Search results for: classification tree method

Authors: B. Zerouali, M. Kara, B. Hamaidi, H. Mahdjoub, S. Rouabhia

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In this paper, a safety analysis for crude oil tanks to prevent undesirable events that may cause catastrophic accidents. The estimation of the probability of damage to industrial systems is carried out through a series of steps, and in accordance with a specific methodology. In this context, this work involves developing an assessment tool and risk analysis at the level of crude oil tanks system, based primarily on identification of various potential causes of crude oil tanks fire and explosion by the use of Fault Tree Analysis (FTA), then improved risk modelling by Bayesian Networks (BNs). Bayesian approach in the evaluation of failure and quantification of risks is a dynamic analysis approach. For this reason, have been selected as an analytical tool in this study. Research concludes that the Bayesian networks have a distinct and effective method in the safety analysis because of the flexibility of its structure; it is suitable for a wide variety of accident scenarios.

Keywords: bayesian networks, crude oil tank, fault tree, prediction, safety

Procedia PDF Downloads 645

Authors: Chia-Hau Liu, Tai-Yue Wang

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Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.

Keywords: multivariate control chart, statistical process control, one-class classification method, non-normal data

Procedia PDF Downloads 413

Authors: Meera Tandan, Mohan Timilsina, Martin Cormican, Akke Vellinga

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Background: In general practice, many infections are treated empirically without microbiological confirmation. Understanding susceptibility of antimicrobials during empirical prescribing can be helpful to reduce inappropriate prescribing. This study aims to apply a prediction model using a decision tree approach to predict the antimicrobial resistance (AMR) of urinary tract infections (UTI) based on non-clinical features of patients over 65 years. Decision tree models are a novel idea to predict the outcome of AMR at an initial stage. Method: Data was extracted from the database of the microbiological laboratory of the University Hospitals Galway on all antimicrobial susceptibility testing (AST) of urine specimens from patients over the age of 65 from January 2011 to December 2014. The primary endpoint was resistance to common antimicrobials (Nitrofurantoin, trimethoprim, ciprofloxacin, co-amoxiclav and amoxicillin) used to treat UTI. A classification and regression tree (CART) model was generated with the outcome ‘resistant infection’. The importance of each predictor (the number of previous samples, age, gender, location (nursing home, hospital, community) and causative agent) on antimicrobial resistance was estimated. Sensitivity, specificity, negative predictive (NPV) and positive predictive (PPV) values were used to evaluate the performance of the model. Seventy-five percent (75%) of the data were used as a training set and validation of the model was performed with the remaining 25% of the dataset. Results: A total of 9805 UTI patients over 65 years had their urine sample submitted for AST at least once over the four years. E.coli, Klebsiella, Proteus species were the most commonly identified pathogens among the UTI patients without catheter whereas Sertia, Staphylococcus aureus; Enterobacter was common with the catheter. The validated CART model shows slight differences in the sensitivity, specificity, PPV and NPV in between the models with and without the causative organisms. The sensitivity, specificity, PPV and NPV for the model with non-clinical predictors was between 74% and 88% depending on the antimicrobial. Conclusion: The CART models developed using non-clinical predictors have good performance when predicting antimicrobial resistance. These models predict which antimicrobial may be the most appropriate based on non-clinical factors. Other CART models, prospective data collection and validation and an increasing number of non-clinical factors will improve model performance. The presented model provides an alternative approach to decision making on antimicrobial prescribing for UTIs in older patients.

Keywords: antimicrobial resistance, urinary tract infection, prediction, decision tree

Procedia PDF Downloads 244

Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Ghada S. Moussa

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With the increase in population along with economic prosperity, an enormous increase in the number and types of vehicles on the roads occurred. This fact brings a growing need for efficiently yet effectively classifying vehicles into their corresponding categories, which play a crucial role in many areas of infrastructure planning and traffic management. This paper presents two vehicle-type classification approaches; 1) geometric-based and 2) appearance-based. The two classification approaches are used for two tasks: multi-class and intra-class vehicle classifications. For the evaluation purpose of the proposed classification approaches’ performance and the identification of the most effective yet efficient one, 10-fold cross-validation technique is used with a large dataset. The proposed approaches are distinguishable from previous research on vehicle classification in which: i) they consider both geometric and appearance attributes of vehicles, and ii) they perform remarkably well in both multi-class and intra-class vehicle classification. Experimental results exhibit promising potentials implementations of the proposed vehicle classification approaches into real-world applications.

Keywords: appearance attributes, geometric attributes, support vector machine, vehicle classification

Procedia PDF Downloads 325

Authors: Y. Kourd, D. Lefebvre, N. Guersi

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This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty behaviors Models (NNFM's). NNFM's are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it becomes possible to take the appropriate decision regarding the actual process behavior by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.

Keywords: neural networks, decision trees, diagnosis, behaviors

Procedia PDF Downloads 484

Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

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Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

Procedia PDF Downloads 195

Authors: Ravi Vishwanath, Saumya Kumaar, S. N. Omkar

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Brain-computer interfacing (BCI) is a technology that is almost four decades old, and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering, etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, however, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the controllability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

Keywords: brain-computer interface, classification, machine learning, unmanned aerial vehicles

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

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Authors: Rabia Shabbir, Sheikh Saeed Ahmad, Amna Butt

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Rapid land use changes have taken place in Islamabad, the capital city of Pakistan, over the past decades due to accelerated urbanization and industrialization. In this study, land use changes in the metropolitan area of Islamabad was observed by the combined use of GIS and satellite remote sensing for a time period of 15 years. High-resolution Google Earth images were downloaded from 2000-2015, and object-based classification method was used for accurate classification using eCognition software. The information regarding urban settlements, industrial area, barren land, agricultural area, vegetation, water, and transportation infrastructure was extracted. The results showed that the city experienced a spatial expansion, rapid urban growth, land use change and expanding transportation infrastructure. The study concluded the integration of GIS and remote sensing as an effective approach for analyzing the spatial pattern of urban growth and land use change.

Keywords: land use change, urban growth, Islamabad, object-based classification, Google Earth, remote sensing, GIS

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Authors: Feras Hanandeh, Majdi Shannag

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This research studies the different factors that could affect the Faculty of Information Technology in Hashemite University students’ accumulative average. The research paper verifies the student information, background, their academic records, and how this information will affect the student to get high grades. The student information used in the study is extracted from the student’s academic records. The data mining tools and techniques are used to decide which attribute(s) will affect the student’s accumulative average. The results show that the most important factor which affects the students’ accumulative average is the student Acceptance Type. And we built a decision tree model and rules to determine how the student can get high grades in their courses. The overall accuracy of the model is 44% which is accepted rate.

Keywords: data mining, classification, extracting rules, decision tree

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Authors: Adel Ali Elshaibani

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Risk management indicators augment the reporting infrastructure, particularly for the board and senior management, to identify, monitor, and manage risks. This enhancement facilitates improved decision-making throughout the banking organization. Decision tree analysis is a tool that visually outlines potential outcomes, costs, and consequences of complex decisions. It is particularly beneficial for analyzing quantitative data and making decisions based on numerical values. By calculating the expected value of each outcome, decision tree analysis can help assess the best course of action. In the context of banking, decision tree analysis can assist lenders in evaluating a customer’s creditworthiness, thereby preventing losses. However, applying these tools in developing countries may face several limitations, such as data availability, lack of technological infrastructure and resources, lack of skilled professionals, cultural factors, and cost. Moreover, decision trees can create overly complex models that do not generalize well to new data, known as overfitting. They can also be sensitive to small changes in the data, which can result in different tree structures and can become computationally expensive when dealing with large datasets. In conclusion, while risk management indicators and decision tree analysis are beneficial for decision-making in banks, their effectiveness is contingent upon how they are implemented and utilized by the board of directors, especially in the context of developing countries. It’s important to consider these limitations when planning to implement these tools in developing countries.

Keywords: risk management indicators, decision tree analysis, developing countries, board of directors, bank performance, risk management strategy, banking institutions

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

Procedia PDF Downloads 266

Authors: Filippo Portera

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Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

Procedia PDF Downloads 78

Authors: Tanvi P. Patel, Warish D. Patel

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Association rule mining forms the core of data mining and it is termed as one of the well-known methodologies of data mining. Objectives of mining is to find interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Hence, association rule mining is imperative to mine patterns and then generate rules from these obtained patterns. For efficient targeted query processing, finding frequent patterns and itemset mining, there is an efficient way to generate an itemset tree structure named Memory Efficient Itemset Tree. Memory efficient IT is efficient for storing itemsets, but takes more time as compare to traditional IT. The proposed strategy generates maximal frequent itemsets from memory efficient itemset tree by using levelwise pruning. For that firstly pre-pruning of items based on minimum support count is carried out followed by itemset tree reconstruction. By having maximal frequent itemsets, less number of patterns are generated as well as tree size is also reduced as compared to MEIT. Therefore, an enhanced approach of memory efficient IT proposed here, helps to optimize main memory overhead as well as reduce processing time.

Keywords: association rule mining, itemset mining, itemset tree, meit, maximal frequent pattern

Procedia PDF Downloads 357

Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

Procedia PDF Downloads 131

Authors: Mohammed Alkiyumi

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Educational systems are currently paying special attention to developing learners' higher thinking skills to develop the capabilities of human resources to deal with contemporary challenges. Although psychologists disagree about the concept of higher-order thinking skills and the skills they include, there is unlimited effort in designing them and building strategies for their implementation. The most important factor helping to develop these skills is their classification according to specific criteria, and the most important of these classifications is Bloom's classification, which is dominant in most educational systems at all levels. Previous classifications have many limitations, including the comprehensiveness of the skills they contain, the logical structure of their hierarchy, and classification criteria. Therefore, this article puts another step in this area by providing a new classification of higher-order thinking skills that includes five categories: the first response stage, transformative stage, application, reasoning stage, and the production stage with a logical justification for this classification, with some techniques to developing it among learners.

Keywords: high-order thinking skills, classification, teaching, education

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Authors: Kamel Belammi, Houria Fatrim

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imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method.

Keywords: multilayer neural networks, k- nearest neighbor, support vector machine, imbalanced medical data, least mean square algorithm, diabetes

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Authors: Mohd Suhaizan Shamsuddin

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Urbanization exacerbates the environment quality and pressures of matured urban trees' growth and development in changing environment. The growth of struggled matured urban tree-roots by spreading within the existences of infrastructures, resulting in large damage to the structured and declined growth. Many physiological growths declined or damages by the present and installations of infrastructures within and nearby root zone. Afford to remain both matured urban tree and infrastructures as a service provider causes damage and death, respectively. Inasmuch, spending more expenditure on fixing both or removing matured urban trees as risky to the future environment as the mitigation methods to reduce the problems are unconcerned. This paper aims to explain mitigation method practices of reducing the encountered problems of matured urban tree-roots settling and infrastructures while modified urban soil to sustain at an optimum level. Three categories capturing encountered conflicts growth of matured urban tree-roots growth within and nearby infrastructures by mitigating the problems of limited soil spaces, poor soil structures and soil space barrier installations and maintenance. The limited soil space encountered many conflicts and identified six methods that mitigate the survival tree-roots, such as soil volume/mounding, soil replacement/amendment for the radial trench, soil spacing-root bridge, root tunneling, walkway/pavement rising/diverted, and suspended pavement. The limited soil spaces are mitigation affords of inadequate soil-roots and spreading root settling and modification of construction soil media since the barrier existed and installed in root trails or zones. This is the reason for enabling tree-roots spreading and finds adequate sources (nutrients, water uptake and oxygen), spaces and functioning to stability stand of root anchorage since the matured tree grows larger. The poor soil structures were identified as three methods to mitigate soil materials' problems, and fewer soil voids comprise skeletal soil, structural soil, and soil cell. Mitigation of poor soil structure is altering the existing and introducing new structures by modifying the quantities and materials ratio allowing more voids beneath for roots spreading by considering the above structure of foot and vehicle traffics functioning or load-bearing. The soil space barrier installations and maintenance recognized to sustain both infrastructures and tree-roots grown in limited spaces and its benefits, the root barrier installations and root pruning are recommended. In conclusion, these recommended methods attempt to mitigate the present problems encountered at a particular place and problems among tree-roots and infrastructures exist. The combined method is the best way to alleviates the conflicts since the recognized conflicts are between tree-roots and man-made while the urban soil is modified. These presenting methods are most considered to sustain the matured urban trees' lifespan growth in the urban environment.

Keywords: urban tree-roots, limited soil spaces, poor soil structures, soil space barrier and maintenance

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Sunita Singh, Rajani Srivastava

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For natural resource management and in other applications about earth observation revolutionary remote sensing technology plays a significant role. One of such application in monitoring and classification of crop types at spatial and temporal scale, as it provides latest, most precise and cost-effective information. Present study emphasizes the use of three different vegetation indices of Rapid Eye imagery on crop type classification. It also analyzed the effect of each indices on classification accuracy. Rapid Eye imagery is highly demanded and preferred for agricultural and forestry sectors as it has red-edge and NIR bands. The three indices used in this study were: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) and all of these incorporated the Red Edge band. The study area is Varanasi district of Uttar Pradesh, India and Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Classification was performed with these three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 85% was obtained using three vegetation indices. The study concluded that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the Rapid Eye imagery can get satisfactory results of classification accuracy without original bands.

Keywords: GNDVI, NDRE, NDVI, rapid eye, vegetation indices

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Authors: Ming Lu, Xiaojun Li, Bodi Lu, Juehui Xing

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Earthquake damage phenomenon of each large earthquake gives comprehensive and profound real test to the dynamic performance and failure mechanism of different engineering structures. Cognitive engineering structure characteristics through seismic damage phenomenon are often far superior to expensive shaking table experiments. After the earthquake, people will record a variety of different types of engineering damage photos. However, a large number of earthquake damage photographs lack sufficient information and reduce their using value. To improve the research value and the use efficiency of engineering seismic damage photographs, this paper objects to explore and show seismic damage background information, which includes the earthquake magnitude, earthquake intensity, and the damaged structure characteristics. From the research requirement in earthquake engineering field, the authors use the 2008 China Wenchuan M8.0 earthquake photographs, and provide four kinds of attribute indexes and classification, which are seismic information, structure types, earthquake damage parts and disaster causation factors. The final object is to set up an engineering structural seismic damage database based on these four attribute indicators and classification, and eventually build a website providing seismic damage photographs.

Keywords: attribute index, classification method, earthquake damage picture, engineering structure

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Authors: Zhanfeng Ma, Zhiping Xiong, Hu Yin, Zhengwei She, Aditya P. Gurajada, Tianlun Chen, Ying Li

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Many RDBMS vendors use B-tree index to achieve high performance for point queries and range queries, and some of them also employ hash index to further enhance the performance as hash table is more efficient for point queries. However, there are extra overheads to maintain a separate hash index, for example, hash mapping for all data records must always be maintained, which results in more memory space consumption; locking, logging and other mechanisms are needed to guarantee ACID, which affects the concurrency and scalability of the system. To relieve the overheads, Hash Cached B-tree (HCB) index is proposed in this paper, which consists of a standard disk-based B-tree index and an additional in-memory lock-free hash table. Initially, only the B-tree index is constructed for all data records, the hash table is built on the fly based on runtime workload, only data records accessed by point queries are indexed using hash table, this helps reduce the memory footprint. Changes to hash table are done using compare-and-swap (CAS) without performing locking and logging, this helps improve the concurrency and avoid contention. The hash table is also optimized to be cache conscious. HCB index is implemented in SAP ASE database, compared with the standard B-tree index, early experiments and customer adoptions show significant performance improvement. This paper provides an overview of the design of HCB index and reports the experimental results.

Keywords: B-tree, compare-and-swap, lock-free hash table, point queries, range queries, SAP ASE database

Procedia PDF Downloads 276

Authors: E. Younsi, H. Andriamboavonjy, A. David, S. Dokou, B. Lemrabet

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Software and time optimization are very important factors in financial markets, which are competitive fields, and emergence of new computer tools further stresses the challenge. In this context, any improvement of technical indicators which generate a buy or sell signal is a major issue. Thus, many tools have been created to make them more effective. This worry about efficiency has been leading in present paper to seek best (and most innovative) way giving largest improvement in these indicators. The approach consists in attaching a signature to frequent market configurations by application of frequent patterns extraction method which is here most appropriate to optimize investment strategies. The goal of proposed trading algorithm is to find most accurate signatures using back testing procedure applied to technical indicators for improving their performance. The problem is then to determine the signatures which, combined with an indicator, outperform this indicator alone. To do this, the FP-Tree algorithm has been preferred, as it appears to be the most efficient algorithm to perform this task.

Keywords: quantitative analysis, back-testing, computational models, apriori algorithm, pattern recognition, data mining, FP-tree

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Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

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Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: ring recognition, edge detection, X-ray computed tomography, dendrochronology

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Joserra Díez, Ainhoa Meñika, Iñaki Sanz-Azkue, Arritokieta Ortuzar

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Biodiversity provides humans with a great range of ecosystemic services; it is therefore an indispensable resource and a legacy to coming generations. However, in the last decades, the increasing exploitation of the Planet has caused a great loss of biodiversity and its acquaintance has decreased remarkably; especially in urbanized areas, due to the decreasing attachment of humans to nature. Yet, the Primary Education curriculum primes the identification of flora and fauna to guarantee the knowledge of children on their surroundings, so that they care for the environment as well as for themselves. In order to produce effective didactic material that meets the needs of both teachers and pupils, it is fundamental to diagnose the current situation. In the present work, the knowledge on biodiversity of 3^rd cycle Primary Education students in Biscay (n=98) and its relation to the size of the town/city of their school is discussed. Two tests have been used with such aim: one for tree identification and the other one so that the students enumerated the species of trees and animals they knew. Results reveal that knowledge of students on tree identification is scarce regardless the size of the city/town and of their school. On the other hand, animal species are better known than tree species.

Keywords: biodiversity, population, tree identification, animal identification

Procedia PDF Downloads 183

Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

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Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: landcover classification, artificial neural network, remote sensing, SPOT 5

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Authors: Essam Al Daoud

Abstract:

This study solves a phylogeny problem by using modified wild dog pack optimization. The least squares error is considered as a cost function that needs to be minimized. Therefore, in each iteration, new distance matrices based on the constructed trees are calculated and used to select the alpha dog. To test the suggested algorithm, ten homologous genes are selected and collected from National Center for Biotechnology Information (NCBI) databanks (i.e., 16S, 18S, 28S, Cox 1, ITS1, ITS2, ETS, ATPB, Hsp90, and STN). The data are divided into three categories: 50 taxa, 100 taxa and 500 taxa. The empirical results show that the proposed algorithm is more reliable and accurate than other implemented methods.

Keywords: least square, neighbor joining, phylogenetic tree, wild dog pack

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Authors: Rosa L. Figueroa, Christopher A. Flores, Qing Zeng-Treitler

Abstract:

A regular expression consists of sequence characters which allow describing a text path. Usually, in clinical research, regular expressions are manually created by programmers together with domain experts. Lately, there have been several efforts to investigate how to generate them automatically. This article presents a text classification algorithm based on regexes. The algorithm named REX was designed, and then, implemented as a simplified method to create regexes to classify Spanish text automatically. In order to classify ambiguous cases, such as, when multiple labels are assigned to a testing example, REX includes an information gain method Two sets of data were used to evaluate the algorithm’s effectiveness in clinical text classification tasks. The results indicate that the regular expression based classifier proposed in this work performs statically better regarding accuracy and F-measure than Support Vector Machine and Naïve Bayes for both datasets.

Keywords: information gain, regular expressions, smith-waterman algorithm, text classification

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