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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: Pu, Shihua | Liu, Zuohua
Article Type: Research Article
Abstract: Under the highly valued environment of intelligent breeding, rapid and accurate detection of pigs in the breeding process can scientifically monitor the health of pigs and improve the welfare level of pigs. At present, the methods of live pig detection cannot complete the detection task in real time and accurately, so a pig detection model named TR-YOLO is proposed. Using cameras to collect data at the pig breeding site in Rongchang District, Chongqing City, LabelImg software is used to mark the position of pigs in the image, and data augmentation methods are used to expand the data samples, thus constructing …a pig dataset. The lightweight YOLOv5n is selected as the baseline detection model. In order to complete the pig detection task more accurately, a C3DW module constructed by depth wise separable convolution with large convolution kernels is used to replace the C3 module in YOLOv5n, which enhances the receptive field of the whole detection model; a C3TR module constructed by Transformer structure is used to extract more refined global feature information. Contrast with the baseline model YOLOv5n, the new detection model does not increase additional computational load, and improves the accuracy of detection by 1.6 percentage points. Compared with other lightweight detection models, the new detection model has corresponding advantages in terms of parameter quantity, computational load, detection accuracy and so on. It can detect pigs in feeding more accurately while satisfying the real-time performance of target detection, providing an effective method for live monitoring and analysis of pigs at the production site. Show more
Keywords: Pig, deep learning, target detection, detection network, transformer
DOI: 10.3233/JIFS-236674
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5263-5273, 2024
Authors: Li, Hai | Gao, Mingjian | Lin, Zhizhan | Peng, Jian | Xie, Liangzhen | Ma, Junjie
Article Type: Research Article
Abstract: Background: Atrial fibrillation (AF), one of the most prevalent heart rhythm disorders, may lead to thromboembolism, heart failure, and sudden death. However, the mechanism of AF has not yet been fully explained. Objective: This study aims to identify novel gene signatures and to investigate the potential therapeutic targets of AF with an integrated bioinformatic approach. Methods: The gene expression and methylation datasets of AF were obtained through the Gene Expression Omnibus (GEO) database. Subsequently, a set of differentially expressed genes and differential methylation sites were identified. Gene functional annotation analysis was conducted to explore the potential …function of differentially-methylated/expressed genes. Then, we constructed a PPI network and TF–miRNA–mRNA network. Finally, weighted gene co-expression network analysis (WGCNA) was presented to study critical modules of AF. Results: Seven hypomethylated-high expression genes and nine hypermethylated-low expression genes were acquired from AF patients. Functional enrichment results indicated that the differentially-methylated/expressed genes were mainly concentrated in decidualization, maternal placenta development, regulation of nitric-oxide synthase activity, and osteoclast differentiation. Based on the results of the PPI, we defined 4 key genes namely FHL2, STC2, ALPK3, and RAP1GAP2 as the core genes, playing essential roles in the TF-miRNA-mRNA network. In the end, we constructed two co-expression modules that highly correlated with AF-related phenotype. Conclusion: In our study, we found critical genes for AF that might help understand the molecular changes in AF. Show more
Keywords: Atrial fibrillation, differentially expressed genes, DNA methylation, hub genes, miRNA, co-expression analysis
DOI: 10.3233/JIFS-234306
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5275-5285, 2024
Authors: Xhafaj, Evgjeni | Qendraj, Daniela Halidini | Salillari, Denisa
Article Type: Research Article
Abstract: The adoption of online banking is a big challenge as well as an emergent paradigm which is evolving quickly. The study develops a new exploration model that is used to determine significant constructs of the UTAUT theory that influence the adoption of online-banking. A study was conducted through an online survey of online banking users. Three methods were used; firstly, PLS-SEM was used to determine which of the constructs have a significant impact on behavioral intention to use e-banking, secondly, an incorporated neural network model was used to classify the relative impact of significant variables obtained from PLS-SEM, and finally …a hybrid procedure is incorporated from PLS-SEM to initialize the Fuzzy TOPSIS. The findings of the research paper constitute the ranked results and a comparison between them. The results of PLS-SEM and ANN analysis showed the same ranking for the constructs, while the decision making method Fuzzy TOPSIS introduced some changes in the ranking. This study presents valuable insights for the banking system to bring effective projects that increase the possibilities of using online banking. Show more
Keywords: PLS-SEM, ANN, online banking, fuzzy TOPSIS, UTAUT
DOI: 10.3233/JIFS-235388
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5287-5297, 2024
Authors: Zheng, Xuehui | Wang, Jun | Gao, You
Article Type: Research Article
Abstract: Selecting appropriate Cluster Heads (CHs) can significantly enhance the lifetime of the wireless sensor networks (WSNs). Fuzzy logic is an effective approach for CH election. However, existing fuzzy-logic-based CH election methods usually require a large number of fuzzy rules, making the CH election procedure inefficiency. In this study, a data-driven CH election method is proposed based on a compact set of fuzzy rules, which are learned by group sparse Takagi-Sugeno-Kang (GS-TSK) fuzzy system. Specifically, five linguistic variables were first used as features to describe the status of sensor nodes. After that, a compact set of fuzzy rules were learned by …GS-TSK, and they were then used to predict the chance of each sensor node becoming a CH. Based on the selected CHs, the clusters are generated. Simulation results show that the GS-TSK can select CHs with fewer rules more accurately. Besides, by using the proposed DD-FLC, an average improvement of WSN was shown in terms of first node dead (FND), 10% of nodes dead (10PND), quarter of nodes dead (QND), half of nodes dead (HND). Show more
Keywords: Wireless sensor network, sparse learning, TSK fuzzy system, GS-TSK
DOI: 10.3233/JIFS-224252
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5299-5311, 2024
Authors: Rani, R.M. | Dwarakanath, B. | Kathiravan, M. | Murugesan, S. | Bharathiraja, N. | Vinoth Kumar, M.
Article Type: Research Article
Abstract: Liver cancer is a leading cause of death worldwide and poses a significant challenge to physicians in terms of accurate diagnosis and treatment. AI-powered segmentation and classification algorithms can play a vital role in assisting physicians in detecting and diagnosing liver tumors. However, liver tumor classification is a difficult task due to factors such as noise, non-homogeneity, and significant appearance variations in cancerous tissue. In this study, we propose a novel approach to automatically segmenting and classifying liver tumors. Our proposed framework comprises three main components: a preprocessing unit to enhance picture contrast, a Masked Recurrent Convolutional Neural Network (RCNN) …for liver segmentation, and a pixel-wise classification unit for identifying abnormalities in the liver. When our models are applied to the challenging MICCAI’2027 liver tumor segmentation (LITS) database, we achieve Dice similarity coefficients of 96% and 98% for liver segmentation and lesion identification, respectively. We also demonstrate the efficiency of our proposed framework by comparing it with similar strategies for tumor segmentations. The proposed approach achieved high accuracy, sensitivity, specificity, and F1 score parameters for liver segmentation and lesion identification. These results were evaluated using the Dice similarity coefficient and compared with similar strategies for tumor segmentation. Our approach holds promise for improving the accuracy and speed of liver tumor detection and diagnosis, which could have significant implications for patient outcomes. Show more
Keywords: Liver segmentation, classification, deep learning, and mask RCNN
DOI: 10.3233/JIFS-232195
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5313-5328, 2024
Authors: Mohananthini, N. | Rajeshkumar, K. | Ananth, C.
Article Type: Research Article
Abstract: Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents …a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment. Show more
Keywords: Heart disease detection, healthcare, blockchain, security, fuzzy logic, feature selection
DOI: 10.3233/JIFS-232902
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5329-5342, 2024
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. 46, no. 2, pp. 5343-5358, 2024
Authors: Qin, Xiwen | Ji, Xing | Zhang, Siqi | Xu, Dingxin
Article Type: Research Article
Abstract: The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy …data’s misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model. Show more
Keywords: Credit scoring, improved PSO, Fuzzy C-means, undersampling, ensemble model
DOI: 10.3233/JIFS-233334
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5359-5376, 2024
Authors: Wu, Yixun | Wang, Taiyu | Gu, Runze | Liu, Chao | Xu, Boqiang
Article Type: Research Article
Abstract: In order to address the problem of decreased accuracy in vehicle object detection models when facing low-light conditions in nighttime environments, this paper proposes a method to enhance the accuracy and precision of object detection by using the image translation technology based on the Generative Adversarial Network (GAN) in the field of computer vision, specifically the CycleGAN, from the perspective of improving the training set of object detection models. This is achieved by transforming the existing well-established daytime vehicle dataset into a nighttime vehicle dataset. The proposed method adopts a comparative experimental approach to obtain translation models with different degrees …of fitting by changing the training set capacity, and selects the optimal model based on the evaluation of the effect. The translated dataset is then used to train the YOLO-v5-based object detection model, and the quality of the nighttime dataset is evaluated through the evaluation of annotation confidence and effectiveness. The research results indicate that utilizing the translated nighttime vehicle dataset for training the object detection model can increase the area under the PR curve and the peak F1 score by 10.4% and 9% respectively. This approach improves the annotation accuracy and precision of vehicle object detection models in nighttime environments without requiring additional labeling of vehicles in monitoring videos. Show more
Keywords: Vehicle object detection, CycleGAN, nighttime vehicle image dataset, deep learning, machine vision
DOI: 10.3233/JIFS-233899
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5377-5389, 2024
Authors: Li, Jie
Article Type: Research Article
Abstract: In response to the evolving landscape of the modern era, the requirements for engineering audit have undergone significant changes. To achieve efficient audit tasks and obtain accurate and reliable results, the integration of machine learning and wireless network technology has become essential, leading to the emergence of digital and information-based audit modes. This paper focuses on the development of a digital audit system that combines engineering audit management fusion with machine learning and wireless network technology. Such an approach reflects the dynamic shift in internal audit functions and objectives, providing clear guidelines for the future of digital audit management. By …harnessing the power of machine learning and wireless networks, the digital audit system effectively addresses challenges associated with data management, sharing, exchange, and security during the audit process. Through seamless integration, it enables comprehensive electronic and digital management of internal and audit business processes. This research explores the platform’s functionalities and its potential application, using actual audit data for analysis. The proposed digital audit system showcases superior real-time data querying performance, heightened accuracy in checks, and enhanced retrieval capabilities. The simulation results validate the system’s efficacy, highlighting its ability to deliver true and dependable audit outcomes. By embracing digital transformation, the engineering audit field can harness the potential of cutting-edge technologies, thus paving the way for a more efficient, reliable, and future-ready approach to audit management. Show more
Keywords: Machine learning, wireless network technology, digital engineering audit, audit management strategy
DOI: 10.3233/JIFS-230759
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5391-5403, 2024
Authors: Zhang, Yehua | Zhang, Yan
Article Type: Research Article
Abstract: With the advancement of modern medical concepts, the beneficial effects of music on human health have gradually become accepted, and the corresponding music therapy has gradually become a new research direction that has received much attention in recent years. However, folk music has certain peculiarities that lead to the fact that there is no efficient way of selecting repertoire that can be carried out directly throughout the repertoire selection. This paper combines deep learning theory with ethnomusic therapy based on previous research and proposes a deep learning-based approach to ethnomusic therapy song selection. Since the feature extraction process in the …traditional sense has insufficient information on each frame, excessive redundancy, inability to process multiple frames of continuous music signals containing relevant music features and weak noise immunity, it increases the computational effort and reduces the efficiency of the system. To address the above shortcomings, this paper introduces deep learning methods into the feature extraction process, combining the feature extraction process of the Deep Auto-encoder (DAE) with the music classification process of Gaussian mixture model, which forms a new DAE-GMM music classification model. Finally, in terms of music therapy selection, this paper compares the music selection method based on co-matrix and physiological signal with the one in this paper. From the theoretical and simulation plots, it can be seen that the method proposed in this paper can achieve both good music classifications from a large number of music and further optimize the process of music therapy song selection from both subjective and objective aspects by considering the therapeutic effect of music on patients. Through this article research results found that the depth of optimization feature vector to construct double the accuracy of the classifier is higher, in addition, compared with the characteristics of the original optimization classification model, using the gaussian mixture model can more accurately classify music, the original landscape “hometown” score of 0.9487, is preferred, insomnia patients mainly ceramic flute style soft tone, without excitant, low depression, have composed of nourishing the heart function. Show more
Keywords: Ethnic music, music therapy, repertoire selection, deep learning
DOI: 10.3233/JIFS-230893
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5405-5414, 2024
Authors: Sureka, V. | Kavya, G.
Article Type: Research Article
Abstract: Automobiles have undergone a transformation during the past two decades due to the merger of the electronics and automotive industries. The combination of autos and electronic sensors has resulted in a new generation of vehicles known as autonomous vehicles (AVs). These AVs have a few hundred thousand sensors, producing an enormous amount of raw data for computation. Data from the vehicular network can be offloaded to existing telecommunication infrastructure to address the problem of processing resources. In order to address vehicular network requirements, large-capacity servers deployed in major telecommunications networks are first used to offload resource-intensive tasks. Mobile Cloud Computing …(MCC) is a critical enabling technology for 5 G networks, which has a key feature of offloading to divide application tasks into local and cloud server execution components. This paper proposes a novel Three TierEdge cloud computing (T2 EC2 ) system which uses an Energy-aware Dynamic Task offloading and collaborative task execution algorithm (EA-DTOCTE) for multilayer vehicular cloud computing networks. The EA-DTOCTE algorithm is included in the decision-making engine in the proposed system, which selects whether to offload the task to the remote environment or implement it locally. EA-DTOCTE focuses on consumption of energy by tasks both locally and remotely since its goal is to efficiently and dynamically split the application into tasks and schedule them on local devices and cloud resources. The proposed T2 EC2 has been evaluated in terms of parameters such as energy consumption, completion time, and throughput. Experimental results indicate that the proposed T2 EC2 can save up to 28% of system energy consumption compared with other state-of-art techniques. Show more
Keywords: Autonomous vehicles, mobile cloud computing, application partitioning, offloading, scheduling, EA-DTOCTE, decision making engine, collaborative task execution
DOI: 10.3233/JIFS-220970
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5415-5427, 2024
Authors: Cao, Peng | Xiao, Jing
Article Type: Research Article
Abstract: The Belt and Road (B&R) plan is put out within the framework of global economics and strategic growth. This study examines the written material of popular tourist sites along B&R and the tourism assets from the viewpoint of B&R, based on the wireless network and AI technology, and using a big data platform and the Internet of Things (IoT) User Generated Content (UGC) network structure. To manage tourist pictures from customers’ views, online travel notes are first utilized as examples. Next, tourism texts’ keywords are extracted using Python big data and AI technology to understand consumers’ perceptions of scenic spot …preferences, tourism facilities and services, and social and cultural customs. The findings demonstrate that, when compared to the conventional tourism brand development strategy, the integrated development strategy based on the AI big data platform can not only increase the effectiveness of managing tourists’ perceptions of scenic locations but can also encourage the common development of national sports event components and intelligent tourism image management. Several sports tourist boutique picturesque locations have also been built along B&R following years of development of intelligent tourism and sports projects, which will strengthen the effect of multicultural exchanges. Show more
Keywords: The Belt and Road, traditionalsports, tourism brand, big data, artificial intelligence
DOI: 10.3233/JIFS-230547
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5429-5439, 2024
Authors: Mahalakshmi, G. | Uma, E.
Article Type: Research Article
Abstract: Intelligent Transportation Systems have become integral to daily life, with VANETs (vehicular ad-hoc networks) playing the pivotal role. VANETs, the subsets of MANETs, employ vehicles as nodes to establish intelligent transport systems. However, due to critical applications such as military use, these networks are susceptible to attacks. With features like high mobility, dynamic network topology, and coverage issues, security breaches are a concern. This necessitates a secure routing algorithm to mitigate attacks and ensure message delivery. In our study, we utilize the UNSW-NB15 intrusion detection dataset to develop training and testing models. Our proposed novel intrusion detection system employs a …feature selection algorithm that prioritizes significant arriving traffic attributes. This algorithm enhances abnormal activity detection while minimizing associated features. To achieve this, we modify the Conditional Random Field algorithm with fuzzy-based rules, resulting in a more efficient selection of influential and contributing features for detecting attacks such as DoS, Worms, Fuzzers, and Shellcode. Through appropriate feature selection using the modified Conditional Random Field and Support Vector Machine classification system in our experiments, we demonstrate a notable increase in security by reducing the false positive rate. Additionally, our approach excels in detecting accuracy of Fuzzers (98.86%), DoS (98.80%), Worms (34.45%), and Shellcode (89.308%), ultimately enhancing network performance. These findings underscore the effectiveness of our proposed method in enhancing intrusion detection and overall network efficiency. Show more
Keywords: Vehicular ad-hoc networks, intrusion detection, feature selection, classification
DOI: 10.3233/JIFS-234192
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5441-5453, 2024
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. 46, no. 2, pp. 5455-5463, 2024
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. 46, no. 2, pp. 5465-5478, 2024
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. 46, no. 2, pp. 5479-5489, 2024
Authors: Al-Jamaan, Rawabe | Ykhlef, Mourad | Alothaim, Abdulrahman
Article Type: Correction
DOI: 10.3233/JIFS-219331
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5491-5491, 2024
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