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  • Open Access

    ARTICLE

    Towards Improving the Intrusion Detection through ELM (Extreme Learning Machine)

    Iftikhar Ahmad1, *, Rayan Atteah Alsemmeari1

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1097-1111, 2020, DOI:10.32604/cmc.2020.011732 - 20 August 2020

    Abstract An IDS (intrusion detection system) provides a foremost front line mechanism to guard networks, systems, data, and information. That’s why intrusion detection has grown as an active study area and provides significant contribution to cyber-security techniques. Multiple techniques have been in use but major concern in their implementation is variation in their detection performance. The performance of IDS lies in the accurate detection of attacks, and this accuracy can be raised by improving the recognition rate and significant reduction in the false alarms rate. To overcome this problem many researchers have used different machine learning… More >

  • Open Access

    ARTICLE

    Discrete Wavelet Transmission and Modified PSO with ACO Based Feed Forward Neural Network Model for Brain Tumour Detection

    Machiraju Jayalakshmi1, *, S. Nagaraja Rao2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1081-1096, 2020, DOI:10.32604/cmc.2020.011710 - 20 August 2020

    Abstract In recent years, the development in the field of computer-aided diagnosis (CAD) has increased rapidly. Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images. The existing algorithms have drawbacks with respect to their accuracy, efficiency, and limited learning processes. To address these issues, we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast, 2D- discrete wavelet transformation (2D-DWT) to extract the features, probabilistic principal component analysis (PPCA) and linear discriminant analysis (LDA) to normalize and reduce the features, and More >

  • Open Access

    ARTICLE

    Mitigating and Monitoring Smart City Using Internet of Things

    Sudan Jha1, Lewis Nkenyereye2, *, Gyanendra Prasad Joshi3, Eunmok Yang4

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1059-1079, 2020, DOI:10.32604/cmc.2020.011754 - 20 August 2020

    Abstract The present trends in smart world reflects the extensive use of limited resources through information and communication technology. The limited resources like space, mobility, energy, etc., have been consumed rigorously towards creating optimized but smart instances. Thus, a new concept of IoT integrated smart city vision is yet to be proposed which includes a combination of systems like noise and air loss monitoring, web monitoring and fire detection systems, smart waste bin systems, etc., that have not been clearly addressed in the previous researches. This paper focuses on developing an effective system for possible monitoring More >

  • Open Access

    ARTICLE

    DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge Computing

    Abdu Gumaei1, 2, *, Mabrook Al-Rakhami1, 2, Hussain AlSalman2, Sk. Md. Mizanur Rahman3, Atif Alamri1, 2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1033-1057, 2020, DOI:10.32604/cmc.2020.011740 - 20 August 2020

    Abstract Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them. Deep learning has gained momentum for identifying activities through sensors, smartphones or even surveillance cameras. However, it is often difficult to train deep learning models on constrained IoT devices. The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model More >

  • Open Access

    ARTICLE

    Second Law Analysis and Optimization of Elliptical Pin Fin Heat Sinks Using Firefly Algorithm

    Nawaf N. Hamadneh1, Waqar A. Khan2, Ilyas Khan3, *

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1015-1032, 2020, DOI:10.32604/cmc.2020.011476 - 20 August 2020

    Abstract One of the most significant considerations in the design of a heat sink is thermal management due to increasing thermal flux and miniature in size. These heat sinks utilize plate or pin fins depending upon the required heat dissipation rate. They are designed to optimize overall performance. Elliptical pin fin heat sinks enhance heat transfer rates and reduce the pumping power. In this study, the Firefly Algorithm is implemented to optimize heat sinks with elliptical pin-fins. The pin-fins are arranged in an inline fashion. The natureinspired metaheuristic algorithm performs powerfully and efficiently in solving numerical… More >

  • Open Access

    ARTICLE

    Complementary Kalman Filter as a Baseline Vector Estimator for GPS-Based Attitude Determination

    Dah-Jing Jwo1, *

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 993-1014, 2020, DOI:10.32604/cmc.2020.011592 - 20 August 2020

    Abstract The Global Positioning System (GPS) offers the interferometer for attitude determination by processing the carrier phase observables. By using carrier phase observables, the relative positioning is obtained in centimeter level. GPS interferometry has been firstly used in precise static relative positioning, and thereafter in kinematic positioning. The carrier phase differential GPS based on interferometer principles can solve for the antenna baseline vector, defined as the vector between the antenna designated master and one of the slave antennas, connected to a rigid body. Determining the unknown baseline vectors between the antennas sits at the heart of… More >

  • Open Access

    ARTICLE

    C5.0 Decision Tree Model Using Tsallis Entropy and Association Function for General and Medical Dataset

    Uma K.V1,*, Appavu alias Balamurugan S2

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 61-70, 2020, DOI:10.31209/2019.100000153

    Abstract Real world data consists of lot of impurities. Entropy measure will help to handle impurities in a better way. Here, data selection is done by using Naïve Bayes’ theorem. The sample which has posterior probability value greater than that of the threshold value is selected. C5.0 decision tree classifier is taken as base and modified the Gain calculation function using Tsallis entropy and Association function. The proposed classifier model provides more accuracy and smaller tree for general and Medical dataset. Precision value obtained for Medical dataset is more than that of existing method. More >

  • Open Access

    ARTICLE

    Enhancing the Classification Accuracy in Sentiment Analysis with Computational Intelligence Using Joint Sentiment Topic Detection with MEDLDA

    PCD Kalaivaani1,*, Dr. R Thangarajan2

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 71-79, 2020, DOI:10.31209/2019.100000152

    Abstract Web mining is the process of integrating the information from web by traditional data mining methodologies and techniques. Opinion mining is an application of natural language processing to extract subjective information from web. Online reviews require efficient classification algorithms for analysing the sentiments, which does not perform an in–depth analysis in current methods. Sentiment classification is done at document level in combination with topics and sentiments. It is based on weakly supervised Joint Sentiment-Topic mode which extends the topic model Maximum Entropy Discrimination Latent Dirichlet Allocation by constructing an additional sentiment layer. It is assumed More >

  • Open Access

    ARTICLE

    Laparoscopic Training Exercises Using HTC VIVE

    Ayesha Hoor Chaudhry*, Faisal Bukhari, Waheed Iqbal, Zubair Nawaz, Muhammad Kamran Malik

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 53-59, 2020, DOI:10.31209/2019.100000149

    Abstract Laparoscopic surgery is a relatively new field in developing countries. There is a scarcity of laparoscopically trained doctors due to a lack of training and resources available in hospitals. Training and evaluation of medical professionals to develop laparoscopic surgical skills are important and essential as it improves the success rate and reduces the risk during real surgery. The purpose of this research is to develop a series of training exercises based on virtual reality using HTC Vive headset to emulate real-world training of doctors. This virtual training not only gives the trainee doctors mastery in More >

  • Open Access

    ARTICLE

    Detecting Outlier Behavior of Game Player Players Using Multimodal Physiology Data

    Shinjin Kang1, Taiwoo Park2,*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 205-214, 2020, DOI:10.31209/2019.100000141

    Abstract This paper describes an outlier detection system based on a multimodal physiology data clustering algorithm in a PC gaming environment. The goal of this system is to provide information on a game player’s abnormal behavior with a bio-signal analysis. Using this information, the game platform can easily identify players with abnormal behavior in specific events. To do this, we propose a mouse device that measures the wearer's skin conductivity, temperature, and motion. We also suggest a Dynamic Time Warping (DTW) based clustering algorithm. The developed system examines the biometric information of 50 players in a More >

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