indicators serve as a yardstick to measure the progress of each of the 9 targets. This research takes an in-depth look at the perspectives of SDG 5 –Gender Inequalities, its targets, and indicators. Furthermore, explanatory data analysis and numerical association rule mining alongside QuantMiner are applied to the generated Indian …datasets on SDG 5 to extract patterns and associations among the fourteen indicators of SDG 5. The association rule mining carried out on the indicators reveals the pattern of association among these indicators. Legal provision for women and the rate of crimes against women have a perfect association of 100% while the association between legal provision for women and women who have experienced physical violence stands at 80%. The full relationships of all the 14 indicators are discussed extensively in the result and discussion section. Overall, it is established that these indicators are interdependent. This will make it easier for academics, the general public, and governmental and non-governmental organizations to understand the trends and form informed opinions on issues relating to gender inequality and SDG 5. Show more

Keywords: Sustainable development goals (SDG), gender equality, indicators, numerical association rule mining, knowledge extraction

DOI: 10.3233/JIFS-222384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5151-5162, 2023

Authors: Karthikeyan, N. | Gugan, I. | Kavitha, M.S. | Karthik, S.

Article Type: Research Article

Abstract: The drastic advancements in the field of Information Technology make it possible to analyze, manage and handle large-scale environment data and spatial information acquired from diverse sources. Nevertheless, this process is a more challenging task where the data accessibility has been performed in an unstructured, varied, and incomplete manner. The appropriate extraction of information from diverse data sources is crucial for evaluating natural disaster management. Therefore, an effective framework is required to acquire essential information in a structured and accessible manner. This research concentrates on modeling an efficient ontology-based evaluation framework to facilitate the queries based on the flood disaster …location. It offers a reasoning framework with spatial and feature patterns to respond to the generated query. To be specific, the data is acquired from the urban flood disaster environmental condition to perform data analysis hierarchically and semantically. Finally, data evaluation can be accomplished by data visualization and correlation patterns to respond to higher-level queries. The proposed ontology-based evaluation framework has been simulated using the MATLAB environment. The result exposes that the proposed framework obtains superior significance over the existing frameworks with a lesser average query response time of 7 seconds. Show more

Keywords: Flood disaster management, ontology framework, spatial information, data pre-processing

DOI: 10.3233/JIFS-223000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5163-5178, 2023

Authors: Yang, Hai-Long | Ren, Huan-Huan

Article Type: Research Article

Abstract: In this paper, we focus on the three-way decision model on incomplete single-valued neutrosophic information tables. Firstly, we define the minimum and maximum similarity measures between single-valued neutrosophic numbers (SVNNs) which may contain unknown values. On this basis, the notion of θ-weak similarity measure is given. Then, we introduce the conception of an incomplete single-valued neutrosophic information table (ISVNIT). For an incomplete single-valued neutrosophic information table, a new similarity relation is proposed based on the θ-weak similarity measure. Some properties are also studied. By using Bayesian decision theory and this similarity relation, we construct a three-way decision model on an …ISVNIT. Finally, an example of choosing product service providers is explored to illustrate the rationality and feasibility of the proposed model. We also discuss the influence of parameters in the model on decision results. Show more

Keywords: Three-way decision, single-valued neutrosophic number, incomplete single-valued neutrosophic information table, similarity measure

DOI: 10.3233/JIFS-221942

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5179-5193, 2023

Authors: Lei, Fan | Cai, Qiang | Wei, Guiwu | Mo, Zhiwen | Guo, Yanfeng

Article Type: Research Article

Abstract: The emergence of new energy electric vehicles (NEEV) can effectively reduce vehicle fuel consumption and alleviate the contradiction between fuel supply and demand. It has made great contributions to improving the atmospheric environment and promoting the development of environmental protection. However, the insufficient number of new energy electric vehicle charging stations (NEEVCSs) and unreasonable coverage areas have become obstacles to the large-scale promotion of new energy electric vehicles. Therefore, we build a multi-attribute decision making (MADM) model based on probabilistic double hierarchy linguistic weight Maclaurin symmetric mean (PDHLWMSM) operator and a MADM model based on probabilistic double hierarchy linguistic weight …power Maclaurin symmetric mean (PDHLWPMSM) operator to select the best charging station construction point from multiple alternative sites. In addition, the model constructed in this paper is compared with the existing MADM models to verify the scientificity of the model proposed in this paper. Show more

Keywords: Multiple attribute decision making (MADM), probabilistic double hierarchy linguistic term set (PDHLTS), PDHLWMSM operator, PDHLWPMSM operator, new energy electric vehicle charging station (NEEVS)

DOI: 10.3233/JIFS-221979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5195-5216, 2023