Computer Systems Science and Engineering

About the Journal

The Computer Systems Science and Engineering journal is devoted to the publication of high quality papers on theoretical developments in computer systems science, and their applications in computer systems engineering. Original research papers, state-of-the-art reviews and technical notes are invited for publication. Computer Systems Science and Engineering is published monthly by Tech Science Press.

Indexing and Abstracting

Science Citation Index (Web of Science): 2019 Impact Factor 0.278; Scopus Cite Score (Impact per Publication 2019): 1.2; SNIP (Source Normalized Impact per Paper 2019): 0.897; ACM Digital Library;

Previously published by CRL Publishing (, Computer Systems Science and Engineering starts to be published by Tech Science Press from the fifth issue of 2020 and supports Open Access Policy.

  • Generalized Class of Mean Estimators with Known Measures for Outliers Treatment
  • Abstract In estimation theory, the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares (OLS) method or robust regression techniques for estimating regression coefficients. But when the correlation is negative and the outliers are presented, the results can be distorted and the OLS-type estimators may give misleading estimates or highly biased estimates. Hence, this paper mainly focuses on such issues through the use of non-conventional measures of dispersion and a robust estimation method. Precisely, we have proposed generalized estimators by using the ancillary information of non-conventional measures… More
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  • Optimization of Bio-Implantable Power Transmission Efficiency Based on Input Impedance
  • Abstract Recently, the inductive coupling link is the most robust method for powering implanted biomedical devices, such as micro-system stimulators, cochlear implants, and retinal implants. This research provides a novel theoretical and mathematical analysis to optimize the inductive coupling link efficiency driven by efficient proposed class-E power amplifiers using high and optimum input impedance. The design of the coupling link is based on two pairs of aligned, single-layer, planar spiral circular coils with a proposed geometric dimension, operating at a resonant frequency of 13.56 MHz. Both transmitter and receiver coils are small in size. Implanted device resistance varies from 200 Ω… More
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  • Human-Animal Affective Robot Touch Classification Using Deep Neural Network
  • Abstract Touch gesture recognition is an important aspect in human–robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human–animal affective robot touch (HAART) using a deep learning algorithm. The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015. The dataset was tested with numerous subjects performing different HAART gestures; each touch was performed on a robotic animal covered by a pressure sensor skin. A convolutional neural network algorithm is proposed to implement the touch… More
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  • Residential Electricity Classification Method Based On Cloud Computing Platform and Random Forest
  • Abstract With the rapid development and popularization of new-generation technologies such as cloud computing, big data, and artificial intelligence, the construction of smart grids has become more diversified. Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents, which is essential to ensure the normal operation of the power system, energy management and planning. Based on the distributed architecture of cloud computing, this paper designs an improved random forest residential electricity classification method. It uses the unique out-of-bag error of random forest and combines the Drosophila… More
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  • An Efficient Medium Access Control Mechanism for Flying Ad-hoc Networks
  • Abstract The Flying Ad-hoc Networks (FANETs) is characterized by the transition from a single large Unmanned Aerial Vehicle (UAV) to multiple small UAVs connected in an ad-hoc fashion. Since high mobility is the core feature of such networks, they are prone to route breaks within the links. The issue of connectivity loss can be coped with, to some extent, by making use of omnidirectional antennas. Such modification, however, curtails Quality-of-Service (QoS) requirements of networks in terms of bandwidth, media access delay, coverage and others. Alternately, directional antennas have advantages over omnidirectional antennas such as improved transmission range, spatial reuse and high… More
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  • Differential Evolution Algorithm Based Self-adaptive Control Strategy for Fed-batch Cultivation of Yeast
  • Abstract In the fed-batch cultivation of Saccharomyces cerevisiae, excessive glucose addition leads to increased ethanol accumulation, which will reduce the efficiency of glucose utilization and inhibit product synthesis. Insufficient glucose addition limits cell growth. To properly regulate glucose feed, a different evolution algorithm based on self-adaptive control strategy was proposed, consisting of three modules (PID, system identification and parameter optimization). Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental cultivations. In the simulation, cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration, more stable ethanol concentration around the… More
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  • A New Generalized Weibull Model: Classical and Bayesian Estimation
  • Abstract Statistical distributions play a prominent role in applied sciences, particularly in biomedical sciences. The medical data sets are generally skewed to the right, and skewed distributions can be used quite effectively to model such kind of data sets. In the present study, therefore, we propose a new family of distributions suitable for modeling right-skewed medical data sets. The proposed family may be called a new generalized-X family. A special sub-model of the proposed family called a new generalized-Weibull distribution is discussed in detail. The maximum likelihood estimators of the model parameters are obtained. A brief Monte Carlo simulation study is… More
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  • Smart Contract: Security and Privacy
  • Abstract Smart contracts are simply self-activated contracts between two parties. The idea behind their implementation relies on the concept of blockchain, wherein the details and execution of the contract are turned into code and distributed among users of a network. This process controls counterfeiting and money laundering by its ability to trace who owes whom. It also boosts the general economy. This research paper shows how smart contracts in modern-day systems have changed the approach to money tracing. We present case studies about the uses of smart contracts with high levels of security and privacy. As a building block of smart… More
  •   Views:68       Downloads:63        Download PDF
  • A Network Security Risk Assessment Method Based on a B_NAG Model
  • Abstract Computer networks face a variety of cyberattacks. Most network attacks are contagious and destructive, and these types of attacks can be harmful to society and computer network security. Security evaluation is an effective method to solve network security problems. For accurate assessment of the vulnerabilities of computer networks, this paper proposes a network security risk assessment method based on a Bayesian network attack graph (B_NAG) model. First, a new resource attack graph (RAG) and the algorithm E-Loop, which is applied to eliminate loops in the B_NAG, are proposed. Second, to distinguish the confusing relationships between nodes of the attack graph… More
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  • Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity for Data Classification
  • Abstract

    Classification is one of the data mining processes used to predict predetermined target classes with data learning accurately. This study discusses data classification using a fuzzy soft set method to predict target classes accurately. This study aims to form a data classification algorithm using the fuzzy soft set method. In this study, the fuzzy soft set was calculated based on the normalized Hamming distance. Each parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation function. In the classification step, a generalized normalized Euclidean distance is used to determine… More

  •   Views:128       Downloads:69        Download PDF