A Novel Image Encryption Using an Integration Technique of Blocks Rotation Based on the Magic Cube and the AES Algorithm

In recent years, several encryption algorithms have been proposed to protect digital images from cryptographic attacks. These encryption algorithms typically use a relatively small key space and therefore, provide safe, especially if they are of a dimension. In this paper proposes an encryption algorithm for a new image protection scheme based on the rotation of the faces of a Magic Cube. The original image is divided into six sub-images and these sub-images are divided amongst a number of blocks and attached to the faces of a Magic Cube. The faces are then scrambled using rotation of the Magic Cube. Then the rotated image is fed to the AES algorithm which is applied to the pixels of the image to encrypt the scrambled image. Finally, experimental results and security analysis show that the proposed image encryption scheme not only encrypts the picture to achieve perfect hiding, but the algorithm can also withstand exhaustive, statistical and differential attacks

Convolutional-NN and Word Embedding for Making an Effective Product Recommendation Based on Enhanced Contextual Understanding of a Product Review

E-commerce is one of the most popular service applications in the world in the last decade. It has become a revolutionary model from traditional shopping transaction to entire internet commerce. E-commerce needs essential artificial intelligence (AI) to provide the customer with information about a product, called a recommendation machine. Collaborative filtering is a model of a recommendation algorithm that relies on rating as the fundamental calculation to make a recommendation. It has been successfully implemented in e-commerce. Even so, this model has a weakness in sparse product data in which the rating number is very low or sparse. Mostly, only less than 3% of the total user population rate a product, leading to the rise of sparse data. A text sentence document is a part of customers’ feedback that can be converted into a product rating.  According to a traditional approach, bag of word and lexicon model are ignored in a contextual approach. This experiment, it developed a new model to increase the contextuality of text sentences, leading to a more effective rating prediction. We employed a kind of convolutional neural network to generate item latent factor vectors that could be incorporated with probabilistic matrix factorization to make rating prediction. Our method outperformed several previous works based on a metric evaluation using the Root Mean Squared Error (RMSE). In this experiment, we analyzed MovieLens and IMDB datasets, which contained a movie product review

Enhanced degree of injury classification model: determination critical indicator and criteria degree of injury from Visum et Repertum (Ver) in Pekanbaru, Indonesia

Abstract Background This study aims to obtain criteria and indicator as parameters to determine the degree of injury from Visum et Repertum (VeR). The approach is done by adopting quantitative descriptive learning from VeR data. This study is conducted to retrieve the independent variable, either one variable or more. The techniques applied in this study are (Analytical Hierarchy Process) AHP and (Logistic Regession) LR to the opinion of experts according to the VeR data for a new knowledge discovery. Survey methods used in Bhayangkara Hospital Pekanbaru in the period from 2013 until 2016. The data sample used in this study is secondary data which are injury data from VeR. Result The finding of this study reveals that the model developed using the AHP and LR has good ability to determine and analyze the parameters for the degree of injury from VeR based on experts’ opinion. Conclusions The LR results also showed the physical factors that influence the degree of injury. Which are Respiration Rate (RR) and Systolic Blood Pressure (SBP). The higher Respiration Rate (RR) indicated the degree of injury is lighter. On the contrary, the lower Respiration Rate (RR) indicated the degree of injury is rising. While, the higher Systolic Blood Pressure (SBP) explained the degree of injury is lighter, and the lower level of Systolic Blood Pressure (SBP) explained the degree of injury level increase