A novel perceptually adaptive image watermarking scheme by selecting adaptive threshold in dht domain

This paper proposed a novel image watermarking technique by applying the characteristics of the human visual system, in Hadamard transform domain. Statistical information measures were used to select proper blocks for data embedding. Watermark was embedded by the modification of Discrete Hadamard transform (DHT) coefficients of selected blocks. Threshold and modification value were selected adaptively for each image block, which improved robustness and transparency. The proposed algorithm was able to withstand a variety of attacks and image processing operations like rotation, cropping, noise addition, resizing, lossy compression and etc. The experimental results showed good performance of the proposed scheme in comparison with some of the recently reported watermarking techniques.Keywords: Digital image watermarking, Hadamard transform, Entropy, Lossy compression, Adaptive Threshol

Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model

The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorithm (SVMAK) based on a support vector machine model and the theory of adjusting kernel scaling. The kernel function’s choice was evaluated by applying the likelihood-ratio chi-square and weighting measures. The empirical assessment was performed on the 2017 National Household Travel Survey–California dataset. The performance of the SVMAK model was compared with several other models, including neural networks, XGBoost, Bayesian Network, standard support vector machine model, and some SVM-based models that were previously developed to handle the imbalanced datasets. The SVMAK model outperformed these models, and in some cases improved the accuracy of the minority class classification. For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution