Search results for: message passing neural network

Authors: Tri Pratiwi, Sufyarma Marsidin, Hermawati Syarif, Yahya

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The 21st-century skills being highly promoted today are Creativity and Innovation, Critical Thinking and Problem Solving, Communication and Collaboration. Communication Skill is one of the essential skills that should be mastered by the students. To master Communication Skills, students must first master their Language Skills. Language Skills is one of the main supporting factors in improving Communication Skills of a person because by learning Language Skills students are considered capable of communicating well and correctly so that the message or how to deliver the message to the listener can be conveyed clearly and easily understood. However, it cannot be denied that English output or learning outcomes which are less optimal is the problem which is frequently found in the implementation of the learning process. This research aimed to improve students’ language skills by developing learning model in English subject for VIII graders of SMP N 1 Uram Jaya through Directed-Project Based Learning (DPjBL) implementation. This study is designed in Research and Development (R & D) using ADDIE model development. The researcher collected data through observation, questionnaire, interview, test, and documentation which were then analyzed qualitatively and quantitatively. The results showed that DPjBL is effective to use, it is seen from the difference in value between the pretest and posttest of the control class and the experimental class. From the results of a questionnaire filled in general, the students and teachers agreed to DPjBL learning model. This learning model can increase the students' English achievement.

Keywords: language skills, learning model, Directed-Project Based Learning (DPjBL), English achievement

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Authors: Hassan Abubakar

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This paper will serve as a guide to good design and implementation of wireless networking for campus institutions in Nigeria. It can be implemented throughout the primary, secondary and tertiary institutions. This paper describe the some technical functions, standard configurations and layouts of the 802.11 wireless LAN(Local Area Network) that can be implemented across the campus network. The paper also touches upon the wireless infrastructure standards involved with enhanced services, such as voice over wireless and wireless guest hotspot. The paper also touch the benefits derived from implementing campus wireless network and share some lights on how to arrive at the success in increasing the performance of wireless and using the campus wireless to share resources like software applications, printer and documents.

Keywords: networking, standards, wireless local area network (WLAN), radio frequency (RF), campus

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Authors: Y. B. Kirankumar, J. D. Mallapur

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Wireless Sensor Network (WSN) is an emerging technology, which has great invention for various low cost applications both for mass public as well as for defence. The wireless sensor communication technology allows random participation of sensor nodes with particular applications to take part in the network, which results in most of the uncovered simulation area, where fewer nodes are located at far distances. The drawback of such network would be that the additional energy is spent by the nodes located in a pattern of dense location, using more number of nodes for a smaller distance of communication adversely in a region with less number of nodes and additional energy is again spent by the source node in order to transmit a packet to neighbours, thereby transmitting the packet to reach the destination. The proposed work is intended to develop Energy Efficient Node Placement Algorithm (EENPA) in order to place the sensor node efficiently in simulated area, where all the nodes are equally located on a radial path to cover maximum area at equidistance. The total energy consumed by each node compared to random placement of nodes is less by having equal burden on fewer nodes of far location, having distributed the nodes in whole of the simulation area. Calculating the network lifetime also proves to be efficient as compared to random placement of nodes, hence increasing the network lifetime, too. Simulation is been carried out in a qualnet simulator, results are obtained on par with random placement of nodes with EENP algorithm.

Keywords: energy, WSN, wireless sensor network, energy approach

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Authors: Fariba Ebrahimi, Mehdi Ghorbani

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Achieving to integrated Water management fundamentally needs to effective relation, coordination, collaboration and synergy among various actors who have common but different responsibilities. In this sense, the foundation of comprehensive and integrated management is not compatible with centralization and top-down strategies. The aim of this paper is analysis institutional network of water relevant stakeholders and water policy monitoring in Shemiranat. In this study collaboration networks between informal and formal institutions co-management process have been investigated. Stakeholder network analysis as a quantitative method has been implicated in this research. The results of this study indicate that institutional cohesion is medium; sustainability of institutional network is about 40 percent (medium). Additionally the core-periphery index has measured in this study according to reciprocity index. Institutional capacities for integrated natural resource management in regional level are measured in this study. Furthermore, the necessity of centrality reduction and promote stakeholders relations and cohesion are emphasized to establish a collaborative natural resource governance.

Keywords: policy monitoring, water management, social network, stakeholder, shemiranat

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Authors: Lee Soo-Kyoung, Kim Young-Su

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This study established a network related to metabolic syndrome intervention by conducting a social network analysis of titles, keywords, and abstracts, and it identified emerging topics of research. It visualized an interconnection between critical keywords and investigated their frequency of appearance to construe the trends in metabolic syndrome intervention measures used in studies conducted over 38 years (1979–2017). It examined a collection of keywords from 8,285 studies using text rank analyzer, NetMiner 4.0. The analysis revealed 5 groups of newly emerging keywords in the research. By examining the relationship between keywords with reference to their betweenness centrality, the following clusters were identified. Thus if new researchers refer to existing trends to establish the subject of their study and the direction of the development of future research on metabolic syndrome intervention can be predicted.

Keywords: intervention, metabolic syndrome, network analysis, research, the trend

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: Sanat Ramesh, Tarun Dutt, Abhilasha Aswal, Anushka Chandrababu, G. N. Srinivasa Prasanna

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Optimal Schedule Generation for a large national railway network operating thousands of passenger trains with tens of thousands of kilometers of track is a grand computational challenge in itself. We present heuristics based on a Mixed Integer Program (MIP) formulation for local optimization. These methods provide flexibility in scheduling new trains with varying speed and delays and improve utilization of infrastructure. We propose methods that provide a robust solution with hundreds of trains being scheduled over a portion of the railway network without significant increases in delay. We also provide techniques to validate the nominal schedules thus generated over global correlated variations in travel times thereby enabling us to detect conflicts arising due to delays. Our validation results which assume only the support of the arrival and departure time distributions takes an order of few minutes for a portion of the network and is computationally efficient to handle the entire network.

Keywords: mixed integer programming, optimization, railway network, train scheduling

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Authors: Nopsarun Vannasirikul

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The purposes of this research paper were to study the current situations of community development, women’s potentials, women’s participation, network, and government policy as well as to study the factors influencing women’s potentials, women’s participation, network, and government policy that have on the community development. The population included the women age of 18 years old who were living in the communities of Bangkok areas. This study was a mix research method of quantitative and qualitative method. A simple random sampling method was utilized to obtain 400 sample groups from 50 districts of Bangkok and to perform data collection by using questionnaire. Also, a purposive sampling method was utilized to obtain 12 informants for an in-depth interview to gain an in-sight information for quantitative method.

Keywords: community development, participation, network, women’s right, management

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Authors: Hanbhin Ryu, Ilkwon Yun

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Automotive industry targets to provide an improvement in safety and convenience through realizing fully autonomous vehicle. For partially realizing fully automated driving, Current vehicles already feature varieties of advanced driver assistance system (ADAS) for safety and infotainment systems for the driver’s convenience. This paper presents Cloud Connected Vehicle (CCV) which connected vehicles with cloud data center via the access network to control the vehicle for achieving next autonomous driving form and describes its features. This paper also describes the shortcoming of the existing In-Vehicle Network (IVN) to be a next generation IVN of CCV and organize the 802.3 Ethernet, the next generation of IVN, related research issue to verify the feasibility of using Ethernet. At last, this paper refers to additional considerations to adopting Ethernet-based IVN for CCV.

Keywords: autonomous vehicle, cloud connected vehicle, ethernet, in-vehicle network

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Authors: Amrah Maryam

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The emergence of online social networks (OSNs) has transformed the way we pursue and share information. On the one hand, OSNs provide great ease for the spreading of positive information while, on the other hand, they may also become a channel for the spreading of malicious rumors and misinformation throughout the social network. Thus, to assure the trustworthiness of OSNs to its users, it is of vital importance to detect the misinformation propagation in the network by placing network monitors. In this paper, we aim to place monitors near the suspected nodes with the intent to limit the diffusion of misinformation in the social network, and then we also detect the most significant nodes in the network for propagating true information in order to minimize the effect of already diffused misinformation. Thus, we initiate two heuristic monitor placement using articulation points and truth propagation using eigenvector centrality. Furthermore, to provide real-time workings of the system, we integrate both the monitor placement and truth propagation entities as well. To signify the effectiveness of the approaches, we have carried out the experiment and evaluation of Stanford datasets of online social networks.

Keywords: online social networks, monitor placement, independent cascade model, spread of misinformation

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Authors: Farnaz Farhangi

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Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Camille Intson

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This paper endeavors to explore the opportunities, challenges, and ethics of reconstructing and re-enacting archives of memory through virtual reality (VR) performance, using Jordan Tannahill’s Draw Me Close as an exemplary case study. Draw Me Close is a 1:1 virtual reality (VR) performance in which the artist’s childhood memories, experiences, and interactions with his mother are reconstructed in the wake of her passing. Solo audience members are positioned as Jordan (the subject and character) and taken through a series of narratives, (virtual) spaces, and interactions with his “mother,” played by a live actor. Piece by piece, audiences are brought into the world of the “shifting” archive, inhabiting Jordan’s reconstructed virtual world from his early explorations of queer sexuality through to his mother’s cancer diagnosis and passing. This paper will explore how the world of Draw Me Close represents a “touching” and/or “queering” of time within its archive, blurring and transgressing the boundaries between the animate and the inanimate, life and death. On a philosophical level, considering foundational queer performance scholarship and archival theory, it will also examine how performance’s ephemerality rewards its artists with the dual advantages of visibility and protection, allowing for an ethical exploration of traumatic memory and loss within a disappearing medium. Finally, this provocation will use Draw Me Close as a point of departure from which to outline future possibilities for performance and emerging technologies’ engagements with archival theory and practice. By positioning virtual reality (VR) as an archive-constructing medium, it aims to move beyond the question of how we can take performances seriously as archives towards how personal archive construction is itself a performative act.

Keywords: intermedial theatre, new media arts, queer performance, virtual reality

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Authors: H. Gorine, M. Ramadan Elmezughi

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In this paper, we investigate security issues and challenges facing researchers in wireless sensor networks and countermeasures to resolve them. The broadcast nature of wireless communication makes Wireless Sensor Networks prone to various attacks. Due to resources limitation constraint in terms of limited energy, computation power and memory, security in wireless sensor networks creates different challenges than wired network security. We will discuss several attempts at addressing the issues of security in wireless sensor networks in an attempt to encourage more research into this area.

Keywords: wireless sensor networks, network security, light weight encryption, threats

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Authors: Angga S. Fajar, A. Aminullah, J. Kiyono, R. A. Safitri

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This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved.

Keywords: bridge structures, ANN, optimization, back propagation

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Authors: Mostafa Sefidgar, Kaamran Raahemifar, Hossein Bazmara, Madjid Soltani

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The computational methods provide condition for investigation related to the process of drug delivery, such as convection and diffusion of drug in extracellular matrices, and drug extravasation from microvascular. The information of this process clarifies the mechanisms of drug delivery from the injection site to absorption by a solid tumor. In this study, an advanced numerical method is used to solve fluid flow and solute transport equations simultaneously to show how capillary network structure induced by tumor affects drug delivery. The effect of heterogeneous capillary network induced by tumor on interstitial fluid flow and drug delivery is investigated by this multi scale method. The sprouting angiogenesis model is used for generating capillary network induced by tumor. Fluid flow governing equations are implemented to calculate blood flow through the tumor-induced capillary network and fluid flow in normal and tumor tissues. The Starling’s law is used for closing this system of equations and coupling the intravascular and extravascular flows. Finally, convection-diffusion-reaction equation is used to simulate drug delivery. The dynamic approach which changes the capillary network structure based on signals sent by hemodynamic and metabolic stimuli is used in this study for more realistic assumption. The study indicates that drug delivery to solid tumors depends on the tumor induced capillary network structure. The dynamic approach generates the irregular capillary network around the tumor and predicts a higher interstitial pressure in the tumor region. This elevated interstitial pressure with irregular capillary network leads to a heterogeneous distribution of drug in the tumor region similar to in vivo observations. The investigation indicates that the drug transport properties have a significant role against the physiological barrier of drug delivery to a solid tumor.

Keywords: solid tumor, physiological barriers to drug delivery, angiogenesis, microvascular network, solute transport

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Authors: Jinan Fiaidhi, Sabah Mohammed, Petros Zezos

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Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn's disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours to reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning, including deep learning, has failed to report robust results because of the lack of large samples to train its neural nets. In this paper, we are describing a thick data approach that learns from a few anchor images. We are using sound datasets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies in any CE video. We are identifying candidate frames based on feature extraction to provide representative measures of the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach reported accuracy of detecting Crohn's Disease based on the availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine-tune our approach by adding more thick data methods for enhancing diagnosis accuracy.

Keywords: thick data analytics, capsule endoscopy, Crohn’s disease, siamese neural network, decision tree

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Authors: Sayed Hadi Sadeghi

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This research study examines the current state of support services for e-network practice in an Australian and an American university. It identifies information that will be of assistance to Australian and American universities to improve their existing online programs. The study investigated the two universities using a quantitative methodological approach. Participants were students, lecturers and admins of universities engaged with online courses and learning management systems. The support services for e-network practice variables, namely academic support services, administrative support and technical support, were investigated for e-practice. Evaluations of e-network support service and its sub factors were above average and excellent in both countries, although the American admins and lecturers tended to evaluate this factor higher than others did. Support practice was evaluated higher by all participants of an American university than by Australians. One explanation for the results may be that most suppliers of the Australian university e-learning system were from eastern Asian cultural backgrounds with a western networking support perspective about e-learning.

Keywords: support services, e-Network practice, Australian universities, United States universities

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Authors: Elif D. Oguz Erkal, David Krackhardt, Erica Cochran-Hameen

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Even though social network analysis (SNA) is an abundantly studied concept for many organizations and industries, a clear SNA approach to the project teams has not yet been adopted by the construction industry. The main challenges for performing SNA in construction and the apparent reason for this gap is the unique and complex structure of each construction project, the comparatively high circulation of project team members/contributing parties and the variety of authentic problems for each project. Additionally, there are stakeholders from a variety of professional backgrounds collaborating in a high-stress environment fueled by time and cost constraints. Within this case study on Project RE, a design & build project performed at the Urban Design Build Studio of Carnegie Mellon University, social network analysis of the project design team will be performed with the main goal of applying social network theory to construction project environments. The research objective is to determine a correlation between the network of how individuals relate to each other on one’s perception of their own professional strengths and weaknesses and the communication patterns within the team and the group dynamics. Data is collected through a survey performed over four rounds conducted monthly, detailed follow-up interviews and constant observations to assess the natural alteration in the network with the effect of time. The data collected is processed by the means of network analytics and in the light of the qualitative data collected with observations and individual interviews. This paper presents the full ethnography of this construction design team of fourteen architecture students based on an elaborate social network data analysis over time. This study is expected to be used as an initial step to perform a refined, targeted and large-scale social network data collection in construction projects in order to deduce the impacts of social networks on project performance and suggest better collaboration structures for construction project teams henceforth.

Keywords: construction design teams, construction project management, social network analysis, team collaboration, network analytics

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Authors: Arjumand Warsy

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Among the Muslims, the Holy Quran is taught from early childhood and generally by the age of 7-8 years the reading of the entire Quran is completed by most of the children in Muslim families. During this period excellent reciter’s are selected to teach and emphasis is laid on correct reading, pronunciation and memorization. Following these years, the parents lay emphasis on the recitation of the Quran on daily basis. During the month of Ramadan the entire Quran is read one or more times and there are considerable number of Muslims who complete the entire Quran once or more each calendar month. Many Muslims do not know Arabic and for them message in the Quran is what others tell them and often they have no idea about this Guidance sent to them. This deficiency is reflected in many ways, both among people living in Muslim or non-Muslim countries. Due to the deficiency in knowledge about Islamic teachings, the foundations of Islam are being eroded by a variety of forces. In an attempt to guard against the non-Islamic influences, every Muslim must have a clear understanding of the Islamic teachings and requirements. The best guidance can be provided by the understanding of the Holy Quran. However, we are faced with the problem that often the Quran is taught in a way that fails to develop an interest and understanding of the message from Allah. Looking at the teaching of other subjects both scientific and non-scientific, at school, college or University levels, it is obvious that the advances in teaching methodologies using electronic technology have had a major impact, where both the understanding and the interest of the students are significantly elevated. We attempted to teach the meaning of the Holy Quran to children and adults using a scientific and modern approach using slide presentation and animations. The results showed almost 100% increase in the understanding of the Quran message; all attendees claimed they developed an increased interest in the study of the Holy Quran and did not lose track or develop boredom throughout the lectures. They learnt the information and remembered it more effectively. The love for Allah and Prophet Mohammad (PBUH) increased significantly. The fear of Allah and love of Heaven developed significantly. Historical facts and the stories of the past nations became clearer and the Greatness of the Creator was strongly felt. Several of attendees wanted to become better Muslims and to spread the knowledge of Islam. In this presentation, the adopted teaching method will be first presented and demonstrated to the audience using a short Surah from the Quran, followed by discussion on the results achieved during our study. We will endeavor to convey to the audience that there is a need to adopt a more scientific approach to teach the Quran so that a greater benefit is achieved by all.

Keywords: The Holy Quran, Muslims, presentations, technology

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Authors: Yeliz Ekinci, Melis Koyuncu

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Successful planning of solid waste management systems requires successful prediction of the amount of solid waste generated in an area. Waste management planning can protect the environment and human health, hence it is tremendously important for countries. The lack of information in waste generation can cause many environmental and health problems. Turkey is a country that plans to join European Union, hence, solid waste management is one of the most significant criteria that should be handled in order to be a part of this community. Solid waste management system requires a good forecast of solid waste generation. Thus, this study aims to forecast solid waste generation in Turkey. Artificial Neural Network and Linear Regression models will be used for this aim. Many models will be run and the best one will be selected based on some predetermined performance measures.

Keywords: forecast, solid waste generation, solid waste management, Turkey

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Authors: Taehan Lee

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We consider a network design problem which has shortest routing restriction based on the values determined by the installed facilities on each arc. In conventional multicommodity network design problem, a commodity can be routed through any possible path when the capacity is available. But, we consider a problem in which the commodity between two nodes must be routed on a path which has shortest metric value and the link metric value is determined by the installed facilities on the link. By this routing restriction, the problem has a distinct characteristic. We present an integer programming formulation containing the primal-dual optimality conditions to the shortest path routing. We give some computational results for the model.

Keywords: integer programming, multicommodity network design, routing, shortest path

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Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

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This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.

Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Muhammad Arif Khan, Haroon Idrees, Imran Aziz, Sidra Mushtaq

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Social platforms like online discussion and mailing groups are well aligned with academic as well as professional learning spaces. Professional communities are increasingly moving to online forums for sharing and capturing the intellectual abilities. This study investigated dynamics of social connectivity of yahoo mailing groups of Pakistani Library and Information Science (LIS) professionals using Graph Theory technique. Design/Methodology: Social Network Analysis is the increasingly concerned domain for scientists in identifying whether people grow together through online social interaction or, whether they just reflect connectivity. We have conducted a longitudinal study using Network Graph Theory technique to analyze the large data-set of email communication. The data was collected from three yahoo mailing groups using network analysis software over a period of six months i.e. January to June 2016. Findings of the network analysis were reviewed through focus group discussion with LIS experts and selected respondents of the study. Data were analyzed in Microsoft Excel and network diagrams were visualized using NodeXL and ORA-Net Scene package. Findings: Findings demonstrate that professionals and students exhibit intellectual growth the more they get tied within a network by interacting and participating in communication through online forums. The study reports on dynamics of the large network by visualizing the email correspondence among group members in a network consisting vertices (members) and edges (randomized correspondence). The model pair wise relationship between group members was illustrated to show characteristics, reasons, and strength of ties. Connectivity of nodes illustrated the frequency of communication among group members through examining node coupling, diffusion of networks, and node clustering has been demonstrated in-depth. Network analysis was found to be a useful technique in investigating the dynamics of the large network.

Keywords: emailing networks, network graph theory, online social platforms, yahoo mailing groups

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Authors: Behnam Mahdiyan Nasl

Abstract:

In a lab-scale research, the effects of feeding whey into the biogas system and how to solve the probable problems arising were analysed. In the study a semi-continuous glass reactor, having a total capacity of 13 liters and having a working capacity of 10 liters, was placed in an incubator, and the temperature was tried to be held at 38 °C. At first, the reactor was operated by adding 5 liters of animal manure and water with a ratio of 1/1. By passing time, the production rate of the gas reduced intensively that on the fourth day there was no production of gas and the system stopped working. In this condition, the pH was adjusted and by adding NaOH, it was increased from 5.4 to 7. On 48th day, the first gas measurement was done and an amount of 12.07 % of CH₄ was detected. After making buffer in the ambient, the number of bacteria existing in the cattle’s manure and contributing to the gas production was thought to be not adequate, and up to 20 % of its volume 2 liters of mud was added to the reactor. 7 days after adding the anaerobic mud, second gas measurement was carried out, and biogas including 43 % CH₄ was obtained. From the 61st day of the study, the cheese whey with the animal manure was started to be added with an amount of 40 mL per day. However, by passing time, the raising of the microorganisms existed in the whey (especially Ni and Co), the percent of methane in the biogas decreased. In fact, 2 weeks after adding PAS, the gas measurement was done and 36,97 % CH₄ was detected. 0,06 mL Ni-Co (to gain a concentration of 0.05 mg/L in the reactor’s mixture) solution was added to the system for 15 days. To find out the effect of the solution on archaea, 7 days after stopping addition of the solution, methane gas was found to have a 9,03 % increase and reach 46 %. Lastly, the effects of adding molasses to the reactor were investigated. The effects of its activity on the bacteria was analysed by adding 4 grams of it to the system. After adding molasses in 10 days, according to the last measurement, the amount of methane gas reached up to 49%.

Keywords: biogas, cheese whey, cattle manure, energy

Procedia PDF Downloads 337

Authors: Christian Kapuku, Seung-Young Kho

Abstract:

An accurate representation of the transportation system serving the region is one of the important aspects of transportation modeling. Such representation often requires developing an abstract model of the system elements, which also requires important amount of data, surveys and time. However, in some cases such as in developing countries, data deficiencies, time and budget constraints do not always allow such accurate representation, leaving opportunities to assumptions that may negatively affect the quality of the analysis. With the emergence of Internet open source data especially in the mapping technologies as well as the advances in Geography Information System, opportunities to tackle these issues have raised. Therefore, the objective of this paper is to demonstrate such application through a practical case of the development of the transportation network for the city of Kinshasa. The GIS geo-referencing was used to construct the digitized map of Transportation Analysis Zones using available scanned images. Centroids were then dynamically placed at the center of activities using an activities density map. Next, the road network with its characteristics was built using OpenStreet data and other official road inventory data by intersecting their layers and cleaning up unnecessary links such as residential streets. The accuracy of the final network was then checked, comparing it with satellite images from Google and Bing. For the validation, the final network was exported into Emme3 to check for potential network coding issues. Results show a high accuracy between the built network and satellite images, which can mostly be attributed to the use of open source data.

Keywords: geographic information system (GIS), network construction, transportation database, open source data

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Authors: Z. Nougrara, J. Meunier

Abstract:

In this paper, we present a new approach for extracting roads, there road network and its nodes from satellite image representing regions in Algeria. Our approach is related to our previous research work. It is founded on the information theory and the mathematical morphology. We therefore have to define objects as sets of pixels and to study the shape of these objects and the relations that exist between them. The main interest of this study is to solve the problem of the automatic mapping from satellite images. This study is thus applied for that the geographical representation of the images is as near as possible to the reality.

Keywords: nodes, road network, satellite image, updating a road map

Procedia PDF Downloads 426

Authors: Nicole Pearcy, Jonathan J. Crofts, Nadia Chuzhanova

Abstract:

In this paper, we employ a directed hypergraph model to investigate the extent to which environmental variability influences the set of available biochemical reactions within a living cell. Such an approach avoids the limitations of the usual complex network formalism by allowing for the multilateral relationships (i.e. connections involving more than two nodes) that naturally occur within many biological processes. More specifically, we extend the concept of network reciprocity to complex hyper-networks, thus enabling us to characterize a network in terms of the existence of mutual hyper-connections, which may be considered a proxy for metabolic network complexity. To demonstrate these ideas, we study 115 metabolic hyper-networks of bacteria, each of which can be classified into one of 6 increasingly varied habitats. In particular, we found that reciprocity increases significantly with increased environmental variability, supporting the view that organism adaptability leads to increased complexities in the resultant biochemical networks.

Keywords: complexity, hypergraphs, reciprocity, metabolism

Procedia PDF Downloads 299