Search results for: e-content producing algorithm

Authors: Ashish James, Anusha James

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The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

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Authors: Sonia Anand Knowlton

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There are some cases where we as a society feel deeply uncomfortable with the use of Artificial Intelligence (AI) tools in the judicial decision-making process, and justifiably so. A perfect example is COMPAS, an algorithmic model that predicts recidivism rates of offenders to assist in the determination of their bail conditions. COMPAS turned out to be extremely racist: it massively overpredicted recidivism rates of Black offenders and underpredicted recidivism rates of white offenders. At the same time, there are certain uses of AI in the judicial decision-making process that many would feel more comfortable with and even support. Take, for example, a “super-breathalyzer,” an (albeit imaginary) tool that uses AI to deliver highly detailed information about the subject of the breathalyzer test to the legal decision-makers analyzing their drunk-driving case. This article evaluates the point at which a judge’s use of AI tools begins to undermine the public’s trust in the administration of justice. It argues that the answer to this question depends on whether the AI tool is in a role in which it must perform a moral evaluation of a human being.

Keywords: artificial intelligence, judicial reasoning, morality, technology, algorithm

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Authors: Eric Huang, Coleman DeLude, Justin Romberg, Saibal Mukhopadhyay, Madhavan Swaminathan

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High performance computing (HPC) based emulators can be used to model the scattering from multiple stationary and moving targets for RADAR applications. These emulators rely on the RADAR Cross Section (RCS) of the targets being available in complex scenarios. Representing the RCS using tables generated from electromagnetic (EM) simulations is often times cumbersome leading to large storage requirement. This paper proposed a spherical harmonic based anisotropic scatterer model to represent the RCS of complex targets. The problem of finding the locations and reflection profiles of all scatterers can be formulated as a linear least square problem with a special sparsity constraint. This paper solves this problem using a modified Orthogonal Matching Pursuit algorithm. The results show that the spherical harmonic based scatterer model can effectively represent the RCS data of complex targets.

Keywords: RADAR, RCS, high performance computing, point scatterer model

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Authors: Xuan Zhou, Litian Zhang, Shuixiang Li

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Mesh deformation using radial basis function interpolation method has been demonstrated to produce quality meshes with relatively little computational cost using a concise algorithm. However, it still suffers from the limited deformation ability, especially in large deformation. In this paper, a pre-displacement improvement is proposed to improve the problem that illegal meshes always appear near the moving inner boundaries owing to the large relative displacement of the nodes near inner boundaries. In this improvement, nodes near the inner boundaries are first associated to the near boundary nodes, and a pre-displacement based on the displacements of associated boundary nodes is added to the nodes near boundaries in order to make the displacement closer to the boundary deformation and improve the deformation capability. Several 2D and 3D numerical simulation cases have shown that the pre-displacement improvement for radial basis function (RBF) method significantly improves the mesh quality near inner boundaries and deformation capability, with little computational burden increasement.

Keywords: mesh deformation, mesh quality, background mesh, radial basis function

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Authors: Ghada S. Moussa

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Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

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Authors: Mahmoud M. Othman, Almoataz Y. Abdelaziz, Yasser G. Hegazy

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This paper presents a load control strategy based on modification of the Big Bang Big Crunch optimization method. The proposed strategy aims to determine the optimal load to be controlled and the corresponding time of control in order to minimize the energy purchased from substation. The presented strategy helps the distribution network operator to rely on the renewable energy sources in supplying the system demand. The renewable energy sources used in the presented study are modeled using the diagonal band Copula method and sequential Monte Carlo method in order to accurately consider the multivariate stochastic dependence between wind power, photovoltaic power and the system demand. The proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder. Several case studies are done and the subsequent discussions show the effectiveness of the proposed algorithm.

Keywords: big bang big crunch, distributed generation, load control, optimization, planning

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Authors: Bazla Manzoor, Aqsa Yasin

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Pakistan is the country which is progressing by leaps and bounds through agricultural and industrial growth. The main area, which presents the largest income rate through industrial activities, is Faisalabad from the Province of Punjab. Faisalabad’s main occupations include agriculture and industry. As these sectors i.e. agriculture and industry is developing day by day, they are earning much income for the country and generating thousands of job vacancies. On one hand the city, i.e. Faisalabad is on the way of development through industrial growth, while on the other hand this industrial growth is producing a bad impact on the environment. In return, that damaged environment is affecting badly on the people and built environment. This research is chiefly based on one of the above-mentioned factors i.e. adverse environmental impacts on the built structures. Faisalabad is an old city, therefore; it is having many old structures especially from British Era. Many of those structures are still surviving and are functioning as the government, private and public buildings. However, these structures are getting in a poor condition with the passage of time due to bad maintenance and adverse environmental impacts. Bad maintenance is a factor, which can be controlled by financial assistance and management. The factor needs to be seriously considered is the other one i.e. adverse environmental impacts on British Era structures of the city because this factor requires controlled and refined human activities and actions. For this reason, a research was required to conserve the British Era structures of Faisalabad so that these structures can function well. The other reason to conserve them is that these structures are historically important and are the heritage of the city. For doing this research, literature has been reviewed which was present in the libraries of the city. Department of Environment, Town Municipal Administration, Faisalabad Development Authority and Lyallpur Heritage Foundation were visited to collect the existing data available. Various British Era structures were also visited to note down the environmental impacts on them. From all the structures “Clock Tower,” was deeply studied as it is one of the oldest and most important heritage structures of the city because the earlier settlements of the city were planned based on its location by The British Government. The architectural and environmental analyses were done for The Clock Tower. This research study found the deterioration factors of the tower according to which suggestions have been made.

Keywords: lyallpur, heritage, architecture, environment

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Authors: Abdulatif Abdulsalam Mohamed Shaban

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This paper states that the art of Procedure Entry Array (PEA) plan with a focus on control system applications. This paper begins with an impression of PEA technology development, followed by an arrangement of design technologies, and the use of programmable description languages and system-level design tools. They allow a practical approach based on a unique model for complete engineering electronics systems. There are three main design rules are implemented in the system. These are algorithm based fine-tuning, modularity, and the control act and the architectural constraints. An overview of contributions and limits of PEAs is also given, followed by a short survey of PEA-based gifted controllers for recent engineering systems. Finally, two complete and timely case studies are presented to illustrate the benefits of a PEA implementation when using the proposed system modelling and devise attitude. These consist of the direct control for training motor drives and the control of a diesel-driven stand-alone generator with the help of logical design.

Keywords: control (DC), engineering electronics systems, training motor drives, procedure entry array

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Authors: Yushuai Wang, Feng Xu, Junbo Tan, Xueqian Wang, Bin Liang

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In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results.

Keywords: fault detection, linear parameter varying, model predictive control, set theory

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Authors: M. Sagarika, M. Chandra Sekhar

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Agricultural residues are producing in large quantities in India and account for abundant but underutilized source of renewable biomass in agriculture. In India, the amount of crop residues available is estimated to be approximately 686 million tons. Anaerobic digestion is a promising option to utilize the surplus agricultural residues and can produce biogas and digestate. Biogas is mainly methane (CH4), which can be utilized as an energy source in replacement for fossil fuels such as natural gas, oil, in other hand, digestate contains high amounts of nutrients, can be employed as fertilizer. Solid state anaerobic digestion (total solids ≥ 15%) is suitable for agricultural residues, as it reduces the problems like stratification and floating issues that occur in liquid anaerobic digestion (total solids < 15%). The major concern in solid-state anaerobic digestion is the low mass transfer of feedstock and inoculum that resulting in low performance. To resolve this low mass transfer issue, effective mixing of feedstock and inoculum is required. Mechanical mixing using stirrer at the time of digestion process can be done, but it is difficult to operate the stirring of feedstock with high solids percentage and high viscosity. Complete premixing of feedstock and inoculum is an alternative method, which is usual in lab scale studies but may not be affordable due to high energy demand in large-scale digesters. Developing partial premixing methods may reduce this problem. Current study is to improve the performance of solid-state anaerobic digestion of corn stover at feedstock to inoculum ratios 3 and 5, by applying partial premixing methods and to compare the complete premixing method with two partial premixing methods which are two alternative layers of feedstock and inoculum and three alternative layers of feedstock and inoculum where higher inoculum ratios in the top layers. From experimental studies it is observed that, partial premixing method with three alternative layers of feedstock and inoculum yielded good methane.

Keywords: anaerobic digestion, premixing methods, methane yield, corn stover, volatile solids

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Authors: John Lamb, Robert Epstein, Vasundhara L. Bhupathi, Sanjeev Kumar Marimekala

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As we enter the new era of Artificial Intelligence (AI) and Cloud Computing, we mostly rely on the Machine and Natural Language Processing capabilities of AI, and Energy Efficient Hardware and Software Devices in almost every industry sector. In these industry sectors, much emphasis is on developing new and innovative methods for producing and conserving energy and sustaining the depletion of natural resources. The core pillars of sustainability are economic, environmental, and social, which is also informally referred to as the 3 P's (People, Planet and Profits). The 3 P's play a vital role in creating a core Sustainability Model in the Enterprise. Natural resources are continually being depleted, so there is more focus and growing demand for renewable energy. With this growing demand, there is also a growing concern in many industries on how to reduce carbon emissions and conserve natural resources while adopting sustainability in corporate business models and policies. In our paper, we would like to discuss the driving forces such as Climate changes, Natural Disasters, Pandemic, Disruptive Technologies, Corporate Policies, Scaled Business Models and Emerging social media and AI platforms that influence the 3 main pillars of Sustainability (3P’s). Through this paper, we would like to bring an overall perspective on enterprise strategies and the primary focus on bringing cultural shifts in adapting energy-efficient operational models. Overall, many industries across the globe are incorporating core sustainability principles such as reducing energy costs, reducing greenhouse gas (GHG) emissions, reducing waste and increasing recycling, adopting advanced monitoring and metering infrastructure, reducing server footprint and compute resources (Shared IT services, Cloud computing, and Application Modernization) with the vision for a sustainable environment.

Keywords: climate change, pandemic, disruptive technology, government policies, business model, machine learning and natural language processing, AI, social media platform, cloud computing, advanced monitoring, metering infrastructure

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Authors: Jane E. Souyave, Judith Bower

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This study is an ongoing collaborative project between the University of Central Lancashire and the Alzheimer’s Society to design and test the idea of using interactive tools for a person living with dementia and their carers. It is hoped that the tools will fulfill the possible needs of engagement and interaction as dementia progresses, therefore enhancing wellbeing and improving quality of life for the person with dementia and their carers. The project was informed by Kitwood’s five psychological needs for producing wellbeing and explored evidence that fidgeting is often seen as a form of agitation and a negative symptom of dementia. Although therapy for agitation may be well established, there is a lack of appropriate items aimed at people in the later stage of dementia, that are not childlike or medical in their aesthetic. Individuals may fidget in a particular way and the tools in the Fidget Widget Toolkit have been designed to encourage repetitive movements of the hand, specifically to address the abilities of people with relatively advanced dementia. As an intervention, these tools provided a new approach that had not been tested in dementia care. Prototypes were created through an iterative design process and tested with a number of people with dementia and their carers, using quantitative and qualitative methods. Dementia Care Mapping was used to evaluate the impact of the intervention in group settings. Cohen Mansfield’s Agitation Inventory was used to record the daily use and interest of the intervention for people in their usual place of residence. The results informed the design of a new set of devices to promote safe, stigma free fidgeting as a positive experience, meaningful activity and enhance wellbeing for people in the later stage of dementia. The outcomes addressed the needs of individuals by reducing agitation and restlessness through helping them to connect, engage and act independently, providing the means of doing something for themselves that they were able to do. The next stage will be to explore the commercial feasibility of the Fidget Widget Toolkit so that it can be introduced as good practice and innovation in dementia care. It could be used by care homes, with carers and their families to support wellbeing and lead the way in providing some positive experiences and person-centred approaches that are lacking in the later stage of dementia.

Keywords: dementia, design, fidgeting, healthcare, positive moments, quality of life, wellbeing

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Authors: Fahimeh Rafiezade, Fatemeh Kakoyi Dinaki, Maryam Soufi

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This research studies and identifies the important elements of the living style of the residents of one of the neighborhoods of Tehran. The study will also study the role, the degree, and extent of the influence of neighborhood trainings in the lives of these people. Saraymahaleh is one of the centers established by Tehran municipality in various neighborhoods of Tehran in order to provide educational, cultural, etc. services. We carried out our study according to demography, field study, observation, 30 interviews, and 2 focus group discussions (FGD) at Sahebalzaman neighborhood in district 18 of Tehran municipality. We interpreted our observations and interviews with the neighborhoods’ supervisors and city council assistants (Shorayar), supervisor of Saraymahaleh and people who refer to them. We used this information to study the citizens’ lifestyle, values, behavioral, motivational, and attitude preferences in their religious and environmental orientations, cultural consumptions, and spare times, and the influence of Saraymahaleh on these aspects according to specific economic, cultural, and ethnic characteristics. Sahebalzaman neighborhood is considered an underprivileged district in terms of economy, high illiteracy, and low but structured migration of young people. The interviews we made helped us classify the people referring to Saraymahaleh based on their demographic attributes and attitudes and the reason of referring and finally the influence of the rendered services on their lifestyles. The studies indicate that women made the most part of people referring to Saraymahaleh Sahebalzaman. They were mostly young, in their midlives, and generally unemployed without a specialized skill. People referred to Saraymahaleh Sahebalzaman mostly to receive services or for entertainment and recreation purposes, i.e. they did not take part actively. In addition to creating welfare and cultural facilities, Saraymahaleh increases the level of skill training, empowerment, innovation and creativity, and issues skill certificates and documents that helps to increase job and income producing opportunities for the neighborhood residents, improve the quality of their live, and increase their hope for life.

Keywords: lifestyle, living in neighborhood, Saraymahaleh, Tehran municipality, urban life, demography

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

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With the world climate projected to warm and major cities in developing countries becoming increasingly populated and polluted, governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of an adaptable model of these risks. Simulations are performed using the EnergyPlus building physics software. An accurate metamodel is formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) are compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: neural networks, radial basis functions, metamodelling, python machine learning libraries

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Authors: Ime R. Udotong, Ofonime U. M. John, Justina I. R. Udotong

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Ibeno, Nigeria hosts the operational base of Mobil Producing Nigeria Unlimited (MPNU), a subsidiary of ExxonMobil and the current highest oil and condensate producer in Nigeria. Besides MPNU, other multinational oil companies like Shell Petroleum Development Company Ltd, Elf Petroleum Nigeria Ltd and Nigerian Agip Energy, a subsidiary of ENI E&P operate onshore, on the continental shelf and deep offshore of the Atlantic Ocean in Ibeno, Nigeria, respectively. This study was designed to carry out the survey of the oil impacted sites in Ibeno, Nigeria. A combinations of electrical resistivity (ER), ground penetrating radar (GPR) and physico-chemical as well as microbiological characterization of soils and water samples from the area were carried out. Results obtained revealed that there have been hydrocarbon contaminations of this environment by past crude oil spills as observed from significant concentrations of THC, BTEX and heavy metal contents in the environment. Also, high resistivity values and GPR profiles clearly showing the distribution, thickness and lateral extent of hydrocarbon contamination as represented on the radargram reflector tones corroborates previous significant oil input. Contaminations were of varying degrees, ranging from slight to high, indicating levels of substantial attenuation of crude oil contamination over time. Hydrocarbon pollution of the study area was confirmed by the results of soil and water physico-chemical and microbiological analysis. The levels of THC contamination observed in this study are indicative of high levels of crude oil contamination. Moreover, the display of relatively lower resistivities of locations outside the impacted areas compared to resistivity values within the impacted areas, the 3-D Cartesian images of oil contaminant plume depicted by red, light brown and magenta for high, low and very low oil impacted areas, respectively as well as the high counts of hydrocarbonoclastic microorganisms in excess of 1% confirmed significant recent pollution of the study area.

Keywords: oil-polluted sites, physico-chemical analyses, microbiological characterization, geotechnical investigations, total hydrocarbon content

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Authors: Uraib Sharaha, Guy Beck, Joseph Kapelushnik, Adam H. Agbaria, Itshak Lapidot, Shaul Mordechai, Ahmad Salman, Mahmoud Huleihel

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Infectious diseases cause a significant burden on the public health and the economic stability of societies all over the world for several centuries. A reliable detection of the causative agent of infection is not possible based on clinical features, since some of these infections have similar symptoms, including fever, sneezing, inflammation, vomiting, diarrhea, and fatigue. Moreover, physicians usually encounter difficulties in distinguishing between viral and bacterial infections based on symptoms. Therefore, there is an ongoing need for sensitive, specific, and rapid methods for identification of the etiology of the infection. This intricate issue perplex doctors and researchers since it has serious repercussions. In this study, we evaluated the potential of the mid-infrared spectroscopic method for rapid and reliable identification of bacterial and viral infections based on simple peripheral blood samples. Fourier transform infrared (FTIR) spectroscopy is considered a successful diagnostic method in the biological and medical fields. Many studies confirmed the great potential of the combination of FTIR spectroscopy and machine learning as a powerful diagnostic tool in medicine since it is a very sensitive method, which can detect and monitor the molecular and biochemical changes in biological samples. We believed that this method would play a major role in improving the health situation, raising the level of health in the community, and reducing the economic burdens in the health sector resulting from the indiscriminate use of antibiotics. We collected peripheral blood samples from young 364 patients, of which 93 were controls, 126 had bacterial infections, and 145 had viral infections, with ages lower than18 years old, limited to those who were diagnosed with fever-producing illness. Our preliminary results showed that it is possible to determine the infectious agent with high success rates of 82% for sensitivity and 80% for specificity, based on the WBC data.

Keywords: infectious diseases, (FTIR) spectroscopy, viral infections, bacterial infections.

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Authors: Ali Pour Yazdanpanah, Farideh Foroozandeh Shahraki, Emma Regentova

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The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited to improve the reconstruction quality in sparse-view CT, and the convex constraint in those problems leads to an easy optimization process. However, convex regularizers often result in a biased approximation and inaccurate reconstruction in CT problems. Here, we present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction. We compare our method with previously reported high performance methods which use convex regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV) on the test phantom images. The results show that there are benefits in using the nonconvex regularizer in the sparse-view CT reconstruction.

Keywords: computed tomography, non-convex, sparse-view reconstruction, L1-L2 minimization, difference of convex functions

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Authors: Muhammad Fezan Afzal, Imran Khan, Salma Imtiaz

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The need for human verification and identification requires from centuries for authentication. Since it is being used in big institutes like financial, government and crime departments, a continued struggle is important to make this system more efficient to prevent security breaches. Therefore, multiple devices are used to authenticate the biometric for each individual. A large number of devices are required to cover a large number of users. As the number of devices increases, cost will automatically increase. Furthermore, it is time-consuming for biometrics due to the devices being insufficient and are not available at every door. In this paper, we propose the framework and algorithm where the mobile of each individual can also perform the biometric authentication of attendance and security. Every mobile has a biometric authentication system that is used in different mobile applications for security purposes. Therefore, each individual can use the biometric system mobile without moving from one place to another. Moreover, by using the biometrics mobile, the cost of biometric systems can be removed that are mostly deployed in different organizations for the attendance of students, employees and for other security purposes.

Keywords: fingerprint, fingerprint authentication, mobile verification, mobile biometric verification, mobile fingerprint sensor

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Authors: Nora Geczy

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In today’s architectural practice, during the process of designing public, educational, healthcare and cultural space, human-centered architectural designs helping spatial orientation, safe space usage and the appropriate spatial sequence of actions are gaining increasing importance. Related to the methodology of designing public buildings, several scientific experiments in spatial recognition, spatial analysis and spatial psychology with regard to the components of space producing mental and physiological effects have been going on at the Department of Architectural Design and the Interdisciplinary Student Workshop (IDM) at the Széchenyi István University, Győr since 2013. Defining the creation of preventive, anticipated spatial design and the architectural tools of spatial comfort of public buildings and their practical usability are in the limelight of our research. In the experiments applying eye-tracking cameras, we studied the way public spaces are used, especially concentrating on the characteristics of spatial behaviour, orientation, recognition, the sequence of actions, and space usage. Along with the role of mental maps, human perception, and interaction problems in public spaces (at railway stations, galleries, and educational institutions), we analyzed the spatial situations influencing psychological and ergonomic factors. We also analyzed the eye movements of the experimental subjects in dynamic situations, in spatial procession, using stairs and corridors. We monitored both the consequences and the distorting effects of the ocular dominance of the right eye on spatial orientation; we analyzed the gender-based differences of women and men’s orientation, stress-inducing spaces, spaces affecting concentration and the spatial situation influencing territorial behaviour. Based on these observations, we collected the components of creating public interior spaces, which -according to our theory- contribute to the optimal usability of public spaces. We summed up our research in criteria for design, including 10 points. Our further goals are testing design principles needed for optimizing orientation and space usage, their discussion, refinement, and practical usage.

Keywords: architecture, eye-tracking, human-centered spatial design, public interior spaces, visual perception

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Authors: Fiffy Hanisdah Saikim, Bruce Prideaux

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One of the aims of wildlife tourism is to educate visitors about the threats facing wildlife, in general, and the actions needed to protect the environment and maintain biodiversity. Annually, millions of tourists visit natural areas and zoos primarily to view flagship species such as rhinos and elephants. Venues rely on the inherent charisma of these species to increase visitation and anchor conservation efforts. Expected visitor outcomes from the use of flagships include raised levels of awareness and pro-conservation behaviors. However, the role of flagships in wildlife tourism has been criticized for not delivering conservation benefits for species of interest or biodiversity and producing negative site impacts. Furthermore, little is known about how the connection to a species influences conservation behaviors. This paper addresses this gap in knowledge by extending previous work exploring wildlife tourism to include the emotional connection formed with wildlife species and pro-conservation behaviors for individual species and biodiversity. This paper represents a substantial contribution to the field because (a) it incorporates the role of the experience in understanding how tourists connect with a species and how this connection influences pro-conservation behaviors; and (b) is the first attempt to operationalize Conservation Caring as a measure of tourists’ connection with a species. Existing studies have investigated how specific elements, such as interpretation or species’ morphology may influence programmatic goals or awareness. However, awareness is a poor measure of an emotional connection with an animal. Furthermore, there has not been work done to address the holistic nature of the wildlife viewing experience, and its subsequent influence on behaviors. Results based on the structural equation modelling, support the validity of Conservation Caring as a factor; the ability of wildlife tourism to influence Conservation Caring; and that this connection is a strong predictor of conservation awareness behaviors. These findings suggest wildlife tourism can deliver conservation outcomes. The studies in this paper also provide a valuable framework for structuring wildlife tourism experiences to align with flagship related conservation outcomes, and exploring a wider assemblage of species as potential flagships.

Keywords: wildlife tourism, conservation caring, conservation awareness, structural equation modelling

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: H. Alijani, M. Jabari, S. Matroodi, H. Zolqarnein, A. Sharafi, I. Zamani

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Actinomycetes, Gram-positive bacteria, are interesting as a main producer of secondary metabolites and are important industrially and pharmaceutically. The marine environment is a potential source for new actinomycetes, which can provide novel bioactive compounds and industrially important enzymes. The aims of this study were to isolate and identify novel actinomycetes from Persian Gulf sediments and screen these isolates for the production of secondary metabolites, especially antibiotics, Using phylogenetic (16S rRNA gene sequence), morphological and biochemical analyses. 15 different actinomycete strains from Persian Gulf sediments at a depth of 5-10 m were identified. DNA extraction was done using Cinnapure DNA Kit. PCR amplification of 16S rDNA gene was performed using F27 and R1492 primers. Phylogenetic tree analysis was performed using the MEGA 6 software. Most of the isolated strains belong to the genus namely Streptomyces (14), followed by Nocardiopsis (1). Antibacterial assay of the isolates supernatant was performed using a standard disc diffusion assay with replication (n=3). The results of disk diffusion assay showed that most active strain against Proteus volgaris and Bacillus cereus was AMJ1 (16.46±0.2mm and 13.78±0.2mm, respectively), against Salmonella sp. AMJ7 was the most effective strain (10.13±0.2mm), and AMJ1 and AHA5 showed more inhibitory activity against Escherichia coli (8.04±0.02 mm and 8.2±0.03 ). The AMJ6 strain showed best antibacterial activity against Klebsiella sp. (8.03±0.02mm). Antifungal activity of AMJ2 showed that it was most active strain against complex (16.05±0.02mm) and against Aspergillus flavus strain AMJ1 was most active strain (16.4±0.2mm) and highest antifungal activity against Trichophyton mentagrophytes, Microsporum gyp serum and Candida albicans, were shown by AHA1 (21.03±0.02mm), AHA3 and AHA7 (18±0.03mm), AMJ6 (21.03±0.2mm) respectively. Our results revealed that the marine actinomycetes of Persian Gulf sediments were potent source of novel antibiotics and bioactive compounds and indicated that the antimicrobial metabolites were extracellular. Most of the secondary metabolites and antibiotics are extracellular in nature and extracellular products of actinomycetes show potent antimicrobial activities.

Keywords: antibacterial activity, antifungal activity, marine actinomycetes, Persian Gulf

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Authors: Shangdong Zhu, Yunzhou Zhang, Jie Zhang, Hang Hu, Yazhou Zhang

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Image stitching is a very important branch in the field of computer vision, especially for panoramic map. In order to eliminate shape distortion, a novel stitching method is proposed based on gradually changing matrix when images are horizontal. For images captured horizontally, this paper assumes that there is only translational operation in image stitching. By analyzing each parameter of the homography matrix, the global homography matrix is gradually transferred to translation matrix so as to eliminate the effects of scaling, rotation, etc. in the image transformation. This paper adopts matrix approximation to get the minimum value of the energy function so that the shape distortion at those regions corresponding to the homography can be minimized. The proposed method can avoid multiple horizontal images stitching failure caused by accumulated shape distortion. At the same time, it can be combined with As-Projective-As-Possible algorithm to ensure precise alignment of overlapping area.

Keywords: image stitching, gradually changing matrix, horizontal direction, matrix approximation, homography matrix

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Authors: Motahareh Fadaei

Abstract:

Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: clustering, energy consumption‎, learning automata, underwater sensor networks

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Authors: Frederico Fialho Teixeira, Islam Mashaly, Maryam Shafiei, Jurij Karlovsek

Abstract:

The ubiquity of data gathering and smart technologies, advancements in virtual technologies, and the development of the internet of things (IoT) have created urgent demands for the development of frameworks and efficient workflows for data collection, visualisation, and analysis. Digital twin, in different scales of the city into the building, allows for bringing together data from different sources to generate fundamental and illuminating insights for the management of current facilities and the lifecycle of amenities as well as improvement of the performance of current and future designs. Over the past two decades, there has been growing interest in the topic of digital twin and their applications in city and building scales. Most such studies look at the urban environment through a homogeneous or generalist lens and lack specificity in particular characteristics or identities, which define an urban university campus. Bridging this knowledge gap, this paper offers a framework for developing a digital twin for a university campus that, with some modifications, could provide insights for any large-scale digital twin settings like towns and cities. It showcases how currently unused data could be purposefully combined, interpolated and visualised for producing analysis-ready data (such as flood or energy simulations or functional and occupancy maps), highlighting the potential applications of such a framework for campus planning and policymaking. The research integrates campus-level data layers into one spatial information repository and casts light on critical data clusters for the digital twin at the campus level. The paper also seeks to raise insightful and directive questions on how digital twin for campus can be extrapolated to city-scale digital twin. The outcomes of the paper, thus, inform future projects for the development of large-scale digital twin as well as urban and architectural researchers on potential applications of digital twin in future design, management, and sustainable planning, to predict problems, calculate risks, decrease management costs, and improve performance.

Keywords: digital twin, smart campus, framework, data collection, point cloud

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Authors: M. R. Ebrahimi, B. Mahdaviani

Abstract:

Conventional electric distribution systems are radial in nature, supplied at one end through a main source. These networks generally have a simple protection system usually implemented using fuses, re-closers, and over-current relays. Recently, great attention has been paid to applying Distributed energy resources (DERs) throughout electric distribution systems. Presence of such generation in a network leads to losing coordination of protection devices. Therefore, it is desired to develop an algorithm which is capable of protecting distribution systems that include DER. On the other hand smart grid brings opportunities to the power system. Fast advancement in communication and measurement techniques accelerates the development of multi agent system (MAS). So in this paper, a new approach for the protection of distribution networks in the presence of DERs is presented base on MAS. The proposed scheme has been implemented on a sample 27-bus distribution network.

Keywords: distributed energy resource, distribution network, protection, smart grid, multi agent system

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

Abstract:

In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: A. Movaghar, R. A. Mahdavinejad

Abstract:

To analyze the effect of various parameters of fluid on the material properties such as surface and depth defects and/or cracks, it is possible to determine the affection of pressure field on these specifications. Stress tensor analysis is also able to determine the points in which the probability of defection creation is more. Besides, from pressure field, it is possible to analyze the affection of various fluid specifications such as viscosity and density on defect created in the material. In this research, the concerned boundary conditions are analyzed first. Then the solution network and stencil used are mentioned. With the determination of relevant equation on the fluid flow between notch and matrix and their discretion according to the governed boundary conditions, these equations can be solved. Finally, with the variation creations on fluid parameters such as density and viscosity, the affection of these variations can be determined on pressure field. In this direction, the flowchart and solution algorithm with their results as vortex and current function contours for two conditions with most applications in pressing process are introduced and discussed.

Keywords: pressing, notch, matrix, flow function, vortex

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Authors: Sagir M. Yusuf, Chris Baber

Abstract:

We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: L. S. Chathurika

Abstract:

Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

Procedia PDF Downloads 120