Showing papers in "Advances in intelligent systems and computing in 2017"
TL;DR: This work proposes FairAccess as a new decentralized pseudonymous and privacy preserving authorization management framework that leverages the consistency of blockchain technology to manage access control on behalf of constrained devices.
Abstract: Access control face big challenges in IoT. Unfortunately, it is hard to implement current access control standards on smart object due to its constrained nature while the introduction of powerful and trusted third party to handle access control logic could harm user privacy. In this work we show how blockchain, the promising technology behind Bitcoin, can be very attractive to face those arising challenges. We therefore propose FairAccess as a new decentralized pseudonymous and privacy preserving authorization management framework that leverages the consistency of blockchain technology to manage access control on behalf of constrained devices.
309 citations
TL;DR: In this paper, three novel methods were reported to solve the problem of recognition of Indian sign language gestures effectively by combining Neural Network (NN) with Genetic Algorithm (GA), Evolutionary algorithm (EA) and Particle Swarm Optimization (PSO) separately to attain novel NN-GA, NN -EA and NNPSO methods; respectively.
Abstract: Recognition of sign languages has gained reasonable interest by the researchers in the last decade. An accurate sign language recognition system can facilitate more accurate communication of deaf and dumb people. The wide variety of Indian Sign Language (ISL) led to more challenging learning process. In the current work, three novel methods was reported to solve the problem of recognition of ISL gestures effectively by combining Neural Network (NN) with Genetic Algorithm (GA), Evolutionary algorithm (EA) and Particle Swarm Optimization (PSO) separately to attain novel NN-GA, NN-EA and NN-PSO methods; respectively. The input weight vector to the NN has been optimized gradually to achieve minimum error. The proposed methods performance was compared to NN and the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifiers. Several performance metrics such as the accuracy, precision, recall, F-measure and kappa statistic were calculated. The experimental results established that the proposed algorithm achieved considerable improvement over the performance of existing works in order to recognize ISL gestures. The NN-PSO outperformed the other approaches with 99.96 accuracy, 99.98 precision, 98.29 recall, 99.63 F-Measure and 0.9956 Kappa Statistic.
66 citations
TL;DR: A novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF.
Abstract: A mosquito borne pathogen called Dengue virus (DENV) has been emerged as one of the most fatal threats in the recent time. Infections can be in two main forms, namely the DF (Dengue Fever), and DHF (Dengue Hemorrhagic Fever). An efficient detection method for both fever types turns out to be a significant task. Thus, in the present work, a novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF. The ANN’s input weight vector are optimized using PSO to achieve the expected accuracy and to avoid premature convergence toward the local optima. Therefore, a gene expression data (GDS5093 dataset) available publicly is used. The dataset contains gene expression data for DF, DHF, convalescent and healthy control patients of total 56 subjects. Greedy forward selection method has been applied to select most promising genes to identify the DF, DHF and normal (either convalescent or healthy controlled) patients. The proposed system performance was compared to the multilayer perceptron feed-forward neural network (MLP-FFN) classifier. Results proved the dominance of the proposed method with achieved accuracy of 90.91 %.
56 citations
TL;DR: The novel integration of ML and optimization which can be applied to the complex and dynamic contexts of Robot learning is described and with the aid of an educational Robotics kit the proposed methodology is evaluated.
Abstract: Learning ability in Robotics is acknowledged as one of the major challenges facing artificial intelligence. Although in the numerous areas within Robotics machine learning (ML) has long identified as a core technology, recently Robot learning, in particular, has been witnessing major challenges due to the theoretical advancement at the boundary between optimization and ML. In fact the integration of ML and optimization reported to be able to dramatically increase the decision-making quality and learning ability in decision systems. Here the novel integration of ML and optimization which can be applied to the complex and dynamic contexts of Robot learning is described. Furthermore with the aid of an educational Robotics kit the proposed methodology is evaluated.
52 citations
TL;DR: It is argued that gesture-based and haptic interface technologies hold the promise of creating richer and more natural interaction than the traditional vision- and audio-based interfaces that dominate the current market.
Abstract: While user interfaces for in-vehicle systems in the market are mostly button- and screen-based, advances in electronic technology provide designers with new design opportunities. In this paper, we propose applications of these novel technologies for several aspects of the current and future driving context. We explore opportunities for gesture-based and haptic interfaces in three different areas: establishing shared control between the driver and the autonomous vehicle; providing situation awareness to users of autonomous vehicles while engaged in other activities; connecting drivers to fellow drivers. We argue that these interface technologies hold the promise of creating richer and more natural interaction than the traditional vision- and audio-based interfaces that dominate the current market. We conclude by outlining steps for further research.
42 citations
TL;DR: In this paper, the role of human factors in mixed traffic flow was investigated using a high-fidelity driving simulator, and complementary information was collected using questionnaires to assist in developing accurate, realistic, and robust microscopic traffic flow models.
Abstract: Connected and automated vehicle technologies are widely expected to revolutionize transport systems, enhancing the mobility and quality of life while reducing the environmental impact. However, in the foreseeable future, connected and automated vehicles will have to co-exist with traditional vehicles, indicating a great research need of modelling mixed traffic flow. In few attempts of modelling mixed traffic flow recently, human factors are largely ignored, despite their critical roles in understanding traffic flow dynamics and effective operation and control of this mixed traffic flow. To properly investigate the role of human factors in mixed traffic, we have designed a series of experiments using a high-fidelity driving simulator. Complementary information is collected using questionnaires. This study can assist in developing accurate, realistic, and robust microscopic traffic flow models.
40 citations
TL;DR: An exploratory study aimed to compare the players’ experience while performing a video game in an immersive (virtual reality) and in a non-immersive (tablet) condition and the experimental design and results will be presented and discussed.
Abstract: Despite the rapid and significant growth of virtual reality based video games, scientific studies have not yet been conducted to highlight the outstanding differences of this kind of immersive video games as compared to the more traditional kind (i.e. not immersive, for instance tablet or console games). Peculiarly, very little information is provided about the players’ experience during a virtual reality game. On the basis of these observations, the paper presents an exploratory study aimed to compare the players’ experience while performing a video game in an immersive (virtual reality) and in a non-immersive (tablet) condition. In order to address this objective, 10 participants, within the age range of 18 to 35 years old, were asked to play Smash Hit, a first person game in which the player is provided with an inventory of metallic spheres with which to aim and break glass obstacles. The video game was played by participants on to different display modalities: immersive (virtual reality) and non-immersive (tablet) condition. Psychometric (self-report questionnaires assessing emotional responses and usability of the video game) and physiological (heart rate) measures were used as quantitative dependent variables. The experimental design and results of this exploratory study will be presented and discussed.
34 citations
TL;DR: In this article, a study with 49 participants was conducted to investigate the relevance of mode awareness and mode errors in the context of vehicle automation, and the influence of a cognitive-auditive and a visual-motoric non-driving-related task as well as an adapted HMI was examined.
Abstract: In near future, several complex automation modes – like SAE-level 2 and 3 – may be employed in one vehicle. In order to investigate the relevance of mode awareness and mode errors in the context of vehicle automation, a study with 49 participants was conducted. In the experiment, the participants experienced two stages: one stage with alternating partially and conditionally automated driving and another stage with only partially automated driving. Mode awareness and the occurrence of mode errors were compared in the two stages in order to examine the effect of shifting between the two modes. Additionally, the influence of a cognitive-auditive and a visual-motoric non-driving-related task as well as an adapted HMI was examined. Results showed that depending on the type of the non-driving-related task shifting between partially and conditionally automated driving leads to a loss of mode awareness and results in more mode errors compared to having only one automation mode. An enhancement of mode awareness by the suggested adapted HMI could not be found.
33 citations
TL;DR: The human gait analysis by using wavelets transform of signal obtained from six inertial ProMove mini sensors is proposed in this work and the flexion - extension of joint angles of the knees were calculated for healthy people and with impaired locomotion system.
Abstract: The human gait analysis by using wavelets transform of signal obtained from six inertial ProMove mini sensors is proposed in this work. The angular velocity data measured by the gyro sensors were used to estimate the translational acceleration in the gait analysis. As a result, the flexion - extension of joint angles of the knees were calculated for healthy people and with impaired locomotion system. After measurements we propose to use one of wavelet transform (wavelet type) in order to analyze the signals, indicate a characteristic feature and compare them.
29 citations
TL;DR: The impact of agent transparency on operator performance (decision accuracy), response time, perceived workload, perceived usability of the agent, and operator trust in the agent is examined.
Abstract: This paper discusses two studies testing the effects of agent transparency in joint cognitive systems involving supervisory control and decision-making. Specifically, we examine the impact of agent transparency on operator performance (decision accuracy), response time, perceived workload, perceived usability of the agent, and operator trust in the agent. Transparency has a positive impact on operator performance, usability, and trust, yet the depiction of uncertainty has potentially negative effects on usability and trust. Guidelines and considerations for displaying transparency in joint cognitive systems are discussed.
27 citations
TL;DR: The experiment revealed that inattentional deafness to single auditory alarms could take place as the pilots missed a mean number of 12.5 alarms occurring mostly during the complex maneuvering condition, when the EEG engagement index was high.
Abstract: The inability to detect auditory alarms is a critical issue in many domains such as aviation. An interesting prospect for flight safety is to understand the neural mechanisms underpinning auditory alarm misperception under actual flight condition. We conducted an experiment in which four pilots were to respond by button press when they heard an auditory alarm. The 64 channel Cognionics dry-wireless EEG system was used to measure brain activity in a 4 seat light aircraft. An instructor was present on all flights and in charge of initiating the various scenarios to induce two levels of task engagement (simple navigation task vs. complex maneuvering task). Our experiment revealed that inattentional deafness to single auditory alarms could take place as the pilots missed a mean number of 12.5 alarms occurring mostly during the complex maneuvering condition, when the EEG engagement index was high.
TL;DR: The findings show that the random forest is a powerful method to predict the trends of fluctuations of the gold price and validate that, by using the random Forest algorithm, there were only two factors must be considered to ensure the performance of the prediction, which were DJIA and S&P500.
Abstract: Gold price fluctuation trend prediction is an important issue in the financial world. Even small improvements in predictive performance can make lots of profits. In order to improve the prediction, various factors were considered in related literatures, such as US dollar index (USDX), the crude oil price (COP), Dow Jones Industrial Average (DJIA), the CPI of US (USCPI), the prices of US ten year bond futures (US10BFP), the Hang Seng Index (HIS) and the Standard & Poor’s 500 Index (S&P500), etc. However, the more factors should be considered, the more difficult data can be gathered. This paper used the random forest method to predict the trend of fluctuations of the gold price. Our predictions are one month ahead. Extensive experiments based on real world data were conducted. Our findings show that (1) the random forest is a powerful method to predict the trends of fluctuations of the gold price and (2) the results also validated that, by using the random forest algorithm, there were only two factors must be considered to ensure the performance of the prediction, which were DJIA and S&P500.
TL;DR: In this article, the authors examined an HRI scenario using an automation trust scale and a robotic trust scale, and found that the two scales examine separate constructs and are therefore not interchangeable, and that future evaluations are required to identify appropriate context applications for either automation or robotic operations.
Abstract: When studying Human–Robot Interaction (HRI), we often employ measures of trust. Trust is essential in HRI, as inappropriate levels of trust result in misuse, abuse, or disuse of that robot. Some measures of trust specifically target automation, while others specifically target HRI. Although robots are a type of automation, it is unclear which of the broader factors that define automation are shared by robots. However, measurements of trust in automation and trust in robots should theoretically still yield similar results. We examined an HRI scenario using (1) an automation trust scale and (2) a robotic trust scale. Findings indicated conflicting results coming from these respective trust scales. It may well be that these two trust scales examine separate constructs and are therefore not interchangeable. This discord shows us that future evaluations are required to identify scale appropriate context applications for either automation or robotic operations.
TL;DR: A linguistic rule-based approach is devised which identifies the aspects from movie reviews, locates opinion about that aspect and computes the sentiment polarity of that opinion using linguistic approaches, and generates an aspect-level opinion summary.
Abstract: Aspect-level sentiment analysis refers to sentiment polarity detection from unstructured text at a fine-grained feature or aspect level This paper presents our experimental work on aspect-level sentiment analysis of movie reviews Movie reviews generally contain user opinion about different aspects such as acting, direction, choreography, cinematography, etc We have devised a linguistic rule-based approach which identifies the aspects from movie reviews, locates opinion about that aspect and computes the sentiment polarity of that opinion using linguistic approaches The system generates an aspect-level opinion summary The experimental design is evaluated on datasets of two movies The results achieved good accuracy and shows promise for deployment in an integrated opinion profiling system
TL;DR: In this article, the authors present a comparative analysis of the specific features of great teacher qualities descriptions presented in nine regions worldwide and the main pecularities of the requirements to a teacher in different countries are systematized by the authors.
Abstract: There are the results of comparative analysis of the specific features of great teacher qualities descriptions presented in nine regions worldwide. The main pecularities of the requirements to a teacher in different countries are systematized by the authors in the article. The rating of the qualities of a successful teacher allowed to single out a number of qualities which can be named as universal. Empathy belongs to this group of universal qualities.
TL;DR: The main security challenges for IoT are listed and the security requirements for IoT healthcare system are defined for the first time.
Abstract: The Internet of Things (IoT) is the tomorrow’s Internet. It is being used in our everyday life where objects possessing sensing capabilities such as users, computing systems, and others are combined for convenience and economic benefits. Connecting various such devices is a challenging activity, as each device can have its architecture and security concerns. Various proposals are available in the literature to connect devices effectively, and various systems are there in the markets that are using this IoT concept. Hence dealing with all such interacting devices would be a challenging task when it comes to security. Also, various proposals are available that list the security issues present in IoT, but they are not defining the security requirements clearly for IoT. Therefore, in this paper, we are going to list the main security challenges for IoT and define the security requirements for IoT healthcare system.
TL;DR: For the first time in scientific community worldwide, a dynamic approach independent of all factors namely usage of file or dictionary, word-length,word-frequency, and training dataset is presented focusing on automatic and dynamic identification of a complete list of Gujarati stop words.
Abstract: Stop words removal is an important step in many natural language processing (NLP) tasks. Till now, there is no standardized, exhaustive, and dynamic stop word list created for documents written in Indian Gujarati language which is spoken by nearly 66 million people worldwide. Most of the existing stop words removal approaches are file or dictionary based, wherein a hard-coded static, nonstandardized, and individually created list of stop words is used. The existing approaches are time consuming and complex owing to file or dictionary preparation by collecting possible stop words from a large vocabulary, complex framework and a morphologically variant Gujarati document. Even the other proposed approaches in the literature are also very restricted due to their dependence on word-length, word-frequency, and/or training data set. For the first time in scientific community worldwide, this paper proposes a dynamic approach independent of all factors namely usage of file or dictionary, word-length, word-frequency, and training dataset. An 11 rule-based approach is presented focusing on automatic and dynamic identification of a complete list of Gujarati stop words. Extensive empirical evidence has been presented through deployment of proposed algorithm on nearly 600 Gujarati documents, categorized into routine and domain-specific categories. The respective results with 98.10 and 94.08% average accuracy show that the proposed approach is effective and promising enough for implementation in NLP tasks involving Gujarati written documents.
TL;DR: Based on the result the multi-objective of PSO for Numerical Association Rule Mining Problem with Cauchy Distribution (PARCD) showed the better result than the method of Multi-objectives Particle Swarm Optimization for Association Rule mining (MOPAR).
Abstract: The numerical problem of association rule mining is an updated issue. Numerous authors propose some methods to solved it. A number of them are using the optimization approach by Particle Swarm Optimization (PSO). The problem is that the PSO trapped in local optima when searched the best particle in every iteration. Many researchers solved this problem by combining with Cauchy distribution because it is tremendous for searching in a large neighborhood. Hence, that combination will be implemented to accomplish the numerical association rule mining problem for some objective functions such as confidence, comprehensibility, interestingness. Based on the result the multi-objective of PSO for Numerical Association Rule Mining Problem with Cauchy Distribution (PARCD) showed the better result than the method of Multi-objective Particle Swarm Optimization for Association Rule Mining (MOPAR).
TL;DR: In this paper, the authors propose the use of the prognostic approach, the didactic forecasting, for designing the learning process in professional educational institutions, which is based on socioeconomic, scientific, technical, cultural, and technological forecasting.
Abstract: The article is devoted to the development of didactic bases for designing the learning process in professional educational institutions. From this standpoint, the authors propose the use of the prognostic approach, the didactic forecasting. The future state of the pedagogical process is determined on the basis of socioeconomic, scientific, technical, cultural, technological forecasting. A characteristic feature of the didactic design of the learning process in a professional educational institution is the presence of two components of the learning process: theoretical and industrial training, which necessitates the design of theoretical and production training. Designing theoretical training is associated with the development of modular programs of general educational, general technical and special subjects, pedagogical technologies that ensure the implementation of innovative approaches to the development of educational and cognitive activities. The design of production training is associated with the design of the production process, the material and technical and socio-technical environment. The design of the material and technical environment is associated with the equipment of study rooms, training workshops, with the definition of quantitative and qualitative characteristics of the raw materials, with technical and technological capabilities for manufacturing products. The design of the social and industrial sphere is associated with production, economic relations, and professional communication. Also, the article discusses the features of designing the learning process in a professional school related to establishing links between theoretical and production training, pedagogical and production tasks, between professional knowledge and production activities.
TL;DR: A user study is presented in which it is investigated which visual feedback leads to the best performance for grasping virtual objects and shows that users are supported most when additional Hand Color Feedback is provided in the VR environment.
Abstract: Digital Human Models (DHMs) are widely used in big industry whereas they are not used in small and medium-sized enterprises. One of the main reasons is the complexity and usability. Engineers need a notable amount of training to be able to use DHM software. The authors suggest a new interactive Virtual Reality (VR) interface to instruct DHMs. Within this VR environment, engineers can naturally interact with virtual objects using their hands. These interactions are used as an instruction for DHMs. To support the software user best it is necessary to provide feedback to be able to grasp the virtual object most efficiently. In this work we present a user study in which we investigate which visual feedback leads to the best performance for grasping virtual objects. The results show that users are supported most when additional Hand Color Feedback is provided in the VR environment.
TL;DR: Evaluated effects of proximity and speed of approach on trust in human-robot interaction and trust levels were measured by self-report on the Human Robot Trust Scale and the Trust in Automation Scale.
Abstract: This experiment was designed to evaluate the effects of proximity and speed of approach on trust in human-robot interaction (HRI). The experimental design used a 2 (Speed) × 2 (Proximity) mixed factorial design and trust levels were measured by self-report on the Human Robot Trust Scale and the Trust in Automation Scale. Data analyses indicate proximity [F(2, 146) = 6.842, p < 0.01, partial ŋ2 = 0.086] and speed of approach [F(2, 146) = 2.885, p = 0.059, partial ŋ2 = 0.038] are significant factors contributing to changes in trust levels.
TL;DR: A new method is proposed for the better management of the traffic of emergency vehicles through the use of internet of things (IOT), which enables the emergency vehicles to signal the traffic signal controller placed in the traffic junction regarding their arrival so that the traffic will be regulated.
Abstract: Increase in traffic in cities makes emergency vehicles, like ambulance, to take more time to reach the destination from the source due to which the life of human beings are in danger. So, emergency vehicles, like ambulance and fire engines, require better traffic management for safe and fast travel to safeguard the lives of human beings. In this paper, a new method is proposed for the better management of the traffic of emergency vehicles through the use of internet of things (IOT). The proposed method enables the emergency vehicles to signal the traffic signal controller placed in the traffic junction regarding their arrival so that the traffic will be regulated. This system requires the users traveling in the emergency vehicle to signal the traffic controller hardware through the android application deployed in their mobile phones. We have also proposed the idea for an advanced system which controls the traffic automatically.
TL;DR: This work presents a Question Answering system which combines multiple knowledge bases, with a Natural Language parser to transform questions into SPARQL queries or other query language and demonstrates the feasibility to build such a semantic QA system and the accuracy and relevance of the returned results.
Abstract: Due to the massive growth of information on the Web, information retrieval systems come to play a more critical role. Most of these systems are based on content matching rather than the meaning, therefore the returned results are not always relevant to the user. To solve this problem, the next generation of information retrieval systems focus on the meaning of the user query and search data using ontologies that provide the vocabulary and structure associated with metadata. In this work we present a Question Answering system which combines multiple knowledge bases, with a Natural Language parser to transform questions into SPARQL queries or other query language. We demonstrate the feasibility to build such a semantic QA system and the accuracy and relevance of the returned results.
TL;DR: System of engineering support of upper limb diagnosis was developed and verified by correlating waveforms angles in joints for a physiotherapist with the waveforms of people who exercise in the developed system.
Abstract: System of engineering support of upper limb diagnosis was developed and verified as a part of the research. Virtual Cave 3D, System VRTouchDevice and inertial motion analysis system MVNBiomech were used to build this system. In cooperation with the physiotherapist 2 applications were developed to motivate to do exercises during which kinematic parameters of the examined people were registered. The prepared system was positively verified by correlating waveforms angles in joints for a physiotherapist with the waveforms of people who exercise in the developed system.
TL;DR: Fuzzy Analytic Hierarchy Process is applied during the pre-negotiation stage to identify security risks for better assessment and it may be helpful to developers for better management performance at late stage of development life cycle.
Abstract: Software development is a field which is filled with different types of risks. In nowdays, secure software development is very difficult task. Security risk mitigation is the activity which aims to identify and clear most of the security threats before it could harm the system software. This paper is focusing on identifying and mitigating security risks which are affect the duration of secure software after development. A hierarchical structure of durability risk factors with respect to security in software development is established. This paper aims to apply Fuzzy Analytic Hierarchy Process (FAHP) during the pre-negotiation stage to identify security risks factor. This paper aims to apply Fuzzy Analytic Hierarchy Process (FAHP) during the prenegotiation stage to identify security risks for better assessment. With the help of this prioritization, it may be helpful to developers for better management performance at late stage of development life cycle. After applying this prioritization, organizations might improve longevity of secure software.
TL;DR: More relevant features which will profile the authors more accurately are described about, which include readability metrics, vocabulary richness, and emotional status which are taken into consideration.
Abstract: Author profiling is one of the active researches in the field of data mining. Rather than only concentrated on the syntactic as well as stylometric features, this paper describes about more relevant features which will profile the authors more accurately. Readability metrics, vocabulary richness, and emotional status are the features which are taken into consideration. Age and gender are detected as the metrics for author profiling. Stylometry is defined by using deep learning algorithm. This approach has attained an accuracy of 97.7% for gender and 90.1% for age prediction.
TL;DR: This study investigated the design of city tunnel side wall in Shanghai city and the fuzzy elevation method was applied to elevate the new design patterns and the results provide guidance to the design pattern of city Tunnel side wall.
Abstract: When driving in the tunnel drivers often feel that the visual environment is not comfortable and the ability of speed perception drops. At the same time the enclosed environment of tunnel makes drivers feel nervous. All of these characteristics of driving in the tunnel are the causing factors for the accidents. This study focused on the design pattern of city tunnel side wall and investigated the design of city tunnel side wall in Shanghai city. On the base of color psychology and human factors engineering, the design pattern of city tunnel side wall including design style, color and the spacing of the pattern was analyzed and studied. The fuzzy elevation method was applied to elevate the new design patterns. The comfort and rationality of the new design patterns had been verified and the results provide guidance to the design pattern of city tunnel side wall.
TL;DR: The objective of this paper is to select the most important and crucial parameters in order to provide an optimized ANN for Pattern Recognition which is able to detect attacks including the recently developed ones.
Abstract: The Artificial Neural Network (ANN) enables systems to think and act intelligently. In recent years, ANNs are applied in security of network. Therefore, there are several researches in this area, particularly in Intrusion Detection System which are based on ANN. The objective of this paper is to select the most important and crucial parameters in order to provide an optimized ANN for Pattern Recognition which is able to detect attacks including the recently developed ones. First of all, we have taken some and all of the basic attributes to aliment the networks input and to verify the dependence between these parameters and attacks. Then, we have added the parameters relating to content and time-based ones in order to demonstrate their utility and performance and also to present in which case they are crucial.
TL;DR: This research, through the employment of gray relational analysis (GRA) prioritizes 14 barriers identified from literature, according to the degree of their negative impact and reveals that “Unauthorized Reuse of Health Care Waste” and Implementation of “Poor Segregation Practices” are perceived as the two most significant barriers of HCWM in India.
Abstract: The waste generated by health care units has been contributing a dreadful share in terms of life threatening diseases and environmental pollution. Erroneous management of this waste has not only invited a serious threat to the environment but also to the personnel associated with it; mainly health care experts, patients, workers as well as the general community. A number of studies advocate that there exists certain factors that inhibit effectiveness of health care waste management (HCWM). Prior knowledge of these factors and their relative importance will be helpful for decision makers to better handle these barriers and improve HCWM effectiveness. This research, through the employment of gray relational analysis (GRA) prioritizes 14 barriers identified from literature, according to the degree of their negative impact. The study reveals that “Unauthorized Reuse of Health Care Waste” and Implementation of “Poor Segregation Practices” ranked 1 are perceived as the two most significant barriers while “Lack of Accountability of Authorities of Health Care Facilities towards HCWM” and “Inadequate Awareness and Training Programs” ranked 5 are perceived as the least important barriers of HCWM in India.
TL;DR: In this article, the authors proposed a system QZTool, which is capable of generating origin-destination (OD) matrices automatically starting from floating phone data (FPD) as raw input.
Abstract: Models describing human travel patterns are indispensable to plan and operate road, rail and public transportation networks. For most kind of analyses in the field of transportation planning, there is a need for origin-destination (OD) matrices, which specify the travel demands between the origin and destination zones in the network. The preparation of OD matrices is traditionally a time consuming and cumbersome task. The presented system, QZTool, reduces the necessary effort as it is capable of generating OD matrices automatically. These matrices are produced starting from floating phone data (FPD) as raw input. This raw input is processed by a Hadoop-based big data system. A graphical user interface allows for an easy usage and hides the complexity from the operator. For evaluation, we compare a FDP-based OD matrix to an OD matrix created by a traffic demand model. Results show that both matrices agree to a high degree, indicating that FPD-based OD matrices can be used to create new, or to validate or amend existing OD matrices.