Abdelaziz A. Abdelhamid^1,3,*, Sultan R. Alotaibi²

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2305-2321, 2022, DOI:10.32604/cmc.2022.025739 - 29 March 2022

Abstract The design of microstrip antennas is a complex and time-consuming process, especially the step of searching for the best design parameters. Meanwhile, the performance of microstrip antennas can be improved using metamaterial, which results in a new class of antennas called metamaterial antenna. Several parameters affect the radiation loss and quality factor of this class of antennas, such as the antenna size. Recently, the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning, which presents a better alternative to simulation tools and trial-and-error processes. However, the prediction accuracy depends… More >

Mubashir Ali¹, Anees Baqir², Hafiz Husnain Raza Sherazi^3,*, Asad Hussain⁴, Asma Hassan Alshehri⁵, Muhammad Ali Imran⁶

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2411-2427, 2022, DOI:10.32604/cmc.2022.024704 - 29 March 2022

Abstract With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades, social media platforms (such as Facebook, Twitter, and Instagram) have consumed a large proportion of time in our daily lives. People tend to stay alive on their social media with recent updates, as it has become the primary source of interaction within social circles. Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities. Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues… More >

Mohammad Hafiz Mohd Yusof^1,*, Abdullah Mohd Zin², Nurhizam Safie Mohd Satar²

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2445-2466, 2022, DOI:10.32604/cmc.2022.023571 - 29 March 2022

Abstract Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had… More >

Salman Khan¹, Mukhtaj Khan^1,2, Nadeem Iqbal¹, Mohd Amiruddin Abd Rahman^3,*, Muhammad Khalis Abdul Karim³

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2243-2258, 2022, DOI:10.32604/cmc.2022.022901 - 29 March 2022

Abstract Piwi-interacting Ribonucleic acids (piRNAs) molecule is a well-known subclass of small non-coding RNA molecules that are mainly responsible for maintaining genome integrity, regulating gene expression, and germline stem cell maintenance by suppressing transposon elements. The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development. Due to the vital roles of the piRNA in computational biology, the identification of piRNAs has become an important area of research in computational biology. This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods. The More >

Yu Ren¹, Jiaohua Qin^1,*, Yun Tan¹, Neal N. Xiong²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1439-1453, 2022, DOI:10.32604/iasc.2022.025485 - 24 March 2022

Abstract In the field of reversible data hiding in encrypted images (RDH-EI), predict an image effectively and embed a message into the image with lower distortion are two crucial aspects. However, due to the linear regression prediction being sensitive to outliers, it is a challenge to improve the accuracy of predictions. To address this problem, this paper proposes an RDH-EI scheme based on adaptive prediction-error label map. In the prediction stage, an adaptive threshold estimation algorithm based on local complexity is proposed. Then, the pixels selection method based on gradient of image is designed to train More >

Vineetha Mathai^*, P. Indumathi

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1729-1746, 2022, DOI:10.32604/iasc.2022.024234 - 24 March 2022

Abstract An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings. From the simulation, it can be inferred that Weibull fading outperforms all the other… More >

J. Faritha Banu^1,*, S. B. Rajeshwari², Jagadish S. Kallimani², S. Vasanthi³, Ahmed Mateen Buttar⁴, M. Sangeetha⁵, Sanjay Bhargava⁶

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1393-1405, 2022, DOI:10.32604/iasc.2022.024176 - 24 March 2022

Abstract The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, noisy, and chaotic data, stock market prediction is… More >

R. Saravana Ram¹, K. Venkatachalam², Mehedi Masud³, Mohamed Abouhawwash^4,5,*

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1639-1652, 2022, DOI:10.32604/iasc.2022.023962 - 24 March 2022

Abstract In current scenario, Wireless Sensor Networks (WSNs) has been applied on variety of applications such as targets tracking, natural resources investigation, monitoring on unapproachable place and so on. Through the sensor nodes, the information for the applications is gathered and transferred. The physical coordination of these sensor nodes is determined, and it is called as localization. The WSN localization methods are studied widely for recent research with the study of small proportion of the sensor node called anchor nodes and their positions are determined through the GPS devices. Sometimes sensor nodes can be a IoT… More >

Jie Zhang, Jiaxiang Zhao^*, Pengchang Xu

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 905-917, 2022, DOI:10.32604/cmes.2022.019097 - 14 March 2022

Abstract Accurately, reliably and rapidly identifying intrinsically disordered (IDPs) proteins is essential as they often play important roles in various human diseases; moreover, they are related to numerous important biological activities. However, current computational methods have yet to develop a network that is sufficiently deep to make predictions about IDPs and demonstrate an improvement in performance. During this study, we constructed a deep neural network that consisted of five identical variant models, ResNet18, combined with an MLP network, for classification. Resnet18 was applied for the first time as a deep model for predicting IDPs, which allowed More >

Sridharan Kannan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 677-694, 2022, DOI:10.32604/cmes.2022.018580 - 14 March 2022

Abstract With the worldwide analysis, heart disease is considered a significant threat and extensively increases the mortality rate. Thus, the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System (CDSS). Generally, CDSS is used to predict the individuals’ heart disease and periodically update the condition of the patients. This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers. Here, the Synthetic Over-sampling prediction model is integrated with the… More >