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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: Agordzo, George K. | Fang, Xianwen | Li, Juan
Article Type: Research Article
Abstract: In today’s digital age, log files are crucial. However, the conversion of text log files into images has only recently been developed. The security of log files is a major source of concern, and the security of the systems in which the logs are stored determines the safety of the log file in process mining. This calls for the first conversion of a text log file into an image file. Thus, this research aims to convert the log files into images in a mugshot database and detect illegal activity and criminals from the converted images employing a novel Convolutional Neural …Network (CNN). The developed model has three stages: pre-processing, feature extraction, and detection and matching. The pre-processing was performed by min-max normalization, and in feature extraction, the deep learning method was used. Moreover, in the detection phase, CNN is employed for detecting illegal activities, and the matching process is performed for detecting illegal activities from converted images and criminals in the mugshot database. The model’s performance is evaluated in terms of precision, F1-score, recall, and accuracy values of 99.6%, 98.5%, 98.7%, and 99.8%, respectively. A further comparison has been performed to show the effectiveness of the suggested model over other methods. Show more
Keywords: Privacy, log file, convolutional neural network, process mining, machine learning algorithm
DOI: 10.3233/JIFS-224486
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1-12, 2023
Authors: Abarna, S. | Sheeba, J.I. | Pradeep Devaneyan, S.
Article Type: Research Article
Abstract: Schools and universities shuttered as a result of the worldwide COVID-19 pandemic lockdown, and student screen time skyrocketed. Since the programs are delivered online, a spike in social media use during lockdown resulted in many pupils becoming victims of cyberbullying, which includes criticizing one another, posting sexual comments on images of young ladies, and using fake accounts to bully others. Machine Learning (ML) and Natural Language Processing (NLP) techniques are being used in a growing body of work on automated cyberbullying detection. Different machine learning methods, however, are unable to converge to the requisite accuracy. Thus, numerous classifier systems known …as “ensemble learning” are proposed in order to improve predictive performance by aggregating the predictions from various models. In our proposed system, we use a novel method of detecting online harassment (cyberbullying) on the Instagram dataset. The attributes of abusive words are initially analyzed from feature selection and pre-trained word embedding language models like Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMO). A knowledge-based frequent pattern method is used to find the intention of the harasser and is created by the Knowledge-BERT (K-BERT). The unsupervised approaches such as Latent Semantic Analysis (LSA), Frequent pattern growth (FP-Growth), and a clustering technique K-Means. The results from the detection models are ensembled using Extreme Gradient Boosting (XGBoost) for classifying the categories of online harassment. The performance of the ensemble model is then cross-validated using machine learning metrics and compared with various existing techniques. An ensemble model performs better with a higher F1 score of 92.04% with less error rate in the classification of harassment categories. Show more
Keywords: Cyber-harassment, ensemble learning, K-BERT, BERT, ELMO, FP-growth, LSA, K-means, XGBoost, NLP
DOI: 10.3233/JIFS-230346
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 13-36, 2023
Authors: Martinez-Gil, Jorge | Chaves-Gonzalez, Jose Manuel
Article Type: Research Article
Abstract: Recently, transfer learning strategies have become ideal for reusing acquired knowledge through a training phase. The key idea is that reusing such knowledge brings advantages such as increased accuracy and considerable resource savings. In this work, we design a novel strategy for effective and efficient transfer learning in semantic similarity. Our approach is based on generating and transferring optimal models obtained through a symbolic regression process being able to stack evaluation scores from several fundamental techniques. After an exhaustive empirical study, the results lead to high accuracy in addition to significant savings in terms of training time consumed in most …of the scenarios considered. Show more
Keywords: Knowledge engineering, Transfer learning, Semantic textual similarity
DOI: 10.3233/JIFS-230141
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 37-49, 2023
Authors: Kong, Lingxing | Liu, Kailong | Fu, Deyi | Liu, Boyong | Ma, Jingkai | Sun, Huini | Bai, Shuang
Article Type: Research Article
Abstract: Accurately evaluating the technological improvement effects of wind turbines is crucial for wind farm operators. To this end, this paper proposes an innovative approach that employs a wind power regression model which leverages external environmental information to predict the output power of wind turbines. The effectiveness of technological improvements can be evaluated by comparing the predicted output power with the measured output power. In this paper, a model called stacked LSTM networks with attention mechanisms is designed. In the proposed model, the stacked LSTM networks are used to enhance the nonlinear fitting ability and capture deeper features of the input …sequence. Furthermore, temporal attention mechanisms are employed to make the model focus on important time-series information of the data. In addition, a hierarchical attention mechanism is designed to explore the correlation among the outputs of the stacked LSTM networks and enrich the model’s output information. The experiments on the data from a wind farm show that the proposed method outperforms various wind power prediction benchmarks, achieving lower RMSE, MAE, and MAPE values of 142.82, 104.2, and 4.85%, respectively. Show more
Keywords: Wind power regression prediction, evaluation of technological improvement effect, stacked LSTMs, temporal attention mechanism, hierarchical attention mechanism
DOI: 10.3233/JIFS-230403
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 51-62, 2023
Authors: Zhao, Yating | Zhou, Yanping | Chen, Huiying | Zhang, Yang
Article Type: Research Article
Abstract: In the context of open innovation, selecting partners for knowledge collaboration is crucial for knowledge-intensive enterprises, and matching cooperation is key to successful intellectual property cooperation. To provide enterprises with practical tools for partner selection, this paper analyzes the evaluation factors of intellectual property partners. We establish a collaborative innovation intellectual property partner selection model by combining the maximum entropy model with grey relational method, and calculating the comprehensive evaluation value of candidate enterprises by using the improved Pythagorean Fuzzy Hybrid Aggregation (PF-HA) operator. An application example illustrates the feasibility and advantage of the improved PF-HA method improving the selection …of intellectual property partners. Compared with other methods, the advantages of PF-HA are shown in that it can simultaneously optimize the use efficiency of multi-partner and multi-dimensional evaluation data, and effectively deal with the ambiguity of expert decision information and the flexibility of index weight in the partner evaluation process. Show more
Keywords: Collaborative innovation, partner selection, intellectual property cooperation, Pythagorean fuzzy hybrid aggregation, grey correlation
DOI: 10.3233/JIFS-230412
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 63-75, 2023
Authors: Anandan, D. | Hariharan, S. | Sasikumar, R.
Article Type: Research Article
Abstract: Liver Tumour (LT) develops when healthy cells undergo abnormal DNA changes that cause them to grow and divide uncontrollably. In manual examination, evaluation might be changed by the unique perception of the observers, which depends on their expertise and subjectivity. Therefore, computer-aided intelligent tools are established to eliminate subjectivity and increase the performance. To overcome these challenges, a novel Two-fold Segmentation of Liver Tumour (TFSLT) model for accurately detecting the liver tumour using computed tomography (CT) images. Initially, the CT images are pre-processed using Normalized-Modified Anisotropic Diffusion Filtering (NMADF) Algorithm to reduce the noise artifacts. These pre-processed CT images are …taken as input to the Canny Edge Detector (CED) for detecting the edges of the liver. Based on these edges, the first-fold segmentation process is performed using the Jaccard metric-based Watershed (JMWS) algorithm to accurately segment the liver region. Improved Deep Neural Network (IDNN) is utilized to classify the LT into normal, Hepatocellular carcinoma (HCC), Cholangio carcinoma (CC) and Metastatic tumour (MT). Modified Elephant Herd Optimization (MEHO) algorithm for the MEHO algorithm for selecting the features of the images. Finally, the Improved Expectation-Maximization (IEM) Algorithm as second-fold segmentation process to segment the different abnormal classes. The performance of the proposed TFSLT approach is assessed using the specific metrics like recall, precision, specificity, accuracy and F1 score. The experimental findings reveal that the proposed TFSLT approach achieves a better accuracy range of 99.57% for detecting LT in its early stages. Show more
Keywords: Liver tumour, computed tomography two-fold segmentation, improved deep neural network, modified elephant herd optimization
DOI: 10.3233/JIFS-230694
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 77-92, 2023
Authors: Liu, Qian | Hou, Jundan | Dong, Qi
Article Type: Research Article
Abstract: Tourism is the most culturally loaded industry. In the new era, China’s tourism industry is developing rapidly and the public’s need for diversified culture is growing. The integration of culture and tourism, the development of new forms of cultural tourism industry, is also an important means to enhance the country’s cultural soft power, promote the development of China’s tourism culture, and solve the contradiction between cultural supply and demand. The industrial competitiveness evaluation of regional cultural tourism is looked as the multiple attribute group decision-making (MAGDM) problem. This paper proposed extended MARCOS method in probabilistic hesitant fuzzy sets (PHFSs) circumstance. …Meanwhile, the CRITIC method is used to evaluate the criterion weights. Then we give a case study for industrial competitiveness evaluation of regional cultural tourism to measure the novel model’s validity. Show more
Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets (PHFSs), MARCOS method, CRITIC method, industrial competitiveness evaluation
DOI: 10.3233/JIFS-224491
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 93-103, 2023
Authors: Noon, Serosh Karim | Amjad, Muhammad | Qureshi, Muhammad Ali | Mannan, Abdul
Article Type: Research Article
Abstract: For the last decade, the use of deep learning techniques in plant leaf disease recognition has seen a lot of success. Pretrained models and the networks trained from scratch have obtained near-ideal accuracy on various public and self-collected datasets. However, symptoms of many diseases found on various plants look similar, which still poses an open challenge. This work takes on the task of dealing with classes with similar symptoms by proposing a trained-from-scratch shallow and thin convolutional neural network employing dilated convolutions and feature reuse. The proposed architecture is only four layers deep with a maximum width of 48 features. …The utility of the proposed work is twofold: (1) it is helpful for the automatic detection of plant leaf diseases and (2) it can be used as a virtual assistant for a field pathologist to distinguish among classes with similar symptoms. Since dealing with classes with similar-looking symptoms is not well studied, there is no benchmark database for this purpose. We prepared a dataset of 11 similar-looking classes and 5, 108 images for experimentation and have also made it publicly available. The results demonstrate that our proposed model outperforms other recent and state-of-the-art models in terms of the number of parameters, training & inference time, and classification accuracy. Show more
Keywords: Plant disease, similar-looking symptoms, shallow CNN models, lightweight models, agriculture
DOI: 10.3233/JIFS-223554
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 105-120, 2023
Authors: Li, Fuxue | Chi, Chuncheng | Yan, Hong | Liu, Beibei | Shao, Mingzhi
Article Type: Research Article
Abstract: Transformer-based neural machine translation (NMT) has achieved state-of-the-art performance in the NMT paradigm. However, it relies on the availability of copious parallel corpora. For low-resource language pairs, the amount of parallel data is insufficient, resulting in poor translation quality. To alleviate this issue, this paper proposes an efficient data augmentation (DA) method named STA. Firstly, the pseudo-parallel sentence pairs are generated by translating sentence trunks with the target-to-source NMT model. Furthermore, two strategies are introduced to merge the original data and pseudo-parallel corpus to augment the training set. Experimental results on simulated and real low-resource translation tasks show that the …proposed method improves the translation quality over the strong baseline, and also outperforms other data augmentation methods. Moreover, the STA method can further improve the translation quality when combined with the back-translation method with the extra monolingual data. Show more
Keywords: Data augmentation, neural machine translation, sentence trunk, mixture, concatenation
DOI: 10.3233/JIFS-230682
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 121-132, 2023
Authors: Huang, Ying | Cao, Zhiying | Chen, Siyuan | Zhang, Xiuguo | Wang, Peipeng | Cao, Qilei
Article Type: Research Article
Abstract: Most existing Web service recommendation models based on machine learning do not fully consider the high-order features interaction between users and services and with poor interpretability. In this paper, an Interpretable Web Service Recommendation model based on Disentangled Representation Learning (WSR-DRL) is proposed. First of all, to make full use of the service description information to improve the accuracy of Web service recommendation, the features representation of service name is obtained by using BERT model, and the local and global features representation of service description information is further obtained by combining 2-D CNN and Bi-LSTM. Then the disentangled convolution neural …network is used to generate the high-order interaction features between users and services, and the neighborhood routing algorithm is used to mine the latent factors in these features. That improves the accuracy of Web service recommendation and make it interpretable. Finally, in order to verify the effectiveness of the model, several groups of experiments are carried out on real data sets. The experimental results show that compared with latest models such as DMF, DeepFM, DKN, GCMC, NDCG model and WSR-MGAT model, the WSR-DRL model proposed in this paper shows better performance on Precision@10, Recall@10, F1@10 and NDCG@10 evaluation metrics. Show more
Keywords: Web service recommendation, Disentangled representation learning, BERT
DOI: 10.3233/JIFS-223306
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 133-145, 2023
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