Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 434 journals)
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 Arabian Journal for Science and EngineeringJournal Prestige (SJR): 0.303 Citation Impact (citeScore): 1Number of Followers: 5      Hybrid journal (It can contain Open Access articles) ISSN (Print) 2193-567X - ISSN (Online) 2191-4281 Published by Springer-Verlag  [2652 journals]
• Computer-Aided Segmentation of Liver Lesions in CT Scans Using Cascaded
Convolutional Neural Networks and Genetically Optimised Classifier
• Abstract: Abdominal CT scans have been widely studied and researched by medical professionals in recent years. CT scans have proved effective for the task of detection of liver abnormalities in patients. Computer-aided automatic segmentation of the liver can serve as an elementary step for radiologists to trace anomalies in the liver. In this paper, we have explored deep learning techniques first and foremost for the extraction of liver from the abdominal CT scan and then, consequently, to segment the lesions from a tumour-ridden liver. A cascaded model of convolutional neural networks is used to segment lesions once tumour has been detected in the liver by GA-ANN which has been fed textural liver features using LTEM for its classification procedure. A high DICE index has been obtained of 0.9557 for liver segmentation and 0.6976 for lesion segmentation.
PubDate: 2019-04-01
DOI: 10.1007/s13369-019-03735-8

• Image Inpainting Algorithm Based on Saliency Map and Gray Entropy
• Abstract: Image inpainting algorithms based on separated priority are easily misled by image texture information, have poor accuracy in searching for matching patches with high priority and often result in inconsistent texture propagation and edge structure. Additionally, it is difficult to obtain the best-matching patch within a fixed range based on only color information. By considering the attention point of human vision and the statistical information of an image, an image inpainting algorithm based on saliency mapping and gray entropy is proposed. A saliency map is added to the priority stage, which ensures that the parts with strong structural information and visual importance are completed preferentially. The best-matching patch is determined by comprehensively considering the color information and saliency features. The search range of the matching patch is adaptively controlled based on gray entropy. Experiments concerning scratch damage, text removal and large area object removal are compared. The results of the proposed method have better visual effects and are superior in regard to the consistency of the edge structure and texture. The efficiency is similar to methods with a fixed local search range. The objective evaluation results also validate the performance of the proposed method.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3592-5

• A Neighborhood Search-Based Heuristic for the Fixed Spectrum Frequency
Assignment Problem
• Abstract: This article proposes a heuristic for the fixed spectrum frequency assignment (FS-FA) problem of telecommunications networks. A network composes of many connections, and each connection needs a frequency from the spectrum. The assignment of frequencies to the transmitters should satisfy a set of constraints. The constraints specify the separation which is necessary between frequencies of different transmitters. Violation of constraints creates interference. The goal of the FS-FA problem is to find an assignment of frequencies for the transmitters, which has minimum interference. The proposed heuristic has two main components: a local search heuristic and a compound move. The local search heuristic employs one-change moves (i.e., a move that changes the frequency of one transmitter at a time). It also employs a lookup table that classifies all possible one-change moves as positive or negative. The local search heuristic chooses positive/negative moves until it traps in a locally minimal solution. The compound-move operation shifts the local search to a new location in the search space. We can repeatedly apply the local search and compound move for many iterations. The proposed heuristic has been evaluated on the same benchmarks as used by others in the recently published literature. We have compared our algorithm with two existing tabu-search-based algorithms: dynamic-list-based tabu search (DTS) (Montemanni et al. in IEEE Trans Veh Technol 52(4):891–901, 2003. https://doi.org/10.1109/TVT.2003.810976) and heuristic manipulation technique-based TS (Montemanni and Smith in Comput Oper Res 37(3):543–551, 2010. https://doi.org/10.1016/j.cor.2008.08.006) (HMT). The solution quality of the proposed algorithm is found to be better than or equal to the HMT and DTS in 88% and 79% of test problems, respectively.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3393-x

• Integrating the Local Patches of Weber Orientation with Sparse
Distribution Method for Object Recognition
• Abstract: In this paper, a new scheme of feature extraction named as sparse Weber-oriented visual features is proposed by the integration of sparse and dense data representations. Image orientation, magnitude and pixel intensity are aggregated in horizontal direction to obtain the feature vector that provides more discriminative information of an image objects. Instead of merely considering individual pixel intensities being highly susceptible to image local variations, orientation is calculated using horizontal and vertical local patches along current pixel. The magnitude component is computed using dense chromatic data representation by second-order symmetric kernel function. Pixels randomly selected from sparse data are binarized with winner-take-all principle to generate feature vector directly from pixel intensities. Experiments using KNN classifier resulted in object recognition accuracy of 94.43%, 96.21% and 71.8% on three standard available colored image datasets that are COIL-100, ALOI and PVOC 2007, respectively. Results evaluation depicts the performance of proposed method as compared to state-of-the-art object recognition schemes.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3612-5

• Keyword Binning-Based Efficient Search on Encrypted Cloud Data
• Abstract: Cloud provides storage facility to its users at a reduced cost, but storing confidential data on the cloud is a security concern. To provide data privacy, the confidential data are encrypted before outsourcing to the cloud. Storing encrypted data in the cloud affects the utilization of data as it makes document searching inefficient. Although recent research has tried to make searching efficient, still there is a trade-off between the search efficiency and search accuracy. As the cloud is based on the pay-per-use model, more is the time required to retrieve the relevant documents more will be the financial burden on end users which affects their cloud-usage satisfaction. In this paper, the concept of keyword binning is proposed where document indexes are assigned to multiple buckets based on the contained keywords and search is performed only in relevant buckets. So, we use the number of comparisons as one of the metrics as it affects the search time. With ranking incorporated, the retrieved results are ranked efficiently and in 84% of the cases 4 out of the top-5 results of the proposed scheme match the 4 results in top-5 plain-text ranked results. For enhanced query privacy, we also propose an efficient query randomization scheme. The experimental results using Reuters-21578 dataset show that the proposed scheme is privacy-preserving and efficient with 100% recall and 98.45% precision.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3580-9

• Very High Capacity Image Steganography Technique Using Quotient Value
Differencing and LSB Substitution
• Abstract: This article proposes a very high capacity steganography technique using differencing and substitution mechanisms. It divides the image into non-overlapped $$3{\times }3$$ pixel blocks. For every pixel of a block, least significant bit (LSB) substitution is applied on two LSBs and quotient value differencing (QVD) is applied on the remaining six bits. Thus, there are two levels of embedding: (i) LSB substitution at lower bit planes and (ii) QVD at higher bit planes. If a block after embedding in this fashion suffers with fall off boundary problem, then that block is undone from the above hybrid embedding and modified 4-bit LSB substitution is applied. Experimentally, it is evidenced that the hiding capacity is improved to a greater extent. It is also experimentally proved that pixel difference histogram and RS analysis techniques cannot detect the proposed steganography technique.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3372-2

• Deep Learning Based Sentiment Analysis Using Convolution Neural Network
• Abstract: Sentiment analysis (SA) of natural language text is an important and challenging task for many applications of Natural Language Processing. Till now, researchers have used different types of SA techniques such as lexicon based and machine learning to perform SA for different languages such as English, Chinese. Inspired by the gain in popularity of deep learning models, we conducted experiments using different configuration settings of convolutional neural network (CNN) and performed SA of Hindi movie reviews collected from online newspapers and Web sites. The dataset has been manually annotated by three native speakers of Hindi to prepare it for training of the model. The experiments are conducted using different numbers of convolution layers with varying number and size of filters. The CNN models are trained on 50% of the dataset and tested on remaining 50% of the dataset. For the movie reviews dataset, the results given by our CNN model are compared with traditional ML algorithms and state-of-the-art results. It has been observed that our model is able to achieve better performance than traditional ML approaches and it has achieved an accuracy of 95%.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3500-z

• Novel Automatic Food Trading System Using Consortium Blockchain
• Abstract: Conventional food trading platforms face several issues, such as quickly to find trading objects and protect the reliability of transaction information. With e-commerce developing rapidly, food trading has also recently shifted to the online domain. Blockchain has changed many industries owing to its robustness, decentralization and end-to-end credibility. This paper proposes a novel Food Trading System with COnsortium blockchaiN (FTSCON) to improve trust and security issues in transactions. It uses consortium blockchain technology to set permission and authentication for different roles in food transaction, which meet the challenge of the privacy protection of multi-stakeholders. The algorithm of optimized transaction combination is designed for the purpose of helping users find suitable transaction objects. It can choose the optimized trading portfolio for buyers. The online double auction mechanism is used to eliminate competition. And the improved PBFT (iPBFT) is used to enhance efficiency of system. Moreover, a smart-contract life- cycle management method is introduced, and security analysis shows that FTSCON improves transaction security and privacy protection. Experiment results based on a series of data indicate that the proposed algorithm can achieve profit improvement of merchants.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3537-z

• DNA-Based AES with Silent Mutations
• Abstract: We present a new version of Advanced Encryption Standard (AES), called DNAES, based on deoxyribonucleic acid (DNA) sequences with silent mutations. We present how to encode and decode data in a DNA sequence and how to perform the different steps of AES. The proposed cipher has the following features: (1) it can be applied to any type of data (e.g., text, videos, images); (2) it has the same security level as AES; (3) it can be implemented in a biological environment or on DNA computers; (4) because the ciphertext generated by DNAES does not actually change the amino acid sequence of the protein, side effects are avoided; and (5) besides encryption, the proposed cipher can be used to hide data in a DNA sequence.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3520-8

• Development of a Real-Time, Simple and High-Accuracy Fall Detection System
for Elderly Using 3-DOF Accelerometers
• Abstract: Falls represent a major problem for the elderly people aged 60 or above. There are many monitoring systems which are currently available to detect the fall. However, there is a great need to propose a system which is of optimal effectiveness. In this paper, we propose to develop a low-cost fall detection system to precisely detect an event when an elderly person accidentally falls. The fall detection algorithm compares the acceleration with lower fall threshold and upper fall threshold values to accurately detect a fall event. The post-fall recognition module is the combination of posture recognition and vertical velocity estimation that has been added to our proposed method to enhance the performance and accuracy. In case of a fall, our device will transmit the location information to the contacts instantly via SMS and voice call. A smartphone application will ensure that the notifications are delivered to the elderly person’s relatives so that medical attention can be provided with minimal delay. The system was tested by volunteers and achieved 100% sensitivity and accuracy. This was confirmed by testing with public datasets and it also achieved the same percentage in sensitivity and accuracy as in our recorded datasets.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3496-4

• Detection and Defense Mechanisms on Duplicate Address Detection Process in
IPv6 Link-Local Network: A Survey on Limitations and Requirements
• Abstract: The deployment of Internet Protocol Version 6 (IPv6) has progressed at a rapid pace. IPv6 has introduced new features and capabilities that is not available in IPv4. However, new security risks and challenges emerge with any new technology. Similarly, Duplicate Address Detection (DAD), part of Neighbor Discovery Protocol in IPv6 protocol, is subject to security threats such as denial-of-service attacks. This paper presents a comprehensive review on detection and defense mechanisms for DAD on fixed network. The strengths and weaknesses of each mechanism to Secure-DAD process are discussed from the perspective of implementation and processing time. Finally, challenges and future directions are presented along with feature requirements for the new security mechanism to secure DAD procedure in an IPv6 link-local network.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3643-y

• Comparing Hyperparameter Optimization in Cross- and Within-Project Defect
Prediction: A Case Study
• Abstract: Various studies related to the cross-project defect prediction (CPDP) have been done in defect prediction literature. These studies are based on the methodology which takes training and testing data sets from different projects or varied versions of same project that could have same number of features. Configurable parameters of machine learning algorithms should not be disregarded during defect prediction. In this study, the effects of hyperparameter optimization are investigated in CPDP and within-project defect prediction (WPDP). To this end, this work proposes a novel method that shows how hyperparameter optimization should be performed in CPDP. Thus, two new procedures are proposed by regarding the structure of heterogeneous data sets. Firstly, a defect prediction model is established on 20 data sets. Various hyperparameters are optimized and the success of CPDP and WPDP is compared afterward. According to the obtained results: (i) CPDP is averagely superior to WPDP in hyperparameter optimization; (ii) linear kernel of SVM is better than polynomial and radial kernels in terms of hyperparameter optimization; (iii) max tree depth (interaction.depth) is crucial in increasing accuracy if a tree-based algorithm is used.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3564-9

• Analysis of the Message Propagation on the Highway in VANET
• Abstract: Intelligent transportation systems are becoming more and more important nowadays. In these systems, vehicles and possibly the infrastructure communicate with each other by vehicular ad hoc networks (VANETs). VANETs are being deployed and widely used in urban as well as in highway applications. Several standard use cases have been identified over the last decade (i.e., alert messages, car following support, data exchange between vehicles). In this paper, we focus on the alert message propagation on the highway. We derive the stationary and the transient solution of the message propagation distance by constant vehicle speed. Since these messages frequently indicate an accident on the road leading to a traffic jam, we extend the model to take the queueing system due to the traffic jam also into consideration. Our analytical results are compared with SUMO-/Veins-based simulations.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3535-1

• A New Leaf Venation Detection Technique for Plant Species Classification
• Abstract: This paper presents a novel approach to classify the leaf shape and to identify plant species using venation detection. The proposed approach consists of five main steps to extract the leaf venation, including canny edge detection, remove leaf boundary, extract curve, and produce hue normalization image and image fusion. Moreover, to localize the edge direction efficiently, the lines that extracted from pre-processing are further divided into smaller segments. Thirty-two leaf images of Malaysian plants are analysed and evaluated with two different datasets, Flavia and Acer. The average accuracy is obtained by 98.6 and 89.83% for Flavia and Acer datasets, respectively. Experimental results show the effectiveness of the proposed approach for shape recognition with high accuracy.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3504-8

• Delay-Aware Resource Allocation for M2M Communications Over LTE-A Networks
• Abstract: Machine-to-machine (M2M) communication is now a frequently used term owing to the knowledge of the Internet of Things. As the applications of the M2M increase, the number of M2M devices is predicted to highly increase in the next coming years. Presently, there is an attempt to improve the cellular networks to handle both human-to-human (H2H) and M2M communications. Integrating the M2M communication across the usual H2H communication is an important target owing to the predictable growth in the number of M2M devices that includes the distinctive features of the M2M traffic. To increase the efficiency of the usage of the LTE resource, the same resource is predicted to be used for H2H and M2M communications. Consequently, an efficient resource-scheduling plan is essential to manage an LTE network system including both M2M devices and H2H users. In this paper, we propose a delay-aware time-slotted resource allocation with a priority-based queuing model, which is designed especially for the LTE network including both M2M devices and H2H users. Resource scheduling provides the highest priority to H2H, in contrast to M2M, which is given the lowest priority. The high arrival rate of the users of H2H results in widespread starvation of M2M users; hence, it is likely to transmit some resources to M2M devices by postponing the H2H users up to QoS value, which does not have a negative impact on their quality. In the proposed schemes, the H2H users’ priority is relaxed by delaying them in the LTE network up to the level that does not have a negative impact on the quality of the H2H users. Then, in addition to protecting the H2H users, the proposed schemes can increase the utilization of the M2M resource usage and reduce its average waiting delay. In addition, simulation and analytical models are developed in order to assess the performance measures of M2M in terms of average waiting delay (W) and average system delay (T). The results show that the proposed schemes provide better M2M performance while controlling the QoS level for H2H services.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3622-3

• Service-Level Agreement—Energy Cooperative Quickest Ambulance Routing
for Critical Healthcare Services
• Abstract: In this study, the problem of critical ambulance routing scheme, which is a significant variant of the quickest path problem (QPP), was investigated. The proposed QPP incorporates additional factors, such as service-level agreement (SLA) and energy cooperation, to compute the SLA-energy cooperative quickest route (SEQR) for a real-time critical healthcare service vehicle (e.g., ambulance). The continuity of critical healthcare services depends on the performance of the transport system. Therefore, in this research, SLA and energy were proposed as important measures for quantifying the performance. The developed algorithm (SEQR) evaluates the SLA-energy cooperative quickest ambulance route according to the user’s service requirements. The SEQR algorithm was tested with various transport networks. The SLAs and energy variation were quantified through the mean candidate s–t qualifying service set (QSS) routes for the service, average hop count, and average energy efficiency.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3687-z

• A Fibrosis Diagnosis Clinical Decision Support System Using Fuzzy
Knowledge
• Abstract: Liver cirrhosis, the end stage of chronic liver disease, is one of the major risk factors for the development of liver cancer, and may result in premature death. This research proposes a fuzzy fibrosis decision support (F2DS) system. It is a fuzzy knowledge-based expert system for liver fibrosis stage prediction. F2DS is carefully based on a set of knowledge acquisition and machine learning techniques. In addition, the system depends on domain expert knowledge for designing the membership functions and validating the fuzzy knowledge base. It depends on a suitable list of 17 symptoms, and laboratory test features that can accurately and significantly describe fibrosis patients. The experimental results of the expert system were obtained using a real dataset from the Liver Institute, Mansoura University, Egypt, of 119 patients infected by chronic viral hepatitis C. The performance of the system was evaluated with many metrics, achieving a testing accuracy of 95.7%. The evaluation of proposed fuzzy expert system shows its capability of diagnosing the stages of liver fibrosis with a high degree of accuracy, and it can be embedded as a component in a healthcare system to assist physicians in their daily practice. In addition, students training in medicine can benefit from this system.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3670-8

• Estimation of Noise Using Non-local Regularization Frameworks for Image
Denoising and Analysis
• Abstract: In this paper, we propose a novel model which adaptively estimates the noise probability distribution and noise parameters from the input image and restores the data accordingly choosing appropriate regularization model designed for it. In most imaging applications the noise characteristics are assumed prior to the restoration process. This assumption is generally based on the previous experimental study of the images from a specific modality. The adaptive detection of the noise distribution from the data makes it robust and highly suitable for automated signal and image restoration systems. The non-local framework implemented using fast numerical solvers catalyzes the convergence rate of the model. Here we analyze three different noise distributions such as Gamma, Poisson, and Gaussian. Among this Gaussian is additive and source independent, Gamma is multiplicative and source dependent, and finally Poisson is data dependent (neither multiplicative nor additive). The model can be extended to the other source-dependent distributions such as Rayleigh and Rician by appropriately tuning it. The experimental results conform to the assumption regarding the noise distribution and noise parameters estimation capability of the model.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3542-2

• A Framework for Efficient Matching of Large-Scale Metadata Models
• Abstract: Despite the success achieved in the metadata models matching area, large-scale matching does not preserve high match quality and efficiency at the same time. To deal with these challenges, we introduce a generic matching framework, called MetMat, to identify and discover corresponding entities across XML schemas and/or ontologies (metadata models). In particular, the proposed framework is based on a parallelized clustering-based matching approach, which first splits the original matching task into smaller independent tasks. These independent tasks are then carried out in parallel exploiting desktop platform features that are equipped with parallelism enabled multi-core processors. To this end, we develop three different parallel strategies: inter-, intra-, and hybrid-matching strategies. To obtain high quality, a set of matchers are exploited. The proposed framework is validated through an extensive set of experiments over small and large data sets. We also compared the MetMat framework to top matching tools participating in the OAEI (Ontology Alignment Evaluation Initiative) (http://oaei.ontologymatching.org/) for the last three years. The results show that the MetMat framework with the intra-parallel matching strategy outperforms other matching strategies in terms of processing time while preserving the same quality. Moreover, the tool acquires a good position through OAEI for the last three years.
PubDate: 2019-04-01
DOI: 10.1007/s13369-018-3443-4

• A Lightweight Authentication Scheme for Multi-gateway Wireless Sensor
Networks Under IoT Conception
• Abstract: Internet of Things (IoT) is known as a hot topic in current decade. As a widely accepted IoT technology, wireless sensor network (WSN) is employed in fire alarming, agriculture, etc. However, how to devise a secure authentication scheme for WSNs is an open issue. In recent several years, authentication schemes about WSN with multi-gateway architecture have turned up. Unfortunately, security weaknesses may happen in historical schemes. Rather than simply improve the old schemes, we present a lightweight authentication scheme for multi-gateway WSNs in this paper. Based on the place of aim sensor, the process is divided into two cases: visiting sensor in and out of the scope of gateway. After the proof with ProVerif and analysis of the security properties, we deem that our scheme is away from usual attacks. Furthermore, the presented scheme is better than other recent schemes by performance comparison and it is practical by simulation study via the famous tool NS-3.
PubDate: 2019-04-01
DOI: 10.1007/s13369-019-03752-7

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