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  • Open Access

    ARTICLE

    Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

    S. Nandagopal1,*, G. Karthy2, A. Sheryl Oliver3, M. Subha4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1719-1733, 2023, DOI:10.32604/csse.2023.028003

    Abstract Human Action Recognition (HAR) and pose estimation from videos have gained significant attention among research communities due to its application in several areas namely intelligent surveillance, human robot interaction, robot vision, etc. Though considerable improvements have been made in recent days, design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle, occlusion, background, movement speed, and so on. From the literature, it is observed that hard to deal with the temporal dimension in the action recognition process. Convolutional neural network (CNN) models could… More >

  • Open Access

    ARTICLE

    Deep Learning Enabled Social Media Recommendation Based on User Comments

    K. Saraswathi1,*, V. Mohanraj2, Y. Suresh2, J. Senthilkumar2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1691-1702, 2023, DOI:10.32604/csse.2023.027987

    Abstract Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn’t filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method’s problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The first step is to collect… More >

  • Open Access

    ARTICLE

    Study on Real-Time Heart Rate Detection Based on Multi-People

    Qiuyu Hu1, Wu Zeng1,*, Yi Sheng1, Jian Xu1, Weihua Ou2, Ruochen Tan3

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1397-1408, 2023, DOI:10.32604/csse.2023.027980

    Abstract Heart rate is an important vital characteristic which indicates physical and mental health status. Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly. Therefore, the study of non-contact heart rate measurement methods is of great importance. Based on the principles of photoelectric volumetric tracing, we use a computer device and camera to capture facial images, accurately detect face regions, and to detect multiple facial images using a multi-target tracking algorithm. Then after the regional segmentation of the facial image, the signal acquisition of the region of interest is further resolved. Finally, frequency detection… More >

  • Open Access

    ARTICLE

    Deep-BERT: Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

    Md. Anwar Hussen Wadud1, M. F. Mridha1, Jungpil Shin2,*, Kamruddin Nur3, Aloke Kumar Saha4

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

    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 and multilingual offensive texts by… More >

  • Open Access

    ARTICLE

    An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

    M. Usharani1,*, B. Sakthivel2, S. Gayathri Priya3, T. Nagalakshmi4, J. Shirisha5

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1647-1657, 2023, DOI:10.32604/csse.2023.027744

    Abstract Approximate computing is a popular field for low power consumption that is used in several applications like image processing, video processing, multimedia and data mining. This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier. The multiplier is the most essential element used for approximate computing where the power consumption is majorly based on its performance. There are several researchers are worked on the approximate multiplier for power reduction for a few decades, but the design of low power approximate multiplier is not so easy. This seems a bigger challenge for digital industries to design an… More >

  • Open Access

    ARTICLE

    An Optimized Transfer Learning Model Based Kidney Stone Classification

    S. Devi Mahalakshmi*

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

    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 of DenseNet169, MobileNetv2 and GoogleNet… More >

  • Open Access

    ARTICLE

    Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

    P. Rahul1,*, N. Kanthimathi1, B. Kaarthick2, M. Leeban Moses1

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1583-1600, 2023, DOI:10.32604/csse.2023.027424

    Abstract Recently, the fundamental problem with Hybrid Mobile Ad-hoc Networks (H-MANETs) is to find a suitable and secure way of balancing the load through Internet gateways. Moreover, the selection of the gateway and overload of the network results in packet loss and Delay (DL). For optimal performance, it is important to load balance between different gateways. As a result, a stable load balancing procedure is implemented, which selects gateways based on Fuzzy Logic (FL) and increases the efficiency of the network. In this case, since gateways are selected based on the number of nodes, the Energy Consumption (EC) was high. This… More >

  • Open Access

    ARTICLE

    Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

    Ashit Kumar Dutta1,*, Yasser Albagory2, Manal Al Faraj1, Majed Alsanea3, Abdul Rahaman Wahab Sait4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1419-1432, 2023, DOI:10.32604/csse.2023.027377

    Abstract Accurate soil prediction is a vital parameter involved to decide appropriate crop, which is commonly carried out by the farmers. Designing an automated soil prediction tool helps to considerably improve the efficacy of the farmers. At the same time, fuzzy logic (FL) approaches can be used for the design of predictive models, particularly, Fuzzy Cognitive Maps (FCMs) have involved the concept of uncertainty representation and cognitive mapping. In other words, the FCM is an integration of the recurrent neural network (RNN) and FL involved in the knowledge engineering phase. In this aspect, this paper introduces effective fuzzy cognitive maps with… More >

  • Open Access

    ARTICLE

    Study on Recognition Method of Similar Weather Scenes in Terminal Area

    Ligang Yuan1,*, Jiazhi Jin1, Yan Xu2, Ningning Zhang3, Bing Zhang4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1171-1185, 2023, DOI:10.32604/csse.2023.027221

    Abstract Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality… More >

  • Open Access

    ARTICLE

    Efficient Object Detection and Classification Approach Using HTYOLOV4 and M2RFO-CNN

    V. Arulalan*, Dhananjay Kumar

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1703-1717, 2023, DOI:10.32604/csse.2023.026744

    Abstract Object detection and classification are the trending research topics in the field of computer vision because of their applications like visual surveillance. However, the vision-based objects detection and classification methods still suffer from detecting smaller objects and dense objects in the complex dynamic environment with high accuracy and precision. The present paper proposes a novel enhanced method to detect and classify objects using Hyperbolic Tangent based You Only Look Once V4 with a Modified Manta-Ray Foraging Optimization-based Convolution Neural Network. Initially, in the pre-processing, the video data was converted into image sequences and Polynomial Adaptive Edge was applied to preserve… More >

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