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  • Open Access

    ARTICLE

    Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

    Ibrar Amin1, Saima Hassan1, Samir Brahim Belhaouari2,*, Muhammad Hamza Azam3

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

    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 system, which showed promising results.… More >

  • Open Access

    ARTICLE

    A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

    Farah Batool1, Abdul Rehman2, Dongsun Kim2,*, 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

    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 the higher value of α… More >

  • Open Access

    ARTICLE

    A Transaction Frequency Based Trust for E-Commerce

    Dong Huang1,*, Sean Xu2

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

    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 transaction frequency module which can… More >

  • Open Access

    ARTICLE

    High-Bandwidth, Low-Power CMOS Transistor Based CAB for Field Programmable Analog Array

    Ameen Bin Obadi1, Alaa El-Din Hussein2, Samir Salem Al-Bawri3,4,*, Kabir Hossain5, Abdullah Abdulhameed4, Muzammil Jusoh1,6,7, Thennarasan Sabapathy1,6, 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 >

  • Open Access

    ARTICLE

    Exploiting Human Pose and Scene Information for Interaction Detection

    Manahil Waheed1, Samia Allaoua Chelloug2,*, 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 >

  • Open Access

    ARTICLE

    Using Informative Score for Instance Selection Strategy in Semi-Supervised Sentiment Classification

    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 >

  • Open Access

    ARTICLE

    An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-II

    Afia Zafar1, Muhammad Aamir2, Nazri Mohd Nawi1, Ali Arshad3, Saman Riaz3, Abdulrahman Alruban4,*, Ashit Kumar Dutta5, Badr Almutairi6, Sultan Almotairi7,8

    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 >

  • Open Access

    ARTICLE

    Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

    Mohammad Yamin1,*, 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 >

  • Open Access

    REVIEW

    A Review of Machine Learning Techniques in Cyberbullying Detection

    Daniyar Sultan1,2,*, 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 >

  • Open Access

    ARTICLE

    Chaotic Metaheuristics with Multi-Spiking Neural Network Based Cloud Intrusion Detection

    Mohammad Yamin1,*, 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 >

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