Muath Jarrah¹, Hussam Al Hamadi^4,*, Ahmed Abu-Khadrah², Taher M. Ghazal^1,3

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 19-33, 2023, DOI:10.32604/cmc.2023.032775 - 08 June 2023

Abstract The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. There is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising rapidly. The IoMT is a novel reality transforming our daily lives. It can renovate modern healthcare by delivering a more personalized, protective, and collaborative approach to care. However, More >

Atif Ikram^1,2,*, Masita Abdul Jalil¹, Amir Bin Ngah¹, Saqib Raza⁶, Ahmad Salman Khan³, Yasir Mahmood^3,4, Nazri Kama⁴, Azri Azmi⁴, Assad Alzayed⁵

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1871-1886, 2023, DOI:10.32604/cmc.2023.030818 - 22 September 2022

Abstract Software maintenance is the process of fixing, modifying, and improving software deliverables after they are delivered to the client. Clients can benefit from offshore software maintenance outsourcing (OSMO) in different ways, including time savings, cost savings, and improving the software quality and value. One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’ projects. The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients. The projects belong to… More >

Muhammad Adnan Khan, Taher M. Ghazal^2,3, Sang-Woong Lee^1,*, Abdur Rehman⁴

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3399-3413, 2022, DOI:10.32604/cmc.2022.020173 - 27 September 2021

Abstract In recent years, the infrastructure of Wireless Internet of Sensor Networks (WIoSNs) has been more complicated owing to developments in the internet and devices’ connectivity. To effectively prepare, control, hold and optimize wireless sensor networks, a better assessment needs to be conducted. The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis. This study investigates the methodology of Real Time Sequential Deep Extreme Learning Machine (RTS-DELM) implemented to wireless Internet of Things (IoT) enabled sensor networks for the detection of… More >

Aaqib Inam^1,*, Zhuli¹, Ayesha Sarwar¹, Salah-ud-din², Ayesha Atta³, Iftikhar Naaseer⁴, Shahan Yamin Siddiqui^5,6, Muhammad Adnan Khan⁷

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 701-712, 2021, DOI:10.32604/iasc.2021.014235 - 01 March 2021

Abstract The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for… More >

Fahd Alhaidari¹, Sultan H. Almotiri², Mohammed A.Al Ghamdi², Muhammad Adnan Khan^3,*, Abdur Rehman⁴, Sagheer Abbas⁴, Khalid Masood Khan³, Atta-ur-Rahman⁵

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1269-1285, 2021, DOI:10.32604/cmc.2021.013303 - 12 January 2021

Abstract In recent years, the infrastructure, instruments, and resources of network systems are becoming more complex and heterogeneous, with the rapid development of current internet and mobile communication technologies. In order to efficaciously prepare, control, hold and optimize networking systems, greater intelligence needs to be deployed. However, due to the inherently dispensed characteristic of conventional networks, Machine Learning (ML) techniques are hard to implement and deployed to govern and operate networks. Software-Defined Networking (SDN) brings us new possibilities to offer intelligence in the networks. SDN’s characteristics (e.g., logically centralized control, global network view, software-based site visitor… More >

Amir Haider¹, Muhammad Adnan Khan², Abdur Rehman³, Muhib Ur Rahman⁴, Hyung Seok Kim^1,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1785-1798, 2021, DOI:10.32604/cmc.2020.013910 - 26 November 2020

Abstract In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in More >

Muhammad Adnan Khan^1,*, Abdur Rehman², Khalid Masood Khan¹, Mohammed A. Al Ghamdi³, Sultan H. Almotiri³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 467-480, 2021, DOI:10.32604/cmc.2020.013121 - 30 October 2020

Abstract Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations… More >

Dildar Hussain¹, Muhammad Adnan Khan^2,*, Sagheer Abbas³, Rizwan Ali Naqvi⁴, Muhammad Faheem Mushtaq⁵, Abdur Rehman³, Afrozah Nadeem²

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 141-156, 2021, DOI:10.32604/cmc.2020.013458 - 30 October 2020

Abstract The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information. Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach. In this article, we discuss recent progress of applying machine learning into vehicle networks for… More >

Muhammad Adnan Khan^{1, *}, Sagheer Abbas², Khalid Masood Khan¹, Mohammad A. Al Ghamdi³, Abdur Rehman²

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1329-1342, 2020, DOI:10.32604/cmc.2020.011155 - 30 June 2020

Abstract An epidemic is a quick and widespread disease that threatens many lives and damages the economy. The epidemic lifetime should be accurate so that timely and remedial steps are determined. These include the closing of borders schools, suspension of community and commuting services. The forecast of an outbreak effectively is a very necessary but difficult task. A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available. This work proposes and examines a prediction model based on a deep extreme learning machine… More >