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.

  • Parallel Cloth Simulation Using OpenGL Shading Language
  • Abstract The primary goal of cloth simulation is to express object behavior in a realistic manner and achieve real-time performance by following the fundamental concept of physic. In general, the mass–spring system is applied to real-time cloth simulation with three types of springs. However, hard spring cloth simulation using the mass–spring system requires a small integration time-step in order to use a large stiffness coefficient. Furthermore, to obtain stable behavior, constraint enforcement is used instead of maintenance of the force of each spring. Constraint force computation involves a large sparse linear solving operation. Due to the large computation, we implement a… More
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  • Evaluation of NFC-Guidable System to Manage Polypharmacy in Elderly Patients
  • Abstract A complete and thorough understanding by patients of their prescriptions is one of the most critical components of a successful treatment journey. Being unfamiliar with the intricacies of prescribed medication can cause serious health risks due to not adhering to prescription instructions or noting potential drug interactions, which can lead to life-threatening injuries. Pharmacists face communication barriers (including non-English speaking patients), lack of time, lack of knowledge, workload, and frequent interruption when dispensing medicines often preventing them from providing the necessary guidance to their patients. To minimize this risk, an NFC-Guidable polypharmacy system was developed integrating Near Field Technology (NFC)… More
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  • Relative Time Quantum-based Enhancements in Round Robin Scheduling
  • Abstract Modern human life is heavily dependent on computing systems and one of the core components affecting the performance of these systems is underlying operating system. Operating systems need to be upgraded to match the needs of modern-day systems relying on Internet of Things, Fog computing and Mobile based applications. The scheduling algorithm of the operating system dictates that how the resources will be allocated to the processes and the Round Robin algorithm (RR) has been widely used for it. The intent of this study is to ameliorate RR scheduling algorithm to optimize task scheduling. We have carried out an experimental… More
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  • Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing
  • Abstract Cloud computing is a high network infrastructure where users, owners, third users, authorized users, and customers can access and store their information quickly. The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently. This cloud is nowadays highly affected by internal threats of the user. Sensitive applications such as banking, hospital, and business are more likely affected by real user threats. An intruder is presented as a user and set as a member of the network. After becoming an insider in the network, they will… More
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  • Optimal Placement and Sizing of Distributed Generation Using Metaheuristic Algorithm
  • Abstract Power loss and voltage uncertainty are the major issues prevalently faced in the design of distribution systems. But such issues can be resolved through effective usage of networking reconfiguration that has a combination of Distributed Generation (DG) units from distribution networks. In this point of view, optimal placement and sizing of DGs are effective ways to boost the performance of power systems. The optimum allocation of DGs resolves various problems namely, power loss, voltage profile improvement, enhanced reliability, system stability, and performance. Several research works have been conducted to address the distribution system problems in terms of power loss, energy… More
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  • Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services
  • Abstract Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other hand, trip planning is an… More
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  • Semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection
  • Abstract The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable. Phishing websites also create traffic in the entire network. Another phishing issue is the broadening malware of the entire network, thus highlighting the demand for their detection while massive datasets (i.e., big data) are processed. Despite the application of boosting mechanisms in phishing detection, these methods are prone to significant errors in their output, specifically due to the combination of all website features in the training state. The upcoming big data system requires MapReduce, a popular… More
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  • FACT: An Air-Ground Communication Framework for Seeding Quality Control of Aircraft
  • Abstract A new type of air-ground communication application framework named FACT (framework for air-ground communication technology with weather-modification aircraft) is presented to track and command weather-modification aircraft to perform ideal cloud seeding. FACT provides a set of solutions from three perspectives, namely, onboard, onground and air-to-ground, with the core purpose of solving the problems of the rapid exchange of information, contract analysis and identifying potential seeding areas when flight plans and meteorological conditions change. On board, the observed data are processed centrally and transmitted downward through air-to-ground communication. The real-time application and sharing of aircraft detection data are strengthened on the… More
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  • Evaluating the Impacts of Security-Durability Characteristic: Data Science Perspective
  • Abstract Security has always been a vital research topic since the birth of web application. A great deal of research has been conducted to determine the ways of identifying and classifying security issues or goals However, in the recent years, it has been noticed that high secure web applications have less durability; thus reducing their business continuity. High security features of a web application are worthless unless they provide effective services to the user and meet the standards of commercial viability. Hence, there is a need to bridge the gap between security and durability of the web application. Indeed, security mechanisms… More
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  • Enhanced Neuro-Fuzzy-Based Crop Ontology for Effective Information Retrieval
  • Abstract Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers. Familiarizing ontology as information retrieval (IR) aids in augmenting the searching effects of user-required relevant information. The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet, mapping the connection between keywords and information, and categorizing the retrieval outcomes. The prevailing procedures for IR consume considerable time, and they could not recover information proficiently. In this study, through applying a modified neuro-fuzzy algorithm (MNFA), the IR time is mitigated, and the retrieval accuracy is enhanced for trouncing the… More
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  • A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection
  • Abstract A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small… More
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  • Graphics Evolutionary Computations in Higher Order Parametric Bezier Curves
  • Abstract This work demonstrates in practical terms the evolutionary concepts and computational applications of Parametric Curves. Specific cases were drawn from higher order parametric Bezier curves of degrees 2 and above. Bezier curves find real life applications in diverse areas of Engineering and Computer Science, such as computer graphics, robotics, animations, virtual reality, among others. Some of the evolutionary issues explored in this work are in the areas of parametric equations derivations, proof of related theorems, first and second order calculus related computations, among others. A Practical case is demonstrated using a graphical design, physical hand sketching, and programmatic implementation of… More
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  • Exploring and Modelling IoT Offloading Policies in Edge Cloud Environments
  • Abstract The Internet of Things (IoT) has recently become a popular technology that can play increasingly important roles in every aspect of our daily life. For collaboration between IoT devices and edge cloud servers, edge server nodes provide the computation and storage capabilities for IoT devices through the task offloading process for accelerating tasks with large resource requests. However, the quantitative impact of different offloading architectures and policies on IoT applications’ performance remains far from clear, especially with a dynamic and unpredictable range of connected physical and virtual devices. To this end, this work models the performance impact by exploiting a… More
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  • Analysis of the Desynchronization Attack Impact on the E2EA Scheme
  • Abstract The healthcare IoT system is considered to be a significant and modern medical system. There is broad consensus that these systems will play a vital role in the achievement of economic growth in numerous growth countries. Among the major challenges preventing the fast and widespread adoption of such systems is the failure to maintain the data privacy of patients and the integrity of remote clinical diagnostics. Recently, the author proposed an end-to-end authentication scheme for healthcare IoT systems (E2EA), to provide a mutual authentication with a high data rate between the communication nodes of the healthcare IoT systems. Although the… More
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  • Brain Image Classification Using Time Frequency Extraction with Histogram Intensity Similarity
  • Abstract Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image… More
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  • Optimal Data Placement and Replication Approach for SIoT with Edge
  • Abstract Social networks (SNs) are sources with extreme number of users around the world who are all sharing data like images, audio, and video to their friends using IoT devices. This concept is the so-called Social Internet of Things (SIot). The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources, and this task demands an efficient storage procedure. For this kind of large volume of data storage, the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low latency. The major issue is… More
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  • Early Detection of Pancreatic Cancer Using Jaundiced Eye Images
  • Abstract Pancreatic cancer is one of the deadliest cancers, with less than 9% survival rates. Pancreatic Ductal Adeno Carcinoma (PDAC) is common with the general public affecting most people older than 45. Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages (advanced stage). One of the earlier symptoms of PDAC is Jaundice. Patients with diabetes, obesity, and alcohol consumption are also at higher risk of having pancreatic cancer. A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge. Features such as Mean Hue, Mean Saturation, Mean… More
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  • A New Method of Image Restoration Technology Based on WGAN
  • Abstract With the development of image restoration technology based on deep learning, more complex problems are being solved, especially in image semantic inpainting based on context. Nowadays, image semantic inpainting techniques are becoming more mature. However, due to the limitations of memory, the instability of training, and the lack of sample diversity, the results of image restoration are still encountering difficult problems, such as repairing the content of glitches which cannot be well integrated with the original image. Therefore, we propose an image inpainting network based on Wasserstein generative adversarial network (WGAN) distance. With the corresponding technology having been adjusted and… More
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  • Algorithms to Calculate the Most Reliable Maximum Flow in Content Delivery Network
  • Abstract Calculating the most reliable maximum flow (MRMF) from the edge cache node to the requesting node can provide an important reference for selecting the best edge cache node in a content delivery network (CDN). However, SDBA, as the current state-of-the-art MRMF algorithm, is too complex to meet real-time computing needs. This paper proposes a set of MRMF algorithms: NWCD (Negative Weight Community Deletion), SCPDAT (Single-Cycle Preference Deletion Approximation algorithm with Time constraint) and SCPDAP (Single-Cycle Preference Deletion Approximation algorithm with Probability constraint). NWCD draws on the “flow-shifting” algorithm of minimum cost and maximum flow, and further defines the concept of… More
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  • A Generalized State Space Average Model for Parallel DC-to-DC Converters
  • Abstract The high potentiality of integrating renewable energies, such as photovoltaic, into a modern electrical microgrid system, using DC-to-DC converters, raises some issues associated with controller loop design and system stability. The generalized state space average model (GSSAM) concept was consequently introduced to design a DC-to-DC converter controller in order to evaluate DC-to-DC converter performance and to conduct stability studies. This paper presents a GSSAM for parallel DC-to-DC converters, namely: buck, boost, and buck-boost converters. The rationale of this study is that modern electrical systems, such as DC networks, hybrid microgrids, and electric ships, are formed by parallel DC-to-DC converters with… More
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  • Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection
  • Abstract Breast cancer has become the second leading cause of death among women worldwide. In India, a woman is diagnosed with breast cancer every four minutes. There has been no known basis behind it, and detection is extremely challenging among medical scientists and researchers due to unknown reasons. In India, the ratio of women being identified with breast cancer in urban areas is 22:1. Symptoms for this disease are micro calcification, lumps, and masses in mammogram images. These sources are mostly used for early detection. Digital mammography is used for breast cancer detection. In this study, we introduce a new hybrid… More
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  • Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes
  • Abstract Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system… More
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  • Lightweight and Secure Mutual Authentication Scheme for IoT Devices Using CoAP Protocol
  • Abstract Internet of things enables every real world objects to be seamlessly integrated with traditional internet. Heterogeneous objects of real world are enhanced with capability to communicate, computing capabilities and standards to interoperate with existing network and these entities are resource constrained and vulnerable to various security attacks. Huge number of research works are being carried out to analyze various possible attacks and to propose standards for securing communication between devices in internet of things (IoT). In this article, a robust and lightweight authentication scheme for mutual authentication between client and server using constrained application protocol is proposed. Internet of things… More
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  • Measuring Mental Workload Using ERPs Based on FIR, ICA, and MARA
  • Abstract Mental workload is considered to be strongly linked to human performance, and the ability to measure it accurately is key for balancing human health and work. In this study, brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload. In addition, a finite impulse response (FIR) filter, independent component analysis (ICA), and multiple artifact rejection algorithms (MARAs) were used to filter event-related potentials (ERPs). Then, the data consisting of ERPs, subjective ratings of mental workload, and task performance, were analyzed through the use of variance and Spearman’s correlation during a simulated computer… More
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  • On Mixed Model for Improvement in Stock Price Forecasting
  • Abstract Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. But the fact is that forecasting stock prices by using various models has been suffering from low accuracy, slow convergence, and complex parameters. This study aims to employ a mixed model to improve the accuracy of stock price prediction. We present how to use a random walk based on jump-diffusion, to obtain stock predictions with a good-fitting degree by adjusting different parameters. Aimed at getting better parameters and then using the time series model to predict the data, we employed the time… More
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  • Healthcare Device Security Assessment through Computational Methodology
  • Abstract The current study discusses the different methods used to secure healthcare devices and proposes a quantitative framework to list them in order of significances. The study uses the Hesitant Fuzzy (HF), Analytic Hierarchy Process (AHP) integrated with Fuzzy Technical for Order Preference by Similarities to Ideal Solution (TOPSIS) to classify the best alternatives to security techniques for healthcare devices to securing the devices. The technique is enlisted to rate the alternatives based on the degree of satisfaction of their weights. The ranks of the alternatives consequently decide the order of priority for the techniques. A1 was the most probable alternative… More
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  • Modified Mackenzie Equation and CVOA Algorithm Reduces Delay in UASN
  • Abstract In Underwater Acoustic Sensor Network (UASN), routing and propagation delay is affected in each node by various water column environmental factors such as temperature, salinity, depth, gases, divergent and rotational wind. High sound velocity increases the transmission rate of the packets and the high dissolved gases in the water increases the sound velocity. High dissolved gases and sound velocity environment in the water column provides high transmission rates among UASN nodes. In this paper, the Modified Mackenzie Sound equation calculates the sound velocity in each node for energy-efficient routing. Golden Ratio Optimization Method (GROM) and Gaussian Process Regression (GPR) predicts… More
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  • Stochastic Gradient Boosting Model for Twitter Spam Detection
  • Abstract

    In today’s world of connectivity there is a huge amount of data than we could imagine. The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time. These social networks give the complete independence to the user to post the data either political, commercial or entertainment value. Some data may be sensitive and have a greater impact on the society as a result. The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter. Due to the large… More

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