Journal of Intelligent & Fuzzy Systems - Volume 46, issue 2

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