Amado Alvarez^1,2, Enrique Rubiños Panta¹, Francisco Gavi Reyes¹, Juan José Alarcón Cabañero², Elizabeth Hernández Acosta³, Carlos Ramírez Ayala¹, Enrique Mejía Saenz¹, Francisco Pedrero Salcedo², Emilio Nicolas Nicolas², Enrique Salazar Sosa⁴

Phyton-International Journal of Experimental Botany, Vol.75, pp. 71-83, 2006, DOI:10.32604/phyton.2006.75.071

Abstract A water quality index as a standardized method to compare the category in an integral way, between locations and through time, of the different water, river and stream storages of the Amajac river basin was developed. In addition, it is possible to predict the degree of contamination and establish planning strategies in the management of the water resources for the river basin in issue. The used methodology was based in the quantitative expression of water quality. Three samplings were made (2005-2006) and Dissolved Oxygen, Coliform in feaces, pH, Oxygen Biochemical Demand, Nitrates, Total Phosphorus, Turbidity and Dissolved Total Solids were… More >

N. Kins Burk Sunil^{1, *}, R. Ganesan², B. Sankaragomathi³

CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.2, pp. 351-375, 2019, DOI:10.31614/cmes.2018.04484

Abstract Obstructive Sleep Apnea (OSA) is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation. The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea (SA) activity. In the proposed method, the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted. These features are applied to the Classification and Regression Tree (CART)-Particle Swarm Optimization (PSO) classifier which classifies the signal into normal breathing signal… More >

Syed Muhammad Saqlain Shah^1,*, Tahir Afzal Malik², Robina khatoon¹, Syed Saqlain Hassan³, Faiz Ali Shah⁴

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 535-553, 2019, DOI:10.32604/cmc.2019.07948

Abstract Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting… More >

Yang Yang^1,*, Jinbao Duan¹, Haitao Yu¹, Zhipeng Gao¹, Xuesong Qiu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 555-575, 2018, DOI:10.31614/cmes.2018.04249

Abstract The development of deep learning has revolutionized image recognition technology. How to design faster and more accurate image classification algorithms has become our research interests. In this paper, we propose a new algorithm called stochastic depth networks with deep energy model (SADIE), and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics. First, the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training. Then in the backpropagation process, the energy function is designed to optimize the target loss… More >

Kuan-Peng Chen^1,3, An-Cheng Yang^1,3, Wen-Jay Lee¹, Yi-Ming Tseng², Nien-Ti Tsou², Nan-Yow Chen^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.3, pp. 54-54, 2019, DOI:10.32604/icces.2019.05401

Abstract Crystallographic classification of microstructure is a very important issue in material science especially numerous data were generated by experiments or Molecular Dynamic (MD) simulations. Some analysis tools were purposed, such as coordination analysis and Honeycutt-Anderson (HA) pair analysis [1], however, to analyze these huge amounts of data is still quite difficult. Sometimes, crystallography prior knowledge of their structures is also desired in the classification procedures. Not only the task is very labor intensive but also the result is susceptible to errors and is usually lack of objectivity. In this study, we developed a computational workflow which can get characteristic quantities… More >

F. Keissarian¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.12, No.4, pp. 137-144, 2009, DOI:10.3970/icces.2009.012.137

Abstract In this paper, a new image compression technique is proposed in which variable block size technique is adopted, using quadtree decomposition, for coding images at low bit rates. The proposed algorithm decomposes the host image into blocks of variable sizes according to histogram analysis of the block residuals. Variable block sizes are then encoded at different rates based on their visual activity levels. To preserve edge integrity, a high-detail block is coded by a set of parameters associated with the pattern appearing inside the block. The use of these parameters at the receiver together with the quadtree code reduces the… More >

Princy Matlani^1,*, Manish Shrivastava¹

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 427-458, 2019, DOI:10.32604/cmes.2019.04985

Abstract Real-time wild smoke detection utilizing machine based identification method is not produced proper accuracy, and it is not suitable for accurate prediction. However, various video smoke detection approaches involve minimum lighting, and it is required for the cameras to identify the existence of smoke particles in a scene. To overcome such challenges, our proposed work introduces a novel concept like deep VGG-Net Convolutional Neural Network (CNN) for the classification of smoke particles. This Deep Feature Synthesis algorithm automatically generated the characteristics for relational datasets. Also hybrid ABC optimization rectifies the problem related to the slow convergence since complexity is reduced.… More >

Mohammad Behrouzian Nejad¹, Mohammad Ebrahim Shiri Ahmadabadi^{1, 2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.1, pp. 185-205, 2019, DOI:10.32604/cmes.2019.01838

Abstract The main target of this paper is presentation of an efficient method for MRI images classification so that it can be used to diagnose patients and non-patients. Image classification is one of the prominent subset topics of machine learning and data mining that the most important image technique is the auto-categorization of images. MRI images with high resolution and appropriate accuracy allow physicians to decide on the diagnosis of various diseases and treat them. The auto categorization of MRI images toward diagnosing brain diseases has been being used to accurately diagnose hospitals, clinics, physicians and medical research centers. In this… More >

Guangyong Yang^1,*, Mengke Yang^2,*, Shafaq Salam³, Jianqiu Zeng⁴

Journal of Cyber Security, Vol.1, No.1, pp. 19-28, 2019, DOI:10.32604/jcs.2019.05947

Abstract Big data is becoming increasingly important because of the enormous information generation and storage in recent years. It has become a challenge to the data mining technique and management. Based on the characteristics of geometric explosion of information in the era of big data, this paper studies the possible approaches to balance the maximum value and privacy of information, and disposes the Nine-Cells information matrix, hierarchical classification. Furthermore, the paper uses the rough sets theory to proceed from the two dimensions of value and privacy, establishes information classification method, puts forward the countermeasures for information security. Taking spam messages for… More >

Qi Liu^1,2, Zhiyun Yang¹, Xiaodong Liu³, Scholas Mbonihankuye^4,*

Journal of New Media, Vol.1, No.2, pp. 101-113, 2019, DOI:10.32604/jnm.2019.06958

Abstract This paper aims to study energy consumption in a house. Home energy man-agement system (HEMS) has become very important, because energy consumption of a residential sector accounts for a significant amount of total energy consumption. However, a conventional HEMS has some architectural limitations among dimensional variables reusability and interoperability. Furthermore, the cost of implementation in HEMS is very expensive, which leads to the disturbance of the spread of a HEMS. Therefore, this study proposes an Internet of Things (IoT) based HEMS with lightweight photovoltaic (PV) system over dynamic home area networks (DHANs), which enables the construction of a HEMS to… More >