Alaa Omran Almagrabi^1,*, A. K. Bashir²

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3649-3667, 2021, DOI:10.32604/cmc.2021.015056

Abstract The design of distributed ledger, Asymmetric Key Algorithm (AKA) blockchain systems, is prominent in administering security and access control in various real-time services and applications. The assimilation of blockchain systems leverages the reliable access and secure service provisioning of the services. However, the distributed ledger technology’s access control and chained decisions are defaced by pervasive and service unawareness. It results in degrading security through unattended access control for limited-service users. In this article, a service-aware access control procedure (SACP) is introduced to address the afore-mentioned issue. The proposed SACP defines attended access control for all the service session by identifying… More >

Sharaf J. Malebary¹, Yaser Daanial Khan^2,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2863-2881, 2021, DOI:10.32604/cmc.2021.015041

Abstract With the advancement in cellular biology, the use of antimicrobial peptides (AMPs) against many drug-resistant pathogens has increased. AMPs have a broad range of activity and can work as antibacterial, antifungal, antiviral, and sometimes even as anticancer peptides. The traditional methods of distinguishing AMPs from non-AMPs are based only on wet-lab experiments. Such experiments are both time-consuming and expensive. With the recent development in bioinformatics more and more researchers are contributing their effort to apply computational models to such problems. This study proposes a prediction algorithm for classifying AMPs and distinguishing between AMPs and non-AMPs. The proposed methodology uses machine… More >

Muhammad Attique Khan¹, Abdul Majid¹, Nazar Hussain¹, Majed Alhaisoni², Yu-Dong Zhang³, Seifedine Kadry⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3381-3399, 2021, DOI:10.32604/cmc.2021.014983

Abstract In the area of medical image processing, stomach cancer is one of the most important cancers which need to be diagnose at the early stage. In this paper, an optimized deep learning method is presented for multiple stomach disease classification. The proposed method work in few important steps—preprocessing using the fusion of filtering images along with Ant Colony Optimization (ACO), deep transfer learning-based features extraction, optimization of deep extracted features using nature-inspired algorithms, and finally fusion of optimal vectors and classification using Multi-Layered Perceptron Neural Network (MLNN). In the feature extraction step, pre-trained Inception V3 is utilized and retrained on… More >

Neelum Noreen^1,*, Sellapan Palaniappan¹, Abdul Qayyum², Iftikhar Ahmad³, Madini O. Alassafi³

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3967-3982, 2021, DOI:10.32604/cmc.2021.014158

Abstract Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges of low accuracy and high… More >

Mustafa M. M. Al Qudah, Ahmad S. A. Mohamed^*, Syaheerah L. Lutfi

Computer Systems Science and Engineering, Vol.37, No.1, pp. 47-62, 2021, DOI:10.32604/csse.2021.015222

Abstract The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects due to its ability to measure the facial transient temperature, which is correlated with human affects and robustness against illumination changes. Therefore, studies have increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation of light conditions and revealing original human affect. Moreover, the thermal-based imaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and… More >

Neha Srivastava^1,*, Akshay Shrivastav², Rajeev Singh³, Mohammed Abohashrh⁴, K. R. Srivastava¹, Safia Irfan⁵, Manish Srivastava¹, P. K. Mishra¹, Vijai Kumar Gupta⁶, Vijay Kumar Thakur^6,*

Journal of Renewable Materials, Vol.9, No.4, pp. 615-636, 2021, DOI:10.32604/jrm.2021.014374

Abstract Increased environmental pollution due to the organic wastes over the world is one of the most burning issues. These organic wastes lie under the category of biodegradable waste and can be effectively degraded from their complex compound into simple one by the action of microbes or other living organisms. Moreover, lignocellulosic biomass is a major part of the biodegradable waste and belongs to the group of renewable energy source, which can be very effective for bioenergy production. Biomasses are made up of different compounds such as cellulose, hemicelluloses, lignin and protein. Apart from these components, based on the structural analysis… More >

J. Banumathi¹, A. Muthumari², S. Dhanasekaran³, S. Rajasekaran⁴, Irina V. Pustokhina⁵, Denis A. Pustokhin⁶, K. Shankar^7,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2393-2407, 2021, DOI:10.32604/cmc.2021.015605

Abstract Due to the advancements in remote sensing technologies, the generation of hyperspectral imagery (HSI) gets significantly increased. Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis. The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials. Presently, deep learning (DL) models particularly, convolutional neural networks (CNNs) become useful for HSI classification owing to the effective feature representation and high performance. In this view, this paper introduces a new DL based Xception model for HSI analysis and classification, called Xcep-HSIC model. Initially, the… More >

Gulzar Ahmad¹, Saad Alanazi², Madallah Alruwaili², Fahad Ahmad^3,6, Muhammad Adnan Khan^4,*, Sagheer Abbas¹, Nadia Tabassum⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2585-2600, 2021, DOI:10.32604/cmc.2021.015080

Abstract Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect lives. Operators monitor CCTV; however, it is difficult for a single person to monitor the actions of multiple people at one time. Consequently, there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study, we have designed a new Intelligent Ammunition Detection and Classification (IADC) system using… More >

Jae Dong Lee^1,2, Hyo Soung Cha¹, Shailendra Rathore², Jong Hyuk Park^2,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1537-1553, 2021, DOI:10.32604/cmc.2021.014774

Abstract In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture… More >

Yasemin Gültepe^*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2015-2028, 2021, DOI:10.32604/cmc.2021.014631

Abstract In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine learning is a branch of artificial intelligence that allows computers to quickly identify patterns within complex and large datasets by learning from existing data. Machine-learning techniques have been improving rapidly and are increasingly used by medical professionals for the successful classification and diagnosis of early-stage disease. They are widely used in cancer diagnosis. In particular, machine learning has been used in the diagnosis of lung cancer… More >