CMC-Computers, Materials & Continua

About the Journal

Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.

Indexing and Abstracting

SCI: 2019 Impact Factor 4.89; Scopus CiteScore (Impact per Publication 2019): 3.8; SNIP (Source Normalized Impact per Paper 2019): 4.801; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.

  • Low Area PRESENT Cryptography in FPGA Using TRNG-PRNG Key Generation
  • Abstract Lightweight Cryptography (LWC) is widely used to provide integrity, secrecy and authentication for the sensitive applications. However, the LWC is vulnerable to various constraints such as high-power consumption, time consumption, and hardware utilization and susceptible to the malicious attackers. In order to overcome this, a lightweight block cipher namely PRESENT architecture is proposed to provide the security against malicious attacks. The True Random Number Generator-Pseudo Random Number Generator (TRNG-PRNG) based key generation is proposed to generate the unpredictable keys, being highly difficult to predict by the hackers. Moreover, the hardware utilization of PRESENT architecture is optimized using the Dual port… More
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  • A New Medical Image Enhancement Algorithm Based on Fractional Calculus
  • Abstract The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might influence the accuracy of the diagnosis process. To overcome this problem, this paper presents a new fractional integral entropy (FITE) that estimates the unforeseeable probabilities of image pixels, posing as the main contribution of the paper. The proposed model dynamically enhances the image based on the image contents. The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’… More
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  • Wave Propagation Model in a Human Long Poroelastic Bone under Effect of Magnetic Field and Rotation
  • Abstract This article is aimed at describing the way rotation and magnetic field affect the propagation of waves in an infinite poroelastic cylindrical bone. It offers a solution with an exact closed form. The authors got and examined numerically the general frequency equation for poroelastic bone. Moreover, they calculated the frequencies of poroelastic bone for different values of the magnetic field and rotation. Unlike the results of previous studies, the authors noticed little frequency dispersion in the wet bone. The proposed model will be applicable to wide-range parametric projects of bone mechanical response. Examining the vibration of surface waves in rotating… More
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  • Multi-Head Attention Graph Network for Few Shot Learning
  • Abstract The majority of existing graph-network-based few-shot models focus on a node-similarity update mode. The lack of adequate information intensifies the risk of overtraining. In this paper, we propose a novel Multi-head Attention Graph Network to excavate discriminative relation and fulfill effective information propagation. For edge update, the node-level attention is used to evaluate the similarities between the two nodes and the distribution-level attention extracts more in-deep global relation. The cooperation between those two parts provides a discriminative and comprehensive expression for edge feature. For node update, we embrace the label-level attention to soften the noise of irrelevant nodes and optimize… More
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  • Analyzing Customer Reviews on Social Media via Applying Association Rule
  • Abstract The rapid growth of the use of social media opens up new challenges and opportunities to analyze various aspects and patterns in communication. In-text mining, several techniques are available such as information clustering, extraction, summarization, classification. In this study, a text mining framework was presented which consists of 4 phases retrieving, processing, indexing, and mine association rule phase. It is applied by using the association rule mining technique to check the associated term with the Huawei P30 Pro phone. Customer reviews are extracted from many websites and Facebook groups, such as re-view.cnet.com, CNET. Facebook and amazon.com technology, where customers from… More
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  • HLR-Net: A Hybrid Lip-Reading Model Based on Deep Convolutional Neural Networks
  • Abstract

    Lip reading is typically regarded as visually interpreting the speaker’s lip movements during the speaking. This is a task of decoding the text from the speaker’s mouth movement. This paper proposes a lip-reading model that helps deaf people and persons with hearing problems to understand a speaker by capturing a video of the speaker and inputting it into the proposed model to obtain the corresponding subtitles. Using deep learning technologies makes it easier for users to extract a large number of different features, which can then be converted to probabilities of letters to obtain accurate results. Recently proposed methods for… More

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  • Brain Cancer Tumor Classification from Motion-Corrected MRI Images Using Convolutional Neural Network
  • Abstract Detection of brain tumors in MRI images is the first step in brain cancer diagnosis. The accuracy of the diagnosis depends highly on the expertise of radiologists. Therefore, automated diagnosis of brain cancer from MRI is receiving a large amount of attention. Also, MRI tumor detection is usually followed by a biopsy (an invasive procedure), which is a medical procedure for brain tumor classification. It is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive procedures. Convolutional neural network (CNN) is deemed to be one of the best machine learning… More
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  • General Steganalysis Method of Compressed Speech Under Different Standards
  • Abstract Analysis-by-synthesis linear predictive coding (AbS-LPC) is widely used in a variety of low-bit-rate speech codecs. Most of the current steganalysis methods for AbS-LPC low-bit-rate compressed speech steganography are specifically designed for a specific coding standard or category of steganography methods, and thus lack generalization capability. In this paper, a general steganalysis method for detecting steganographies in low-bit-rate compressed speech under different standards is proposed. First, the code-element matrices corresponding to different coding standards are concatenated to obtain a synthetic code-element matrix, which will be mapped into an intermediate feature representation by utilizing the pre-trained dictionaries. Then, bidirectional long short-term memory… More
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  • Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm
  • Abstract Fuzzy C-means (FCM) is a clustering method that falls under unsupervised machine learning. The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres. Artificial Bee Colony (ABC) is a type of swarm algorithm that strives to improve the members’ solution quality as an iterative process with the utilization of particular kinds of randomness. However, ABC has some weaknesses, such as balancing exploration and exploitation. To improve the exploration process within the ABC algorithm, the mean artificial bee colony (MeanABC) by its modified search equation that depends… More
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  • System Performance of Wireless Sensor Network Using LoRa–Zigbee Hybrid Communication
  • Abstract Wireless sensor network (WSN) is considered as the fastest growing technology pattern in recent years because of its applicability in varied domains. Many sensor nodes with different sensing functionalities are deployed in the monitoring area to collect suitable data and transmit it to the gateway. Ensuring communications in heterogeneous WSNs, is a critical issue that needs to be studied. In this research paper, we study the system performance of a heterogeneous WSN using LoRa–Zigbee hybrid communication. Specifically, two Zigbee sensor clusters and two LoRa sensor clusters are used and combined with two Zigbee-to-LoRa converters to communicate in a network managed… More
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  • Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA
  • Abstract Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis… More
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  • Modeling Bacterial Species: Using Sequence Similarity with Clustering Techniques
  • Abstract Existing studies have challenged the current definition of named bacterial species, especially in the case of highly recombinogenic bacteria. This has led to considering the use of computational procedures to examine potential bacterial clusters that are not identified by species naming. This paper describes the use of sequence data obtained from MLST databases as input for a k-means algorithm extended to deal with housekeeping gene sequences as a metric of similarity for the clustering process. An implementation of the k-means algorithm has been developed based on an existing source code implementation, and it has been evaluated against MLST data. Results… More
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  • Multi Sensor-Based Implicit User Identification
  • Abstract Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner’s personal information and services against the stored passwords. As a result of this potential scenario, this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by smartphone sensors. A set of preprocessing schemes are applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features, then further optimized… More
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  • Experimental Study of Heat Transfer Enhancement in Solar Tower Receiver Using Internal Fins
  • Abstract The receiver is an important element in solar energy plants. The principal receiver’s tubes in power plants are devised to work under extremely severe conditions, including excessive heat fluxes. Half of the tube’s circumference is heated whilst the other half is insulated. This study aims to improve the heat transfer process and reinforce the tubes’ structure by designing a new receiver; by including longitudinal fins of triangular, circular and square shapes. The research is conducted experimentally using Reynolds numbers ranging from 28,000 to 78,000. Triangular fins have demonstrated the best improvement for heat transfer. For Reynolds number value near 43,000… More
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  • Resonator Rectenna Design Based on Metamaterials for Low-RF Energy Harvesting
  • Abstract In this paper, the design of a resonator rectenna, based on metamaterials and capable of harvesting radio-frequency energy at 2.45 GHz to power any low-power devices, is presented. The proposed design uses a simple and inexpensive circuit consisting of a microstrip patch antenna with a mushroom-like electromagnetic band gap (EBG), partially reflective surface (PRS) structure, rectifier circuit, voltage multiplier circuit, and 2.45 GHz Wi-Fi module. The mushroom-like EBG sheet was fabricated on an FR4 substrate surrounding the conventional patch antenna to suppress surface waves so as to enhance the antenna performance. Furthermore, the antenna performance was improved more by utilizing… More
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  • An Optimal Classification Model for Rice Plant Disease Detection
  • Abstract Internet of Things (IoT) paves a new direction in the domain of smart farming and precision agriculture. Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent. In smart farming, IoT devices are linked among one another with new technologies to improve the agricultural practices. Smart farming makes use of IoT devices and contributes in effective decision making. Rice is the major food source in most of the countries. So, it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and… More
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  • Research on Crowdsourcing Price Game Model in Crowd Sensing
  • Abstract Crowd-Sensing is an innovative data acquisition method that combines the perception of mobile devices with the idea of crowdsourcing. It is a new application mode under the development of the Internet of Things. The perceptual data that mobile users can provide is limited. Multiple crowdsourcing parties will share this limited data, but the cost that the crowdsourcing party can pay is limited, and enough mobile users are needed to complete the perceptual task, making the group wisdom is really played. In this process, there is bound to be a game between the crowds and the mobile users. Most of the… More
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  • GPS Vector Tracking Loop with Fault Detection and Exclusion
  • Abstract In this paper, both the integrity monitoring and fault detection and exclusion (FDE) mechanisms are incorporated into the vector tracking loop (VTL) architecture of the Global Positioning System (GPS) receiver for reliability enhancement. For the VTL, the tasks of signal tracking and navigation state estimation no longer process separately and a single extended Kalman filter (EKF) is employed to simultaneously track the received signals and estimate the receiver’s position, velocity, etc. In contrast to the scalar tracking loop (STL) which utilizes the independent parallel tracking loop approach, the VTL technique is beneficial from the correlation of each satellite signal and… More
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  • Mathematical Model Validation of Search Protocols in MP2P Networks
  • Abstract Broadcasting is a basic technique in Mobile ad-hoc network (MANET), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique, floods the network with query messages, while the random walk technique operates by contacting the subsets of every node’s neighbors at each step, thereby restricting the search space. One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource. Many… More
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  • KSUTraffic: A Microscopic Traffic Simulator for Traffic Planning in Smart Cities
  • Abstract Simulation is a powerful tool for improving, evaluating and analyzing the performance of new and existing systems. Traffic simulators provide tools for studying transportation systems in smart cities as they describe the evolution of traffic to the highest level of detail. There are many types of traffic simulators that allow simulating traffic in modern cities. The most popular traffic simulation approach is the microscopic traffic simulation because of its ability to model traffic in a realistic manner. In many cities of Saudi Arabia, traffic management represents a major challenge as a result of expansion in traffic demands and increasing number… More
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  • Quranic Script Optical Text Recognition Using Deep Learning in IoT Systems
  • Abstract Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things (IoT) systems, much research has been done in using machine learning methods to recognize IoT sensors data. This is particularly the case for optical character recognition of handwritten scripts. Recognizing text in images has several useful applications, including content-based image retrieval, searching and document archiving. The Arabic language is one of the mostly used tongues in the world. However, Arabic text recognition in imagery is still very much in the nascent stage, especially handwritten text. This is mainly due to the language complexities, different writing… More
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  • Modelling and Analysis of Bacteria Dependent Infectious Diseases with Variable Contact Rates
  • Abstract In this research, we proposed a non-linear SIS model to study the effect of variable interaction rates and non-emigrating population of the human habitat on the spread of bacteria-infected diseases. It assumed that the growth of bacteria is logistic with an intrinsic growth rate is a linear function of infectives. In this model, we assume that contact rates between susceptibles and infectives as well as between susceptibles and bacteria depend on the density of the non-emigrating population and the total population of the habitat. The stability theory has been analyzed to analyzed to study the crucial role played by bacteria… More
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  • Cogent and Energy Efficient Authentication Protocol for WSN in IoT
  • Abstract Given the accelerating development of Internet of things (IoT), a secure and robust authentication mechanism is urgently required as a critical architectural component. The IoT has improved the quality of everyday life for numerous people in many ways. Owing to the predominantly wireless nature of the IoT, connected devices are more vulnerable to security threats compared to wired networks. User authentication is thus of utmost importance in terms of security on the IoT. Several authentication protocols have been proposed in recent years, but most prior schemes do not provide sufficient security for these wireless networks. To overcome the limitations of… More
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  • Utilizing Blockchain Technology to Improve WSN Security for Sensor Data Transmission
  • Abstract This paper proposes a method for improving the data security of wireless sensor networks based on blockchain technology. Blockchain technology is applied to data transfer to build a highly secure wireless sensor network. In this network, the relay stations use microcontrollers and embedded devices, and the microcontrollers, such as Raspberry Pi and Arduino Yun, represents mobile databases. The proposed system uses microcontrollers to facilitate the connection of various sensor devices. By adopting blockchain encryption, the security of sensing data can be effectively improved. A blockchain is a concatenated transaction record that is protected by cryptography. Each section contains the encrypted… More
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  • Code Smell Detection Using Whale Optimization Algorithm
  • Abstract Software systems have been employed in many fields as a means to reduce human efforts; consequently, stakeholders are interested in more updates of their capabilities. Code smells arise as one of the obstacles in the software industry. They are characteristics of software source code that indicate a deeper problem in design. These smells appear not only in the design but also in software implementation. Code smells introduce bugs, affect software maintainability, and lead to higher maintenance costs. Uncovering code smells can be formulated as an optimization problem of finding the best detection rules. Although researchers have recommended different techniques to… More
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  • Spatial-Resolution Independent Object Detection Framework for Aerial Imagery
  • Abstract Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) object detection framework to make… More
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  • Black Hole and Sink Hole Attack Detection in Wireless Body Area Networks
  • Abstract In Wireless Body Area Networks (WBANs) with respect to health care, sensors are positioned inside the body of an individual to transfer sensed data to a central station periodically. The great challenges posed to healthcare WBANs are the black hole and sink hole attacks. Data from deployed sensor nodes are attracted by sink hole or black hole nodes while grabbing the shortest path. Identifying this issue is quite a challenging task as a small variation in medicine intake may result in a severe illness. This work proposes a hybrid detection framework for attacks by applying a Proportional Coinciding Score (PCS)… More
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  • Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks
  • Abstract An IoT-based wireless sensor network (WSN) comprises many small sensors to collect the data and share it with the central repositories. These sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting it. During data sharing, security is an important concern in such networks as they are prone to many threats, of which the deadliest is the wormhole attack. These attacks are launched without acquiring the vital information of the network and they highly compromise the communication, security, and performance of the network. In the IoT-based network environment, its mitigation… More
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  • Enhancement of Sentiment Analysis Using Clause and Discourse Connectives
  • Abstract The sentiment of a text depends on the clausal structure of the sentence and the connectives’ discourse arguments. In this work, the clause boundary, discourse argument, and syntactic and semantic information of the sentence are used to assign the text’s sentiment. The clause boundaries identify the span of the text, and the discourse connectives identify the arguments. Since the lexicon-based analysis of traditional sentiment analysis gives the wrong sentiment of the sentence, a deeper-level semantic analysis is required for the correct analysis of sentiments. Hence, in this study, explicit connectives in Malayalam are considered to identify the discourse arguments. A… More
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  • Energy-Efficient Routing Algorithm Based on Multipath Routing in Large-Scale Networks
  • Abstract A reduction in network energy consumption and the establishment of green networks have become key scientific problems in academic and industrial research. Existing energy efficiency schemes are based on a known traffic matrix, and acquiring a real-time traffic matrix in current complex networks is difficult. Therefore, this research investigates how to reduce network energy consumption without a real-time traffic matrix. In particular, this paper proposes an intra-domain energy-efficient routing scheme based on multipath routing. It analyzes the relationship between routing availability and energy-efficient routing and integrates the two mechanisms to satisfy the requirements of availability and energy efficiency. The main… More
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  • Gastric Tract Disease Recognition Using Optimized Deep Learning Features
  • Abstract Artificial intelligence aids for healthcare have received a great deal of attention. Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy (WCE). Early diagnosis facilitates appropriate treatment and saves lives. Deep learning-based techniques have been used to identify gastrointestinal ulcers, bleeding sites, and polyps. However, small lesions may be misclassified. We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images. Initially, we use hybrid contrast enhancement to distinguish diseased from normal regions. Then, a pretrained model is fine-tuned, and further training is done via transfer learning. Deep features are… More
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  • An Adaptive Anomaly Detection Algorithm Based on CFSFDP
  • Abstract CFSFDP (Clustering by fast search and find of density peak) is a simple and crisp density clustering algorithm. It does not only have the advantages of density clustering algorithm, but also can find the peak of cluster automatically. However, the lack of adaptability makes it difficult to apply in intrusion detection. The new input cannot be updated in time to the existing profiles, and rebuilding profiles would waste a lot of time and computation. Therefore, an adaptive anomaly detection algorithm based on CFSFDP is proposed in this paper. By analyzing the influence of new input on center, edge and discrete… More
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  • Transmitter-Receiver Path Selection for Cell Range Extension Using Multi-Hop D2D
  • Abstract Conventional approach of dealing with more users per coverage area in cellular networks implies densifying the amount of (Access Point) AP which will eventually result in a larger carbon footprint. In this paper, we propose a base station off-loading and cell range extension (CRE) scheme based on multi-hop device-to-device (MHD2D) path selection between transmitter and receiver node. The paper also provides derivations of upper and lower bounds for energy efficiency, capacity, and transmit power. The proposed path selection scheme is inspired by the foraging behavior of honey bees. We present the algorithm as a modified variant of the artificial bee… More
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  • Reconfigurable Compact Wideband Circularly Polarised Dielectric Resonator Antenna for Wireless Applications
  • Abstract In this work, a novel compact wideband reconfigurable circularly polarised (CP) dielectric resonator antenna (DRA) is presented. The L-shaped Dielectric resonator antenna is excited by an inverted question mark shaped feed. This arrangement of feed-line helps to generate two orthogonal modes inside the DR, which makes the design circularly polarised. A thin micro-strip line placed on the defected ground plane not only helps to generate a wideband response but also assist in the positioning of the two diode switches. These switches located at the left and right of the micro-strip line helps in performing two switching operations. The novel compact… More
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  • Accurate and Computational Efficient Joint Multiple Kronecker Pursuit for Tensor Data Recovery
  • Abstract This paper addresses the problem of tensor completion from limited samplings. Generally speaking, in order to achieve good recovery result, many tensor completion methods employ alternative optimization or minimization with SVD operations, leading to a high computational complexity. In this paper, we aim to propose algorithms with high recovery accuracy and moderate computational complexity. It is shown that the data to be recovered contains structure of Kronecker Tensor decomposition under multiple patterns, and therefore the tensor completion problem becomes a Kronecker rank optimization one, which can be further relaxed into tensor Frobenius-norm minimization with a constraint of a maximum number… More
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  • Transmission Control under Multi-Service Disciplines in Wireless Sensor Networks
  • Abstract The wireless sensor network (WSN), as the terminal data acquisition system of the 5G network, has attracted attention due to advantages such as low cost and easy deployment. Its development is mainly restricted by energy. The traditional transmission control scheme is not suitable for WSNs due to the significant information interaction. A switchable transmission control scheme for WSNs based on a queuing game (SQGTC) is proposed to improve network performance. Considering that sensor nodes compete for the resources of sink nodes to realize data transmission, the competitive relationship between nodes is described from the perspective of a game. Different types… More
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  • Game-Oriented Security Strategy Against Hotspot Attacks for Internet of Vehicles
  • Abstract With the rapid development of mobile communication technology, the application of internet of vehicles (IoV) services, such as for information services, driving safety, and traffic efficiency, is growing constantly. For businesses with low transmission delay, high data processing capacity and large storage capacity, by deploying edge computing in the IoV, data processing, encryption and decision-making can be completed at the local end, thus providing real-time and highly reliable communication capability. The roadside unit (RSU), as an important part of edge computing in the IoV, fulfils an important data forwarding function and provides an interactive communication channel for vehicles and server… More
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  • Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network
  • Abstract The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially. This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network (F-RCNN). The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions. Furthermore, image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation. The permanent changes in… More
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  • Power Allocation Strategy for Secret Key Generation Method in Wireless Communications
  • Abstract Secret key generation (SKG) is an emerging technology to secure wireless communication from attackers. Therefore, the SKG at the physical layer is an alternate solution over traditional cryptographic methods due to wireless channels’ uncertainty. However, the physical layer secret key generation (PHY-SKG) depends on two fundamental parameters, i.e., coherence time and power allocation. The coherence time for PHY-SKG is not applicable to secure wireless channels. This is because coherence time is for a certain period of time. Thus, legitimate users generate the secret keys (SKs) with a shorter key length in size. Hence, an attacker can quickly get information about… More
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  • Intelligent Autonomous-Robot Control for Medical Applications
  • Abstract The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic. This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods (including medicines) that is needed to prevent infection and treatment for infected patients. The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic. The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways, particularly in the control… More
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  • Improved Hybrid Precoding Technique with Low-Resolution for MIMO-OFDM System
  • Abstract This paper proposes an improved hybrid beamforming system based on multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. The proposed beamforming system improves energy efficiency compared to the conventional hybrid beamforming system. Both sub-connected and full-connected structure are considered to apply the proposed algorithm. In the conventional hybrid beamforming, the usage of radio frequency (RF) chains and phase shifter (PS) gives high power and hardware complexity. In this paper, the phase over sampling (POS) with switches (SW) is used in hybrid beamforming system to improve the energy efficiency. The POS-SW structure samples the value of analog beamformer to make lower… More
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  • Real-Time Recognition and Location of Indoor Objects
  • Abstract Object recognition and location has always been one of the research hotspots in machine vision. It is of great value and significance to the development and application of current service robots, industrial automation, unmanned driving and other fields. In order to realize the real-time recognition and location of indoor scene objects, this article proposes an improved YOLOv3 neural network model, which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network, which is applied to the detection and recognition of objects in indoor scenes. In this article, RealSense D415 RGB-D camera is used to obtain the… More
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  • Machine Learning Approach for COVID-19 Detection on Twitter
  • Abstract Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this… More
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  • Propagation Characterization and Analysis for 5G mmWave Through Field Experiments
  • Abstract The 5G network has been intensively investigated to realize the ongoing early deployment stage as an effort to match the exponential growth of the number of connected users and their increasing demands for high throughput, bandwidth with Quality of Service (QoS), and low latency. Given that most of the spectrums below 6 GHz are nearly used up, it is not feasible to employ the traditional spectrum, which is currently in use. Therefore, a promising and highly feasible effort to satisfy this insufficient frequency spectrum is to acquire new frequency bands for next-generation mobile communications. Toward this end, the primary effort… More
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  • Grey Wolf Optimization Based Tuning of Terminal Sliding Mode Controllers for a Quadrotor
  • Abstract The research on Unmanned Aerial Vehicles (UAV) has intensified considerably thanks to the recent growth in the fields of advanced automatic control, artificial intelligence, and miniaturization. In this paper, a Grey Wolf Optimization (GWO) algorithm is proposed and successfully applied to tune all effective parameters of Fast Terminal Sliding Mode (FTSM) controllers for a quadrotor UAV. A full control scheme is first established to deal with the coupled and underactuated dynamics of the drone. Controllers for altitude, attitude, and position dynamics become separately designed and tuned. To work around the repetitive and time-consuming trial-error-based procedures, all FTSM controllers’ parameters for… More
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  • A Novel Technique for Early Detection of COVID-19
  • Abstract COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the… More
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  • Verifiable Identity-Based Encryption with Keyword Search for IoT from Lattice
  • Abstract Internet of Things (IoT), which provides the solution of connecting things and devices, has increasingly developed as vital tools to realize intelligent life. Generally, source-limited IoT sensors outsource their data to the cloud, which arises the concerns that the transmission of IoT data is happening without appropriate consideration of the profound security challenges involved. Though encryption technology can guarantee the confidentiality of private data, it hinders the usability of data. Searchable encryption (SE) has been proposed to achieve secure data sharing and searching. However, most of existing SE schemes are designed under conventional hardness assumptions and may be vulnerable to… More
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  • Emotion Analysis: Bimodal Fusion of Facial Expressions and EEG
  • Abstract With the rapid development of deep learning and artificial intelligence, affective computing, as a branch field, has attracted increasing research attention. Human emotions are diverse and are directly expressed via non-physiological indicators, such as electroencephalogram (EEG) signals. However, whether emotion-based or EEG-based, these remain single-modes of emotion recognition. Multi-mode fusion emotion recognition can improve accuracy by utilizing feature diversity and correlation. Therefore, three different models have been established: the single-mode-based EEG-long and short-term memory (LSTM) model, the Facial-LSTM model based on facial expressions processing EEG data, and the multi-mode LSTM-convolutional neural network (CNN) model that combines expressions and EEG. Their… More
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  • Cluster-Based Group Mobility Support for Smart IoT
  • Abstract IPv6 over Low Power Wireless Personal Area Network (6LoWPAN) connects the highly constrained sensor nodes with the internet using the IPv6 protocol. 6LoWPAN has improved the scalability of the Internet of Things (IoTs) infrastructure and allows mobile nodes to send packets over the IEEE 802.15.4 wireless network. Several mobility managements schemes have been suggested for handling the registration and handover procedures in 6LoWPAN. However, these schemes have performance constraints, such as increased transmission cost, signalling overhead, registration, and handover latency. To address these issues, we propose a novel cluster-based group mobility scheme (CGM6) for 6LoWPAN. To reduce the signalling cost… More
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  • Predicted Oil Recovery Scaling-Law Using Stochastic Gradient Boosting Regression Model
  • Abstract In the process of oil recovery, experiments are usually carried out on core samples to evaluate the recovery of oil, so the numerical data are fitted into a non-dimensional equation called scaling-law. This will be essential for determining the behavior of actual reservoirs. The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data. This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity, which dynamics of the process. One of the practical machine learning (ML)… More
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  • Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients
  • Abstract The National Health Interview Survey (NHIS) shows that there are 13.2% of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder (ADHD), globally. The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy. These traditional methods, namely therapy, need a large number of visits to hospitals and include medication. Neurogames could be used for the effective treatment of ADHD. It could be a helpful tool in improving children and ADHD patients’ cognitive skills by using Brain–Computer Interfaces (BCI). BCI enables the user to interact with the computer through brain activity using… More
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  • Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records
  • Abstract Background: Electronic Health Record (EHR) systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients globally. However, the existing EHR systems mostly lack in providing appropriate security, entrusted access control and handling privacy and secrecy issues and challenges in current hospital infrastructures. Objective: To solve this delicate problem, we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR systems. Methodology: In our EHR blockchain system, Peer nodes from various organizations (stakeholders) create a ledger network, where… More
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  • Design, Implementation and Verification of Topology Network Architecture of Smart Home Tree
  • Abstract Smart home technology provides consumers with network connectivity, automation or enhanced services for home devices. With the Internet of Things era, a vast data flow makes business platforms have to own the same computing power to match their business services. It achieves computing power through implementing big data algorithms deployed in the cloud data center. However, because of the far long geographical distance between the client and the data center or the massive data capacity gap, potentially high latency and high packet loss will reduce the usability of smart home systems if service providers deploy all services in the cloud… More
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  • Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model
  • Abstract Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for abnormality detection over MRI data… More
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  • HealthyBlockchain for Global Patients
  • Abstract An emerging healthcare delivery model is enabling a new era of clinical care based on well-informed decision-making processes. Current healthcare information systems (HISs) fall short of adopting this model due to a conflict between information security needed to implement the new model and those already enforced locally to support traditional care models. Meanwhile, in recent times, the healthcare sector has shown a substantial interest in the potential of using blockchain technology for providing quality care to patients. No blockchain solution proposed so far has fully addressed emerging cross-organization information-sharing needs in healthcare. In this paper, we aim to study the… More
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  • A Genetic Based Leader Election Algorithm for IoT Cloud Data Processing
  • Abstract In IoT networks, nodes communicate with each other for computational services, data processing, and resource sharing. Most of the time huge data is generated at the network edge due to extensive communication between IoT devices. So, this tidal data is transferred to the cloud data center (CDC) for efficient processing and effective data storage. In CDC, leader nodes are responsible for higher performance, reliability, deadlock handling, reduced latency, and to provide cost-effective computational services to the users. However, the optimal leader selection is a computationally hard problem as several factors like memory, CPU MIPS, and bandwidth, etc., are needed to… More
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  • Single-Layer Wideband Circularly Polarized Antenna Using Non-Uniform Metasurface for C-band Applications
  • Abstract A single-layer design of non-uniform metasurface (MS) based circularly polarized (CP) antenna with wideband operation characteristic is proposed and investigated in this paper. The antenna is excited by a truncated corner squared patch as a primary radiating CP source. Then, a non-uniform MS is placed in the same layer with the driven patch. Besides increasing the impedance bandwidth, the non-uniform MS also generates two additional CP bands in the high frequency band, leading to significantly increase the antenna’s overall performances. The use of non-uniform MS distinguishes our design from the other CP MS based antennas in literature, in which the… More
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  • Remote Health Monitoring Using IoT-Based Smart Wireless Body Area Network
  • Abstract A wireless body area network (WBAN) consists of tiny health-monitoring sensors implanted in or placed on the human body. These sensors are used to collect and communicate human medical and physiological data and represent a subset of the Internet of Things (IoT) systems. WBANs are connected to medical servers that monitor patients’ health. This type of network can protect critical patients’ lives due to the ability to monitor patients’ health continuously and remotely. The inter-WBAN communication provides a dynamic environment for patients allowing them to move freely. However, during patient movement, the WBAN patient nodes may become out of range… More
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  • A Novel Hybrid Tag Identification Protocol for Large-Scale RFID Systems
  • Abstract

    Radio frequency identification technology is one of the main technologies of Internet of Things (IoT). Through the transmission and reflection of wireless radio frequency signals, non-contact identification is realized, and multiple objects identification can be realized. However, when multiple tags communicate with a singleton reader simultaneously, collision will occur between the signals, which hinders the successful transmissions. To effectively avoid the tag collision problem and improve the reading performance of RFID systems, two advanced tag identification algorithms namely Adaptive M-ary tree slotted Aloha (AMTS) based on the characteristics of Aloha-based and Query tree-based algorithms are proposed. In AMTS, the reader… More

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  • Flower Pollination Heuristics for Parameter Estimation of Electromagnetic Plane Waves
  • Abstract For the last few decades, the parameter estimation of electromagnetic plane waves i.e., far field sources, impinging on antenna array geometries has attracted a lot of researchers due to their use in radar, sonar and under water acoustic environments. In this work, nature inspired heuristics based on the flower pollination algorithm (FPA) is designed for the estimation problem of amplitude and direction of arrival of far field sources impinging on uniform linear array (ULA). Using the approximation in mean squared error sense, a fitness function of the problem is developed and the strength of the FPA is utilized for optimization… More
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  • Eye Gaze Detection Based on Computational Visual Perception and Facial Landmarks
  • Abstract The pandemic situation in 2020 brought about a ‘digitized new normal’ and created various issues within the current education systems. One of the issues is the monitoring of students during online examination situations. A system to determine the student’s eye gazes during an examination can help to eradicate malpractices. In this work, we track the users’ eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier. We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network (CNN) models, namely the AlexNet model and the… More
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  • Frequency Reconfigurable Antenna for Multi Standard Wireless and Mobile Communication Systems
  • Abstract In this paper, low profile frequency reconfigurable monopole antenna is designed on FR-4 substrate with a compact size of 30 mm mm3. The antenna is tuned to four different modes through three pin diode switches. In Mode 1 (SW1 to ), antenna covers a wideband of 3.15–8.51 GHz. For Mode 2 (, SW2 to ), the proposed antenna resonates at 3.5 GHz. The antenna shows dual band behavior and covers 2.6 and 6.4 GHz in Mode 3 (SW1 and , ). The same antenna covers three different bands of 2.1, 5 and 6.4 GHz when operating in Mode 4 (SW1… More
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  • Security-Critical Components Recognition Algorithm for Complex Heterogeneous Information Systems
  • Abstract With the skyrocketing development of technologies, there are many issues in information security quantitative evaluation (ISQE) of complex heterogeneous information systems (CHISs). The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation urgently. In this paper, we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE process. The evaluation model establishes a framework for ISQE of CHISs that are updated iteratively. Firstly, with the support of asset identification and topology data, we… More
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  • Uncertainty Analysis on Electric Power Consumption
  • Abstract The analysis of large time-series datasets has profoundly enhanced our ability to make accurate predictions in many fields. However, unpredictable phenomena, such as extreme weather events or the novel coronavirus 2019 (COVID-19) outbreak, can greatly limit the ability of time-series analyses to establish reliable patterns. The present work addresses this issue by applying uncertainty analysis using a probability distribution function, and applies the proposed scheme within a preliminary study involving the prediction of power consumption for a single hotel in Seoul, South Korea based on an analysis of 53,567 data items collected by the Korea Electric Power Corporation using robotic… More
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  • A Reversible Data Hiding Algorithm Based on Image Camouflage and Bit-Plane Compression
  • Abstract Reversible data hiding in encrypted image (RDHEI) is a widely used technique for privacy protection, which has been developed in many applications that require high confidentiality, authentication and integrity. Proposed RDHEI methods do not allow high embedding rate while ensuring losslessly recover the original image. Moreover, the ciphertext form of encrypted image in RDHEI framework is easy to cause the attention of attackers. This paper proposes a reversible data hiding algorithm based on image camouflage encryption and bit plane compression. A camouflage encryption algorithm is used to transform a secret image into another meaningful target image, which can cover both… More
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  • A Practical Quantum Network Coding Protocol Based on Non-Maximally Entangled State
  • Abstract

    In many earlier works, perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled states. However, in actual quantum networks, a maximally entangled state as auxiliary resource is hard to be obtained or easily turned into a non-maximally entangled state subject to all kinds of environmental noises. Therefore, we propose a more practical quantum network coding scheme with the assist of non-maximally entangled states. In this paper, a practical quantum network coding protocol over grail network is proposed, in which the non-maximally entangled resource is assisted and even… More

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  • Optimal Cost-Aware Paradigm for Off-Grid Green Cellular Networks in Oman
  • Abstract

    Green wireless networks or energy-efficient wireless networks have gained popularity as a research topic due to the ecological and economic concerns of cellular operators. The specific power supply requirements for the cellular base station, such as cost-effectiveness, efficiency, sustainability, and reliability, can be met by utilizing the technological advances in renewable energy. There are numerous drivers for the deployment of renewable energy technologies and the transition towards green energy. Renewable energy is free, clean, and abundant in most locations throughout the year. Accordingly, this work proposes a novel framework for energy-efficient solar-powered base stations for the Oman site, specifically for… More

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  • A Data-Semantic-Conflict-Based Multi-Truth Discovery Algorithm for a Programming Site
  • Abstract With the extensive application of software collaborative development technology, the processing of code data generated in programming scenes has become a research hotspot. In the collaborative programming process, different users can submit code in a distributed way. The consistency of code grammar can be achieved by syntax constraints. However, when different users work on the same code in semantic development programming practices, the development factors of different users will inevitably lead to the problem of data semantic conflict. In this paper, the characteristics of code segment data in a programming scene are considered. The code sequence can be obtained by… More
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  • Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences
  • Abstract Recognition of human gait is a difficult assignment, particularly for unobtrusive surveillance in a video and human identification from a large distance. Therefore, a method is proposed for the classification and recognition of different types of human gait. The proposed approach is consisting of two phases. In phase I, the new model is proposed named convolutional bidirectional long short-term memory (Conv-BiLSTM) to classify the video frames of human gait. In this model, features are derived through convolutional neural network (CNN) named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information. In phase II,… More
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  • Using Semantic Web Technologies to Improve the Extract Transform Load Model
  • Abstract Semantic Web (SW) provides new opportunities for the study and application of big data, massive ranges of data sets in varied formats from multiple sources. Related studies focus on potential SW technologies for resolving big data problems, such as structurally and semantically heterogeneous data that result from the variety of data formats (structured, semi-structured, numeric, unstructured text data, email, video, audio, stock ticker). SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data. In the current research, we implement a new semantic Extract Transform Load (ETL)… More
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  • A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques
  • Abstract The writer identification (WI) of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist organizations. It is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies, including old national and religious archives. In this study, we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on blocks. This modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis… More
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  • Decision Making in Internet of Vehicles Using Pervasive Trusted Computing Scheme
  • Abstract Pervasive schemes are the significant techniques that allow intelligent communication among the devices without any human intervention. Recently Internet of Vehicles (IoVs) has been introduced as one of the applications of pervasive computing that addresses the road safety challenges. Vehicles participating within the IoV are embedded with a wide range of sensors which operate in a real time environment to improve the road safety issues. Various mechanisms have been proposed which allow automatic actions based on uncertainty of sensory and managed data. Due to the lack of existing transportation integration schemes, IoV has not been completely explored by business organizations.… More
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  • A Technical Framework for Selection of Autonomous UAV Navigation Technologies and Sensors
  • Abstract The autonomous navigation of an Unmanned Aerial Vehicle (UAV) relies heavily on the navigation sensors. The UAV’s level of autonomy depends upon the various navigation systems, such as state measurement, mapping, and obstacle avoidance. Selecting the correct components is a critical part of the design process. However, this can be a particularly difficult task, especially for novices as there are several technologies and components available on the market, each with their own individual advantages and disadvantages. For example, satellite-based navigation components should be avoided when designing indoor UAVs. Incorporating them in the design brings no added value to the final… More
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  • Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities
  • Abstract With increasingly more smart cameras deployed in infrastructure and commercial buildings, 3D reconstruction can quickly obtain cities’ information and improve the efficiency of government services. Images collected in outdoor hazy environments are prone to color distortion and low contrast; thus, the desired visual effect cannot be achieved and the difficulty of target detection is increased. Artificial intelligence (AI) solutions provide great help for dehazy images, which can automatically identify patterns or monitor the environment. Therefore, we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning. First, we propose a fine transmission image deep convolutional… More
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