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  • Open Access

    ARTICLE

    Defected Ground Structure Multiple Input-Output Antenna For Wireless Applications

    Ramya Sridhar1,*, Vijayalakshimi Patteeswaran2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2109-2122, 2023, DOI:10.32604/csse.2023.036781

    Abstract In this paper, the investigation of a novel compact 2 × 2, 2 × 1, and 1 × 1 Ultra-Wide Band (UWB) based Multiple-Input Multiple-Output (MIMO) antenna with Defected Ground Structure (DGS) is employed. The proposed Electromagnetic Radiation Structures (ERS) is composed of multiple radiating elements. These MIMO antennas are designed and analyzed with and without DGS. The feeding is introduced by a microstrip-fed line to significantly moderate the radiating structure’s overall size, which is 60 × 40 × 1 mm. The high directivity and divergence characteristics are attained by introducing the microstrip-fed lines perpendicular to each other. And the… More >

  • Open Access

    ARTICLE

    Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network

    Saud Yonbawi1, Sultan Alahmari2, B. R. S. S. Raju3, Chukka Hari Govinda Rao4, Mohamad Khairi Ishak5, Hend Khalid Alkahtani6, José Varela-Aldás7,*, Samih M. Mostafa8

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2319-2335, 2023, DOI:10.32604/csse.2023.036721

    Abstract Artificial intelligence (AI) technologies and sensors have recently received significant interest in intellectual agriculture. Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture. Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques. Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist. With this motivation, this study develops a modified black widow optimization with a… More >

  • Open Access

    ARTICLE

    Data Analytics on Unpredictable Pregnancy Data Records Using Ensemble Neuro-Fuzzy Techniques

    C. Vairavel1,*, N. S. Nithya2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2159-2175, 2023, DOI:10.32604/csse.2023.036598

    Abstract The immune system goes through a profound transformation during pregnancy, and certain unexpected maternal complications have been correlated to this transition. The ability to correctly examine, diagnoses, and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is scalable. Many approaches for disease diagnosis/classification have been established with the use of data mining concepts. However, such methods do not provide an appropriate classification/diagnosis model. Furthermore, single learning approaches are used to create the bulk of these systems. Classification issues may be made more accurate by combining predictions from many… More >

  • Open Access

    ARTICLE

    Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

    S. Hema1,*, Y. Sukhi2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1503-1519, 2023, DOI:10.32604/csse.2023.036577

    Abstract Smart grids and their technologies transform the traditional electric grids to assure safe, secure, cost-effective, and reliable power transmission. Non-linear phenomena in power systems, such as voltage collapse and oscillatory phenomena, can be investigated by chaos theory. Recently, renewable energy resources, such as wind turbines, and solar photovoltaic (PV) arrays, have been widely used for electric power generation. The design of the controller for the direct Current (DC) converter in a PV system is performed based on the linearized model at an appropriate operating point. However, these operating points are ever-changing in a PV system, and the design of the… More >

  • Open Access

    ARTICLE

    Improved Siamese Palmprint Authentication Using Pre-Trained VGG16-Palmprint and Element-Wise Absolute Difference

    Mohamed Ezz, Waad Alanazi, Ayman Mohamed Mostafa*, Eslam Hamouda, Murtada K. Elbashir, Meshrif Alruily

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2299-2317, 2023, DOI:10.32604/csse.2023.036567

    Abstract Palmprint identification has been conducted over the last two decades in many biometric systems. High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues. This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication. The proposed model has two stages of learning; the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model. The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity. The proposed model achieves robust and reliable end-to-end Palmprint… More >

  • Open Access

    ARTICLE

    Augmenting Android Malware Using Conditional Variational Autoencoder for the Malware Family Classification

    Younghoon Ban, Jeong Hyun Yi, Haehyun Cho*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2215-2230, 2023, DOI:10.32604/csse.2023.036555

    Abstract Android malware has evolved in various forms such as adware that continuously exposes advertisements, banking malware designed to access users’ online banking accounts, and Short Message Service (SMS) malware that uses a Command & Control (C&C) server to send malicious SMS, intercept SMS, and steal data. By using many malicious strategies, the number of malware is steadily increasing. Increasing Android malware threats numerous users, and thus, it is necessary to detect malware quickly and accurately. Each malware has distinguishable characteristics based on its actions. Therefore, security researchers have tried to categorize malware based on their behaviors by conducting the familial… More >

  • Open Access

    ARTICLE

    Optimization of Quantum Cost for Low Energy Reversible Signed/Unsigned Multiplier Using Urdhva-Tiryakbhyam Sutra

    Marwa A. Elmenyawi1,2,*, Radwa M. Tawfeek1

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1827-1844, 2023, DOI:10.32604/csse.2023.036474

    Abstract One of the elementary operations in computing systems is multiplication. Therefore, high-speed and low-power multipliers design is mandatory for efficient computing systems. In designing low-energy dissipation circuits, reversible logic is more efficient than irreversible logic circuits but at the cost of higher complexity. This paper introduces an efficient signed/unsigned 4 × 4 reversible Vedic multiplier with minimum quantum cost. The Vedic multiplier is considered fast as it generates all partial product and their sum in one step. This paper proposes two reversible Vedic multipliers with optimized quantum cost and garbage output. First, the unsigned Vedic multiplier is designed based on… More >

  • Open Access

    ARTICLE

    Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

    Mohammad Yamin1,*, Sarah Basahel1, Saleh Bajaba2, Mona Abusurrah3, E. Laxmi Lydia4

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1901-1916, 2023, DOI:10.32604/csse.2023.036455

    Abstract Recently, there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy (DR). DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed world. Initial detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus images. This article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy (DLLSHDM-DR) on Retinal Fundus Images. The proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making method. In the DLLSHDM-DR technique, image preprocessing is initially… More >

  • Open Access

    ARTICLE

    Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

    Junjie Zhou, Hongkui Xu*, Zifeng Zhang, Jiangkun Lu, Wentao Guo, Zhenye Li

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2277-2297, 2023, DOI:10.32604/csse.2023.036419

    Abstract Fraud cases have been a risk in society and people’s property security has been greatly threatened. In recent studies, many promising algorithms have been developed for social media offensive text recognition as well as sentiment analysis. These algorithms are also suitable for fraudulent phone text recognition. Compared to these tasks, the semantics of fraudulent words are more complex and more difficult to distinguish. Recurrent Neural Networks (RNN), the variants of RNN, Convolutional Neural Networks (CNN), and hybrid neural networks to extract text features are used by most text classification research. However, a single network or a simple network combination cannot… More >

  • Open Access

    ARTICLE

    Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

    N. Sharmili1, Saud Yonbawi2, Sultan Alahmari3, E. Laxmi Lydia4, Mohamad Khairi Ishak5, Hend Khalid Alkahtani6,*, Ayman Aljarbouh7, Samih M. Mostafa8

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2247-2262, 2023, DOI:10.32604/csse.2023.036377

    Abstract Facial expression recognition (FER) remains a hot research area among computer vision researchers and still becomes a challenge because of high intra-class variations. Conventional techniques for this problem depend on hand-crafted features, namely, LBP, SIFT, and HOG, along with that a classifier trained on a database of videos or images. Many execute perform well on image datasets captured in a controlled condition; however not perform well in the more challenging dataset, which has partial faces and image variation. Recently, many studies presented an endwise structure for facial expression recognition by utilizing DL methods. Therefore, this study develops an earthworm optimization… More >

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