Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Gnanasundari, P. | Sheela Sobana Rani, K.

Article Type: Research Article

Abstract: Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with …WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption & decryption time, delay, energy consumption, delivery ratio and throughput. Show more

Keywords: Node Validation, energy efficiency, cybersecurity, blockchain, WSN

DOI: 10.3233/JIFS-230020

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Anandha Kumar, M. | Shanmuga Priya, M. | Arunprakash, R.

Article Type: Research Article

Abstract: In the past couple of years, neural networks have gained widespread use in network security analysis. This type of analysis is usually performed in a nonlinear and highly correlated manner. Due to the immense amount of data traffic, the current models are prone to false alarms and poor detection. Deep-learning models can help security researchers identify and extract data features that are related to an attack. They can also minimize the data’s dimensionality and detect intrusions. Unfortunately, the complexity of the network structure and hidden neurons of a deep-learning model can be set by error-prone procedures. In order to improve …the performance of deep learning models, a new algorithm is proposed. This method combines a gradient boost regression and particle swarm optimization. The proposes a method called the Spark-DBN-SVM-GBR algorithm. The simulations conducted proposed algorithm revealed that it has a better accuracy rate than other deep learning models and the experiments conducted on the PSO-GBR algorithm revealed that it performed better than the current optimization technique when detecting unauthorized attack activities. Show more

Keywords: Intrusion detection, Apache Spark, Support vector machine (SVM), particle swarm optimization and gradient boost regression

DOI: 10.3233/JIFS-221351

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2023

Authors: Li, Junwei | Liu, Huanyu | Jin, Yong | Zhao, Aoxiang

Article Type: Research Article

Abstract: Research on conflict evidence fusion is an important topic of evidence theory. When fusing conflicting evidence, Dempster-Shafer evidence theory sometimes produces counter-intuitive results. Thus, this work proposes a conflict evidence fusion method based on improved conflict coefficient and belief entropy. Firstly, the proposed method uses an improved conflict coefficient to measure the degree of conflict, and the conflict matrix is constructed to get the support degree of evidence. Secondly, in order to measure the uncertainty of evidence, an improved belief entropy is proposed, and the information volume of evidence is obtained by the improve entropy. Next, connecting with the support …degree and information volume, We get the weight coefficient, and use it to modify the evidence. Finally, using the combination rule of Dempster for fusion. Simulation experiments have demonstrated the effectiveness and superiority of the proposed method in this paper. Show more

Keywords: Evidence theory, conflict evidence, conflict coefficient, beleief entropy, combination rule

DOI: 10.3233/JIFS-221507

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Khatab, Hussein Ageel | Shareef, Salah Gazi

Article Type: Research Article

Abstract: The conjugate gradient (CG) techniques are a class of unconstrained optimization algorithms with strong local and global convergence qualities and minimal memory needs. While the quasi-Newton methods are reliable and efficient on a wide range of problems and these methods are converge faster than the conjugate gradient methods and require fewer function evaluations, however, they are request substantially more storage, and if the problem is ill-conditioned, they may require several iterations. There is another class, termed preconditioned conjugate gradient method, it is a technique that combines two methods conjugate gradient with quasi-Newton. In this work, we proposed a new two …limited memory preconditioned conjugate gradient methods (New1 and New2), to solve nonlinear unconstrained minimization problems, by using new modified symmetric rank one (NMSR1) and new modified Davidon, Fletcher, Powell (NMDFP), and also using projected vectors. We proved that these modifications fulfill some conditions. Also, the descent condition of the new technique has been proved. The numerical results showed the efficiency of the proposed new algorithms compared with some standard nonlinear, unconstrained problems. Show more

Keywords: Unconstrained optimization, projected quasi-newton methods, preconditioned conjugate gradient methods, limited memory preconditioned conjugate gradient methods

DOI: 10.3233/JIFS-233081

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Adar, Tuba | Delice, Elif Kılıç | Delice, Orhan

Article Type: Research Article

Abstract: Accurate and rapid diagnosis is a significant factor in reducing incidence rate; especially when the number of people inflicted with a disease is considerably high. In the healthcare sector, the decision-making process might be a complex and error-prone one due to excessive workload, negligence, time restrictions, incorrect or incomplete evaluation of medical reports and analyses, and lack of experience as well as insufficient knowledge and skills. Clinical decision support systems (CDSSs) are those developed to improve effectiveness of decisions by supporting physicians’ decision-making process regarding their patients. In this study, a new artificial intelligence-based CDSS and a user-friendly interface for …this system were developed to ensure rapid and accurate detection of pandemic diseases. The proposed CDSS, which is called panCdss, uses hybrid models consisting of the Convolutional Neural Network (CNN) model and Machine Learning (ML) methods in order to detect covid-19 from lung computed tomography (CT) images. Transfer Learning (TL) models were used to detect monkeypox from skin lesion images and covid-19 from chest X-Ray images. The results obtained from these models were evaluated according to accuracy, precision, recall and F1-score performance metrics. Of these models, the ones with the highest classification performance were used in the panCdss. The highest classification values obtained for each dataset were as follows: % 91.71 accuracy, % 92.07 precision, % 90.29 recall and % 91.71 F1-score for covid-19 CT dataset by using CNN+RF hybrid model; % 99.56 accuracy, % 100 precision, % 99.12 recall and % 99.55 F1-score for covid-19 X-ray dataset by using VGG16 model; and % 90.38 accuracy, % 93.32 precision, % 88.11 recall and % 90.64 F1-score for monkeypox dataset by using MobileNetV2. It is believed that panCdss can be successfully employed for rapid and accurate classification of pandemic diseases and can help reduce physicians’ workload. Furthermore, the study showed that the proposed CDSS is an adaptable, flexible and dynamic system that can be practiced not only for the detection of pandemic diseases but also for other diseases. To the authors’ knowledge, this proposed CDSS is the first CDSS developed for pandemic disease detection. Show more

Keywords: Clinical decision support system, artificial intelligence, deep learning, user interface, pandemic diseases

DOI: 10.3233/JIFS-232477

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023

Authors: Ramesh, Janjhyam Venkata Naga | Sucharitha, G. | Sankardass, Veeramalai | Rani, R. | Bhat, Nagaraj | Kiran, Ajmeera | Rajaram, A.

Article Type: Research Article

Abstract: Although difficult, robust and reliable synchronization of multimodal medical pictures has several practical uses. For instance, in MR-TRUS fusing guided prostate treatments, picture registration between the two modalities is essential. However, due to the significant variety in image appearance and correlation, MR-TRUS picture registration remains a challenging issue. In this research, we suggest employing deep convolutional neural networks (CNN) i.e. three dimensional CNN U-NET (3D-Conv-Net) to develop a resemblance measure for MR-TRUS registration. Finally, for the second-order optimal of the taught measure, we apply a composite optimisation method that searches the solution space for an appropriate starting point. We also …use a multi-stage process to improve the optimisation metric. Show more

Keywords: Image registration, convolutional neural networks, multimodal image fusion, and prostate cancer

DOI: 10.3233/JIFS-235744

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Kaijun, Zhao

Article Type: Research Article

Abstract: To enhance the psychological resilience of athletes, a method for evaluating the psychological resilience of High-intensity Interval Training (HIIT) athletes based on evolutionary neural networks is studied. From the six criteria of frustration coping, personal characteristics, self-promotion, self-regulation, internal protection and external protection, the evaluation index of psychological resilience of athletes in sports High-intensity Interval Training is selected; the audition indicators are qualitatively analyzed according to the principle of indicator selection, and the indicators that do not meet the requirements are eliminated; Cluster analysis and coefficient of variation analysis are used to carry out quantitative analysis on the remaining evaluation …indicators after qualitative analysis; the indicators after quantitative analysis are improved, to build the assessment index system of psychological resilience of athletes in high-intensity sports training. The Back Propagation (BP) neural network is optimized by a genetic algorithm, and the evolutionary neural network is constructed. The index data set is input into the evolutionary neural network as a sample, and the index weight value is output through training. The evaluation result and corresponding evaluation grade are determined based on the index weight value and membership degree. The experimental results show that when the number of hidden layers is 3, the calculation of evaluation index weights is the best; The weight of personal traits obtained from the evaluation results is the highest (0.206), while the weight of external protection is the lowest (0.151), and the evaluation results are basically consistent with the expert results. The above results show that this method can accurately evaluate the psychological resilience of athletes and significantly enhance their psychological resilience. Show more

Keywords: Evolutionary neural network, evaluation of psychological resilience, index system construction, genetic algorithm, weight calculation

DOI: 10.3233/JIFS-233299

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Wang, Hui | Liu, Ensheng | Wei, Hokai

Article Type: Research Article

Abstract: A machine for tunnel boring machine (TBM ) is recognized as productive equipment for tunnel construction. A dependable and precise tunnel boring machine’s performance (such as penetration rate (ROP )) prediction could reduce the cost and help choose the suitable construction method. Hence, this research develops new integrated artificial intelligence methods, i.e., biogeography-based multilayer perceptron neural network (BMLP ) and biogeography-based support vector regression (BSVR ), to forecast TBM PR . Using the biogeography-based optimization (BBO ) algorithm aims to improve the developed model’s performance by determining the optimized neuron number of hidden layers for MLP models and …the ideal values of the essential variables of SVR method. The results show that advanced methods can productively make a nonlinear relation among the ROP and its forecasters to obtain a satisfying forecast. Amongst the BMLP models with several hidden substrates, BM 5L with five hidden substrates could attain the total ranking score (TRS ) greatest rate, with root mean squared error (RMSE ) and coefficient of determination (R 2 ) equal to 0.017 and 0.9969. Simultaneously, the BSVR was the supreme model because of the fewer RMSE (0.00497 m /hr ) and a larger R 2 (0.999) compared with BMLP models. Overall, the acquired TRS s show that the BSVR outperforms the BMLP in terms of performance. As a consequence, the BSVR model may have been chosen as the suggested model if it had been able to accurately forecast the observed value even better than BM 5L . Show more

Keywords: Tunnel boring machine, penetration rate, biogeography-based multilayer perceptron neural network (BMLP), biogeography-based support vector regression (BSVR)

DOI: 10.3233/JIFS-232989

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Zhou, Sijiang | Mo, Kanglin | Yang, Xia | Ning, Zong

Article Type: Research Article

Abstract: OBJECTIVE: This research aims to pinpoint key biomarkers and immunological infiltration of idiopathic pulmonary fibrosis (IPF) through bioinformatics analysis. METHODS: From the GEO database, 12 gene expression profiles were obtained. The LIMMA tool in Bioconductor accustomed to identify the genes that are expressed differently (DEGs), and analyses of functional enrichment were performed. A protein-protein interaction network (PPI) was constructed using STRING and Cytoscape, and a modular analysis was performed. Analysis of the immunological infiltration of lung tissue between IPF and healthy groups was done using the CIBERSORTx method. RESULTS: 11,130 genes with differential expression (including 7,492 …up-regulated and 3,638 down-regulated) were found. The selected up-regulated DEGs were mainly involved in the progression of pulmonary fibrosis and the selected down-regulated DEGs maintain the relative stability of intracellular microenvironment, according to functional enrichment analysis. KEGG enrichment analysis revealed that up-regulated DEGs were primarily abundant in the PI3K-Akt signaling mechanism, whereas down-regulated DEGs were associated with cancer pathways. The most significant modules involving 8 hub genes were found after the PPI network was analyzed. IPF lung tissue had a greater percentage of B memory cells, plasma cells, T cells follicular helper, T cells regulatory, T cells gamma delta, macrophages M0 and resting mast cells. while a relatively low proportion of T cells CD4 memory resting, NK cells resting and neutrophils. CONCLUSION: This research demonstrates the differences of hub genes and immunological infiltration in IPF. Show more

Keywords: Idiopathic pulmonary fibrosis, biomarkers, immunological infiltration, lung tissue, bioinformatics analysis

DOI: 10.3233/JIFS-234957

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Shanthi, A.S. | Ignisha Rajathi, G. | Velumani, R. | Srihari, K.

Article Type: Research Article

Abstract: In older people, mild cognitive impairment (MCI) is a precursor to more severe forms of dementia like AD (AD). In diagnosing patients with primary AD and amnestic MCI, modern neuroimaging techniques, especially MRI, play a key role. To efficiently categorize MRI images as normal or abnormal, the research presents a machine learning-based automatic labelling system, with a focus on boosting performance via texture feature analysis. To this end, the research implements a preprocessing phase employing Log Gabor filters, which are particularly well-suited for spatial frequency analysis. In addition, the research uses Gray Wolf Optimization (GWO) to acquire useful information from …the images. For classification tasks using the MRI images, the research also make use of DenseNets, a form of deep neural network. The proposed method leverages Log Gabor filters for preprocessing, Gray Wolf Optimization (GWO) for feature extraction, and DenseNets for classification, resulting in a robust approach for categorizing MRI images as normal or abnormal. When compared to earlier trials performed without optimization, the proposed systematic technique shows a significant increase in classification accuracy of 15% . For neuroimaging applications, our research emphasizes the use of Log Gabor filters for preprocessing, GWO for feature extraction, and DenseNets for classification, which can help with the early detection and diagnosis of MCI and AD. Show more

Keywords: Dementia, mild cognitive impairment, MRI, AD, Gray Wolf Optimization, DenseNets, log gabor filter

DOI: 10.3233/JIFS-235118

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2023