Search results for: research network

Authors: K. Govender, M. Sinclair

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This paper demonstrates a proof of concept whereby shorter trips and land use densification can be promoted through an alternative approach to planning and implementation of road infrastructure in the South African context. It briefly discusses how the development of the Compact City concept relies on a combination of promoting shorter trips and densification through a change in focus in road infrastructure provision. The methodology developed in this paper uses a traffic model to test the impact of synthesized deterrence functions on congestion locations in the road network through the assignment of traffic on the study network. The results from this study demonstrate that intelligent planning of road infrastructure can indeed promote reduced urban sprawl, increased residential density and mixed-use areas which are supported by an efficient public transport system; and reduced dependence on the freeway network with a fixed road infrastructure budget. The study has resonance for all cities where urban sprawl is seemingly unstoppable.

Keywords: compact cities, densification, road infrastructure planning, transportation modelling

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Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

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Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

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Authors: M. Khajezadeh, M. Saied Fallah Niasar, S. Ali Asli, D. Davani Davari, M. Godarzi, Y. Asgari

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This study aims to explore the joint application of both Boston and Ansoff matrices in the operational development of the product. We conduct deep analysis, by utilizing the Artificial Neural Network, to predict the position of the product in the market while the company is interested in increasing its share. The data are gathered from two industries, called hygiene and detergent. In doing so, the effort is being made by investigating the behavior of top player companies and, recommend strategic orientations. In conclusion, this combination analysis is appropriate for operational development; as well, it plays an important role in providing the position of the product in the market for both hygiene and detergent industries. More importantly, it will elaborate on the company’s strategies to increase its market share related to a combination of the Boston Consulting Group (BCG) Matrix and Ansoff Matrix.

Keywords: artificial neural network, portfolio analysis, BCG matrix, Ansoff matrix

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Authors: Jingting Luo, Yong Wang, Ying Wang

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Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Ardavan Zamanpour, Somaieh Yassari

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ZigBee protocol network was developed in industries and MIT laboratory in 1997. ZigBee is a wireless networking technology by alliance ZigBee which is designed to low board and low data rate applications. It is a Protocol which connects between electrical devises with very low energy and cost. The first version of IEEE 802.15.4 which was formed ZigBee was based on 2.4GHZ MHZ 912MHZ 868 frequency band. The name of system is often reminded random directions that bees (BEES) traversing during pollination of products. Such as alloy of the ways in which information packets are traversed within the mesh network. This paper aims to study the performance and effectiveness of this protocol in wireless networks.

Keywords: ZigBee, protocol, wireless, networks

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Henni Mansour Abdelwaheb

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This study discusses the design of a bounded position/force feedback controller developed to ensure position and force tracking for bilateral teleoperation arms operating with variable delay, and actuator saturation. Also, an adaptive robust Radial Basis Function (RBF) neural network is used to estimate the environment torque. The parameters of the environment torque are then sent from the slave site to the master site as a non-power signal to avoid passivity problems. Moreover, a nonlinear function is applied to each controller term as a smooth saturation function, providing a bounded control signal and preserving the system’s actuators. Lastly, the Lyapunov approach demonstrates the global stability of the controlled system, and numerical experiment results further confirm the validity of the presented strategy.

Keywords: teleoperation manipulators system, time-varying delay, actuator saturation, adaptive robust rbf neural network approximation, uncertainties

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Authors: Wojciech Kulesza

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The north-western part of the Poland, specifically West Pomerania and Lubuskie provinces, from several years are the subject of research of the Department of Archaeology of Early Middle Ages of Institute of Archaeology of Nicolaus Copernicus University in Toruń. This area has a dense network of rivers and numerous lakes, where many of them are connected to the southern part of the Baltic Sea. During the many years of research in this area, archaeologists discovered the remains of the early Middle Ages settlement located on several islands and in most cases were encountered relics of early Middle Ages bridges linking those islands with the mainland. During the excavation, work was carried out both under water and on land for the accurate identification of islands and adjacent to them underwater areas. The result of this work is a graphic documentation, made in a three-dimensional technique, not only for the underwater trenches but also relics of bridges and objects discovered during exploration, which as the main theme will be presented in the full presentation.

Keywords: Poland, underwater archaeology, Nicolaus Copernicus University, early middle ages

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

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Authors: Ojin Kwon, Yong-Jin Yoon, Liu Xin, Zhang Hongbao

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Wireless Body Area Network (WBAN) is a short-range wireless communication around human body for various applications such as wearable devices, entertainment, military, and especially medical devices. WBAN attracts the attention of continuous health monitoring system including diagnostic procedure, early detection of abnormal conditions, and prevention of emergency situations. Compared to cellular network, WBAN system is more difficult to control inter- and inner-cell interference due to the limited power, limited calculation capability, mobility of patient, and non-cooperation among WBANs. In this paper, we compare the performance of resource allocation scheme based on several Pseudo Orthogonal Codewords (POCs) to mitigate inter-WBAN interference. Previously, the POCs are widely exploited for a protocol sequence and optical orthogonal code. Each POCs have different properties of auto- and cross-correlation and spectral efficiency according to its construction of POCs. To identify different WBANs, several different pseudo orthogonal patterns based on POCs exploits for resource allocation of WBANs. By simulating these pseudo orthogonal resource allocations of WBANs on MATLAB, we obtain the performance of WBANs according to different POCs and can analyze and evaluate the suitability of POCs for the resource allocation in the WBANs system.

Keywords: wireless body area network, body sensor network, resource allocation without feedback, interference mitigation, pseudo orthogonal pattern

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Authors: Fadhl Alakwaa

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One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method.

Keywords: gene regulatory network, biclustering, denoising, system biology

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Authors: Ashish Payal, C. S. Rai, B. V. R. Reddy

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With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m² grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.

Keywords: localization, wireless sensor networks, artificial neural network, radial basis function, multi-layer perceptron, backpropagation, RSSI, GPS

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Authors: Haoqi Gao, Koichi Ogawara

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Retinal vascular segmentation of color fundus is the basis of ophthalmic computer-aided diagnosis and large-scale disease screening systems. Early screening of fundus diseases has great value for clinical medical diagnosis. The traditional methods depend on the experience of the doctor, which is time-consuming, labor-intensive, and inefficient. Furthermore, medical images are scarce and fraught with legal concerns regarding patient privacy. In this paper, we propose a new Generative Adversarial Network based on CycleGAN for retinal fundus images. This method can generate not only synthetic fundus images but also generate corresponding segmentation masks, which has certain application value and challenge in computer vision and computer graphics. In the results, we evaluate our proposed method from both quantitative and qualitative. For generated segmented images, our method achieves dice coefficient of 0.81 and PR of 0.89 on DRIVE dataset. For generated synthetic fundus images, we use ”Toy Experiment” to verify the state-of-the-art performance of our method.

Keywords: retinal vascular segmentations, generative ad-versarial network, cyclegan, fundus images

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Authors: Li Guo, Jianhong Wang, Dian Huang, Shengzhong Feng

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Computing networks as a new network architecture has shown great promise in boosting the utilization of different resources, such as computing, caching, and communications. To maximise the efficiency of resource orchestration in computing network control systems (CNCSs), this work proposes a dynamic orchestration strategy of a different resource based on task requirements from computing power requestors (CPRs). Specifically, computing power providers (CPPs) in CNCSs could share information with each other through communication channels on the basis of blockchain technology, especially their current idle resources. This dynamic process is modeled as a cooperative game in which CPPs have the same target of maximising long-term rewards by improving the resource utilization ratio. Meanwhile, the task requirements from CPRs, including size, deadline, and calculation, are simultaneously considered in this paper. According to task requirements, the proposed orchestration strategy could schedule the best-fitting resource in CNCSs, achieving the maximum long-term rewards of CPPs and the best quality of experience (QoE) of CRRs at the same time. Based on the EdgeCloudSim simulation platform, the efficiency of the proposed strategy is achieved from both sides of CPRs and CPPs. Besides, experimental results show that the proposed strategy outperforms the other comparisons in all cases.

Keywords: computing network control systems, resource orchestration, dynamic scheduling, blockchain, cooperative game

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Authors: Abhishek Jain, Kavish Kejriwal, V. Balaji Rao, Abhigna Chavda

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Increasing environmental and social concerns are forcing companies to take a fresh view of the impact of supply chain operations on environment and society when designing a supply chain. A challenging task in today’s food industry is the distribution of high-quality food items throughout the food supply chain. Improper storage and unwanted transportation are the major hurdles in food supply chain and can be tackled by making dynamic storage facility location decisions with the distribution network. Since food supply chain in India is one of the biggest supply chains in the world, the companies should also consider environmental impact caused by the supply chain. This project proposes a multi-objective optimization model by integrating sustainability in decision-making, on distribution in a food supply chain network (SCN). A Multi-Objective Mixed-Integer Linear Programming (MOMILP) model between overall cost and environmental impact caused by the SCN is formulated for the problem. The goal of MOMILP is to determine the pareto solutions for overall cost and environmental impact caused by the supply chain. This is solved by using GAMS with CPLEX as third party solver. The outcomes of the project are pareto solutions for overall cost and environmental impact, facilities to be operated and the amount to be transferred to each warehouse during the time horizon.

Keywords: multi-objective mixed linear programming, food supply chain network, GAMS, multi-product, multi-period, environment

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Authors: Xiaoqing Wang, Jianjian Zong, Li Li, Yanxing Zheng, Jinrong Tong, Mao Zhan

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With the rapid development of network and communication technology, a great deal of data has been generated in different domains of a network. These data show a trend of increasing scale and more complex structure. Therefore, an effective and flexible cross-domain data-sharing system is needed. The Cross-domain Data Sharing System(CDSS) in a net-centric environment is composed of three sub-systems. The data distribution sub-system provides data exchange service through publish-subscribe technology that supports asynchronism and multi-to-multi communication, which adapts to the needs of the dynamic and large-scale distributed computing environment. The access control sub-system adopts Attribute-Based Access Control(ABAC) technology to uniformly model various data attributes such as subject, object, permission and environment, which effectively monitors the activities of users accessing resources and ensures that legitimate users get effective access control rights within a legal time. The cross-domain access security negotiation subsystem automatically determines the access rights between different security domains in the process of interactive disclosure of digital certificates and access control policies through trust policy management and negotiation algorithms, which provides an effective means for cross-domain trust relationship establishment and access control in a distributed environment. The CDSS’s asynchronous,multi-to-multi and loosely-coupled communication features can adapt well to data exchange and sharing in dynamic, distributed and large-scale network environments. Next, we will give CDSS new features to support the mobile computing environment.

Keywords: data sharing, cross-domain, data exchange, publish-subscribe

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Kherbache Mehdi

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Within the framework of the validation of the Master in business administration marketing and sales in INSIM institute international in management Blida, we get the opportunity to do a professional internship in Sonelgaz Enterprise and a thesis. The thesis deals with the integration of social networking in the communication strategy of a company. The problematic is: How communicate with social network can be a solution for companies? The challenges stressed by this thesis were to suggest limits and recommendations to Sonelgaz Enterprise concerning social networks. The whole social networks represent more than a billion people as a potential target for the companies. Thanks to research and a qualitative approach, we have identified tree valid hypothesis. The first hypothesis allows confirming that using social networks cannot be ignored by any company in its communication strategy. However, the second hypothesis demonstrates that it’s necessary to prepare a strategy that integrates social networks in the communication plan of the company. The risk of this strategy is very limited because failure on social networks is not a restraint for the enterprise, social networking is not expensive and, a bad image which could result from it is not as important in the long-term. Furthermore, the return on investment is difficult to evaluate. Finally, the last hypothesis shows that firms establish a new relation between consumers and brands thanks to the proximity allowed by social networks. After the validation of the hypothesis, we suggested some recommendations to Sonelgaz Enterprise regarding the communication through social networks. Firstly, the company must use the interactivity of social network in order to have fruitful exchanges with the community. We also recommended having a strategy to treat negative comments. The company must also suggest delivering resources to the community thanks to a community manager, in order to have a good relation with the community. Furthermore, we advised using social networks to do business intelligence. Sonelgaz Enterprise can have some creative and interactive contents with some amazing applications on Facebook for example. Finally, we recommended to the company to be not intrusive with “fans” or “followers” and to be open to all the platforms: Twitter, Facebook, Linked-In for example.

Keywords: social network, buzz, communication, consumer, return on investment, internet users, web 2.0, Facebook, Twitter, interaction

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Authors: Marisa P. De Brito

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The internet is affecting the way the industry operates and communicates. It is also becoming a customary means for leisure, tourism, and hospitality consumers to seek and exchange information and views on hotels, destinations events and attractions, or to develop social ties with other users. On the one hand, the internet is a rich field to conduct leisure, tourism, and hospitality research; on the other hand, however, there are few researchers formally embracing online methods of research, such as netnography. Within social sciences, netnography falls under the interpretative/ethnographic research methods umbrella. It is an adaptation of anthropological techniques such as participant and non-participant observation, used to study online interactions happening on social media platforms, such as Facebook. It is, therefore, a research method applied to the study of online communities, being the term itself a contraction of the words network (as on internet), and ethnography. It was developed in the context of marketing research in the nineties, and in the last twenty years, it has spread to other contexts such as education, psychology, or urban studies. Since netnography is not universally known, it may discourage researchers and educators from using it. This work offers guidelines for researchers wanting to apply this method in the field of leisure, tourism, and hospitality or for educators wanting to teach about it. This is done by means of a double approach: a content analysis of the literature side-by-side with educational data, on the use of netnography. The content analysis is of the incidental research using netnography in leisure, tourism, and hospitality in the last twenty years. The educational data is the author and her colleagues’ experience in coaching students throughout the process of writing a paper using primary netnographic data - from identifying the phenomenon to be studied, selecting an online community, collecting and analyzing data to writing their findings. In the end, this work puts forward, on the one hand, a research agenda, and on the other hand, an educational roadmap for those wanting to apply netnography in the field or the classroom. The educator’s roadmap will summarise what can be expected from mini-netnographies conducted by students and how to set it up. The research agenda will highlight for which issues and research questions the method is most suitable; what are the most common bottlenecks and drawbacks of the method and of its application, but also where most knowledge opportunities lay.

Keywords: netnography, online research, research agenda, educator's roadmap

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Authors: Vangelis Papadimitropoulos

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This paper is part of the research program “Techno-Social Innovation in the Collaborative Economy”, funded by the Hellenic Foundation for Research and Innovation (H.F.R.I.) for the years 2022-2024. The paper showcases the Open Food Network (OFN) as an open-sourced digital platform supporting short food supply chains in local agricultural production and consumption. The paper outlines the research hypothesis, the theoretical framework, and the methodology of research as well as the findings and conclusions. Research hypothesis: The model of open cooperativism as a vehicle for systemic change in the agricultural sector. Theoretical framework: The research reviews the OFN as an illustrative case study of the three-zoned model of open cooperativism. The OFN is considered a paradigmatic case of the model of open cooperativism inasmuch as it produces commons, it consists of multiple stakeholders including ethical market entities, and it is variously supported by local authorities across the globe, the latter prefiguring the mini role of a partner state. Methodology: Research employs Ernesto Laclau and Chantal Mouffe’s discourse analysis -elements, floating signifiers, nodal points, discourses, logics of equivalence and difference- to analyse the breadth of empirical data gathered through literature review, digital ethnography, a survey, and in-depth interviews with core OFN members. Discourse analysis classifies OFN floating signifiers, nodal points, and discourses into four themes: value proposition, governance, economic policy, and legal policy. Findings: OFN floating signifiers align around the following nodal points and discourses: “digital commons”, “short food supply chains”, “sustainability”, “local”, “the elimination of intermediaries” and “systemic change”. The current research identifies a lack of common ground of what the discourse of “systemic change” signifies on the premises of the OFN’s value proposition. The lack of a common mission may be detrimental to the formation of a common strategy that would be perhaps deemed necessary to bring about systemic change in agriculture. Conclusions: Drawing on Laclau and Mouffe’s discourse theory of hegemony, research introduces a chain of equivalence by aligning discourses such as “agro-ecology”, “commons-based peer production”, “partner state” and “ethical market entities” under the model of open cooperativism, juxtaposed against the current hegemony of neoliberalism, which articulates discourses such as “market fundamentalism”, “privatization”, “green growth” and “the capitalist state” to promote corporatism and entrepreneurship. Research makes the case that for OFN to further agroecology and challenge the current hegemony of industrial agriculture, it is vital that it opens up its supply chains into equivalent sectors of the economy, civil society, and politics to form a chain of equivalence linking together ethical market entities, the commons and a partner state around the model of open cooperativism.

Keywords: sustainability, the digital commons, open cooperativism, innovation

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Authors: Sukhbir Singh

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This paper describes the modeling of novel solar air collector (NSAC) system by using artificial neural network (ANN) model. The objective of the study is to demonstrate the application of the ANN model to predict the performance of the NSAC with acetamide as a phase change material (PCM) storage. Input data set consist of time, solar intensity and ambient temperature wherever as outlet air temperature of NSAC was considered as output. Experiments were conducted between 9.00 and 24.00 h in June and July 2014 underneath the prevailing atmospheric condition of Kurukshetra (city of the India). After that, experimental results were utilized to train the back propagation neural network (BPNN) to predict the outlet air temperature of NSAC. The results of proposed algorithm show that the BPNN is effective tool for the prediction of responses. The BPNN predicted results are 99% in agreement with the experimental results.

Keywords: Evacuated tube solar air collector, Artificial neural network, Phase change material, solar air collector

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Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu

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This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.

Keywords: piezoelectricity, energy harvesting, simulation-based optimization, artificial neural network, genetic algorithm

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Authors: Go Woon Kwak, Siyoung Jun, Soyun Maeng, Haeyoung Lee

Abstract:

Entry is an education platform used in South Korea, created to help students learn to program, in which they can learn to code while playing. Using the online version of the entry, teachers can easily assign programming homework to the student and the students can make programs simply by linking programming blocks. However, the programs may be made by others, so that the authors of the programs should be identified. In this paper, as the first step toward author identification of entry programs, we present an artificial neural network based classification approach to identify genders of authors of a program written in an entry. A neural network has been trained from labeled training data that we have collected. Our result in progress, although preliminary, shows that the proposed approach could be feasible to be applied to the online version of entry for gender identification of authors. As future work, we will first use a machine learning technique for age identification of entry programs, which would be the second step toward the author identification.

Keywords: artificial intelligence, author identification, deep neural network, gender identification, machine learning

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Authors: Weera Weerasophon

Abstract:

This research paper is to study the development of a management model for promoting sustainable tourism of Rajabhat universities in Thailand. Mixed Method Research is applied under the said topic. The researcher has developed a management model to promote sustainable tourism. The objectives of the research are 1) to study the readiness in management sustainable tourism of Rajabhat universities in Thailand 2) to develop a management model for promoting sustainable tourism of those universities. The process of this research is organized in two steps according to the objectives. The results of the research are as in the following: 1. Rajabhat universities have the readiness in management for promoting sustainable tourism. The universities can be developed to be sustainable tourist attraction under the admistrators who have vision and realize the importance of tourism, eager to promote sustainable tourism of the universities by specifying obvious policy plans and management. 2. The management model for promoting sustainable tourism of Rajabhat universities is consisted of the main following factors : 2.1 Master plan and policy, 2.2 Rajabhat universities organization management and personnel administration, 2.3 Assignment and authority, leadership, 2.4 Join network, 2.5 Assurance of quality and controlling, 2.6 Budget management, 2.7 Human Resources management, 2.8 Alliance and co-ordination, 2.9 Tool of marketing. There are also other communal factors for promoting sustainable tourism. They are: local communities, local communities, tourism activities, government and private sectors, communicative technology system, history, tourist attractive, art and culture, internal and external environment including local wisdom heritage. The management model for promoting sustainable tourism can be concluded from these main and communal factors mentioned above.

Keywords: tourism, sustainable tourism, management, Rajabhat University

Procedia PDF Downloads 419

Authors: Kemka Ihemelandu, Chukwuemeka Ihemelandu

Abstract:

Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

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Authors: Rania Khadim, Mohammed Erritali, Abdelhakim Maaden

Abstract:

Nowadays Wireless Sensor Networks have attracted worldwide research and industrial interest, because they can be applied in various areas. Geographic routing protocols are very suitable to those networks because they use location information when they need to route packets. Obviously, location information is maintained by Location-Based Services provided by network nodes in a distributed way. In this paper we choose to evaluate the performance of two hierarchical rendezvous location based-services, GLS (Grid Location Service) and HLS (Hierarchical Location Service) coupled to the GPSR routing protocol (Greedy Perimeter Stateless Routing) for Wireless Sensor Network. The simulations were performed using NS2 simulator to evaluate the performance and power of the two services in term of location overhead, the request travel time (RTT) and the query Success ratio (QSR). This work presents also a new scalability performance study of both GLS and HLS, specifically, what happens if the number of nodes N increases. The study will focus on three qualitative metrics: The location maintenance cost, the location query cost and the storage cost.

Keywords: location based-services, routing protocols, scalability, wireless sensor networks

Procedia PDF Downloads 375