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

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 system. The architecture of the… 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

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 critically examining the importance of… 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

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 useful; thus identifying the informative… 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

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 require different combinations of models… 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

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 technique exploits Oppositional Salp Swarm… 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

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 of 13 papers from four… More >

Mohammad Yamin^1,*, Saleh Bajaba², Zenah Mahmoud AlKubaisy¹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.033677

Abstract Cloud Computing (CC) provides data storage options as well as computing services to its users through the Internet. On the other hand, cloud users are concerned about security and privacy issues due to the increased number of cyberattacks. Data protection has become an important issue since the users’ information gets exposed to third parties. Computer networks are exposed to different types of attacks which have extensively grown in addition to the novel intrusion methods and hacking tools. Intrusion Detection Systems (IDSs) can be used in a network to manage suspicious activities. These IDSs monitor the activities of the CC environment… More >

Khadijah Mohammad Alfadli, Alaa Omran Almagrabi^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5871-5883, 2023, DOI:10.32604/cmc.2023.033603

Abstract Heart diseases are the undisputed leading causes of death globally. Unfortunately, the conventional approach of relying solely on the patient’s medical history is not enough to reliably diagnose heart issues. Several potentially indicative factors exist, such as abnormal pulse rate, high blood pressure, diabetes, high cholesterol, etc. Manually analyzing these health signals’ interactions is challenging and requires years of medical training and experience. Therefore, this work aims to harness machine learning techniques that have proved helpful for data-driven applications in the rise of the artificial intelligence era. More specifically, this paper builds a hybrid model as a tool for data… More >

Khalid Masood¹, Mahmoud M. Al-Sakhnini^2,3, Waqas Nawaz^4,*, Tauqeer Faiz^5,6, Abdul Salam Mohammad⁷, Hamza Kashif⁸

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5417-5430, 2023, DOI:10.32604/cmc.2023.033590

Abstract Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and… More >

Badriyya B. Al-onazi¹, Saud S. Alotaib², Saeed Masoud Alshahrani^3,*, Najm Alotaibi⁴, Mrim M. Alnfiai⁵, Ahmed S. Salama⁶, Manar Ahmed Hamza⁷

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5447-5465, 2023, DOI:10.32604/cmc.2023.033564

Abstract The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic. A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features. In this… More >