Deep learning is presently an effective research area in machine learning technique and pattern classification association. This has achieved big success in the areas of application namely computer vision, speech recognition, and NLP. This paper gives the impact of feature extraction that used in a deep learning technique such as Convolutional Neural Network (CNN). The purpose of this paper presents an emerged survey of actual literatures on feature extraction methods since past five years. As the raising of application demand increases, a large study and analysis in the feature extraction field became very efficient. In this paper, we presented a detailed study on deep learning, and a described some of existing methodology of feature extraction.

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.

In smart cities, managing the energy in the industrial Internet of Things (IIoT) IoT networks is a challenging issue. In a cluster-based IIoT, Cluster Heads (CHs) near the sink quickly deplete their energy as they are responsible for relaying a large amount of data. It results in isolating the sink from the network and causes an energy hole or hot spot problem. Many solutions exist for this problem, including sink mobility, unequal clustering, use of gateways, etc. Each of these solutions has its own merits and demerits. It is well known that combining clustering and energy-aware routing can significantly extend the IIoT’s lifetime. In this work, we propose an advanced energy management and harvesting system for enhancement of network lifetime of IIoT in smart cities by addressing the hot spot problems. The system is based on energy-aware clustering and routing algorithms that employ energy-harvesting nodes deployed along with the normal sensor nodes. We simulate the proposed system extensively for various cases of network topology. A couple of existing algorithms are analyzed and compared with simulated results. It is shown that the network lifetime is increased significantly compared to the existing ones.