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  • Open Access

    ARTICLE

    Scheduling Flexible Flow Shop in Labeling Companies to Minimize the Makespan

    Chia-Nan Wang1, Hsien-Pin Hsu2, Hsin-Pin Fu3,*, Nguyen Ky Phuc Phan4, Van Thanh Nguyen5

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 17-36, 2022, DOI:10.32604/csse.2022.016992 - 26 August 2021

    Abstract In the competitive global marketplace, production scheduling plays a vital role in planning in manufacturing. Scheduling deals directly with the time to output products quickly and with a low production cost. This research examines case study of a Radio-Frequency Identification labeling department at Avery Dennison. The main objective of the company is to have a method that allows for the sequencing and scheduling of a set of jobs so it can be completed on or before the customer’s due date to minimize the number of late orders. This study analyzes the flexible flow shop scheduling More >

  • Open Access

    ARTICLE

    An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images

    Anas Basalamah1, Shadikur Rahman2,*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 375-388, 2022, DOI:10.32604/csse.2022.016949 - 26 August 2021

    Abstract This paper demonstrates empirical research on using convolutional neural networks (CNN) of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction. Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge. In this study, CNN architectures such as VGG-16, VGG-19, RestNet50, RestNet18 are compared, and an optimized model for feature extraction in X-ray images from various domains involving several classes is proposed. An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for More >

  • Open Access

    ARTICLE

    Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

    A. Thasil Mohamed, Sundar Santhoshkumar*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 191-205, 2022, DOI:10.32604/csse.2022.016754 - 26 August 2021

    Abstract Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases… More >

  • Open Access

    ARTICLE

    Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm

    Nagham A. Al-Madi*, Adnan A. Hnaif

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 65-74, 2022, DOI:10.32604/csse.2022.016730 - 26 August 2021

    Abstract Current traffic signals in Jordan suffer from severe congestion due to many factors, such as the considerable increase in the number of vehicles and the use of fixed timers, which still control existing traffic signals. This condition affects travel demand on the streets of Jordan. This study aims to improve an intelligent road traffic management system (IRTMS) derived from the human community-based genetic algorithm (HCBGA) to mitigate traffic signal congestion in Amman, Jordan’s capital city. The parameters considered for IRTMS are total time and waiting time, and fixed timers are still used for control. By More >

  • Open Access

    ARTICLE

    A Coordinated Search Algorithm for a Lost Target on the Plane

    Sundus Naji Al-Aziz1,*, Abd Al-Aziz Hosni El-Bagoury2, W. Afifi2,3

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 127-137, 2022, DOI:10.32604/csse.2022.016007 - 26 August 2021

    Abstract Concepts in search theory have developed since World War II. The study of search plans has found considerable interest among searchers due to its wide applications in our life. Searching for lost targets either located or moved is often a time-critical issue, especially when the target is very important . In many commercial and scientific missions at sea, it is of crucial importance to find lost targets underwater. We illustrate a technique known as coordinated search, that completely characterizes the search for a randomly located target on a plane. The idea is to avoid wasting… More >

  • Open Access

    ARTICLE

    Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

    Jiaming Mao1,*, Mingming Zhang1, Mu Chen2, Lu Chen2, Fei Xia1, Lei Fan1, ZiXuan Wang3, Wenbing Zhao4

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 373-390, 2021, DOI:10.32604/csse.2021.018086 - 12 August 2021

    Abstract The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning… More >

  • Open Access

    ARTICLE

    FREPD: A Robust Federated Learning Framework on Variational Autoencoder

    Zhipin Gu1, Liangzhong He2, Peiyan Li1, Peng Sun3, Jiangyong Shi1, Yuexiang Yang1,*

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 307-320, 2021, DOI:10.32604/csse.2021.017969 - 12 August 2021

    Abstract Federated learning is an ideal solution to the limitation of not preserving the users’ privacy information in edge computing. In federated learning, the cloud aggregates local model updates from the devices to generate a global model. To protect devices’ privacy, the cloud is designed to have no visibility into how these updates are generated, making detecting and defending malicious model updates a challenging task. Unlike existing works that struggle to tolerate adversarial attacks, the paper manages to exclude malicious updates from the global model’s aggregation. This paper focuses on Byzantine attack and backdoor attack in… More >

  • Open Access

    ARTICLE

    Finding the Time-dependent Term in 2D Heat Equation from Nonlocal Integral Conditions

    M.J. Huntul*

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 415-429, 2021, DOI:10.32604/csse.2021.017924 - 12 August 2021

    Abstract The aim of this paper is to find the time-dependent term numerically in a two-dimensional heat equation using initial and Neumann boundary conditions and nonlocal integrals as over-determination conditions. This is a very interesting and challenging nonlinear inverse coefficient problem with important applications in various fields ranging from radioactive decay, melting or cooling processes, electronic chips, acoustics and geophysics to medicine. Unique solvability theorems of these inverse problems are supplied. However, since the problems are still ill-posed (a small modification in the input data can lead to bigger impact on the ultimate result in the… More >

  • Open Access

    ARTICLE

    Hybrid Sooty Tern Optimization and Differential Evolution for Feature Selection

    Heming Jia1,2,*, Yao Li2, Kangjian Sun2, Ning Cao1, Helen Min Zhou3

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 321-335, 2021, DOI:10.32604/csse.2021.017536 - 12 August 2021

    Abstract In this paper, a hybrid model based on sooty tern optimization algorithm (STOA) is proposed to optimize the parameters of the support vector machine (SVM) and identify the best feature sets simultaneously. Feature selection is an essential process of data preprocessing, and it aims to find the most relevant subset of features. In recent years, it has been applied in many practical domains of intelligent systems. The application of SVM in many fields has proved its effectiveness in classification tasks of various types. Its performance is mainly determined by the kernel type and its parameters.… More >

  • Open Access

    ARTICLE

    Flood Forecasting of Malaysia Kelantan River using Support Vector Regression Technique

    Amrul Faruq1, Aminaton Marto2, Shahrum Shah Abdullah3,*

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 297-306, 2021, DOI:10.32604/csse.2021.017468 - 12 August 2021

    Abstract The rainstorm is believed to contribute flood disasters in upstream catchments, resulting in further consequences in downstream area due to rise of river water levels. Forecasting for flood water level has been challenging, presenting complex task due to its nonlinearities and dependencies. This study proposes a support vector machine regression model, regarded as a powerful machine learning-based technique to forecast flood water levels in downstream area for different lead times. As a case study, Kelantan River in Malaysia has been selected to validate the proposed model. Four water level stations in river basin upstream were… More >

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