Akhil Muralidharan^1,2, V. Sugumaran¹, K.P Soman³, M. Amarnath⁴

Structural Durability & Health Monitoring, Vol.10, No.1, pp. 55-80, 2014, DOI:10.3970/sdhm.2014.010.055

Abstract Gears are machine elements that transmit motion by means of successively engaging teeth. In purely scientific terms, gears are used to transmit motion. A faulty gear is a matter of serious concern as it affects the functionality of a machine to a great extent. Thus it is essential to diagnose the faults at an initial stage so as to reduce the losses that might be incurred. This necessitates the need for continuous monitoring of the gears. The vibrations produced by gears from good and simulated faulty conditions can be effectively used to detect the faults in these gears. The introduction… More >

Tang Y¹, SS Jin²

Phyton-International Journal of Experimental Botany, Vol.87, pp. 280-285, 2018, DOI:10.32604/phyton.2018.87.280

Abstract Persistent seed banks have been detected in the Horqin Sandy Land, China using experimental methods. In this study, we used seed traits (i.e. seed mass and seed shape) to predict the persistence of seed banks using the random forest algorithm. The results showed that the mean decrease in accuracy for seed mass and seed shape was 18.26 and 9.90, respectively, suggesting that seed mass was a better predictor than seed shape. With increasing seed mass, the log of P (where P is the ratio of the number of votes selecting existence of a persistent seed bank to the number of… More >

M. Rajalakshmi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 209-220, 2018, DOI:10.3970/cmes.2018.114.209

Abstract Amongst several biometric traits, Vein pattern biometric has drawn much attention among researchers and diverse users. It gains its importance due to its difficulty in reproduction and inherent security advantages. Many research papers have dealt with the topic of new generation biometric solutions such as iris and vein biometrics. However, most implementations have been based on small datasets due to the difficulties in obtaining samples. In this paper, a deeper study has been conducted on previously suggested methods based on Convolutional Neural Networks (CNN) using a larger dataset. Also, modifications are suggested for implementation using ensemble methods. Ensembles were used… More >

Kemal Akyol^1,*, Baha Şen²

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 69-80, 2019, DOI:10.32604/cmc.2019.08143

Abstract The popularity of news, which conveys newsworthy events which occur during day to people, is substantially important for the spectator or audience. People interact with news website and share news links or their opinions. This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources. These techniques consist of basically two phrases: a) the training data is sent as input to the classifier algorithm, b) the performance of pre-learned algorithm is tested on the testing data. And so, a knowledge discovery from the data is performed. In this context, firstly, twelve datasets… More >

Alia Alabdulkarim¹, Mznah Al-Rodhaan², Yuan Tian^*,3, Abdullah Al-Dhelaan²

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 585-601, 2019, DOI:10.32604/cmc.2019.05637

Abstract Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust. These systems are used to improve physicians’ diagnostic processes in terms of speed and accuracy. Using data-mining techniques, a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms. In this work, we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’ information and exposing them to cyber and network attacks. Solving the same problem with a… More >

Changting Shi^{1, *}

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 635-652, 2018, DOI:10.32604/cmc.2018.03754

Abstract In the last decade, IoT has been widely used in smart cities, autonomous driving and Industry 4.0, which lead to improve efficiency, reliability, security and economic benefits. However, with the rapid development of new technologies, such as cognitive communication, cloud computing, quantum computing and big data, the IoT security is being confronted with a series of new threats and challenges. IoT device identification via Radio Frequency Fingerprinting (RFF) extracting from radio signals is a physical-layer method for IoT security. In physical-layer, RFF is a unique characteristic of IoT device themselves, which can difficultly be tampered. Just as people’s unique fingerprinting,… More >