Search results for: social network analysis (SNA).

Authors: Hamid R. S. Mojaveri, Seyed S. Mousavi, Mojtaba Heydar, Ahmad Aminian

Abstract:

The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare data for entering into forecasting models. In second step, the modeling step, an artificial neural network and support vector machine is presented after defining Mean Absolute Percentage Error index for measuring error. The structure of artificial neural network is selected based on previous researchers' results and in this article the accuracy of network is increased by using sensitivity analysis. The best forecast for classical forecasting methods (Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend) is resulted based on prepared data and this forecast is compared with result of support vector machine and proposed artificial neural network. The results show that artificial neural network can forecast more precisely in comparison with other methods. Finally, forecasting methods' stability is analyzed by using raw data and even the effectiveness of clustering analysis is measured.

Keywords: Artificial Neural Networks (ANN), bullwhip effect, demand forecasting, Support Vector Machine (SVM).

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Authors: S. Chebbi, A. Meddeb

Abstract:

The distribution networks are often exposed to harmful incidents which can halt the electricity supply of the customer. In this context, we studied a real case of a critical zone of the Tunisian network which is currently characterized by the dysfunction of its plan of protection. In this paper, we were interested in the harmonization of the protection plan settings in order to ensure a perfect selectivity and a better continuity of service on the whole of the network.

Keywords: Distribution network Gabes-Tunisia, NEPLAN©DACH, protection plan settings, selectivity.

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Authors: Safaa R. Amer

Abstract:

Missing data yields many analysis challenges. In case of complex survey design, in addition to dealing with missing data, researchers need to account for the sampling design to achieve useful inferences. Methods for incorporating sampling weights in neural network imputation were investigated to account for complex survey designs. An estimate of variance to account for the imputation uncertainty as well as the sampling design using neural networks will be provided. A simulation study was conducted to compare estimation results based on complete case analysis, multiple imputation using a Markov Chain Monte Carlo, and neural network imputation. Furthermore, a public-use dataset was used as an example to illustrate neural networks imputation under a complex survey design

Keywords: Complex survey, estimate, imputation, neural networks, variance.

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Authors: Mohamed Othmani, Yassine Khlifi

Abstract:

This paper presents a technique for compact three dimensional (3D) object model reconstruction using wavelet networks. It consists to transform an input surface vertices into signals,and uses wavelet network parameters for signal approximations. To prove this, we use a wavelet network architecture founded on several mother wavelet families. POLYnomials WindOwed with Gaussians (POLYWOG) wavelet families are used to maximize the probability to select the best wavelets which ensure the good generalization of the network. To achieve a better reconstruction, the network is trained several iterations to optimize the wavelet network parameters until the error criterion is small enough. Experimental results will shown that our proposed technique can effectively reconstruct an irregular 3D object models when using the optimized wavelet network parameters. We will prove that an accurateness reconstruction depends on the best choice of the mother wavelets.

Keywords: 3D object, optimization, parametrization, Polywog wavelets, reconstruction, wavelet networks.

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Authors: Wenbin Zhang, Hantian Wu, Jian Tang

Abstract:

An artificial neural network is a mathematical model inspired by biological neural networks. There are several kinds of neural networks and they are widely used in many areas, such as: prediction, detection, and classification. Meanwhile, in day to day life, people always have to make many difficult decisions. For example, the coach of a soccer club has to decide which offensive player to be selected to play in a certain game. This work describes a novel Neural Network using a combination of the General Regression Neural Network and the Probabilistic Neural Networks to help a soccer coach make an informed decision.

Keywords: General Regression Neural Network, Probabilistic Neural Networks, Neural function.

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Authors: S.Baskar, P.S.Ramkumar, R.Kesavan

Abstract:

This paper presents a new heuristic algorithm useful for long-term planning of survivable WDM networks. A multi-period model is formulated that combines network topology design and capacity expansion. The ability to determine network expansion schedules of this type becomes increasingly important to the telecommunications industry and to its customers. The solution technique consists of a Genetic Algorithm that allows generating several network alternatives for each time period simultaneously and shortest-path techniques to deduce from these alternatives a least-cost network expansion plan over all time periods. The multi-period planning approach is illustrated on a realistic network example. Extensive simulations on a wide range of problem instances are carried out to assess the cost savings that can be expected by choosing a multi-period planning approach instead of an iterative network expansion design method.

Keywords: Wavelength Division Multiplexing, Genetic Algorithm, Network topology, Multi-period reliable network planning

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Authors: Mohamed Watfa, William Daher, Hisham Al Azar

Abstract:

The main idea behind in network aggregation is that, rather than sending individual data items from sensors to sinks, multiple data items are aggregated as they are forwarded by the sensor network. Existing sensor network data aggregation techniques assume that the nodes are preprogrammed and send data to a central sink for offline querying and analysis. This approach faces two major drawbacks. First, the system behavior is preprogrammed and cannot be modified on the fly. Second, the increased energy wastage due to the communication overhead will result in decreasing the overall system lifetime. Thus, energy conservation is of prime consideration in sensor network protocols in order to maximize the network-s operational lifetime. In this paper, we give an energy efficient approach to query processing by implementing new optimization techniques applied to in-network aggregation. We first discuss earlier approaches in sensors data management and highlight their disadvantages. We then present our approach “Energy Efficient Indexed Aggregation" (EEIA) and evaluate it through several simulations to prove its efficiency, competence and effectiveness.

Keywords: Sensor Networks, Data Base, Data Fusion, Aggregation, Indexing, Energy Efficiency

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Authors: Katsumi Hirata

Abstract:

Certain systems can function well only if they recognize the sound environment as humans do. In this research, we focus on sound classification by adopting a convolutional neural network and aim to develop a method that automatically classifies various environmental sounds. Although the neural network is a powerful technique, the performance depends on the type of input data. Therefore, we propose an approach via a slice bispectrogram, which is a third-order spectrogram and is a slice version of the amplitude for the short-time bispectrum. This paper explains the slice bispectrogram and discusses the effectiveness of the derived method by evaluating the experimental results using the ESC‑50 sound dataset. As a result, the proposed scheme gives high accuracy and stability. Furthermore, some relationship between the accuracy and non-Gaussianity of sound signals was confirmed.

Keywords: Bispectrum, convolutional neural network, environmental sound, slice bispectrogram, spectrogram.

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Authors: Mehmet Balci, Sakir Tasdemir, Mustafa Altin, Ozlem Bozok

Abstract:

Our globalizing world has become almost a small village and everyone can access any information at any time. Everyone lets each other know who does whatever in which place. We can learn which social events occur in which place in the world. From the perspective of education, the course notes that a lecturer use in lessons in a university in any state of America can be examined by a student studying in a city of Africa or the Far East. This dizzying communication we have mentioned happened thanks to fast developments in computer and internet technologies. While these developments occur in the world, Turkey that has a very large young population and whose electronic infrastructure rapidly improves has also been affected by these developments. Nowadays, mobile devices have become common and thus, it causes to increase data traffic in social networks. This study was carried out on students in the different age groups in Selcuk University Vocational School of Technical Sciences, the Department of Computer Technology. Students’ opinions about the use of internet and social media were obtained. The features such as using the Internet and social media skills, purposes, operating frequency, accessing facilities and tools, social life and effects on vocational education and so forth were explored. The positive effects and negative effects of both internet and social media use on the students in this department and findings are evaluated from different perspectives and results are obtained. In addition, relations and differences were found out statistically.

Keywords: Computer technologies, internet use, social network, higher vocational school.

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Authors: Weifeng Pan

Abstract:

Complex networks have been intensively studied across many fields, especially in Internet technology, biological engineering, and nonlinear science. Software is built up out of many interacting components at various levels of granularity, such as functions, classes, and packages, representing another important class of complex networks. It can also be studied using complex network theory. Over the last decade, many papers on the interdisciplinary research between software engineering and complex networks have been published. It provides a different dimension to our understanding of software and also is very useful for the design and development of software systems. This paper will explore how to use the complex network theory to analyze software structure, and briefly review the main advances in corresponding aspects.

Keywords: Metrics, measurement, complex networks, software.

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Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

Abstract:

Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: Modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition.

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Authors: Vishnu Pratap Singh Kirar

Abstract:

Wireless sensor network (WSN) is a network of many interconnected networked systems, they equipped with energy resources and they are used to detect other physical characteristics. On WSN, there are many researches are performed in past decades. WSN applicable in many security systems govern by military and in many civilian related applications. Thus, the security of WSN gets attention of researchers and gives an opportunity for many future aspects. Still, there are many other issues are related to deployment and overall coverage, scalability, size, energy efficiency, quality of service (QoS), computational power and many more. In this paper we discus about various applications and security related issue and requirements of WSN.

Keywords: Wireless Sensor Network (WSN), Wireless Network Attacks, Wireless Network Security.

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Authors: Roza Dastres, Mohsen Soori

Abstract:

Networks are vulnerable devices due to their basic feature of facilitating remote access and data communication. The information in the networks needs to be kept secured and safe in order to provide an effective communication and sharing device in the web of data. Due to challenges and threats of the data in networks, the network security is one of the most important considerations in information technology infrastructures. As a result, the security measures are considered in the network in order to decrease the probability of accessing the secured data by the hackers. The purpose of network security is to protect the network and its components from unauthorized access and abuse in order to provide a safe and secured communication device for the users. In the present research work a review in recent development of network threats and security measures is presented and future research works are also suggested. Different attacks to the networks and security measured against them are discussed in order to increase security in the web of data. So, new ideas in the network security systems can be presented by analyzing the published papers in order to move forward the research field.

Keywords: Network threats, network security, security measures, firewalls.

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Authors: Justinian Anatory, Ekael Stephen Manase

Abstract:

The use of mobile phones is growing tremendously all over the world. In Tanzania there are a number of operators licensed by Tanzania Communications Regulatory Authority (TCRA) aiming at attracting customers into their networks. So far telecommunications market competition has been very stiff. Various measures are being taken by mobile operators to survive in the market. Such measure include introducing of different air time bundles on daily, weekly and monthly at lower tariffs. Other measures include the introduction of normal tariff, tourist package and one network. Despite of all these strategies, there is a dynamic competition in the market which needs to be explored. Some influences which attract customers to choose a certain mobile operator are of particular interest. This paper is investigating if the network effects and Quality of Services (QoS) influence mobile customers in selection of their mobile network operators. Seventy seven students from high learning institutions in Dodoma Municipality in Tanzania participated in responding to prepared questionnaires. The data was analyzed using Statistical Package for Social Science (SPSS) Software. The results indicate that, network coverage does influence customers in selection of mobile operators. In addition, this paper proposes further research in some areas especially where the study came up with different findings from what the theory has in place.

Keywords: Network effects, Quality of services, Consumer Buying, mobile operators.

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Authors: Lina Yan, Jingjing Di, Ke Wang

Abstract:

A new basis function neural network algorithm is proposed for numerical integration. The main idea is to construct neural network model based on spline basis functions, which is used to approximate the integrand by training neural network weights. The convergence theorem of the neural network algorithm, the theorem for numerical integration and one corollary are presented and proved. The numerical examples, compared with other methods, show that the algorithm is effective and has the characteristics such as high precision and the integrand not required known. Thus, the algorithm presented in this paper can be widely applied in many engineering fields.

Keywords: Numerical integration, Spline basis function, Neural network algorithm

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Authors: Radek Malinský, Ivan Jelínek

Abstract:

The current web has become a modern encyclopedia, where people share their thoughts and ideas on various topics around them. This kind of encyclopedia is very useful for other people who are looking for answers to their questions. However, with the growing popularity of social networking and blogging and ever expanding network services, there has also been a growing diversity of technologies along with a different structure of individual web sites. It is therefore difficult to directly find a relevant answer for a common Internet user. This paper presents a web application for the real-time end-to-end analysis of selected Internet trends where the trend can be whatever the people post online. The application integrates fully configurable tools for data collection and analysis using selected webometric algorithms, and for its chronological visualization to user. It can be assumed that the application facilitates the users to evaluate the quality of various products that are mentioned online.

Keywords: Trend, visualizer, web analysis, web 2.0.

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Authors: Dalibor Dobrilovic, Borislav Odadžic

Abstract:

In this paper is being described a possible use of virtualization technology in teaching computer networks. The virtualization can be used as a suitable tool for creating virtual network laboratories, supplementing the real laboratories and network simulation software in teaching networking concepts. It will be given a short description of characteristic projects in the area of virtualization technology usage in network simulation, network experiments and engineering education. A method for implementing laboratory has also been explained, together with possible laboratory usage and design of laboratory exercises. At the end, the laboratory test results of virtual laboratory are presented as well.

Keywords: Computer network simulation software, teaching networking concepts, virtual network laboratory, virtualization technology.

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Authors: Kenichiro Hida, Shin-Ichi Kuribayashi

Abstract:

In a conventional network, most network devices, such as routers, are dedicated devices that do not have much variation in capacity. In recent years, a new concept of network functions virtualisation (NFV) has come into use. The intention is to implement a variety of network functions with software on general-purpose servers and this allows the network operator to select their capacities and locations without any constraints. This paper focuses on the allocation of NFV-based routing functions which are one of critical network functions, and presents the virtual routing function allocation algorithm that minimizes the total power consumption. In addition, this study presents the useful allocation policy of virtual routing functions, based on an evaluation with a ladder-shaped network model. This policy takes the ratio of the power consumption of a routing function to that of a circuit and traffic distribution between areas into consideration. Furthermore, the present paper shows that there are cases where the use of NFV-based routing functions makes it possible to reduce the total power consumption dramatically, in comparison to a conventional network, in which it is not economically viable to distribute small-capacity routing functions.

Keywords: Virtual routing function, NFV, resource allocation, minimum power consumption.

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Authors: K. Vidya, V.Rhymend Uthariaraj

Abstract:

Traffic Engineering (TE) is the process of controlling how traffic flows through a network in order to facilitate efficient and reliable network operations while simultaneously optimizing network resource utilization and traffic performance. TE improves the management of data traffic within a network and provides the better utilization of network resources. Many research works considers intra and inter Traffic Engineering separately. But in reality one influences the other. Hence the effective network performances of both inter and intra Autonomous Systems (AS) are not optimized properly. To achieve a better Joint Optimization of both Intra and Inter AS TE, we propose a joint Optimization technique by considering intra-AS features during inter – AS TE and vice versa. This work considers the important criterion say latency within an AS and between ASes. and proposes a Bi-Criteria Latency optimization model. Hence an overall network performance can be improved by considering this jointoptimization technique in terms of Latency.

Keywords: Inter-Domain Routing , Measurement, OptimizationPerformance, Traffic Engineering.

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Authors: Dong-Young Yoo, Jong-Whoi Shin, Gang Shin Lee, Jae-Il Lee

Abstract:

As the disfunctions of the information society and social development progress, intrusion problems such as malicious replies, spam mail, private information leakage, phishing, and pharming, and side effects such as the spread of unwholesome information and privacy invasion are becoming serious social problems. Illegal access to information is also becoming a problem as the exchange and sharing of information increases on the basis of the extension of the communication network. On the other hand, as the communication network has been constructed as an international, global system, the legal response against invasion and cyber-attack from abroad is facing its limit. In addition, in an environment where the important infrastructures are managed and controlled on the basis of the information communication network, such problems pose a threat to national security. Countermeasures to such threats are developed and implemented on a yearly basis to protect the major infrastructures of information communication. As a part of such measures, we have developed a methodology for assessing the information protection level which can be used to establish the quantitative object setting method required for the improvement of the information protection level.

Keywords: Information Security Evaluation Methodology, Critical Information Infrastructure Protection.

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Authors: Omar Gaci, Stefan Balev

Abstract:

In this paper we introduce the notion of protein interaction network. This is a graph whose vertices are the protein-s amino acids and whose edges are the interactions between them. Using a graph theory approach, we identify a number of properties of these networks. We compare them to the general small-world network model and we analyze their hierarchical structure.

Keywords: interaction network, protein structure, small-world network.

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Authors: Khaing Win Mar, Thinn Thu Naing

Abstract:

Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.

Keywords: Precipitation prediction, monthly precipitation, neural network models, Myanmar.

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Authors: Suk-Hyun Yu, Heeyong Kwon

Abstract:

Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.

Keywords: AI, air quality prediction, neural networks, pattern recognition, PM-10.

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Authors: Abigail Qian Zhou

Abstract:

In today's China, the well-educated middle class, with stable jobs and above-average income, are the driving force behind its Internet society. Through the analysis of data from the 2015 Chinese General Social Survey and 50 interviewees, this study investigates the current situation of this group’s specific internet usage. The findings of this study demonstrate that daily life among the members of this socioeconomic group is closely tied to the Internet. For Chinese middle class, the Internet is used to socialize and entertain self and others. It is also used to search for and share information as well as to build their identities. The empirical results of this study will provide a reference, supported by factual data, for enterprises seeking to target the Chinese middle class through online marketing efforts.

Keywords: China, internet use, middle class, network behavior, online marketing.

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Authors: Mikko Elomaa, Aarne Halme

Abstract:

A novel method using bearing-only SLAM to estimate node positions of a localization network is proposed. A group of simple robots are used to estimate the position of each node. Each node has a unique ID, which it can communicate to a robot close by. Initially the node IDs and positions are unknown. A case example using RFID technology in the localization network is introduced.

Keywords: Localization network, Multi-robot, RFID, SLAM

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Authors: Jing Zhou, Steven Su, Aihuang Guo

Abstract:

COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.

Keywords: BP Neural Network, Exercising Testing, Fault Detection and Identification, Principal Component Analysis.

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Authors: S. M. Mahbobur Rahman

Abstract:

Whole building energy simulation models are widely used for predicting future energy consumption, performance diagnosis and optimum control.  Black box building energy modeling approach has been heavily studied in the past decade. The thermal response of a building can also be modeled using a network of interconnected resistors (R) and capacitors (C) at each node called R-C network. In this study, a model building, Case 600, as described in the “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program”, ASHRAE standard 140, is studied along with a 3R2C thermal network model and the ASHRAE clear sky solar radiation model. Although building an energy model involves two important parts of building component i.e., the envelope and internal mass, the effect of building internal mass is not considered in this study. All the characteristic parameters of the building envelope are evaluated as on Case 600. Finally, monthly building energy consumption from the thermal network model is compared with a simple-box energy model within reasonable accuracy. From the results, 0.6-9.4% variation of monthly energy consumption is observed because of the south-facing windows.

Keywords: ASHRAE case study, clear sky solar radiation model, energy modeling, thermal network model.

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Authors: W. Rowan, Y. O’Connor, L. Lynch, C. Heavin

Abstract:

Situation Awareness can offer the potential for conscious dynamic reflection. In an era of online health data sharing, it is becoming increasingly important that users of health social networks (HSNs) have the information necessary to make informed decisions as part of the registration process and in the provision of eConsent. This research aims to leverage an adapted Situation Awareness (SA) model to explore users’ decision making processes in the provision of eConsent. A HSN platform was used to investigate these behaviours. A mixed methods approach was taken. This involved the observation of registration behaviours followed by a questionnaire and focus group/s. Early results suggest that users are apt to automatically accept eConsent, and only later consider the long-term implications of sharing their personal health information. Further steps are required to continue developing knowledge and understanding of this important eConsent process. The next step in this research will be to develop a set of guidelines for the improved presentation of eConsent on the HSN platform.

Keywords: eConsent, health social network, mixed methods, situation awareness.

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Authors: Shivaleela E. S., Shrikant S. Tangade

Abstract:

In this paper optical code-division multiple-access (OCDMA) packet network is considered, which offers inherent security in the access networks. Two types of random access protocols are proposed for packet transmission. In protocol 1, all distinct codes and in protocol 2, distinct codes as well as shifted versions of all these codes are used. O-CDMA network performance using optical orthogonal codes (OOCs) 1-D and two-dimensional (2-D) wavelength/time single-pulse-per-row (W/T SPR) codes are analyzed. The main advantage of using 2-D codes instead of onedimensional (1-D) codes is to reduce the errors due to multiple access interference among different users. In this paper, correlation receiver is considered in the analysis. Using analytical model, we compute and compare packet-success probability for 1-D and 2-D codes in an O-CDMA network and the analysis shows improved performance with 2-D codes as compared to 1-D codes.

Keywords: Optical code-division multiple-access, optical CDMA correlation receiver, wavelength/time optical CDMA codes.

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Authors: Rania Alshikhe, Vinita Jindal

Abstract:

Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from trav-eling vehicles, such as taxis through installed global positioning sys-tem (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

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