Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Purchase individual online access for 1 year to this journal.
Price: EUR 315.00Impact Factor 2024: 1.7
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: Sridhar, M. | Pankajavalli, P.B.
Article Type: Research Article
Abstract: In the resource-constrained wireless sensor network (WSN) geographic routing has been considered as an attractive method where it exploits the location data instead of global topology to transmit the data. The geographic routing protocol faces the routing issues when it is used by a heterogeneous device and utilizes high energy during the propagation of data. The lifespan of the sensor network depends on the efficiency of energy and capacity of the battery. Hence, successful data transmission, enrichment of battery capacity and energy utilization is necessary for WSN. To attain this requirements an effective change is made in the data transmission …environment and network topology. In this paper proposed a dynamic cluster based duty cycle scheduling is initiated for the data transmission. The cluster-based scheduling and routing in geographic routing protocol (CSRGR) utilize the clustering mechanism which in turn reduces the consumption of energy and maximizes the throughput. The objective function of the proposed approach provides a scheduling and routing strategy. The demonstration of simulation results shows the effective cluster size balancing with data transmission range dynamically. The proposed algorithm is compared with the existing approach and from the results, the energy consumption is minimum for diverse scenarios. Show more
Keywords: Duty cycle schedule, throughput, energy efficiency, routing, scheduling cluster, and geographic routing
DOI: 10.3233/JIFS-220966
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 951-961, 2023
Authors: Saravana Kumar, K. | Ramasubramanian, S.
Article Type: Research Article
Abstract: Cardiovascular disease (CVD) is a severe public health concern globally. Early and accurate CVD diagnosis is a difficult task but a necessary endeavour required to prevent further damage and protect patients’ lives. Machine Learning (ML)-based Clinical Decision Support Systems (CDSS) have the potential to assist healthcare providers in making accurate CVD diagnoses and treatments. Clinical data usually contains missing values (MVs); hence, the incorporated imputation techniques for ML have become a critical consideration when working with real-world medical datasets. Furthermore, removing instances with MVs will lead to essential data loss and produce incorrect results. To overcome these issues, this paper …proposes an efficient and reliable CDSS with Ensemble Two-Fold Classification (ETC) framework for classifying heart diseases. The effectiveness of the proposed ETC framework using different supervised ML algorithms is evaluated with four distinct imputation methods for handling MVs over the standard benchmark dataset, viz., the University of California, Irwin (UCI). Experimental results show that our proposed ETC framework with the k-Nearest Neighbors(k-NN) imputation method achieves better classification accuracy of 0.9999 and a lesser error rate of 0.0989 compared to other imputation methods and classifiers with similar execution times. Show more
Keywords: Clinical dataset, classification, data pre-processing, decision support system, heart disease prediction, imputation, machine learning algorithms, missing values
DOI: 10.3233/JIFS-221165
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 963-980, 2023
Authors: Sahoo, Santosh Kumar
Article Type: Research Article
Abstract: Social distance is considered one of the most effective prevention techniques to prevent the spread of Covid19 disease. To date, there is no proper system available to monitor whether social distancing protocol is being followed by individuals or not in public places. This research has proposed a hybrid deep learning-based model for predicting whether individuals maintain social distancing in public places through video object detection. This research has implemented a customized deep learning model using Detectron2 and IOU for monitoring the process. The base model adapted is RCNN and the optimization algorithm used is Stochastic Gradient Descent algorithm. The model …has been tested on real time images of people gathered in textile shops to demonstrate the real time application of the developed model. The performance evaluation of the proposed model reveals that the precision is 97.9% and the mAP value is 84.46, which makes it clear that the model developed is good in monitoring the adherence of social distancing by individuals. Show more
Keywords: Covid19, social-distancing, deep learning, Detectron 2, Intersection over Union, video object detection
DOI: 10.3233/JIFS-221174
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 981-999, 2023
Authors: Song, Hui-Hui | Wang, Ying-Ming | Jia, Xiang | Meng, Meng-Jun
Article Type: Research Article
Abstract: In order to avoid the hesitation of choosing between aggressive and benevolent strategies, we propose two cross-efficiency models to get interval cross-efficiency (ICE) from the relatively neutral angle in fuzzy environment, and then propose a novel aggregation method for ICE to solve the full ranking of Decision-Making Units (DMUs). Firstly, regard the expected value of fuzzy data as the input and output of Data Envelopment Analysis (DEA) method based on fuzzy set theory. Secondly, construct the cross-efficiency models based on the fuzzy expected values from the relatively neutral angle, and generate the lower and upper bounds of ICE for all …DMUs, which determines the interval cross-efficiency matrix (ICEM). Thirdly, project all ICE onto the plane as points, then seek the optimal rally point for each DMU based on ICEM as the comprehensive ICE. Fourthly, rank the comprehensive ICE to obtain the complete ranking of DMUs by using the optimal number sorting method. Finally, the proposed model is applied to the evaluation of manufacturing enterprises, and the results are compared with different models to prove its effectiveness. Show more
Keywords: Interval cross-efficiency DEA, fuzzy sets, fuzzy numbers, the optimal rally point, aggregation method
DOI: 10.3233/JIFS-221482
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1001-1015, 2023
Authors: Alqahtani, Yahya | Jamil, Muhammad Kamran | Alshehri, Hamdan | Ahmad, Ali | Azeem, Muhammad
Article Type: Research Article
Abstract: In November of 2019 year, there was the first case of COVID-19 (Coronavirus) recorded, and up to 3rd of April of 2020, 1,116,643 confirmed positive cases, and around 59,158 dying were recorded. Novel antiviral structures of the 2019 pandemic disease Coronavirus are discussed in terms of the metric basis of their molecular graph. These structures are named arbidol, chloroquine, hydroxy-chloroquine, thalidomide, and theaflavin. Metric dimension or metric basis is a concept in which the whole vertex set of a structure is uniquely identified with a chosen subset named as resolving set. Moreover, the fault-tolerant concept of those structures …is also included in this study. By this concept of vertex-metric resolvability of COVID antiviral drug structures are uniquely identified and help to study the structural properties of the structure. Show more
Keywords: COVID antiviral drug structures, vertex metric dimension, vertex fault-tolerant metric dimension, locating number, locating set
DOI: 10.3233/JIFS-220964
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1017-1028, 2023
Authors: Shobana Nageswari, C. | Vimal Kumar, M.N. | Vini Antony Grace, N. | Thiyagarajan, J.
Article Type: Research Article
Abstract: Ultrasound image quality management and assessment are an important stage in clinical diagnosis. This operation is often carried out manually, which has several issues, including reliance on the operator’s experience, lengthy labor, and considerable intra-observer variance. As a result, automatic quality evaluation of Ultrasound images is particularly desirable in medical applications. This research work plans to perform the fetal heart chamber segmentation and classification using the novel intelligent technology named as hybrid optimization algorithm Tunicate Swarm-based Grey Wolf Algorithm (TS-GWA). Initially, the US fetal images data is collected and data undergoes the preprocessing using the total variation technique. From the …preprocessed images, the optimal features are extracted using the TF-IDF approach. Then, Segmentation is processed on optimally selected features using Spatially Regularized Discriminative Correlation Filters (SRDCF) method. In the final step, the classification of fetal images is done using the Modified Long Short-Term Memory (MLSTM) Network. The fitness function behind the optimal feature selection as well as the hidden neuron optimization of MLSTM is the maximization of PSNR and minimization of MSE. The PSNR value is improved from 3.1 to 9.8 in the proposed method and accuracy of the proposed classification algorithm is improved from 1.9 to 12.13 compared to other existing techniques. The generalization ability and the adaptability of proposed TS-GWA method are described by conducting the various performance analysis. Extensive performance result shows that proposed intelligent techniques performs better than the existing segmentation methods. Show more
Keywords: Fetal heart chamber segmentation, optimal feature selection, modified long short term memory tunicate swarm-based grey wolf algorithm, fetal heart chamber classification
DOI: 10.3233/JIFS-221654
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1029-1041, 2023
Authors: Zhao, Guiping | Wang, Hongmei | Li, Zhanfa
Article Type: Research Article
Abstract: The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA ). Although meticulous experimental studies are needed to determine a rock’s CWA , predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR ) models were created: a traditional SVR , two …ensembled models, and a hybrid SVR model using the whale optimization technique (WOA - SVR ). Results show that all models have acceptable performance in predicting the CWA with R 2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA - SVR model compared to other methods. Show more
Keywords: Capillary water absorption, building stones, prediction, support vector regression, ensembled SVR, hybrid SVR
DOI: 10.3233/JIFS-221207
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1043-1055, 2023
Authors: Nithinsha, S. | Anusuya, S.
Article Type: Research Article
Abstract: The objective of the research work is to propose an intrusion detection system in a cloud environment using K-Means clustering-based outlier detection. In the open access and dispersed cloud architecture, the main problem is security and confidentiality because these are easily susceptible to intruders. Intrusion Detection System (IDS) is a commonly used method to identify the various attacks on the cloud which is easy to access from a remote area. The existing process can’t provide the data to transmit securely. This work describes and notifies the modernly established IDS and alarm management methods by giving probable responses to notice and …inhibit the intrusions in the cloud computing environment and to overcome the security and privacy issue. Proposed K-means Clustering based Outlier Detection (KmCOD) is used to detect the intruders and efficiently secure the data from malicious activity, where it is formulated respectively to increase the trustworthiness of the system by using applying intrusion detection techniques to virtual machines thus keeping the system safe and free from intrusion also provides system reliability. The parametric measures such as the detection rate, trace preprocessing, and correctly identified and incorrectly identified malicious activity are chosen. The performance analysis shows the accuracy of outlier detection as 81%, detection rate achieves 76%, packet arrival rate reaches 79%, pre-processing trace achieves 74%, and malicious activity rate of 21%. Show more
Keywords: Cloud, intrusion detection, data security, clustering algorithm, outlier detection, data privacy, anomaly
DOI: 10.3233/JIFS-220574
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1057-1068, 2023
Authors: Senthilkumar, D. | Reshmy, A.K. | Paulraj, S.
Article Type: Research Article
Abstract: Multi-Target Regression (MTR) is used to study the relationship between the same set of input variables and multiple continuous target variables simultaneously. A dataset with many input and output variables is the prime issue to address in the MTR, which is computationally complex to build a prediction model. Also, dimensionality reduction from multiple target variables is a challenging and essential task that aims to reduce the size of the dataset to optimize the time complexity of analysis and remove the redundant and irrelevant variables. This paper proposes an efficient feature selection strategy, Multi-Target Feature Subset Selection (MTFSS), for MTR that …constructs a unique subset of features by considering multiple targets. On the other hand, two feature evaluators, correlation and ReliefF, support the MTR dataset without discretization. Furthermore, two new score functions, weighted mean aggregation strategy and threshold function, are introduced to identify the significant features. To evaluate the effectiveness of the proposed MTFSS, experiments were carried out on a benchmark dataset. The experimental results demonstrate that the proposed MTFSS can select fewer features and perform better than the original dataset results. Also, the correlation-based feature evaluator performs better than ReliefF with better performance. Show more
Keywords: Multi-target regression, feature selection, correlation, ReliefF
DOI: 10.3233/JIFS-220412
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1069-1083, 2023
Authors: George, Remya | Jose, Reshma | Meenakshy, K. | Jarin, T. | Senthil Kumar, S.
Article Type: Research Article
Abstract: Law enforcement teams across the globe experience the highest occupational stress and stress-related diseases. Physical exercise and an active lifestyle are recommended as part of their profession to equip them to fight stress and related health adversities. The research is carried out using objective measures of Heart Rate Variability (HRV), Electro Dermal Activity (EDA), Heart Rate Recovery (HRR), and subjective questionnaires. HRV was generated with an electrocardiogram (ECG) signal acquired using NI myRIO 1900 interfaced with the Vernier EKG sensor. HRR was acquired with the help of a Polar chest strap exercise heart rate monitor and EDA acquisition was carried …out with Mindfield E-Sense electrodes. Then statistical features are extracted from the collected data, and feed to the AQCNN (Aquila convolution neural network) classifier to predict the stress. Signal analyses were done in Kubios 4.0, Ledalab V3.x in a MATLAB environment. The results pointed out that exercise training is effective in increasing the vagal tone of the Autonomic Nervous System (ANS) and hence improves the recovery potential of the cardiovascular system from stress. The proposed AQCNN method improves the accuracy by 95.12% which is better than 93.13%, 85.36% and 80.13% from Statistical technique, CNN and ML-SVM respectively. The findings have the potential to influence decision-making in the selection and training of recruits in high-stress positions, hence optimizing the cost and time of training by identifying maladaptive recruits early. Show more
Keywords: Exercise training, ANS adaptation, machine learning, stress-recovery, heart rate variability, heart rate recovery, electrodermal activity
DOI: 10.3233/JIFS-221588
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1085-1097, 2023
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl