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): 2020 Impact Factor 1.486; Scopus Cite Score (Impact per Publication 2020): 1.4; SNIP (Source Normalized Impact per Paper 2020): 0.382; ACM Digital Library;

Previously published by CRL Publishing (http://crl-publishing.co.uk/), Computer Systems Science and Engineering starts to be published by Tech Science Press from the fifth issue of 2020 and supports Open Access Policy.

  • Secured Vehicle Life Cycle Tracking Using Blockchain and Smart Contract
  • Abstract Life Cycle Tracking (LCT) involves continuous monitoring and analysis of various activities associated with a vehicle. The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders. Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties. Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions performed. Machine learning techniques when combined decentralized nature of blockchains can be used to develop… More
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  • Detection of Fuel Adulteration Using Wave Optical with Machine Learning Algorithms
  • Abstract Fuel is a very important factor and has considerable influence on the air quality in the environment, which is the heart of the world. The increase of vehicles in lived-in areas results in greater emission of carbon particles in the environment. Adulterated fuel causes more contaminated particles to mix with breathing air and becomes the main source of dangerous pollution. Adulteration is the mixing of foreign substances in fuel, which damages vehicles and causes more health problems in living beings such as humans, birds, aquatic life, and even water resources by emitting high levels of hydrocarbons, nitrogen oxides, and carbon… More
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  • Short Text Mining for Classifying Educational Objectives and Outcomes
  • Abstract Most of the international accreditation bodies in engineering education (e.g., ABET) and outcome-based educational systems have based their assessments on learning outcomes and program educational objectives. However, mapping program educational objectives (PEOs) to student outcomes (SOs) is a challenging and time-consuming task, especially for a new program which is applying for ABET-EAC (American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission) accreditation. In addition, ABET needs to automatically ensure that the mapping (classification) is reasonable and correct. The classification also plays a vital role in the assessment of students’ learning. Since the PEOs are… More
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  • Nonlinear Identification and Control of Laser Welding Based on RBF Neural Networks
  • Abstract A laser beam is a heat source with a high energy density; this technology has been rapidly developed and applied in the field of welding owing to its potential advantages, and supplements traditional welding techniques. An in-depth analysis of its operating process could establish a good foundation for its application in China. It is widely understood that the welding process is a highly nonlinear and multi-variable coupling process; it comprises a significant number of complex processes with random uncertain factors. Because of their nonlinear mapping and self-learning characteristics, artificial neural networks (ANNs) have certain advantages in comparison to traditional methods… More
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  • Cost Optimized Switching of Routing Protocol Scheme for IoT Applications
  • Abstract In this work, we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks. The work planned has two stages i) Selection of suitable routing protocol (RP) for given IoT applications using higher cognitive process and ii) Deployment of the corresponding routing protocol. We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability, path delay, energy utilization, and throughput. The chosen routing protocol will be set in the sensor network using a software-defined networking controller in… More
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  • Secured Data Storage Using Deduplication in Cloud Computing Based on Elliptic Curve Cryptography
  • Abstract The tremendous development of cloud computing with related technologies is an unexpected one. However, centralized cloud storage faces few challenges such as latency, storage, and packet drop in the network. Cloud storage gets more attention due to its huge data storage and ensures the security of secret information. Most of the developments in cloud storage have been positive except better cost model and effectiveness, but still data leakage in security are billion-dollar questions to consumers. Traditional data security techniques are usually based on cryptographic methods, but these approaches may not be able to withstand an attack from the cloud server's… More
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  • A Secure IoT-Cloud Based Healthcare System for Disease Classification Using Neural Network
  • Abstract The integration of the Internet of Things (IoT) and cloud computing is the most popular growing technology in the IT world. IoT integrated cloud computing technology can be used in smart cities, health care, smart homes, environmental monitoring, etc. In recent days, IoT integrated cloud can be used in the health care system for remote patient care, emergency care, disease prediction, pharmacy management, etc. but, still, security of patient data and disease prediction accuracy is a major concern. Numerous machine learning approaches were used for effective early disease prediction. However, machine learning takes more time and less performance while classification.… More
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  • Phishing Websites Detection by Using Optimized Stacking Ensemble Model
  • Abstract Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for… More
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  • Correlation Analysis between Economic Growth and Environmental Quality
  • Abstract With the rapid development of the economy, China’s environment has been damaged severely, which has attracted much attention from scholars and the local government. The concept of green development has been an underlying trend since 2012, and it is of great significance to explore the relationship between economic growth and environmental quality. Huzhou is a prefecture-level city under the jurisdiction of Zhejiang Province, and it is one of the 27 cities in the central area of the Yangtze River Delta. In recent years, this city develops well not only in economic development but also in maintaining a green environment. In… More
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  • Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction
  • Abstract Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from using perturb observation method in… More
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  • Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments
  • Abstract This paper proposes anoptimal fuzzy-based model for obtaining crisp priorities for Fuzzy-AHP comparison matrices. Crisp judgments cannot be given for real-life situations, as most of these include some level of fuzziness and complexity. In these situations, judgments are represented by the set of fuzzy numbers. Most of the fuzzy optimization models derive crisp priorities for judgments represented with Triangular Fuzzy Numbers (TFNs) only. They do not work for other types of Triangular Shaped Fuzzy Numbers (TSFNs) and Trapezoidal Fuzzy Numbers (TrFNs). To overcome this problem, a sum of squared error (SSE) based optimization model is proposed. Unlike some other methods,… More
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  • Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System
  • Abstract Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from… More
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  • Classification of Foot Pressure Images Using Machine Learning Algorithm
  • Abstract Arthritis is an acute systemic disease of a joint accompanied by pain. In developed countries, it mainly causes disability among people over 50 years of age. Rheumatoid Arthritis is a type of arthritis that occurs commonly among elders. The incidence of arthritis is higher in females than in males. There is no permanent diagnosis method for arthritis, but if it was identified in the early stages based on the foot pressure, it can be diagnosed before attaining the critical stage of Rheumatoid Arthritis. The analysis and study of arthritis patients were done using design thinking methodology. Design thinking is a… More
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  • On Relations for Moments of Generalized Order Statistics for Lindley–Weibull Distribution
  • Abstract Moments of generalized order statistics appear in several areas of science and engineering. These moments are useful in studying properties of the random variables which are arranged in increasing order of importance, for example, time to failure of a computer system. The computation of these moments is sometimes very tedious and hence some algorithms are required. One algorithm is to use a recursive method of computation of these moments and is very useful as it provides the basis to compute higher moments of generalized order statistics from the corresponding lower-order moments. Generalized order statistics provides several models of ordered data… More
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  • Novel Power-Aware Optimization Methodology and Efficient Task Scheduling Algorithm
  • Abstract The performance of central processing units (CPUs) can be enhanced by integrating multiple cores into a single chip. Cpu performance can be improved by allocating the tasks using intelligent strategy. If Small tasks wait for long time or executes for long time, then CPU consumes more power. Thus, the amount of power consumed by CPUs can be reduced without increasing the frequency. Lines are used to connect cores, which are organized together to form a network called network on chips (NOCs). NOCs are mainly used in the design of processors. However, its performance can still be enhanced by reducing power… More
  •   Views:70       Downloads:48        Download PDF
  • Extensive Study of Cloud Computing Technologies, Threats and Solutions Prospective
  • Abstract Infrastructure as a Service (IaaS) provides logical separation between data, network, applications and machines from the physical constrains of real machines. IaaS is one of the basis of cloud virtualization. Recently, security issues are also gradually emerging with virtualization of cloud computing. Different security aspects of cloud virtualization will be explored in this research paper, security recognizing potential threats or attacks that exploit these vulnerabilities, and what security measures are used to alleviate such threats. In addition, a discussion of general security requirements and the existing security schemes is also provided. As shown in this paper, different components of virtualization… More
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  • Consensus-Based Ensemble Model for Arabic Cyberbullying Detection
  • Abstract Due to the proliferation of internet-enabled smartphones, many people, particularly young people in Arabic society, have widely adopted social media platforms as a primary means of communication, interaction and friendship making. The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world. However, such networks expose young people to cyberbullying and offensive content that puts their safety and emotional well-being at serious risk. Although, many solutions have been proposed to automatically detect cyberbullying, most of the existing solutions have been designed for English speaking consumers. The… More
  •   Views:64       Downloads:47        Download PDF
  • Adaptive Scheme for Crowd Counting Using off-the-Shelf Wireless Routers
  • Abstract Since the outbreak of the world-wide novel coronavirus pandemic, crowd counting in public areas, such as in shopping centers and in commercial streets, has gained popularity among public health administrations for preventing the crowds from gathering. In this paper, we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information (CSI) by using common commercial wireless routers. Compared with previous researches on device-free crowd counting, our proposed method is more adaptive to the change of environment and can achieve high accuracy of crowd count estimation. Because the distance between access point (AP) and monitor point (MP)… More
  •   Views:83       Downloads:57        Download PDF
  • Green Measurements for Software Product Based on Sustainability Dimensions
  • Abstract Software is a central component in the modern world and vastly affects the environment’s sustainability. The demand for energy and resource requirements is rising when producing hardware and software units. Literature study reveals that many studies focused on green hardware; however, limited efforts were made in the greenness of software products. Green software products are necessary to solve the issues and problems related to the long-term use of software, especially from a sustainability perspective. Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment, the mentioned benefits will not be attained. Currently,… More
  •   Views:74       Downloads:51        Download PDF
  • RFID Positioning and Physiological Signals for Remote Medical Care
  • Abstract The safety of patients and the quality of medical care provided to them are vital for their wellbeing. This study establishes a set of RFID (Radio Frequency Identification)-based systems of patient care based on physiological signals in the pursuit of a remote medical care system. The RFID-based positioning system allows medical staff to continuously observe the patient's health and location. The staff can thus respond to medical emergencies in time and appropriately care for the patient. When the COVID-19 pandemic broke out, the proposed system was used to provide timely information on the location and body temperature of patients who… More
  •   Views:76       Downloads:55        Download PDF
  • Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19
  • Abstract The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required… More
  •   Views:115       Downloads:64        Download PDF
  • A Survey on Technologies and Challenges of LTE-U
  • Abstract The rapid growth of mobile data traffic has caused great pressure on the limited spectrum resources, and there must be some better methods to deal with this problem. The innovative technology of Long-Term Evolution (LTE) using the unlicensed spectrum, known as LTE-Unlicensed (LTE-U), has been proposed to effectively alleviate the shortage of authorized band resources. LTE-U has explored a lot of potential capacity in mobile communication systems with limited authorized spectrum resources, and improved the spectrum utilization of unauthorized frequency bands. However, LTE-U is still facing challenges in its application. In this paper, we summarize the key features of LTE-U… More
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  • Ensemble Variable Selection for Naive Bayes to Improve Customer Behaviour Analysis
  • Abstract Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner. Further investigation of customer patterns helps the firm to develop efficient decisions and in turn, helps to optimize the enterprise’s business and maximizes consumer satisfaction correspondingly. To conduct an effective assessment about the customers, Naive Bayes(also called Simple Bayes), a machine learning model is utilized. However, the efficacious of the simple Bayes model is utterly relying on the consumer data used, and the existence of uncertain and redundant attributes in the… More
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  • Image Manipulation Detection Through Laterally Linked Pixels and Kernel Algorithms
  • Abstract In this paper, copy-move forgery in image is detected for single image with multiple manipulations such as blurring, noise addition, gray scale conversion, brightness modifications, rotation, Hu adjustment, color adjustment, contrast changes and JPEG Compression. However, traditional algorithms detect only copy-move attacks in image and never for different manipulation in single image. The proposed LLP (Laterally linked pixel) algorithm has two dimensional arrays and single layer is obtained through unit linking pulsed neural network for detection of copied region and kernel tricks is applied for detection of multiple manipulations in single forged image. LLP algorithm consists of two channels such… More
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  • A Novel Big Data Storage Reduction Model for Drill Down Search
  • Abstract Multi-level searching is called Drill down search. Right now, no drill down search feature is available in the existing search engines like Google, Yahoo, Bing and Baidu. Drill down search is very much useful for the end user to find the exact search results among the huge paginated search results. Higher level of drill down search with category based search feature leads to get the most accurate search results but it increases the number and size of the file system. The purpose of this manuscript is to implement a big data storage reduction binary file system model for category based… More
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  • Autism Spectrum Disorder Diagnosis Using Ensemble ML and Max Voting Techniques
  • Abstract Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder (ASD) diseases. These diseases can affect the nerves at any stage of the human being in childhood, adolescence, and adulthood. ASD is known as a behavioral disease due to the appearances of symptoms over the first two years that continue until adulthood. Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD. The detection of ASD is a very challenging task among various researchers. Machine learning (ML) algorithms still act very… More
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  • Statistical Inference of Sine Inverse Rayleigh Distribution
  • Abstract We study in this manuscript a new one-parameter model called sine inverse Rayleigh (SIR) model that is a new extension of the classical inverse Rayleigh model. The sine inverse Rayleigh model is aiming to provide more fitting for real data sets of purposes. The proposed extension is more flexible than the original inverse Rayleigh (IR) model and it hasmany applications in physics and medicine. The sine inverse Rayleigh distribution can havea uni-model and right skewed probability density function (PDF). The hazard rate function (HRF) of sine inverse Rayleigh distribution can be increasing and J-shaped. Several of thenew model’s fundamental characteristics,… More
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  • Facial Expression Recognition Using Enhanced Convolution Neural Network with Attention Mechanism
  • Abstract Facial Expression Recognition (FER) has been an interesting area of research in places where there is human-computer interaction. Human psychology, emotions and behaviors can be analyzed in FER. Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces. Recently, Deep Learning Techniques (DLT) have gained popularity in applications of real-world problems including recognition of human emotions. The human face reflects emotional states and human intentions. An expression is the most natural and powerful way of communicating non-verbally. Systems which form communications between the two are termed Human Machine Interaction (HMI)… More
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