Search Results (3)

Pneumonia is one of the leading causes of morbidity and mortality in children, making its early detection critical for effective treatment. The objective of this study is to develop and evaluate a hybrid deep learning framework that combines convolutional neural networks with intuitionistic fuzzy estimators to enhance the accuracy, sensitivity, and robustness of pneumonia detection in pediatric chest X-rays. The main background is the use of intuitionistic fuzzy estimators (IFEs). The hybrid model integrates the powerful feature extraction capabilities of CNNs with the uncertainty handling and decision-making strengths of intuitionistic fuzzy logic. By incorporating an IFE, the model is better equipped to deal with ambiguity and noise in medical imaging data, resulting in more accurate and robust pneumonia detection. Experimental results on pediatric chest X-ray datasets demonstrate the effectiveness of the proposed method, achieving higher sensitivity and specificity compared to traditional CNN approaches. The hybrid system achieved a classification accuracy of 94.93%, confirming its strong diagnostic performance. In conclusion, this hybrid model offers a promising tool to assist healthcare professionals in the early and accurate diagnosis of pneumonia in children. Full article

The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods. The investigation presents in a methodological way the whole process of IFDNN development, starting with the simplest form—an intuitionistic fuzzy neural network (IFNN) with one layer with single-input neuron, passing through IFNN with one layer with one multi-input neuron, further subsequent complication—an IFNN with one layer with many multi-input neurons, and finally—the true IFDNN with many layers with many multi-input neurons. The formulas for strongly optimistic, optimistic, average, pessimistic and strongly pessimistic formulas for NN parameters estimation, represented in the form of intuitionistic fuzzy pairs, are given here for the first time for each one of the presented IFNNs. To demonstrate its workability, an example of an IFDNN application to biomedical data is here presented. Full article

In recent years, the presence of malware has been growing exponentially, resulting in enormous demand for efficient malware classification methods. However, the existing machine learning-based classifiers have high false positive rates and cannot effectively classify malware variants, packers, and obfuscation. To address this shortcoming, this paper proposes an efficient deep learning-based method named AIFS-IDL (Atanassov Intuitionistic Fuzzy Sets-Integrated Deep Learning), which uses static features to classify malware. The proposed method first extracts six types of features from the disassembly and byte files and then fuses them to solve the single-feature problem in traditional classification methods. Next, Atanassov’s intuitionistic fuzzy set-based method is used to integrate the result of the three deep learning models, namely, GRU (Temporal Convolutional Network), TCN (Temporal Convolutional Network), and CNN (Convolutional Neural Networks), which improves the classification accuracy and generalizability of the classification model. The proposed method is verified by experiments and the results show that the proposed method can effectively improve the accuracy of malware classification compared to the existing methods. Experiments were carried out on the six types of features of malicious code and compared with traditional classification algorithms and ensemble learning algorithms. A variety of comparative experiments show that the classification accuracy rate of integrating multi-feature, multi-model aspects can reach 99.92%. The results show that, compared with other static classification methods, this method has better malware identification and classification ability. Full article