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  • Open Access


    Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

    Musaed Alrashidi*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 371-387, 2023, DOI:10.32604/csse.2023.034739

    Abstract The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones. Mosques are the type of buildings that have a unique energy usage pattern. Nevertheless, these types of buildings have minimal consideration in the ongoing energy efficiency applications. This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks. Therefore, this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh, Saudi Arabia. In this study, and by harvesting the load consumption of the… More >

  • Open Access


    A Novel Mixed Precision Distributed TPU GAN for Accelerated Learning Curve

    Aswathy Ravikumar, Harini Sriraman*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 563-578, 2023, DOI:10.32604/csse.2023.034710

    Abstract Deep neural networks are gaining importance and popularity in applications and services. Due to the enormous number of learnable parameters and datasets, the training of neural networks is computationally costly. Parallel and distributed computation-based strategies are used to accelerate this training process. Generative Adversarial Networks (GAN) are a recent technological achievement in deep learning. These generative models are computationally expensive because a GAN consists of two neural networks and trains on enormous datasets. Typically, a GAN is trained on a single server. Conventional deep learning accelerator designs are challenged by the unique properties of GAN, like the enormous computation stages… More >

  • Open Access


    Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

    El-Sayed M. El-kenawy1, Abdelaziz A. Abdelhamid2,3, Abdelhameed Ibrahim4, Mostafa Abotaleb5, Tatiana Makarovskikh5, Amal H. Alharbi6,*, Doaa Sami Khafaga6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 883-896, 2023, DOI:10.32604/csse.2023.034697

    Abstract : Several instances of pneumonia with no clear etiology were recorded in Wuhan, China, on December 31, 2019. The world health organization (WHO) called it COVID-19 that stands for “Coronavirus Disease 2019,” which is the second version of the previously known severe acute respiratory syndrome (SARS) Coronavirus and identified in short as (SARSCoV-2). There have been regular restrictions to avoid the infection spread in all countries, including Saudi Arabia. The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread.Methodology: Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive… More >

  • Open Access


    Constructing an AI Compiler for ARM Cortex-M Devices

    Rong-Guey Chang, Tam-Van Hoang*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 999-1019, 2023, DOI:10.32604/csse.2023.034672

    Abstract The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code, which often requires specific treatment for each platform. The problem becomes critical on embedded devices, where computational and memory resources are strictly constrained. Compilers play an essential role in deploying source code on a target device through the backend. In this work, a novel backend for the Open Neural Network Compiler (ONNC) is proposed, which exploits machine learning to optimize code for the ARM Cortex-M device. The backend requires minimal changes to Open Neural Network Exchange (ONNX) models. Several novel optimization… More >

  • Open Access


    Anomaly Detection Based on Discrete Wavelet Transformation for Insider Threat Classification

    Dong-Wook Kim1, Gun-Yoon Shin1, Myung-Mook Han2,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 153-164, 2023, DOI:10.32604/csse.2023.034589

    Abstract Unlike external attacks, insider threats arise from legitimate users who belong to the organization. These individuals may be a potential threat for hostile behavior depending on their motives. For insider detection, many intrusion detection systems learn and prevent known scenarios, but because malicious behavior has similar patterns to normal behavior, in reality, these systems can be evaded. Furthermore, because insider threats share a feature space similar to normal behavior, identifying them by detecting anomalies has limitations. This study proposes an improved anomaly detection methodology for insider threats that occur in cybersecurity in which a discrete wavelet transformation technique is applied… More >

  • Open Access


    Adversarial Examples Protect Your Privacy on Speech Enhancement System

    Mingyu Dong, Diqun Yan*, Rangding Wang

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1-12, 2023, DOI:10.32604/csse.2023.034568

    Abstract Speech is easily leaked imperceptibly. When people use their phones, the personal voice assistant is constantly listening and waiting to be activated. Private content in speech may be maliciously extracted through automatic speech recognition (ASR) technology by some applications on phone devices. To guarantee that the recognized speech content is accurate, speech enhancement technology is used to denoise the input speech. Speech enhancement technology has developed rapidly along with deep neural networks (DNNs), but adversarial examples can cause DNNs to fail. Considering that the vulnerability of DNN can be used to protect the privacy in speech. In this work, we… More >

  • Open Access


    The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

    S. Navaneethan1,*, P. Siva Satya Sreedhar2, S. Padmakala3, C. Senthilkumar4

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 125-135, 2023, DOI:10.32604/csse.2023.034546

    Abstract The pupil recognition method is helpful in many real-time systems, including ophthalmology testing devices, wheelchair assistance, and so on. The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size, occlusion of eyelids, and eyelashes. Deep Convolutional Neural Networks (DCNN) are being used in pupil recognition systems and have shown promising results in terms of accuracy. To improve accuracy and cope with larger datasets, this research work proposes BOC (BAT Optimized CNN)-IrisNet, which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm… More >

  • Open Access


    The Correlation Coefficient of Hesitancy Fuzzy Graphs in Decision Making

    N. Rajagopal Reddy, S. Sharief Basha*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 579-596, 2023, DOI:10.32604/csse.2023.034527

    Abstract The hesitancy fuzzy graphs (HFGs), an extension of fuzzy graphs, are useful tools for dealing with ambiguity and uncertainty in issues involving decision-making (DM). This research implements a correlation coefficient measure (CCM) to assess the strength of the association between HFGs in this article since CCMs have a high capacity to process and interpret data. The CCM that is proposed between the HFGs has better qualities than the existing ones. It lowers restrictions on the hesitant fuzzy elements’ length and may be used to establish whether the HFGs are connected negatively or favorably. Additionally, a CCM-based attribute DM approach is… More >

  • Open Access


    Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

    Naqqash Dilshad1, Taimoor Khan2, JaeSeung Song1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 749-764, 2023, DOI:10.32604/csse.2023.034475

    Abstract To prevent economic, social, and ecological damage, fire detection and management at an early stage are significant yet challenging. Although computationally complex networks have been developed, attention has been largely focused on improving accuracy, rather than focusing on real-time fire detection. Hence, in this study, the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment. The proposed model architecture is inspired by the VGG16 network, with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4. This results in higher performance with a reduced number of… More >

  • Open Access


    Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

    Mohamed Amine Ouamri1,2, Reem Alkanhel3,*, Daljeet Singh4, El-sayed M. El-kenaway5, Sherif S. M. Ghoneim6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 73-92, 2023, DOI:10.32604/csse.2023.034461

    Abstract Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive for dense cellular networks. Therefore,… More >

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