Noman Mazhar^1,2, Rosli Saleh^1,*, Reza Zaba^1,3, Muhammad Zeeshan⁴, M. Muzaffar Hameed¹, Nauman Khan¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3099-3118, 2022, DOI:10.32604/cmc.2022.028285

Abstract The advent of the latest technologies like the Internet of things (IoT) transforms the world from a manual to an automated way of lifestyle. Meanwhile, IoT sector open numerous security challenges. In traditional networks, intrusion detection and prevention systems (IDPS) have been the key player in the market to ensure security. The challenges to the conventional IDPS are implementation cost, computing power, processing delay, and scalability. Further, online machine learning model training has been an issue. All these challenges still question the IoT network security. There has been a lot of research for IoT based detection systems to secure the… More >

R. Bhaskaran¹, P. S. Sujith Kumar², G. Shanthi³, L. Raja⁴, Gyanendra Prasad Joshi⁵, Woong Cho^6,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 1-17, 2023, DOI:10.32604/csse.2023.024973

Abstract In recent times, real time wireless networks have found their applicability in several practical applications such as smart city, healthcare, surveillance, environmental monitoring, etc. At the same time, proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks. This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization (IM-EECNL) approach for real-time wireless networks. The proposed IM-EECNL technique involves two major processes namely node localization and clustering. Firstly, Chaotic Water Strider Algorithm based Node Localization (CWSANL) technique to determine the unknown position of the nodes. Secondly, an Oppositional Archimedes Optimization… More >

Muhammad Sanaullah^1,*, Muhammad Kashif², Babar Ahmad², Tauqeer Safdar², Mehdi Hassan³, Mohd Hilmi Hasan⁴, Amir Haider⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2501-2519, 2022, DOI:10.32604/cmc.2022.021990

Abstract Communication is a basic need of every human being to exchange thoughts and interact with the society. Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so. Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society. The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs. The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively. The signs for singular… More >

Anita Gehlot¹, Rajesh Singh¹, Sweety Siwach², Shaik Vaseem Akram¹, Khalid Alsubhi³, Aman Singh^4,*, Irene Delgado Noya^4,5, Sushabhan Choudhury²

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 999-1015, 2022, DOI:10.32604/cmc.2022.023861

Abstract Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise. Moreover, it is widely utilizing for preventing injuries of athletes during a practice session and in few cases, it leads to muscle fatigue. At present, emerging technology like the internet of things (IoT) and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity. In this study, an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram (sEMG) sensor. Normally, the EMG signal is utilized to display muscle activity. Arduino… More >

Duraimurugan Nagendiran^1,*, S. P. Chokkalingam²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 983-996, 2022, DOI:10.32604/iasc.2022.023982

Abstract Uncontrollable growth of cells may lead to brain tumors and may cause permanent damages to the brain or even death. To make early diagnosis and treatment, identifying the position and size of tumors is identified as a tedious and troublesome problem among the existing computer-aided diagnosis systems. Moreover, the progression of tumors may vary among the patients with respect to shape, location, and volume. Therefore, to effectively classify and diagnose the brain tumor images according to severity stages follows the sequence of processing such as pre-processing, segmentation, feature extraction, and classification techniques to carrying out the appropriate treatment. To enhance… More >

G. Anitha^1,*, S. Baghavathi Priya²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 87-103, 2022, DOI:10.32604/csse.2022.020361

Abstract Human fall detection plays a vital part in the design of sensor based alarming system, aid physical therapists not only to lessen after fall effect and also to save human life. Accurate and timely identification can offer quick medical services to the injured people and prevent from serious consequences. Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments. At present times, deep learning (DL) models particularly convolutional neural networks (CNNs) have gained much importance in the fall detection tasks. With this motivation, this paper presents a new vision based elderly fall event detection using… More >

Hanan A. Hosni Mahmoud, Norah S. Alghamdi, Amal H. Alharbi^*

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1423-1434, 2022, DOI:10.32604/iasc.2022.020586

Abstract COVID-19 pandemic outbreak became one of the serious threats to humans. As there is no cure yet for this virus, we have to control the spread of Coronavirus through precautions. One of the effective precautions as announced by the World Health Organization is mask wearing. Surveillance systems in crowded places can lead to detection of people wearing masks. Therefore, it is highly urgent for computerized mask detection methods that can operate in real-time. As for now, most countries demand mask-wearing in public places to avoid the spreading of this virus. In this paper, we are presenting an object detection technique… More >

Batyrkhan Omarov^1,2,*, Nurbek Saparkhojayev², Shyrynkyz Shekerbekova³, Oxana Akhmetova¹, Meruert Sakypbekova¹, Guldina Kamalova³, Zhanna Alimzhanova¹, Lyailya Tukenova³, Zhadyra Akanova⁴

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2815-2833, 2022, DOI:10.32604/cmc.2022.019246

Abstract Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of… More >

Reem K. Alkhodhairi¹, Shahad R. Aljalhami¹, Norah K. Rusayni¹, Jowharah F. Alshobaili¹, Amal A. Al-Shargabi^1,*, Abdulatif Alabdulatif²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3215-3233, 2021, DOI:10.32604/cmc.2021.016881

Abstract Currently, Bitcoin is the world’s most popular cryptocurrency. The price of Bitcoin is extremely volatile, which can be described as high-benefit and high-risk. To minimize the risk involved, a means of more accurately predicting the Bitcoin price is required. Most of the existing studies of Bitcoin prediction are based on historical (i.e., benchmark) data, without considering the real-time (i.e., live) data. To mitigate the issue of price volatility and achieve more precise outcomes, this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open, high, low, and close (OHLC)—prices. Seeking a better prediction model, the present study… More >

Ruilong Chen¹, Guangfu Zeng¹, Ke Wang², Lei Luo^1,*, Zhiping Cai¹

Journal on Internet of Things, Vol.2, No.2, pp. 55-64, 2020, DOI:10.32604/jiot.2020.09814

Abstract Smoking is the main reason for fire disaster and pollution in petrol station, construction site and warehouse. Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of smoking in unmanned scenarios. With the developments of closed circuit television (CCTV) system, vision-based methods for object detection, mostly driven by deep learning techniques, were introduced recently. However, the massive GPU computing hardware required by the deep learning algorithm made these methods hard to be deployed. This paper aims at solving the smoking detection problem on edge and proposes the solution that has fast detection speed,… More >