Fatma S. Alrayes¹, Nuha Alshuqayran², Mohamed K Nour³, Mesfer Al Duhayyim^4,*, Abdullah Mohamed⁵, Amgad Atta Abdelmageed Mohammed⁶, Gouse Pasha Mohammed⁶, Ishfaq Yaseen⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6737-6753, 2023, DOI:10.32604/cmc.2023.032591 - 28 December 2022

Abstract Recently, Internet of Things (IoT) devices have developed at a faster rate and utilization of devices gets considerably increased in day to day lives. Despite the benefits of IoT devices, security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security operation. Consequently, IoT devices are vulnerable to different kinds of attacks. A single attack on networking system/device could result in considerable data to data security and privacy. But the emergence of artificial intelligence (AI) techniques can be exploited for attack detection and classification… More >

El-Sayed M. El-kenawy^1,2, Abdelaziz A. Abdelhamid^3,4, Abdelhameed Ibrahim⁵, Seyedali Mirjalili^6,7, Nima Khodadad⁸, Mona A. Al duailij⁹, Amel Ali Alhussan^9,*, Doaa Sami Khafaga⁹

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1917-1934, 2023, DOI:10.32604/csse.2023.032497 - 03 November 2022

Abstract Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields. Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), genetic algorithm (GA), and gravitational search algorithm (GSA). According to the literature, no one metaheuristic optimization algorithm can handle all present optimization problems. Hence novel optimization methodologies are still needed. The Al-Biruni earth radius (BER) search optimization algorithm is proposed in this paper. The More >

Doaa Sami Khafaga¹, Amel Ali Alhussan^1,*, Abdelaziz A. Abdelhamid^2,3, Abdelhameed Ibrahim⁴, Mohamed Saber⁵, El-Sayed M. El-kenawy^6,7

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1469-1482, 2023, DOI:10.32604/csse.2023.031943 - 03 November 2022

Abstract Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of… More >

K. Karthikeyan^1,*, Liyakathunisa², Eman Aljohani², Thavavel Vaiyapuri³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1641-1654, 2023, DOI:10.32604/csse.2023.031582 - 03 November 2022

Abstract Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based… More >

Mashael M Asiri¹, Heba G. Mohamed², Mohamed K Nour³, Mesfer Al Duhayyim^4,*, Amira Sayed A. Aziz⁵, Abdelwahed Motwakel⁶, Abu Sarwar Zamani⁶, Mohamed I. Eldesouki⁷

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1679-1694, 2023, DOI:10.32604/csse.2023.031063 - 03 November 2022

Abstract Due to exponential increase in smart resource limited devices and high speed communication technologies, Internet of Things (IoT) have received significant attention in different application areas. However, IoT environment is highly susceptible to cyber-attacks because of memory, processing, and communication restrictions. Since traditional models are not adequate for accomplishing security in the IoT environment, the recent developments of deep learning (DL) models find beneficial. This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection (HMFS-SDLCAD) model. The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks… More >

Reem Alkanhel^1,*, Doaa Sami Khafaga², El-Sayed M. El-kenawy³, Abdelaziz A. Abdelhamid^4,5, Abdelhameed Ibrahim⁶, Rashid Amin⁷, Mostafa Abotaleb⁸, B. M. El-den⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2695-2709, 2023, DOI:10.32604/cmc.2023.033153 - 31 October 2022

Abstract The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That’s why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper… More >

Radwa Marzouk¹, Eatedal Alabdulkreem², Mohamed K. Nour³, Mesfer Al Duhayyim^4,*, Mahmoud Othman⁵, Abu Sarwar Zamani⁶, Ishfaq Yaseen⁶, Abdelwahed Motwakel⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4435-4451, 2023, DOI:10.32604/cmc.2023.033091 - 31 October 2022

Abstract The recent developments in Multimedia Internet of Things (MIoT) devices, empowered with Natural Language Processing (NLP) model, seem to be a promising future of smart devices. It plays an important role in industrial models such as speech understanding, emotion detection, home automation, and so on. If an image needs to be captioned, then the objects in that image, its actions and connections, and any silent feature that remains under-projected or missing from the images should be identified. The aim of the image captioning process is to generate a caption for image. In next step, the… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,3, Faten Khalid Karim^1,*, Mostafa Abotaleb⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, D. L. Elsheweikh⁸

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4531-4545, 2023, DOI:10.32604/cmc.2023.033042 - 31 October 2022

Abstract Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2ⁿ possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristics-based feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy², Fadwa Alrowais^1,*, Sunil Kumar³, Abdelhameed Ibrahim⁴, Abdelaziz A. Abdelhamid^5,6

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4027-4041, 2023, DOI:10.32604/cmc.2023.033039 - 31 October 2022

Abstract In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2ⁿ potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead… More >

Mohammad Alamgeer¹, Amal Al-Rasheed², Ahmad Alhindi³, Manar Ahmed Hamza^4,*, Abdelwahed Motwakel⁴, Mohamed I. Eldesouki⁵

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2725-2738, 2023, DOI:10.32604/cmc.2023.029163 - 31 October 2022

Abstract Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In More >