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.

  • Lexicalized Dependency Paths Based Supervised Learning for Relation Extraction
  • Abstract Log-linear models and more recently neural network models used for supervised relation extraction requires substantial amounts of training data and time, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in a dependency tree which we call lexicalized dependency paths (LDPs). We show that this representation is fast, efficient and transparent. We further propose representations utilizing entity types and its subtypes to refine our model and alleviate the data sparsity problem. We apply lexicalized dependency paths to supervised learning using the ACE corpus and show that… More
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  • A Novel Soft Clustering Approach for Gene Expression Data
  • Abstract Gene expression data represents a condition matrix where each row represents the gene and the column shows the condition. Micro array used to detect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messenger RNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with the volume of data containing imprecision and outliers increases the challenges in dealing with them. Clustering methods are hence essential to identify the patterns present in… More
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  • Secure Key Management Based Mobile Authentication in Cloud
  • Abstract Authentication is important to the security of user data in a mobile cloud environment. Because of the server’s user credentials, it is subject to attacks. To maintain data authentication, a novel authentication mechanism is proposed. It consists of three independent phases: Registration, login, and authentication and key agreement. The user registers with the Registration Center (RC) by producing a secret number that isn’t stored in the phone, which protects against privileged insider attacks. The user and server generate a nonce for dynamic user identity and agree on a session secret key for safe communication. The passwords are not stored on… More
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  • An Animated GIF Steganography Using Variable Block Partition Scheme
  • Abstract The paper presents a novel Graphics Interchange Format (GIF) Steganography system. The algorithm uses an animated (GIF) file format video to apply on, a secured and variable image partition scheme for data embedding. The secret data could be any character text, any image, an audio file, or a video file; that is converted in the form of bits. The proposed method uses a variable partition scheme structure for data embedding in the (GIF) file format video. The algorithm estimates the capacity of the cover (GIF) image frames to embed data bits. Our method built variable partition blocks in an empty… More
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  • A Learning Model to Detect Android C&C Applications Using Hybrid Analysis
  • Abstract Smartphone devices particularly Android devices are in use by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks launched the Distributed denial of services (DDoS) that causes to steal of sensitive data, remote access, and spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic analysis. In this paper, a novel hybrid model, the combination of static and dynamic methods… More
  •   Views:132       Downloads:48        Download PDF
  • Adaptive Particle Swarm Optimization Data Hiding for High Security Secret Image Sharing
  • Abstract The main aim of this work is to improve the security of data hiding for secret image sharing. The privacy and security of digital information have become a primary concern nowadays due to the enormous usage of digital technology. The security and the privacy of users’ images are ensured through reversible data hiding techniques. The efficiency of the existing data hiding techniques did not provide optimum performance with multiple end nodes. These issues are solved by using Separable Data Hiding and Adaptive Particle Swarm Optimization (SDHAPSO) algorithm to attain optimal performance. Image encryption, data embedding, data extraction/image recovery are the… More
  •   Views:126       Downloads:45        Download PDF
  • Primary Contacts Identification for COVID-19 Carriers from Surveillance Videos
  • Abstract COVID-19 (Coronavirus disease of 2019) is caused by SARS-CoV2 (Severe Acute Respiratory Syndrome Coronavirus 2) and it was first diagnosed in December 2019 in China. As of 25th Aug 2021, there are 165 million confirmed COVID-19 positive cases and 4.4 million deaths globally. As of today, though there are approved COVID-19 vaccine candidates only 4 billion doses have been administered. Until 100% of the population is safe, no one is safe. Even though these vaccines can provide protection against getting seriously ill and dying from the disease, it does not provide 100% protection from getting infected and passing it on… More
  •   Views:209       Downloads:71        Download PDF
  • Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing
  • Abstract Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome… More
  •   Views:89       Downloads:46        Download PDF
  • Hybrid Cloud Security by Revocable KUNodes-Storage with Identity-Based Encryption
  • Abstract Cloud storage is a service involving cloud service providers providing storage space to customers. Cloud storage services have numerous advantages, including convenience, high computation, and capacity, thereby attracting users to outsource data in the cloud. However, users outsource data directly via cloud stage services that are unsafe when outsourcing data is sensitive for users. Therefore, cipher text-policy attribute-based encryption is a promising cryptographic solution in a cloud environment, and can be drawn up for access control by data owners (DO) to define access policy. Unfortunately, an outsourced architecture applied with attribute-based encryption introduces numerous challenges, including revocation. This issue is… More
  •   Views:99       Downloads:37        Download PDF
  • Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning
  • Abstract

    There is a large amount of information in the network data that we can exploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem. Meanwhile, there is always an unpredictable distribution of class clusters output by graph representation learning. Therefore, we propose an improved density peak clustering algorithm (ILDPC) for the community detection problem, which improves the local density mechanism in the original algorithm and can better accommodate class clusters of different shapes. And we study the… More

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  • Modeling and Experimental Verification of Electric Vehicles Off-Grid Photovoltaic Powered Charging Station
  • Abstract With the increasing development of EVs, the energy demand from the conventional utility grid increases in proportion. On the other hand, photovoltaic (PV) energy sources can overcome several problems when charging EVs from the utility grid especially in remote areas. This paper presents an effective photovoltaic stand-alone charging station for EV applications. The proposed charging station incorporates PV array, a lithium-ion battery representing the EV battery, and a lead-acid battery representing the energy storage system (ESS). A bidirectional DC-DC converter is employed for charging/discharging the ESS and a unidirectional DC-DC converter is utilized for charging the EV battery. The proposed… More
  •   Views:86       Downloads:42        Download PDF
  • Optimized Cognitive Learning Model for Energy Efficient Fog-BAN-IoT Networks
  • Abstract In Internet of Things (IoT), large amount of data are processed and communicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integration of IoT and Artificial Intelligence (AI). The amalgamation of above mentioned tools has taken the new peak in terms of diagnosis and treatment process especially in the pandemic period. But the real challenges such as low latency, energy consumption high throughput still remains in the dark side of the research. This paper proposes a novel optimized cognitive learning based BAN model based on Fog-IoT technology as a… More
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  • Classification of Glaucoma in Retinal Images Using EfficientnetB4 Deep Learning Model
  • Abstract Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma. Glaucoma is an incurable and unavoidable eye disease that damages the vision of optic nerves and quality of life. Classification of Glaucoma has been an active field of research for the past ten years. Several approaches for Glaucoma classification are established, beginning with conventional segmentation methods and feature-extraction to deep-learning techniques such as Convolution Neural Networks (CNN). In contrast, CNN classifies the input images directly using tuned parameters of convolution and pooling layers by extracting features. But, the volume of training… More
  •   Views:150       Downloads:60        Download PDF
  • Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM
  • Abstract In the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging. The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation. The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary. Also, this manual technique requires a large number of image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique… More
  •   Views:94       Downloads:47        Download PDF
  • Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization
  • Abstract Link prediction has attracted wide attention among interdisciplinary researchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks. Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connected graph. However, the complexity of the real world makes the complex networks abstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes.… More
  •   Views:168       Downloads:120        Download PDF
  • QL-CBR Hybrid Approach for Adapting Context-Aware Services
  • Abstract A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically. Most existing context-aware systems provide context-aware services based on supervised algorithms. Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trial-and-error interactions. They also have the ability to build excellent self-adaptive systems. In this study, we aim to incorporate reinforcement algorithms (Q-learning) into a context-aware system to provide relevant services based on a user’s dynamic context. To accelerate the convergence of reinforcement learning (RL) algorithms and provide the correct services in… More
  •   Views:99       Downloads:52        Download PDF
  • A Novel Optimizer in Deep Neural Network for Diabetic Retinopathy Classification
  • Abstract In severe cases, diabetic retinopathy can lead to blindness. For decades, automatic classification of diabetic retinopathy images has been a challenge. Medical image processing has benefited from advances in deep learning systems. To enhance the accuracy of image classification driven by Convolutional Neural Network (CNN), balanced dataset is generated by data augmentation method followed by an optimized algorithm. Deep neural networks (DNN) are frequently optimized using gradient (GD) based techniques. Vanishing gradient is the main drawback of GD algorithms. In this paper, we suggest an innovative algorithm, to solve the above problem, Hypergradient Descent learning rate based Quasi hyperbolic (HDQH)… More
  •   Views:86       Downloads:41        Download PDF
  • Energy-efficient and Secure Wireless Communication for Telemedicine in IoT
  • Abstract The Internet of Things (IoT) represents a radical shifting paradigm for technological innovations as it can play critical roles in cyberspace applications in various sectors, such as security, monitoring, medical, and environmental sectors, and also in control and industrial applications. The IoT in E-medicine unleashed the design space for new technologies to give instant treatment to patients while also monitoring and tracking health conditions. This research presents a system-level architecture approach for IoT energy efficiency and security. The proposed architecture includes functional components that provide privacy management and system security. Components in the security function group provide secure communications through… More
  •   Views:78       Downloads:41        Download PDF
  • Enhanced Reliability in Network Function Virtualization by Hybrid Hexagon-Cost Efficient Algorithm
  • Abstract In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network node function and to build a block that may connect the chain, communication services. Mainly, Virtualized Network Function Forwarding Graph (VNF-FG) has been used to define the connection between the VNF and to give the best end-to-end services. In the existing method, VNF mapping and backup VNF were proposed but there was no profit and reliability improvement of the backup and mapping of the primary VNF. As a consequence, this paper offers a Hybrid Hexagon-Cost Efficient algorithm for determining the best… More
  •   Views:91       Downloads:36        Download PDF
  • Image Inpainting Detection Based on High-Pass Filter Attention Network
  • Abstract Image inpainting based on deep learning has been greatly improved. The original purpose of image inpainting was to repair some broken photos, such as inpainting artifacts. However, it may also be used for malicious operations, such as destroying evidence. Therefore, detection and localization of image inpainting operations are essential. Recent research shows that high-pass filtering full convolutional network (HPFCN) is applied to image inpainting detection and achieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, we introduce the squeezed excitation blocks (SE) and propose a high-pass… More
  •   Views:170       Downloads:123        Download PDF
  • Speak-Correct: A Computerized Interface for the Analysis of Mispronounced Errors
  • Abstract Any natural language may have dozens of accents. Even though the equivalent phonemic formation of the word, if it is properly called in different accents, humans do have audio signals that are distinct from one another. Among the most common issues with speech, the processing is discrepancies in pronunciation, accent, and enunciation. This research study examines the issues of detecting, fixing, and summarising accent defects of average Arabic individuals in English-speaking speech. The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues. The proposed SpeakCorrect computerized interface employs a… More
  •   Views:87       Downloads:46        Download PDF
  • An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases
  • Abstract Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However, there is no standard guideline for data transformation from relational database to NoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processing time when compared to the relational database query processing time. However, these approaches produced redundant tables in the resulted schema that in turn consume large unnecessary storage size and produce high query processing time due to the generated schema with redundant column families in the transformed column-oriented… More
  •   Views:70       Downloads:40        Download PDF
  • Vibrating Particles System Algorithm for Solving Classification Problems
  • Abstract Big data is a term that refers to a set of data that, due to its largeness or complexity, cannot be stored or processed with one of the usual tools or applications for data management, and it has become a prominent word in recent years for the massive development of technology. Almost immediately thereafter, the term “big data mining” emerged, i.e., mining from big data even as an emerging and interconnected field of research. Classification is an important stage in data mining since it helps people make better decisions in a variety of situations, including scientific endeavors, biomedical research, and… More
  •   Views:97       Downloads:46        Download PDF
  • Analysis of Cognitive Radio for LTE and 5G Waveforms
  • Abstract Spectrum sensing is one of the major concerns in reaching an efficient Quality of service (QOS) in the advanced mobile communication system. The advanced engineering sciences such as 5G, device 2 device communications (D2D), Internet of things (IoT), MIMO require a large spectrum for better service. Orthogonal frequency division multiplexing (OFDM) is not a choice in advanced radio due to the Cyclic Prefix (CP), wastage of the spectrum, and so on. Hence, it is important to explore the spectral efficient advanced waveform techniques and combine a cognitive radio (CR) with the 5G waveform to sense the idle spectrum, which overcomes… More
  •   Views:98       Downloads:41        Download PDF
  • Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5
  • Abstract Safety helmet-wearing detection is an essential part of the intelligent monitoring system. To improve the speed and accuracy of detection, especially small targets and occluded objects, it presents a novel and efficient detector model. The underlying core algorithm of this model adopts the YOLOv5 (You Only Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (Complete Intersection Over Union) Loss function, and the Mish activation function. First, it applies the attention mechanism in the feature extraction. The network can learn the weight of each channel independently and enhance… More
  •   Views:174       Downloads:137        Download PDF
  • Improving Throughput of Transmission Control Protocol Using Cross Layer Approach
  • Abstract Most of the internet users connect through wireless networks. Major part of internet traffic is carried by Transmission Control Protocol (TCP). It has some design constraints while operated across wireless networks. TCP is the traditional predominant protocol designed for wired networks. To control congestion in the network, TCP used acknowledgment to delivery of packets by the end host. In wired network, packet loss signals congestion in the network. But rather in wireless networks, loss is mainly because of the wireless characteristics such as fading, signal strength etc. When a packet travels across wired and wireless networks, TCP congestion control theory… More
  •   Views:80       Downloads:40        Download PDF
  • Object Detection in Remote Sensing Images Using Picture Fuzzy Clustering and MapReduce
  • Abstract In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order to perform next steps in image processing. Remote sensing images usually have large size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detect objects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reduce the runtime… More
  •   Views:94       Downloads:39        Download PDF
  • Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry
  • Abstract Currently, mobile communication is one of the widely used means of communication. Nevertheless, it is quite challenging for a telecommunication company to attract new customers. The recent concept of mobile number portability has also aggravated the problem of customer churn. Companies need to identify beforehand the customers, who could potentially churn out to the competitors. In the telecommunication industry, such identification could be done based on call detail records. This research presents an extensive experimental study based on various deep learning models, such as the 1D convolutional neural network (CNN) model along with the recurrent neural network (RNN) and deep… More
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  • Air Quality Predictions in Urban Areas Using Hybrid ARIMA and Metaheuristic LSTM
  • Abstract Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor air quality leads to rising health issues in the human’s life in many ways especially respiratory infections, heart disease, asthma, stroke and lung cancer. The contaminated air comprises harmful ingredients such as sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter of PM10, PM2.5, and an Air Quality Index (AQI). These pollutant ingredients are very harmful to human’s health and also leads to death. So, it is necessary to develop a prediction model for air quality as regular on… More
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  • An Improved Genetic Algorithm for Berth Scheduling at Bulk Terminal
  • Abstract Berth and loading and unloading machinery are not only the main factors that affecting the terminal operation, but also the main starting point of energy saving and emission reduction. In this paper, a genetic Algorithm Framework is designed for the berth allocation with low carbon and high efficiency at bulk terminal. In solving the problem, the scheduler’s experience is transformed into a regular way to obtain the initial solution. The individual is represented as a chromosome, and the sub-chromosomes are encoded as integers, the roulette wheel method is used for selection, the two-point crossing method is used for cross, and… More
  •   Views:172       Downloads:130        Download PDF
  • Cyber Secure Framework for Smart Containers Based on Novel Hybrid DTLS Protocol
  • Abstract The Internet of Things (IoTs) is apace growing, billions of IoT devices are connected to the Internet which communicate and exchange data among each other. Applications of IoT can be found in many fields of engineering and sciences such as healthcare, traffic, agriculture, oil and gas industries, and logistics. In logistics, the products which are to be transported may be sensitive and perishable, and require controlled environment. Most of the commercially available logistic containers are not integrated with IoT devices to provide controlled environment parameters inside the container and to transmit data to a remote server. This necessitates the need… More
  •   Views:3882       Downloads:553        Download PDF
  • Low-Cost IMU Sensors for Satellite Maturity Improvement
  • Abstract The satellite technology proves its impact in the modern era with its wide benefits and applications. However, the cost of the development in this field presents gaps in many countries, almost the developed countries. Therefore, this paper provides a rich platform around low-cost sensors in order to improve maturity in space technology, mostly the system of attitude determination and control. The development of this knowledge turns out to be very interesting in order to achieve a space mission which leads to the progression of the spatial technology readiness level (TRL) defined by the international measurement scale which is able to… More
  •   Views:85       Downloads:39        Download PDF
  • Customized Share Level Monitoring System for Users in OSN-Third Party Applications
  • Abstract Preserving privacy of the user is a very critical requirement to be met with all the international laws like GDPR, California privacy protection act and many other bills in place. On the other hand, Online Social Networks (OSN) has a wide spread recognition among the users, as a means of virtual communication. OSN may also acts as an identity provider for both internal and external applications. While it provides a simplified identification and authentication function to users across multiple applications, it also opens the users to a new spectrum of privacy threats. The privacy breaches costs to the users as… More
  •   Views:101       Downloads:37        Download PDF
  • Multi-Site Air Pollutant Prediction Using Long Short Term Memory
  • Abstract The current pandemic highlights the significance and impact of air pollution on individuals. When it comes to climate sustainability, air pollution is a major challenge. Because of the distinctive nature, unpredictability, and great changeability in the reality of toxins and particulates, detecting air quality is a puzzling task. Simultaneously, the ability to predict or classify and monitor air quality is becoming increasingly important, particularly in urban areas, due to the well documented negative impact of air pollution on resident’s health and the environment. To better comprehend the current condition of air quality, this research proposes predicting air pollution levels from… More
  •   Views:110       Downloads:52        Download PDF
  • Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies
  • Abstract Digitalization has changed the way of information processing, and new techniques of legal data processing are evolving. Text mining helps to analyze and search different court cases available in the form of digital text documents to extract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reduced time. The rapid development of judicial ontologies seems to deliver interesting problem solving to legal knowledge formalization. Mining context information through ontologies from corpora is a challenging and interesting field. This research paper presents a three tier contextual text… More
  •   Views:356       Downloads:169        Download PDF