Fuzzy Deep Learning Research Articles

A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning

Different oncologists make their own decisions about the detection and classification of the type of ovarian cancer from histopathological whole slide images. However, it is necessary to have an automated system that is more accurate and standardized for decision-making, which is essential for early detection of ovarian cancer. To help doctors, an automated detection and classification of ovarian cancer system is proposed. This model starts by extracting the main features from the histopathology images based on the ResNet-50 model to detect and classify the cancer. Then, recursive feature elimination based on a decision tree is introduced to remove unnecessary features extracted during the feature extraction process. Adam optimizers were implemented to optimize the network’s weights during training data. Finally, the advantages of combining deep learning and fuzzy logic are combined to classify the images of ovarian cancer. The dataset consists of 288 hematoxylin and eosin (H&E) stained whole slides with clinical information from 78 patients. H&E-stained Whole Slide Images (WSIs), including 162 effective and 126 invalid WSIs were obtained from different tissue blocks of post-treatment specimens. Experimental results can diagnose ovarian cancer with a potential accuracy of 98.99%, sensitivity of 99%, specificity of 98.96%, and F1-score of 98.99%. The results show promising results indicating the potential of using fuzzy deep-learning classifiers for predicting ovarian cancer.

Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics

The Internet of Things (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfaction. Still, IoT remains to transform the consumer electronics field; security in IoT becomes critical, and it is utilized by cyber attackers to pose risks to public safety, compromise data privacy, gain unauthorized access, and even disrupt operations. Robust security measures are crucial for maintaining trust in the proliferation and adoption of interconnected technologies, mitigating those risks, protecting sensitive data, and certifying the integrity of the IoT ecosystem. An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. This work presents an Improved Crayfish Optimization Algorithm with Interval Type-2 Fuzzy Deep Learning (ICOA-IT2FDL) technique for Intrusion Detection on IoT infrastructure. The main intention of the ICOA-IT2FDL technique is to utilize a hyperparameter-tuned improved deep learning (DL) method for intrusion detection, thereby improving safety in the IoT infrastructure. BC technology can be used to accomplish security among consumer electronics. The ICOA-IT2FDL technique employs a linear scaling normalization (LSN) approach for data normalization. In addition, features are selected using an improved crayfish optimization algorithm (ICOA). This is followed by the ICOA-IT2FDL technique, which applies the interval type-2 fuzzy deep belief network (IT2-FDBN) model to identify intrusions. Finally, the bald eagle search (BES) model strategy improves the intrusion recognition rate. A series of investigations is accomplished to ensure the enhanced accomplishment of the ICOA-IT2FDL model. The experimentation results specified that the ICOA-IT2FDL model shows better recognition results compared to recent models.

Fuzzy Deep Learning for the Diagnosis of Alzheimer's Disease: Approaches and Challenges

Fuzzy Deep Learning for the Diagnosis of Alzheimer's Disease: Approaches and Challenges

Near real-time spatial prediction of earthquake-triggered landslides based on global inventories from 2008 to 2022

Near real-time prediction of earthquake-triggered landslides can rapidly forecast the spatial distribution of coseismic landslides just after a great earthquake, and provide effective support for emergency response. However, the prediction of earthquake-triggered landslides has always been a great challenge because of low accuracy and high false alarms. This work proposes a novel fuzzy deep learning (FuDL) model for near real-time earthquake-triggered landslide spatial prediction. Fuzzy learning theory is for the first time employed in earthquake-triggered landslide prediction. The FuDL has high generalization and robustness, effectively improving the accuracy of earthquake-triggered landslide prediction. Eighteen earthquake-triggered landslide inventories worldwide from 2008 to 2022 are employed to conduct ETL prediction. According to the chronological order, 15 earthquake-triggered landslides from 2008 to 2018 are adopted to train the FuDL model, and 3 earthquake-triggered landslides from 2019 to 2022 are utilized for near real-time earthquake-triggered landslide prediction. Furthermore, this work reveals that ground movement, relatively steep and high topography, and strong seismic intensity are critical factors affecting the spatial distribution of earthquake-triggered landslides. In addition, this work conducted a detailed analysis of the distribution patterns of earthquake-triggered landslides on a global scale.

A systematic survey of fuzzy deep learning for uncertain medical data

Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. It is important and significant to review the contributions of fuzzy deep learning for uncertain medical data, because fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the presence of noise, artifact or high dimensional unstructured information in uncertain medical data. Therefore, taking focus on the intersection of both different fuzzy deep learning models and several types of uncertain medical data, the paper first constructs four types of frameworks of fuzzy deep learning models used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning models, uncertain medical data and application scenarios. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. This work has some original points: (1) four types of frameworks of applying fuzzy deep learning models for uncertain medical data are first proposed. (2) Seven fuzzy deep learning models, five types of uncertain medical data, and five application scenarios are reviewed in details, respectively. (3) The advantages, challenges, and future research directions of fuzzy deep learning for uncertain medical data are critically analyzed, providing valuable suggestions for further deep research.

Survival estimation of oral cancer using fuzzy deep learning

BackgroundOral cancer is a deadly disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop a fuzzy deep learning (FDL)-based model to estimate the survival time based on clinicopathologic data of oral cancer.MethodsElectronic medical records of 581 oral squamous cell carcinoma (OSCC) patients, treated with surgery with or without radiochemotherapy, were collected retrospectively from the Oral and Maxillofacial Surgery Clinic and the Regional Cancer Center from 2011 to 2019. The deep learning (DL) model was trained to classify survival time classes based on clinicopathologic data. Fuzzy logic was integrated into the DL model and trained to create FDL-based models to estimate the survival time classes.ResultsThe performance of the models was evaluated on a test dataset. The performance of the DL and FDL models for estimation of survival time achieved an accuracy of 0.74 and 0.97 and an area under the receiver operating characteristic (AUC) curve of 0.84 to 1.00 and 1.00, respectively.ConclusionsThe integration of fuzzy logic into DL models could improve the accuracy to estimate survival time based on clinicopathologic data of oral cancer.

Fuzzy Deep Learning Recurrent Neural Network Algorithm to Detect Corn Leaf Disease

Corn is a major commodity after rice in supporting food self-sufficiency in Indonesia. However, due to leaf disease, the quality and quantity of corn plants are greatly reduced. The problem with detecting corn leaf diseases is that the detection method is still manual, making it inefficient and ineffective. Therefore, in this study, disease detection on corn leaves was performed using the Fuzzy C-Means (FCM) and Long Short-Term Memory (LSTM) methods. First, oversampling was carried out to ensure an equal amount of data in all classes, then the corn leaf images were pre-processed before being input into the LSTM algorithm. After completing clustering process in the [Formula: see text] algorithm, the next step involved extracting texture features using the Gray Level Co-occurrence Matrix (GLCM) technique, followed by classification using LSTM. To assess their performance, both algorithms underwent evaluation using the k-fold cross-validation method, and their accuracy and speed were compared. The results of the k-fold cross-validation demonstrated that the [Formula: see text] algorithm achieved an accuracy of 63.53%, whereas the LSTM algorithm achieved an accuracy of 80.24%. In terms of the time required for training and prediction, the LSTM algorithm took 13[Formula: see text]min and 18[Formula: see text]s for training on corn leaf disease images, while the prediction process only took 1.59[Formula: see text]s. The training and prediction time required for the [Formula: see text] algorithm were 65[Formula: see text]min and 24[Formula: see text]s and 5[Formula: see text]min and 44[Formula: see text]s, respectively. The conclusion of this study is that the LSTM algorithm has better accuracy and time compared to [Formula: see text] on the dataset used in this study in terms of corn leaf disease detection.

Fuzzy deep learning for modeling uncertainty in character recognition using EEG signals

Classification of brain signals for character recognition is a challenging task associated with uncertainties. This study aims to present an Improved Type-2 Fuzzy Deep Learning (IT2FDL) framework for uncertainty management in character recognition using EEG signals by learning rules from each other in the learning layer. The Evolutionary Algorithm (EA) was employed for this study's optimum design of the proposed IT2FDL model. This algorithm was used to determine the T2F set parameters. The model parameters were tuned by Parallel Swarm Optimization (PSO) Algorithm. The present framework optimized the fusion layer of the model. This layer is a multi-layered Recurrent Type2 Fuzzy Neural Network (RT2FNN) with a learning layer composed of RNN cells. For performance evaluation, the models were examined using three public databases: BCI Competition III-Dataset II, BCI Competition II-Dataset IIb, and EEG Dataset. The optimized IT2FDL model outperformed the counterpart models with a significant improvement of test score of 2.5% for Competition III-Dataset II at epoch 15, 2.5% for Competition II-Dataset IIb at epoch 3, and 1% for EEG Dataset at epoch 15. The proposed IT2FDL-PSO (GRU) model showed a mean test score of 96.2% at epoch 15 for all 58 subjects, which outperformed other models with many subjects for character recognition.

Fuzzy Deep Learning in Text Processing: An Application to Medical Expert System

Fuzzy Deep Learning in Text Processing: An Application to Medical Expert System

An Enhanced Fake News Detection System with Fuzzy Deep-Learning

An Enhanced Fake News Detection System with Fuzzy Deep-Learning

Design of Super Resolution and Fuzzy Deep Learning Architecture for the Classification of Land Cover and Landsliding using Aerial Remote Sensing Data

Design of Super Resolution and Fuzzy Deep Learning Architecture for the Classification of Land Cover and Landsliding using Aerial Remote Sensing Data

Conditional Aggregation Operator Defined by the Power Information Concerning Type-2 Fuzzy Deep Learning Algorithm for Financial Investment Data Decision-Making

Conditional Aggregation Operator Defined by the Power Information Concerning Type-2 Fuzzy Deep Learning Algorithm for Financial Investment Data Decision-Making

Optimizing Human Vitality - A Fuzzy Deep Learning Approach for Enhancing Organ Endurance in Healthcare

Optimizing Human Vitality - A Fuzzy Deep Learning Approach for Enhancing Organ Endurance in Healthcare

Fuzzy Deep Learning Model for Prediction of Conformational Epitope

Fuzzy Deep Learning Model for Prediction of Conformational Epitope

Pose recognition of dancing images using fuzzy deep learning technique in anIoTenvironment

AbstractWith the development of smart technologies (e.g., Internet of Things (IoT), Artificial Intelligence (AI), and Big Data), people have start using them for various purposes. The real‐time IoT devices generate an enormous amount of video and imaging data, leading to the concept of complex data structure. And people face major challenges in mining and extraction of useful features and information from such data. How to efficiently analyse and process video data to obtain valuable information has become a key research topic. The traditional manual annotation methods are unable to meet the current demand for the growing number of videos. Therefore, a more convenient method for processing video data needs to be developed. The research objective of this paper is dance videos, and the goal is to realize automatic recognition of dance movements. In this paper, a Dual Convolutional Neural Network Algorithm (DCNNA) is proposed for the automatic recognition of different dance movements in live and remote videos. DCNNA can extract video information more comprehensively and efficiently. It can simultaneously extract the light flow features corresponding to the action changes and the information contained in each frame of the video. Therefore, the dance movements can be more accurately identified. In the experiments, the performance of DCNNA is evaluated based on dance videos and compared with Inception V3 and 3D‐CNN. All the experiments illustrate the superior performance of the proposed DCNN algorithm. From the experimental results, it is quite obvious that the F1 score of the proposed DCNNA is 11% and 6% higher than that of the Inception V3 and 3D‐CNN, respectively.

Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection Approach to Aid Elderly People

Over the last few decades, the processes of mobile communications and the Internet of Things (IoT) have been established to collect human and environmental data for a variety of smart applications and services. Remote monitoring of disabled and elderly persons living in smart homes was most difficult because of possible accidents which can take place due to day-to-day work like falls. Fall signifies a major health problem for elderly people. When the condition is not alerted in time, then this causes death or impairment in the elderly which decreases the quality of life. For elderly persons, falls can be assumed to be the main cause for the demise of posttraumatic complications. Therefore, early detection of elderly persons’ falls in smart homes is required for increasing their survival chances or offering vital support. Therefore, the study presents a Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection (CSA-IDFLFD) technique. The CSA-IDFLFD technique helps elderly persons with the identification of fall actions and improves their quality of life. The CSA-IDFLFD technique involves two phases of operations. In the initial phase, the CSA-IDFLFD technique involves the design of the IDFL model for the identification and classification of fall events. Next, in the second phase, the parameters related to the IDFL method can be optimally selected by the design of CSA. To validate the performance of the CSA-IDFLFD technique in the fall detection (FD) process, a widespread experimental evaluation process takes place. The extensive outcome stated the improved detection results of the CSA-IDFLFD technique.

Novel fuzzy deep learning approach for automated detection of useful COVID-19 tweets

Coronavirus (COVID-19) is a newly discovered viral disease from the SARS-CoV-2 family. This has caused a moral panic resulting in the spread of informative and uninformative information about COVID-19 and its effects. Twitter is a popular social media platform used extensively during the current outbreak. This paper aims to predict informative tweets related to COVID-19 on Twitter using a novel set of fuzzy rules involving deep learning techniques. This study focuses on identifying informative tweets during the pandemic to provide the public with trustworthy information and forecast how quickly diseases could spread. In this case, we have implemented RoBERTa and CT-BERT models using the fuzzy methodology to identify COVID-19 patient tweets. The proposed architecture combines deep learning transformer models RoBERTa and CT-BERT with the fuzzy technique to categorize posts as INFORMATIVE or UNINFORMATIVE. We performed a comparative analysis of our method with machine learning models and deep learning approaches. The results show that our proposed model can classify informative and uninformative tweets with an accuracy of 91.40% and an F1-score of 91.94% using the COVID-19 English tweet dataset. The proposed model is accurate and ready for real-world application.

FCUnet: Refined remote sensing image segmentation method based on a fuzzy deep learning conditional random field network

AbstractImage segmentation is pivotal for the understanding of high‐resolution remote sensing images (HRRS). However, because of the inherent uncertainties in remote sensing images and the highly complex resolution of HRRS, ambiguity often occurs among some geographic entities in the segmentation process, and the fine segmentation of HRRS is not considered sufficiently for most existing segmentation methods. Therefore, in this paper, the authors propose a new collaborative neural network structure called fuzzy deep learning conditional random field network (FCUnet) to solve the refined segmentation of HRRS. First, the authors design a fuzzy U‐Net classification network to obtain effective feature information, which introduces the fuzzy logic unit into the network to process the ambiguity and uncertainty of HRRS. Then, the authors introduce the conditional random field (CRF) at the end of FCUnet to optimize the image segmentation results. Finally, the authors validated the effectiveness and superiority of their approach on three data sets. The experiment results revealed that FCUnet had better refined segmentation performance and generalization ability than state‐of‐the‐art methods.

Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

This paper addresses the detection and identification of flaws in Plug valves. The Plug valve thermal image is acquired using a Fluke thermal camera [TiS20]. Thermal images of the Plug valve are used for the identification of flaws such as Crack, Porosity, Corrosion, and Internal defects. The thermal images detect the surface flaws and never subsurface flaws in Plug valves. The subsurface flaws detection is a challenging problem in valve inspection. In this paper, the thermal images obtained after the dye penetrates the surface valve detect the surface flaws more efficiently after applying the Fuzzy Deep Learning Algorithms. DyePenetrating Test (DPT) combined with Infrared Thermography is proposed to identify heat flux changes and flaws in the faulty metal surface of Plug valves. In DPT, thinned paint is employed on the metal surface that displays metal porosity and even fine cracks. After DPT, thermal images of the Plug valve are processed through the Fuzzy Deep Learning Algorithm to evaluate flaws. The Fuzzy Algorithm is utilized prior to Deep Learning to simplify and speed up the classification task. The flaws are identified using Slicing operation and the following parametric quantities such as Accuracy, Mathew’s Correlation Coefficient (MCC), Local Self-Similarity Descriptor (LSS), Precision/Recall, F-measure, and Jaccard Index. The parametric quantities depict corresponding variations with regard to surface coarseness and metal flaws. The DPT and Fuzzy Deep Learning Algorithm identify metal defects with 80.67% accuracy.

Evolutionary Optimization of COVID-19 Vaccine Distribution With Evolutionary Demands

Vaccination uptake has become the key factor that will determine our success in containing the coronavirus pneumonia (COVID-19) pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel COVID-19 pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can predistribute the forecasted number of vaccines to the satellites in advance; after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions; when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and, hence, accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts and, hence, contribute to accelerating the achievement of herd immunity.