Modified CSLBP

Image hashing is an efficient way to handle digital data authentication problem. Image hashing represents quality summarization of image features in compact manner. In this paper, the modified center symmetric local binary pattern (CSLBP) image hashing algorithm is proposed. Unlike CSLBP 16 bin histogram, Modified CSLBP generates 8 bin histogram without compromise on quality to generate compact hash. It has been found that, uniform quantization on a histogram with more bin results in more precision loss. To overcome quantization loss, modified CSLBP generates the two histogram of a four bin. Uniform quantization on a 4 bin histogram results in less precision loss than a 16 bin histogram. The first generated histogram represents the nearest neighbours and second one is for the diagonal neighbours. To enhance quality in terms of discrimination power, different weight factor are used during histogram generation. For the nearest and the diagonal neighbours, two local weight factors are used. One is the Standard Deviation (SD) and other is the Laplacian of Gaussian (LoG). Standard deviation represents a spread of data which captures local variation from mean. LoG is a second order derivative edge detection operator which detects edges well in presence of noise. The proposed algorithm is resilient to the various kinds of attacks. The proposed method is tested on database having malicious and non-malicious images using benchmark like NHD and ROC which confirms theoretical analysis. The experimental results shows good performance of the proposed method for various attacks despite the short hash length

Adaptive CSLBP compressed image hashing

Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images

A framework for cloud cover prediction using machine learning with data imputation

The climatic conditions of a region are affected by multiple factors. These factors are dew point temperature, humidity, wind speed, and wind direction. These factors are closely related to each other. In this paper, the correlation between these factors is studied and an approach has been proposed for data imputation. The idea is to utilize all these features to obtain the prediction of the total cloud cover of a region instead of removing the missing values. Total cloud cover prediction is significant because it affects the agriculture, aviation, and energy sectors. Based on the imputed data which is obtained as the output of the proposed method, a machine learning-based model is proposed. The foundation of this proposed model is the bi-directional approach of the long short-term memory (LSTM) model. It is trained for 8 stations for two different approaches. In the first approach, 80% of the entire data is considered for training and 20% of the data is considered for testing. In the second approach, 90% of the entire data is accounted for training and 10% of the data is accounted for testing. It is observed that in the first approach, the model gives less error for prediction