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Equilibrial service composition model in Cloud manufacturing (ESCM) based on non-cooperative and cooperative game theory for healthcare service equipping PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-03-01 Ehsan Vaziri Goudarzi; Mahmoud Houshmand; Omid Fatahi Valilai; Vahidreza Ghezavati; Shahrooz Bamdad
Industry 4.0 is the digitalization of the manufacturing systems based on Information and Communication Technologies (ICT) for developing a manufacturing system to gain efficiency and improve productivity. Cloud Manufacturing (CM) is a paradigm of Industry 4.0. Cloud Manufacturing System (CMS) considers anything as a service. The end product is developed based on the service composition in the CMS according
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Gait recognition using a few gait frames PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-03-01 Lingxiang Yao; Worapan Kusakunniran; Qiang Wu; Jian Zhang
Gait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize individuals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused by exterior factors. However, most of these methods are developed based on sufficient input gait frames
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Age of information of a server with energy requirements PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-03-01 Josu Doncel
We investigate a system with Poisson arrivals to two queues. One queue stores the status updates of the process of interest (or data packets) and the other handles the energy that is required to deliver the updates to the monitor. We consider that the energy is represented by packets of discrete unit. When an update ends service, it is sent to the energy queue and, if the energy queue has one packet
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Evaluation of Rust code verbosity, understandability and complexity PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-26 Luca Ardito; Luca Barbato; Riccardo Coppola; Michele Valsesia
Rust is an innovative programming language initially implemented by Mozilla, developed to ensure high performance, reliability, and productivity. The final purpose of this study consists of applying a set of common static software metrics to programs written in Rust to assess the verbosity, understandability, organization, complexity, and maintainability of the language. To that extent, nine different
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Optimal sequence for chain matrix multiplication using evolutionary algorithm PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-26 Umer Iqbal; Ijaz Ali Shoukat; Ihsan Elahi; Afshan Kanwal; Bakhtawar Farrukh; Mohammed A. Alqahtani; Abdul Rauf; Jehad Saad Alqurni
The Chain Matrix Multiplication Problem (CMMP) is an optimization problem that helps to find the optimal way of parenthesization for Chain Matrix Multiplication (CMM). This problem arises in various scientific applications such as in electronics, robotics, mathematical programing, and cryptography. For CMMP the researchers have proposed various techniques such as dynamic approach, arithmetic approach
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Low-cost intelligent surveillance system based on fast CNN PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-25 Zaid Saeb Sabri; Zhiyong Li
Smart surveillance systems are used to monitor specific areas, such as homes, buildings, and borders, and these systems can effectively detect any threats. In this work, we investigate the design of low-cost multiunit surveillance systems that can control numerous surveillance cameras to track multiple objects (i.e., people, cars, and guns) and promptly detect human activity in real time using low
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Efficient video face recognition based on frame selection and quality assessment PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-25 Angelina Kharchevnikova; Andrey V. Savchenko
The article is considering the problem of increasing the performance and accuracy of video face identification. We examine the selection of the several best video frames using various techniques for assessing the quality of images. In contrast to traditional methods with estimation of image brightness/contrast, we propose to utilize the deep learning techniques that estimate the frame quality by using
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The classification of movement intention through machine learning models: the identification of significant time-domain EMG features PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-25 Ismail Mohd Khairuddin; Shahrul Naim Sidek; Anwar P.P. Abdul Majeed; Mohd Azraai Mohd Razman; Asmarani Ahmad Puzi; Hazlina Md Yusof
Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals
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Renyi entropy driven hierarchical graph clustering PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-25 Frédérique Oggier; Anwitaman Datta
This article explores a graph clustering method that is derived from an information theoretic method that clusters points in ${{\mathbb{R}}^{n}}$Rn relying on Renyi entropy, which involves computing the usual Euclidean distance between these points. Two view points are adopted: (1) the graph to be clustered is first embedded into ${\mathbb{R}}^{d}$Rd for some dimension d so as to minimize the distortion
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Robust adaptive PD-like control of lower limb rehabilitation robot based on human movement data PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-24 Ningning Hu; Aihui Wang; Yuanhang Wu
The combination of biomedical engineering and robotics engineering brings hope of rehabilitation to patients with lower limb movement disorders caused by diseases of the central nervous system. For the comfort during passive training, anti-interference and the convergence speed of tracking the desired trajectory, this paper analyzes human body movement mechanism and proposes a robust adaptive PD-like
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Implementation of the computer tomography parallel algorithms with the incomplete set of data PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-24 Mariusz Pleszczyński
Computer tomography has a wide field of applicability; however, most of its applications assume that the data, obtained from the scans of the examined object, satisfy the expectations regarding their amount and quality. Unfortunately, sometimes such expected data cannot be achieved. Then we deal with the incomplete set of data. In the paper we consider an unusual case of such situation, which may occur
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Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-23 Adi Alhudhaif; Zafer Cömert; Kemal Polat
Background Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone. Methods In this study, a novel computer-aided
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Analysis of historical road accident data supporting autonomous vehicle control strategies PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-23 Sándor Szénási
It is expected that most accidents occurring due to human mistakes will be eliminated by autonomous vehicles. Their control is based on real-time data obtained from the various sensors, processed by sophisticated algorithms and the operation of actuators. However, it is worth noting that this process flow cannot handle unexpected accident situations like a child running out in front of the vehicle
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MeteoMex: open infrastructure for networked environmental monitoring and agriculture 4.0 PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-23 Robert Winkler
Air, water, and soil are essential for terrestrial life, but pollution, overexploitation, and climate change jeopardize the availability of these primary resources. Thus, assuring human health and food production requires efficient strategies and technologies for environmental protection. Knowing key parameters such as soil moisture, air, and water quality is essential for smart farming and urban development
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Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-22 Tharun J. Iyer; Alex Noel Joseph Raj; Sushil Ghildiyal; Ruban Nersisson
The pandemic of Coronavirus Disease-19 (COVID-19) has spread around the world, causing an existential health crisis. Automated detection of COVID-19 infections in the lungs from Computed Tomography (CT) images offers huge potential in tackling the problem of slow detection and augments the conventional diagnostic procedures. However, segmenting COVID-19 from CT Scans is problematic, due to high variations
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CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-22 Malik Yousef; Ege Ülgen; Osman Uğur Sezerman
Most of the traditional gene selection approaches are borrowed from other fields such as statistics and computer science, However, they do not prioritize biologically relevant genes since the ultimate goal is to determine features that optimize model performance metrics not to build a biologically meaningful model. Therefore, there is an imminent need for new computational tools that integrate the
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Adaptive neural PD controllers for mobile manipulator trajectory tracking PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-19 Jesus Hernandez-Barragan; Jorge D. Rios; Javier Gomez-Avila; Nancy Arana-Daniel; Carlos Lopez-Franco; Alma Y. Alanis
Artificial intelligence techniques have been used in the industry to control complex systems; among these proposals, adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent versions of the most used controller in the industry. This work presents an adaptive neuron PD controller and a multilayer neural PD controller for position tracking of a mobile manipulator. Both controllers
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Comparing general and specialized word embeddings for biomedical named entity recognition PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-18 Rigo E. Ramos-Vargas; Israel Román-Godínez; Sulema Torres-Ramos
Increased interest in the use of word embeddings, such as word representation, for biomedical named entity recognition (BioNER) has highlighted the need for evaluations that aid in selecting the best word embedding to be used. One common criterion for selecting a word embedding is the type of source from which it is generated; that is, general (e.g., Wikipedia, Common Crawl), or specific (e.g., biomedical
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COVID-19: a new deep learning computer-aided model for classification PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-18 Omar M. Elzeki; Mahmoud Shams; Shahenda Sarhan; Mohamed Abd Elfattah; Aboul Ella Hassanien
Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further
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Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-18 Anant R. Bhatt; Amit Ganatra; Ketan Kotecha
Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant conditions by analyzing swab images. Various computer vision techniques can be explored to identify potential precancerous and cancerous lesions by analyzing the Pap
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Application and evaluation of knowledge graph embeddings in biomedical data PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-18 Mona Alshahrani; Maha A. Thafar; Magbubah Essack
Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search capabilities. However, linked data and bio-ontologies are more recently being used to represent
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Open-source data management system for Parkinson’s disease follow-up PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-17 João Paulo Folador; Marcus Fraga Vieira; Adriano Alves Pereira; Adriano de Oliveira Andrade
Background Parkinson’s disease (PD) is a neurodegenerative condition of the central nervous system that causes motor and non-motor dysfunctions. The disease affects 1% of the world population over 60 years and remains cureless. Knowledge and monitoring of PD are essential to provide better living conditions for patients. Thus, diagnostic exams and monitoring of the disease can generate a large amount
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An adaptive weighting mechanism for Reynolds rules-based flocking control scheme PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-16 Duc N. M. Hoang; Duc M. Tran; Thanh-Sang Tran; Hoang-Anh Pham
Cooperative navigation for fleets of robots conventionally adopts algorithms based on Reynolds's flocking rules, which usually use a weighted sum of vectors for calculating the velocity from behavioral velocity vectors with corresponding fixed weights. Although optimal values of the weighting coefficients giving good performance can be found through many experiments for each particular scenario, the
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A novel framework for storage assignment optimization inspired by finite element method PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-16 Seyed-Kourosh Tabatabaei; Omid Fatahi Valilai; Ali Abedian; Mohammad Khalilzadeh
Considering necessary fundamental and structural changes in the production and manufacturing industries to fulfill the industry 4.0 paradigm, the proposal of new ideas and frameworks for operations management of production and manufacturing system is inevitable. This research focuses on traditional methods proposed for storage assignment problem and struggles for new methods and definitions for industry
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Efficient steganalysis using convolutional auto encoder network to ensure original image quality PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-16 Mallikarjuna Reddy Ayaluri; Sudheer Reddy K.; Srinivasa Reddy Konda; Sudharshan Reddy Chidirala
Steganalysis is the process of analyzing and predicting the presence of hidden information in images. Steganalysis would be most useful to predict whether the received images contain useful information. However, it is more difficult to predict the hidden information in images which is computationally difficult. In the existing research method, this is resolved by introducing the deep learning approach
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Optical character recognition system for Baybayin scripts using support vector machine PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-15 Rodney Pino; Renier Mendoza; Rachelle Sambayan
In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines’ national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates the characters of both scripts. The proposed system considers the normalization of an individual character to
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Correct and stable sorting for overflow streaming data with a limited storage size and a uniprocessor PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-12 Suluk Chaikhan; Suphakant Phimoltares; Chidchanok Lursinsap
Tremendous quantities of numeric data have been generated as streams in various cyber ecosystems. Sorting is one of the most fundamental operations to gain knowledge from data. However, due to size restrictions of data storage which includes storage inside and outside CPU with respect to the massive streaming data sources, data can obviously overflow the storage. Consequently, all classic sorting algorithms
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Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-11 Arpan Srivastava; Sonakshi Jain; Ryan Miranda; Shruti Patil; Sharnil Pandya; Ketan Kotecha
In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it
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From ECG signals to images: a transformation based approach for deep learning PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-10 Mahwish Naz; Jamal Hussain Shah; Muhammad Attique Khan; Muhammad Sharif; Mudassar Raza; Robertas Damaševičius
Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record
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A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-10 Omar M. Elzeki; Mohamed Abd Elfattah; Hanaa Salem; Aboul Ella Hassanien; Mahmoud Shams
Background and Purpose COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people’s health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method
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Numerical behavior of NVIDIA tensor cores PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-10 Massimiliano Fasi; Nicholas J. Higham; Mantas Mikaitis; Srikara Pranesh
We explore the floating-point arithmetic implemented in the NVIDIA tensor cores, which are hardware accelerators for mixed-precision matrix multiplication available on the Volta, Turing, and Ampere microarchitectures. Using Volta V100, Turing T4, and Ampere A100 graphics cards, we determine what precision is used for the intermediate results, whether subnormal numbers are supported, what rounding mode
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Software evolution: the lifetime of fine-grained elements PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-09 Diomidis Spinellis; Panos Louridas; Maria Kechagia
A model regarding the lifetime of individual source code lines or tokens can estimate maintenance effort, guide preventive maintenance, and, more broadly, identify factors that can improve the efficiency of software development. We present methods and tools that allow tracking of each line’s or token’s birth and death. Through them, we analyze 3.3 billion source code element lifetime events in 89 revision
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Comparison of machine learning and deep learning techniques in promoter prediction across diverse species PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-09 Nikita Bhandari; Satyajeet Khare; Rahee Walambe; Ketan Kotecha
Gene promoters are the key DNA regulatory elements positioned around the transcription start sites and are responsible for regulating gene transcription process. Various alignment-based, signal-based and content-based approaches are reported for the prediction of promoters. However, since all promoter sequences do not show explicit features, the prediction performance of these techniques is poor. Therefore
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Addressing multiple bit/symbol errors in DRAM subsystem PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-09 Ravikiran Yeleswarapu; Arun K. Somani
As DRAM technology continues to evolve towards smaller feature sizes and increased densities, faults in DRAM subsystem are becoming more severe. Current servers mostly use CHIPKILL based schemes to tolerate up-to one/two symbol errors per DRAM beat. Such schemes may not detect multiple symbol errors arising due to faults in multiple devices and/or data-bus, address bus. In this article, we introduce
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Petri Net based modeling and analysis for improved resource utilization in cloud computing PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-08 Muhammad Rizwan Ali; Farooq Ahmad; Muhammad Hasanain Chaudary; Zuhaib Ashfaq Khan; Mohammed A. Alqahtani; Jehad Saad Alqurni; Zahid Ullah; Wasim Ullah Khan
The cloud is a shared pool of systems that provides multiple resources through the Internet, users can access a lot of computing power using their computer. However, with the strong migration rate of multiple applications towards the cloud, more disks and servers are required to store huge data. Most of the cloud storage service providers are replicating full copies of data over multiple data centers
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A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-05 Bhavana R. Bhamare; Jeyanthi Prabhu
Due to the massive progression of the Web, people post their reviews for any product, movies and places they visit on social media. The reviews available on social media are helpful to customers as well as the product owners to evaluate their products based on different reviews. Analyzing structured data is easy as compared to unstructured data. The reviews are available in an unstructured format.
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OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-04 Shiu Kumar; Ronesh Sharma; Alok Sharma
A human–computer interaction (HCI) system can be used to detect different categories of the brain wave signals that can be beneficial for neurorehabilitation, seizure detection and sleep stage classification. Research on developing HCI systems using brain wave signals has progressed a lot over the years. However, real-time implementation, computational complexity and accuracy are still a concern. In
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Survey on graph embeddings and their applications to machine learning problems on graphs PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-04 Ilya Makarov; Dmitrii Kiselev; Nikita Nikitinsky; Lovro Subelj
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature
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Search, access, and explore life science nanopublications on the Web PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-04 Fabio Giachelle; Dennis Dosso; Gianmaria Silvello
Nanopublications are Resource Description Framework (RDF) graphs encoding scientific facts extracted from the literature and enriched with provenance and attribution information. There are millions of nanopublications currently available on the Web, especially in the life science domain. Nanopublications are thought to facilitate the discovery, exploration, and re-use of scientific facts. Nevertheless
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Data-flow-based adaption of the System-Theoretic Process Analysis for Security (STPA-Sec) PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-03 Jinghua Yu; Stefan Wagner; Feng Luo
Security analysis is an essential activity in security engineering to identify potential system vulnerabilities and specify security requirements in the early design phases. Due to the increasing complexity of modern systems, traditional approaches lack the power to identify insecure incidents caused by complex interactions among physical systems, human and social entities. By contrast, the System-Theoretic
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A novel IoT-based health and tactical analysis model with fog computing PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-03 Aykut Karakaya; Sedat Akleylek
In sports competitions, depending on the conditions such as excitement, stress, fatigue, etc. during the match, negative situations such as disability or loss of life may occur for players and spectators. Therefore, it is extremely important to constantly check their health. In addition, some strategic analyzes are made during the match. According to the results of these analyzes, the technical team
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Using algorithmic trading to analyze short term profitability of Bitcoin PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-03 Iftikhar Ahmad; Muhammad Ovais Ahmad; Mohammed A. Alqarni; Abdulwahab Ali Almazroi; Muhammad Imran Khan Khalil
Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading
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Solution strategy based on Gaussian mixture models and dispersion reduction for the capacitated centered clustering problem PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-03 Santiago-Omar Caballero-Morales
The Capacitated Centered Clustering Problem (CCCP)—a multi-facility location model—is very important within the logistics and supply chain management fields due to its impact on industrial transportation and distribution. However, solving the CCCP is a challenging task due to its computational complexity. In this work, a strategy based on Gaussian mixture models (GMMs) and dispersion reduction is presented
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On the classification of Microsoft-Windows ransomware using hardware profile PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-02 Sana Aurangzeb; Rao Naveed Bin Rais; Muhammad Aleem; Muhammad Arshad Islam; Muhammad Azhar Iqbal
Due to the expeditious inclination of online services usage, the incidents of ransomware proliferation being reported are on the rise. Ransomware is a more hazardous threat than other malware as the victim of ransomware cannot regain access to the hijacked device until some form of compensation is paid. In the literature, several dynamic analysis techniques have been employed for the detection of malware
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On automated RBAC assessment by constructing a centralized perspective for microservice mesh PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-02-01 Dipta Das; Andrew Walker; Vincent Bushong; Jan Svacina; Tomas Cerny; Vashek Matyas
It is important in software development to enforce proper restrictions on protected services and resources. Typically software services can be accessed through REST API endpoints where restrictions can be applied using the Role-Based Access Control (RBAC) model. However, RBAC policies can be inconsistent across services, and they require proper assessment. Currently, developers use penetration testing
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Steganography in color images with random order of pixel selection and encrypted text message embedding PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-28 Krasimir Kordov; Stanimir Zhelezov
Information security is major concern in modern digital ages, and the outdated algorithms need to be replaced with new ones or to be improved. In this article a new approach for hiding secret text message in color images is presented, combining steganography and cryptography. The location and the order of the image pixels chosen for information embedding are randomly selected using chaotic pseudo-random
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Enhancement of small doppler frequencies detection for LFMCW radar PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-28 Sameh Ghanem
Detection of targets with small Doppler frequencies of linear-frequency modulated continuous wave radars is the main task of this article. The moving target indicator (MTI) is used to reject the fixed targets and high-speed targets through the radar research area. In this work, targets with small Doppler frequencies can be detected perfectly based on the frequency response of a single delay line canceller
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MKL-GRNI: A parallel multiple kernel learning approach for supervised inference of large-scale gene regulatory networks PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-28 Nisar Wani; Khalid Raza
High throughput multi-omics data generation coupled with heterogeneous genomic data fusion are defining new ways to build computational inference models. These models are scalable and can support very large genome sizes with the added advantage of exploiting additional biological knowledge from the integration framework. However, the limitation with such an arrangement is the huge computational cost
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Explainable stock prices prediction from financial news articles using sentiment analysis PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-28 Shilpa Gite; Hrituja Khatavkar; Ketan Kotecha; Shilpi Srivastava; Priyam Maheshwari; Neerav Pandey
The stock market is very complex and volatile. It is impacted by positive and negative sentiments which are based on media releases. The scope of the stock price analysis relies upon ability to recognise the stock movements. It is based on technical fundamentals and understanding the hidden trends which the market follows. Stock price prediction has consistently been an extremely dynamic field of exploration
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ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-26 Alex Noel Joseph Raj; Haipeng Zhu; Asiya Khan; Zhemin Zhuang; Zengbiao Yang; Vijayalakshmi G. V. Mahesh; Ganesan Karthik
Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19
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Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-25 Seungjin Lee; Azween Abdullah; Nz Jhanjhi; Sh Kok
The Industrial Revolution 4.0 began with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories. A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating
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Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-25 Md Akizur Rahman; Ravie chandren Muniyandi; Dheeb Albashish; Md Mokhlesur Rahman; Opeyemi Lateef Usman
Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem
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Data augmentation based malware detection using convolutional neural networks PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-22 Ferhat Ozgur Catak; Javed Ahmed; Kevser Sahinbas; Zahid Hussain Khand
Due to advancements in malware competencies, cyber-attacks have been broadly observed in the digital world. Cyber-attacks can hit an organization hard by causing several damages such as data breach, financial loss, and reputation loss. Some of the most prominent examples of ransomware attacks in history are WannaCry and Petya, which impacted companies’ finances throughout the globe. Both WannaCry and
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Multi-objective simulated annealing for hyper-parameter optimization in convolutional neural networks PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-04 Ayla Gülcü; Zeki Kuş
In this study, we model a CNN hyper-parameter optimization problem as a bi-criteria optimization problem, where the first objective being the classification accuracy and the second objective being the computational complexity which is measured in terms of the number of floating point operations. For this bi-criteria optimization problem, we develop a Multi-Objective Simulated Annealing (MOSA) algorithm
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Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model PeerJ Comput. Sci. (IF 3.091) Pub Date : 2021-01-04 Felix Velicia-Martin; Juan-Pedro Cabrera-Sanchez; Eloy Gil-Cordero; Pedro R. Palos-Sanchez
Background The expansion of the coronavirus pandemic and the extraordinary confinement measures imposed by governments have caused an unprecedented intense and rapid contraction of the global economy. In order to revive the economy, people must be able to move safely, which means that governments must be able to quickly detect positive cases and track their potential contacts. Different alternatives
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Latent based temporal optimization approach for improving the performance of collaborative filtering PeerJ Comput. Sci. (IF 3.091) Pub Date : 2020-12-21 Ismail Ahmed Al-Qasem Al-Hadi; Nurfadhlina Mohd Sharef; Md Nasir Sulaiman; Norwati Mustapha; Mehrbakhsh Nilashi
Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used
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Application of particle swarm optimization in optimal placement of tsunami sensors PeerJ Comput. Sci. (IF 3.091) Pub Date : 2020-12-18 Angelie Ferrolino; Renier Mendoza; Ikha Magdalena; Jose Ernie Lope
Rapid detection and early warning systems demonstrate crucial significance in tsunami risk reduction measures. So far, several tsunami observation networks have been deployed in tsunamigenic regions to issue effective local response. However, guidance on where to station these sensors are limited. In this article, we address the problem of determining the placement of tsunami sensors with the least
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State-of-the-art IoV trust management a meta-synthesis systematic literature review (SLR) PeerJ Comput. Sci. (IF 3.091) Pub Date : 2020-12-14 Abdul Rehman; Mohd Fadzil Hassan; Kwang Hooi Yew; Irving Paputungan; Duc Chung Tran
In the near future, the Internet of Vehicles (IoV) is foreseen to become an inviolable part of smart cities. The integration of vehicular ad hoc networks (VANETs) into the IoV is being driven by the advent of the Internet of Things (IoT) and high-speed communication. However, both the technological and non-technical elements of IoV need to be standardized prior to deployment on the road. This study
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Data augmentation-based conditional Wasserstein generative adversarial network-gradient penalty for XSS attack detection system PeerJ Comput. Sci. (IF 3.091) Pub Date : 2020-12-14 Fawaz Mahiuob Mohammed Mokbal; Dan Wang; Xiaoxi Wang; Lihua Fu
The rapid growth of the worldwide web and accompanied opportunities of web applications in various aspects of life have attracted the attention of organizations, governments, and individuals. Consequently, web applications have increasingly become the target of cyberattacks. Notably, cross-site scripting (XSS) attacks on web applications are increasing and have become the critical focus of information
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Identifying Twitter users who repost unreliable news sources with linguistic information PeerJ Comput. Sci. (IF 3.091) Pub Date : 2020-12-14 Yida Mu; Nikolaos Aletras
Social media has become a popular source for online news consumption with millions of users worldwide. However, it has become a primary platform for spreading disinformation with severe societal implications. Automatically identifying social media users that are likely to propagate posts from handles of unreliable news sources sometime in the future is of utmost importance for early detection and prevention
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