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MATLAB is an elite language for specialized registering. It coordinates calculation, perception, and programming condition. In addition, it has powerful built-in routines that enable a very wide variety of computations. Regression is that the method of fitting models to information. Regression analysis is employed to model the link between a response variable and one or many predictor variables. In this paper, we exercised refined MATLAB code for linear regression analysis and solved problems are demonstrated through graphs.

With a transience rate of over 18 million per year, Heart Disease (HD) has emerged out to be the lethal disease of the world. Data mining-based heart disease diagnosis systems can surely aid cardiac professionals in a timely diagnosis of the patient's condition. In this proposed work, a Python-based data mining system capable of diagnosing the HD using a Decision Tree has been developed. In the methodology, the UCI data repository was taken into consideration with 14 Attributes. In the dataset, there are few missing values (yet found to be hyperparameter), and pre-processing with such missing values is a common yet challenging problem. A mere substitution will give biased results from the data to be observed for HD diagnosis and will certainly affect the value of the learning process in Machine Learning. Therefore, in the proposed work, a missing value imputation is done, which gave better accuracy, and it is trustable.

This Open source innovation has become an important part of development and research in many areas. Traditional open Source projects and research indexing databases works using search keywords and key Value pair match parameters for the published well-structured data, a lot of undocumented research literature, Partial results and datasets gets discarded which might contain some valuable insights which could be later picked later for further research and development. In the proposed system, geo tagging is used on such data aiding to faster discovery, better indexing, with less battery utilization capability compared to traditional GPS techniques.

The growing importance of information security over recent years has prompted development of secure encryption algorithms. Most of the traditional algorithms such as AES and DES are computationally intensive. Hence, in this paper we propose a simple but secure symmetric key encryption algorithm. The image encryption approach makes use of two pseudorandom number generators to encrypt images. Middle Square Algorithm is used to swap the columns of the image followed by the swapping of the rows. This permutation step produces the intermediary cipher image. Subsequently, RC4 algorithm is used to produce a stream of pseudo-random numbers. These numbers are used to substitute the intensity of pixels of the intermediary cipher image, which gives the final encrypted image. Various analysis tests are performed on the quality of the encrypted image. The results of these tests prove that the proposed algorithm is secure and efficient.

Partial hepatectomy leads to an orchestrated regenerative response, activating a cascade of cell signaling events necessary for cell cycle progression and proliferation of hepatocytes. However, the identity of the humoral factors that trigger the activation of these pathways in the concerted regenerative response in hepatocytes remains elusive. In recent years, extracellular ATP has emerged as a rapidly acting signaling molecule that influences a variety of liver functions, but its role in hepatocyte growth and regeneration is unknown. In this study, we sought to determine if purinergic signaling can lead to the activation of c-jun N-terminal kinase (JNK), a known central player in hepatocyte proliferation and liver regeneration. Hepatocyte treatment with ATPgammaS, a nonhydrolyzable ATP analog, recapitulated early signaling events associated with liver regeneration-that is, rapid and transient activation of JNK signaling, induction of immediate early genes c-fos and c-jun, and activator protein-1 (AP-1) DNA-binding activity. The rank order of agonist preference, UTP>ATP>ATPgammaS, suggests that the effects of extracellular ATP is mediated through the activation of P2Y2 receptors in hepatocytes. ATPgammaS treatment alone and in combination with epidermal growth factor (EGF) substantially increased cyclin D1 and proliferating cell nuclear antigen (PCNA) protein expression and hepatocyte proliferation in vitro. Extracellular ATP as low as 10 nM was sufficient to potentiate EGF-induced cyclin D1 expression. Infusion of ATP by way of the portal vein directly activated hepatic JNK signaling, while infusion of a P2 purinergic receptor antagonist prior to partial hepatectomy inhibited JNK activation. In conclusion, extracellular ATP is a hepatic mitogen that can activate JNK signaling and hepatocyte proliferation in vitro and initiate JNK signaling in regenerating liver in vivo. These findings have implications for enhancing our understanding of novel factors involved in the initiation of regeneration, liver growth, and development.

With the advent of better computers and high computing speeds use of images and videos have drastically increased. Today images are a very integral part of our lives from the entertainment industry to medical sciences. In the field of medicine, image processing plays a very important role when it comes to medical imaging. Image processing is utilized to get a clear denoised image for clear and easy diagnostic study. With increase in the usage, need to compress the images and store as many as possible in limited spaces have thus become a necessity. The emphasis being on the ability to convert them back into clear crisp image with minimum noise when the need be. Compressing an image is significantly different than compressing raw binary data. General purpose compression programs can be used to compress images, but the result is less than optimal. This is because images have certain statistical properties which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image can be sacrificed for the sake of saving a little more bandwidth or storage space. This also means that lossy compression techniques can be used in this area. In this work, noisy image is decomposed using three different DWT transforms (haar/db6/coif5). Input images are processed with various noise levels (20, 50 and 80 %) with both salt and pepper and Additive White Gaussian Noise (AWGN). Various error metrics such as Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), Root Mean Squared Error (RMSD), Mean Absolute Error (MAE), and Structural Similarity index Measure (SSIM) are computed and compared with other state of art methods for stability performance.

In this paper, Spatial video denoising using wavelet transform has been focussed as it requires less computation and more suitable for real-time applications. Two specific techniques for spatial video denoising using wavelet transform are considered in this work, 2D Discrete Wavelet Transform (2D DWT) and Integer wavelet transform. Each of these techniques has its advantages and disadvantages. The first technique gives less quality at high levels of noise but consumes less time while the second gives high quality video while consuming long. In this work, we introduce an intelligent denoising system that makes a trade-off between the quality of the denoised video and the time required for denoising. The simulation results show that the proposed system is more suitable for real time applications where the time is critical while giving high quality videos especially at low to moderate levels of noise.