Chih-Ta Yen^1,*, Cheng-Hong Liao²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1973-1986, 2022, DOI:10.32604/cmc.2022.022679 - 03 November 2021

Abstract In this study, single-channel photoplethysmography (PPG) signals were used to estimate the heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A deep learning model was proposed using a long-term recurrent convolutional network (LRCN) modified from a deep learning algorithm, the convolutional neural network model of the modified inception deep learning module, and a long short-term memory network (LSTM) to improve the model's accuracy of BP and HR measurements. The PPG data of 1,551 patients were obtained from the University of California Irvine Machine Learning Repository. How to design a filter of More >

Wafaa Alsaggaf^1,*, Felwa Abukhodair¹, Amani Tariq Jamal², Sayed Abdel-Khalek³, Romany F. Mansour⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1189-1203, 2022, DOI:10.32604/cmc.2022.022469 - 03 November 2021

Abstract In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents… More >

Anupam Garg¹, Anshu Parashar¹, Dipto Barman², Sahil Jain³, Divya Singhal³, Mehedi Masud⁴, Mohamed Abouhawwash^5,6,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1459-1471, 2022, DOI:10.32604/cmc.2022.022170 - 03 November 2021

Abstract Autism Spectrum Disorder (ASD) is a developmental disorder whose symptoms become noticeable in early years of the age though it can be present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completely but can be reduced if detected early. An early diagnosis is hampered by the variation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergence of deep learning approaches in various fields, medical experts can be assisted in… More >

Pudi Sekhar¹, T. J. Benedict Jose², Velmurugan Subbiah Parvathy³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Kuntha Pin⁶, Yunyoung Nam^7,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1473-1487, 2022, DOI:10.32604/cmc.2022.022110 - 03 November 2021

Abstract With the incorporation of distributed energy systems in the electric grid, transactive energy market (TEM) has become popular in balancing the demand as well as supply adaptively over the grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peer-to-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At the same time, there is a need to predict the load for effectual P2P energy trading and can be… More >

Zuhaira Muhammad Zain^1,*, Sapiah Sakri¹, Nurul Halimatul Asmak Ismail², Reza M. Parizi³

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1521-1546, 2022, DOI:10.32604/cmc.2022.022085 - 03 November 2021

Abstract Developing successful software with no defects is one of the main goals of software projects. In order to provide a software project with the anticipated software quality, the prediction of software defects plays a vital role. Machine learning, and particularly deep learning, have been advocated for predicting software defects, however both suffer from inadequate accuracy, overfitting, and complicated structure. In this paper, we aim to address such issues in predicting software defects. We propose a novel structure of 1-Dimensional Convolutional Neural Network (1D-CNN), a deep learning architecture to extract useful knowledge, identifying and modelling the… More >

Emad M. Ahmed^1,*, Mohamed A. Ahmed¹, Ziad M. Ali^2,3, Imran Khan⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 231-254, 2022, DOI:10.32604/cmc.2022.022005 - 03 November 2021

Abstract The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances.… More >

Swati Singh¹, Isha Gupta², Sheifali Gupta², Deepika Koundal^3,*, Sultan Aljahdali⁴, Shubham Mahajan⁵, Amit Kant Pandit⁵

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1849-1866, 2022, DOI:10.32604/cmc.2022.021875 - 03 November 2021

Abstract In Agriculture Sciences, detection of diseases is one of the most challenging tasks. The mis-interpretations of plant diseases often lead to wrong pesticide selection, resulting in damage of crops. Hence, the automatic recognition of the diseases at earlier stages is important as well as economical for better quality and quantity of fruits. Computer aided detection (CAD) has proven as a supportive tool for disease detection and classification, thus allowing the identification of diseases and reducing the rate of degradation of fruit quality. In this research work, a model based on convolutional neural network with 19… More >

Alaa A. El-Demerdash, Sherif E. Hussein, John FW Zaki^*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 941-959, 2022, DOI:10.32604/cmc.2022.021839 - 03 November 2021

Abstract Sentiment analysis attracts the attention of Egyptian Decision-makers in the education sector. It offers a viable method to assess education quality services based on the students’ feedback as well as that provides an understanding of their needs. As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels, this research uses a dataset for tweets' sentiments to assess a few machine learning techniques. After dataset preprocessing to remove symbols, necessary stemming and lemmatization is performed for features extraction. This is followed by several machine learning techniques and… More >

Wei Pei¹, Yan Li¹, Siuly Siuly^1,*, Peng Wen²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 889-905, 2022, DOI:10.32604/cmc.2022.021830 - 03 November 2021

Abstract Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on… More >

P. Suresh^1,*, R. H. Aswathy¹, Sridevi Arumugam², Amani Abdulrahman Albraikan³, Fahd N. Al-Wesabi⁴, Anwer Mustafa Hilal⁵, Mohammad Alamgeer⁶

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1713-1728, 2022, DOI:10.32604/cmc.2022.021789 - 03 November 2021

Abstract In India, water wastage in agricultural fields becomes a challenging issue and it is needed to minimize the loss of water in the irrigation process. Since the conventional irrigation system needs massive quantity of water utilization, a smart irrigation system can be designed with the help of recent technologies such as machine learning (ML) and the Internet of Things (IoT). With this motivation, this paper designs a novel IoT enabled deep learning enabled smart irrigation system (IoTDL-SIS) technique. The goal of the IoTDL-SIS technique focuses on the design of smart irrigation techniques for effectual water… More >