Journal of Intelligent & Fuzzy Systems - Volume 45, issue 4

Authors: Suresh Babu, D. | Ramakrishnan, M.

Article Type: Research Article

Abstract: A severe problem that regularly affects cloud systems are intrusions. Ignore how the expansion of Internet of Things (IoT) devices will result in enormous intrusions. To distinguish intrusions from authorized network activity, detection is a crucial procedure. An Enhanced Lion Optimization Algorithm (ELOA) is utilized in this research, IoT intrusion detection system. Intrusions are classified using the Deep Belief Network (DBN) and an SDN controller technique. The proposed ELOA-based Intrusion Detection System uses the optimal weight in DBN to train the neurons to categorize the data in a network as normal and attacked during the training phase. In the testing …step that follows training, data from nodes are examined, and by contrasting the training results, they are categorized as normal and attacked data. By using the proposed ELOA and DBN algorithms, our intrusion detection system can successfully identify intrusions. Based on the creation of blacklists for detecting IoT intrusions, the (SDN) Software Defined Networking controller can effectively prohibit harmful devices. In order to demonstrate that the proposed ELOA finds network intrusions more successfully, its performance is compared to that of other existing techniques. The node sizes of the algorithms are run and evaluated for 1000, 2000, 3000, 4000, and 5000 respectively. At highest node 5000, the Proposed ELOA and DPN have precision, recall, f-score and accuracy becomes as 97.8, 96.22, 97.5 and 98.67 respectively. Show more

Keywords: Internet of Things, intrusion detection, Enhanced Lion Optimization Algorithm, deep belief network, SDN controller

DOI: 10.3233/JIFS-232532

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6605-6615, 2023

Authors: Suganya, S. | Selvamuthukumaran, S.

Article Type: Research Article

Abstract: Hadoop is a big data processing system that enables the distributed processing of massive data sets across multiple computers using straightforward programming techniques. Hadoop has been extensively investigated in many attacks as a result of its growing significance in industry. A company may learn about the actions of invaders as well as the weaknesses of the Hadoop cluster by examining a significant quantity of data from the log file. In a Big Data setting, the goal of the paper is to generate an analytical classification for intrusion detection. In this study, Hadoop log files were examined based on assaults that …were recorded in the log files. Prior to analysis, the log data is cleaned and improved using a Hadoop preprocessing tool. For feature extraction, the hybrid Improved Sparrow Search Algorithm with Mutual Information Maximization (H-ISSA-MIM). Then the CNN (Convolutional Neural Network) classifier will detect the intrusions. The implementation is performed using the MATLAB 2020a software. The performance metrics like accuracy, precision, F-score, recall, specificity, FPR, FNR are calculated for the proposed methodology and it is compared with the existing techniques like Decision Tree (DT), Principal Components Analysis (PCA)- K means, Long Short Time Memory (LSTM). The maximum value of accuracy finds out in the proposed method 98% . Show more

Keywords: Hadoop attacks, log file, intrusion detection, big data environment and feature extraction, convolutional neural networks

DOI: 10.3233/JIFS-233579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6617-6628, 2023

Authors: Baqer, Ihsan A. | Jaber, Alaa Abdulhady | Soud, Wafa A.

Article Type: Research Article

Abstract: Belt drive contamination is considered one of the most common failure modes that could be developed in the belts due to harsh operation conditions, high humidity, and sunlight exposure, reducing the belt’s performance. If the belt failure has not been detected early, a sudden shutdown may happen, producing safety and economic consequences. However, most maintenance personnel use their senses of sight, hearing, smell, and touch to identify the cause of the problem while diagnosing a belt drive condition. Hence, this research involves developing an intelligent contamination status detection system based on vibration signal analysis for a pulley-belt rotating system. Time-domain …signal analysis was employed to extract some suggestive features such as the root mean square, kurtosis, and skewness from the vibration data. An artificial neural network (ANN) model was built to detect the simulated different operating conditions. The vibration data was gathered with the help of two MEMS accelerometers (ADXL335) interfaced with an NI USB-6009 data acquisition device. A signal capture, analysis, and feature extraction system was developed using Matlab Simulink. The simulated operating conditions include clean, wet, and powder-contaminated belts. The results showed that the designed system could identify the pulley-belt operation conditions with 100% overall accuracy. Show more

Keywords: Condition monitoring, fault diagnosis, preventive maintenance, time-domain signal analysis, machine learning

DOI: 10.3233/JIFS-222438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6629-6643, 2023

Authors: Lin, Tao | Chen, Biao | Wang, Ruixia | Zhang, Yabo | Shi, Yu | Jiang, Nan

Article Type: Research Article

Abstract: Vision-based Continuous Sign Language Recognition (CSLR) is a challenging and weakly supervised task aimed at segmenting sign language from weakly annotated image stream sequences for recognition. Compared with Isolated Sign Language Recognition (ISLR), the biggest challenge of this work is that the image stream sequences have ambiguous time boundaries. Recent CSLR works have shown that the visual-level sign language recognition task focuses on image stream feature extraction and feature alignment, and overfitting is the most critical problem in the CSLR training process. After investigating the advanced CSLR models in recent years, we have identified that the key to this study …is the adequate training of the feature extractor. Therefore, this paper proposes a CSLR model with Multi-state Feature Optimization (MFO), which is based on Fully Convolutional Network (FCN) and Connectionist Temporal Classification (CTC). The MFO mechanism supervises the multiple states of each Sign Gloss in the modeling process and provides more refined labels for training the CTC decoder, which can effectively solve the overfitting problem caused by training, while also significantly reducing the training cost in time. We validate the MFO method on the popular CSLR dataset and demonstrate that the model has better performance. Show more

Keywords: Continuous sign language recognition, fully convolutional network, multi-state feature optimization, connectionist temporal classification, adequate training

DOI: 10.3233/JIFS-223601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6645-6654, 2023

Authors: Wang, Libo | Zhao, Jun | Guo, Shizhong

Article Type: Research Article

Abstract: Concrete is known as one of the most important materials in the world. Concrete composites consisting of cement, water, aggregates, and some additives are used to improve the properties of concrete. These concrete have a certain compressive strength that can be increased depending on the type of concrete. In line with these ideas, high-performance concrete (HPC) has been produced, which can have high compressive strength by adding materials such as fly ash, silica fume, etc. This type of concrete is used in bridges, dams, and special constructions. However, obtaining the mixture design of HPC is problematic and complex, for this …reason, the machine learning methods can make it easy to achieve the output by saving time and energy. This study has used support vector regression (SVR) to predict the compressive strength of HPC. Moreover, this study provided two meta-heuristic algorithms for obtaining suitable and optimized results, which are contained the artificial hummingbird algorithm (AHA) and Sine Cosine Algorithm (SCA). The model by coupling with algorithms created the hybrid method in the framework of SVR-AHA and SVR-SCA. Furthermore, some criteria indicators have been used for determining the most desirable hybrid model, which is included coefficient of correlation (R2 ), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and weight absolute percentage error (WAPE). As a result, the AHA algorithm could have a more satisfying association model with the SVR model, and the results were RMSE = 2.00 (MPa), R2  = 98.59%, MAE = 0.717 (MPa), MAPE = 1.22 (MPa), and WAPE = 0.114 (MPa). Show more

Keywords: High-performance concrete, sine cosine algorithm, artificial hummingbird algorithm, support vector regression, compressive strength

DOI: 10.3233/JIFS-230132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6655-6666, 2023

Authors: Li, Jiacheng | Wang, Jianhua | Liu, Wenjie | Gao, Shengxia | Du, Shiqiang

Article Type: Research Article

Abstract: The Dunhuang murals, notably the paintings on the interior walls of China’s Dunhuang Grottoes, are considered international cultural treasure. The Dunhuang murals were ruined to varied degrees after a lengthy period of erosion. Deep learning networks were utilized to reconstruct broken parts of murals in order to better preserve their important historical and cultural values. Due to the presence of various damages, such as large peeling, mold and scratches, and multi-scale objects in the mural, a simple porting of existing working methods is suboptimal. In this paper, we propose a progressive Dunhuang murals inpainting (PDMI) based on recurrent feature reasoning …network to progressively infer the pixel values of hole centers by a progressive approach, aiming to obtain visually reasonable and semantically consistent inpainted results. PDMI consists mainly of the FFC-based recurrent feature reasoning (RFR) module and Multi-scale Knowledge Consistent Attention (MKCA) module. The RFR module first fills in the feature value at the feature map’s hole border, then utilizes the obtained feature value as a clue for further inference. The module steadily improved the limitation of hole centers, making the inpainted results more explicit; MKCA enables feature maps in RFR to handle richer background information from distant location information in a flexible manner while preventing misuse. After several round-robin inferences provide multiple feature maps, these feature maps are fused using an adaptive feature weighted fusion mechanism, then the fused feature maps decode back to RGB image. Experiments on a publicly available dataset and a self-made Dunhuang mural dataset reveal that the proposed method outperforms the comparison algorithm in both qualitative and quantitative aspects. Show more

Keywords: Image inpainting, Dunhuang murals, progressive inpainting, feature reasoning

DOI: 10.3233/JIFS-230320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6667-6678, 2023

Authors: Du, Jinze | Wang, Chang

Article Type: Research Article

Abstract: Based on the quaternion system, we give a new representation of the complex vague soft set, and related logical operations. This new representation contains more information than before. Three quaternion distance measures are proposed and a decision model is established. The disease diagnosis of breast cancer is applied to the model to reflect the superiority of the model. By comparing the diagnostic errors under the different distance measures, the most suitable distance measure for this dataset is selected.

Keywords: Quaternion, vague soft set, complex vague soft set

DOI: 10.3233/JIFS-231270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6679-6690, 2023

Authors: Mythily, M. | David, Beaulah | Venkatesan, R. | Joseph, Iwin Thanakumar

Article Type: Research Article

Abstract: Emerging daily, new devices and software-driven advancements pose challenges in software development, including errors, bugs, and evolving requirements. This leads to delays in delivery. Ensuring software security within the Software Development Life Cycle (SDLC) is crucial. To address this, the research focuses on incorporating security aspects early in the SDLC through model transformation. Platform-independent models with security attributes like Integrity, Privacy, Security Audit, non-repudiation, and authentication are generated. A template-based source code generator is utilized to create the structure of the source model. The Secure Business Process Model (SBPM) encompasses Unified Modeling Language (UML) artifacts, such as analysis level classes …and sequence diagrams, enriched with security attributes derived from the activity model. Security requirements are linked to elements extracted from the source model, and structural codes with security-enabled members are produced. Automation in software development is inevitable, though not complete, as it plays a vital role in addressing these challenges and improving the security of software applications. Show more

Keywords: Index Terms: Object-oriented modeling, software design, software safety, software reusability, software tools.

DOI: 10.3233/JIFS-231359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6691-6705, 2023

Authors: Yang, Ze | Jiang, Xianliang | Jin, Guang | Bai, Jie

Article Type: Research Article

Abstract: Accurate and fast pest detection is crucial for ensuring high crop yield and quality in modern agriculture. However, there are significant challenges in using deep learning for pest detection, such as the small proportion of pest individuals in the image area, complex backgrounds in light-trapped pest images, and an unbalanced distribution of pest species. To address these problems, we propose MFSPest, a multi-scale feature selection network for detecting agricultural pests in trapping scenes. We design a novel selective kernel spatial pyramid pooling structure (SKSPP) in the feature extraction stage to enhance the network’s feature extraction ability for key regions and …reduce its focus on irrelevant background information. Furthermore, we present the equalized loss to increase the loss weights of rare categories and improve the distribution imbalance among pest categories. Finally, we build LAPD, a light-trapping agricultural pest dataset containing nine pest categories. We conducted experiments on this dataset and demonstrated that our proposed method achieves state-of-the-art performance, with Accuracy, Recall, and mean Average Precision (mAP) of 89.9%, 92.8%, and 93.6%, respectively. Our method satisfies the requirements of pest detection applications in practical scenarios and has practical value and economic benefits for use in agricultural pest trapping and management. Show more

Keywords: Deep learning, object detection, agricultural light-trapped pests, pest detection

DOI: 10.3233/JIFS-231590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6707-6720, 2023

Authors: Duan, Qi | Zong, Yi

Article Type: Research Article

Abstract: It is worth exploring how “novices in academic entrepreneurship” can more clearly judge their performance in academic entrepreneurship process, self-diagnose the interaction effect between internal and external factors, and improve the effectiveness of entrepreneurial activities. This research takes Chinese academic entrepreneurs as the object, through the qualitative research method of grounded theory analysis, constructs the behavior map of academic entrepreneurship. The main stages of academic entrepreneurship chain are divided, including four stages: starting point, finding technology application, stabilizing technology application, and enterprise mature development. The common decision logic of academic entrepreneurs in each stage is explained. At the same time, …the map shows the main influencing factors of academic entrepreneurial behavior and the logic of these factors’ influence on academic entrepreneurial behavior. The above results not only enrich the research theory in the field of academic entrepreneurship process, but also have guiding significance for the practical activities of “novices in academic entrepreneurship”. Show more

Keywords: Academic entrepreneurship, behavior map, grounded theory analysis

DOI: 10.3233/JIFS-232240

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6721-6733, 2023