Search results for: small baseline subset algorithm

Authors: Afrah Aladwani, Alexander Mullen, Mohammad AlRashidi, Omamah Alfarisi, Faisal Alterkit, Abdulwahab Aladwani, Asit Kumar, Emad Eldosouky

Abstract:

Introduction: Trastuzumab is a HER-2 targeted humanized monoclonal antibody that significantly improves the therapeutic outcomes of metastatic and non-metastatic breast cancer. However, it is associated with increased risk of cardiotoxicity that ranges from mild decline in the cardiac ejection fraction to permanent cardiomyopathy. Concerns have been raised in treating eligible older patients. This study compares trastuzumab outcomes between two age cohorts in the Kuwait Cancer Control Centre (KCCC). Methods: In a prospective comparative observational study, 93 HER-2 positive breast cancer patients undergoing different chemotherapy protocols + trastuzumab were included and divided into two cohorts based on their age (˂60 and ≥60 years old). The baseline left ventricular ejection fraction (LVEF) was assessed and monitored every three months during trastuzumab treatment. Event of cardiotoxicity was defined as ≥10% decline in the LVEF from the baseline. The lower accepted normal limit of the LVEF was 50%. Results: The median baseline LVEF was 65% in both age cohorts (IQR 8% and 9% for older and younger patients respectively). Whereas, the median LVEF post-trastuzumab treatment was 51% and 55% in older and younger patients respectively (IQR 8%; p-value = 0.22), despite the fact that older patients had significantly lower exposure to anthracyclines compared to younger patients (60% and 84.1% respectively; p-value ˂0.001). 86.7% and 55.6% of older and younger patients, respectively, developed ≥10% decline in their LVEF from the baseline. Among those, only 29% of older and 27% of younger patients reached a LVEF value below 50% (p-value = 0.88). Statistically, age was the only factor that significantly correlated with trastuzumab induced cardiotoxicity (OR 4; p-value ˂0.012), but it did not increase the requirement for permanent discontinuation of treatment. A baseline LVEF value below 60% contributed to developing a post-treatment value below normal ranges (50%). Conclusion: Breast cancer patients aged 60 years and above in Kuwait were at 4-fold higher risk of developing ≥10% decline in their LVEF from the baseline than younger patients during trastuzumab treatment. Surprisingly, previous exposure to anthracyclines and multiple comorbidities were not associated with significant increased risk of cardiotoxicity.

Keywords: breast cancer, elderly, Trastuzumab, cardiotoxicity

Procedia PDF Downloads 180

Authors: Soma Kanta Baral

Abstract:

Background: Since 1981, when the first AIDS case was reported, worldwide, more than 34 million people have been infected with HIV. Almost 95 percent of the people infected with HIV live in developing countries. As HBV & HIV share similar routes of transmission by sexual intercourse or drug use by parenteral injection, co-infection is common. Because of the limited access to healthcare & HIV treatment in developing countries, HIV-infected individuals are present late for care. Enumeration of CD4+ T cell count at the time of diagnosis has been useful to initiate the therapy in HIV infected individuals. The baseline CD4+ T cell count shows high immunological variability among patients. Methods: This prospective study was done in the serology section of the Department of Microbiology over a period of one year from august 2012 to July 2013. A total of 13037 individuals subjected for HIV test were included in the study comprising of 4982 males & 8055 females. Blood sample was collected by vein puncture aseptically with standard operational procedure in clean & dry test-tube. All blood samples were screened for HIV as described by WHO algorithm by Immuno-chromatography rapid kits. Further confirmation was done by biokit ELISA method as per the manufacturer’s guidelines. After informed consent, HIV positive individuals were screened for HBsAg by Immuno-chromatography rapid kits (Hepacard). Further confirmation was done by biokit ELISA method as per the manufacturer’s guidelines. EDTA blood samples were collected from the HIV sero-positive individuals for baseline CD4+ T count. Then, CD4+ T cells count was determined by using FACS Calibur Flow Cytometer (BD). Results: Among 13037 individuals screened for HIV, 104 (0.8%) were found to be infected comprising of 69(66.34%) males & 35 (33.65%) females. The study showed that the high infection was noted in housewives (28.7%), active age group (30.76%), rural area (56.7%) & in heterosexual route (80.9%) of transmission. Out of total HIV infected individuals, distribution of HBV co-infection was found to be 6(5.7%). All co- infected individuals were married, male, above the age of 25 years & heterosexual route of transmission. Baseline CD4+ T cell count of HIV infected patient was found higher (mean CD4+ T cell count; 283cells/cu.mm) than HBV co-infected patients (mean CD4+ T cell count; 91 cells/cu.mm). Majority (77.2%) of HIV infected & all co-infected individuals were presented in our center late (CD4+ T cell count;< 350/cu. mm) for diagnosis and care. Majority of co- infected 4 (80%) were late presented with advanced AIDS stage (CD4+ count; <200/cu.mm). Conclusions: The study showed a high percentage of HIV sero-positive & co- infected individuals. Baseline CD4+ T cell count of majority of HIV infected individuals was found to be low. Hence, more sustained and vigorous awareness campaigns & counseling still need to be done in order to promote early diagnosis and management.

Keywords: HIV/AIDS, HBsAg, co-infection, CD4+

Procedia PDF Downloads 188

Authors: Maxat Bokambayev, Bella Tussupova, Aisha Mamyrova, Erlan Izbasarov

Abstract:

Optimization of business processes in trading companies is reviewed in the report. There is the presentation of the “Wholesale Customer Order Handling Process” business process model applicable for small and medium businesses. It is proposed to apply the algorithm for automation of the customer order processing which will significantly reduce labor costs and time expenditures and increase the profitability of companies. An optimized business process is an element of the information system of accounting of spare parts trading network activity. The considered algorithm may find application in the trading industry as well.

Keywords: business processes, discrete-event simulation, management, trading industry

Procedia PDF Downloads 318

Authors: Chaghoub Soraya, Zhang Xiaoyan

Abstract:

This research presents the first constant approximation algorithm to the p-median network design problem with multiple cable types. This problem was addressed with a single cable type and there is a bifactor approximation algorithm for the problem. To the best of our knowledge, the algorithm proposed in this paper is the first constant approximation algorithm for the p-median network design with multiple cable types. The addressed problem is a combination of two well studied problems which are p-median problem and network design problem. The introduced algorithm is a random sampling approximation algorithm of constant factor which is conceived by using some random sampling techniques form the literature. It is based on a redistribution Lemma from the literature and a steiner tree problem as a subproblem. This algorithm is simple, and it relies on the notions of random sampling and probability. The proposed approach gives an approximation solution with one constant ratio without violating any of the constraints, in contrast to the one proposed in the literature. This paper provides a (21 + 2)-approximation algorithm for the p-median network design problem with multiple cable types using random sampling techniques.

Keywords: approximation algorithms, buy-at-bulk, combinatorial optimization, network design, p-median

Procedia PDF Downloads 165

Authors: Ariel Barel, Rotem Manor, Alfred M. Bruckstein

Abstract:

We present a probabilistic gathering algorithm for agents that can only detect the presence of other agents in front of or behind them. The agents act in the plane and are identical and indistinguishable, oblivious, and lack any means of direct communication. They do not have a common frame of reference in the plane and choose their orientation (direction of possible motion) at random. The analysis of the gathering process assumes that the agents act synchronously in selecting random orientations that remain fixed during each unit time-interval. Two algorithms are discussed. The first one assumes discrete jumps based on the sensing results given the randomly selected motion direction, and in this case, extensive experimental results exhibit probabilistic clustering into a circular region with radius equal to the step-size in time proportional to the number of agents. The second algorithm assumes agents with continuous sensing and motion, and in this case, we can prove gathering into a very small circular region in finite expected time.

Keywords: control, decentralized, gathering, multi-agent, simple sensors

Procedia PDF Downloads 139

Authors: Kalpana Poudel-Tandukar

Abstract:

Background: Human Immunodeficiency Virus (HIV) infection has been frequently associated with vitamin D deficiency and depression. Vitamin D deficiency increases the risk of depression in people without HIV. We assessed the cross-sectional and prospective associations between serum concentrations of 25-hydroxyvitamin D (25[OH]D) and depression in a HIV-positive people. Methods: A survey was conducted among 316 HIV-positive people aged 20-60 years residing in Kathmandu, Nepal for a cross-sectional association at baseline, and among 184 participants without depressive symptoms at baseline who responded to both baseline (2010) and follow-up (2011) surveys for prospective association. The competitive protein-binding assay was used to measure 25(OH)D levels and the Beck Depression Inventory-Ia method was used to measure depression, with cut off score 20 or higher. Relationships were assessed using multiple logistic regression analysis with adjustment of potential confounders. Results: The proportion of participants with 25(OH)D level of <20ng/mL, 20-30ng/mL, and >30ng/mL were 83.2%, 15.5%, and 1.3%, respectively. Only four participants with 25(OH)D level of >30ng/mL were excluded in the further analysis. The mean 25(OH)D level in men and women were 15.0ng/mL and 14.4ng/mL, respectively. Twenty six percent of participants (men:23%; women:29%) were depressed. Participants with 25(OH)D level of < 20 ng/mL had a 1.4 fold higher odds of depression in a cross-sectional and 1.3 fold higher odds of depression after 18 months of baseline compared to those with 25(OH)D level of 20-30ng/mL (p=0.40 and p=0.78, respectively). Conclusion: Vitamin D may not have significant impact against depression among HIV-positive people with 25(OH)D level below normal ( > 30ng/mL).

Keywords: depression, HIV, Nepal, vitamin D

Procedia PDF Downloads 305

Authors: Tomasz Grzes, Maciej Kopczynski, Jaroslaw Stepaniuk

Abstract:

The rough sets theory developed by Prof. Z. Pawlak is one of the tools that can be used in the intelligent systems for data analysis and processing. Banking, medicine, image recognition and security are among the possible fields of utilization. In all these fields, the amount of the collected data is increasing quickly, but with the increase of the data, the computation speed becomes the critical factor. Data reduction is one of the solutions to this problem. Removing the redundancy in the rough sets can be achieved with the reduct. A lot of algorithms of generating the reduct were developed, but most of them are only software implementations, therefore have many limitations. Microprocessor uses the fixed word length, consumes a lot of time for either fetching as well as processing of the instruction and data; consequently, the software based implementations are relatively slow. Hardware systems don’t have these limitations and can process the data faster than a software. Reduct is the subset of the decision attributes that provides the discernibility of the objects. For the given decision table there can be more than one reduct. Core is the set of all indispensable condition attributes. None of its elements can be removed without affecting the classification power of all condition attributes. Moreover, every reduct consists of all the attributes from the core. In this paper, the hardware implementation of the two-stage greedy algorithm to find the one reduct is presented. The decision table is used as an input. Output of the algorithm is the superreduct which is the reduct with some additional removable attributes. First stage of the algorithm is calculating the core using the discernibility matrix. Second stage is generating the superreduct by enriching the core with the most common attributes, i.e., attributes that are more frequent in the decision table. Described above algorithm has two disadvantages: i) generating the superreduct instead of reduct, ii) additional first stage may be unnecessary if the core is empty. But for the systems focused on the fast computation of the reduct the first disadvantage is not the key problem. The core calculation can be achieved with a combinational logic block, and thus add respectively little time to the whole process. Algorithm presented in this paper was implemented in Field Programmable Gate Array (FPGA) as a digital device consisting of blocks that process the data in a single step. Calculating the core is done by the comparators connected to the block called 'singleton detector', which detects if the input word contains only single 'one'. Calculating the number of occurrences of the attribute is performed in the combinational block made up of the cascade of the adders. The superreduct generation process is iterative and thus needs the sequential circuit for controlling the calculations. For the research purpose, the algorithm was also implemented in C language and run on a PC. The times of execution of the reduct calculation in a hardware and software were considered. Results show increase in the speed of data processing.

Keywords: data reduction, digital systems design, field programmable gate array (FPGA), reduct, rough set

Procedia PDF Downloads 189

Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

Abstract:

The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: degree, initial cluster center, k-means, minimum spanning tree

Procedia PDF Downloads 377

Authors: Richard Vuijk, Arnoud Arntz

Abstract:

Rationale: To our knowledge treatment of personality disorder comorbidity in adults with autism spectrum disorder (ASD) is understudied and is still in its infancy: We do not know if treatment of personality disorders may be applicable to adults with ASD. In particular, it is unknown whether patients with ASD benefit from experiential techniques that are part of schema therapy developed for the treatment of personality disorders. Objective: The aim of the study is to investigate the efficacy of a schema mode focused treatment with adult clients with ASD and comorbid personality pathology (i.e. at least one personality disorder). Specifically, we investigate if they can benefit from both cognitive-behavioral, and experiential interventions. Study design: A multiple baseline case series study. Study population: Adult individuals (age > 21 years) with ASD and at least one personality disorder. Participants will be recruited from Sarr expertise center for autism in Rotterdam. The study requires 12 participants. Intervention: The treatment protocol consists of 35 weekly offered sessions, followed by 10 monthly booster sessions. A multiple baseline design will be used with baseline varying from 5 to 10 weeks, with weekly supportive sessions. After baseline, a 5-week exploration phase follows with weekly sessions during which current and past functioning, psychological symptoms, schema modes are explored, and information about the treatment will be given. Then 15 weekly sessions with cognitive-behavioral interventions and 15 weekly sessions with experiential interventions will be given. Finally, there will be a 10-month follow-up phase with monthly booster sessions. Participants are randomly assigned to baseline length, and respond weekly during treatment and monthly at follow-up on Belief Strength of negative core beliefs (by VAS), and fill out SMI, SCL-90 and SRS-A 7 times during screening procedure (i.e. before baseline), after baseline, after exploration, after cognitive and behavioral interventions, after experiential interventions, and after 5- and 10- month follow-up. The SCID-II will be administered during screening procedure (i.e. before baseline), at 5- and at 10-month follow-up. Main study parameters: The primary study parameter is negative core beliefs. Secondary study parameters include schema modes, personality disorder manifestations, psychological symptoms, and social interaction and communication. Discussion: To the best of author’s knowledge so far no study has been published on the application of schema mode focused interventions in adult patients with ASD and comorbid PD(s). This study offers the first systematic test of application of schema therapy for adults with ASD. The results of this study will provide initial evidence for the effectiveness of schema therapy in treating adults with both ASD and PD(s). The study intends to provide valuable information for future development and implementation of therapeutic interventions for adults with both ASD and PD(s).

Keywords: adults, autism spectrum disorder, personality disorder, schema therapy

Procedia PDF Downloads 205

Authors: Ali Kassem, Aly Taha, Abeer Hassan, Kazuhide Higuchi

Abstract:

Introduction: Trans arterial chemoembolization (TACE) for hepatocellular carcinoma (HCC) is usually followed by hepatic dysfunction that limits its efficacy. L-carnitine is recently studied as hepatoprotective agent. Our aim is to evaluate the L-carnitine effects against the deterioration of liver functions after TACE. Method: 53 patients with intermediate stage HCC were assigned into two groups; L-carnitine group (26 patients) who received L-carnitine 300 mg tablet twice daily from 2 weeks before to 12 weeks after TACE and control group (27 patients) without L-carnitine therapy. 28 of studied patients received branched chain amino acids granules. Results: There were significant differences between L-carnitine Vs. control group in mean serum albumin change from baseline to 1 week and 4 weeks after TACE (p < 0.05). L-Carnitine maintained Child-Pugh score at 1 week after TACE and exhibited improvement at 4 weeks after TACE (p < 0.01 Vs 1 week after TACE). Control group has significant Child-Pugh score deterioration from baseline to 1 week after TACE (p < 0.05) and 12 weeks after TACE (p < 0.05). There were significant differences between L-carnitine and control groups in mean Child-Pugh score change from baseline to 4 weeks (p < 0.05) and 12 weeks after TACE (p < 0.05). L-carnitine displayed improvement in (PT) from baseline to 1 week, 4 w (p < 0.05) and 12 weeks after TACE. PT in control group declined less than baseline along all follow up intervals. Total bilirubin in L-carnitine group decreased at 1 week post TACE while in control group, it significantly increased at 1 week (p = 0.01). ALT and C-reactive protein elevation were suppressed at 1 week after TACE in Lcarnitine group. The hepatoprotective effects of L-carnitine were enhanced by concomitant use of branched chain amino acids. Conclusion: L-carnitine and BCAA combination therapy offer a novel supportive strategy after TACE in HCC patients.

Keywords: hepatocellular carcinoma, L-carnitine, liver functions , trans-arterial embolization

Procedia PDF Downloads 121

Authors: Kangkang Wu, Kaizhi Wang, Zhijun Yuan

Abstract:

This paper deals with a mini radar system aimed at detecting small targets at the low latitude. The radar operates at Ku-band in the frequency modulated continuous wave (FMCW) mode with two receiving channels. The radar system has the characteristics of compactness, mobility, and low power consumption. This paper focuses on the implementation of the radar system, and the Block least mean square (Block LMS) algorithm is applied to minimize the fortuitous distortion. It is validated from a series of experiments that the track of the unmanned aerial vehicle (UAV) can be easily distinguished with the radar system.

Keywords: unmanned aerial vehicle (UAV), interference, Block Least Mean Square (Block LMS) Algorithm, Frequency Modulated Continuous Wave (FMCW)

Procedia PDF Downloads 291

Authors: Archana Singh, Jyoti Agarwal, Ajay Rana

Abstract:

Data Mining is an efficient technology to discover patterns in large databases. Association Rule Mining techniques are used to find the correlation between the various item sets in a database, and this co-relation between various item sets are used in decision making and pattern analysis. In recent years, the problem of finding association rules from large datasets has been proposed by many researchers. Various research papers on association rule mining (ARM) are studied and analyzed first to understand the existing algorithms. Apriori algorithm is the basic ARM algorithm, but it requires so many database scans. In DIC algorithm, less amount of database scan is needed but complex data structure lattice is used. The main focus of this paper is to propose a new optimized algorithm (Friendly Algorithm) and compare its performance with the existing algorithms A data set is used to find out frequent itemsets and association rules with the help of existing and proposed (Friendly Algorithm) and it has been observed that the proposed algorithm also finds all the frequent itemsets and essential association rules from databases as compared to existing algorithms in less amount of database scan. In the proposed algorithm, an optimized data structure is used i.e. Graph and Adjacency Matrix.

Keywords: association rules, data mining, dynamic item set counting, FP-growth, friendly algorithm, graph

Procedia PDF Downloads 389

Authors: Ji Eun Shin, Dong Hoon Lim

Abstract:

Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

Procedia PDF Downloads 401

Authors: Sunghun Jung

Abstract:

The existing web map service providers contract with the satellite operators to update their maps by paying an astronomical amount of money, but the cost could be minimized by operating a cheap and small UAV. In contrast to the satellites, we only need to replace aged battery packs from time to time for the usage of UAVs. Utilizing both a regular camera and an infrared camera mounted on a small, solar-powered, long-endurance, and hoverable UAV, daytime ground surface photographs, and nighttime infrared photographs will be continuously and repeatedly uploaded to the web map server and overlapped with the existing ground surface photographs in real-time. The real-time web map service using a small, solar-powered, long-endurance, and hoverable UAV can also be applied to the surveillance missions, in particular, to detect border area intruders. The improved real-time image stitching algorithm is developed for the graphic map data overlapping. Also, a small home server will be developed to manage the huge size of incoming map data. The map photographs taken at tens or hundreds of kilometers by a UAV would improve the map graphic resolution compared to the map photographs taken at thousands of kilometers by satellites since the satellite photographs are limited by weather conditions.

Keywords: long-endurance, real-time web map service (RWMS), solar-powered, unmanned aerial vehicle (UAV)

Procedia PDF Downloads 247

Authors: J. Kaabi, Y. Harrath

Abstract:

This paper studied the flow shop scheduling problem under machine availability constraints. The machines are subject to flexible preventive maintenance activities. The nonresumable scenario for the jobs was considered. That is, when a job is interrupted by an unavailability period of a machine it should be restarted from the beginning. The objective is to minimize the total tardiness time for the jobs and the advance/tardiness for the maintenance activities. To solve the problem, a genetic algorithm was developed and successfully tested and validated on many problem instances. The computational results showed that the new genetic algorithm outperforms another earlier proposed algorithm.

Keywords: flow shop scheduling, genetic algorithm, maintenance, priority rules

Procedia PDF Downloads 447

Authors: Mohammad H. Almomani

Abstract:

In this paper, we argue the effect of the initial sample size, and the increment in simulation samples on the performance of a sequential approach that used in selecting the top m designs when the number of alternative designs is very large. The sequential approach consists of two stages. In the first stage the ordinal optimization is used to select a subset that overlaps with the set of actual best k% designs with high probability. Then in the second stage the optimal computing budget is used to select the top m designs from the selected subset. We apply the selection approach on a generic example under some parameter settings, with a different choice of initial sample size and the increment in simulation samples, to explore the impacts on the performance of this approach. The results show that the choice of initial sample size and the increment in simulation samples does affect the performance of a selection approach.

Keywords: Large Scale Problems, Optimal Computing Budget Allocation, ordinal optimization, simulation optimization

Procedia PDF Downloads 326

Authors: Nasiruddin Khan, Nasser M. Al-Daghri, Dara A. Al-Disi, Asim Al-Fadda, Mohamed Al-Seif, Gyanendra Tripathi, A. L. Harte, Philip G. Mcternan

Abstract:

For the last few decades, the prevalence of type 2 diabetes mellitus (T2DM) in the Kingdom of Saudi Arabia (KSA) is increasing alarmingly high and is unprecedented at 31.6 %. Preventive measures should be taken to curb down the increasing incidence. In this prospective, 3-month study, we aimed to determine whether dietary modification program would confer favorable affects among overweight and obese adult Saudi women with or without T2DM. A total of 92 Saudi women [18 healthy controls, 24 overweight subjects and 50 overweight or obese patients with early onset T2DM were included in this prospective study. Baseline anthropometrics and fasting blood samples were taken at baseline and after 3 months. Fasting blood sugar and lipid profile were measured routinely. A 500 Kcal deficit energy diet less than their daily recommended dietary allowances were prescribed to all participants. After 3 months of follow-up visit, significant improvements were observed in both the overweight and DMT2 group as compared to baseline with decreased mean BMI [Overweight Group 28.54±1.49 versus 27.95±2.25, p<0.05; DMT2 group 35.24±7.67 versus 35.04±8.07, p<0.05] and hip circumference [Overweight group 109.67±5.01 versus 108.07±4.07, p<0.05; DMT2 group 112.3±13.43 versus 109.21±12.71, p<0.01]. Moreover, in the overweight group, baseline HDL-cholesterol was significantly associated with protein intake and inversely associated with carbohydrate intake in controls. In the DMT2 group, carbohydrate intake at baseline was significantly associated with BMI. A 3-month 500kcal/day deficit dietary modification alone is probably effective among adult overweight or obese Saudi females without or with T2DM. Longer prospective studies are to determine whether the dietary intervention alone can reduce progression of T2DM among high-risk adult Arabs.

Keywords: diet, lipid, obesity, T2DM

Procedia PDF Downloads 448

Authors: Omar Dib, Alexandre Caminada, Marie-Ange Manier

Abstract:

The purpose of this study is to introduce a novel approach to solve the one-to-one shortest path problem. A directed connected graph is assumed in which all edges’ weights are positive. Our method is based on a memetic algorithm in which we combine a genetic algorithm (GA) and a variable neighborhood search method (VNS). We compare our approximate method with two exact algorithms Dijkstra and Integer Programming (IP). We made experimentations using random generated, complete and real graph instances. In most case studies, numerical results show that our method outperforms exact methods with 5% average gap to the optimality. Our algorithm’s average speed is 20-times faster than Dijkstra and more than 1000-times compared to IP. The details of the experimental results are also discussed and presented in the paper.

Keywords: shortest path problem, Dijkstra’s algorithm, integer programming, memetic algorithm

Procedia PDF Downloads 436

Authors: Noha El-Gohary, Thanaa El-Shater, A. A. Mahfouz, M. M. Sakr

Abstract:

Searching for high power conversion efficiency and long lifetime are important goals when designing a power supply subsystem for satellite applications. To fulfill these goals, this paper presents a power supply subsystem for small satellites in which flywheel energy storage system is used as a secondary power source instead of chemical battery. In this paper, the model of flywheel energy storage system is introduced; a DC bus regulation control algorithm for charging and discharging of flywheel based on space vector pulse width modulation technique and motor current control is also introduced. Simulation results showed the operation of the flywheel for charging and discharging mode during illumination and shadowed period. The advantages of the proposed system are confirmed by the simulation results of the power supply system.

Keywords: small-satellites, flywheel energy storage system, space vector pulse width modulation, power conversion

Procedia PDF Downloads 373

Authors: Arman S. Kussainov, Altynbek K. Beisekov

Abstract:

This work focuses on building an efficient universal procedure to construct a single density of states from the multiple pieces of data provided by the parallel implementation of the Wang Landau Monte Carlo based algorithm. The Ising and Pott models were used as the examples of the two-dimensional spin lattices to construct their densities of states. Sampled energy space was distributed between the individual walkers with certain overlaps. This was made to include the latest development of the algorithm as the density of states replica exchange technique. Several factors of immediate importance for the seamless stitching process have being considered. These include but not limited to the speed and universality of the initial parallel algorithm implementation as well as the data post-processing to produce the expected smooth density of states.

Keywords: density of states, Monte Carlo, parallel algorithm, Wang Landau algorithm

Procedia PDF Downloads 367

Authors: Tayfun Çay, Yasar İnceyol, Abdurrahman Özbeyaz

Abstract:

Land reallocation is one of the most important steps in land consolidation projects. Many different models were proposed for land reallocation in the literature such as Fuzzy Logic, block priority based land reallocation and Spatial Decision Support Systems. A model including four parts is considered for automatic block reallocation with genetic algorithm method in land consolidation projects. These stages are preparing data tables for a project land, determining conditions and constraints of land reallocation, designing command steps and logical flow chart of reallocation algorithm and finally writing program codes of Genetic Algorithm respectively. In this study, we designed the first three steps of the considered model comprising four steps.

Keywords: land consolidation, landholding, land reallocation, optimization, genetic algorithm

Procedia PDF Downloads 395

Authors: Dimitri Golenko-Ginzburg

Abstract:

A CPM network project with deterministic activity durations, in which activities require homogenous resources with fixed capacities, is considered. The problem is to determine the optimal schedule of starting times for all network activities within their maximal allowable limits (in order not to exceed the network's critical time) to minimize the maximum required resources for the project at any point in time. In case when a non-critical activity may start only at discrete moments with the pregiven time span, the problem becomes NP-complete and an optimal solution may be obtained via a look-over algorithm. For the case when a look-over requires much computational time an approximate algorithm is suggested. The algorithm's performance ratio, i.e., the relative accuracy error, is determined. Experimentation has been undertaken to verify the suggested algorithm.

Keywords: resource smoothing problem, CPM network, lookover algorithm, lexicographical order, approximate algorithm, accuracy estimate

Procedia PDF Downloads 274

Authors: Yosep Chong, Yejin Kim, Jingyun Choi, Hwanjo Yu, Eun Jung Lee, Chang Suk Kang

Abstract:

For the past decades, immunohistochemistry (IHC) has been playing an important role in the diagnosis of human neoplasms, by helping pathologists to make a clearer decision on differential diagnosis, subtyping, personalized treatment plan, and finally prognosis prediction. However, the IHC performed in various tumors of daily practice often shows conflicting and very challenging results to interpret. Even comprehensive diagnosis synthesizing clinical, histologic and immunohistochemical findings can be helpless in some twisted cases. Another important issue is that the IHC data is increasing exponentially and more and more information have to be taken into account. For this reason, we reached an idea to develop an expert supporting system to help pathologists to make a better decision in diagnosing human neoplasms with IHC results. We gave probabilistic decision tree algorithm and tested the algorithm with real case data of lymphoid neoplasms, in which the IHC profile is more important to make a proper diagnosis than other human neoplasms. We designed probabilistic decision tree based on Bayesian theorem, program computational process using MATLAB (The MathWorks, Inc., USA) and prepared IHC profile database (about 104 disease category and 88 IHC antibodies) based on WHO classification by reviewing the literature. The initial probability of each neoplasm was set with the epidemiologic data of lymphoid neoplasm in Korea. With the IHC results of 131 patients sequentially selected, top three presumptive diagnoses for each case were made and compared with the original diagnoses. After the review of the data, 124 out of 131 were used for final analysis. As a result, the presumptive diagnoses were concordant with the original diagnoses in 118 cases (93.7%). The major reason of discordant cases was that the similarity of the IHC profile between two or three different neoplasms. The expert supporting system algorithm presented in this study is in its elementary stage and need more optimization using more advanced technology such as deep-learning with data of real cases, especially in differentiating T-cell lymphomas. Although it needs more refinement, it may be used to aid pathological decision making in future. A further application to determine IHC antibodies for a certain subset of differential diagnoses might be possible in near future.

Keywords: database, expert supporting system, immunohistochemistry, probabilistic decision tree

Procedia PDF Downloads 204

Authors: Aishik Deb, Rituparna Sinha

Abstract:

We have developed an algorithm to detect the abnormal segment/"structural variation in the genome across a number of samples. We have worked on simulated as well as real data from the BAM Files and have designed a segmentation algorithm where abnormal segments are detected. This algorithm aims to improve the accuracy and performance of the existing CNV-CH algorithm. The next-generation sequencing (NGS) approach is very fast and can generate large sequences in a reasonable time. So the huge volume of sequence information gives rise to the need for Big Data and parallel approaches of segmentation. Therefore, we have designed a map-reduce approach for the existing CNV-CH algorithm where a large amount of sequence data can be segmented and structural variations in the human genome can be detected. We have compared the efficiency of the traditional and map-reduce algorithms with respect to precision, sensitivity, and F-Score. The advantages of using our algorithm are that it is fast and has better accuracy. This algorithm can be applied to detect structural variations within a genome, which in turn can be used to detect various genetic disorders such as cancer, etc. The defects may be caused by new mutations or changes to the DNA and generally result in abnormally high or low base coverage and quantification values.

Keywords: cancer detection, convex hull segmentation, map reduce, next generation sequencing

Procedia PDF Downloads 102

Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

Abstract:

Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

Procedia PDF Downloads 260

Authors: Rohit Sane, Snehal Dongre, Pravin Ghadigaonkar, Rahul Mandole

Abstract:

The present study was designed to evaluate efficacy of heart failure reversal therapy (HFRT) that uses herbal procedure (panchakarma) and allied therapies, in chronic heart failure (CHF) patients with low ejection fraction. Methods: This efficacy study was conducted in CHF patients (aged: 25-65 years, ejection fraction (EF) < 30%) wherein HFRT (60-75 minutes) consisting of snehana (external oleation), swedana (passive heat therapy), hrudaydhara(concoction dripping treatment) and basti(enema) was administered twice daily for 7 days. During this therapy and next 30 days, patients followed the study dinarcharya and were prescribed ARJ kadha in addition to their conventional treatment. The primary endpoint of this study was evaluation of maximum aerobic capacity uptake (MAC) as assessed by 6-minute walk distance (6MWD) using Cahalins equation from baseline, at end of 7 day treatment, follow-up after 30 days and 90 days. EF was assessed by 2D Echo at baseline and after 30 days of follow-up. Results: CHF patients with < 30% EF (N=52, mean [SD] age: 58.8 [10.8], 85% men) were enrolled in the study. There was a 100% compliance to study therapy. A significant improvement was observed in MAC levels (7.11%, p =0.029), at end of 7 day therapy as compared to baseline. This improvement was maintained at two follow-up visits. Moreover, ejection fraction was observed to be increased by 6.38%, p=0,012 as compared to baseline at day 7 of the therapy. Conclusions: This 90 day follow up study highlights benefit of HFRT, as a part of maintenance treatment for CHF patients with reduced ejection fraction.

Keywords: chronic heart failure, functional capacity, heart failure reversal therapy, oxygen uptake, panchakarma

Procedia PDF Downloads 203

Authors: Ashit Syngle, Nidhi Garg, Pawan Krishan

Abstract:

Background: Endothelial Progenitor Cells (EPCs) are depleted and contribute to increased cardiovascular (CV) risk in rheumatoid arthritis (RA). Statins exert a protective effect in CAD partly by promoting EPC mobilization. This vasculoprotective effect of statin has not yet been investigated in RA. We aimed to investigate the effect of rosuvastatin on EPCs in RA. Methods: 50 RA patients were randomized to receive 6 months of treatment with rosuvastatin (10 mg/day, n=25) and placebo (n=25) as an adjunct to existing stable antirheumatic drugs. EPCs (CD34+/CD133+) were quantified by Flow Cytometry. Inflammatory measures included DAS28, CRP and ESR were measured at baseline and after treatment. Lipids and pro-inflammatory cytokines (TNF-α, IL-6, and IL-1) were estimated at baseline and after treatment. Results: At baseline, inflammatory measures and pro-inflammatory cytokines were elevated and EPCs depleted among both groups. At baseline, EPCs inversely correlated with DAS28 and TNF-α in both groups. EPCs increased significantly (p < 0.01) after treatment with rosuvastatin but did not show significant change with placebo. Rosuvastatin exerted positive effect on lipid spectrum: lowering total cholesterol, LDL, non HDL and elevation of HDL as compared with placebo. At 6 months, DAS28, ESR, CRP, TNF-α and IL-6 improved significantly in rosuvastatin group. Significant negative correlation was observed between EPCs and DAS28, CRP, TNF-α, and IL-6 after treatment with rosuvastatin. Conclusion: First study to show that rosuvastatin improves inflammation and EPC biology in RA possibly through its anti-inflammatory and lipid lowering effect. This beneficial effect of rosuvastatin may provide a novel strategy to prevent cardiovascular events in RA.

Keywords: RA, Endothelial Progenitor Cells, rosuvastatin, cytokines

Procedia PDF Downloads 232

Authors: Haifeng Wang, Haili Zhang

Abstract:

Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

Procedia PDF Downloads 235

Authors: Kristian Bautista, Ruben A. Idoy

Abstract:

A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

Procedia PDF Downloads 200

Authors: Fariborz Ahmadi, Reza Tati

Abstract:

Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn’t produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only do not this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones.

Keywords: hardware, genetic algorithm, computer science, engineering

Procedia PDF Downloads 468