Maryam Jamal¹, Nazir Ahmad Zafar², Atta-ur-Rahman^3,*, Dhiaa Musleh³, Mohammed A. Gollapalli⁴, Sghaier Chabani⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3331-3348, 2022, DOI:10.32604/cmc.2022.025205

Abstract The formal modeling and verification of aircraft takeoff is a challenge because it is a complex safety-critical operation. The task of aircraft takeoff is distributed amongst various computer-based controllers, however, with the growing malicious threats a secure communication between aircraft and controllers becomes highly important. This research serves as a starting point for integration of BB84 quantum protocol with petri nets for secure modeling and verification of takeoff procedure. The integrated model combines the BB84 quantum cryptographic protocol with powerful verification tool support offered by petri nets. To model certain important properties of BB84, a new variant of petri nets… More >

Fadwa Alrowais¹, Ahmed S. Almasoud², Radwa Marzouk³, Fahd N. Al-Wesabi^4,5, Anwer Mustafa Hilal^6,*, Mohammed Rizwanullah⁶, Abdelwahed Motwakel⁶, Ishfaq Yaseen⁶

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2783-2795, 2022, DOI:10.32604/cmc.2022.025204

Abstract Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling… More >

Ala Saleh Alluhaidan¹, Masoud Alajmi², Fahd N. Al-Wesabi^3,4, Anwer Mustafa Hilal⁵, Manar Ahmed Hamza^5,*, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2713-2727, 2022, DOI:10.32604/cmc.2022.025202

Abstract Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based feature extractor is derived to… More >

Mousa Al-Akhras^1,2,*, Zainab Darwish², Samer Atawneh¹, Mohamed Habib^1,3

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3719-3749, 2022, DOI:10.32604/cmc.2022.025196

Abstract Association rules’ learning is a machine learning method used in finding underlying associations in large datasets. Whether intentionally or unintentionally present, noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy. This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier. Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning. This paper utilizes them to remove noise from the dataset before training the association rules classifier. Extensive experiments were conducted to assess the accuracy of association rules with different instance reduction techniques, namely:… More >

Anuchit Aurairat, Boonyang Plangklang^*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2375-2395, 2022, DOI:10.32604/cmc.2022.025122

Abstract Nowadays, the single state inverter for the grid-connected photovoltaic (PV) systems is becoming more and more popular as they can reduce circuit complexity resulting in less power losses of the inverter. This paper focuses on the use of model predictive control (MPC) to control a 3-phase and 2-level single-state grid-connected inverter in order to regulate the PV maximum power point (MPP). The algorithm of MPC scheme was done to measure the simultaneous current signal including predicting the next sampling current flow. The reference current (I_d*) was used to control the distribution of electrical power from the solar cell to the… More >

Dahyeon Kim¹, Chulsu Kim², Junho Ahn^1,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2765-2781, 2022, DOI:10.32604/cmc.2022.025116

Abstract When firefighters are engaged in search and rescue missions inside a building at a risk of collapse, they have difficulty in field command and rescue because they can only simply monitor the situation inside the building utilizing old building drawings or robots. To propose an efficient solution for fast search and rescue work of firefighters, this study investigates the generation of up-to-date digital maps for disaster sites by tracking the collapse situation, and identifying the information of obstacles which are risk factors, using an artificial intelligence algorithm based on low-cost robots. Our research separates the floor by using the mask… More >

Mustafa Al-Fayoumi¹, Mohammad Al-Fawa’reh², Shadi Nashwan^3,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3091-3111, 2022, DOI:10.32604/cmc.2022.025103

Abstract The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet, as many employees have transitioned to working from home. Furthermore, with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network (VPN) or Tor Browser (dark web) to keep their data privacy and hidden, network traffic encryption is rapidly becoming a universal approach. This affects and complicates the quality of service (QoS), traffic monitoring, and network security provided by Internet Service Providers (ISPs), particularly for… More >

Rajesh Kumar Kaushal¹, Naveen Kumar¹, Vinay Kukreja¹, Fahd S. Alharithi², Ahmed H. Almulihi², Arturo Ortega Mansilla^3,4, Shikha Rani^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2565-2579, 2022, DOI:10.32604/cmc.2022.025049

Abstract The probability of medical staff to get affected from COVID19 is much higher due to their working environment which is more exposed to infectious diseases. So, as a preventive measure the body temperature monitoring of medical staff at regular intervals is highly recommended. Infrared temperature sensing guns have proved its effectiveness and therefore such devices are used to monitor the body temperature. These devices are either used on hands or forehead. As a result, there are many issues in monitoring the temperature of frontline healthcare professionals. Firstly, these healthcare professionals keep wearing PPE (Personal Protective Equipment) kits during working hours… More >

Shaveta Gupta¹, Dinesh Grover², Ahmad Ali AlZubi^3,*, Nimit Sachdeva⁴, Mirza Waqar Baig⁵, Jimmy Singla⁶

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2665-2682, 2022, DOI:10.32604/cmc.2022.025048

Abstract With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS attacks. However,… More >

Nighat Parveen Shaikh^*, Mumtaz Hussain Mahar

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3145-3158, 2022, DOI:10.32604/cmc.2022.024843

Abstract Recent patterns of human sentiments are highly influenced by emoji based sentiments (EBS). Social media users are widely using emoji based sentiments (EBS) in between text messages, tweets and posts. Although tiny pictures of emoji contains sufficient information to be considered for construction of classification model; but due to the wide range of dissimilar, heterogynous and complex patterns of emoji with similar meanings (SM) have become one of the significant research areas of machine vision. This paper proposes an approach to provide meticulous assistance to social media application (SMA) users to classify the EBS sentiments. Proposed methodology consists upon three… More >