D. Jerusha^*, T. Jaya

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1121-1132, 2022, DOI:10.32604/csse.2022.022997

Abstract Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in… More >

Taifeng Pan^*

Journal of Quantum Computing, Vol.3, No.4, pp. 161-171, 2021, DOI:10.32604/jqc.2021.025373

Abstract The rapid development of Internet of Things (IoT) technology has brought great convenience to people’s life. However, the security protection capability of IoT is weak and vulnerable. Therefore, more protection needs to be done for the security of IoT. The paper proposes an intrusion detection method for IoT based on multi GBDT feature reduction and hierarchical traffic detection model. Firstly, GBDT is used to filter the features of IoT traffic data sets BoT-IoT and UNSW-NB15 to reduce the traffic feature dimension. At the same time, in order to improve the reliability of feature filtering, this paper constructs multiple GBDT models… More >

Venkateswari Pichaimani¹, K. R. Manjula^2,*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 383-397, 2022, DOI:10.32604/iasc.2022.023105

Abstract The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset… More >

Mohammad Yamin^1,*, Adnan Ahmed Abi Sen², Zenah Mahmoud AlKubaisy¹, Rahaf Almarzouki¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2283-2298, 2021, DOI:10.32604/cmc.2021.017433

Abstract COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the… More >

Yasemin Gültepe^*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2015-2028, 2021, DOI:10.32604/cmc.2021.014631

Abstract In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine learning is a branch of artificial intelligence that allows computers to quickly identify patterns within complex and large datasets by learning from existing data. Machine-learning techniques have been improving rapidly and are increasingly used by medical professionals for the successful classification and diagnosis of early-stage disease. They are widely used in cancer diagnosis. In particular, machine learning has been used in the diagnosis of lung cancer… More >

Yan Zhou, Lili Pan^*, Rongyu Chen, Weizhi Shao

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 77-86, 2020, DOI:10.32604/jihpp.2020.010486

Abstract In large-scale image retrieval, deep features extracted by Convolutional Neural Network (CNN) can effectively express more image information than those extracted by traditional manual methods. However, the deep feature dimensions obtained by Deep Convolutional Neural Network (DCNN) are too high and redundant, which leads to low retrieval efficiency. We propose a novel image retrieval method, which combines deep features selection with improved DCNN and hash transform based on high-dimension features reduction to gain lowdimension deep features and realizes efficient image retrieval. Firstly, the improved network is based on the existing deep model to build a more profound and broader network… More >

Soukaina Mjahed^1,*, Khadija Bouzaachane¹, Ahmad Taher Azar^2,3, Salah El Hadaj¹, Said Raghay¹

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 459-494, 2020, DOI:10.32604/cmes.2020.010791

Abstract This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the “Higgs machine learning challenge 2014” data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of both hard and fuzzy clustering… More >

Yanqiu Cao¹, Chao Tan¹, Genlin Ji^{1, *}

Journal on Big Data, Vol.2, No.1, pp. 19-31, 2020, DOI:10.32604/jbd.2020.01003

Abstract In the last few years, smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents, which causes many scholars to pay attention to autonomous driving. For this complexity scene, one of the major challenges is mining information comprehensively from massive features in vehicle video. This paper proposes a multi-label classification method MCM-VV (Multi-label Classification Method for Vehicle Video) for vehicle video to judge the label of road condition for unmanned system. Method MCM-VV includes a process of feature extraction and a process of multi-label classification. During feature extraction, grayscale, lane line and the edge of… More >

Lianggui Liu^1,*, Wei Li¹, Huiling Jia¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 97-106, 2018, DOI:10.32604/cmc.2018.03511

Abstract With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the combination of dynamic time warping… More >