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In this manuscript, a deep learning-based Generative Adversarial Networks (GAN) classifier optimized with the Water Strider Optimization Algorithm (WSOA) is proposed to solve the issue of fake tweets in Twitter. Initially, input Twitter data is taken from the "Twitter API" dataset. Then, pre-processing is done in the Twitter dataset. After that, the pre-processed tweet features are selected based on the Bag of Words model, mutual information, and the Chi-square method. Then the selected features are given into the Generative Adversarial Networks classifier for detecting fake tweets. Here, the Generative Adversarial Networks classifier is used to fake tweets detection. But it does not expose any optimization strategies adopted to determine the optimum parameters to confirm the accurate categorization of fake tweets. Therefore, Water Strider Optimization Algorithm is applied to optimize the parameter in Generative Adversarial Networks classifier (FT-GAN-WSOAC). The proposed method is activated in MATLAB and the performance is analyzed with performance metrics. The experimental results show that the proposed FT-GAN-WSOA classifier provides higher accuracy of 25.14%, 25.21%, 20.44%, and 34.38% than the existing methods, such as fake tweet detection with content-based features using a custom rule-based algorithm (FT-CBF-CRBA) method, fake tweet detection with long/short-term memory using a random forest algorithm (FT-LSTM-RFA) method, fake tweet detection with zero-shot learning using bidirectional encoder representations from transformers (FT-ZSL-BERT) method, and fake tweet detection with a hybrid convolution neural network and recurrent neural network with long/short-term memory (FT-Hyb CNN-RNN-LSTM) method, respectively. [ABSTRACT FROM AUTHOR]

In this manuscript, a novel method of rational-dilation wavelet transform (RDWT) is proposed for carotid plaque characterisation and classification in Ultrasound images. RDWT is mainly utilised for image acquisition, pre-processing, feature extraction and ensemble classification in automated plaque classification. Here, the transition bands are constructed from the transition function. The statistical features, viz mean, standard deviation, skewness, Renyi entropy, energy are extracted from the sub-bands of RDWT. The Salp Swarm Algorithm (SSA) is mainly used for selecting the optimum features. In this for selecting optimum features using SSA algorithm two conditions are satisfied such as, in the first approach, mean, standard deviation, skewness are selected and then utilised for converting the continual version of salp swarm algorithm to binary. Subsequently, the crossover operator is utilised to select the Renyi entropy, energy features including transfer functions for replacing the average operator and enhancing the characteristics of research method. Plaque Classification uses K Nearest Neighbour (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) classifiers. Experimental outcomes show the efficiency of the proposed method depending on accuracy, specificity and sensitivity. The proposed method attains accuracy of 93%, sensitivity of 90% and specificity of 94% when likened to the existing techniques, such as KNN, PNN and SVM. [ABSTRACT FROM AUTHOR]