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Blockchain and cloud computing are two of the main topics related to the distributed computing paradigm, and in the last decade, they have seen exponential growth in their adoption. Cloud computing has long been established as the main mechanism to test, develop, and deliver new applications and services in a distributed manner across the World Wide Web. Large data centers host many services and store

Direct numerical simulations (DNS) of turbulent flows have increasing importance because they not only provide fundamental understanding of turbulent flows but also complement and extend experimental results. DNS of high Reynolds numbers, however, require huge computing cost so high-performance computing has been strongly pursued. In this study, we examine the feasibility of cost-efficient DNS on Intel

Today, the internet of things (IoT) is becoming more common and finds applications in several domains, especially in the healthcare sector. Due to the rising demands of IoT, a massive quantity of sensing data gets generated from diverse sensing devices. Artificial intelligence (AI) techniques are vital for providing a scalable and precise analysis of data in real time. But the design and development

Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification

Vehicular networks are considered as one of the most pertinent research topics in intelligent transportation systems and anchors for future smart city environment due to the ability to provide road safety and precautionary measures for the drivers and passengers. Due to the characteristics of vehicular networks, security, privacy, and trust management are challenging issues. Blockchain is an emerging

Sentiment analysis in Danmaku video interaction aims at measuring public mood in respect of the video, which is helpful for the potential applications in behavioral science. Once these sentiments are discovered, this feedback can help video creators improve the video quality and greatly enhance online users’ watching experience. Predicting these entity-level sentiments is challenging because there

For measuring the efficiency of workflow scheduling, determining makespan and execution cost is essential. As estimating makespan and cost is difficult in a Cloud environment, designing an efficient computation of workflow scheduling remains a challenge. The Cloud resources are scaled up and down in accordance with user demand by following a scheduling policy. The scalability of the work environment

In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize the network topology. Therefore, choosing the right route in this type of network due to resource constraints and their operating environment is one of the most important challenges in these networks. Therefore, the main purpose of these

Phase unwrapping is an integral part of multiple algorithms with diverse applications. Detailed phase unwrapping is also necessary for achieving high-accuracy metric sensing using laser feedback-based self-mixing interferometry (SMI). Among SMI specific phase unwrapping approaches, a technique called Improved Phase Unwrapping Method (IPUM) provides the highest accuracy. However, due to its complex

The implementation of a wireless sensor network has been utilized in many applications of urban and rural areas. One such application is smart-grid Internet of Things, (IoT)-based monitoring systems that work on real-time communication. They require an intelligent scheduling mechanism to scrap delay in dynamic traffic flows without undergoing any efficiency loss. This study presents a data-driven TDMA-based

As enterprise information systems grow in scale and computing resources remain limited, some computing system services run into occasional abnormalities such as degraded stability and the failure to respond in time. Fewer monitoring tools mean that system maintenance managers may not be notified when abnormal events occur. It becomes difficult to diagnose and manage the problems promptly, to decrease

Multicore systems are in demand due to their high performance thus making application mapping an important research area in this field. Breaking an application into multiple parallel tasks efficiently and task-core assignment decisions can drastically influence system performance. This has created an urgency to find potent mapping techniques which can handle the complexity of these systems. Task assignment

Numerical simulation of multi-physical processes requires a lot of processor time, especially when solving ill-conditional linear systems arising in fluid dynamics problems. This paper is devoted to the development of efficient parallel methods for such systems for FireStar3D wildfire modeling code. Two alternative approaches are discussed and analyzed, based on the MILU-preconditioned conjugate gradient

This work aimed to explore chest computed tomography (CT) image segmentation of patients with large-cell neuroendocrine carcinoma (LCNEC) based on deep learning, as well as the clinical manifestations and imaging and pathological features of LCNEC patients. Clinical data of 40 patients with LCNEC confirmed by pathological examination in the X Hospital from December 2015 to December 2017 were retrospectively

Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal

Increasingly, research has analyzed human behavior in various fields. The fourth industrial revolution technology is very useful for analyzing human behavior. From the viewpoint of the residential space monitoring system, the life patterns in human living spaces vary widely, and it is very difficult to find abnormal situations. Therefore, this study proposes a living space-based monitoring system.

In this paper, a theoretical framework for Optical Wireless Communication using RGB color model with computer monitor display and digital camera is proposed. The motivation is to find a cheaper alternative to physical network switches and wired and optical cables in communication networks that can be leveraged for computer clusters, thus reducing time and costs for purchase, setup, maintenance, power

LTE networks consist of tracking areas (TAs) or a group of cells, while several TAs constitute a TA list (TAL). The LTE network adopts TAL-based registration, where, if the user equipment (UE) enters a TA that is not in its current TAL, the UE registers the TA to inform the network of its new location. A central policy for TAL allocation, known as a TA-based central policy, was proposed for TAL-based

Hyperspectral images (HSIs) record scenes at different wavelength channels, providing detailed spatial and spectral information. How to storage and process this high-dimensional data plays a vital role in many practical applications, where classification technologies have emerged as excellent processing tools. However, their high computational complexity and energy requirements bring some challenges

Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms

To improve the accuracy and computational efficiency of the MapReduce distributed parallel computing framework, thereby mining the diagnosis and treatment data of Kashin-Beck Disease (KBD) of the knee joint. Based on the shortcomings of the traditional K-means Clustering Algorithm (KCA), a simplified method for distance calculation was proposed. The Manhattan distance was used instead of Euclidean

The objective is to demonstrate that a probabilistic cellular automata rule can place reliably a maximal number of dominoes in different active area shapes, exemplarily evaluated for the square and diamond. The basic rule forms domino patterns, but the number of dominoes is not necessarily maximal and the patterns are not always stable. It works with templates derived from domino tiles. The first proposed

The deluge of dark data is about to happen. Lacking data management capabilities, especially in the field of supercomputing, and missing data documentation (i.e., missing metadata annotation) constitute a major source of dark data. The present work contributes to addressing this challenge by presenting ExtractIng, a generic automated metadata extraction toolkit. Existing metadata information of simulation

With the recent developments in Internet of Things (IoT), the number of sensors that generate raw data with high velocity, variety, and volume is tremendously increased. By employing complex event processing (CEP) systems, valuable information can be extracted from raw data and used for further applications. CEP is a stream processing technology that matches atomic events to complex events via predefined

Internet of Things (IoT) is a novel paradigm that connects several physical devices and the cyber world over the Internet. IoT technology is growing rapidly and soon will have an enormous innovation in our daily lives. With the increasing number of connected IoT devices making our daily lives more convenient, it puts personal data at serious risk too. Radio frequency identification (RFID) contributes

Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that

This work presents a structure-preserving non-photorealistic rendering (NPR) framework that can produce an effective structure-preserving abstracted and stylized output by manipulating visual features from 2D color image. The proposed framework distills the prominent structural features, dominant edges, medium-scale details, curved discontinued edges, silhouette, dendritic structures and curved boundaries

Quantum image processing focuses on the use of quantum computing in the field of digital image processing. In the last few years, this technique has emerged since the properties inherent to quantum mechanics would provide the computing power required to solve hard problems much faster than classical computers. Binarization is often recognized to be one of the most important steps in image processing

Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both methodologies promise similar benefits, they are based on different foundations. SPL encourages systematic reuse that exploits the commonalities of various products belonging to a common domain and manages their variations systematically. In

Fast Fourier transform is widely used to solve numerous scientific and engineering problems. In particular, this transform is behind the software dealing with speech and image recognition, signal analysis, modeling of properties of new materials and substances, etc. Newly emerging high-performance hybrid computing systems, as well as systems with alternative architectures, require research on discrete

The purpose of this study is to investigate gait in patients with neurological disorders using accelerometers. Accelerometers were placed on both ankles of participants undergoing gait analysis. Data were collected during the 10-min walk test from healthy participants (n = 20) and patients with neurological deficits (n = 22) scheduled for surgery. Additional data were obtained after surgery for comparison

A goniometer is a device used to measure the angular range of motion of various human joints and muscle groups, such as the elbow, knee or waist. However, the majority of mechanical goniometers are heavy, provide inaccurate measurements and are affected by dust or stains. Electronic and optical fiber-based goniometers demonstrate better capability than their mechanical equivalents; however, due to

Quantum-dot cellular automata (QCA) are a new technology used to fabricate digital circuits on the nanoscale in place of CMOS technology, which has limitations in device density. QCA devices are low in power consumption and high in speed due to their structure. Although some defects may occur during chemical fabrication, QCA gates and circuits can be designed to be fault-tolerant. The majority gate

Telecare medicine information system (TMIS) is recognized as an important tool for improving the quality and protection of healthcare services. In addition to protecting the privacy of patients, many authentication techniques are being introduced in TMIS. After investigations, it is observed that many authentication techniques have security breaches. In this article, we propose an efficient, secure

Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating

Malware detection from the smartphone has become a challenging issue for academicians and researchers. In this research paper, we applied five distinct machine learning algorithms and three different ensemble methods to develop a model for detecting malware from an Android-based smartphone. In this study, we proposed a framework that helps in selecting the right sets of the feature with an aim to improve

The railway transportation industry is one of the essential factors to promote economic development, so research is made on improving the investment benefit of construction planning of the railway transportation industry, and on this basis, the national emergency attribute is analyzed. A prediction model of railway transportation investment benefits and national economic attributes based on EEMD-LSTM

Across the globe, health cognizant among the people is increasing and everyone wants to maintain a healthy and normal life. But due to the fast moving world, obesity and other related issue becomes the major health problem among the human beings. According to medical experts, a person is defined as obese when their BMI is greater than 30 kg/m2. Obesity leads to many diseases like high cholesterol,

The distributed alternating direction method of multipliers (ADMM) is an effective algorithm for solving large-scale optimization problems. However, its high communication cost limits its scalability. An asynchronous lazy ADMM algorithm based on hierarchical sparse allreduce communication mode (HSAC-ALADMM) is proposed to reduce the communication cost of the distributed ADMM: firstly, this paper proposes

This paper defines software maintenance activities and develops a model for maintenance cost estimation of package software. First, we classified software maintenance activities which were collected from the literature reviews. Second, we developed a cost structure for package software maintenance based on the identified activities. Third, the activity-based software maintenance cost estimation model

In this paper, we propose a parallel source separation system designed to extract heart and lung sounds from single-channel mixtures. The proposed system is based on a non-negative matrix factorization (NMF) approach and a clustering strategy together with a soft-masking filtering. Furthermore, we propose an offline and online implementation of the framework which can be applied in many real-time scenarios

Generating random numbers is important for many real-world applications, including cryptography, statistical sampling and Monte Carlo simulations. Quantum systems subject to a measurement produce random results via Born’s rule, and thus it is natural to study the possibility of using such systems in order to generate high-quality random numbers. However, current quantum devices are subject to errors

The integration of information technologies into the current power grid has raised significant security concerns for the advanced metering infrastructure (AMI). Evidently, without employing proper security measures, illegal or malicious entities could launch miscellaneous attacks. Thus, scholars have presented several key agreement schemes, which can be used by different parties in the AMI guaranteeing

Wireless sensor networks (WSNs), as an integral part of most Internet of Things (IoT) devices, are currently proliferating providing a new paradigm of emerging technologies. It is estimated that the number of globally connected products will increase exponentially in the next decade. Therefore, it is not surprising finding applications in different areas such as smart homes, smart cities, industry

Workflows that consume and produce large amounts of data are being widely used in modern scientific computing and data processing pipelines. Scheduling of data-intensive workflows requires a careful management of data transfers between tasks, since network contention can significantly impact the workflow execution time. The paper presents and evaluates several scheduling algorithms, data transfer strategies

Visual methods were used for pre-cluster assessment and useful cluster partitions. Existing visual methods, such as visual assessment tendency (VAT), spectral VAT (SpecVAT), cosine-based VAT (cVAT), and multi-viewpoints cosine-based similarity VAT (MVS-VAT), effectively assess the knowledge about the number of clusters or cluster tendency. Tweets data partitioning is underlying the problem of social

The Internet of Medical Things (IoMT) is the next frontier in the digital revolution and it leverages IoT in the healthcare domain. The underlying technology has changed the current healthcare system by collecting real-time data of patients and providing a patient motioning system. But IoMT also presents a big challenge for data storage management, security, and privacy due to cloud-based storage.

Although Cloud computing is gaining popularity by supporting data analysis in an outsourced and cost-effective way, it brings serious privacy issues when sending the original data to Cloud servers. Sensitive data have a significant value, and any infringement of privacy can cause great loss in terms of money and reputation. Thus, for any Cloud ecosystem to be accepted and easily adopted by different

Extensive use of multimedia services and Internet Data Center applications demand distributed deployment of these applications. It is implemented using edge computing with server clusters. To increase the availability of the services, applications are deployed redundantly in server clusters. In this situation, an efficient server allocation strategy is essential to improve execution fairness in server

Quantum dot cellular automaton (QCA) is a novel emerging nanometer-scale-based circuit design using nanocomputing technology, which overcomes the limitations of complementary MOS technology in the precondition of the circuit design area, power, and latency/delay. This paper presents an efficient design of crossover single-layer (coplanar) and multilayer novel hybrid full adder–subtractor circuits by

Achieving the minimum average coflow completion time(CCT) and the isolation guarantees for multi-tenant, is considered a challenge in a cloud environment. This is because the minimum average CCT and isolation guarantees are two conflicting targets, and they cannot be achieved simultaneously. Prior solutions have implemented a single target either minimizing the average CCT or isolation guarantees.

In the clustering method, the cluster head node loses much energy between transmissions to the base station, confirming that determining the cluster heads is crucial. A robust determination protocol needs to pick cluster heads depending on the node's region and its residual energy. We proposed an innovative approach to the method of selecting cluster heads in this work. The target of the cluster head

Link prediction (LP) is a crucial issue in the online social network (OSN) evolution analysis. Since OSNs are growing in size on a daily basis, a growing need for scalable LP algorithms is being felt. OSNs are innately evolutionary, such that the characteristics, behavior, and activities of their components (including nodes and links) change over time. In analyzing social networks which are based on

In recent years, with the rapid development of e-commerce and network technologies, many people do online shopping through the Internet. If a physical product is bought by a client, the store will entrust their logistics to deliver the goods to the client. However, there have been many cases of switched goods which were purchased by clients recently. Some scholars proposed a security mechanism with

Mobile Ad hoc NETworks (MANET) are networks without infrastructure. The communication range among nodes is limited, where several hops are needed to transmit a packet from the source to the destination. These networks have a constantly changing topology due to its mobile nodes and their arbitrary connections, which make it vulnerable for different attacks. One of the most important attacks in MANET

We present CamNav, a vision-based navigation system that provides users with indoor navigation services. CamNav captures images in real time while the user is walking to recognize their current location. It does not require any installation of indoor localization devices. In this paper, we describe the techniques of our system that improve the recognition accuracy of an existing system that uses oriented

Energy optimization has become an emerging concern for cloud service providers. Existing methods focus on reducing the energy consumption of either server inside the data center or data transmission between data centers. Moreover, most of the works are based on assumptions that servers and workloads are homogeneous. This is not in accordance with the fact that modern data centers are built from various

Most researchers establish research directions in their study of new fields by providing expert advice or publishing expert papers. The existing academic search services display papers by field but do not provide experts by field. Therefore, researchers are left to judge experts in each field by analyzing the papers for themselves. In this paper, we design and implement an expert search system based

In the social Web era, crowdfunding has become an increasingly important channel for entrepreneurs to raise funds from the crowd for their start-up projects. Previous studies have examined various factors, such as textual information of projects and social capital of investors. However, multimedia information on projects such as audio information was rarely studied for analysing crowdfunding successes