Search results for: outbreak prediction

Authors: Folajimi Oladimeji Shorunke

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Background: As of October 9, 2023, diphtheria has been noted to be re-emerging in four African countries: Algeria, Guinea, Niger, and Nigeria. 14,587 cases with a case fatality rate of 4.1% have been reported across these regions, with Nigeria alone responsible for over 90% of the cases. In Taraba State Nigeria, the index case of Diphtheria was reported on epidemic week 34, August 24, 2023 with 75 confirmed cases found 3 months after the index case and a case fatality of 1.3%. it described the distribution, trend and common symptoms found during the Outbreak. Methods: The Taraba State Diphtheria Outbreak line list on the Surveillance Outbreak Response Management & Analysis System (SORMAS) for all its 16 local government areas (LGAs) was analyzed using descriptive statistics (graphs, chats and maps) for the period between 24th August to 25th November 2023. Primary data was collected through the use of case investigation forms and variables like Age, gender, date of disease onset, LGA of residence, and symptoms exhibited were collected. Naso-pharyngeal and oro-pharyngeal samples were also collected for Laboratory confirmation. The most common diphtheria symptoms during the outbreak were also highlighted. Results: A total of 75 Diphtheria cases were diagnosed in 10 of the 16 LGAs in Taraba State between 24th August to 25th November 2023, 72% of the cases were female, with the age range 0-9 years having the highest proportion of 34 (45.3%), the number of positive diagnosis reduces with age among cases. The Northern part of the State had the highest proportion of cases, 68 (90.7%), with Ardo-Kola LGA having the highest 28 (29%). The remaining 9.2% of cases is shared among the middle belt and southern part of the State. The Epi-curve took the characteristic shape of a propagated infection with peaks at the 37th, 39th and 45th epidemic weeks. The most common symptoms found in cases were fever 71 (94.7%), pharyngitis 65( 86.7%), tonsillitis 60 (80%), and laryngitis 53 (71%). Conclusions: The number of confirmed cases of Diphtheria in Taraba State, Nigeria between 24th August to 25th November 2023 is 75. The condition is higher among females than male and mostly affected children between ages 0-9 with the northern part of the state most affected. The most common symptoms exhibited by cases include fever, pharyngitis, tonsillitis and laryngitis.

Keywords: diphtheria outbreak, taraba nigeria, descriptive epidemiology, trend

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Authors: Zhou Yang, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction.

Keywords: traffic prediction, raw data utilization, context building, data reduction

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set.

Keywords: Epilepsy, seizure, phase correlation, fluctuation, deviation.

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Authors: Jeena R. S., Sukesh Kumar A.

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Stroke is one of the major reasons of adult disability and morbidity in many of the developing countries like India. Early diagnosis of stroke is essential for timely prevention and cure. Various conventional statistical methods and computational intelligent models have been developed for predicting the risk and outcome of stroke. This research work focuses on a multilevel approach for predicting the occurrence of stroke based on various risk factors and invasive techniques like retinal imaging. This risk prediction model can aid in clinical decision making and help patients to have an improved and reliable risk prediction.

Keywords: prediction, retinal imaging, risk factors, stroke

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Authors: Geumchan Hwang

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Professional sport leagues in Europe and North America are shut down due to novel coronavirus disease (COVID-19) outbreak. Football leagues in Europe (e.g., La Liga, English Premier League, Bundesliga, Serie A, and Ligue 1) and big four professional sport leagues in North America (e.g., National Football League, Major League Baseball, National Basketball Association, and National Hockey League) are indefinitely suspended or delayed. COVID-19 outbreak has a growing negative impact on economics of sport leagues. For example, loss of revenue in Europe’s top five leagues due to the COVID-19 pandemic was estimated at € 4 billion and loss of revenue in the NBA was estimated at $650 million as of March 2020. In the unprecedented difficult situation, sport teams and leagues try to communicate with sport fans through diverse media platforms. In sport, however, very few studies have been done regarding how sport organizations effectively communicate with sport fans during pandemics, such as COVID-19 outbreak. Understanding sport organizations’ crisis communication is important to develop effective crisis management strategies for sport organizations. Therefore, the purpose of the study is to examine how sport organizations communicate with sport fans via online platforms in COVID-19 outbreak and how sport fans evaluate their communication strategies. 9 official sport league sites (i.e., five major football leagues in Europe and four major sport leagues in North America) and COVID-19 news articles published between January and June in 2020 will be analyzed in terms of coronavirus information, teams and players’ live update, fan interaction, fan support, and community engagement. In addition, comments posted on social media sites (i.e., Facebook and Twitter) of major sport leagues will be also analyzed to examine how sport fans perceive online messages provided by sport leagues as an effective communication strategy. To measure the effectiveness of crisis communication performance, five components (i.e., prompt, compassionate, honest, informative, and interactive) of crisis communication will be collected from leagues’ official websites information and social media posts. Upon completing data collection, content analysis method will be used to evaluate effectiveness of crisis communication among 9 professional sport leagues. The results of the study will provide athletic directors, administrators, and public relations managers in sport organizations with practical information regarding how athlete celebrities and sport organizations should interact with their fans in pandemic situations. In particular, this study will contribute to developing specific crisis management plan for sport organizations. For instance, football teams and leagues in Europe will be able to create standard manuals to minimize damages caused by disease outbreak, such as COVID-19 outbreak.

Keywords: COVID-19, communication, sport leagues, fans

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Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma

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Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.

Keywords: accelerometer, AdaBoost, GPS, mode prediction, support vector machine

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Authors: Carl van Walraven, Meltem Tuna

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Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

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Authors: Arvinder Kaur, Deepti Chopra

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Software Entropy Metrics for bug prediction have been validated on various software systems by different researchers. In our previous research, we have validated that Software Entropy Metrics calculated for Mozilla subsystem’s predict the future bugs reasonably well. In this study, the Software Entropy metrics are calculated for a subsystem of Android and it is noticed that these metrics are not suitable for bug prediction. The results are compared with a subsystem of Mozilla and a comparison is made between the two software systems to determine the reasons why Software Entropy metrics are not applicable for Android.

Keywords: android, bug prediction, mining software repositories, software entropy

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Authors: Chang Su Woo, Hyun Sung Park

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Useful lifetime evaluation of chevron rubber spring was very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of chevron rubber spring. In this study, we performed characteristic analysis and useful lifetime prediction of chevron rubber spring. Rubber material coefficient was obtained by curve fittings of uni-axial tension, equi bi-axial tension and pure shear test. Computer simulation was executed to predict and evaluate the load capacity and stiffness for chevron rubber spring. In order to useful lifetime prediction of rubber material, we carried out the compression set with heat aging test in an oven at the temperature ranging from 50°C to 100°C during a period 180 days. By using the Arrhenius plot, several useful lifetime prediction equations for rubber material was proposed.

Keywords: chevron rubber spring, material coefficient, finite element analysis, useful lifetime prediction

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Authors: Amit R. Bhende, G. K. Awari

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Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Bearing degradation data at three different conditions from run to failure is used. A RUL prediction model is separately built in each condition. Feed forward back propagation neural network models are developed for prediction modeling.

Keywords: bearing degradation data, remaining useful life (RUL), back propagation, prognosis

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Authors: Qiang Zhang, Chun Yuan

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Since the PU (Prediction Unit) decision process is the most time consuming part of the emerging HEVC (High Efficient Video Coding) standardin intra and inter frame coding, this paper proposes the fast PU decision algorithm and speed up the algorithm using CUDA (Compute Unified Device Architecture). In intra frame coding, the fast PU decision algorithm uses the texture features to skip intra-frame prediction or terminal the intra-frame prediction for smaller PU size. In inter frame coding of HEVC, the fast PU decision algorithm takes use of the similarity of its own two Nx2N size PU's motion vectors and the hierarchical structure of CU (Coding Unit) partition to skip some modes of PU partition, so as to reduce the motion estimation times. The accelerate algorithm using CUDA is based on the fast PU decision algorithm which uses the GPU to make the motion search and the gradient computation could be parallel computed. The proposed algorithm achieves up to 57% time saving compared to the HM 10.0 with little rate-distortion losses (0.043dB drop and 1.82% bitrate increase on average).

Keywords: HEVC, PU decision, inter prediction, intra prediction, CUDA, parallel

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Authors: J. K. Alhassan, C. S. Actsu

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This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

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Authors: Mohammad Ghavami, Reza S. Dilmaghani

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This paper presents an adaptive framework for modelling financial markets using equity risk premiums, risk free rates and volatilities. The recorded economic factors are initially used to train four adaptive filters for a certain limited period of time in the past. Once the systems are trained, the adjusted coefficients are used for modelling and prediction of an important financial market index. Two different approaches based on least mean squares (LMS) and recursive least squares (RLS) algorithms are investigated. Performance analysis of each method in terms of the mean squared error (MSE) is presented and the results are discussed. Computer simulations carried out using recorded data show MSEs of 4% and 3.4% for the next month prediction using LMS and RLS adaptive algorithms, respectively. In terms of twelve months prediction, RLS method shows a better tendency estimation compared to the LMS algorithm.

Keywords: adaptive methods, LSE, MSE, prediction of financial Markets

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: Godfrey Nsereko, Daniel Kadobera, Denis Okethwangu, Joyce Nguna, Alex Riolexus Ario

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Background: Malaria is a leading cause of morbidity and mortality in Uganda. In April 2018, malaria cases surged in Nwoya District, northern Uganda, exceeding the action thresholds. We investigated to assess the outbreak’s magnitude, identify transmission risk factors, and recommend evidence-based control measures. Methods: We defined a malaria case as onset of fever in a resident of Nwoya District with a positive Rapid Diagnostic Test or microscopy for malaria P. falciparum from 1 February to 22 May 2018. We reviewed medical records in all health facilities of affected sub-counties to find cases. In a case-control study, we compared exposure risk factors between 107 case-persons and 107 asymptomatic controls matched by age and village. We conducted entomological assessment on vector-density and behavior. Results: We identified 3,879 case-persons (attack rate [AR]=6.5%) and 2 deaths (case-fatality rate=5.2/10,000). Females (AR=8.1%) were more affected than males (AR=4.7%). Of all age groups, the 5-18 year age group (AR=8.4%) was most affected. Heavy rain started on 4 March; a propagated outbreak began during the week of 2 April. In the case-control study, 55% (59/107) of case-patients and 18% (19/107) of controls had stagnant water around households for several days following rainfall (ORM-H=5.6, 95%CI=3.0-11); 25% (27/107) of case-patients and 51% (55/107) of controls wore long-sleeve cloths during evening hours (ORM-H=0.30, 95%CI=0.20-0.60); 29% (31/107) of case-patients and 15% (16/107) of controls did not sleep under a long-lasting insecticide-treated net (LLIN) (ORM-H=2.3, 95%CI=1.1-4.9); 37% (40/107) of case-patients and 52% (56/107) of controls had ≥1 LLIN per 2 household members (ORM-H=0.54, 95%CI=0.30-0.97). Entomological assessment indicated active breeding sites; Anopheles gambiae sensu lato species were the predominant vector. Conclusion: Increased vector breeding sites after heavy rainfall, together with inadequate malaria preventive measures caused this outbreak. We recommended increasing coverage for LLINs and larviciding breeding sites.

Keywords: malaria outbreak, Plasmodium falciparum, global health security, Uganda

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Authors: Jiun Jeon, Byung-Ju Yi

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This paper investigates modeling and shape prediction of elastic kinematic chains such as colonoscopy. 2D and 3D models of elastic kinematic chains are suggested and their behaviors are demonstrated through simulation. To corroborate the effectiveness of those models, experimental work is performed using a magnetic sensor system.

Keywords: elastic kinematic chain, shape prediction, colonoscopy, modeling

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Yu-Tzu Hsu, Alice Wu, Hsiang-Kuang Tseng

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Introduction: Hepatitis A virus causes acute hepatitis and is usually transmitted by a fecal-oral route of food contamination, which is more prevalent in areas with poor hygienic practices. However, we described a hepatitis A outbreak associated with a fecal-oral route of sexual behavior in men who has sex with men (MSM) in Northern Taiwan. Methods: We retrospectively collected patients with acute HAV infection in MacKay Memorial Hospital, Taipei, Taiwan between July 2015 and November 2016. Demographic data (age, gender, onset time and infection risk), laboratory data (GOT, GPT, bilirubin, HIV status, HBsAg, HCV antibody and syphilis), clinical symptoms and travel history with a foreign tour were analyzed. We compared variables between HIV and non-HIV group. Unless otherwise stated, continuous variables were expressed as mean ± SD, and categorical variables were expressed as number (percentage) for each item. The t test for continuous variables was applied for the comparison between two groups and chi-square for categorical variables were applied for measures of association. Results: We collected 80 cases during the study period. Among them, 54 (67.5%) cases were MSM and 43 (53.8%) cases were HIV positive. The average age was 32.6±7.59 years-old. The average value of initial liver function was 1324 IU/L for AST (GOT), 2100 IU/L for ALT (GPT), and 5.82 mg/dL for bilirubin. We found seven (8.6%) cases were in the status of HBV carrier, five (6.3%) cases were positive for HCV antibody, and 15 (18.6%) cases were co-infected with syphilis. With regards to associated symptoms, 32 (40%) had fever, 46 (57.5%) had nausea, 34 (42.5%) had abdominal discomfort and 46 (57.5%) had general malaise. To compare the non-HIV patients with HIV patients, HIV patients were more likely to be male (p=0.008), MSM (p=0.000), co-infected syphilis (p=0.000) and slowly improving liver function of transaminases (p=0.033, 0.027). Conclusion: The HAV outbreak in Northern Taiwan was mainly occurred in MSM population. Hereafter, our cohort data support a policy in Taiwan to provide one dose of free HAV vaccine shot in this population. Hopefully, the outbreak could be stop by the free vaccine policy and public education.

Keywords: acute hepatitis A, men who has sex with men, human immunodeficiency virus, vaccine

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Authors: Mohammad Mehdizadeh, Sara Miri

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Due to the recent events surrounding the global health crisis and the spread of the coronavirus (COVID-19), the activities of many businesses and start-up companies have been disrupted. It solves many economic problems and can reduce unemployment in countries because governments can take advantage of their potential without direct investment. However, with the help of their innovative ideas and new technologies, these companies can develop and grow the economy. But it is essential to consider that there will be no guarantee of their success in the event of unforeseen events, as the coronavirus outbreak in the last two years has seriously damaged these companies and, like other businesses, challenges and stagnation have started. The startup companies' challenge in the face of coronavirus begins with its impact on customers. Changing customer behavior can affect their products and distribution channels. On the other hand, to prevent countless losses in this crisis, startup companies require creative solutions to address challenges in various areas of human capital, supply chain management, sales and marketing, and so on. Therefore, all business leaders must consider and plan for the current crisis and the future; after overcoming these conditions and returning to regular business routines, it will no longer be an option, and new situations will prevail in a competitive environment. The essential strategies for developing and growing startups during the Coronavirus outbreak can be connecting with the global startup ecosystem, hosting webinars, providing podcasts and free question and answer sessions, mentoring services to growing teams, and consulting pointed out this to firms for digitalization.

Keywords: business, COVID-19, digitalization, startups

Procedia PDF Downloads 134

Authors: Helen Nelly Naiga, Jane Frances Zalwango, Saudah N. Kizito, Brian Agaba, Brenda N Simbwa, Maria Goretti Zalwango, Richard Migisha, Benon Kwesiga, Daniel Kadobera, Alex Ario Riolexus, Sarah Paige, Julie R. Harris

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Background: Traditional community beliefs and practices can facilitate the spread of Ebola virus during outbreaks. On September 20, 2022, Uganda declared a Sudan Virus Disease (SVD) outbreak after a case was confirmed in Mubende District. During September–November 2022, the outbreak spread to eight additional districts. We investigated the role of community beliefs and practices in the spread of SUDV in Uganda in 2022. Methods: A qualitative study was conducted in Mubende, Kassanda, and Kyegegwa districts in February 2023. We conducted nine focus group discussions (FGDs) and six key informant interviews (KIIs). FGDs included SVD survivors, household members of SVD patients, traditional healers, religious leaders, and community leaders. Key informants included community, political, and religious leaders, traditional healers, and health workers. We asked about community beliefs and practices to understand if and how they contributed to the spread of SUDV. Interviews were recorded, translated, transcribed, and analyzed thematically. Results: Frequently-reported themes included beliefs that the community deaths, later found to be due to SVD, were the result of witchcraft or poisoning. Key informants reported that SVD patients frequently first consulted traditional healers or spiritual leaders before seeking formal healthcare, and noted that traditional healers treated patients with signs and symptoms of SVD without protective measures. Additional themes included religious leaders conducting laying-on-of-hands prayers for SVD patients and symptomatic contacts, SVD patients and their symptomatic contacts hiding in friends’ homes, and exhumation of SVD patients originally buried in safe and dignified burials, to enable traditional burials. Conclusion: Multiple community beliefs and practices likely promoted SVD outbreak spread during the 2022 outbreak in Uganda. Engaging traditional and spiritual healers early during similar outbreaks through risk communication and community engagement efforts could facilitate outbreak control. Targeted community messaging, including clear biological explanations for clusters of deaths and information on the dangers of exhuming bodies of SVD patients, could similarly facilitate improved control in future outbreaks in Uganda.

Keywords: Ebola, Sudan virus, outbreak, beliefs, traditional

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Tanapat Chongkamunkong

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The objective of this paper is to present a practical use of Geographic Information System (GIS) to the public health from spatial correlation between multiple factors and dengue fever outbreak. Meteorological factors, demographic factors and environmental factors are compiled using GIS techniques along with the Global Satellite Mapping Remote Sensing (RS) data. We use monthly dengue fever cases, population density, precipitation, Digital Elevation Model (DEM) data. The scope cover study area under climate change of the El Niño–Southern Oscillation (ENSO) indicated by sea surface temperature (SST) and study area in 12 provinces of Thailand as remote sensing (RS) data from January 2007 to December 2014.

Keywords: dengue fever, sea surface temperature, Geographic Information System (GIS), remote sensing

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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Authors: Katawut Kaewbanjong

Abstract:

We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: K. Petcharaporn, S. Kumchoo

Abstract:

The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes.

Keywords: tomato, quality, prediction, transmittance, titratable acidity, citric acid

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Authors: Venkat S. Somayajula

Abstract:

Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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