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  • Open Access

    ARTICLE

    Tuberculosis Diagnosis and Visualization with a Large Vietnamese X-Ray Image Dataset

    Nguyen Trong Vinh1, Lam Thanh Hien1, Ha Manh Toan2, Ngo Duc Vinh3, Do Nang Toan2,*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 281-299, 2024, DOI:10.32604/iasc.2024.045297

    Abstract Tuberculosis is a dangerous disease to human life, and we need a lot of attempts to stop and reverse it. Significantly, in the COVID-19 pandemic, access to medical services for tuberculosis has become very difficult. The late detection of tuberculosis could lead to danger to patient health, even death. Vietnam is one of the countries heavily affected by the COVID-19 pandemic, and many residential areas as well as hospitals have to be isolated for a long time. Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessing medical services, such as an automatic tuberculosis… More >

  • Open Access

    ARTICLE

    Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

    Dmitry Gura1,2, Bo Dong3,*, Duaa Mehiar4, Nidal Al Said5

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1995-2014, 2024, DOI:10.32604/cmc.2024.048238

    Abstract The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake Detection Challenge, of which 199… More >

  • Open Access

    ARTICLE

    KurdSet: A Kurdish Handwritten Characters Recognition Dataset Using Convolutional Neural Network

    Sardar Hasen Ali*, Maiwan Bahjat Abdulrazzaq

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 429-448, 2024, DOI:10.32604/cmc.2024.048356

    Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits, texts, and symbols. The dataset… More >

  • Open Access

    ARTICLE

    Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

    Zhuoqun Xia1, Hangyu Hu1, Wenjing Li2,3, Qisheng Jiang1, Lan Pu1, Yicong Shu1, Arun Kumar Sangaiah4,5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 409-430, 2024, DOI:10.32604/cmes.2024.030052

    Abstract Early screening of diabetes retinopathy (DR) plays an important role in preventing irreversible blindness. Existing research has failed to fully explore effective DR lesion information in fundus maps. Besides, traditional attention schemes have not considered the impact of lesion type differences on grading, resulting in unreasonable extraction of important lesion features. Therefore, this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator (MPAG) and a lesion localization module (LLM). Firstly, MPAG is used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained… More >

  • Open Access

    ARTICLE

    Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

    Asifa Mehmood Qureshi1, Naif Al Mudawi2, Mohammed Alonazi3, Samia Allaoua Chelloug4, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3683-3701, 2024, DOI:10.32604/cmc.2024.043611

    Abstract Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts. The extracted frames… More >

  • Open Access

    ARTICLE

    A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

    Navaneetha Krishnan Muthunambu1, Senthil Prabakaran2, Balasubramanian Prabhu Kavin3, Kishore Senthil Siruvangur4, Kavitha Chinnadurai1, Jehad Ali5,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172

    Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks (RNN) integrated with… More >

  • Open Access

    ARTICLE

    SciCN: A Scientific Dataset for Chinese Named Entity Recognition

    Jing Yang, Bin Ji, Shasha Li*, Jun Ma, Jie Yu

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4303-4315, 2024, DOI:10.32604/cmc.2023.035594

    Abstract Named entity recognition (NER) is a fundamental task of information extraction (IE), and it has attracted considerable research attention in recent years. The abundant annotated English NER datasets have significantly promoted the NER research in the English field. By contrast, much fewer efforts are made to the Chinese NER research, especially in the scientific domain, due to the scarcity of Chinese NER datasets. To alleviate this problem, we present a Chinese scientific NER dataset–SciCN, which contains entity annotations of titles and abstracts derived from 3,500 scientific papers. We manually annotate a total of 62,059 entities, and these entities are classified… More >

  • Open Access

    ARTICLE

    Analysis of large datasets for identifying molecular targets in intestinal polyps and metabolic disorders

    SHAN OU#, YUN XU#, QINGLAN LIU, TIANWEN YANG, WEI CHEN, XIU YUAN, XIN ZUO, PENG SHI*, JIE YAO*

    BIOCELL, Vol.48, No.3, pp. 415-429, 2024, DOI:10.32604/biocell.2024.046178

    Abstract Background: The interrelation between intestinal polyps, metabolic syndrome (MetS), and colorectal cancer (CRC) is a critical area of study. This research focuses on pinpointing potential molecular targets to understand the link between intestinal polyp formation, metabolic irregularities, and CRC progression. Methods: We examined clinical samples from patients with intestinal polyps coexisting with MetS and compared them with samples from patients with standard intestinal polyps. Transcriptome sequencing and public database analysis were employed to identify significant pathways and genes. These targets were then validated through immunohistochemistry (IHC). Following the RNA interference of key target expression, a series of experiments, including the… More > Graphic Abstract

    Analysis of large datasets for identifying molecular targets in intestinal polyps and metabolic disorders

  • Open Access

    ARTICLE

    MDCN: Modified Dense Convolution Network Based Disease Classification in Mango Leaves

    Chirag Chandrashekar1, K. P. Vijayakumar1,*, K. Pradeep1, A. Balasundaram1,2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697

    Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Digital Image Forgery Detection Using Transfer Learning

    Emad Ul Haq Qazi1,*, Tanveer Zia1, Muhammad Imran2, Muhammad Hamza Faheem1

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 225-240, 2023, DOI:10.32604/iasc.2023.041181

    Abstract Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified. In the current cyber world where deepfakes have shaken the global community, confirming the legitimacy of a digital image is of great importance. With the advancements made in deep learning techniques, now we can efficiently train and develop state-of-the-art digital image forensic models. The most traditional and widely used method by researchers is convolution neural networks (CNN) for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training.… More >

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