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  • Open Access


    Bitcoin Candlestick Prediction with Deep Neural Networks Based on Real Time Data

    Reem K. Alkhodhairi1, Shahad R. Aljalhami1, Norah K. Rusayni1, Jowharah F. Alshobaili1, Amal A. Al-Shargabi1,*, Abdulatif Alabdulatif2

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3215-3233, 2021, DOI:10.32604/cmc.2021.016881

    Abstract Currently, Bitcoin is the world’s most popular cryptocurrency. The price of Bitcoin is extremely volatile, which can be described as high-benefit and high-risk. To minimize the risk involved, a means of more accurately predicting the Bitcoin price is required. Most of the existing studies of Bitcoin prediction are based on historical (i.e., benchmark) data, without considering the real-time (i.e., live) data. To mitigate the issue of price volatility and achieve more precise outcomes, this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open, high, low, and close (OHLC)—prices. Seeking a better prediction model, the present study… More >

  • Open Access


    A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

    D. Venugopal1, T. Jayasankar2, Mohamed Yacin Sikkandar3, Mohamed Ibrahim Waly3, Irina V. Pustokhina4, Denis A. Pustokhin5, K. Shankar6,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2877-2893, 2021, DOI:10.32604/cmc.2021.015480

    Abstract Data fusion is one of the challenging issues, the healthcare sector is facing in the recent years. Proper diagnosis from digital imagery and treatment are deemed to be the right solution. Intracerebral Haemorrhage (ICH), a condition characterized by injury of blood vessels in brain tissues, is one of the important reasons for stroke. Images generated by X-rays and Computed Tomography (CT) are widely used for estimating the size and location of hemorrhages. Radiologists use manual planimetry, a time-consuming process for segmenting CT scan images. Deep Learning (DL) is the most preferred method to increase the efficiency of diagnosing ICH. In… More >

  • Open Access


    HLR-Net: A Hybrid Lip-Reading Model Based on Deep Convolutional Neural Networks

    Amany M. Sarhan1, Nada M. Elshennawy1, Dina M. Ibrahim1,2,*

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1531-1549, 2021, DOI:10.32604/cmc.2021.016509


    Lip reading is typically regarded as visually interpreting the speaker’s lip movements during the speaking. This is a task of decoding the text from the speaker’s mouth movement. This paper proposes a lip-reading model that helps deaf people and persons with hearing problems to understand a speaker by capturing a video of the speaker and inputting it into the proposed model to obtain the corresponding subtitles. Using deep learning technologies makes it easier for users to extract a large number of different features, which can then be converted to probabilities of letters to obtain accurate results. Recently proposed methods for… More >

  • Open Access


    Human-Animal Affective Robot Touch Classification Using Deep Neural Network

    Mohammed Ibrahim Ahmed Al-mashhadani1, Theyazn H. H. Aldhyani2,*, Mosleh Hmoud Al-Adhaileh3, Alwi M. Bamhdi4, Mohammed Y. Alzahrani5, Fawaz Waselallah Alsaade6, Hasan Alkahtani1,6

    Computer Systems Science and Engineering, Vol.38, No.1, pp. 25-37, 2021, DOI:10.32604/csse.2021.014992

    Abstract Touch gesture recognition is an important aspect in human–robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human–animal affective robot touch (HAART) using a deep learning algorithm. The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015. The dataset was tested with numerous subjects performing different HAART gestures; each touch was performed on a robotic animal covered by a pressure sensor skin. A convolutional neural network algorithm is proposed to implement the touch… More >

  • Open Access


    Imperative Dynamic Routing Between Capsules Network for Malaria Classification

    G. Madhu1,*, A. Govardhan2, B. Sunil Srinivas3, Kshira Sagar Sahoo4, N. Z. Jhanjhi5, K. S. Vardhan1, B. Rohit6

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 903-919, 2021, DOI:10.32604/cmc.2021.016114

    Abstract Malaria is a severe epidemic disease caused by Plasmodium falciparum. The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications. The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis. Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels. Further, convolutions can capture spatial invariances but, cannot capture rotational invariances. Hence to overcome these limitations, this research, develops an Imperative Dynamic routing mechanism with fully trained capsule networks for… More >

  • Open Access


    Paddy Leaf Disease Detection Using an Optimized Deep Neural Network

    Shankarnarayanan Nalini1,*, Nagappan Krishnaraj2, Thangaiyan Jayasankar3, Kalimuthu Vinothkumar4, Antony Sagai Francis Britto5, Kamalraj Subramaniam6, Chokkalingam Bharatiraja7

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1117-1128, 2021, DOI:10.32604/cmc.2021.012431

    Abstract Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop. Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. A novel deep neural network (DNN) classification model is proposed for the identification of paddy leaf disease using plant image data. Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search… More >

  • Open Access


    Machine Learning in Detecting Schizophrenia: An Overview

    Gurparsad Singh Suri1, Gurleen Kaur1, Sara Moein2,*

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 723-735, 2021, DOI:10.32604/iasc.2021.015049

    Abstract Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data are high dimensional. In this… More >

  • Open Access


    Mammographic Image Classification Using Deep Neural Network for Computer-Aided Diagnosis

    Charles Arputham1,*, Krishnaraj Nagappan2, Lenin Babu Russeliah3, AdalineSuji Russeliah4

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 747-759, 2021, DOI:10.32604/iasc.2021.012077

    Abstract Breast cancer detection is a crucial topic in the healthcare sector. Breast cancer is a major reason for the increased mortality rate in recent years among women, specifically in developed and underdeveloped countries around the world. The incidence rate is less in India than in developed countries, but awareness must be increased. This paper focuses on an efficient deep learning-based diagnosis and classification technique to detect breast cancer from mammograms. The model includes preprocessing, segmentation, feature extraction, and classification. At the initial level, Laplacian filtering is applied to identify the portions of edges in mammogram images that are highly sensitive… More >

  • Open Access


    Roughsets-based Approach for Predicting Battery Life in IoT

    Rajesh Kaluri1, Dharmendra Singh Rajput1, Qin Xin2,*, Kuruva Lakshmanna1, Sweta Bhattacharya1, Thippa Reddy Gadekallu1, Praveen Kumar Reddy Maddikunta1

    Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 453-469, 2021, DOI:10.32604/iasc.2021.014369

    Abstract Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from the Chicago district beach water.… More >

  • Open Access


    An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network

    Bin Yang1,*, Lingyun Xiang2, Xianyi Chen3, Wenjing Jia4

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 951-964, 2021, DOI:10.32604/cmc.2021.014839

    Abstract Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network. However, due to the complexity of the human body, there are still many challenges to face in that process. One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients, online. This paper presents a novel chronic disease prediction system based on an incremental deep neural network. The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner. With time, the system can predict diabetes more and more… More >

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