A. Chinnappa^*, C. Vijayakumaran

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4017-4031, 2022, DOI:10.32604/cmc.2022.027858

Abstract Internet of Things (IoT) allows several low resources and controlled devices to interconnect, calculate processes and make decisions in the communication network. In the heterogeneous environment for IoT devices, several challenging issues such as energy, storage, efficiency, and security. The design of encryption techniques enables the transmission of the data in the IoT environment in a secured way. The proper selection of optimal keys helps to boost the encryption performance. With this motivation, the study presents a signcryption with quantum chaotic krill herd algorithm for secured data transmission (SCQCKH-SDT) in IoT environment. The proposed SCQCKH-SDT technique aims to effectively encrypts… More >

Huiping Jiang^1,*, Demeng Wu¹, Xingqun Tang¹, Zhongjie Li¹, Wenbo Wu²

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2697-2712, 2022, DOI:10.32604/cmc.2022.027856

Abstract Emotions serve various functions. The traditional emotion recognition methods are based primarily on readily accessible facial expressions, gestures, and voice signals. However, it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications. Electroencephalogram (EEG) signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage. Although EEG signals are commonly used in current emotional recognition research, the accuracy is low when using traditional methods. Therefore, this study presented an optimized hybrid pattern with an attention mechanism (FFT_CLA) for EEG emotional recognition. First, the… More >

Rami Sameer Ahmad Al Kloub^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2801-2820, 2022, DOI:10.32604/cmc.2022.027847

Abstract Mainland China has a poor distribution of meteorological stations. Existing models’ estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature, and low for relative humidity and wind speed (few studies reported). This study compared the typical generalized additive model (GAM) and autoencoder-based residual neural network (hereafter, residual network for short) in terms of predicting three meteorological parameters, namely air temperature, relative humidity, and wind speed, using data from 824 monitoring stations across China’s mainland in 2015. The performance of the two models was assessed using a 10-fold cross-validation procedure. The air temperature models employ basic… More >

Sungan Yoon, Jeongho Cho^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2821-2834, 2022, DOI:10.32604/cmc.2022.027840

Abstract Road potholes can cause serious social issues, such as unexpected damages to vehicles and traffic accidents. For efficient road management, technologies that quickly find potholes are required, and thus researches on such technologies have been conducted actively. The three-dimensional (3D) reconstruction method has relatively high accuracy and can be used in practice but it has limited application owing to its long data processing time and high sensor maintenance cost. The two-dimensional (2D) vision method has the advantage of inexpensive and easy application of sensor. Recently, although the 2D vision method using the convolutional neural network (CNN) has shown improved pothole… More >

Muhammad Fahad Khan^1,2,*, Khalid Saleem¹, Mohammed Alotaibi³, Mohammad Mazyad Hazzazi⁴, Eid Rehman², Aaqif Afzaal Abbasi², Muhammad Asif Gondal⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2679-2696, 2022, DOI:10.32604/cmc.2022.027655

Abstract Internet of Things is an ecosystem of interconnected devices that are accessible through the internet. The recent research focuses on adding more smartness and intelligence to these edge devices. This makes them susceptible to various kinds of security threats. These edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the field. In this regard, block cipher has been one of the most reliable options through which data security is accomplished. The strength of block encryption algorithms against different attacks is dependent on its nonlinear primitive which is called Substitution Boxes. For the… More >

Mofreh A. Hogo^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2947-2966, 2022, DOI:10.32604/cmc.2022.027300

Abstract Data size plays a significant role in the design and the performance of data mining models. A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy. Features selection algorithms aim at selecting the best features and eliminating unnecessary ones, which in turn simplifies the structure of the data mining model as well as increases its performance. This paper introduces a robust features selection algorithm, named Features Ranking Voting Algorithm FRV. It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly; based… More >

Dhivya Chandrasekaran^*, Vijay Mago

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2257-2274, 2022, DOI:10.32604/cmc.2022.027236

Abstract Student mobility or academic mobility involves students moving between institutions during their post-secondary education, and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student. In general, this process involves domain experts comparing the learning outcomes of the courses, to decide on offering transfer credits to the incoming students. This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity. The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing (NLP) to effectively automate… More >

Tariq Ahamed Ahanger^1,*, Abdulaziz Aldaej¹, Mohammed Atiquzzaman², Imdad Ullah¹, Mohammed Yousuf Uddin¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3199-3217, 2022, DOI:10.32604/cmc.2022.027212

Abstract DDoS attacks in the Internet of Things (IoT) technology have increased significantly due to its spread adoption in different industrial domains. The purpose of the current research is to propose a novel technique for detecting botnet attacks in user-oriented IoT environments. Conspicuously, an attack identification technique inspired by Recurrent Neural networks and Bidirectional Long Short Term Memory (BLRNN) is presented using a unique Deep Learning (DL) technique. For text identification and translation of attack data segments into tokenized form, word embedding is employed. The performance analysis of the presented technique is performed in comparison to the state-of-the-art DL techniques. Specifically,… More >

Wenzheng Yu¹, Mingxuan Zhu¹, Li Shao¹, Yanbo Shen^2,3,*, Haitao Liu¹, Tianliang Chen¹, Hanxiaoya Zhang⁴

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3771-3793, 2022, DOI:10.32604/cmc.2022.027094

Abstract Qinghai Lake Basin area in Gangcha county is selected as the study area in terms of desertification change features in this paper. Based on the remote sensing (RS) and global positioning system (GPS) technologies, the desertification information range from 1989 to 2014 in the study area is extracted. Using the method of the decision tree, the desertification in the research area is been divided into four grades including mild desertification, moderate desertification, severe desertification and serious desertification. The change characteristics of desertification in the study area were analyzed in detail, which showed that the desertification in the study area experienced… More >

Areej A. Malibari¹, Reem M. Alshehri², Fahd N. Al-Wesabi³, Noha Negm³, Mesfer Al Duhayyim⁴, Anwer Mustafa Hilal^5,*, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4277-4290, 2022, DOI:10.32604/cmc.2022.027030

Abstract In bioinformatics applications, examination of microarray data has received significant interest to diagnose diseases. Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes. Microarray data classification incorporates multiple disciplines such as bioinformatics, machine learning (ML), data science, and pattern classification. This paper designs an optimal deep neural network based microarray gene expression classification (ODNN-MGEC) model for bioinformatics applications. The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale. Besides, improved fruit fly optimization (IFFO) based feature selection technique is used… More >