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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: Jun, Dai | Huijie, Shi | Yanqin, Li | Junwei, Zhao | Naohiko, Hanajima
Article Type: Research Article
Abstract: Cylinder liner is an internal part of the automobile engine, which plays an important role in the automobile internal combustion engine. Therefore, it is a top priority to accurately and quickly detect the cylinder liner surface defects. In order to effectively achieve the classification and localization of surface defects on the cylinder liner, this paper establishes a dataset for surface defects on cylinder liner and proposes a based on improved YOLOv5 algorithm for detecting surface defects on cylinder liner. Firstly, a machine vision system is established to acquire on-site images and perform manual annotation to build the dataset of surface …defects on cylinder liner. Secondly, the GSConv SlimNeck mechanism is introduced to reduce the model complexity; the Bi-directional Feature Pyramid Network (BiFPN) is used to fuse the feature information at different scales to enhance the detection accuracy of small surface defects on cylinder liner; and embedding the SimAM attention mechanism to focus on the object region of interest and improve the accuracy and robustness of the model. The final improved YOLOv5 model reduces the number of model parameters by 15.8% compared to the non-improved YOLOv5. And the experimental results on our self-built dataset for cylinder liner defects show that the mAP0.5 is improved by 0.4%. This means that the accuracy of model detection was not compromised. This method can be applied to actual production processes. Show more
Keywords: Cylinder liner defect detection, YOLOv5, GSConv SlimNeck, BiFPN, SimAM
DOI: 10.3233/JIFS-237793
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Hu, Man | Sun, Dezhi | Bai, Yihan | Xiao, Han | You, Fucheng
Article Type: Research Article
Abstract: In the realm of graph representation learning, Graph Neural Networks (GNNs) have demonstrated exceptional efficacy across diverse tasks. Typically, GNNs employ message-passing schemes to disseminate node features along graph structures, culminating in learned graph representations. However, their heavy reliance on smoothed node features over graph structures, coupled with limited expressiveness in the presence of node attributes, often constrains link prediction performance. To surmount this challenge, we propose GTLP, a Graph Transformer based link prediction framework. GTLP integrates unsupervised GNNs and structure encoding, enabling a holistic consideration of both topological structures and node features. This approach preserves critical node location and …role information, enhancing the model’s expressiveness. By introducing the Graph Transformer model, GTLP adeptly incorporates neighbor information, refining embedding quality and bolstering the model’s learning and generalization capabilities. Notably, our method exhibits superior scalability, accommodating diverse techniques for information extraction, embedding learning, and sampling. Experimental results underscore GTLP’s state-of-the-art performance, outpacing various baselines across five real-world datasets. Show more
Keywords: Deep learning, graph neural networks, graph transformer, link prediction
DOI: 10.3233/JIFS-237506
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Chen, Xinying | Hu, Mingjie
Article Type: Research Article
Abstract: With the rapid proliferation of substantial textual data from sources such as social media, online comments, and news articles, sentiment analysis has become increasingly crucial. However, existing deep learning methods have overlooked the significance of part-of-speech (POS) and emotional words in understanding the emotion of text. Based on this, this paper proposes a sentiment analysis approach that combines multiple features with a dual-channel network. Firstly, the vector representation of the text is obtained through Robustly Optimized BERT Pretraining Approach (RoBERTa). Secondly, the POS features and word emotional features are separately updated using self-attention to calculate weights. Concatenating words, POS and …emotion, feature dimension reduction and fusion are achieved through a linear layer. Finally, the fused feature vector is input into a dual-channel network composed of Bidirectional Gated Recurrent Unit (BiGRU) and Deep Pyramid Convolutional Neural Network (DPCNN). Experimental results demonstrate that the proposed method achieves higher classification accuracy than the comparative methods on three sentiment analysis datasets. Moreover, the experimental results fully validate the effectiveness of the proposed approach. Show more
Keywords: Sentiment analysis, part-of-speech, RoBERTa, bidirectional gated recurrent unit, deep pyramid convolutional neural network
DOI: 10.3233/JIFS-237749
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Nisha, B. Muthu | Selvakumar, J. | Nithya, V.
Article Type: Research Article
Abstract: The provision of secure and sustainable energy services is ensured by this research, also contributing to the advancement of technology align with the Sustainable Development Goals (SDGs). The motivation behind this study stems from the critical need to bolster hardware security within cutting-edge smart grid infrastructure, and more specifically, for smart energy metering technology. To address this need, this paper introduces a feasible and modular approach for enhancing the security through the implementation of a cryptographic key generator. This key generator is based on a modified Delay-based Physically Unclonable Function (PUF), which incorporates the innovative concept of a Delay Locked …Loop(DLL).The reliability of the proposed PUFs has been rigorously assessed, demonstrating impressive performance levels of 98.02% and 99.1% across a wide temperature and supply voltage, spanning from -40°C to 80°C and (3.0-3.6) V. This is showcasing exceptional functionality within the smart meter’s operational parameters.The effectiveness of this approach is confirmed through practical testing conducted on the ZYNQ-7 ZC 702 Field-Programmable Gate Array (FPGA) platform.The outcomes are encouraging by substantial uniqueness (55.96% and 56.2%) and uniformity (51.2% and 49.15%). This research significantly advances the state of the art by surpassing previous investigations into XOR Arbiter PUF (XOR APUF) and Configurable Ring Oscillator PUF (CRO PUF) designs. Furthermore, the paper delves into an examination of the proposed design’s resilience against modeling attacks, along with comprehensive security assessments. Show more
Keywords: Sustainable development goals, smart energy meter, delay locked loop, physically unclonable function, field programmable gate array
DOI: 10.3233/JIFS-240099
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Guo, Yan | Tang, Dezhao | Cai, Qiqi | Tang, Wei | Wu, Jinghua | Tang, Qichao
Article Type: Research Article
Abstract: Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the …final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture. Show more
Keywords: Machine learning, precision agriculture, digital agriculture, STL, attentional mechanisms
DOI: 10.3233/JIFS-235843
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Gowri, S. | Vennila, B. | Antony Crispin Sweety, C.
Article Type: Research Article
Abstract: The primary focus of this work is to develop the concept of bipolar N-neutrosophic supra topological spaces. Also, extended some concepts such as closure and interior operators of N-neutrosophic supra topological spaces to Bipolar N-neutrosophic supra topological spaces. The properties and relationship between weak forms of bipolar N-neutrosophic supra topological open sets are also established. Further, suggested several separations amongst bipolar N-neutrosophic supra sets. Some distance between bipolar N-neutrosophic sets is introduced and an efficient approachfor group multi-criteria decision making based on bipolar N-neutrosophic sets is proposed.
Keywords: Bipolar N-neutrosophic supra topology, bipolar N-neutrosophic supra α-open set, bipolar N-neutrosophic supra semi-open, bipolar N-neutrosophic supra β-open and bipolar N-neutrosophic supra pre-open, N-valued interval neutrosophic sets
DOI: 10.3233/JIFS-224450
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Vallejos, Sebastian | Armentano, Marcelo G. | Berdun, Luis | Schiaffino, Silvia | González Císaro, Sandra | Nigro, Oscar | Balduzzi, Leonardo | Cuesta, Ignacio
Article Type: Research Article
Abstract: Product classification is a critical task for the smooth running of the purchase process in e-commerce websites. When it comes to P2P marketplaces, users can act both as sellers and as buyers, and they need to assign predefined categories to the products they want to sell. Besides being tedious for users, this task can result in ambiguous or inaccurate assignments. This article presents a method for the automatic categorization of items offered in a local P2P marketplace using a multi-level classification approach. Our experiments demonstrated a significant improvement in the classification results of the proposed solution compared to a traditional …direct classification approach. Show more
Keywords: Classification, e-commerce, NLP, P2P marketplace
DOI: 10.3233/JIFS-219344
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Brännström, Andreas | Nieves, Juan Carlos
Article Type: Research Article
Abstract: This paper introduces an automated decision-making framework for providing controlled agent behavior in systems dealing with human behavior-change. Controlled behavior in such settings is important in order to reduce unexpected side-effects of a system’s actions. The general structure of the framework is based on a psychological theory, the Theory of Planned Behavior (TPB), capturing causes to human motivational states, which enables reasoning about dynamics of human motivation. The framework consists of two main components: 1) an ontological knowledge-base that models an individual’s behavioral challenges to infer motivation states and 2) a transition system that, in a given motivation state, decides …on motivational support, resulting in transitions between motivational states. The system generates plans (sequences of actions) for an agent to facilitate behavior change. A particular use-case is modeled regarding children with Autism Spectrum Conditions (ASC) who commonly experience difficulties in everyday social situations. An evaluation of a proof-of-concept prototype is performed that presents consistencies between ASC experts’ suggestions and plans generated by the system. Show more
Keywords: Interactive agents, strategic decision-making, behavior-change systems, theory of planned behavior, Autism
DOI: 10.3233/JIFS-219335
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Li, Fuxue | Chi, Chuncheng | Yan, Hong | Zhang, Zhen | Zhao, Zhongchao
Article Type: Research Article
Abstract: Transformer-based neural machine translation (NMT) models have achieved state-of-the-art performance in the machine translation paradigm. These models learn the translation knowledge from the bilingual corpus through the attention mechanism automatically. This differs from the way human translators approach sentence translation, where prior knowledge plays a significant role. Inspired by this, a word translation augmentation (WTA) method is proposed to improve the Transformer-based NMT model. The main steps are as follows: Firstly, constructing the word alignment rules based on the training set. Next, generating the translation rules for source words according to the word alignment rules. Lastly, incorporating the potential translation …candidates for each source word into the NMT model during the training and testing procedure. In addition, the WTA method introduces the idea of Mixup for translation candidates of a source word and employs two augmentation strategies to augment the encoder. The results of experiments on several translation tasks with high-resource and low-resource indicate the effectiveness of the proposed method compared with the corresponding strong baseline, and the improvement in BLEU score achieved ranges from 0.42 to 0.63. Show more
Keywords: Neural machine translation, transformer, word embedding, word translations
DOI: 10.3233/JIFS-236170
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Jia, Liu
Article Type: Research Article
Abstract: This study explores a predictive approach using a combination of a one-dimensional convolutional neural network and support vector machine to enhance the management of cultural product trade between China and South Korea, addressing the trade deficit challenge. The methodology involves the collection and categorization of diverse data related to the trade of cultural products between the two countries, identifying data mining directions. The research incorporates the design of association rule functions to identify viable data sources, and employs a hybrid data clustering algorithm integrating technology and spectral clustering to cluster available data. The features extracted from the data mining process …are utilized as learning samples for trade prediction. Both a one-dimensional convolutional neural network and support vector machine are employed to model and predict cultural product trade between China and South Korea. Experimental results demonstrate the method’s accuracy in predicting trade situations under parameterized conditions. Throughout the prediction process, credibility measurement values and controllable correlation degrees consistently exceed 19 and 12.5, respectively, while uncertainty discrimination degrees and error coefficients remain below 12 and 6. Show more
Keywords: Big data integration, Chinese and Korean cultural products, trade prediction, data mining, convolutional neural network, support vector machine
DOI: 10.3233/JIFS-238061
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: López-López, Aurelio | Garcıa-Gorrostieta, Jesús Miguel | González-López, Samuel
Article Type: Research Article
Abstract: Emotion detection in educational dialogues, particularly within student-teacher interactions, has become a crucial research area for improving the learning experience. In this paper, we employ two models, one generic Bidirectional Encoder Representations from Transformers (BERT) and the Emotion detection model Robustly Optimized BERT Approach (EmoRoBERTa), to automatically classify emotions in a corpus of student-teacher chat interactions. Then subsequently, we validate these classifications using a scheme based on oracles, employing two generative large language models (ChatGPT and Bard). Experiments on emotion detection in dialogues between students and teachers revealed that EmoRoBERTa exhibited a reasonable level of agreement with the oracles, while …ChatGPT demonstrated the highest consistency with EmoRoBERTa’s predictions. Furthermore, we identified the impact of specific words on emotion classification, offering insights into the decision-making process of these models. The results not only highlight the prominent presence of emotions like approval, gratitude, curiosity, disapproval, amusement, confusion, remorse, joy , and surprise but also provide substantial support for the utilization of the proposed emotion detection model to enhance the student learning environment. Exploring the emotional aspects of educational dialogues holds the potential to enhance instruction methods, provide timely assistance to students in need, and create an improved learning atmosphere. Show more
Keywords: Emotion detection, learning interaction, transfer learning, large language models, active learning
DOI: 10.3233/JIFS-219340
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ratha, Ashoka Kumar | Behera, Santi Kumari | Devi, A. Geetha | Barpanda, Nalini Kanta | Sethy, Prabira Kumar
Article Type: Research Article
Abstract: With the rise of the fruit processing industry, machine learning and image processing have become necessary for quality control and monitoring of fruits. Recently, strong vision-based solutions have emerged in farming industries that make inspections more accurate at a much lower cost. Advanced deep learning methods play a key role in these solutions. In this study, we built an image-based framework that uses the ResNet-101 CNN model to identify different types of papaya fruit diseases with minimal training data and processing power. A case study to identify commonly encountered papaya fruit diseases during harvesting was used to support the results …of the suggested methodology. A total of 983 images of both healthy and defective papaya were considered during the experiment. In this study, we initially used the ResNet-101 CNN model for classification and then combined the deep features drawn out from the activation layer (fc1000) of the ResNet-101 CNN along with a multi-class Support Vector Machine (SVM) to classify papaya fruit defect detection. After comparing the performance of both approaches, it was found that Cubic SVM is the best classifier using the deep feature of ResNet-101 CNN, achieved with an accuracy of 99.5% and an area under the curve (AUC) of 1 without any classification error. The findings of this experiment reveal that the ResNet-101 CNN with the cubic SVM model can categorize good, diseased, and defective papaya pictures. Moreover, the suggested model executed the task in a greater way in terms of the F1- Score (0.99), sensitivity (99.50%), and precision (99.71%). The present work not only assists the end user in determining the type of disease but also makes it possible for them to take corrective measures during farming. Show more
Keywords: Disease classification, CNN (Convolutional Neural Network), ResNet-101, ML (Machine Learning), SVM (Support Vector Machine)
DOI: 10.3233/JIFS-239875
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Nieves, Juan Carlos | Osorio, Mauricio | Rojas-Velazquez, David | Magallanes, Yazmín | Brännström, Andreas
Article Type: Research Article
Abstract: Humans have evolved to seek social connections, extending beyond interactions with living beings. The digitization of society has led to interactions with non-living entities, such as digital companions, aimed at supporting mental well-being. This literature review surveys the latest developments in digital companions for mental health, employing a hybrid search strategy that identified 67 relevant articles from 2014 to 2022. We identified that by the nature of the digital companions’ purposes, it is important to consider person profiles for: a) to generate both person-oriented and empathetic responses from these virtual companions, b) to keep track of the person’s conversations, activities, …therapy, and progress, and c) to allow portability and compatibility between digital companions. We established a taxonomy for digital companions in the scope of mental well-being. We also identified open challenges in the scope of digital companions related to ethical, technical, and socio-technical points of view. We provided documentation about what these issues mean, and discuss possible alternatives to approach them. Show more
Keywords: Conversational agents, well-being, mental health, trustworthy artificial intelligence
DOI: 10.3233/JIFS-219336
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Shi, Xiaolong | Kosari, Saeed | Rangasamy, Parvathi | Nivedhaa, R.K. | Rashmanlou, Hossein
Article Type: Research Article
Abstract: Modern image processing techniques are improving beyond old methods, which include advanced approaches, for example deep learning. Convolutional Neural Networks (CNNs) are excellent at automatic feature extraction, whereas Generative Adversarial Networks (GANs) produce realistic images. Transfer learning uses pre-trained models, whereas semantic segmentation identifies pixels in images. Super-resolution, style transfer, and attention mechanisms can increase the quality of images and understanding. Adversarial defenses address purposeful manipulations, while 3D image processing handles three-dimensional data. These advancements make use of improved computational power and massive datasets to revolutionize image processing capabilities. Traditional image processing algorithms frequently fail to handle the complex and …multidimensional structure of color images, particularly when dealing with uncertainty and imprecision. In this study, the 3D-EIFIM frame work is extented and scaled aggregation operations 3D-EIFIM tailored for image data are proposed. By representing each pixel as an entry of 3D-EIFIM and applying aggregation techniques to enable more effective image analysis, manipulation, and enhancement. The practical implications of this research are significant, as it can lead to advancements in fields such as computer vision, medical imaging, and remote sensing. Show more
Keywords: IFP, conjunction, disjunction, IFIM, EIFIM, 3D-IFIM, 3D-EIFIM
DOI: 10.3233/JIFS-238252
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
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