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  • Open Access


    Intrusion Detection Systems Using Blockchain Technology: A Review, Issues and Challenges

    Salam Al-E’mari1, Mohammed Anbar1,*, Yousef Sanjalawe1,2, Selvakumar Manickam1, Iznan Hasbullah1

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 87-112, 2022, DOI:10.32604/csse.2022.017941

    Abstract Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches. However, the current approaches have challenges in detecting intrusions, which may affect the performance of the overall detection system as well as network performance. For the time being, one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information and maintains trust between individuals no matter… More >

  • Open Access


    Dates Fruit Recognition: From Classical Fusion to Deep Learning

    Khaled Marji Alresheedi1, Suliman Aladhadh2, Rehan Ullah Khan2, Ali Mustafa Qamar1,3,*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 151-166, 2022, DOI:10.32604/csse.2022.017931

    Abstract There are over 200 different varieties of dates fruit in the world. Interestingly, every single type has some very specific features that differ from the others. In recent years, sorting, separating, and arranging in automated industries, in fruits businesses, and more specifically in dates businesses have inspired many research dimensions. In this regard, this paper focuses on the detection and recognition of dates using computer vision and machine learning. Our experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates fruit. Classical machine learning includes the Bayesian network, Support Vector… More >

  • Open Access


    Stochastic Programming For Order Allocation And Production Planning

    Phan Nguyen Ky Phuc*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 75-85, 2022, DOI:10.32604/csse.2022.017793

    Abstract Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material,… More >

  • Open Access


    Stock-Price Forecasting Based on XGBoost and LSTM

    Pham Hoang Vuong1, Trinh Tan Dat1, Tieu Khoi Mai1, Pham Hoang Uyen2, Pham The Bao1,*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 237-246, 2022, DOI:10.32604/csse.2022.017685

    Abstract Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we… More >

  • Open Access


    Repeated Attribute Optimization for Big Data Encryption

    Abdalla Alameen*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 53-64, 2022, DOI:10.32604/csse.2022.017597

    Abstract Big data denotes the variety, velocity, and massive volume of data. Existing databases are unsuitable to store big data owing to its high volume. Cloud computing is an optimal solution to process and store big data. However, the significant issue lies in handling access control and privacy, wherein the data should be encrypted and unauthorized user access must be restricted through efficient access control. Attribute-based encryption (ABE) permits users to encrypt and decrypt data. However, for the policy to work in practical scenarios, the attributes must be repeated. In the case of specific policies, it is not possible to avoid… More >

  • Open Access


    Ensemble Classifier Technique to Predict Gestational Diabetes Mellitus (GDM)

    A. Sumathi*, S. Meganathan

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 313-325, 2022, DOI:10.32604/csse.2022.017484

    Abstract Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification of GDM. Machine Learning (ML) methods are found to be efficient prediction techniques with significant advantage over statistical models. In this view, the current research paper presents an ensemble of ML-based GDM prediction and classification models. The presented model involves three steps such as preprocessing, classification, and ensemble voting process. At first, the input medical data is preprocessed in four levels namely, format conversion, class labeling,… More >

  • Open Access


    Effective Hybrid Content-Based Collaborative Filtering Approach for Requirements Engineering

    Qusai Y. Shambour*, Abdelrahman H. Hussein, Qasem M. Kharma, Mosleh M. Abualhaj

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 113-125, 2022, DOI:10.32604/csse.2022.017221

    Abstract Requirements engineering (RE) is among the most valuable and critical processes in software development. The quality of this process significantly affects the success of a software project. An important step in RE is requirements elicitation, which involves collecting project-related requirements from different sources. Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements. However, the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately. Recommender systems have been widely recognized as an effective solution to such problem. Accordingly, this study proposes an effective hybrid content-based collaborative… More >

  • Open Access


    Blockchain: Secured Solution for Signature Transfer in Distributed Intrusion Detection System

    Shraddha R. Khonde1,2,*, Venugopal Ulagamuthalvi1

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 37-51, 2022, DOI:10.32604/csse.2022.017130

    Abstract Exchange of data in networks necessitates provision of security and confidentiality. Most networks compromised by intruders are those where the exchange of data is at high risk. The main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed network. Malicious activities are monitored and detected by the Intrusion Detection System (IDS) that operates with nodes connected to a distributed network. The IDS operates in two phases, where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest, Convolutional neural network,… More >

  • Open Access


    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

    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 problem with a sequence dependent… More >

  • Open Access


    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

    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 classifying COVID-19 (Negative or Positive).… More >

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