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  • Open Access

    ARTICLE

    Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

    K. Babu1,*, C. Kumar2, C. Kannaiyaraju3

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 317-329, 2022, DOI:10.32604/iasc.2022.023756

    Abstract Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques. Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one’s mind. In the world of computer visions and algorithms, precise facial recognition is tough. In predicting the expression of a face, machine learning/artificial intelligence plays a significant role. The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis. In this article, we use three phases for facial expression recognition techniques. The principal component analysis-based dimensionality reduction techniques are… More >

  • Open Access

    ARTICLE

    A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

    Xiaorui Zhang1,3,*, Wenfang Zhang1, Wei Sun2, Xingming Sun1, Sunil Kumar Jha4

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1043-1056, 2022, DOI:10.32604/csse.2022.022305

    Abstract In a telemedicine diagnosis system, the emergence of 3D imaging enables doctors to make clearer judgments, and its accuracy also directly affects doctors’ diagnosis of the disease. In order to ensure the safe transmission and storage of medical data, a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper. The proposed algorithm employs the principal component analysis (PCA) transform to reduce the data dimension, which can minimize the error between the extracted components and the original data in the mean square sense. Especially, this algorithm helps to create a bacterial foraging model based on particle swarm… More >

  • Open Access

    ARTICLE

    Heart Disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach

    S. Kusuma*, Dr. Jothi K. R

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1273-1289, 2022, DOI:10.32604/csse.2022.021741

    Abstract One of the severe health problems and the most common types of heart disease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurs without any symptoms, it cannot be cured by an intelligent detection system. An effective diagnosis and detection of CHD should prevent human casualties. Moreover, intelligent systems employ clinical-based decision support approaches to assist physicians in providing another option for diagnosing and detecting HD. This paper aims to introduce a heart disease prediction model including phases like (i) Feature extraction, (ii) Feature… More >

  • Open Access

    ARTICLE

    Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

    R. D. Pubudu L. Indrasiri1, Ernesto Lee2, Vaibhav Rupapara3, Furqan Rustam4, Imran Ashraf5,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 489-515, 2022, DOI:10.32604/cmc.2022.019636

    Abstract Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.… More >

  • Open Access

    ARTICLE

    Lysophosphatidylcholine acyltransferase 1 is involved in the regulation of exosome secretion and uptake in colorectal cancer cells

    HAIZHENG LIU1, SHAOFEI CHANG2,*

    BIOCELL, Vol.46, No.2, pp. 453-462, 2022, DOI:10.32604/biocell.2021.015340

    Abstract Lysophosphatidylcholine acyltransferase 1 (LPCAT1) is a phospholipid acyltransferase that promotes phospholipid synthesis and plasma membrane reconstruction. Exosomes play an important role in tumor metastasis. The release and uptake of exosomes are key steps of their functions and depend on plasma membrane fusion and plasma membrane receptors, respectively. The purpose of this study was to explore whether LPCAT1-induced plasma membrane remodeling would change the secretion and uptake behavior of exosomes in tumor cells. We first confirmed the abnormally high expression of LPCAT1 in colorectal cancer cells by quantitative real-time PCR (qPCR) and Western blot analysis. Then, SW620 cells were used as… More >

  • Open Access

    ARTICLE

    Face Image Compression and Reconstruction Based on Improved PCA

    Yu Xue1,2,*, Chen Chen1, ChiShe Wang2, Linguo Li3, Romany F. Mansour4

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 973-982, 2021, DOI:10.32604/iasc.2021.017607

    Abstract Face recognition technology has many usages in the real-world applications, and it has generated extensive interest in recent years. However, the amount of data in a digital image is growing explosively, taking up a lot of storage and transmission resources. There is a lot of redundancy in an image data representation. Thus, image compression has become a hot topic. The principal component analysis (PCA) can effectively remove the correlation of an image and condense the image information into a characteristic image with several main components. At the same time, it can restore different data images according to their principal components… More >

  • Open Access

    ARTICLE

    Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN

    Huizhi Gou1,2,*, Yuncai Ning1

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922

    Abstract Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy problems. To address this research objective, this paper proposes a prediction model based on kernel principal component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks (DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting method of KPCA-MCS-DCNN is established. In… More >

  • Open Access

    ARTICLE

    A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout (Salmo Letnica)

    Sunil Kr. Jha1,*, Ivan Uzunov2, Xiaorui Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 991-1009, 2021, DOI:10.32604/cmes.2021.011821

    Abstract Ohrid trout (Salamo letnica) is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia (FYROM). The growth of Ohrid trout was examined in a controlled environment for a certain period, thereafter released into the lake to grow their natural population. The external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their growth. The data mining methods-based computational model can be used for fast, accurate, reliable, automatic, and improved growth monitoring procedures and classification of Ohrid trout. With this motivation, a combined approach of principal component… More >

  • Open Access

    ARTICLE

    A New Population Initialization of Particle Swarm Optimization Method Based on PCA for Feature Selection

    Shichao Wang, Yu Xue*, Weiwei Jia

    Journal on Big Data, Vol.3, No.1, pp. 1-9, 2021, DOI:10.32604/jbd.2021.010364

    Abstract In many fields such as signal processing, machine learning, pattern recognition and data mining, it is common practice to process datasets containing huge numbers of features. In such cases, Feature Selection (FS) is often involved. Meanwhile, owing to their excellent global search ability, evolutionary computation techniques have been widely employed to the FS. So, as a powerful global search method and calculation fast than other EC algorithms, PSO can solve features selection problems well. However, when facing a large number of feature selection, the efficiency of PSO drops significantly. Therefore, plenty of works have been done to improve this situation.… More >

  • Open Access

    ARTICLE

    Highway Cost Prediction Based on LSSVM Optimized by Intial Parameters

    Xueqing Wang1, Shuang Liu1,*, Lejun Zhang2

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 259-269, 2021, DOI:10.32604/csse.2021.014343

    Abstract The cost of highway is affected by many factors. Its composition and calculation are complicated and have great ambiguity. Calculating the cost of highway according to the traditional highway engineering estimation method is a completely tedious task. Constructing a highway cost prediction model can forecast the value promptly and improve the accuracy of highway engineering cost. This work sorts out and collects 60 sets of measured data of highway engineering; establishes an expressway cost index system based on 10 factors, including main route mileage, roadbed width, roadbed earthwork, and number of bridges; and processes the data through principal component analysis… More >

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