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.

  • Energy-Aware Scheduling for Tasks with Target-Time in Blockchain based Data Centres
  • Abstract

    Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use model. The extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers (DC) leads to massive power consumption. This intensifying scale of DCs has made energy consumption a critical concern. This paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data center. Also, an energy-aware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy consumption.… More

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  • Usability and Security of Arabic Text-based CAPTCHA Using Visual Cryptography
  • Abstract Recently, with the spread of online services involving websites, attackers have the opportunity to expose these services to malicious actions. To protect these services, A Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is a proposed technique. Since many Arabic countries have developed their online services in Arabic, Arabic text-based CAPTCHA has been introduced to improve the usability for their users. Moreover, there exist a visual cryptography (VC) technique which can be exploited in order to enhance the security of text-based CAPTCHA by encrypting a CAPTCHA image into two shares and decrypting it by asking the… More
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  • Video Identification Based on Watermarking Schemes and Visual Cryptography
  • Abstract Related to the growth of data sharing on the Internet and the wide - spread use of digital media, multimedia security and copyright protection have become of broad interest. Visual cryptography () is a method of sharing a secret image between a group of participants, where certain groups of participants are defined as qualified and may combine their share of the image to obtain the original, and certain other groups are defined as prohibited, and even if they combine knowledge of their parts, they can’t obtain any information on the secret image. The visual cryptography is one of the techniques… More
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  • Implementation of K-Means Algorithm and Dynamic Routing Protocol in VANET
  • Abstract With the growth of Vehicular Ad-hoc Networks, many services delivery is gaining more attention from the intelligent transportation system. However, mobility characteristics of vehicular networks cause frequent disconnection of routes, especially during the delivery of data. In both developed and developing countries, a lot of time is consumed due to traffic congestion. This has significant negative consequences, including driver stress due to increased time demand, decreased productivity for various personalized and commercial vehicles, and increased emissions of hazardous gases especially air polluting gases are impacting public health in highly populated areas. Clustering is one of the most powerful strategies for… More
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  • Intelligent Identification and Resolution of Software Requirement Conflicts: Assessment and Evaluation
  • Abstract Considerable research has demonstrated how effective requirements engineering is critical for the success of software projects. Requirements engineering has been established and recognized as one of the most important aspects of software engineering as of late. It is noteworthy to mention that requirement consistency is a critical factor in project success, and conflicts in requirements lead to waste of cost, time, and effort. A considerable number of research studies have shown the risks and problems caused by working with requirements that are in conflict with other requirements. These risks include running overtime or over budget, which may lead to project… More
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  • Mammogram Learning System for Breast Cancer Diagnosis Using Deep Learning SVM
  • Abstract The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces system complexity. The softmax layer… More
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  • Organizational Data Breach: Building Conscious Care Behavior in Incident Response
  • Abstract Organizational and end user data breaches are highly implicated by the role of information security conscious care behavior in respective incident responses. This research study draws upon the literature in the areas of information security, incident response, theory of planned behaviour, and protection motivation theory to expand and empirically validate a modified framework of information security conscious care behaviour formation. The applicability of the theoretical framework is shown through a case study labelled as a cyber-attack of unprecedented scale and sophistication in Singapore’s history to-date, the 2018 SingHealth data breach. The single in-depth case study observed information security awareness, policy,… More
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  • Hybrid Renewable Energy Source Combined Dynamic Voltage Restorer for Power Quality Improvement
  • Abstract In this paper, the hybrid photovoltaic-thermoelectric generator (PV-TEG) combined dynamic voltage restorer (DVR) system is proposed for the power quality disturbances compensation in a single-phase distribution system. The stable and precise level of input voltage is essential for the smooth and trouble-free operation of the electrically sensitive loads which are connected at the utility side to avoid system malfunctions. In this context, the hybrid PV-TEG energy module combined DVR system is proposed in this paper. With the support of the hybrid energy module, the DVR will perform the power quality disturbances compensation effectively with needed voltage and /or power. In… More
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  • An improved CRNN for Vietnamese Identity Card Information Recognition
  • Abstract This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity (CID) card. First, we apply Mask-RCNN to segment and align the CID card from the background. Next, we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST detector. Finally, we introduce a new end-to-end Convolutional Recurrent Neural Network (CRNN) model based on a combination of Connectionist Temporal Classification (CTC) and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions together. The… More
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  • An Architecture Supporting Intelligent Mobile Healthcare Using Human-Computer Interaction HCI Principles
  • Abstract Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare (mHealthcare) services. A patient within the hospital is monitored by several devices. Moreover, upon leaving the hospital, the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health parameters. This raises potential challenges for intelligent monitoring of patient’s health. In this paper, an improved architecture for smart mHealthcare is proposed that is supported by HCI design principles. The HCI also provides the support for the User-Centric Design (UCD) for smart… More
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  • Performance Analysis and Throughput Enhancement of the STET Technique for WLAN IEEE 802.11ad
  • Abstract The IEEE 802.11ad innovation has enabled the impact of remote devices in unauthorized 60 GHz unlicensed frequency band at Giga bits per second information transfer rate in speed concentrated 5G applications. We have presented an innovative work that deals with the upgradation of the ability of IEEE 802.11ad wireless LAN to make it suitable for wireless applications. An exact examination on the IEEE 802.11ad analysis has been carried out in this work to achieve the greatest throughput. This has pulled attraction in broad consideration for accomplishing the pinnacle transmission rate of 8 Gbit/s. IEEE 802.11ad is a convention utilized for… More
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  • Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing
  • Abstract Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task… More
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  • Design of Neural Network Based Wind Speed Prediction Model Using GWO
  • Abstract The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power. Wind power is the clean, free and conservative renewable energy. It is necessary to predict the wind speed, to implement wind power generation. This paper proposes a new model, named WT-GWO-BPNN, by integrating Wavelet Transform (WT), Back Propagation Neural Network (BPNN) and Grey Wolf Optimization (GWO). The wavelet transform is adopted to decompose the original time series data (wind speed) into approximation and detailed band. GWO – BPNN is applied to predict the wind speed. GWO is used to optimize the parameters… More
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  • Desertification Detection in Makkah Region based on Aerial Images Classification
  • Abstract Desertification has become a global threat and caused a crisis, especially in Middle Eastern countries, such as Saudi Arabia. Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification. The vegetation area in Makkah has been damaged because of desertification through wind, floods, overgrazing, and global climate change. The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area. In this paper, we propose an automatic desertification detection system based on Deep Learning techniques. Aerial images are classified using Convolutional Neural Networks… More
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  • Applying Non-Local Means Filter on Seismic Exploration
  • Abstract The seismic reflection method is one of the most important methods in geophysical exploration. There are three stages in a seismic exploration survey: acquisition, processing, and interpretation. This paper focuses on a pre-processing tool, the Non-Local Means (NLM) filter algorithm, which is a powerful technique that can significantly suppress noise in seismic data. However, the domain of the NLM algorithm is the whole dataset and 3D seismic data being very large, often exceeding one terabyte (TB), it is impossible to store all the data in Random Access Memory (RAM). Furthermore, the NLM filter would require a considerably long runtime. These… More
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  • Classification and Diagnosis of Lymphoma’s Histopathological Images Using Transfer Learning
  • Abstract Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lymphoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma. These Lymphoma types… More
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  • Hardware Chip Performance of CORDIC Based OFDM Transceiver for Wireless Communication
  • Abstract The fourth-generation (4G) and fifth-generation (5G) wireless communication systems use the orthogonal frequency division multiplexing (OFDM) modulation techniques and subcarrier allocations. The OFDM modulator and demodulator have inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) respectively. The biggest challenge in IFFT/FFT processor is the computation of imaginary and real values. CORDIC has been proved one of the best rotation algorithms for logarithmic, trigonometric, and complex calculations. The proposed work focuses on the OFDM transceiver hardware chip implementation, in which 8-point to 1024-point IFFT and FFT are used to compute the operations in transmitter and receiver respectively. The coordinate… More
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  • Deep Root Memory Optimized Indexing Methodology for Image Search Engines
  • Abstract Digitization has created an abundance of new information sources by altering how pictures are captured. Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing. This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets. Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional, and c) noisy and less… More
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  • Novel Compact UWB Band Notch Antenna Design for Body-Centric Communications
  • Abstract In this paper, a novel and compact ultra-wideband (UWB) antenna with band-notched characteristics for body-centric communication is examined and implemented. The shape of the designed antenna looks like a ‘swan’ with a slotted patch. The performance parameters of this antenna for both the free space and on-body scenario for body-centric communication are analyzed and investigated through the simulation process using Computer Simulation Technology (CST). This antenna can avoid the interference caused by Wireless Local Area Network (WLAN) (5.15–5.825 GHz) and Worldwide Interoperability for Microwave Access (WiMAX) (5.25–5.85 GHz) systems with a band notch because of newly designed UWB antenna is… More
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  • Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic
  • Abstract The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020, provide critical research criteria to assess the vulnerabilities of our current health system. The paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness, about public health and healthcare mechanisms. In view of this unprecedented health crisis, distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages, and with the higher efficacy. At the implementation level,… More
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  • ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning
  • Abstract Opportunistic Networks (OppNets) is gaining popularity day-by-day due to their various applications in the real-life world. The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication. Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks (Manets) and Wireless Sensor Networks (WSNs), therefore, it inherits most of the traits from both mentioned networking techniques. Apart from its popularity, Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of… More
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  • Integrated Random Early Detection for Congestion Control at the Router Buffer
  • Abstract This paper proposed an Integrated Random Early Detection (IRED) method that aims to resolve the problems of the queue-based AQM and load-based AQM and gain the benefits of both using indicators from both types. The arrival factor (e.g., arrival rate, queue and capacity) and the departure factors are used to estimate the congestion through two integrated indicators. The utilized indicators are mathematically calculated and integrated to gain unified and coherent congestion indicators. Besides, IRED is built based on a new dropping calculation approach that fits the utilized congestion indicators while maintaining the intended buffer management criteria, avoiding global synchronization and… More
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  • Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction
  • Abstract Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students’ behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In… More
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  • Deterministic and Stochastic Fractional Order Model for Lesser Date Moth
  • Abstract In this paper, a deterministic and stochastic fractional order model for lesser date moth (LDM) using mating disruption and natural enemies is proposed and analysed. The interaction between LDM larvae, fertilized LDM female, unfertilized LDM female, LDM male and the natural enemy is investigated. In order to clarify the characteristics of the proposed deterministic fractional order model, the analysis of existence, uniqueness, non-negativity and boundedness of the solutions of the proposed fractional-order model are examined. In addition, some sufficient conditions are obtained to ensure the local and global stability of equilibrium points. The occurrence of local bifurcation near the equilibrium… More
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  • Learning Patterns from COVID-19 Instances
  • Abstract Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization (WHO). Coronavirus disease is also termed COVID-19. It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images. Therefore, Chest X-Ray alone may play a vital role in identifying COVID-19 cases. In this paper, we propose a Machine Learning (ML) approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images. The article also explores traditional, and Deep… More
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  • Secure Data Sharing with Confidentiality, Integrity and Access Control in Cloud Environment
  • Abstract Cloud storage is an incipient technology in today’s world. Lack of security in cloud environment is one of the primary challenges faced these days. This scenario poses new security issues and it forms the crux of the current work. The current study proposes Secure Interactional Proof System (SIPS) to address this challenge. This methodology has a few key essential components listed herewith to strengthen the security such as authentication, confidentiality, access control, integrity and the group of components such as AVK Scheme (Access List, Verifier and Key Generator). It is challenging for every user to prove their identity to the… More
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  • Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems
  • Abstract One of the most important methods used to cope with multipath fading effects, which cause the symbol to be received incorrectly in wireless communication systems, is the use of multiple transceiver antenna structures. By combining the multi-input multi-output (MIMO) antenna structure with non-orthogonal multiple access (NOMA), which is a new multiplexing method, the fading effects of the channels are not only reduced but also high data rate transmission is ensured. However, when the maximum likelihood (ML) algorithm that has high performance on coherent detection, is used as a symbol detector in MIMO NOMA systems, the computational complexity of the system… More
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  • Computerized Detection of Limbal Stem Cell Deficiency from Digital Cornea Images
  • Abstract Limbal Stem Cell Deficiency (LSCD) is an eye disease that can cause corneal opacity and vascularization. In its advanced stage it can lead to a degree of visual impairment. It involves the changing in the semispherical shape of the cornea to a drooping shape to downwards direction. LSCD is hard to be diagnosed at early stages. The color and texture of the cornea surface can provide significant information about the cornea affected by LSCD. Parameters such as shape and texture are very crucial to differentiate normal from LSCD cornea. Although several medical approaches exist, most of them requires complicated procedure… More
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