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Search Results (12)
  • 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 as the need to decrease… 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 pair descriptors to extract optimal… 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 Classification Algorithm (PICA) system is… 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 need to reduce the error… 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 us to recognize their classification… 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 to the calculated feature importance… 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 deviation, energy, contrast, etc., of… 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 Blockchain-based ID3 Decision Tree Classification… More >

  • Open Access


    Performance of Lung Cancer Prediction Methods Using Different Classification Algorithms

    Yasemin Gültepe*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2015-2028, 2021, DOI:10.32604/cmc.2021.014631

    Abstract In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine learning is a branch of artificial intelligence that allows computers to quickly identify patterns within complex and large datasets by learning from existing data. Machine-learning techniques have been improving rapidly and are increasingly used by medical professionals for the successful classification and diagnosis of early-stage disease. They are widely used in cancer diagnosis. In particular, machine learning has been used in the diagnosis of lung cancer… More >

  • Open Access


    Predicting the Type of Crime: Intelligence Gathering and Crime Analysis

    Saleh Albahli1, Anadil Alsaqabi1, Fatimah Aldhubayi1, Hafiz Tayyab Rauf2,*, Muhammad Arif3, Mazin Abed Mohammed4

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2317-2341, 2021, DOI:10.32604/cmc.2021.014113

    Abstract Crimes are expected to rise with an increase in population and the rising gap between society’s income levels. Crimes contribute to a significant portion of the socioeconomic loss to any society, not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy, social parameters, and reputation of a nation. Policing and other preventive resources are limited and have to be utilized. The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are… More >

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