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  • Open Access

    ARTICLE

    Improved Beam Steering Method Using OAM Waves

    Nidal Qasem*, Ahmad Alamayreh

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 417-431, 2023, DOI:10.32604/csse.2023.035603

    Abstract Orbital Angular Momentum (OAM) is an intrinsic feature of electromagnetic waves which has recently found many applications in several areas in radio and optics. In this paper, we use OAM wave characteristics to present a simple method for beam steering over both elevation and azimuth planes. The design overcomes some limitations of traditional steering methods, such as limited dynamic range of steering, the design complexity, bulky size of the steering structure, the limited bandwidth of operation, and low gain. Based on OAM wave characteristics, the proposed steering method avoids design complexities by adopting a simple method for generating the OAM-carrying… More >

  • Open Access

    ARTICLE

    An Anti-Physical Attack Scheme of ARX Lightweight Algorithms for IoT Applications

    Qiang Zhi1, Xiang Jiang1, Hangying Zhang2, Zhengshu Zhou3, Jianguo Ren1, Tong Huang4,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 389-402, 2023, DOI:10.32604/csse.2023.035576

    Abstract The lightweight encryption algorithm based on Add-Rotation-XOR (ARX) operation has attracted much attention due to its high software affinity and fast operation speed. However, lacking an effective defense scheme for physical attacks limits the applications of the ARX algorithm. The critical challenge is how to weaken the direct dependence between the physical information and the secret key of the algorithm at a low cost. This study attempts to explore how to improve its physical security in practical application scenarios by analyzing the masking countermeasures of ARX algorithms and the leakage causes. Firstly, we specify a hierarchical security framework by quantitatively… More >

  • Open Access

    ARTICLE

    Computing of LQR Technique for Nonlinear System Using Local Approximation

    Aamir Shahzad1, Ali Altalbe2,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 853-871, 2023, DOI:10.32604/csse.2023.035575

    Abstract The main idea behind the present research is to design a state-feedback controller for an underactuated nonlinear rotary inverted pendulum module by employing the linear quadratic regulator (LQR) technique using local approximation. The LQR is an excellent method for developing a controller for nonlinear systems. It provides optimal feedback to make the closed-loop system robust and stable, rejecting external disturbances. Model-based optimal controller for a nonlinear system such as a rotatory inverted pendulum has not been designed and implemented using Newton-Euler, Lagrange method, and local approximation. Therefore, implementing LQR to an underactuated nonlinear system was vital to design a stable… More >

  • Open Access

    ARTICLE

    Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

    V. Dinesh1, S. Srinivasan2, Gyanendra Prasad Joshi3, Woong Cho4,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 687-699, 2023, DOI:10.32604/csse.2023.035459

    Abstract In a vehicular ad hoc network (VANET), a massive quantity of data needs to be transmitted on a large scale in shorter time durations. At the same time, vehicles exhibit high velocity, leading to more vehicle disconnections. Both of these characteristics result in unreliable data communication in VANET. A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability. Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs. But one such difficulty was reducing the cluster number under increasing transmitting nodes. This article introduces an Evolutionary Hide… More >

  • Open Access

    ARTICLE

    Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

    Abdulaziz Alorf*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 273-286, 2023, DOI:10.32604/csse.2023.035437

    Abstract Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peak-hours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. This model gives priority to… More >

  • Open Access

    ARTICLE

    An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

    Muhammad Zia Ur Rehman1, Jawad Ahmad2,*, Emad Sami Jaha3, Abdullah Marish Ali3, Mohammed A. Alzain4, Faisal Saeed5

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 897-911, 2023, DOI:10.32604/csse.2023.035382

    Abstract Breast cancer is one of the leading cancers among women. It has the second-highest mortality rate in women after lung cancer. Timely detection, especially in the early stages, can help increase survival rates. However, manual diagnosis of breast cancer is a tedious and time-consuming process, and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience. However, computer-aided medical diagnosis has recently shown promising results, leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages. The research presented in this paper is focused… More >

  • Open Access

    ARTICLE

    MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

    Mehwish Shaikh1, Isma Farah Siddiqui1, Qasim Arain1, Jahwan Koo2,*, Mukhtiar Ali Unar3, Nawab Muhammad Faseeh Qureshi4,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 287-302, 2023, DOI:10.32604/csse.2023.035311

    Abstract Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to accurately categorize CXR images into… More >

  • Open Access

    ARTICLE

    An Intelligent Decision Support System for Lung Cancer Diagnosis

    Ahmed A. Alsheikhy1,*, Yahia F. Said1, Tawfeeq Shawly2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 799-817, 2023, DOI:10.32604/csse.2023.035269

    Abstract Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses a Convolutional Neural Network (CNN)… More >

  • Open Access

    ARTICLE

    A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

    Ahmed Fathalla1, Zakaria Alameer2, Mohamed Abbas3, Ahmed Ali4,5,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 929-950, 2023, DOI:10.32604/csse.2023.035255

    Abstract The oil industries are an important part of a country’s economy. The crude oil’s price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To examine the superiority of the proposed model, this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for… More >

  • Open Access

    ARTICLE

    Price Prediction of Seasonal Items Using Time Series Analysis

    Ahmed Salah1,2, Mahmoud Bekhit3, Esraa Eldesouky4,5, Ahmed Ali4,6,*, Ahmed Fathalla7

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 445-460, 2023, DOI:10.32604/csse.2023.035254

    Abstract The price prediction task is a well-studied problem due to its impact on the business domain. There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change, but there is very limited work to study the price prediction of seasonal goods (e.g., Christmas gifts). Seasonal items’ prices have different patterns than normal items; this can be linked to the offers and discounted prices of seasonal items. This lack of research studies motivates the current work to investigate the problem of seasonal items’ prices as a time series task.… More >

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