The application of confusion and diffusion processes on the three individual components of an RGB image is not secure and efficient, so this problem needs to be addressed. In this paper, a novel RGB image cipher is proposed using chaotic systems, 15-puzzle artificial intelligence problem and DNA computing. First of all the given color image is decomposed into its red, green and blue gray scale images. Then these gray scale images are concatenated to make a single gray scale image. This single gray scale image is further divided into different blocks. A block level permutation (BLP) is proposed on this gray scale image by using the 15-puzzle problem. A pixel level permutation is applied to further randomize the image pixels. This confused image is then DNA encoded. Afterwards, a diffusion process is applied on this DNA encoded image. Lastly this DNA diffused image is converted back into the decimal. Further, this single gray scale image is broken into three gray scale images. These three images are combined to get the final color cipher image. To create the plaintext sensitivity, SHA 256 hash function has been used. Both the simulation and a comprehensive security analyses suggest the robustness and the impregnability of the proposed scheme which in turn signals towards the real world applicability of the scheme.

In the modern era business intelligence (BI) has a pivotal role in articulating a strategy and taking correct measures based on data. Business intelligence plays a pivotal role in an inevitable decision support system that enables the enterprise to perform analysis on data and throughout the process of business. Machine learning predicts the forecasting of future demands of the enterprises. Demand forecasting is one of the main decision-making tasks of enterprise. For demand forecasting first raw sales data is collected from the market, then according to data, the future sale/product demands are forecasted. This prediction is based on collected data that compiles through different sources. The machine learning engine executes data from different modules and determines the weekly, monthly, and quarterly demands of goods/commodities. In demand forecasting, its perfect accuracy is non-compromising, the more accurate system model is more efficient. Furthermore, we test the efficiency by comparing the predicted data with actual data and determine the percentage error. Simulation results show that after applying the purposed solution on real-time organization data, we get up to 92.38 % accuracies for the store in terms of intelligent demand forecasting. INDEX TERMS Business intelligence, demand forecasting, prediction, machine learning, AWS sage maker, sale forecasting.

There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists’ mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity.

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