Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model

Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. Because of the emergence of smart wearable gadgets, fog computing and “Internet of Things” (IoT) solutions have become necessary for diagnosis. The proposed model integrates Edge-Fog-Cloud computing for the accurate and fast delivery of outcomes. The hardware components collect data from different patients. The heart feature extraction from signals is done to get significant features. Furthermore, the feature extraction of other attributes is also gathered. All these features are gathered and subjected to the diagnostic system using an Optimized Cascaded Convolution Neural Network (CCNN). Here, the hyperparameters of CCNN are optimized by the Galactic Swarm Optimization (GSO). Through the performance analysis, the precision of the suggested GSO-CCNN is 3.7%, 3.7%, 3.6%, 7.6%, 67.9%, 48.4%, 33%, 10.9%, and 7.6% more advanced than PSO-CCNN, GWO-CCNN, WOA-CCNN, DHOA-CCNN, DNN, RNN, LSTM, CNN, and CCNN, respectively. Thus, the comparative analysis of the suggested system ensures its efficiency over the conventional models.

Implementation of modified GSO based magic cube keys generation in cryptography

Over the last few decades, tremendous and exponential expansion in digital contents together with their applications has emerged. The Internet represents the essential leading factor for this expansion, which provides low-cost communication tools worldwide. However, the main drawback of the Internet is related to security problems. In order to provide secure communication, enormous efforts have been spent in the cryptographic field. Recently, cryptographic algorithms have become essential for increasing information safety. However, these algorithms require random keys and can be regarded as compromised when the random keys are cracked via the attackers. Therefore, it is substantial that the generation of keys should be random and hard to crack. In this paper, this is guaranteed via one of the most efficient nature-inspired algorithms emerged by inspiring the movements of stars, galaxies, and galaxy superclusters in the cosmos that can be utilized with a mathematical model (magic cube) for generating hardly cracking random number keys. In the proposed cryptographic system, the Modified Galactic Swarm Optimization (GSO) algorithm has been utilized in which every row and column of magic cube faces are randomly rotated until reaching the optimal face, and the optimal random elements are selected as optimal key from the optimal face. The generated optimized magic cube keys are used with several versions of RC6 algorithms to encrypt various secret texts. Furthermore, these generated keys are also used for encrypting images using the logical XOR operation. The obtained results of NIST tests proved that the generated keys are random and uncorrelated. Moreover, the security of the proposed cryptographic system was proved

Generalized type-2 fuzzy logic in galactic swarm optimization: design of an optimal ball and beam fuzzy controller

In this paper we present a modification based on generalized type-2 fuzzy logic to an algorithm that is inspired on the movement of large masses of stars and their attractive force in the universe, known as galactic swarm optimization (GSO). The modification consists on the dynamic adjustment of parameters in GSO using type-1 and type-2 fuzzy logic. The main idea of the proposed approach is the application of fuzzy systems to dynamically adapt the parameters of the GSO algorithm, which is then applied to parameter optimization of the membership functions of the bar and ball fuzzy controller. The experimentation was carried out using the original GSO algorithm, and the type-1 and type-2 fuzzy variants of GSO. In addition a disturbance was added to the bar and ball fuzzy controller plant to be able to validate the effectiveness of the proposed approach in optimizing fuzzy controllers. A formal comparison of results is performed with statistical tests showing that GSO with generalized type-2 fuzzy logic is the best method for optimizing the fuzzy controller.