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Emergence of smart things has revolutionized the conventional internet into a connected network of things, maturing the concept of Internet of Things (IoT). With the evolution of IoT, many attempts were made to realize the notion of smart cities. However, demands for processing enormous amount of data and platform incompatibilities of connected smart things hindered the actual implementation of smart cities. Keeping it in view, we proposed a Big Data analytics embedded smart city architecture, which is further integrated with the web via a smart gateway. Integration with the web provides a universal communication platform to overcome the platform incompatibilities of smart things. We introduced Big Data analytics to enhance data processing speed. Further, we evaluated authentic datasets to determine the threshold values for intelligent decision-making and to present the performance improvement gained in data processing. Finally, we presented a representational state transfer (RESTful) web of things (WoT) integrated smart building architecture (smart home) to reveal the performance improvements of the proposed smart city architecture in terms of network performance and energy management of smart buildings.

Bluetooth Low Energy (BLE) is a popular low power wireless technology, used for short-range communication. In BLE networks, discovery process enables fast and energy-efficient communication, since ...

We aimed to examine whether statin users have a lower risk of hepatocellular carcinoma (HCC) after careful consideration of prevalent statin use and cholesterol levels. During a mean prospective follow-up of 8.4 years in 400,318 Koreans, 1686 individuals were diagnosed with HCC. When prevalent users were included, HCC risk was reduced by >50% in statin users, regardless of adjustment for total cholesterol (TC). When prevalent users were excluded, new users who initiated statins within 6 months after baseline had a 40% lower risk of HCC (hazard ratio [HR] = 0.59) in a TC-unadjusted analysis. However, this relationship disappeared (HR = 1.16, 95% CI = 0.80–1.69) after adjusting for TC levels measured within 6 months before statin initiation. TC levels had strong inverse associations with HCC in each model. High cholesterol levels at statin initiation, not statin use, were associated with reduced risk of HCC. Our study suggests no protective effect of statins against HCC.

One of the major challenges in cybersecurity is the provision of an automated and effective cyber-threats detection technique. In this paper, we present an AI technique for cyber-threats detection, based on artificial neural networks. The proposed technique converts multitude of collected security events to individual event profiles and use a deep learning-based detection method for enhanced cyber-threat detection. For this work, we developed an AI-SIEM system based on a combination of event profiling for data preprocessing and different artificial neural network methods, including FCNN, CNN, and LSTM. The system focuses on discriminating between true positive and false positive alerts, thus helping security analysts to rapidly respond to cyber threats. All experiments in this study are performed by authors using two benchmark datasets (NSLKDD and CICIDS2017) and two datasets collected in the real world. To evaluate the performance comparison with existing methods, we conducted experiments using the five conventional machine-learning methods (SVM, k-NN, RF, NB, and DT). Consequently, the experimental results of this study ensure that our proposed methods are capable of being employed as learning-based models for network intrusion-detection, and show that although it is employed in the real world, the performance outperforms the conventional machine-learning methods.

Future generation networks will accommodate a large amount of data traffic and lower latency. In order to meet these demands, it is essential to look over current spectral use or introduce new frequency bands. Introduction of new frequency bands requires a partial or complete change of already deployed infrastructure, which will have high operation expenditure and capital expenditure. It is more convenient to find other solutions by concentrating on device-related solutions. One of the solutions to achieve higher spectra efficiency is through device-to-device (D2D) communication. This paper presents and compares recent spectral efficiency techniques in 5G through D2D communication. The main focus is on the utilization of different techniques to improve spectrum efficiency. Furthermore, the challenges in interference management, resource utilization, power control, and mode selection of the proposed work are compared.

In an automated steering system of the self-driving vehicles, the steering wheel angle is measured by the absolute angular displacement sensors or relative angle sensors. However, these sensors either encompass global navigation satellite systems (GNSS)/gyroscope – Micro Electromechanical-Sensor (MEMS) based solutions or comprise of the complex gear-based mechanical structure which results in latency and additive bias in the accumulative steering angle assessment. To address these issues, we propose a novel steering angle assessment system based on enhanced gear mechanism along with the adapted rotation paradigm for the customized self-driving vehicles. Additionally, a digital signal processing system has been introduced to resolve the issues in the identification of absolute central and max-bounding steering wheels position in self-driving vehicles. In assistance with the proposed mechanism, an algorithm has also been proposed to optimize the computed steering angle to minimalize the effect of additive bias in the accuracy. The proposed mechanism has been installed in the customized self-driving testbed vehicle and rigor validation has been performed in the straight and curvy road scenarios. Finally, the comparison study has been carried out between the conventional relative sensor and the proposed mechanism to show the accuracy and effectiveness of the proposed mechanism in terms of error rate, stability, and deviation.

The dynamic nature of the electricity market need an efficient energy management and control system to take perfect decisions accordingly. House hold appliances is the contemporary study being adopted to improve the performance and balance the fluctuation between power system and smart home. This article proposes an intelligent home energy management system (IHEMS) incorporated with a prediction model and optimization model. To address the uncertainty of future energy load and its cost, a suitable prediction model based on Bi-directional long short Term memory (Bi-LSTM) is contributed. In collaboration with the prediction model, an optimization model based on reinforcement learning is presented to schedule the home appliances by taking optimal decisions. To validate the performance of the proposed scheme, Intensive simulation is performed with adoptable, un-adoptable and manageable loads of household appliances. The results confirm that the proposed scheme address the problem of energy management for numerous appliances, reduce the total energy consumption with total energy bill and minimize the user comfort level.

Vehicular ad hoc network (VANET) is a special form of Mobile Ad hoc Network (MANET), which plays a key role in the Intelligent Transportation System (ITS). Though many outstanding geographic routing protocols are designed for VANETs, Most of the existing proposals only consider a single factor, which makes the link easy to break. In this research, we propose a Routing Protocol Based on Multi-factor Decision (RMFD), which utilizes several features. The scheme is divided into two parts, namely vehicular decision management and intersection decision management. In the vehicular component, a route is establishes between two adjacent static nodes by calculating a fuzzy performance score using Triangular Fuzzy Number (TFN). In the Intersection management, static nodes located at the intersection are selects to decide which road segment the data will be forwarded to.

Vehicular ad hoc network (VANET) is a special form of mobile ad hoc network (MANET), which plays a key role in the intelligent transportation system (ITS). Though many outstanding geographic routing protocols are designed for VANETs, a majority of them use parameters that only affect routing performance. In this article, we propose an intersection routing based on fuzzy multi-factor decision (IRFMFD), which utilizes several features. The scheme is divided into two parts, namely vehicular decision management and intersection decision management. In the vehicular component, candidate vehicles between two static nodes (SNs) located at two intersections derive potential routing paths considering distance, neighbor quantity, and relative velocity. In the intersection component, the candidate SN was chosen from the current intersection&rsquo, s 2-hop neighbors which were connected with the current intersection by a route that was decided on in part one. To get the best scheme, we also introduced other factors to estimate the number of hops in each link and link lifetime. The simulation shows that the IRFMFD outperforms on delivery ratio and end-to-end delay compared with AODV (Ad hoc on-demand distance vector), GPSR (Greedy perimeter stateless routing) and GeOpps (Geographical opportunistic routing).

Adoption of the Internet of Things for the realization of smart cities in various domains has been pushed by the advancements in Information Communication and Technology. Transportation, power delivery, environmental monitoring, and medical applications are among the front runners when it comes to leveraging the benefits of IoT for improving services through modern decision support systems. Though with the enormous usage of the Internet of Medical Things, security and privacy become intrinsic issues, thus adversaries can exploit these devices or information on these devices for malicious intents. These devices generate and log large and complex raw data which are used by decision support systems to provide better care to patients. Investigation of these enormous and complicated data from a victim&rsquo, s device is a daunting and time-consuming task for an investigator. Different feature-based frameworks have been proposed to resolve this problem to detect early and effectively the access logs to better assess the event. But the problem with the existing approaches is that it forces the investigator to manually comb through collected data which can contain a huge amount of irrelevant data. These data are provided normally in textual form to the investigators which are too time-consuming for the investigations even if they can utilize machine learning or natural language processing techniques. In this paper, we proposed a visualization-based approach to tackle the problem of investigating large and complex raw data sets from the Internet of Medical Things. Our contribution in this work is twofold. Firstly, we create a data set through a dynamic behavioral analysis of 400 malware samples. Secondly, the resultant and reduced data set were then visualized most feasibly. This is to investigate an incident easily. The experimental results show that an investigator can investigate large amounts of data in an easy and time-efficient manner through the effective use of visualization techniques.

The emergence of the Internet of Things (IoT) notion pioneered the implementation of various smart environments. Smart environments intelligibly accommodate inhabitants’ requirements. With rapid resource shrinkage, energy management has recently become an essential concern for all smart environments. Energy management aims to assure ecosystem sustainability, while benefiting both consumers and utility providers. Although energy management emerged as a solution that addresses challenges that arise with increasing energy demand and resource deterioration, further evolution and expansion are hindered due to technological, economical, and social barriers. This review aggregates energy management approaches in smart environments and extensively reviews a variety of recent literature reports on peak load shaving and demand response. Significant benefits and challenges of these energy management strategies were identified through the literature survey. Finally, a critical discussion summarizing trends and opportunities is given as a thread for future research.

Survivors of either a hemorrhagic or ischemic stroke tend to acquire aphasia and experience spontaneous recovery during the first six months. Nevertheless, a considerable number of patients sustain aphasia and require speech and language therapy to overcome the difficulties. As a preliminary study, this article aims to distinguish aphasia caused from a temporoparietal lesion. Typically, temporal and parietal lesions cause Wernicke&rsquo, s aphasia and Anomic aphasia. Differential diagnosis between Anomic and Wernicke&rsquo, s has become controversial and subjective due to the close resemblance of Wernicke&rsquo, s to Anomic aphasia when recovering. Hence, this article proposes a clinical diagnosis system that incorporates normal coupling between the acoustic frequencies of speech signals and the language ability of temporoparietal aphasias to delineate classification boundary lines. The proposed inspection system is a hybrid scheme consisting of automated components, such as confrontation naming, repetition, and a manual component, such as comprehension. The study was conducted involving 30 participants clinically diagnosed with temporoparietal aphasias after a stroke and 30 participants who had experienced a stroke without aphasia. The plausibility of accurate classification of Wernicke&rsquo, s and Anomic aphasia was confirmed using the distinctive acoustic frequency profiles of selected controls. Accuracy of the proposed system and algorithm was confirmed by comparing the obtained diagnosis with the conventional manual diagnosis. Though this preliminary work distinguishes between Anomic and Wernicke&rsquo, s aphasia, we can claim that the developed algorithm-based inspection model could be a worthwhile solution towards objective classification of other aphasia types.

Traffic congestion, collision, and long delay on a traffic signal, etc., are prominent issues in today’s transportation system. To address these issues, literature constitutes different traffic flow models to analyze road conditions. In this context, this paper proposed an optimum scheme called Traffic Signal Delay Avoidance Protocol (TSDAP) based on frequent beacon messages, which aim to estimate speed for vehicles to avoid a long wait on traffic signals. Secondly, the dynamic speed assistance to the driver is another feature of TSDAP. The dynamic speed assistance feature enabled TADAP to avoid a collision. Our simulation results show that the proposed scheme significantly outperforms in terms of speed recommendation for collision and delay avoidance.

Emergence of smart things has matured the Internet of Thing (IoT) notion. Extensive attention drawn towards IoT has inspired the concept of Smart homes. However, actual implementation of smart home architecture is highly challenged by the heterogeneity of smart devices, platform incompatibilities, and energy management demands. Web of Things (WoT) is a common solution platform, which exposes these heterogeneous services in an effective and a transparent manner. Hence, in this paper, we propose a WoT inspired smart home that occupies representational state transfer (REST) architecture for data collection and sharing. The proposed smart home architecture was tested through computer simulation to evaluate its performance efficiency with respect to energy efficiency of domestic appliances, device discovery performance, response time, and failed transmissions.

Big Data and deep computation are among the buzzwords in the present sophisticated digital world. Big Data has emerged with the expeditious growth of digital data. This chapter addresses the problem of employing deep learning algorithms in Big Data analytics. Unlike the traditional algorithms, this chapter comes up with various solutions to employ advanced deep learning mechanisms with less complexity and finally present a generic solution. The deep learning algorithms require less time to process the big amount of data based on different contexts. However, collecting the accurate feature and classifying the context into patterns using neural networks algorithms require high time and complexity. Therefore, using deep learning algorithms in integration with neural networks can bring optimize solutions. Consequently, the aim of this chapter is to provide an overview of how the advance deep learning algorithms can be used to solve various existing challenges in Big Data analytics.

Vehicular ad hoc networks (VANETs) are the preferable choice for Intelligent Transportation Systems (ITS) because of its prevailing significance in both safety and nonsafety applications. Information dissemination in a multihop fashion along with privacy preservation of source node is a serious but challenging issue. We have used the idea of the phantom node as the next forwarder for data dissemination. The phantom node (vehicle) hides the identity of actual source node thus preserving the location privacy. The selection of the phantom node among the set of alternatives’ candidate vehicles is considered as a multicriteria-based problem. The phantom node selection problem is solved by using an analytical network process (ANP) by considering different traffic scenarios. The selection is based on different parameters which are distance, speed, trust, acceleration, and direction. The best alternative (target phantom vehicle) is selected through an ANP where all the alternatives are ranked from best to worst. The vehicle having maximum weight is considered to be the best choice as a phantom node. In order to check the stability of the alternatives’ ranking, sensitivity analysis is performed by taking into account different traffic scenarios and interest level of candidate vehicles.

Software Defined Networks (SDN) has been attracting researchers, scientist, and technology experts from both academia and industry to enhance the current ICT stakes and networking paradigm. The beauty of SDN is the division of Control and Data planes and make it easy for the engineers to modify the networking protocols without visiting onsite devices. Similarly, smart cities concept has been coined recently, where a plethora of smart devices will be connected and providing tons of services to the citizens, officials, and governmental departments. The Internet of Things (IoT) plays a vital role in guaranteeing such services. Few efforts have been made to merge SDN and IoT with the sole purpose of efficient Data retrieval and achieve remotely configurable networks. In this paper, we explicitly define the Internet of Software Defined Things architecture and bring it to Smart Cities as a use-case. Our 3-tier architecture consists of Data Collection, Data Management, and Application levels that are further connected via two intermediate levels working on SDN principles. Followed by the potentials of SDN and IoT for Smart Cities, we evaluated our proposed architecture using Spark and GraphX with Hadoop Ecosystem and the results shows that efficient transfer of Data over SDN for real-time processing is achieved.

BACKGROUND To the authors' knowledge, relatively little is known regarding the interaction of risk factors for hepatocellular carcinoma (HCC) with age, sex, and liver disorder status. METHODS The authors followed 504,646 Korean patients aged 40 to 80 years who underwent routine health checkups between 2002 and 2003 until 2013 via linkage to national hospital discharge records. RESULTS HCC occurred in 2744 individuals. In the sex-adjusted and age-adjusted analysis, cirrhosis increased the incidence of HCC by 42-fold, followed by hepatitis B virus (21-fold), hepatitis C virus (HCV; 19-fold), male sex (4.3-fold), and each 5-year age increment (1.24-fold). In the multivariable adjusted analysis, diabetes increased the risk of HCC by 80%, alcohol consumption ≥80 g/day increased the risk by 75%, alcohol consumption of 40 to 79 g/day increased the risk by 37%, and being a current smoker increased the risk by 25%. The multivariable adjusted hazard ratios of male sex and HCV were 6.27 and 5.72, respectively, at age

The Internet of Things (IoT) lay down a platform for global communication among millions of electronic devices connected to the internet. These devices and electronic appliances included both wireless and wired sensors, home appliances such as Television, refrigerator, etc., radio frequency identifications (RFID), and so. Similarly, heterogeneous networks provide a platform for media independent communications. However, there exist several issues in using heterogeneous technologies for IoT communications. These challenges include the co-existence of wireless technologies such as ZigBee, Bluetooth and WIFI, cross-layer communications, high packet loss due to interferences with electronic devices, etc. Similarly, IoT is a new paradigm for interconnecting electronic devices, thus, it needs major refinement for standardising it for various services. In order to address the aforementioned challenges, we proposed a scheme for enabling a generic IoT framework and platform for various IoT embedded devices. The working of the proposed scheme is twofold, (1) discovering and collecting information from IoT enabled devices using sensors and (2) scheduling the working of these appliances based on the data collected using sensors attached to these devices. Further, the data is transferred to Hadoop ecosystem for processing and analyzing to disseminate the relevant information to the citizen. Moreover, the proposed system is tested in a smart home scenario by installing sensors attached to various home appliances. The energy consumption and packet loss occur due to available electronic appliances, and heterogeneous devices are computed and analyzed for planning an optimal scheduling scheme and load balancing. Similarly, data from various authentic sources is analyzed using Hadoop ecosystem and disseminate it to the citizen in real-time.

Industrial Internet of Things (IIoT) is the other name of industrial Internet. It integrates a variety of existing industrial automation technologies with computing, machine learning, and communication technologies. Vehicular ad hoc network, an application of IIoT, is a self-organized network of vehicles which tends to provide improved road safety, diminished traffic congestion, and ultimate comfort to the travellers. In VANETs, vehicles exchange data with each other directly or through roadside units (RSUs). Data dissemination in VANETs experiences numerous challenging issues including broadcast storm, network partitions, intermittent connectivity between vehicles, and limited bandwidth. In literature, various data dissemination schemes are proposed. However, most of these schemes are designed for either urban or highway VANET scenarios and evaluated under sparse or dense traffic conditions. Moreover, these schemes do not effectively overcome the aforementioned issues simultaneously. In this paper, we present a new data dissemination protocol for VANETs, which disseminates the emergency messages in different scenarios under varying traffic conditions. During dense traffic conditions, DDP4V employs the segmentation of transmission region of a vehicle in order to select the most appropriate next forwarding vehicle (NFV). Accordingly, it divides the transmission region of a vehicle in three distinct segments and selects vehicle(s) inside the highest priority segment to forward the message to all neighbour vehicles, whereas it also uses implicit acknowledgements for guaranteed message delivery during sparse traffic Conditions. Simulation results show that DDP4V protocol outperforms the other existing related protocols in terms of coverage, network overhead, collision, and end-to-end delay.

The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper.

Smart devices, the carriers of a huge amount of private, sensitive and confidential data are pervasive in today's world with innovative and enhanced functionalities. Smartphones have brought tremendous change in people's lives with the launch of a new platform of communication and an ease of access to a wide range of applications. Due to the swift increase in the users of Android smartphones and the increasing demands based on advanced ease and features, developers are working hard to achieve the needful. Easy access to certain features and applications gave rise to the powerfulness and an efficacy of various threats, risks and vulnerabilities that can victimize users' private data residing in smartphone paradigm. With the developments and enhancements in malware, for Android-based smartphones, attacks continue to occur. In this paper, we investigate one of the possibly most destructive attacks for Android, that is, screenshot attack. We have developed “ScreenStealer”application and explored the vulnerabilities which make Android more inclined to risks and threats. Furthermore, we evaluated capture ratio of screenshots, resources consumption and execution time to determine effectiveness, efficiency and stealthiness of such a malicious application.

Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.

The Internet of Things (IoT) provides a global communication network between millions of devices connected to the internet. Similarly, the emergence of heterogeneous wireless networks provides a medium to the IoT communication paradigm. In order to enable an energy-friendly communication in an IoT environment, such as smart home, office, city, etc. we propose an energy-aware communication systems for IoT environments. The proposed scheme works in several phases such as identification of high energy require appliances, deployment of sensors, scheduling, etc. Moreover, the data from the IoT devices are collected through sensors. The data is tested using the Hadoop ecosystem for future planning and efficient usage of the energy in an IoT environment. The proposed architecture is tested in a different scenario against the Wireless Sensor Network (WSN) based IoT architecture in the context of energy consumption. The proposed architecture performs efficiently than WSN in a number of scenarios. Similarly, the efficiency and processing time of the Hadoop system is computed which shows better results.

This paper proposes a new cluster-based on-demand routing protocol to support multihop communication in Bluetooth low energy ad hoc networks. The proposed scheme includes the topology configuration procedure, topology recovery scheme, and on-demand routing protocol. The topology configuration procedure consists of node discovery, piconet configuration, and scatternet formation in a randomly distributed environment. The proposed on-demand routing protocol is designed to minimize the number of route request messages by forwarding them to a master and relay nodes in each cluster during the route request procedure. The performance evaluation shows that our proposed scheme substantially reduces energy consumption, which is the most critical issue on energy constrained networks.

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture.

Internet of Things (IoT) notion gained a tremendous growth with the evolution of smart things. Benefits of IoT and expert attention has inspired various applications including smart home, smart city, etc. Although smart homes are widely accepted, still face with challenges arise from platform incompatibilities and heterogeneity of devices. Consequently, Web of Things (WoT) came into the play as a universal platform that exposes heterogeneous services and devices via the Web. Therefore, in this article, we propose a WoT inspired energy aware smart home management system that incorporates Representational State Transfer (REST) framework to address platform incompatibilities, while data collecting and sharing. Proposed architecture was simulated and tested for performance improvements in terms of energy efficiency of appliances, device discovery performance, response time, and failure transmissions via the RESTful framework.

This paper presents a novel algorithm which is based on the Portable Document Format (PDF) document structure. The PDF document page objects are utilized for watermarking. The secret message is compressing by using Huffman coding, then suitable page objects of PDF document are used for hiding the secret message. Since the embedded watermark information is stored in PDF document page objects, so it will not affect the content, as well as the format of the PDF documents. The experimental result shows that the proposed algorithm is robust with detection accuracy can be up to 97%. The watermark is extracted with high probability, which illustrates that the proposed algorithm is robust and imperceptible. The experiments prove that the proposed method is suitable for PDF documents protection.

Information hiding has attracted the attention of researchers in recent years because digital contents are generated and share online through different communication channels. The verification of original authorship of digital contents is a crucial task. Digital watermarks are used to provide copyright protection and ownership verification solutions. However, digital text watermarking is a challenging job due to limited research and existing schemes like inter-word and paragraph spacing, line and word shift are used to hide information, which is not robust. If spaces are removed between words, lines, and paragraphs, the hidden information is destroyed. The special properties of Word document like variable, bookmarks, and range are appropriate for information hiding. The proposed scheme uses these special properties for watermarking without affecting the content. The watermark information is embedded in the special properties after encryption. Our proposed algorithm shows the results. After applying different attacks, the proposed scheme is robust 99.9%, excellent on imperceptibility 99.9%, and improve embedding capacity from Bytes to KiloBytes.

ObjectivesTo investigate an association between fatty liver disease (FLD) and erosive oesophagitis.Design and settingThis was a cross-sectional study of subjects selected from examinees who underwent health check-up, including oesophagogastroduodenoscopy in one hospital between 2004 and 2011. Erosive oesophagitis was classified according to the Los Angeles classification and FLD was diagnosed with ultrasonography. The anthropometric and laboratory data of the subjects were analysed using χ2test and multivariate logistic regression. Additionally, we have analysed our data with two-stage least square estimation using the Baltagi-Chang one-way model to clarify unobserved confounding variable.Primary outcome measureThe effect of FLD on erosive oesophagitis.ResultsAmong the 14 723 eligible subjects, 4232 (28.7%) subjects diagnosed with FLD were classified into the fatty liver group and 10 491 (71.3%) subjects without FLD were classified into the non-fatty liver group. The incidence rate of erosive oesophagitis was significantly higher in the fatty liver group than in the non-fatty liver group (10.4%vs6.1%, pConclusionFLD diagnosed by ultrasonography is an independent risk factor of erosive oesophagitis. It suggests that FLD-related metabolic abnormality may be associated with erosive oesophagitis.

BACKGROUND The long-term data with direct acting antiviral agents were rare. This study investigated the durability of a sustained virologic response (SVR) and the improvement of fibrosis after daclatasvir and asunaprevir (DCV/ASV) treatment in genotype 1b (GT1b) hepatitis C virus (HCV)-infected patients. METHODS A total of 288 HCV GT1b patients without baseline non-structural 5A (NS5A) resistance-associated substitution (RAS) treated with DCV/ASV were enrolled. Virologic response was measured at 12 weeks and 1 year after treatment completion. In cirrhotic patients, liver function, aspartate transaminase to platelet ratio index (APRI), FIB-4 index, fibrosis index (FI), and liver stiffness measurement (LSM) at baseline and 1 year after treatment completion were evaluated. RESULTS SVR12 was obtained in 278 patients (96.5%). Six patients who checked NS5A RAS after treatment failure were RAS positive. Only one patient showed no durability of SVR. In cirrhotic patients who achieved SVR12 (n = 59), the changes of albumin (3.8 [2.2-4.7] to 4.3 [2.4-4.9] g/dL; P < 0.001), platelet count (99 [40-329] to 118 [40-399] × 10³/mm³; P < 0.001), APRI (1.8 [0.1-14.8] to 0.6 [0.1-4.8]; P < 0.001), FIB-4 index (5.45 [0.6-32.8] to 3.3 [0.4-12.2]; P < 0.001), FI (5.5 [0.6-32.8] to 3.3 [0.4-12.2]; P < 0.001), and LSM (17.2 [5.3-48.0] to 11.2 [3.7-28.1] kPa; P = 0.001) between baseline and 1 year after treatment completion were observed. CONCLUSION DCV/ASV treatment for HCV GT1b infected patients without RAS achieved high SVR rates and showed durable SVR. Cirrhotic patients who achieved SVR12 showed the improvement of liver function and fibrosis markers.

Supporting deep learning is a challenge for Internet of Things hardware with limited computing capacity. Edge computing is a promising solution that supports such hardware as it solves transferring and processing bottlenecks. To allocate appropriate loads to utilize edge computing efficiently, we propose an edge computing solution for smart city environments that assigns some of the deep learning layers to edge nodes in order to support deep learning tasks on Internet of Things devices. The proposed deep learning layer assignment in edge computing algorithm determines the ideal number of deep learning layers to be assigned to each edge considering computing capacity and bandwidth of each edge separately. Simulation results of the proposed algorithm were compared with other existing methods such as Li's offline and online method, fixed assignment, and cloud-only method. The comparison showed that the proposed algorithm handles the most deep learning tasks while maximizing resource utilization of edges.

A Web-oriented architecture using REST framework is proposed to enable warehouse objects to communicate over the Web. In the proposed mechanism, the smart warehouse consists of a data collection module and an administrative module. The former includes a collection of radio-frequency identification sensors to read RFID tags of the products, wireless sensors for operational data collection, and the actuators. It is responsible for the data collection process of products and goods stored in the warehouse as well as the collection of operational parameters, i.e. temperature, humidity, air quality, and pressure. The latter acts as the brain of the smart warehouse by processing data, organizing data, generating events, and executing actions. The warehouse user is provided with the environment to control the functionality of various sensors, things, appliances, and HAVC system of warehouse through the Web. An event decision system is built on top of the Web architecture to control real-time processing of the sensors, things, etc. In the proposed architecture, the smart gateway consists of two layers, namely transport module (TM) and device service module (DSM). The combination of TM and DSM creates the device controller for embedded devices operating on proprietary protocols. The proposed system is simulated and evaluated in various scenarios in context of discovery time, response time, and transmission failure. Its effect is seen in the form of improved performance of warehouse in quick interaction and in high accuracy as well.

Bluetooth Low Energy (BLE) technology has opened a whole new dimension of single-hop wireless communication technology due to its inexpensive and low-power properties. This technology makes it possible for a large number of devices to be connected to the Internet of Things. The BLE mechanism is clearly defined by the standard, however, there is room for improvements in some aspects of the technology such as the discovery process. Although the discovery process of traditional Bluetooth architecture has been intensively investigated through analytical and simulation models over the years, it cannot be applied to the BLE technology because of many changes introduced in the design of the BLE. Recently, there have been several works for performance evaluation of BLE discovery process, but they are not still thorough enough in providing a holistic analysis of the BLE. This has motivated our study for more accurate modeling of the BLE discovery process. Our work focuses on developing an analytical model and carrying out intensive simulations to investigate discovery probability and quantitative examination of the influence of parameter settings on the discovery latency and the energy performance metric of the discovery process.

Spectrum sensing (SS) is one of the key tasks in cognitive radio networks that is performed to get awareness about the usage of electromagnetic spectrum. It enables the secondary user (SU) to detect the presence/absence of primary user (PU) and plan its transmission strategy accordingly. The aim of SS is to detect the presence or absence of PU in a certain frequency band and allow or prevent the SU's transmission on the basis of sensing result. In this paper, a review on different SS techniques is presented in the context of PU detection. We mainly focus on non-cooperative SS methods used for the PU detection. PU is the incumbent user in cognitive radio networks, and its transmission should not be interrupted. Moreover, it can start its transmission at any time, so the SU has to sense a channel before starting its transmission and monitor the arrival of PU continuously as long as it utilizes the channel to avoid any interference. The PU detection techniques are further divided into five main categ...

Bluetooth Low Energy (BLE) gets lots of attention from researchers as one of the most prominent solutions for short range communications. But, the BLE still has many challenging issues which must be resolved before deploying it for the technologies. In this paper, an enhanced discovery mechanism is proposed for BLE devices to avoid collisions during advertisement process, so as to achieve lower latency as well as energy consumption. The proposed scheme is modeled and validated via analytical and simulation methods. The proposed mechanism has shown its effectiveness in avoiding unexpected long latency and much energy consumption by carrier sensing during discovery process in crowded BLE networks.