Hussain Alaaedi^*, Masoud Sabaei

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 633-646, 2023, DOI:10.32604/iasc.2023.032462 - 29 September 2022

Abstract Enabling high mobility applications in millimeter wave (mmWave) based systems opens up a slew of new possibilities, including vehicle communications in addition to wireless virtual/augmented reality. The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links. In this research work, the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated. The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output (MIMO) which… More >

Md. Anwar Hussen Wadud¹, M. F. Mridha¹, Jungpil Shin^2,*, Kamruddin Nur³, Aloke Kumar Saha⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.027841 - 15 June 2022

Abstract Offensive messages on social media, have recently been frequently used to harass and criticize people. In recent studies, many promising algorithms have been developed to identify offensive texts. Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences. In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts. In this study, a detection system has been developed for both monolingual… More >

S. Devi Mahalakshmi^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1387-1395, 2023, DOI:10.32604/csse.2023.027610 - 15 June 2022

Abstract The kidney is an important organ of humans to purify the blood. The healthy function of the kidney is always essential to balance the salt, potassium and pH levels in the blood. Recently, the failure of kidneys happens easily to human beings due to their lifestyle, eating habits and diabetes diseases. Early prediction of kidney stones is compulsory for timely treatment. Image processing-based diagnosis approaches provide a greater success rate than other detection approaches. In this work, proposed a kidney stone classification method based on optimized Transfer Learning(TL). The Deep Convolutional Neural Network (DCNN) models More >

G. Arutperumjothi^1,*, K. Suganya Devi², C. Rani³, P. Srinivasan⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1069-1086, 2023, DOI:10.32604/iasc.2023.028423 - 06 June 2022

Abstract In recent years, Peripheral blood smear is a generic analysis to assess the person’s health status. Manual testing of Peripheral blood smear images are difficult, time-consuming and is subject to human intervention and visual error. This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques. Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images. In order to mitigate this issue Deep Convolution Neural Network (DCNN) based automatic classification technique is introduced with… More >

Sara A. Alameen^*, Areej M. Alhothali

Computer Systems Science and Engineering, Vol.44, No.1, pp. 895-912, 2023, DOI:10.32604/csse.2023.024643 - 01 June 2022

Abstract Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D convolutional neural network (3D-CNN) and long short-term memory (LSTM). The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features… More >

Jie Luo^*, Qingbing Ji, Lvlin Ni

Journal on Artificial Intelligence, Vol.4, No.3, pp. 133-142, 2022, DOI:10.32604/jai.2022.031800 - 01 December 2022

Abstract How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique. Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel. Various VPN protocols make the feature engineering of machine learning extremely difficult. Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification. This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model. SAE can effectively reduce the dimensionality of the data, which not only improves More >

Xintao Duan^1,*, Zhiqiang Shao¹, Wenxin Wang¹, En Zhang¹, Dongli Yue¹, Chuan Qin², Haewoon Nam³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5363-5375, 2022, DOI:10.32604/cmc.2022.027807 - 21 April 2022

Abstract At present, the image steganography method based on CNN has achieved good results. The trained model and its parameters are of great value. Once leaked, the secret image will be exposed. To protect the security of steganographic network model parameters in the transmission process, an idea based on network model parameter scrambling is proposed in this paper. Firstly, the sender trains the steganography network and extraction network, encrypts the extraction network parameters with the key shared by the sender and the receiver, then sends the extraction network and parameters to the receiver through the public… More >

S. Sudha^*, A. Srinivasan, T. Gayathri Devi

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 597-609, 2022, DOI:10.32604/cmc.2022.024065 - 24 February 2022

Abstract Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques… More >

R. Rajakumari^1,*, L. Kalaivani²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1089-1107, 2022, DOI:10.32604/iasc.2022.020178 - 17 November 2021

Abstract Breast cancer is a commonly diagnosed disease in women. Early detection, a personalized treatment approach, and better understanding are necessary for cancer patients to survive. In this work, a deep learning network and traditional convolution network were both employed with the Digital Database for Screening Mammography (DDSM) dataset. Breast cancer images were subjected to background removal followed by Wiener filtering and a contrast limited histogram equalization (CLAHE) filter for image restoration. Wavelet packet decomposition (WPD) using the Daubechies wavelet level 3 (db3) was employed to improve the smoothness of the images. For breast cancer recognition,… More >

Huizhi Gou^1,2,*, Yuncai Ning¹

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922 - 22 July 2021

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 More >