Nimmala Mangathayaru^1,*, Padmaja Rani², Vinjamuri Janaki³, Kalyanapu Srinivas⁴, B. Mathura Bai¹, G. Sai Mohan¹, B. Lalith Bharadwaj¹

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2425-2443, 2021, DOI:10.32604/cmc.2021.016534

Abstract Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine. Detecting arrhythmia from ECG signals is considered a standard approach and hence, automating this process would aid the diagnosis by providing fast, cost-efficient, and accurate solutions at scale. This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography (ECG) signals causing arrhythmia. In this era of applied intelligence, automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions. In this research, our contributions are two-fold. Firstly, the Dual-Tree Complex Wavelet Transform (DT-CWT) method is implied… More >

Mohanad Al-Ghobari¹, Amgad Muneer^2,*, Suliman Mohamed Fati³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1553-1570, 2021, DOI:10.32604/cmc.2021.016348

Abstract Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites, accommodation, and food according to their interests. This objective makes it harder for tourists to decide and plan where to go and what to do. Aside from hiring a local guide, an option which is beyond most travelers’ budgets, the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews. Therefore, this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue. Accordingly, this study proposes location-aware personalized… More >

Xiaolong Yang¹, Xin Yu¹, Liangbo Xie^1,*, Hao Xue², Mu Zhou¹, Qing Jiang¹

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2793-2806, 2021, DOI:10.32604/cmc.2021.016298

Abstract To address the limitations of traditional sleep monitoring methods that highly rely on sleeping posture without considering sleep apnea, an intelligent apnea monitoring system is designed based on commodity WiFi in this paper. By utilizing linear fitting and wavelet transform, the phase error of channel state information (CSI) of the receiving antenna is eliminated, and the noise of the signal amplitude is removed. Moreover, the short-time Fourier transform (STFT) and sliding window method are combined to segment received wireless signals. Finally, several important statistical characteristics are extracted, and a back propagation (BP) neural network model is built to identify apnea… More >

Chia-Nan Wang¹, Hsin-Pin Fu², Hsien-Pin Hsu^3,*, Van Thanh Nguyen⁴, Viet Tinh Nguyen⁴, Ansari Saleh Ahmar⁵

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2339-2353, 2021, DOI:10.32604/cmc.2021.016284

Abstract In developing countries, solar energy is the largest source of energy, accounting for 35%–45% of the total energy supply. This energy resource plays a vital role in meeting the energy needs of the world, especially in Vietnam. Vietnam has favorable natural conditions for this energy production. Because it is hot and humid, and it has much rainfall and fertile soil, biomass develops very quickly. Therefore, byproducts from agriculture and forestry are abundant and continuously increasing. However, byproducts that are considered natural waste have become the cause of environmental pollution; these include burning forests, straw, and sawdust in the North; and… More >

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

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 from the publicly available data… More >

Keshav Thapa, Zubaer Md. Abdhulla AI, Yang Sung-Hyun^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1653-1670, 2021, DOI:10.32604/cmc.2021.015660

Abstract In this era, deep learning methods offer a broad spectrum of efficient and original algorithms to recognize or predict an output when given a sequence of inputs. In current trends, deep learning methods using recent long short-term memory (LSTM) algorithms try to provide superior performance, but they still have limited effectiveness when detecting sequences of complex human activity. In this work, we adapted the LSTM algorithm into a synchronous algorithm (sync-LSTM), enabling the model to take multiple parallel input sequences to produce multiple parallel synchronized output sequences. The proposed method is implemented for simultaneous human activity recognition (HAR) using heterogeneous… More >

Sengul Bayrak^1,2,*, Eylem Yucel², Hidayet Takci³, Ruya Samli²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1427-1446, 2021, DOI:10.32604/cmc.2021.015524

Abstract Today, electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor. These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain, such as epilepsy. Electroencephalogram (EEG) signals are however prone to artefacts. These artefacts must be removed to obtain accurate and meaningful signals. Currently, computer-aided systems have been used for this purpose. These systems provide high computing power, problem-specific development, and other advantages. In this study, a new clinical decision support system was developed for individuals to detect epileptic seizures using EEG signals. Comprehensive classification… More >

Yuzhen Liu^1,3, Xiaoliang Wang^1,*, Xinghui She¹, Ming Yi¹, Yuelong Li¹, Frank Jiang²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2261-2276, 2021, DOI:10.32604/cmc.2021.015501

Abstract An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes, and help people live well in mosquito-infested areas. In this study, we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things. In our method, decision-making is controlled by a deep learning model, and the proposed method uses infrared sensors and an array of pressure sensors to collect data. Moreover the ZigBee protocol is used to transmit the pressure map which… More >

Zakir Ullah¹, Asif Umer¹, Mahdi Zaree², Jamil Ahmad¹, Faisal Alanazi^3,*, Noor Ul Amin¹, Arif Iqbal Umar¹, Ali Imran Jehangiri¹, Muhammad Adnan¹

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2123-2140, 2021, DOI:10.32604/cmc.2021.015445

Abstract Cloud computing is a demanding business platform for services related to the field of IT. The goal of cloud customers is to access resources at a sustainable price, while the goal of cloud suppliers is to maximize their services utilization. Previously, the customers would bid for every single resource type, which was a limitation of cloud resources allocation. To solve these issues, researchers have focused on a combinatorial auction in which the resources are offered by the providers in bundles so that the user bids for their required bundle. Still, in this allocation mechanism, some drawbacks need to be tackled,… More >

Sundresan Perumal¹, Mujahid Tabassum¹, Ganthan Narayana², Suresh Ponnan^3,*, Chinmay Chakraborty⁴, Saju Mohanan⁵, Zeeshan Basit⁵, Mohammad Tabrez Quasim⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1447-1462, 2021, DOI:10.32604/cmc.2021.014854

Abstract A wireless sensor network (WSN) consists of several tiny sensor nodes to monitor, collect, and transmit the physical information from an environment through the wireless channel. The node failure is considered as one of the main issues in the WSN which creates higher packet drop, delay, and energy consumption during the communication. Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets. In this paper, Artificial Neural Network (ANN) based Node Failure Detection (NFD) is developed with cognitive radio for detecting the location of the node failure. The ad hoc on-demand distance vector (AODV)… More >