Sweta Bhattacharya¹, Praveen Kumar Reddy Maddikunta¹, Iyapparaja Meenakshisundaram¹, Thippa Reddy Gadekallu¹, Sparsh Sharma², Mohammed Alkahtani³, Mustufa Haider Abidi^4,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2599-2615, 2021, DOI:10.32604/cmc.2021.016229 - 21 July 2021

Abstract The Internet of Things (IoT) and related applications have witnessed enormous growth since its inception. The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain. Although the applicability of these applications are predominant, battery life remains to be a major challenge for IoT devices, wherein unreliability and shortened life would make an IoT application completely useless. In this work, an optimized deep neural networks based model is used to predict the battery life of the IoT systems. The present study uses the Chicago Park Beach dataset collected More >

Shuai Wang¹, Xianyi Chen^{2, *}

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2065-2077, 2020, DOI:10.32604/cmc.2020.09857 - 16 September 2020

Abstract With the rapid development of computer technology, millions of images are produced everyday by different sources. How to efficiently process these images and accurately discern the scene in them becomes an important but tough task. In this paper, we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification. Specifically, we first extract some high-level features of images under consideration based on available models trained on public datasets. Then, we further design a binary encoding method called one-hot encoding to make the feature representation more efficient. Benefiting from the proposed More >