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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: Zhao, Liang | Wang, Jiawei | Liu, Shipeng | Yang, Xiaoyan
Article Type: Research Article
Abstract: Tunnels water leakage detection in complex environments is difficult to detect the edge information due to the structural similarity between the region of water seepage and wet stains. In order to address the issue, this study proposes a model comprising a multilevel transformer encoder and an adaptive multitask decoder. The multilevel transformer encoder is a layered transformer to extract the multilevel characteristics of water leakage information, and the adaptive multitask decoder comprises the adaptive network branches. The adaptive network branches generate the ground truths of wet stains and water seepage through the threshold value and transmit them to the network …for training. The converged network, the U-net, fuses coarse images from the adaptive multitask decoder, and the fusion images are the final segmentation results of water leakage in tunnels. The experimental results indicate that the proposed model achieves 95.1% Dice and 90.4% MIOU, respectively. This proposed model demonstrates a superior level of precision and generalization when compared to other related models. Show more
Keywords: Water leakage, multilevel transformer encoder, adaptive multitask decoder, adaptive network branches, converged network
DOI: 10.3233/JIFS-224315
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Kadry, Heba | Samak, Ahmed H. | Ghorashi, Sara | Alhammad, Sarah M. | Abukwaik, Abdulwahab | Taloba, Ahmed I. | Zanaty, Elnomery A.
Article Type: Research Article
Abstract: Coronavirus is a new pathogen that causes both the upper and lower respiratory systems. The global COVID-19 pandemic’s size, rate of transmission, and the number of deaths is all steadily rising. COVID-19 instances could be detected and analyzed using Computed Tomography scanning. For the identification of lung infection, chest CT imaging has the advantages of speedy detection, relatively inexpensive, and high sensitivity. Due to the obvious minimal information available and the complicated image features, COVID-19 identification is a difficult process. To address this problem, modified-Deformed Entropy (QDE) algorithm for CT image scanning is suggested. To enhance the number of training …samples for effective testing and training, the suggested method utilizes QDE to generate CT images. The retrieved features are used to classify the results. Rapid innovations in quantum mechanics had prompted researchers to use Quantum Machine Learning (QML) to test strategies for improvement. Furthermore, the categorization of corona diagnosed, and non-diagnosed pictures is accomplished through Quanvolutional Neural Network (QNN). To determine the suggested techniques, the results are related with other methods. For processing the COVID-19 imagery, the study relates QNN with other existing methods. On comparing with other models, the suggested technique produced improved outcomes. Also, with created COVID-19 CT images, the suggested technique outperforms previous state-of-the-art image synthesis techniques, indicating possibilities for different machine learning techniques such as cognitive segmentation and classification. As a result of the improved model training/testing, the image classification results are more accurate. Show more
Keywords: Coronavirus, quantum machine learning, quanvolutional neural network, Q-deformed entropy
DOI: 10.3233/JIFS-233633
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Pradeep, M. | Sivaji, U. | Nithya, B. | Kadiravan, G. | Preethi, D. | Painam, Ranjith Kumar
Article Type: Research Article
Abstract: The mapping function must identify the reference model and detect coordinate arrangement by observing a repository with deep learning. Progression model with coordinate arrangement composition should have various positional displacements from one location to another. A prerogative classification model is an evolution of factor accomplishment in a repository method. Coordinate arrangement with calculation method must formulate a model locality twirl in classification method of a reference in dominance factor of perpetuity position observation by procession of reference localities. In a procession model observation by location, tendency method should be rotated from locality position into another coordinate method, with a PDD …factor measuring DPA of cadent RFT with an origin of 92.6, a cadent DS intermediate factor of 95.2, culmination factor of cadent RFT of 94.1. The docile exploratory arrangement of heuristic parameters is used in existing system to perceive phenomena such as sprout, enrollment discernment, demeanour, gravest perforation measure, Model of a heretic in apprehension method by premonition incongruity. Annotation should identify classification process using a proposed model to obtain massive measure of imputation function, In PDD measure of DPA in Cadent DS, with inception of 96.1, intercession of Cadent RFT in 92.6, with crowning of Cadent RFT in 96.4, 93.2 Show more
Keywords: MRCAI, Goin Twirl, maginot, idiosyncrasy outline, coffer atavism, flocculent utter eminence kedge
DOI: 10.3233/JIFS-234739
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023
Authors: Ahamed, Ayoobkhan Mohamed Uvaze | Joel Devadass Daniel, D.J. | Seenivasan, D. | Rukumani Khandhan, C. | Radhakrishnan, S. | Daya Sagar, K.V. | Bhardwaj, Vivek | Nishant, Neerav
Article Type: Research Article
Abstract: Time-sensitive programs that are linked to smart services, such as smart healthcare as well as smart cities, are supported in large part by the fog computing domain. Due to the increased speed limitation of the cloud, Cloud Computing (CC) is a competent platform for fog in data processing, but it is unable to meet the demands of time-sensitive programs. The procedure of resource provisioning, as well as allocation in either a fog-cloud structure, takes into account dynamic changes in user requirements, and resources with limited access in fog devices are more difficult to manage. Due to the continual changes in …user requirement factors, the deadline represents the biggest obstacle in the fog computing structure. Hence the objective is to minimize the total cost involved in scheduling by maximizing resource utilization. For dynamic scheduling in the fog-cloud computing model, the efficiency of hybridization of the Grey Wolf Optimizer (GWO) and Lion Algorithm (LA) is developed in this study. In terms of energy costs, processing costs, and communication costs, the created GWOMLA-based Deep Belief Network (DBN) performed better and outruns the other traditional models. Show more
Keywords: Fog-cloud computing environment, deep learning, deep belief network (DBN), lion algorithm (LA), grey wolf optimizer (GWO).
DOI: 10.3233/JIFS-234030
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023
Authors: Kalaipriya, O. | Dhandapani, S.
Article Type: Research Article
Abstract: Lung cancer is one of the leading causes of mortality from cancer. Lung cancer is a kind of malignant lung tumor characterized by uncontrolled cell proliferation in lung tissues. Even though CT scans are the most often used imaging technology in medicine, clinicians find it challenging to interpret and diagnose cancer from CT scan pictures. As a result, computer-aided diagnostics can assist clinicians in precisely identifying malignant cells. Many computer-aided approaches were explored and applied, including image processing and machine learning. A comparison of the various classification methodologies will assist in enhancing the accuracy of lung cancer detection systems that …employ robust segmentation and classification algorithms presented in this research. This research proposed to enhance existing segmentation and classification-basedmethodsof human lung cancer detection with optimization in techniques. The workflow includes initial preprocessing of medical images, for segmentation a novel hybrid methodology is developed by combining enhanced k-means clustering and random forest and classification with an Artificial neural network enhanced with PSO parameter and feature optimization. Show more
Keywords: Machine learning, K-means, ANN, random forest, PSO, image processing technique
DOI: 10.3233/JIFS-233845
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Rajesh Kanna, R. | Ulagamuthalvi, V.
Article Type: Research Article
Abstract: Diagnosis is given top priority in terms of farm resource allocation, because it directly affects the GDP of the country. Crop analysis at an early stage is important for verifying the efficient crop output. Computer vision has a number of intriguing and demanding concerns, including disease detection. After China, India is the world’s second-largest creator of wheat. However, there exist algorithms that can accurately identify the most prevalent illnesses of wheat leaves. To help farmers keep track on a large area of wheat plantation, leaf image and data processing techniques have recently been deployed extensively and in pricey systems. In …this study, a hybrid pre-processing practice is used to remove undesired distortions while simultaneously enhancing the images. Fuzzy C-Means (FCM) is used to segment the affected areas from the pre-processed images. The data is then incorporated into a disease classification model using a Convolutional Neural Network (CNN). It was tested using Kaggle data and several metrics to see how efficient the suggested approach was. This study demonstrates that the traditional Long-Short Term Memory (LSTM) technique achieved 91.94% accuracy on the input images, but the hybrid pre-processing model with CNN achieved 95.06 percent accuracy. Show more
Keywords: Plant leaves diseases, convolutional neural network, fuzzy c-means, wheat production, pre-processing techniques
DOI: 10.3233/JIFS-233672
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Prabu Shankar, K.C. | Shyry, S. Prayla
Article Type: Research Article
Abstract: Early detection of diseases in men and women can improve treatment and reduce the risk involved in human life. Nowadays techniques which are non-invasive in nature are popularly used to detect the various types of diseases. Histopathological analysis plays a major role in finding the nature of the disease through medical images. Manual interpretation of these medical imaging takes time, is tedious, subjective, and can have human errors. It has also been discovered that the interpretation of these images varies amongst diagnostic labs. As computer power and memory capacity have increased, methodologies and medical image processing techniques have been developed …to interpret and analyse these images as a substitute for human involvement. The challenge lies in devising an efficient pre-processing technique that helps in analysing, processing and preparing the medical image for further diagnostics. This research provides a hybrid technique that reduces noise in the NITFI medical image by using a 2D adaptive median filter at level 1. The edges of the filtered medical image are preserved using the modified CLAHE algorithm which preserves the local contrast of the image. Expectation Maximization (EM) algorithm extracts the ROI part of the image which helps in easy and accurate identification of the disease. All the three steps are run over the 3D image slices of a NIFTI image. The proposed method proves that it achieves close to ideal RMSE, PSNR and UQI values as well as achieves an average runtime of 37.193 seconds for EM per slice. Show more
Keywords: 2D adaptive, expectation maximization, NIFTI, UQI, edge preservation, 3D slice, computational intelligence
DOI: 10.3233/JIFS-233931
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2023
Authors: Rajendran, Aishwarya | Ganesan, Sumathi | Rathis Babu, T.K.S.
Article Type: Research Article
Abstract: Brain tumor is observed to be grown in irregular shape and presented deep inside the tissues that led to cancer. Human brain tumor identification and categorization are performed with high latency, but also an essential task for the medical experts. The assistance through the automated diagnosis is generally utilized for the advancement in the diagnosis ability in order to get superior accuracy in brain tumor detection. Although the researches are enhancing the brain tumor detection performance, the highly challenging is to segment the brain tumor since it has variability concerning the tumor type, contrast, image modality and also in other …factors. To meet up all the challenges, a novel classification method is introduced using segmentation and machine learning approaches. Initially, the required images are collected from benchmark data sources. The input images are undergone for pre-processing stage, where it is done via “Contrast Limited Adaptive Histogram Equalization (CLAHE) and filtering methods”. Further, the pre-processed imagesare given as input to two classifier models as “Residual Network (ResNet) and Gated Recurrent Unit (GRU)”, in which the model provide the result as normal and abnormal images. In the second part, obtained abnormal image acts an input for segmentation step. In segmentation, it is needed to extract the relevant features by texture and spatial features. The resultant features are subjected for optimizing, where the optimal features are acquired through Adaptive Coyote Optimization Algorithm (ACOA). Then, the extracted features are fed into machine learning model like “Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF)” to render the segmented image. Finally, the hybrid classification named Hybrid ResGRUis developed by integrating the ResNet and GRU, where the hyper parameters are tuned optimally using developed ACOA, thus it is used for classifying the abnormal image that belongs to benign stage or malignant stage. The experimental results are evaluated, and its performance is analyzed by various metrics. Hence, the proposed classification model ensures effective segmentation and classification performance. Show more
Keywords: Brain tumour segmentation and classification, adaptive coyote optimization algorithm, residual network, gated recurrent unit, ensemble machine learning-based tumor segmentation, deep learning-based classification
DOI: 10.3233/JIFS-233546
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Abdus Subhahan, D. | Vinoth Kumar, C.N.S.
Article Type: Research Article
Abstract: The worldwide deforestation rate worsens year after year, ultimately resulting in a variety of severe implications for both mankind and the environment. In order to track the success of forest preservation activities, it is crucial to establish a reliable forest monitoring system. Changes in forest status are extremely difficult to manually annotate due to the tiny size and subtlety of the borders involved, particularly in regions abutting residential areas. Previous forest monitoring systems failed because they relied on low-resolution satellite images and drone-based data, both of which have inherent limitations. Most government organizations still use manual annotation, which is a …slow, laborious, and costly way to keep tabs on data. The purpose of this research is to find a solution to these problems by building a poly-highway forest convolution network using deep learning to automatically detect forest borders so that changes over time may be monitored. Here initially the data was curated using the dynamic decomposed kalman filter. Then the data can be augmented. Afterward the augmented image features can be fused using the multimodal discriminant centroid feature clustering. Then the selected area can be segmented using the iterative initial seeded algorithm (IISA). Finally, the level and the driver of deforestation can be classified using the poly-highway forest convolution network (PHFCN). The whole experimentation was carried out in a dataset of 6048 Landsat-8 satellite sub-images under MATLAB environment. From the result obtained the suggested methodology express satisfied performance than other existing mechanisms. Show more
Keywords: Deforestation, dynamic decomposed kalman filter, multimodal discriminant centroid feature clustering, iterative initial seeded algorithm, poly-highway forest convolution network
DOI: 10.3233/JIFS-233534
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Agrawal, Monika | Moparthi, Nageswara Rao
Article Type: Research Article
Abstract: Sentiment analysis (SA)at the sentence, aspect, and document levels determines the sentiment of particular aspect phrases in a given sentence. Due to their capacity to extract sentiment information from text in aspect-level sentiment classification, neural networks (NNs) have achieved significant success. Generally speaking, sufficiently sizable training corpora are necessary for NNs to be effective. The performance of NN-based systems is reduced by the small size of the aspect-level corpora currently available. In this research, we suggest a gated bilateral recurrent neural network (G-Bi-RNN) as a foundation for multi-source data fusion, their system offers sentiment information that several sources. We develop …a uniform architecture specifically to include information from sentimental lexicons, including aspect- and sentence-level corpora. To further provide aspect-specific phrase representations for SA, we use G-Bi-RNN, a deep bilateral Transformer-based pre-trained language model. We assess our methods using SemEval 2014 datasets for laptops and restaurants. According to experimental findings, our method consistently outperforms cutting-edge techniques on all datasets. We use a number of well-known aspect-level SA datasets to assess the efficacy of our model. Experiments show that when compared to baseline models, the suggested model can produce state-of-the-art results. Show more
Keywords: Sentiment analysis (SA), gated bilateral recurrent neural network (G-Bi-RNN), language model
DOI: 10.3233/JIFS-234076
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
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