Muhammad Umair^1,*, Zafar Saeed¹, Faisal Saeed², Hiba Ishtiaq¹, Muhammad Zubair¹, Hala Abdel Hameed^3,4

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5431-5446, 2023, DOI:10.32604/cmc.2023.033884 - 28 December 2022

Abstract As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing, the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure,… More >

Ibrar Amin¹, Saima Hassan¹, Samir Brahim Belhaouari^2,*, Muhammad Hamza Azam³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6335-6349, 2023, DOI:10.32604/cmc.2023.033860 - 28 December 2022

Abstract Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic More >

Farah Batool¹, Abdul Rehman², Dongsun Kim^2,*, Assad Abbas¹, Raheel Nawaz³, Tahir Mustafa Madni¹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6535-6553, 2023, DOI:10.32604/cmc.2023.033832 - 28 December 2022

Abstract The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information. Primarily, the proposed approach minimizes the network size and eliminates undesirable connections. For that, the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer. Therefore, the proposed approach discards the nodes having a rank (α) lesser than 0.5 to reduce the network complexity. α is the variable value represents the rank of each node that varies between 0 to 1. Node with… More >

Dong Huang^1,*, Sean Xu²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5319-5329, 2023, DOI:10.32604/cmc.2023.033798 - 28 December 2022

Abstract Most traditional trust computing models in E-commerce do not take the transaction frequency among participating entities into consideration, which makes it easy for one party of the transaction to obtain a high trust value in a short time, and brings many disadvantages, uncertainties and even attacks. To solve this problem, a transaction frequency based trust is proposed in this study. The proposed method is composed of two parts. The first part is built on the classic Bayes analysis based trust models which are ease of computing for the E-commerce system. The second part is the More >

Ameen Bin Obadi¹, Alaa El-Din Hussein², Samir Salem Al-Bawri^3,4,*, Kabir Hossain⁵, Abdullah Abdulhameed⁴, Muzammil Jusoh^1,6,7, Thennarasan Sabapathy^1,6, Ahmed Jamal Abdullah Al-Gburi⁸, Mahmoud A. Albreem⁹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5885-5900, 2023, DOI:10.32604/cmc.2023.033789 - 28 December 2022

Abstract This article presents an integrated current mode configurable analog block (CAB) system for field-programmable analog array (FPAA). The proposed architecture is based on the complementary metal-oxide semiconductor (CMOS) transistor level design where MOSFET transistors operating in the saturation region are adopted. The proposed CAB architecture is designed to implement six of the widely used current mode operations in analog processing systems: addition, subtraction, integration, multiplication, division, and pass operation. The functionality of the proposed CAB is demonstrated through these six operations, where each operation is chosen based on the user’s selection in the CAB interface… More >

Manahil Waheed¹, Samia Allaoua Chelloug^2,*, Mohammad Shorfuzzaman³, Abdulmajeed Alsufyani³, Ahmad Jalal¹, Khaled Alnowaiser⁴, Jeongmin Park⁵

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5853-5870, 2023, DOI:10.32604/cmc.2023.033769 - 28 December 2022

Abstract Identifying human actions and interactions finds its use in many areas, such as security, surveillance, assisted living, patient monitoring, rehabilitation, sports, and e-learning. This wide range of applications has attracted many researchers to this field. Inspired by the existing recognition systems, this paper proposes a new and efficient human-object interaction recognition (HOIR) model which is based on modeling human pose and scene feature information. There are different aspects involved in an interaction, including the humans, the objects, the various body parts of the human, and the background scene. The main objectives of this research include… More >

Vivian Lee Lay Shan, Gan Keng Hoon^*, Tan Tien Ping, Rosni Abdullah

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4801-4818, 2023, DOI:10.32604/cmc.2023.033752 - 28 December 2022

Abstract Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service. Existing studies heavily rely on sentiment classification methods that require fully annotated inputs. However, there is limited labelled text available, making the acquirement process of the fully annotated input costly and labour-intensive. Lately, semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods. Nevertheless, some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training. Literature also shows that not all unlabelled instances are equally… More >

Afia Zafar¹, Muhammad Aamir², Nazri Mohd Nawi¹, Ali Arshad³, Saman Riaz³, Abdulrahman Alruban^4,*, Ashit Kumar Dutta⁵, Badr Almutairi⁶, Sultan Almotairi^7,8

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5641-5661, 2023, DOI:10.32604/cmc.2023.033733 - 28 December 2022

Abstract In computer vision, convolutional neural networks have a wide range of uses. Images represent most of today’s data, so it’s important to know how to handle these large amounts of data efficiently. Convolutional neural networks have been shown to solve image processing problems effectively. However, when designing the network structure for a particular problem, you need to adjust the hyperparameters for higher accuracy. This technique is time consuming and requires a lot of work and domain knowledge. Designing a convolutional neural network architecture is a classic NP-hard optimization challenge. On the other hand, different datasets… More >

Mohammad Yamin^1,*, Mishaal Mofleh Almutairi², Saeed Badghish³, Saleh Bajaba⁴

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4965-4981, 2023, DOI:10.32604/cmc.2023.033705 - 28 December 2022

Abstract Due to the rapid increase in urbanization and population, crowd gatherings are frequently observed in the form of concerts, political, and religious meetings. HAJJ is one of the well-known crowding events that takes place every year in Makkah, Saudi Arabia. Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence (AI) applications. The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification (SSODTL-CD2C) model. The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities. To attain this, SSODTL-CD2C… More >

Daniyar Sultan^1,2,*, Batyrkhan Omarov³, Zhazira Kozhamkulova⁴, Gulnur Kazbekova⁵, Laura Alimzhanova¹, Aigul Dautbayeva⁶, Yernar Zholdassov¹, Rustam Abdrakhmanov³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5625-5640, 2023, DOI:10.32604/cmc.2023.033682 - 28 December 2022

Abstract Automatic identification of cyberbullying is a problem that is gaining traction, especially in the Machine Learning areas. Not only is it complicated, but it has also become a pressing necessity, considering how social media has become an integral part of adolescents’ lives and how serious the impacts of cyberbullying and online harassment can be, particularly among teenagers. This paper contains a systematic literature review of modern strategies, machine learning methods, and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet. We undertake an in-depth review… More >