eFC-Evolving Fuzzy Classifier with Incremental Clustering Algorithm Based on Samples Mean Value by Emmanuel Tavares, Gray Farias Moita, Alisson Marques Silva

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Sampling Method Based on Fuzzy Membership for Computing Negative Sample Credibility and Its Applications by Zhijie Ning, Yongbo Tie

“…To overcome this challenge, this study proposes a fuzzy membership-based sampling method for assessing negative sample credibility in the Liangshan Yi Autonomous Prefecture, where credibility is defined as the confidence level of stable nonlandslide samples. …”
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Application of the Fuzzy Classification for Linear Hybrid Prediction Methods by A. S. Taskin, E. M. Mirkes, N. Y. Sirotinina

“…The initial set of attributes is expanded by binary attributes which are derived from the initial set by fuzzy classification. A comparative testing of the discussed forecasting methods on the initial samples and the resulting ones is performed. …”
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Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation by Xiaoli Li, Longlong Zhao, Hongzhong Li, Luyi Sun, Pan Chen, Ruixia Jiang, Jinsong Chen

“…To deal with this problem, the unsupervised image classification algorithm based on fully fuzzy Voronoi tessellation is proposed. …”
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Fuzzy Logic Technique Based on Classification Function Application in Quality Control by Salman Hussien Omran, Salam Waley Shneen, Batool Ibrahim Jameel, Omar Hassoon, Mohammed A. Fayad, Firas Basim Ismail

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Classification algorithm for imbalance data of ECG based on PSOFS and TSK fuzzy system by Xinhui LI, Qing SHEN, Xiongtao ZHANG

“…A new classification model of electrocardiogram (ECG) signal based on particle swarm optimization feature selection (PSOFS) and TSK (Takagi-Sugeno-Kang) fuzzy system was proposed, i.e., parallel ensemble fuzzy neural network based on PSOFS and TSK (PE-PT-FN), which was used for ECG prediction.Each class sample in the training set was randomly sampled, and the samples obtained by randomly sampled were added.Then, the feature selection method PSOFS was carried out independently and parallelly.In PSOFS, particles that were random initial positions represent different feature subsets and converge to the optimal positions after many iterations.Each subset had a corresponding feature subset.Several groups of TSK fuzzy neural network (TSK-FNN) were trained by each feature subset in parallel.Medical researchers could effectively find the correlation between ECG signal data and different types of disease through the interpretability of the fuzzy system and the feature subsets by the PSOFS algorithm.Experiments prove that PE-PT-FN greatly improves the macro-R to 92.35% while retaining interpretability.…”
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Wind Turbine Fault Identification in Sample Imbalance Scenarios Using FRBCS With GAN Oversampling and Metric Learning by Changsheng Kang, Xiaoyi Qian, Lixin Wang, Ziheng Dai, Shuai Guan, Yi Zhao, Wenyao Sun

“…Therefore, this paper proposes a fuzzy rule-based classification system (FRBCS) based on generative adversarial network (GAN) oversampling and metric learning. …”
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Load identification method based on one class classification combined with fuzzy broad learning by Wang Yi, Wang Xiaoyang, Li Songnong, Chen Tao, Hou Xingzhe, Fu Xiuyuan

“…In order to improve the stability and accuracy of the load identification model, a power load identification method combining single classification and fuzzy broad learning is proposed. The one-class K-nearest neighbor method is used to screen samples to detect unknown electrical appliances and control the risk of misjudgment. …”
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FADA-SMOTE-Ms: Fuzzy Adaptative Smote-Based Methods by Roudani Mohammed, El Moutaouakil Karim

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Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network by Xiangqun Li, Jiawen Liang, Jinyu Zhu, Shengping Shi, Fangyu Ding, Jianpeng Sun, Bo Liu

“…A global average pooling (GAP) layer is designed as a replacement for the fully connected layer. Second, fuzzy clustering, instead of the softmax classification layer, is employed in ResNet for its characteristic of requiring no subsequent data training. …”
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Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls by Zhixian Yang, Yinghua Wang, Gaoxiang Ouyang

“…It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. …”
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A novel three-way distance-based fuzzy large margin distribution machine for imbalance classification by Li Liu, Jinrui Guo, Ziqi Yin, Rui Chen, Guojun Huang

“…The large margin distribution machine (LDM) introduces the margin distribution of samples to replace the traditional minimum margin, resulting in extensively enhanced classification performance. …”
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Image Compression Based on Clustering Fuzzy Neural Network by Shahba Khaleel, Jamal Majeed, Bayda Khaleel

“…This new approach includes new objective function, and its minimization by energy function based on unsupervised two dimensional fuzzy Hopfield neural network. New objective function consists of a combination of classification entropy function and average distance between image pixels and cluster centers. …”
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Simple fuzzy classification of metal qualities of Ona River, Ibadan, Nigeria, and its implication for fish production and other uses by Olayiwola Akin Akintola, Foluso Oyedotun Agunbiade, Tolulope Omolayo Ariyomo, Toju Esther Babalola, Bolaji Adelanke Adabembe, Oluwaseun Temitope Faloye, Opeyemi Samuel Sajo

“…These were analyzed according to standard procedures and processed using the Simple Fuzzy Classification (SFC) model with four classes. …”
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Fuzzy rank fusion of deep neural networks for weed identification in groundnut crop by Akshay Dheeraj, Rajnish Kumar Chaturvedi, Sapna Nigam, Md. Ashraful Haque, Sudeep Marwaha

“…Base models EfficientNetV2B0 and DenseNet121 have achieved the classification accuracy of 98.66 ± 0.39 and 98.92 ± 0.19 respectively. …”
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BLSF: Adaptive Learning for Small-Sample Medical Data With Broad Learning System Forest Integration by Dimas Chaerul Ekty Saputra, Khamron Sunat, Tri Ratnaningsih

“…In imbalanced datasets such as PIDD (73.60%), Kidney failure (100%), and Heart disease (91.12%), BLSF consistently surpassed Fuzzy BLS and Intuitionistic Fuzzy BLS. It got a flawless score on the Kidney failure dataset, demonstrating its proficiency in managing difficult classifications with exceptional accuracy. …”
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Fuzzy approaches provide improved spatial detection of coastal dune EU habitats by Emilia Pafumi, Claudia Angiolini, Giovanni Bacaro, Emanuele Fanfarillo, Tiberio Fiaschi, Duccio Rocchini, Simona Sarmati, Michele Torresani, Hannes Feilhauer, Simona Maccherini

“…We observed a great disparity among habitats, with coastal dune scrubs and white dunes generally achieving the highest accuracy. Fuzzy classifications, despite yielding lower overall accuracy than the crisp classification, provided a more realistic representation of vegetation patterns, highlighting the inherent fuzziness of vegetation in coastal dunes. …”
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Mean-Variance optimal portfolio selection integrated with support vector and fuzzy support vector machines by Simrandeep Kaur, Arti Singh, Abha Aggarwal

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Delineation and evaluation of management zones for site-specific nutrient management using a geostatistical and fuzzy C mean cluster approach by Pandit Vaibhav Bhagwan, Theerthala Anjaiah, Chitteti Ravali, Darshanoju Srinivasa Chary, Abu Taha Zamani, Sajid Ullah, Nazih Y. Rebouh, Aqil Tariq

“…Five management zones were delineated by principal component analysis and fuzzy c-means clustering based on fuzzy performance index (FPI) and normalized classification entropy (NCE) indices. …”
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FLSH: A Framework Leveraging Similarity Hashing for Android Malware and Variant Detection by Hassan Jalil Hadi, Alina Khalid, Faisal Bashir Hussain, Naveed Ahmad, Mohammed Ali Alshara

“…To address this, various techniques and algorithms have been employed to improve malware detection and classification. In this paper, we focus on leveraging fuzzy hashes to calculate the similarity index between files, aiding in the identification of malicious content within seemingly legitimate APK files. …”
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