International Journal of Computer and Applications

Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R-squared or R 2) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination (R-squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R-squared as standard metric to evaluate regression analyses in any scientific domain.

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation.

Application of GIS based data driven evidential belief function model to predict groundwater potential zonation

This paper presents a literature review of image steganography techniques in the spatial domain for last 5 years. The research community has already done lots of noteworthy research in image steganography. Even though it is interesting to highlight that the existing embedding techniques may not be perfect, the objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing up-to-date techniques for researchers that are involved in the designing of image steganographic system. In this article, the general structure of the steganographic system and classifications of image steganographic techniques with its properties in spatial domain are exploited. Furthermore, different performance matrices and steganalysis detection attacks are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.

Image steganography in spatial domain: A survey

Ternary radial harmonic Fourier moments based robust stereo image zero-watermarking algorithm

Image steganography is the art of hiding information into a cover image. This paper presents a novel technique for Image steganography based on DWT, where DWT is used to transform original image (cover image) from spatial domain to frequency domain. Firstly two dimensional Discrete Wavelet Transform (2-D DWT) is performed on a gray level cover image of size M × N and Huffman encoding is performed on the secret messages/image before embedding. Then each bit of Huffman code of secret message/image is embedded in the high frequency coefficients resulted from Discrete Wavelet Transform. Image quality is to be improved by preserving the wavelet coefficients in the low frequency subband. The experimental results show that the algorithm has a high capacity and a good invisibility. Moreover PSNR of cover image with stego-image shows the better results in comparison with other existing steganography approaches. Furthermore, satisfactory security is maintained since the secret message/image cannot be extracted without knowing decoding rules and Huffman table.

A novel technique for image steganography based on Block-DCT and Huffman Encoding

Image encryption is a suitable method to protect image data. Image and text data has their unique features. The available encryption algorithms are good for text data. They may not be suitable for multimedia data. In fact the pixels of natural images are highly correlated to their neighboring pixels. Due to this strong correlation any pixel can be practically predicted from the values of its neighbors. In this article, we propose a new location transformation based encryption technique. We redistribute the pixel values to different location using affine transform technique with four 8-bit keys. The transformed image then divided into 2 pixels × 2 pixels blocks and each block is encrypted using XOR operation by four 8-bit keys. The total key size used in our algorithm is 64 bit which proves to be strong enough. The experimental results proved that after the affine transform the correlation between pixel values was significantly decreased.

Image encryption using affine transform and XOR operation

Image steganography is a method of concealing information into a cover image to hide it. Least Significant-Bit (LSB) based approach is most popular steganographic techniques in spatial domain due to its simplicity and hiding capacity. This paper presents a novel technique for Image steganography based on LSB using X-box mapping where we have used several X-boxes having unique data. The embedding part is done by this Steganography algorithm where we use four unique X-boxes with sixteen different values (represented by 4-bits) and each value is mapped to the four LSBs of the cover image. This mapping provides sufficient security to the payload because without knowing the mapping rules no one can extract the secret data (payload).

An image steganography technique using X-box mapping

Long short-term memory LSTM is a special type of recurrent neural network RNN architecture that was designed over simple RNNs for modeling temporal sequences and their long-range dependencies more 

Sentiment Analysis in the Light of LSTM Recurrent Neural Networks

Outbreak of COVID-19, created a disastrous situation in more than 200 countries around the world. Thus the prediction of the future trend of the disease in different countries can be useful for managing the outbreak. Several data driven works have been done for the prediction of COVID-19 cases and these data uses features of past data for future prediction. In this study the machine learning (ML)-guided linear regression model has been used to address the different types of COVID-19 related issues. The linear regression model has been fitted into the dataset to deal with the total number of positive cases, and the number of recoveries for different states in India such as Maharashtra, West Bengal, Kerala, Delhi and Assam. From the current analysis of COVID-19 data it has been observed that trend of per day number of infection follows linearly and then increases exponentially. This property has been incorporated into our prediction and the piecewise linear regression is the best suited model to adopt this property. The experimental results shows the superiority of the proposed scheme and to the best of our knowledge this is a new approach towards the prediction of COVID-19.

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Amitava Nag

Papers

A novel technique for image steganography based on Block-DCT and Huffman Encoding

Image encryption using affine transform and XOR operation

An image steganography technique using X-box mapping

Sentiment Analysis in the Light of LSTM Recurrent Neural Networks

A novel framework for COVID-19 case prediction through piecewise regression in India.