Search results for: mortality prediction

Authors: E. Akpinar, A. Erol, M.F. Cakir

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Precast reinforced concrete (RC) structures are excellent alternatives for construction world all over the globe, thanks to their rapid erection phase, ease mounting process, better quality and reasonable prices. Such structures are rather popular for industrial buildings. For the sake of economic importance of such industrial buildings as well as significance of safety, like every other type of structures, performance assessment and structural risk analysis are important. Fragility curves are powerful tools for damage projection and assessment for any sort of building as well as precast structures. In this study, a comparative review of current knowledge on fragility analysis of industrial precast RC structures were presented and findings in previous studies were compiled. Effects of different structural variables, parameters and building geometries as well as soil conditions on fragility analysis of precast structures are reviewed. It was aimed to briefly present the information in the literature about the procedure of damage probability prediction including fragility curves for such industrial facilities. It is found that determination of the aforementioned structural parameters as well as selecting analysis procedure are critically important for damage prediction of industrial precast RC structures using fragility curves.

Keywords: damage prediction, fragility curve, industrial buildings, precast reinforced concrete structures

Procedia PDF Downloads 192

Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

Procedia PDF Downloads 553

Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

Procedia PDF Downloads 457

Authors: U. N. Bhatty, A. Bhatia, A. George, F. Fiaz

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Post-operatively, patients with the neck of femur fractures are the high-risk of venous thromboembolic events (VTE). NICE have issued guidelines in this regard. We investigated whether these guidelines were being followed. 124 patients undergoing neck of femur fracture surgery were retrospectively analysed at a major orthopaedic centre in England. 9% of patients received adequate anticoagulation (16.5% mortality). An education campaign subsequently took place, circular emails were sent to junior doctors and posters advertised. A reaudit 4 months later showed only 12% of the 68 patients received adequate anticoagulation (11.8% mortality). The education campaign failed to improve prescribing behaviours. Furthermore, as morbidity was not measured, the consequence of poor prescription is underestimated. Perhaps, poor prescribing is because of the silent nature of effective thromboprophylaxis; reducing its perceived effectiveness. Simple interventions are insufficient to change these habits and more intense work is needed; such as compulsory proformas for all high-risk patients.

Keywords: fracture, hip, orthopaedics, thromboembolism

Procedia PDF Downloads 344

Authors: Ekrem Canli, Thomas Glade

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The precise prediction of rainfall in space and time is a key element to most landslide early warning systems. Unfortunately, the spatial variability of rainfall in many early warning applications is often disregarded. A common simplification is to use uniformly distributed rainfall to characterize aerial rainfall intensity. With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on kriging. Because the application of kriging is usually a labor intensive task, a simplified and consequently automated variogram modeling procedure was applied to up-to-date rainfall data. The entire workflow was carried out purely with open source technology. Validation results, albeit promising, pointed out the challenges that are involved in pure distance based, automated geostatistical interpolation techniques for ever-changing environmental phenomena over short temporal and spatial extent.

Keywords: kriging, landslide early warning system, spatial rainfall prediction, variogram modelling, web scraping

Procedia PDF Downloads 282

Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

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Authors: Manasvi Pinnaka, Brianna Chrisman

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Cystic fibrosis patients often develop lung infections that range anywhere in severity from mild to life-threatening due to the presence of thick and sticky mucus that fills their airways. Since many of these infections are chronic, they not only affect a patient’s ability to breathe but also increase the chances of mortality by respiratory failure. With a publicly available dataset of DNA sequences from bacterial species in the lung microbiome of cystic fibrosis patients, the correlations between different microbial species in the lung and the extent of deterioration of lung function were investigated. 16S sequencing technologies were used to determine the microbiome composition of the samples in the dataset. For the statistical analyses, referencing helped distinguish between taxonomies, and the proportions of certain taxa relative to another were determined. It was found that the Fusobacterium, Actinomyces, and Leptotrichia microbial types all had a positive correlation with the FEV1 score, indicating the potential displacement of these species by pathogens as the disease progresses. However, the dominant pathogens themselves, including Pseudomonas aeruginosa and Staphylococcus aureus, did not have statistically significant negative correlations with the FEV1 score as described by past literature. Examining the lung microbiology of cystic fibrosis patients can help with the prediction of the current condition of lung function, with the potential to guide doctors when designing personalized treatment plans for patients.

Keywords: bacterial infections, cystic fibrosis, lung microbiome, 16S sequencing

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Authors: Baboo Ali, Avni Ugur

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Pesticide namely, Diflubenzuron, is tremendously used in pear orchards against different insect pests of pear fruit trees in Turkey. The predatory bug, Anthocoris nemoralis (F.) is found in pear orchard feeding on Cacopsylla pyri (L.) (Homoptera: Psyllidae), is an insect pest of pear fruit trees. In this study, the effectiveness of the above mentioned pesticide on various biological stages of predatory bug were investigated under laboratory conditions of 25±1˚C, 75±5% RH, and photoperiod of 16L: 8D h. Newly emerged 1st, 2nd, 3rd, 4th and 5th instars as well as the female and male stages of the predatory bug were placed on treated petri dishes and their mortality was checked after every 24 hours till the survival of the last individual. Prey consumption of surviving instars as well as the adult stages was determined simultaneously. All biological stages of the predatory bug were fed with eggs of Ephestia kuehniella during the whole research work. Percent hatch of treated eggs was recorded after every 24 hours, and the behavioral test of the male and female stages against Diflubenzuron was also determined using Y-tube olfactometer. Consequently, the mortality rate of 1st, 2nd, 3rd, 4th, and 5th instars was 61.32 %, 67.50%, 74. 91%, 80.11%, and 83.04%, respectively. In case of male and female stages, it has been recorded as 95.47% and 95.50%, respectively. Thus, a significant difference was not found between female and male mortality rates. Prey consumption of 1st, 2nd, 3rd, 4th and 5th surviving instars was noted as 8.01, 11. 72, 13.24, 16.93 and 20.49 number of eggs/day while in females and males, it was 12.05 and 12.71 number of eggs/day, respectively. Hatching ratio of treated eggs of predator was 25.32±4.08. As far as the behavioral test is concerned, it has been indicated that Diflubenzuron has 65% repellent effect on the newly emerged male and female stages of the predatory bug while using Y-tube olfactometer under laboratory conditions.

Keywords: behavior, biological stages, diflubenzuron, effectiveness, pesticide, predatory bug

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

Procedia PDF Downloads 99

Authors: Sila K. Sari, Betty J.L. Laglbauer, Muhammad G. Salim, Irianies C. Gozali, Iqbal Herwata, Fahmi Fahmi, Selvia Oktaviyani, Isabel Ender, Sarah Lewis, Abraham Sianipar, Mark Erdmann

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Taking strides towards the sustainable use of marine stocks requires science-based management of target fish populations and reduction of bycatch in non-selective fisheries. Among elasmobranchs, mobulid rays are faced with high extinction risk due to intrinsic vulnerability to fishing and their conservation has been recognized as a strong priority both in Indonesia and worldwide. Despite their common vulnerabilities to fishing pressure due to slow growth, late maturation and low fecundity, only manta rays, but not devil rays, are protected in Indonesian waters. However, both manta and devil rays are captured in non-selective fisheries, in particular drift gillnets, since their habitat overlaps with fishing grounds for primary target species (e.g. marlin, swordfish and bullet tuna off the coast of Muncar). For this reason, mobulid populations are being heavily impacted, and while national-level protections are crucial to help conservation, they may not suffice alone to insure populations sustainability. In order to assess the potential of applying live-release management measures to conserve mobulids captured as bycatch in drift gillnets, we deployed pop-up survival archival transmitters to assess post-release mortality in Indonesian mobulid rays. We also assessed which fishing practices, in particular, soak duration, affected post-release mortality in order to draw relevant conclusions for management.

Keywords: Mobulid, Devil ray, Manta ray, Bycatch

Procedia PDF Downloads 177

Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Mehrzad Mohabbi Yadollahi, Ramazan Demirboğa, Majid Atashafrazeh

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Geopolymers are highly complex materials which involve many variables which makes modeling its properties very difficult. There is no systematic approach in mix design for Geopolymers. Since the amounts of silica modulus, Na2O content, w/b ratios and curing time have a great influence on the compressive strength an ANFIS (Adaptive neuro fuzzy inference system) method has been established for predicting compressive strength of ground pumice based Geopolymers and the possibilities of ANFIS for predicting the compressive strength has been studied. Consequently, ANFIS can be used for geopolymer compressive strength prediction with acceptable accuracy.

Keywords: geopolymer, ANFIS, compressive strength, mix design

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Authors: A. Brahma

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Drying is a phenomenon that accompanies the hardening of hydraulic materials. It can, if it is not prevented, lead to significant spontaneous dimensional variations, which the cracking is one of events. In this context, cracking promotes the transport of aggressive agents in the material, which can affect the durability of concrete structures. Drying shrinkage develops over a long period almost 30 years although most occurred during the first three years. Drying shrinkage stabilizes when the material is water balance with the external environment. The drying shrinkage of cementitious materials is due to the formation of capillary tensions in the pores of the material, which has the consequences of bringing the solid walls of each other. Knowledge of the shrinkage characteristics of concrete is a necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable shrinkage movement in reinforced or prestressed concrete and the appropriate steps can be taken in design to accommodate this movement. This study is concerned the modelling of drying shrinkage of the hydraulic materials and the prediction of the rate of spontaneous deformations of hydraulic materials during hardening. The model developed takes in consideration the main factors affecting drying shrinkage. There was agreement between drying shrinkage predicted by the developed model and experimental results. In last we show that developed model describe the evolution of the drying shrinkage of high performances concretes correctly.

Keywords: drying, hydraulic concretes, shrinkage, modeling, prediction

Procedia PDF Downloads 341

Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

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The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

Procedia PDF Downloads 193

Authors: Carlos Minguez, Christian F. Sagbay, Erika E. Ordoñez

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Guinea pigs (Cavia porcellus) play prominent roles as experimental models for medical research and as pets. However, in developing countries like South America, the Philippines, and sub-Saharan Africa, the meat of guinea pigs is an economic source of animal protein for the poor and malnourished humans because guinea pigs are mainly fed with forage and do not compete directly with human beings for food resources, such as corn or wheat. To achieve efficient production of guinea pigs, it is essential to provide insurance against vitamin C deficiency. The objective of this research was to investigate the effect of the partial replacement of alfalfa with fresh citrus pulp (Citrus sinensis) in a diet of guinea pigs on the growth performance, slaughter traits and mortality during the fattening period (between 20 and 74 days of age). A total of 300 guinea pigs were housed in collective cages of about ten animals (2 x 1 x 0.4 m) and were distributed into two completely randomized groups. Guinea pigs in both groups were fed ad libitum, with a standard commercial pellet diet (10 MJ of digestible energy/kg, 17% crude protein, 11% crude fiber, and 4.5% crude fat). Control group was supplied with fresh alfalfa as forage. In the treatment group, 30% of alfalfa was replaced by fresh citrus pulp. Growth traits, including body weight (BW), average daily gain (ADG), feed intake (FI), and feed conversion ratio (FCR), were measured weekly. On day 74, the animals were slaughtered, and slaughter traits, including live weight at slaughter (LWS), full gastrointestinal tract weight (FGTW), hot carcass weight (with head; HCW), cold carcass weight (with head; CCW), drip loss percentage (DLP) and dressing out carcass yield percentage (DCY), were evaluated. Contrasts between groups were obtained by calculated generalized least squares values. Mortality was evaluated by Fisher's exact test due to low numbers in some cells. In the first week, there were significant differences in the growth traits BW, ADG, FI, and FCR, which were superior in control group. These differences may have been due to the origin of the young guinea pigs, which, before weaning, were all raised without fresh citrus pulp, and they were not familiarized with the new supplement. In the second week, treatment group had significantly increased ADG compared with control group, which may have been the result of a process of compensatory growth. During subsequent weeks, no significant differences were observed between animals raised in the two groups. Neither were any significant differences observed across the total fattening period. No significant differences in slaughter traits or mortality rate were observed between animals from the two groups. In conclusion, although there were no significant differences in growth performance, slaughter traits, or mortality, the use of fresh citrus pulp is recommended. Fresh citrus pulp is a by-product of orange juice industry and it is cheap or free. Forage made with fresh citrus pulp could reduce about of 30 % the quantity of alfalfa in guinea pig for meat and as consequence, reduce the production costs.

Keywords: fresh citrus, growth, Guinea pig, mortality

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Authors: Asinguza P. Allan

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The Advocacy for Better Health aims to equip citizens with enabling environment and systems to effectively advocate for strong action plans to improve health services. This is because the 2020 Government target for Uganda to transform into a middle income country will be achieved if investment is made in keeping the population healthy and productive. Citizen participation as an important foundation for change has been emphasized to gather data through participatory rural appraisal and inform evidence-based advocacy for recruitment and motivation of human resources. Citizens conduct problem ranking during advocacy forums on staffing levels and health worker absenteeism. Citizens prioritised inadequate number of midwives and absenteeism. On triangulation, health worker to population ratio in Uganda remains at 0.25/1,000 which is far below the World Health Organization (WHO) threshold of 2.3/1,000. Working with IntraHealth, the project advocated for recruitment of critical skilled staff (doctors and midwives) and scale up health workers motivation strategy to reduce Uganda’s Neonatal Mortality Rate of 22/1,000 and Maternal Mortality Ratio of 320/100,000. Government has committed to increase staffing to 80% by 2018 (10 districts have passed ordinances and revived use of duty rosters to address health worker absenteeism. On the other hand, the better health advocacy debate has been elevated with need to increase health sector budget allocations from 8% to 10%. The project has learnt that building a body of evidence from citizens enhances the advocacy agenda. Communities will further monitor government commitments to reduce Neonatal Mortality Rate and Maternal Mortality Ratio. The project has learnt that interface meeting between duty bearers and the community allows for immediate feedback and the process is a strong instrument for empowerment. It facilitates monitoring and performance evaluation of services, projects and government administrative units (like district assemblies) by the community members themselves. This, in turn, makes the human resources in health to be accountable, transparent and responsive to communities where they work. This, in turn, promotes human resource performance.

Keywords: advocacy, empowerment, evidence, human resources

Procedia PDF Downloads 219

Authors: H. Bonakdari, I. Ebtehaj

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The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)

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Authors: Elnaz Erfani, Elahe Erfni

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In this study, 96h LC50 values of dichromate potassium (K2Cr2O7), a highly toxicant heavy metal on sobaity seabream, Sparidebtex hasta of average weight mean weight 3.24 g; mean length 5.35cm was determined. At first, for rang finding test, fish were exposed to K2Cr2O7 at several selected concentrations 5, 10, 20, 30, 40, 50 and 60 mg/L, then fish exposed to five concentrations control, 40, 45, 50 and 55 mg/L of K2Cr2O7 for LC50-96h. The experiment was carried out in triplicate, and 21 fish per each treatment, Physicochemical properties of water were measured continuously throughout the experiment. The temperature, pH, dissolved oxygen and salinity were 26 ◦c, 7.05, 8.84 mgO2 L-1 and 37.5 ppt, respectively. A number of mortality and behavioral responses of fish were recorded after 24, 48, 72 and 96 h. LC50 values were determined with probate analysis. The 96 hour LC50 value of K2Cr2O7 to the fish was found to be 48.82 ppm. In addition, behavioural changes increased with increased concentration. The results obtained in this study clearly revealed the fact that it is necessary to control the use of a heavy metal such as dichromate potassium.

Keywords: marin fish- lc50, dicromat potassium, lc50, mortality

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

Procedia PDF Downloads 118

Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Ahmed Najah Ahmed Al-Mahfoodh, Ali Najah Ahmed Al-Mahfoodh, Ahmed Al-Shafie

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In this study, genetic programming (GP) technique has been investigated in prediction of set of rainfall-runoff data. To assess the effect of input parameters on the model, the sensitivity analysis was adopted. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Correlation Coefficient (CC), Mean Square Error (MSE) and Correlation of Efficiency (CE). The principle aim of this study is to develop a computationally efficient and robust approach for predict of rainfall-runoff which could reduce the cost and labour for measuring these parameters. This research concentrates on the Johor River in Johor State, Malaysia.

Keywords: genetic programming, prediction, rainfall-runoff, Malaysia

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Authors: Theoktisti Makridou, Konstantinos Tsaprailis, George Arvanitakis, Charalampos Kontoes

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In the literature, the data-driven approaches for mosquito abundance prediction relaying on supervised machine learning models that get trained with historical in-situ measurements. The counterpart of this approach is once the model gets trained on pointlevel (specific x,y coordinates) measurements, the predictions of the model refer again to point-level. These point-level predictions reduce the applicability of those solutions once a lot of early warning and mitigation actions applications need predictions for an area level, such as a municipality, village, etc... In this study, we apply a data-driven predictive model, which relies on public-open satellite Earth Observation and geospatial data and gets trained with historical point-level in-Situ measurements of mosquito abundance. Then we propose a methodology to extract information from a point-level predictive model to a broader area-level prediction. Our methodology relies on the randomly spatial sampling of the area of interest (similar to the Poisson hardcore process), obtaining the EO and geomorphological information for each sample, doing the point-wise prediction for each sample, and aggregating the predictions to represent the average mosquito abundance of the area. We quantify the performance of the transformation from the pointlevel to the area-level predictions, and we analyze it in order to understand which parameters have a positive or negative impact on it. The goal of this study is to propose a methodology that predicts the mosquito abundance of a given area by relying on point-level prediction and to provide qualitative insights regarding the expected performance of the area-level prediction. We applied our methodology to historical data (of Culex pipiens) of two areas of interest (Veneto region of Italy and Central Macedonia of Greece). In both cases, the results were consistent. The mean mosquito abundance of a given area can be estimated with similar accuracy to the point-level predictor, sometimes even better. The density of the samples that we use to represent one area has a positive effect on the performance in contrast to the actual number of sampling points which is not informative at all regarding the performance without the size of the area. Additionally, we saw that the distance between the sampling points and the real in-situ measurements that were used for training did not strongly affect the performance.

Keywords: mosquito abundance, supervised machine learning, culex pipiens, spatial sampling, west nile virus, earth observation data

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Authors: Ben Clapperton, Daniel Stahl, Kimberley Goldsmith, Trudie Chalder

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Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared.

Keywords: chronic fatigue syndrome, latent class analysis, prediction modelling, self-organizing maps

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Authors: Osama Zayed

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Aim of the work: The aim of this study is to assess the predictors of pelvic vascular injuries in patients with pelvic fractures from major blunt trauma. Methods: This study was conducted as a tool-assessment study. Forty six patients with pelvic fractures from major blunt trauma will be recruited to the study arriving to department of emergency, Suez Canal University Hospital. Data were collected from questionnaire including; personal data of the studied patients and full medical history, clinical examinations, outcome measures (The Physiological and Operative Severity Score for enumeration of Mortality and morbidity (POSSUM), laboratory and imaging studies. Patients underwent surgical interventions or further investigations based on the conventional standards for interventions. All patients were followed up during conservative, operative and post-operative periods in the hospital for interpretation the predictive scores of vascular injuries. Results: Significant predictors of vascular injuries according to computed tomography (CT) scan include age, male gender, lower Glasgow coma (GCS) scores, occurrence of hypotension, mortality rate, higher physical POSSUM scores, presence of ultrasound collection, type of management, higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) POSSUM scores, presence of abdominal injuries, and poor outcome. Conclusions: There was higher frequency of males than females in the studied patients. There were high probability of morbidity and low probability of mortality among patients. Our study demonstrates that POSSUM score can be used as a predictor of vascular injury in pelvis fracture patients.

Keywords: predictors, pelvic vascular injuries, pelvic fractures, major blunt trauma, POSSUM

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Authors: Brahim Fares Zaidi

Abstract:

Our work aims to improve our Automatic Recognition System for Dysarthria Speech based on the Hidden Models of Markov and the Hidden Markov Model Toolkit to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients and Perceptual Linear Prediction and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers.

Keywords: ARSDS, HTK, HMM, MFCC, PLP

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Authors: Tochukwu Ezechi Ebe, Fechi Njoku-Tony, Ifeyinwa Mgbenena

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Comparative analysis of petroleum ether and aqueous extraction solvents on different stages of Anopheles gambiae was carried out using neem leaf and neem stem. Soxhlet apparatus was used to extract each pulverized plant part. Each plant part extract from both solvents were separately used to test their effects on the developmental stages of Anopheles gambiae. The result showed that the mean mortality of extracts from petroleum ether extraction solvent was higher than that of aqueous extract. It was also observed that mean mortality decreases with increase in developmental stage. Furthermore, extracts from neem leaf was found to be more susceptible than extracts from neem stem using same extraction solvent.

Keywords: petroleum ether, aqueous, developmental, stages, extraction, Anopheles gambiae

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Authors: Sadia Munir

Abstract:

Preeclampsia is the contributor to the worldwide maternal mortality of approximately 100,000 deaths a year. It complicates about 10% of all pregnancies and is the first cause of maternal admission to intensive care units. Predicting preeclampsia is a major challenge in obstetrics. More importantly, no major progress has been achieved in the treatment of preeclampsia. As placenta is the main cause of the disease, the only way to treat the disease is to extract placental and deliver the baby. In developed countries, the cost of an average case of preeclampsia is estimated at £9000. Interestingly, preeclampsia may have an impact on the health of mother or infant, beyond the pregnancy. We performed a systematic search of PubMed including the combination of terms such as preeclampsia, biomarkers, treatment, hypoxia, inflammation, oxidative stress, vascular endothelial growth factor A, activin A, inhibin A, placental growth factor, transforming growth factor β-1, Nodal, placenta, trophoblast cells, microRNAs. In this review, we have summarized current knowledge on the identification of potential biomarkers for the diagnosis of preeclampsia. Although these studies show promising data in early diagnosis of preeclampsia, the current value of these factors as biomarkers, for the precise prediction of preeclampsia, has its limitation. Therefore, future studies need to be done to support some of the very promising and interesting data to develop affordable and widely available tests for early detection and treatment of preeclampsia.

Keywords: activin, biomarkers, growth factors, miroRNA

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Authors: Simin Shahvazi, Sepideh Soltani, Seyed Mehdi Ahmadi, Russell J. De Souza, Amin Salehi-Abargouei

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Background and Objectives: Vitamin D has received attention for its potential to disrupt cancer processes such as attenuating cell proliferation and exacerbating differentiation and apoptosis. However, whether there exists a role for vitamin D in the treatment of prostate cancer specifically remains controversial. We systematically review the literature to assess whether supplementation with vitamin D influences PSA response and overall survival in patients with prostate cancer. Methods: We searched PubMed, Scopus, ISI Web of Science and Google scholar from inception through up to 10 September 2017 for both before-and-after and randomized trials that evaluated the effect of vitamin D supplementation on the prostate specific antigen (PSA) response rate in participants with prostate cancer. The DerSimonian and Laird, inverse-weighted random-effects model was used to pool effect estimates from the studies. Heterogeneity and potential publication bias were evaluated. Subgroup analyses were also performed. Results: Twenty-two studies (16 before-after and 6 randomized controlled trials) were found and included in meta-analysis. The analysis on controlled clinical trials revealed that PSA change from baseline [weighted mean difference (WMD) = -1.66 ng/ml, 95%CI: -0.69, 0.36, P= 0.543)], PSA response (RR=1.18, 95%CI: 0.97, 1.45, P=0.104) and mortality rate (risk ratio (RR) = 1.05, 95% CI: 0.81-1.36; P=0.713) was not significantly different between vitamin D supplementation and placebo groups. Single arm trials revealed that vitamin D supplementation had had a modest effect on PSA response rate: 19% of those enrolled had at least a 50% reduction in PSA by the end of treatment (95% CI: 7% to 31%; p=0.002). Conclusion: We found that vitamin D modestly increases the PSA response rate in single arm studies. No effect on serum PSA levels, PSA response and mortality was seen in randomized controlled clinical trials. It does not seem patients with prostate cancer benefit from vitamin D supplementation.

Keywords: mortality, prostatic neoplasms, PSA response, vitamin D

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Authors: Jay L. Fu

Abstract:

Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Ajuna B. Henry

Abstract:

In recent decades, climate change has demanded biological pesticides; more Bt strains are being discovered worldwide, some containing novel insecticidal genes while others have been modified through molecular approaches for increased yield, toxicity, and wider host target. In this study, B. thuringiensis strain AH-2 (Bt-2) was isolated from the soil and tested for insecticidal activity against Aphis gossypii (Hemiptera: Aphididae) and Lepidoptera insect pests: fall webworm (Hyphantria cunea) and diamondback moth (Plutella xylostella). A commercial strain B. thuringiensis subsp. kurstaki (Btk), and a chemical pesticide, imidacloprid (for Hemiptera) and chlorantraniliprole (for Lepidoptera), were used as positive control and the same media (without bacterial inoculum) as a negative control. For aphidicidal activity, Bt-2 caused a mortality rate of 70.2%, 78.1% or 88.4% in third instar nymphs of A. gossypii (3N) at 10%, 25% or 50% culture concentrations, respectively. Moreover, Bt-2 was effectively produced in cost-effective (PB) supplemented with either glucose (PBG) or sucrose (PBS) and maintained high aphicidal efficacy with 3N mortality rates of 85.9%, 82.9% or 82.2% in TSB, PBG or PBS media, respectively at 50% culture concentration. Bt-2 also suppressed adult fecundity by 98.3% compared to only 65.8% suppression by Btk at similar concentrations but was slightly lower than chemical treatment, which caused 100% suppression. Partial purification of 60 – 80% (NH4)2SO4 fraction of Bt-2 aphicidal proteins purified on anion exchange (DEAE-FF) column revealed a 105 kDa aphicidal protein with LC50 = 55.0 ng/µℓ. For Lepidoptera pests, chemical pesticide, Bt-2, and Btk cultures, mortality of 86.7%, 60%, and 60% in 3rd instar larvae of P. xylostella, and 96.7%, 80.0%, and 93.3% in 6th instar larvae of H. cunea, after 72h of exposure. When the entomopathogenic strains were cultured in a cost-effective PBG or PBS, the insecticidal activity in all strains was not significantly different compared to the use of a commercial medium (TSB). Bt-2 caused a mortality rate of 60.0%, 63.3%, and 50.0% against P. xylostella larvae and 76.7%, 83.3%, and 73.3% against H. cunea when grown in TSB, PBG, and PBS media, respectively. Bt-2 (grown in cost-effective PBG medium) caused a dose-dependent toxicity of 26.7%, 40.0%, and 63.3% against P. xylostella and 46.7%, 53.3%, and 76.7% against H. cunea at 10%, 25% and 50% culture concentration, respectively. The partially purified Bt-2 insecticidal proteins fractions F1, F2, F3, and F4 (extracted at different ratios of organic solvent) caused low toxicity (50.0%, 40.0%, 36.7%, and 30.0%) against P. xylostella and relatively high toxicity (56.7%, 76.7%, 66.7%, and 63.3%) against H. cunea at 100 µg/g of artificial diets. SDS-PAGE analysis revealed that a128kDa protein is associated with toxicity of Bt-2. Our result demonstrates a medium and strong larvicidal activity of Bt-2 against P. xylostella and H. cunea, respectively. Moreover, Bt-2 could be potentially produced using a cost-effective PBG medium which makes it an effective alternative biocontrol strategy to reduce chemical pesticide application.

Keywords: biocontrol, insect pests, larvae/nymph mortality, cost-effective media, aphis gossypii, plutella xylostella, hyphantria cunea, bacillus thuringiensi

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