Vehicle classification is a challenging task in the area of image processing. It involves the classification of various vehicles based on their color, model, and make. A distinctive variety of vehicles belonging to various model categories have been developed in the automobile industry, which has made it necessary to establish a compact system that can classify vehicles within a complex model group. A well-established vehicle classification system has applications in security, vehicle monitoring in traffic cameras, route analysis in autonomous vehicles, and traffic control systems. In this paper, a hybrid model based on the integration of a pre-trained Convolutional Neural Network (CNN) and an evolutionary feature selection model is proposed for vehicle classification. The proposed model performs classification of eight different vehicle categories including sports cars, luxury cars and hybrid power-house SUVs. The used in this work is derived from Stanford car dataset that contains almost 196 cars and vehicle classes. After performing appropriate data preparation and preprocessing steps, feature learning and extraction is carried out using pre-trained VGG16 first that learns and extracts deep features from the set of input images. These features are then taken out of the last fully connected layer of VGG16, and feature optimization phase is carried out using evolution-based nature-inspired optimization model Genetic Algorithm (GA). The classification is performed using numerous SVM kernels where Cubic SVM achieves an accuracy of 99.7% and outperforms other kernels as well as excels in terns of performance as compared to the existing works.

The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms. Some social media sources contribute to the propagation of fake news that has no real validity, but they accumulate over time and begin to appear in the feed of every consumer producing even more ambiguity. To sustain the value of social media, such stories must be distinguished from the true ones. As a result, an automated system is required to save time and money. The classification of fake news and misinformation from social media data corpora is the subject of this research. Several preprocessing and data improvement procedures are used to gather and preprocess two fake news datasets. Deep text features are extracted using word embedding models Word2vec and Global Vectors for Word representation while textual features are extracted using n-gram approaches named Term Frequency-Inverse Document Frequency and Bag of Words from both datasets individually. Bidirectional Encoder Representations from Transformers (BERT) is also employed to derive embedded representations from the input data. Finally, three Machine Learning (ML) and two Deep Learning (DL) algorithms are utilized for fake news classification. BERT also carries out the classification of embedded outcomes generated by it in parallel with the ML and DL models. In terms of overall performance, the DL-based Convolutional Neural Network stands out in the case of the first while BERT performs better in the case of the second dataset.

Amazon products reviews classification based on machine learning, deep learning methods and BERT