Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 509

Search results for: tree count.

509 A High-Speed Multiplication Algorithm Using Modified Partial Product Reduction Tree

Authors: P. Asadee

Abstract:

Multiplication algorithms have considerable effect on processors performance. A new high-speed, low-power multiplication algorithm has been presented using modified Dadda tree structure. Three important modifications have been implemented in inner product generation step, inner product reduction step and final addition step. Optimized algorithms have to be used into basic computation components, such as multiplication algorithms. In this paper, we proposed a new algorithm to reduce power, delay, and transistor count of a multiplication algorithm implemented using low power modified counter. This work presents a novel design for Dadda multiplication algorithms. The proposed multiplication algorithm includes structured parts, which have important effect on inner product reduction tree. In this paper, a 1.3V, 64-bit carry hybrid adder is presented for fast, low voltage applications. The new 64-bit adder uses a new circuit to implement the proposed carry hybrid adder. The new adder using 80 nm CMOS technology has been implemented on 700 MHz clock frequency. The proposed multiplication algorithm has achieved 14 percent improvement in transistor count, 13 percent reduction in delay and 12 percent modification in power consumption in compared with conventional designs.

Keywords: adder, CMOS, counter, Dadda tree, encoder.

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508 Spanning Tree Transformation of Connected Graphs into Single-Row Networks

Authors: S.L. Loh, S. Salleh, N.H. Sarmin

Abstract:

A spanning tree of a connected graph is a tree which consists the set of vertices and some or perhaps all of the edges from the connected graph. In this paper, a model for spanning tree transformation of connected graphs into single-row networks, namely Spanning Tree of Connected Graph Modeling (STCGM) will be introduced. Path-Growing Tree-Forming algorithm applied with Vertex-Prioritized is contained in the model to produce the spanning tree from the connected graph. Paths are produced by Path-Growing and they are combined into a spanning tree by Tree-Forming. The spanning tree that is produced from the connected graph is then transformed into single-row network using Tree Sequence Modeling (TSM). Finally, the single-row routing problem is solved using a method called Enhanced Simulated Annealing for Single-Row Routing (ESSR).

Keywords: Graph theory, simulated annealing, single-rowrouting and spanning tree.

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507 Extraction of Forest Plantation Resources in Selected Forest of San Manuel, Pangasinan, Philippines Using LiDAR Data for Forest Status Assessment

Authors: Mark Joseph Quinto, Roan Beronilla, Guiller Damian, Eliza Camaso, Ronaldo Alberto

Abstract:

Forest inventories are essential to assess the composition, structure and distribution of forest vegetation that can be used as baseline information for management decisions. Classical forest inventory is labor intensive and time-consuming and sometimes even dangerous. The use of Light Detection and Ranging (LiDAR) in forest inventory would improve and overcome these restrictions. This study was conducted to determine the possibility of using LiDAR derived data in extracting high accuracy forest biophysical parameters and as a non-destructive method for forest status analysis of San Manual, Pangasinan. Forest resources extraction was carried out using LAS tools, GIS, Envi and .bat scripts with the available LiDAR data. The process includes the generation of derivatives such as Digital Terrain Model (DTM), Canopy Height Model (CHM) and Canopy Cover Model (CCM) in .bat scripts followed by the generation of 17 composite bands to be used in the extraction of forest classification covers using ENVI 4.8 and GIS software. The Diameter in Breast Height (DBH), Above Ground Biomass (AGB) and Carbon Stock (CS) were estimated for each classified forest cover and Tree Count Extraction was carried out using GIS. Subsequently, field validation was conducted for accuracy assessment. Results showed that the forest of San Manuel has 73% Forest Cover, which is relatively much higher as compared to the 10% canopy cover requirement. On the extracted canopy height, 80% of the tree’s height ranges from 12 m to 17 m. CS of the three forest covers based on the AGB were: 20819.59 kg/20x20 m for closed broadleaf, 8609.82 kg/20x20 m for broadleaf plantation and 15545.57 kg/20x20m for open broadleaf. Average tree counts for the tree forest plantation was 413 trees/ha. As such, the forest of San Manuel has high percent forest cover and high CS.

Keywords: Carbon stock, forest inventory, LiDAR, tree count.

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506 Construction Of Decentralized Lifetime Maximizing Tree for Data Aggregation in Wireless Sensor Networks

Authors: Deepali Virmani , Satbir Jain

Abstract:

To meet the demands of wireless sensor networks (WSNs) where data are usually aggregated at a single source prior to transmitting to any distant user, there is a need to establish a tree structure inside any given event region. In this paper , a novel technique to create one such tree is proposed .This tree preserves the energy and maximizes the lifetime of event sources while they are constantly transmitting for data aggregation. The term Decentralized Lifetime Maximizing Tree (DLMT) is used to denote this tree. DLMT features in nodes with higher energy tend to be chosen as data aggregating parents so that the time to detect the first broken tree link can be extended and less energy is involved in tree maintenance. By constructing the tree in such a way, the protocol is able to reduce the frequency of tree reconstruction, minimize the amount of data loss ,minimize the delay during data collection and preserves the energy.

Keywords: branch energy, decentralized, energy level , lifetime, tree energy, wireless sensor networks.

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505 Balancing of Quad Tree using Point Pattern Analysis

Authors: Amitava Chakraborty, Sudip Kumar De, Ranjan Dasgupta

Abstract:

Point quad tree is considered as one of the most common data organizations to deal with spatial data & can be used to increase the efficiency for searching the point features. As the efficiency of the searching technique depends on the height of the tree, arbitrary insertion of the point features may make the tree unbalanced and lead to higher time of searching. This paper attempts to design an algorithm to make a nearly balanced quad tree. Point pattern analysis technique has been applied for this purpose which shows a significant enhancement of the performance and the results are also included in the paper for the sake of completeness.

Keywords: Algorithm, Height balanced tree, Point patternanalysis, Point quad tree.

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504 An Attribute-Centre Based Decision Tree Classification Algorithm

Authors: Gökhan Silahtaroğlu

Abstract:

Decision tree algorithms have very important place at classification model of data mining. In literature, algorithms use entropy concept or gini index to form the tree. The shape of the classes and their closeness to each other some of the factors that affect the performance of the algorithm. In this paper we introduce a new decision tree algorithm which employs data (attribute) folding method and variation of the class variables over the branches to be created. A comparative performance analysis has been held between the proposed algorithm and C4.5.

Keywords: Classification, decision tree, split, pruning, entropy, gini.

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503 Numbers and Biomass of Bacteria and Fungi Obtained by the Direct Microscopic Count Method

Authors: Ayuko Itsuki, Sachiyo Aburatani

Abstract:

The soil ecology of the organic and mineral soil layers of laurel-leaved and Cryptomeria japonica forest in the Kasuga-yama Hill Primeval Forest (Nara, Japan) was assessed. The number of bacteria obtained by the dilution plate count method was less than 0.05% of those counted by the direct microscopic count. We therefore found that forest soil contains large numbers of non-culturable bacteria compared with agricultural soils. The numbers of bacteria and fungi obtained by both the dilution plate count and the direct microscopic count were larger in the deeper horizons (F and H) of the organic layer than in the mineral soil layer. This suggests that active microbial metabolism takes place in the organic layer. The numbers of bacteria and the length of fungal hyphae obtained by the direct count method were greater in the H horizon than in the F horizon. The direct microscopic count revealed numerous non-culturable bacteria and fungi in the soil. The ratio of fungal to bacterial biomass was lower in the laurel-leaved forest soil. The fungal biomass was therefore relatively low in the laurel-leaved forest soil due to differences in forest vegetation.

Keywords: Bacterial number, Dilution plate count, Direct microscopic count, Forest soil.

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502 Spatial Econometric Approaches for Count Data: An Overview and New Directions

Authors: Paula Simões, Isabel Natário

Abstract:

This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: Spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data.

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501 Improving Fault Resilience and Reconstruction of Overlay Multicast Tree Using Leaving Time of Participants

Authors: Bhed Bahadur Bista

Abstract:

Network layer multicast, i.e. IP multicast, even after many years of research, development and standardization, is not deployed in large scale due to both technical (e.g. upgrading of routers) and political (e.g. policy making and negotiation) issues. Researchers looked for alternatives and proposed application/overlay multicast where multicast functions are handled by end hosts, not network layer routers. Member hosts wishing to receive multicast data form a multicast delivery tree. The intermediate hosts in the tree act as routers also, i.e. they forward data to the lower hosts in the tree. Unlike IP multicast, where a router cannot leave the tree until all members below it leave, in overlay multicast any member can leave the tree at any time thus disjoining the tree and disrupting the data dissemination. All the disrupted hosts have to rejoin the tree. This characteristic of the overlay multicast causes multicast tree unstable, data loss and rejoin overhead. In this paper, we propose that each node sets its leaving time from the tree and sends join request to a number of nodes in the tree. The nodes in the tree will reject the request if their leaving time is earlier than the requesting node otherwise they will accept the request. The node can join at one of the accepting nodes. This makes the tree more stable as the nodes will join the tree according to their leaving time, earliest leaving time node being at the leaf of the tree. Some intermediate nodes may not follow their leaving time and leave earlier than their leaving time thus disrupting the tree. For this, we propose a proactive recovery mechanism so that disrupted nodes can rejoin the tree at predetermined nodes immediately. We have shown by simulation that there is less overhead when joining the multicast tree and the recovery time of the disrupted nodes is much less than the previous works. Keywords

Keywords: Network layer multicast, Fault Resilience, IP multicast

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500 The Mutated Distance between Two Mixture Trees

Authors: Wan Chian Li, Justie Su-Tzu Juan, Yi-Chun Wang, Shu-Chuan Chen

Abstract:

The evolutionary tree is an important topic in bioinformation. In 2006, Chen and Lindsay proposed a new method to build the mixture tree from DNA sequences. Mixture tree is a new type evolutionary tree, and it has two additional information besides the information of ordinary evolutionary tree. One of the information is time parameter, and the other is the set of mutated sites. In 2008, Lin and Juan proposed an algorithm to compute the distance between two mixture trees. Their algorithm computes the distance with only considering the time parameter between two mixture trees. In this paper, we proposes a method to measure the similarity of two mixture trees with considering the set of mutated sites and develops two algorithm to compute the distance between two mixture trees. The time complexity of these two proposed algorithms are O(n2 × max{h(T1), h(T2)}) and O(n2), respectively

Keywords: evolutionary tree, mixture tree, mutated site, distance.

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499 Improved C-Fuzzy Decision Tree for Intrusion Detection

Authors: Krishnamoorthi Makkithaya, N. V. Subba Reddy, U. Dinesh Acharya

Abstract:

As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion. The work also includes identifying best candidate feature sub set to build the efficient c-fuzzy decision tree based Intrusion Detection System (IDS). We investigated the usefulness of c-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Empirical results indicate the usefulness of our approach in developing the efficient IDS.

Keywords: Data mining, Decision tree, Feature selection, Fuzzyc- means clustering, Intrusion detection.

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498 Visualising Energy Efficiency Landscape

Authors: Hairulliza M. Judi, Soon Y. Chee

Abstract:

This paper discusses the landscape design that could increase energy efficiency in a house. By planting trees in a house compound, the tree shades prevent direct sunlight from heating up the building, and it enables cooling off the surrounding air. The requirement for air-conditioning could be minimized and the air quality could be improved. During the life time of a tree, the saving cost from the mentioned benefits could be up to US $ 200 for each tree. The project intends to visually describe the landscape design in a house compound that could enhance energy efficiency and consequently lead to energy saving. The house compound model was developed in three dimensions by using AutoCAD 2005, the animation was programmed by using LightWave 3D softwares i.e. Modeler and Layout to display the tree shadings in the wall. The visualization was executed on a VRML Pad platform and implemented on a web environment.

Keywords: Tree planting, tree shading, energy efficiency, visualization.

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497 The Leaves of a Tree

Authors: Zhu Jiaming, Yu Mengna

Abstract:

In this article, models based on quantitative analysis, physical geometry and regression analysis are established, by using analytic hierarchy process analysis, fuzzy cluster analysis, fuzzy photographic and data fitting. The reasons of various leaf shapes among different species and the differences between the leaf shapes on same tree have been solved by using software, such as Eviews, VB and Matlab. We also successfully estimate the leaf mass of a tree and the correlation with the tree profile.

Keywords: Leaf shape; Mass; Fuzzy cluster; Regression analysis; Eviews; Matlab

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496 Balancing Neural Trees to Improve Classification Performance

Authors: Asha Rani, Christian Micheloni, Gian Luca Foresti

Abstract:

In this paper, a neural tree (NT) classifier having a simple perceptron at each node is considered. A new concept for making a balanced tree is applied in the learning algorithm of the tree. At each node, if the perceptron classification is not accurate and unbalanced, then it is replaced by a new perceptron. This separates the training set in such a way that almost the equal number of patterns fall into each of the classes. Moreover, each perceptron is trained only for the classes which are present at respective node and ignore other classes. Splitting nodes are employed into the neural tree architecture to divide the training set when the current perceptron node repeats the same classification of the parent node. A new error function based on the depth of the tree is introduced to reduce the computational time for the training of a perceptron. Experiments are performed to check the efficiency and encouraging results are obtained in terms of accuracy and computational costs.

Keywords: Neural Tree, Pattern Classification, Perceptron, Splitting Nodes.

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495 Adding Edges between One Node and Every Other Node with the Same Depth in a Complete K-ary Tree

Authors: Kiyoshi Sawada, Takashi Mitsuishi

Abstract:

This paper proposes a model of adding relations between members of the same level in a pyramid organization structure which is a complete K-ary tree such that the communication of information between every member in the organization becomes the most efficient. When edges between one node and every other node with the same depth N in a complete K-ary tree of height H are added, an optimal depth N* = H is obtained by minimizing the total path length which is the sum of lengths of shortest paths between every pair of all nodes.

Keywords: complete K-ary tree, organization structure, shortest path

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494 NOHIS-Tree: High-Dimensional Index Structure for Similarity Search

Authors: Mounira Taileb, Sami Touati

Abstract:

In Content-Based Image Retrieval systems it is important to use an efficient indexing technique in order to perform and accelerate the search in huge databases. The used indexing technique should also support the high dimensions of image features. In this paper we present the hierarchical index NOHIS-tree (Non Overlapping Hierarchical Index Structure) when we scale up to very large databases. We also present a study of the influence of clustering on search time. The performance test results show that NOHIS-tree performs better than SR-tree. Tests also show that NOHIS-tree keeps its performances in high dimensional spaces. We include the performance test that try to determine the number of clusters in NOHIS-tree to have the best search time.

Keywords: High-dimensional indexing, k-nearest neighborssearch.

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493 Historical Landscape Affects Present Tree Density in Paddy Field

Authors: Ha T. Pham, Shuichi Miyagawa

Abstract:

Ongoing landscape transformation is one of the major causes behind disappearance of traditional landscapes, and lead to species and resource loss. Tree in paddy fields in the northeast of Thailand is one of those traditional landscapes. Using three different historical time layers, we acknowledged the severe deforestation and rapid urbanization happened in the region. Despite the general thinking of decline in tree density as consequences, the heterogeneous trend of changes in total tree density in three studied landscapes denied the hypothesis that number of trees in paddy field depend on the length of land use practice. On the other hand, due to selection of planting new trees on levees, existence of trees in paddy field now relies on their values for human use. Besides, changes in land use and landscape structure had a significant impact on decision of which tree density level is considered as suitable for the landscape.

Keywords: Aerial photographs, land use change, traditional landscape, tree in paddy fields.

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492 Decision Tree-based Feature Ranking using Manhattan Hierarchical Cluster Criterion

Authors: Yasmin Mohd Yacob, Harsa A. Mat Sakim, Nor Ashidi Mat Isa

Abstract:

Feature selection study is gaining importance due to its contribution to save classification cost in terms of time and computation load. In search of essential features, one of the methods to search the features is via the decision tree. Decision tree act as an intermediate feature space inducer in order to choose essential features. In decision tree-based feature selection, some studies used decision tree as a feature ranker with a direct threshold measure, while others remain the decision tree but utilized pruning condition that act as a threshold mechanism to choose features. This paper proposed threshold measure using Manhattan Hierarchical Cluster distance to be utilized in feature ranking in order to choose relevant features as part of the feature selection process. The result is promising, and this method can be improved in the future by including test cases of a higher number of attributes.

Keywords: Feature ranking, decision tree, hierarchical cluster, Manhattan distance.

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491 A Thai to English Machine Translation System Using Thai LFG Tree Structure as Interlingua

Authors: Tawee Chimsuk, Surapong Auwatanamongkol

Abstract:

Machine Translation (MT) between the Thai and English languages has been a challenging research topic in natural language processing. Most research has been done on English to Thai machine translation, but not the other way around. This paper presents a Thai to English Machine Translation System that translates a Thai sentence into interlingua of a Thai LFG tree using LFG grammar and a bottom up parser. The Thai LFG tree is then transformed into the corresponding English LFG tree by pattern matching and node transformation. Finally, an equivalent English sentence is created using structural information prescribed by the English LFG tree. Based on results of experiments designed to evaluate the performance of the proposed system, it can be stated that the system has been proven to be effective in providing a useful translation from Thai to English.

Keywords: Interlingua, LFG grammar, Machine translation, Pattern matching.

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490 Reducing Test Vectors Count Using Fault Based Optimization Schemes in VLSI Testing

Authors: Vinod Kumar Khera, R. K. Sharma, A. K. Gupta

Abstract:

Power dissipation increases exponentially during test mode as compared to normal operation of the circuit. In extreme cases, test power is more than twice the power consumed during normal operation mode. Test vector generation scheme is key component in deciding the power hungriness of a circuit during testing. Test vector count and consequent leakage current are functions of test vector generation scheme. Fault based test vector count optimization has been presented in this work. It helps in reducing test vector count and the leakage current. In the presented scheme, test vectors have been reduced by extracting essential child vectors. The scheme has been tested experimentally using stuck at fault models and results ensure the reduction in test vector count.

Keywords: Low power VLSI testing, independent fault, essential faults, test vector reduction.

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489 Semantic Spatial Objects Data Structure for Spatial Access Method

Authors: Kalum Priyanath Udagepola, Zuo Decheng, Wu Zhibo, Yang Xiaozong

Abstract:

Modern spatial database management systems require a unique Spatial Access Method (SAM) in order solve complex spatial quires efficiently. In this case the spatial data structure takes a prominent place in the SAM. Inadequate data structure leads forming poor algorithmic choices and forging deficient understandings of algorithm behavior on the spatial database. A key step in developing a better semantic spatial object data structure is to quantify the performance effects of semantic and outlier detections that are not reflected in the previous tree structures (R-Tree and its variants). This paper explores a novel SSRO-Tree on SAM to the Topo-Semantic approach. The paper shows how to identify and handle the semantic spatial objects with outlier objects during page overflow/underflow, using gain/loss metrics. We introduce a new SSRO-Tree algorithm which facilitates the achievement of better performance in practice over algorithms that are superior in the R*-Tree and RO-Tree by considering selection queries.

Keywords: Outlier, semantic spatial object, spatial objects, SSRO-Tree, topo-semantic.

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488 Accelerating GLA with an M-Tree

Authors: Olli Luoma, Johannes Tuikkala, Olli Nevalainen

Abstract:

In this paper, we propose a novel improvement for the generalized Lloyd Algorithm (GLA). Our algorithm makes use of an M-tree index built on the codebook which makes it possible to reduce the number of distance computations when the nearest code words are searched. Our method does not impose the use of any specific distance function, but works with any metric distance, making it more general than many other fast GLA variants. Finally, we present the positive results of our performance experiments.

Keywords: Clustering, GLA, M-Tree, Vector Quantization .

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487 Decision Tree Based Scheduling for Flexible Job Shops with Multiple Process Plans

Authors: H.-H. Doh, J.-M. Yu, Y.-J. Kwon, J.-H. Shin, H.-W. Kim, S.-H. Nam, D.-H. Lee

Abstract:

This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduling approach that selects the best priority rule combination after many simulation runs, this study suggests a decision tree based approach in which a decision tree is used to select a priority rule combination adequate for a specific system state and hence the burdens required for developing simulation models and carrying out simulation runs can be eliminated. The decision tree based scheduling approach consists of construction and scheduling modules. In the construction module, a decision tree is constructed using a four-stage algorithm, and in the scheduling module, a priority rule combination is selected using the decision tree. To show the performance of the decision tree based approach suggested in this study, a case study was done on a flexible job shop with reconfigurable manufacturing cells and a conventional job shop, and the results are reported by comparing it with individual priority rule combinations for the objectives of minimizing total flow time and total tardiness.

Keywords: Flexible job shop scheduling, Decision tree, Priority rules, Case study.

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486 Using Data Mining Technique for Scholarship Disbursement

Authors: J. K. Alhassan, S. A. Lawal

Abstract:

This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: Decision tree, classification, data mining, scholarship.

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485 Pruning Method of Belief Decision Trees

Authors: Salsabil Trabelsi, Zied Elouedi, Khaled Mellouli

Abstract:

The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. In this paper, we develop a post-pruning method of belief decision trees in order to reduce size and improve classification accuracy on unseen cases. The pruning of decision tree has a considerable intention in the areas of machine learning.

Keywords: machine learning, uncertainty, belief function theory, belief decision tree, pruning.

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484 Defect Cause Modeling with Decision Tree and Regression Analysis

Authors: B. Bakır, İ. Batmaz, F. A. Güntürkün, İ. A. İpekçi, G. Köksal, N. E. Özdemirel

Abstract:

The main aim of this study is to identify the most influential variables that cause defects on the items produced by a casting company located in Turkey. To this end, one of the items produced by the company with high defective percentage rates is selected. Two approaches-the regression analysis and decision treesare used to model the relationship between process parameters and defect types. Although logistic regression models failed, decision tree model gives meaningful results. Based on these results, it can be claimed that the decision tree approach is a promising technique for determining the most important process variables.

Keywords: Casting industry, decision tree algorithm C5.0, logistic regression, quality improvement.

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483 Zero Inflated Strict Arcsine Regression Model

Authors: Y. N. Phang, E. F. Loh

Abstract:

Zero inflated strict arcsine model is a newly developed model which is found to be appropriate in modeling overdispersed count data. In this study, we extend zero inflated strict arcsine model to zero inflated strict arcsine regression model by taking into consideration the extra variability caused by extra zeros and covariates in count data. Maximum likelihood estimation method is used in estimating the parameters for this zero inflated strict arcsine regression model.

Keywords: Overdispersed count data, maximum likelihood estimation, simulated annealing.

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482 Statistical Analysis for Overdispersed Medical Count Data

Authors: Y. N. Phang, E. F. Loh

Abstract:

Many researchers have suggested the use of zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB) models in modeling overdispersed medical count data with extra variations caused by extra zeros and unobserved heterogeneity. The studies indicate that ZIP and ZINB always provide better fit than using the normal Poisson and negative binomial models in modeling overdispersed medical count data. In this study, we proposed the use of Zero Inflated Inverse Trinomial (ZIIT), Zero Inflated Poisson Inverse Gaussian (ZIPIG) and zero inflated strict arcsine models in modeling overdispered medical count data. These proposed models are not widely used by many researchers especially in the medical field. The results show that these three suggested models can serve as alternative models in modeling overdispersed medical count data. This is supported by the application of these suggested models to a real life medical data set. Inverse trinomial, Poisson inverse Gaussian and strict arcsine are discrete distributions with cubic variance function of mean. Therefore, ZIIT, ZIPIG and ZISA are able to accommodate data with excess zeros and very heavy tailed. They are recommended to be used in modeling overdispersed medical count data when ZIP and ZINB are inadequate.

Keywords: Zero inflated, inverse trinomial distribution, Poisson inverse Gaussian distribution, strict arcsine distribution, Pearson’s goodness of fit.

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481 A Comparison of Single of Decision Tree, Decision Tree Forest and Group Method of Data Handling to Evaluate the Surface Roughness in Machining Process

Authors: S. Ghorbani, N. I. Polushin

Abstract:

The machinability of workpieces (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron) in turning operation has been carried out using different types of cutting tool (conventional, cutting tool with holes in toolholder and cutting tool filled up with composite material) under dry conditions on a turning machine at different stages of spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). Experimentation was performed as per Taguchi’s orthogonal array. To evaluate the relative importance of factors affecting surface roughness the single decision tree (SDT), Decision tree forest (DTF) and Group method of data handling (GMDH) were applied.

Keywords: Decision Tree Forest, GMDH, surface roughness, taguchi method, turning process.

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480 An Alternative Approach for Assessing the Impact of Cutting Conditions on Surface Roughness Using Single Decision Tree

Authors: S. Ghorbani, N. I. Polushin

Abstract:

In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: Cutting condition, surface roughness, decision tree, CART algorithm.

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