Sikkim Manipal University

About: Sikkim Manipal University is a education organization based out in Gangtok, India. It is known for research contribution in the topics: Population & Computer science. The organization has 1262 authors who have published 1816 publications receiving 15102 citations. The organization is also known as: SMU.

A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems

Abstract: Recently, wireless technologies have been growing actively all around the world. In the context of wireless technology, fifth-generation (5G) technology has become a most challenging and interesting topic in wireless research. This article provides an overview of the Internet of Things (IoT) in 5G wireless systems. IoT in the 5G system will be a game changer in the future generation. It will open a door for new wireless architecture and smart services. Recent cellular network LTE (4G) will not be sufficient and efficient to meet the demands of multiple device connectivity and high data rate, more bandwidth, low-latency quality of service (QoS), and low interference. To address these challenges, we consider 5G as the most promising technology. We provide a detailed overview of challenges and vision of various communication industries in 5G IoT systems. The different layers in 5G IoT systems are discussed in detail. This article provides a comprehensive review on emerging and enabling technologies related to the 5G system that enables IoT. We consider the technology drivers for 5G wireless technology, such as 5G new radio (NR), multiple-input–multiple-output antenna with the beamformation technology, mm-wave commutation technology, heterogeneous networks (HetNets), the role of augmented reality (AR) in IoT, which are discussed in detail. We also provide a review on low-power wide-area networks (LPWANs), security challenges, and its control measure in the 5G IoT scenario. This article introduces the role of AR in the 5G IoT scenario. This article also discusses the research gaps and future directions. The focus is also on application areas of IoT in 5G systems. We, therefore, outline some of the important research directions in 5G IoT.

Graphene–metal oxide nanohybrids for toxic gas sensor: A review

Abstract: Sensing of gas molecules is critical to environmental monitoring, control of chemical processes, agricultural, and medical applications In particular, the detection of industrial toxic gases such as CO, NO x , and NH 3 is very important for many industries Metal oxides have been widely studied for the sensitivity of their properties to gases even though they do have some limitations Recently, graphene has been considered as a promising material for gas sensing since its electronic properties are strongly affected by the adsorption of foreign molecules Intrinsic graphene has high sensitivity at low gas concentrations; but the sensor selectivity is poor which limits its use in many practical applications Hence, hybrid architectures formed by blending of nanoparticles of metal–oxides with graphene or its derivatives have been explored by several researchers which showed improved gas sensing ability, especially the sensitivity and selectivity at room temperature Here we review the state of the art of gas sensors based on graphene and metal oxide hybrid nanostructures for detection of various common toxic gases

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Abstract: Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.