Handwritten signatures have been widely acclaimed for personal identification viability in educated human society. But, the astronomical growth of population in recent years warrant developing ... Show More

Handwritten signatures have been widely acclaimed for personal identification viability in educated human society. But, the astronomical growth of population in recent years warrant developing mechanized systems to remove the tedium and bias associated with manual checking. Here the proposed system, performing identification with Nearest Neighbor matching between offline signature images collected temporally. The raw images and their extracted features are preserved using Case Based Reasoning and Feature Engineering principles. Image patterns are captured through standard global and local features, along with some profitable indigenously developed features. Outlier feature values, on detection, are automatically replaced by their nearest statistically determined limit values. Search space reduction possibilities within the case base are probed on a few selected key features, applying Hierarchical clustering and Dendogram representation. Signature identification accuracy is found promising when compared with other machine learning techniques and a few existing well known approaches.

Information security dominate the world. All the time we connect to the internet for social media, banking, and online shopping through various applications. Our priceless data may be hacked by... Show More

Information security dominate the world. All the time we connect to the internet for social media, banking, and online shopping through various applications. Our priceless data may be hacked by attackers. There is a necessity for a better encryption method to enhance information security. The distinctive features of Elliptic Curve Cryptography (ECC) in particular the key atomity, speedy ciphering and preserving bandwidth captivating its use in multimedia encipher. An encryption method is proposed by incorporating ECC, Secure Hash Algorithm – 256 (SHA-256), Arnold transform, and hyperchaos. Randomly generated salt values are concatenated with each pixel of an image. SHA-256 hash is imposed which produces a hash value of 32-bit, later used to generate the key in ECC. Stronger ciphering is done by applying Arnold’s transformation and hyperchaos thereby achieved more randomness in image. Simulation outcomes and analysis show that the proposed approach provides more confidentiality for color images.

The successful prediction of the stocks’ future price would produce substantial profit to the investor. In this paper, we propose a framework with the help of various technical indicators of the... Show More

The successful prediction of the stocks’ future price would produce substantial profit to the investor. In this paper, we propose a framework with the help of various technical indicators of the stock market to predict the future prices of the stock using Recurrent Neural Network based Long Short-Term Memory (LSTM) algorithm. The historical transactional data set is amalgamated with the technical indicators to create a more effective input dataset. The historical data is taken from 2010-2019 ten years in total. The dataset is divided into 80% training set and 20% test set. The experiment is carried out in two phases first without the technical indicators and after adding technical indicators. In the experimental setup, it has been observed the LSTM with technical indicators have significantly reduced the error value by 2.42% and improved the overall performance of the system as compared to other machine learning frameworks that are not accounting the effect of technical indicators.