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  • Open Access

    ARTICLE

    Early Detection of Heartbeat from Multimodal Data Using RPA Learning with KDNN-SAE

    A. K. S. Saranya1,*, T. Jaya2

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 545-562, 2023, DOI:10.32604/csse.2023.029975

    Abstract Heartbeat detection stays central to cardiovascular an electrocardiogram (ECG) is used to help with disease diagnosis and management. Existing Convolutional Neural Network (CNN)-based methods suffer from the less generalization problem thus; the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed. In contrast, this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders (KDNN-SAE) that computes the disease before the exact heart rate by combining features from multiple ECG Signals. Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.… More >

  • Open Access

    ARTICLE

    Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

    Zeyad Ghaleb Al-Mekhlafi1, Ebrahim Mohammed Senan2, Taha H. Rassem3, Badiea Abdulkarem Mohammed4,5,*, Nasrin M. Makbol5, Adwan Alownie Alanazi1, Tariq S. Almurayziq1, Fuad A. Ghaleb6

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 775-796, 2022, DOI:10.32604/cmc.2022.024492

    Abstract Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in… More >

  • Open Access

    ARTICLE

    Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease

    Mohd Sadiq1, Mohd Tauheed Khan2,*, Sarfaraz Masood3

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5183-5200, 2022, DOI:10.32604/cmc.2022.020531

    Abstract Parkinson's disease (PD), classified under the category of a neurological syndrome, affects the brain of a person which leads to the motor and non-motor symptoms. Among motor symptoms, one of the major disabling symptom is Freezing of Gait (FoG) that affects the daily standard of living of PD patients. Available treatments target to improve the symptoms of PD. Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual. This work proposed a novel attention-based model for the detection of FoG events and PD, and measuring the intensity of PD on the… More >

  • Open Access

    ARTICLE

    Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

    R. Kanthavel1,*, R. Dhaya2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 257-269, 2022, DOI:10.32604/csse.2022.021606

    Abstract Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee. The regeneration of this cartilage tissue is not possible, and thus physicians typically suggest therapeutic measures to prevent further deterioration over time. Normally, bringing about joint replacement is a remedial course of action. Expose itself in joint pain recognized with a normal X-ray. Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle. It can be used to accurately measure the shape and texture of… More >

  • Open Access

    ARTICLE

    Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

    R. Reena Roy*, G. S. Anandha Mala

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 677-688, 2022, DOI:10.32604/csse.2022.016620

    Abstract Pancreatic cancer is one of the deadliest cancers, with less than 9% survival rates. Pancreatic Ductal Adeno Carcinoma (PDAC) is common with the general public affecting most people older than 45. Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages (advanced stage). One of the earlier symptoms of PDAC is Jaundice. Patients with diabetes, obesity, and alcohol consumption are also at higher risk of having pancreatic cancer. A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge. Features such as Mean Hue, Mean Saturation, Mean… More >

  • Open Access

    ARTICLE

    Early Detection of Alzheimer’s Disease Using Graph Signal Processing and Deep Learning

    Himanshu Padole*, S. D. Joshi, Tapan K. Gandhi

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1655-1669, 2022, DOI:10.32604/iasc.2022.021310

    Abstract Many methods have been proposed in the literature for diagnosis of Alzheimer's disease (AD) in the early stages, among which the graph-based methods have been more popular, because of their capability to utilize the relational information among different brain regions. Here, we design a novel graph signal processing based integrated AD detection model using multimodal deep learning that simultaneously utilizes both the static and the dynamic brain connectivity based features extracted from resting-state fMRI (rs-fMRI) data to detect AD in the early stages. First, our earlier proposed state-space model (SSM) based graph connectivity dynamics characterization method is used to design… More >

  • Open Access

    ARTICLE

    Integrated Random Early Detection for Congestion Control at the Router Buffer

    Ahmad Adel Abu-Shareha*

    Computer Systems Science and Engineering, Vol.40, No.2, pp. 719-734, 2022, DOI:10.32604/csse.2022.018369

    Abstract This paper proposed an Integrated Random Early Detection (IRED) method that aims to resolve the problems of the queue-based AQM and load-based AQM and gain the benefits of both using indicators from both types. The arrival factor (e.g., arrival rate, queue and capacity) and the departure factors are used to estimate the congestion through two integrated indicators. The utilized indicators are mathematically calculated and integrated to gain unified and coherent congestion indicators. Besides, IRED is built based on a new dropping calculation approach that fits the utilized congestion indicators while maintaining the intended buffer management criteria, avoiding global synchronization and… More >

  • Open Access

    ARTICLE

    Early Detection of Lung Carcinoma Using Machine Learning

    A. Sheryl Oliver1, T. Jayasankar2, K. R. Sekar3,*, T. Kalavathi Devi4, R. Shalini5, S. Poojalaxmi5, N. G. Viswesh5

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 755-770, 2021, DOI:10.32604/iasc.2021.016242

    Abstract Lung cancer is a poorly understood disease. Smokers may develop lung cancer due to the inhalation of carcinogenic substances while smoking, but non-smokers may develop this disease as well. Lung cancer can spread to other parts of the body and this process is called metastasis. Because the lung cancer is difficult to identify in the initial stages. The objective of this work is to reduce the mortality rate of the disease by identifying it at an earlier stage based on the existing symptoms. Artificial intelligence plays active roles in tasks such as entropy extraction through preprocessing strategies, ordinal to cardinal… More >

  • Open Access

    ARTICLE

    A Novel Technique for Early Detection of COVID-19

    Mohammad Yamin1,*, Adnan Ahmed Abi Sen2, Zenah Mahmoud AlKubaisy1, Rahaf Almarzouki1

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2283-2298, 2021, DOI:10.32604/cmc.2021.017433

    Abstract COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the… More >

  • Open Access

    ARTICLE

    Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

    Mavra Mehmood1, Ember Ayub1, Fahad Ahmad1,6,*, Madallah Alruwaili2, Ziyad A. Alrowaili3, Saad Alanazi2, Mamoona Humayun2, Muhammad Rizwan1, Shahid Naseem4, Tahir Alyas5

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 641-657, 2021, DOI:10.32604/cmc.2021.013774

    Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information… More >

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