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Search Results (14)
  • Open Access


    Electroencephalography (EEG) Based Neonatal Sleep Staging and Detection Using Various Classification Algorithms

    Hafza Ayesha Siddiqa1, Muhammad Irfan1, Saadullah Farooq Abbasi2,*, Wei Chen1

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1759-1778, 2023, DOI:10.32604/cmc.2023.041970

    Abstract Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual… More >

  • Open Access


    Comparative Evaluation of Data Mining Algorithms in Breast Cancer

    Fuad A. M. Al-Yarimi*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 633-645, 2023, DOI:10.32604/cmc.2023.038858

    Abstract Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and… More >

  • Open Access


    Heart Disease Risk Prediction Expending of Classification Algorithms

    Nisha Mary1, Bilal Khan1, Abdullah A. Asiri2, Fazal Muhammad3,*, Salman Khan3, Samar Alqhtani4, Khlood M. Mehdar5, Hanan Talal Halwani4, Muhammad Irfan6, Khalaf A. Alshamrani2

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6595-6616, 2022, DOI:10.32604/cmc.2022.032384

    Abstract Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today’s lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well… More >

  • Open Access


    Sensors-Based Ambient Assistant Living via E-Monitoring Technology

    Sadaf Hafeez1, Yazeed Yasin Ghadi2, Mohammed Alarfaj3, Tamara al Shloul4, Ahmad Jalal1, Shaharyar Kamal1, Dong-Seong Kim5,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4935-4952, 2022, DOI:10.32604/cmc.2022.023841

    Abstract Independent human living systems require smart, intelligent, and sustainable online monitoring so that an individual can be assisted timely. Apart from ambient assisted living, the task of monitoring human activities plays an important role in different fields including virtual reality, surveillance security, and human interaction with robots. Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions. In this paper, we propose multiple methods such as random occupancy pattern, spatio temporal cloud, way-point trajectory, Hilbert transform, Walsh Hadamard transform and bone More >

  • Open Access


    Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

    Romany F. Mansour1,*, Eatedal Alabdulkreem2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1161-1169, 2023, DOI:10.32604/csse.2023.023307

    Abstract The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image More >

  • Open Access


    Investigating of Classification Algorithms for Heart Disease Risk Prediction

    Nisha Mary1, Bilal Khan1,*, Abdullah A Asiri2, Fazal Muhammad3, Samar Alqhtani4, Khlood M Mehdar5, Hanan Talal Halwani4, Turki Aleyani4, Khalaf A Alshamrani2

    Journal of Intelligent Medicine and Healthcare, Vol.1, No.1, pp. 11-31, 2022, DOI:10.32604/jimh.2022.030161

    Abstract Prognosis of HD is a complex task that requires experience and expertise to predict in the early stage. Nowadays, heart failure is rising due to the inherent lifestyle. The healthcare industry generates dense records of patients, which cannot be managed manually. Such an amount of data is very significant in the field of data mining and machine learning when gathering valuable knowledge. During the last few decades, researchers have used different approaches for the prediction of HD, but still, the major problem is the uncertainty factor in the output data and also there is a… More >

  • Open Access


    Blood Sample Image Classification Algorithm Based on SVM and HOG

    Tianyi Jiang1, Shuangshuang Ying2, Zhou Fang1, Xue Song1, Yinggang Sun2, Dongyang Zhan3,4, Chao Ma2,*

    Journal of New Media, Vol.4, No.2, pp. 85-95, 2022, DOI:10.32604/jnm.2022.027175

    Abstract In the medical field, the classification and analysis of blood samples has always been arduous work. In the previous work of this task, manual classification maneuvers have been used, which are time consuming and laborious. The conventional blood image classification research is mainly focused on the microscopic cell image classification, while the macroscopic reagent processing blood coagulation image classification research is still blank. These blood samples processed with reagents often show some inherent shape characteristics, such as coagulation, attachment, discretization and so on. The shape characteristics of these blood samples also make it possible for… More >

  • Open Access


    Research on Optimization of Random Forest Algorithm Based on Spark

    Suzhen Wang1, Zhanfeng Zhang1,*, Shanshan Geng1, Chaoyi Pang2

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3721-3731, 2022, DOI:10.32604/cmc.2022.015378

    Abstract As society has developed, increasing amounts of data have been generated by various industries. The random forest algorithm, as a classification algorithm, is widely used because of its superior performance. However, the random forest algorithm uses a simple random sampling feature selection method when generating feature subspaces which cannot distinguish redundant features, thereby affecting its classification accuracy, and resulting in a low data calculation efficiency in the stand-alone mode. In response to the aforementioned problems, related optimization research was conducted with Spark in the present paper. This improved random forest algorithm performs feature extraction according More >

  • Open Access


    Multi-Model Detection of Lung Cancer Using Unsupervised Diffusion Classification Algorithm

    N. Jayanthi1,*, D. Manohari2, Mohamed Yacin Sikkandar3, Mohamed Abdelkader Aboamer3, Mohamed Ibrahim Waly3, C. Bharatiraja4

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1317-1329, 2022, DOI:10.32604/iasc.2022.018974

    Abstract Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy and accurate way to detect is by using image processing techniques on the cancer-affected images captured from the patients. This paper proposes a novel lung cancer detection method. Firstly, an adaptive median filter algorithm (AMF) is applied to preprocess those images for improving the quality of the affected area. Then, a supervised image edge detection algorithm (SIED) is presented to segment those images. Then, feature extraction is employed to extract the mean, standard More >

  • Open Access


    Blockchain-Based Decision Tree Classification in Distributed Networks

    Jianping Yu1,2,3, Zhuqing Qiao1, Wensheng Tang1,2,3,*, Danni Wang1, Xiaojun Cao4

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 713-728, 2021, DOI:10.32604/iasc.2021.017154

    Abstract In a distributed system such as Internet of things, the data volume from each node may be limited. Such limited data volume may constrain the performance of the machine learning classification model. How to effectively improve the performance of the classification in a distributed system has been a challenging problem in the field of data mining. Sharing data in the distributed network can enlarge the training data volume and improve the machine learning classification model’s accuracy. In this work, we take data sharing and the quality of shared data into consideration and propose an efficient… More >

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