Recently, the advancements in the field of wireless technologies and micro-electro-mechanical systems lead to the development of potential applications in wireless sensor networks (WSNs). The visual sensors in WSN create a significant impact on computer vision based applications such as pattern recognition and image restoration. generate a massive quantity of multimedia data. Since transmission of images consumes more computational resources, various image compression techniques have been proposed. But, most of the existing image compression techniques are not applicable for sensor nodes due to its limitations on energy, bandwidth, memory, and processing capabilities. In this article, we introduce a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm. The NCS algorithm performs the bit reduction operation and then encoded by a codec (such as PPM, Deflate, and Lempel Ziv Markov chain algorithm.) to further compress the image. The proposed NCS algorithm increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity. Moreover, it achieved a minimum end to end delay of 1074.46 ms at the average bit rate of 4.40 bpp and peak signal to noise ratio of 48.06 on the applied test images. On comparing with state-of-art methods, the proposed method maintains a better tradeoff between compression efficiency and reconstructed image quality.

Bankruptcy prediction and credit risk analysis is one of the most significant problems in the field of accounting and financial decision making. Developing an effective classification rule induction (CRI) framework for bankruptcy prediction and credit risk analysis in appropriate time is essential to prevent the business communities from being bankrupt. Traditional statistical methods and artificial intelligence techniques play a major role to predict bankruptcy and credit risks. Most of the earlier research works were carried out on quantitative methods, while few studies have proposed on qualitative methods to improvise the performance of bankruptcy prediction models. The discovery of bankruptcy prediction in a qualitative way is an important task because it depends on the subjective knowledge of the experts. In this paper, a unified framework for qualitative and quantitative bankruptcy analysis using Ant Colony Optimization (ACO) based ant-miner algorithm is proposed. Three different natured datasets are used to present a trustworthy result. For this experiment, we have collected qualitative_bankruptcy dataset and benchmarked by UCI repository. The proposed method is successfully applied and the performance analysis prove that ant-miner method is better than existing classifiers namely Logistic Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF) and Radial Basis Function (RBF) in terms of various performance analysis factors. Furthermore, the proposed ant-miner model is found to be a more suitable method for bankruptcy prediction when compared to other traditional statistical and artificial intelligence techniques.