Search results for: rank ordered clustering

Authors: Mario Tauzene Afonso Matangue, Ivan Andres Sanchez Ortiz

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The Metropolitan area of Maputo (Mozambique Capital City) is located in semi-arid zone (800 mm annual rainfall) with 1101170 million inhabitants. On the west side, there are the flatlands of Infulene where the Mulauze River flows towards to the Indian Ocean, receiving at this site, the storm water contaminated with sanitary sewage from Maputo, transported through a concrete open channel. In Infulene, local communities grow salads crops such as tomato, onion, garlic, lettuce, and cabbage, which are then commercialized and consumed in several markets in Maputo City. Lettuce is the most daily consumed salad crop in different meals, generally in fast-foods, breakfasts, lunches, and dinners. However, the risk of infection by several pathogens due to the consumption of lettuce, using the Quantitative Microbial Risk Assessment (QMRA) tools, is still unknown since there are few studies or publications concerning to this matter in Mozambique. This work is aimed at determining the annual risk arising from the consumption of lettuce grown in Infulene valley, in Maputo, using QMRA tools. The exposure model was constructed upon the volume of contaminated water remaining in the lettuce leaves, the empirical relations between the number of pathogens and the indicator of microorganisms (E. coli), the consumption of lettuce (g) and reduction of pathogens (days). The reference pathogens were Vibrio cholerae, Cryptosporidium, norovirus, and Ascaris. The water quality samples (E. coli) were collected in the storm water channel from January 2016 to December 2018, comprising 65 samples, and the urban lettuce consumption data were collected through inquiry in Maputo Metropolis covering 350 persons. A non-parametric bootstrap was performed involving 10,000 iterations over the collected dataset, namely, water quality (E. coli) and lettuce consumption. The dose-response models were: Exponential for Cryptosporidium, Kummer Confluent hypergeomtric function (1F1) for Vibrio and Ascaris Gaussian hypergeometric function (2F1-(a,b;c;z) for norovirus. The annual infection risk estimates were performed using R 3.6.0 (CoreTeam) software by Monte Carlo (Latin hypercubes), a sampling technique involving 10,000 iterations. The annual infection risks values expressed by Median and the 95th percentile, per person per year (pppy) arising from the consumption of lettuce are as follows: Vibrio cholerae (1.00, 1.00), Cryptosporidium (3.91x10⁻³, 9.72x 10⁻³), nororvirus (5.22x10⁻¹, 9.99x10⁻¹) and Ascaris (2.59x10⁻¹, 9.65x10⁻¹). Thus, the consumption of the lettuce would result in greater risks than the tolerable levels ( < 10⁻³ pppy or 10⁻⁶ DALY) for all pathogens, and the Vibrio cholerae is the most virulent pathogens, according to the hit-single models followed by the Ascaris lumbricoides and norovirus. The sensitivity analysis carried out in this work pointed out that in the whole QMRA, the most important input variable was the reduction of pathogens (Spearman rank value was 0.69) between harvest and consumption followed by water quality (Spearman rank value was 0.69). The decision-makers (Mozambique Government) must strengthen the prevention measures related to pathogens reduction in lettuce (i.e., washing) and engage in wastewater treatment engineering.

Keywords: annual infections risk, lettuce, non-parametric bootstrapping, quantitative microbial risk assessment tools

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Authors: Elham Alaee, Mousa Shamsi, Hossein Ahmadi, Soroosh Nazem, Mohammad Hossein Sedaaghi

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Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn’t work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region’s area error (0.045) for the proposed algorithm.

Keywords: facial image, segmentation, PCM, FCM, skin error, facial surgery

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Authors: Samson Davoyan, Ashot Davoyan, Ani Khachatryan

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The indexes (Global Competitiveness Index, Economic Freedom Index, Human Development Index, etc.) developed by different international and non-government organizations in time and space express the quantitative and qualitative features of different fields of various reforms implemented in different countries. The main objective of our research is to develop new methodology that we will use to create integral index based on many indexes and that will include many areas of reforms. To achieve our aim we have used econometric methods (regression model for panel data method). The basis of our methodology is the development of the new integral index based on quantitative assessment of the change of two main parameters: the score of the countries by different indexes and the change of the ranks of countries for following two periods of time. As a result of the usage of methods for analyzes we have defined the indexes that are used to create the new integral index and the scales for each of them. Analyzing quantitatively and qualitatively analysis through the integral index for more than 100 countries for 2009-2014, we have defined comparative efficiency that helps to conclude in which directions countries have implemented reforms more effectively compared to others and in which direction reforms have implemented less efficiently.

Keywords: development, rank, reforms, comparative, index, economic, corruption, social, program

Procedia PDF Downloads 321

Authors: Monika Dmitrzak-Weglarz, Aleksandra Rajewska-Rager, Maria Skibinska, Natalia Lepczynska, Piotr Sibilski, Joanna Pawlak, Pawel Kapelski, Joanna Hauser

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Epidermal growth factor (EGF) is a well-known neurotrophic factor that involves in neuronal growth and synaptic plasticity. The proteomic research provided in order to identify novel candidate biological markers for mood disorders focused on elevated EGF serum level in patients during depression episode. However, the EGF association with mood disorder spectrum among adolescents and young adults has not been studied extensively. In this study, we aim to investigate the serum levels of EGF in adolescents and young adults during hypo/manic, depressive episodes and in remission compared to healthy control group. In our study, we involved 80 patients aged 12-24 years in 2-year follow-up study with a primary diagnosis of mood disorder spectrum, and 35 healthy volunteers matched by age and gender. Diagnoses were established according to DSM-IV-TR criteria using structured clinical interviews: K-SADS for child and adolescents, and SCID for young adults. Clinical and biological evaluations were made at baseline and euthymic mood (at 3th or 6th month of treatment and after 1 and 2 years). The Young Mania Rating Scale and Hamilton Rating Scale for Depression were used for assessment. The study protocols were approved by the relevant ethics committee. Serum protein concentration was determined by Enzyme-Linked Immunosorbent Assays (ELISA) method. Human EGF (cat. no DY 236) DuoSet ELISA kit was used (R&D Systems). Serum EGF levels were analysed with following variables: age, age under 18 and above 18 years old, sex, family history of affective disorders, drug-free vs. medicated. Shapiro-Wilk test was used to test the normality of the data. The homogeneity of variance was calculated with Levene’s test. EGF levels showed non-normal distribution and the homogeneity of variance was violated. Non-parametric tests: Mann-Whitney U test, Kruskall-Wallis ANOVA, Friedman’s ANOVA, Wilcoxon signed rank test, Spearman correlation coefficient was applied in the analyses The statistical significance level was set at p<0.05. Elevated EGF level at baseline (p=0.001) and at month 24 (p=0.02) was detected in study subjects compared with controls. Increased EGF level in women at month 12 (p=0.02) compared to men in study group have been observed. Using Wilcoxon signed rank test differences in EGF levels were detected: decrease from baseline to month 3 (p=0.014) and increase comparing: month 3 vs. 24 (p=0.013); month 6 vs. 12 (p=0.021) and vs. 24 (p=0.008). EGF level at baseline was negatively correlated with depression and mania occurrence at 24 months. EGF level at 24 months was positively correlated with depression and mania occurrence at 12 months. No other correlations of EGF levels with clinical and demographical variables have been detected. The findings of the present study indicate that EGF serum level is significantly elevated in the study group of patients compared to the controls. We also observed fluctuations in EGF levels during two years of disease observation. EGF seems to be useful as an early marker for prediction of diagnosis, course of illness and treatment response in young patients during first episode od mood disorders, which requires further investigation. Grant was founded by National Science Center in Poland no 2011/03/D/NZ5/06146.

Keywords: biological marker, epidermal growth factor, mood disorders, prediction

Procedia PDF Downloads 179

Authors: Rugang Tian

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In this study, we generated whole-genome sequence (WGS) data from110 native cattle. Together with whole-genome sequences from world-wide cattle populations, we estimated the genetic diversity and population genetic structure of different cattle populations. Our findings revealed clustering of cattle groups in line with their geographic locations. We identified noticeable genetic diversity between indigenous cattle breeds and commercial populations. Among all studied cattle groups, lower genetic diversity measures were found in commercial populations, however, high genetic diversity were detected in some local cattle, particularly in Rashoki and Mongolian breeds. Our search for potential genomic regions under selection in native cattle revealed several candidate genes related with immune response and cold shock protein on multiple chromosomes such as TRPM8, NMUR1, PRKAA2, SMTNL2 and OXR1 that are involved in energy metabolism and metabolic homeostasis.

Keywords: cattle, whole-genome, population structure, adaptation

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Authors: Uwalomwa Uwuigbe, Olubukola Ranti Uwuigbe, Jinadu Olugbenga, Otekunrin Adegbola

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Corporate reporting has evolved over the years resulting from criticisms of the precedent by shareholders, stakeholders and other relevant financial institutions. Integrated reporting has become a globalized corporate reporting style, with its adoption around the world occurring rapidly to bring about an improvement in the quality of corporate reporting. While some countries have swiftly clinched into reporting in an integrated manner, others have not. In addition, there are ample research that has been conducted on the benefits of adopting integrated reporting, however, the same is not true in developing economies like Nigeria. Hence, this study basically examined the factors determining the adoption of integrated reporting in Nigeria. One hundred (100) copies of questionnaire was administered to financial managers of 20 selected listed companies in the Nigeria stock exchange market. The data obtained was analysed using the Spearman Rank Order Correlation via the Statistical Package for Social Science. This study observed that there is a significant relationship between the social pressures of isomorphic changes and integrated reporting adoption in Nigeria. The study recommends the need for an enforcement mechanism to be put in place while considering the adoption of integrated reporting in Nigeria, enforcement mechanisms should put into consideration the investors demand, the level of economic development, and the degree of corporate social responsibility.

Keywords: corporate social responsibility, isomorphic, integrated reporting, Nigeria, sustainability

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Authors: Greg Zimmerman

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Background: Major depression is a common illness that affects millions of people globally. It is the leading cause of disability and is projected to become the number one cause of the global burden of disease by 2030. Many of those who suffer from depression also suffer from Irritable Bowel Syndrome (IBS). Acupuncture has been shown to help depression. The aim of this study was to investigate the effectiveness of acupuncture in reducing the risk of IBS in patients with depression. Methods: We enrolled patients diagnosed with depression through the Taiwanese National Health Insurance Research Database (NHIRD). Propensity score matching was used to match equal numbers (n=32971) of the acupuncture cohort and no-acupuncture cohort based on characteristics including sex, age, baseline comorbidity, and medication. The Cox regression model was used to compare the hazard ratios (HRs) of IBS in the two cohorts. Results: The basic characteristics of the two groups were similar. The cumulative incidence of IBS was significantly lower in the acupuncture cohort than in the no-acupuncture cohort (Log-rank test, p<0.001). Conclusion: The results provided real-world evidence that acupuncture may have a beneficial effect on IBS risk reduction in patients with depression.

Keywords: acupuncture, depression, irritable bowel syndrome, national health insurance research database, real-world evidence

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Authors: Sirous Eydivandi

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Diacylglycerol-O-acyltransferase (DGAT1) gene encodes diacylglycerol transferase enzyme that plays an important role in glycerol lipid metabolism. DGAT1 is considered to be the key enzyme in controlling the synthesis of triglycerides in adipocytes. This enzyme catalyzes the final step of triglyceride synthesis (transform triacylglycerol (DAG) into triacylglycerol (TAG). A total of 20 DGAT1 gene sequences and corresponding amino acids belonging to 4 species include cattle, goats, sheep and yaks were analyzed, and the differentiation within and among the species was also studied. The length of the DGAT1 gene varies greatly, from 1527 to 1785 bp, due to deletion, insertion, and stop codon mutation resulting in elongation. Observed genetic diversity was higher among species than within species, and Goat had more polymorphisms than any other species. Novel amino acid variation sites were detected within several species which might be used to illustrate the functional variation. Differentiation of the DGAT1 gene was obvious among species, and the clustering result was consistent with the taxonomy in the National Center for Biotechnology Information.

Keywords: DGAT1gene, bioinformatic, ruminnants, biotechnology information

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Authors: Jeonghun Lee

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In this study, we investigated the relationship between stress and thyroid dysfunction in such patients who underwent thyroidectomy. This study included 101 patients who underwent thyroidectomy from January 2015 to June 2020 and experienced hypothyroidism. The included patients had good drug compliance with the same dosage of levothyroxine (LT4). The male-to-female ratio was 1:4.6, and the mean age was 45.4 years at surgery and 50.2 years at stressful events. Eighteen patients underwent lobectomies and, of these, 12 did not take LT4. The mean follow-up period was 49(8-93) months. Statistical analyses were performed using the paired t-test, Wilcoxon signed-rank test, and McNemer test using PROC MIXED with SAS 9.4. Forty-five patients (44.6%) had hypothyroidism with thyroid-stimulating hormone (TSH) >10 μIU/mL. There was distress in 81 patients and eustress in 10 patients. TSH levels increased during a mean 5.8 months (min 1, max 12) in 24 patients who specified the date of their life events. Even though each patient took the same dose of LT4, when the patients were under stress, both the free T4 and T3 decreased and TSH increased, regardless of whether the patient experienced distress or eustress (P <0.001). While adjusting for the effect of the free T4 and T3, TSH increased significantly in the patients after stress (P <0.001). For patients with thyroid cancer who are simultaneously experiencing life events, TSH may be used as a stress biomarker to enable the implementation of appropriate treatment and counseling strategies.

Keywords: endocrine, thyroid, thyroid function, biomarker, stress

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Authors: Choedchoo Kwanhathai

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This study explores attitudes towards ASEAN plus ONE (namely ASEAN plus China) accents of English as a Lingua Franca. The study draws attention to features of ASEAN’s diversity of English and specifically examines the extent of which the English accent in ASEAN countries of three of the ten members plus one were perceived in terms of correctness, acceptability, pleasantness, and familiarity. Three accents were used for this study; Chinese, Philippine and Thai. The participants were ninety eight Thai students enrolled in a foundation course of Suan Dusit Rajabhat University, Bangkok Thailand. The students were asked in questionnaires to rank how they perceived each specifically ASEAN plus One English accent after listening to audio recordings of three stories spoken by the three different ASEAN plus ONE English speakers. SPSS was used to analyze the data. The findings of attitudes towards varieties of English accent from the 98 respondents regarding correctness, acceptability, pleasantness, and familiarity of Thai English accents found that Thai accent was overall at level 3 (X = 2.757, SD= o.33), %Then Philippines accents was at level 2 (X = 2.326, SD = 16.12), and Chinese accents w2as at level 3 (X 3.198, SD = 0.18). Finally, the present study proposes pedagogical implications for teaching regarding awareness of ‘Englishes’ of ASEAN and their respective accents and their lingua cultural background of instructors.

Keywords: English as a lingua franca, English accents, English as an international language, ASEAN plus one, ASEAN English varieties

Procedia PDF Downloads 413

Authors: Swarnadip Ghosh

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Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results.

Keywords: sentiment, Run's Test, cross validation, higher dimensional pmf estimation

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Authors: Trapti Sharma, Devesh Kumar Srivastava

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This paper is basically a analysis study of above MapReduce implementation and also to verify and validate the MapReduce solution model for Parallel K-Mediod algorithm on Hadoop Cluster. MapReduce is a programming model which authorize the managing of huge amounts of data in parallel, on a large number of devices. It is specially well suited to constant or moderate changing set of data since the implementation point of a position is usually high. MapReduce has slowly become the framework of choice for “big data”. The MapReduce model authorizes for systematic and instant organizing of large scale data with a cluster of evaluate nodes. One of the primary affect in Hadoop is how to minimize the completion length (i.e. makespan) of a set of MapReduce duty. In this paper, we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Mediod clustering algorithm. We have found that as the amount of nodes increases the completion time decreases.

Keywords: hadoop, mapreduce, k-mediod, validation, verification

Procedia PDF Downloads 359

Authors: C. Rana, S. K. Jain

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The enormous amount of Web data has challenged its usage in efficient manner in the past few years. As such, a range of techniques are applied to tackle this problem; prominent among them is personalization and recommender system. In fact, these are the tools that assist user in finding relevant information of web. Most of the e-commerce websites are applying such tools in one way or the other. In the past decade, a large number of recommendation algorithms have been proposed to tackle such problems. However, there have not been much research in the evaluation criteria for these algorithms. As such, the traditional accuracy and classification metrics are still used for the evaluation purpose that provides a static view. This paper studies how the evolution of user preference over a period of time can be mapped in a recommender system using a new evaluation methodology that explicitly using time dimension. We have also presented different types of experimental set up that are generally used for recommender system evaluation. Furthermore, an overview of major accuracy metrics and metrics that go beyond the scope of accuracy as researched in the past few years is also discussed in detail.

Keywords: collaborative filtering, data mining, evolutionary, clustering, algorithm, recommender systems

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Authors: A. Peyvan, D. Li, J. Komperda, F. Mashayek

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Turbulence can be observed in a variety fluid motions in nature and industrial applications. Recent investment in high-speed aircraft and propulsion systems has revitalized fundamental research on turbulent flows. In these systems, capturing chaotic fluid structures with different length and time scales is accomplished through the Direct Numerical Simulation (DNS) approach since it accurately simulates flows down to smallest dissipative scales, i.e., Kolmogorov’s scales. The discontinuous spectral element method (DSEM) is a high-order technique that uses spectral functions for approximating the solution. The DSEM code has been developed by our research group over the course of more than two decades. Recently, the code has been improved to run large cases in the order of billions of solution points. Running big simulations requires a considerable amount of RAM. Therefore, the DSEM code must be highly parallelized and able to start on multiple computational nodes on an HPC cluster with distributed memory. However, some pre-processing procedures, such as determining global element information, creating a global face list, and assigning global partitioning and element connection information of the domain for communication, must be done sequentially with a single processing core. A separate code has been written to perform the pre-processing procedures on a local machine. It stores the minimum amount of information that is required for the DSEM code to start in parallel, extracted from the mesh file, into text files (pre-files). It packs integer type information with a Stream Binary format in pre-files that are portable between machines. The files are generated to ensure fast read performance on different file-systems, such as Lustre and General Parallel File System (GPFS). A new subroutine has been added to the DSEM code to read the startup files using parallel MPI I/O, for Lustre, in a way that each MPI rank acquires its information from the file in parallel. In case of GPFS, in each computational node, a single MPI rank reads data from the file, which is specifically generated for the computational node, and send them to other ranks on the node using point to point non-blocking MPI communication. This way, communication takes place locally on each node and signals do not cross the switches of the cluster. The read subroutine has been tested on Argonne National Laboratory’s Mira (GPFS), National Center for Supercomputing Application’s Blue Waters (Lustre), San Diego Supercomputer Center’s Comet (Lustre), and UIC’s Extreme (Lustre). The tests showed that one file per node is suited for GPFS and parallel MPI I/O is the best choice for Lustre file system. The DSEM code relies on heavily optimized linear algebra operation such as matrix-matrix and matrix-vector products for calculation of the solution in every time-step. For this, the code can either make use of its matrix math library, BLAS, Intel MKL, or ATLAS. This fact and the discontinuous nature of the method makes the DSEM code run efficiently in parallel. The results of weak scaling tests performed on Blue Waters showed a scalable and efficient performance of the code in parallel computing.

Keywords: computational fluid dynamics, direct numerical simulation, spectral element, turbulent flow

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Authors: Matthieu-P. Schapranow

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Recruitment of participants for clinical trials requires the screening of a big number of potential candidates, i.e. the testing for trial-specific inclusion and exclusion criteria, which is a time-consuming and complex task. Today, a significant amount of time is spent on identification of adequate trial participants as their selection may affect the overall study results. We introduce a unique patient eligibility metric, which allows systematic ranking and classification of candidates based on trial-specific filter criteria. Our web application enables real-time analysis of patient data and assessment of candidates using freely definable inclusion and exclusion criteria. As a result, the overall time required for identifying eligible candidates is tremendously reduced whilst additional degrees of freedom for evaluating the relevance of individual candidates are introduced by our contribution.

Keywords: in-memory technology, clinical trials, screening, eligibility metric, data analysis, clustering

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Authors: Imam Taufik, Hilkatul Ilmi, Puryani, Mochammad Yuwono, Aty Widyawaruyanti

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Artocarpus champeden Spreng stembark (Moraceae) in Indonesia well known as ‘cempedak’ had been traditionally used for malarial remedies. The difference of growth locations could cause the difference of metabolite profiling. As a consequence, there were difference antimalarial activities in spite of the same plants. The aim of this research was to obtain the profile of metabolites that contained in A. champeden stembark from different locations in Indonesia for authentication and quality control purpose of this extract. The profiling had been performed by HPTLC-Densitometry technique and antimalarial activity had been also determined by HRP2-ELISA technique. The correlation between metabolite fingerprinting and antimalarial activity had been analyzed by Principle Component Analysis, Hierarchical Clustering Analysis and Partial Least Square. As a result, there is correlation between the difference metabolite fingerprinting and antimalarial activity from several different growth locations.

Keywords: antimalarial, artocarpus champeden spreng, metabolite fingerprinting, multivariate analysis

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Authors: Thapthep Paprach, Benya Lertsuwan

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Investment fraud is not a new criminal, but there are still more victims during the Internet of Things era. This kind of criminal has been classified as a national and transnational financial crime problem all over the world. In Thailand, the country has also been attacked by this kind of crime. This research concerns whether the mass media that is supposed to cover news about online investment scams realized and warned Thais about this crime. Thus, this study explores the value of news about investment fraud in terms of frequency. The methodology uses web crawling from the top 5 news agency websites that have the most access. We pull out all information reporting about investment fraud. The findings revealed that the ‘Khaosod’ news agency was the first rank in reporting on investment crime. On the other hand, ‘Matichon’ was the least reported. Thairat news agencies frequently reported such criminals from midnight to very early in the morning, while other news agencies reported during the daytime. The results between the frequency of news reporting about investment fraud and the monthly number of victim reports are not correlated. Although the most cases reported to Thai police were in February 2023, but the most news reported was in January 2023. In conclusion, there might be a negative correlation between the amount of investment fraud news reported and the number of victims.

Keywords: investment fraud, news value, online news report, Ponzi schemes, Romance scam

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Authors: Shiva Kumar, G. S. Vijay, Srinivas Pai P., Shrinivasa Rao B. R.

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In the present study RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tech and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.

Keywords: radial basis function networks, emissions, performance parameters, fuzzy c means

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Authors: Xun Li, Haojie Wang

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Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper.

Keywords: federated learning, backdoor attack, PCA, k-medoids, backdoor defense

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Authors: Arkaprabha Bhattacharyya, Makarand Hastak

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The COVID-19 pandemic is unprecedented in terms of its global reach and economic impacts. Historically, investment in infrastructure development projects has been touted to boost the economic growth of a nation. The State and Local governments responsible for delivering infrastructure assets work under tight budgets. Therefore, it is important to understand which infrastructure projects have the highest potential of boosting economic growth in the post-pandemic era. This paper presents relationships between infrastructure projects and economic growth. Statistical relationships between investment in different types of infrastructure projects (transit, water and wastewater, highways, power, manufacturing etc.) and indicators of economic growth are presented using historic data between 2002 and 2020 from the U.S. Census Bureau and U.S. Bureau of Economic Analysis (BEA). The outcome of the paper is the comparison of statistical correlations between investment in different types of infrastructure projects and indicators of economic growth. The comparison of the statistical correlations is useful in ranking the types of infrastructure projects based on their ability to influence economic prosperity. Therefore, investment in the infrastructures with the higher rank will have a better chance of boosting the economic growth. Once, the ranks are derived, they can be used by the decision-makers in infrastructure investment related decision-making process.

Keywords: economic growth, infrastructure development, infrastructure projects, strategic investment

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Authors: Irina Khutsishvili

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In the present research, a decision support methodology for the multi-attribute group decision-making (MAGDM) problem is developed, namely for the selection of investment projects. The objective of the investment project selection problem is to choose the best project among the set of projects, seeking investment, or to rank all projects in descending order. The project selection is made considering a set of weighted attributes. To evaluate the attributes in our approach, expert assessments are used. In the proposed methodology, lingual expressions (linguistic terms) given by all experts are used as initial attribute evaluations, since they are the most natural and convenient representation of experts' evaluations. Then lingual evaluations are converted into trapezoidal fuzzy numbers, and the aggregate trapezoidal hesitant fuzzy decision matrix will be built. The case is considered when information on the attribute weights is completely unknown. The attribute weights are identified based on the De Luca and Termini information entropy concept, determined in the context of hesitant fuzzy sets. The decisions are made using the extended Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method under a hesitant fuzzy environment. Hence, a methodology is based on a trapezoidal valued hesitant fuzzy TOPSIS decision-making model with entropy weights. The ranking of alternatives is performed by the proximity of their distances to both the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS). For this purpose, the weighted hesitant Hamming distance is used. An example of investment decision-making is shown that clearly explains the procedure of the proposed methodology.

Keywords: In the present research, a decision support methodology for the multi-attribute group decision-making (MAGDM) problem is developed, namely for the selection of investment projects. The objective of the investment project selection problem is to choose the best project among the set of projects, seeking investment, or to rank all projects in descending order. The project selection is made considering a set of weighted attributes. To evaluate the attributes in our approach, expert assessments are used. In the proposed methodology, lingual expressions (linguistic terms) given by all experts are used as initial attribute evaluations since they are the most natural and convenient representation of experts' evaluations. Then lingual evaluations are converted into trapezoidal fuzzy numbers, and the aggregate trapezoidal hesitant fuzzy decision matrix will be built. The case is considered when information on the attribute weights is completely unknown. The attribute weights are identified based on the De Luca and Termini information entropy concept, determined in the context of hesitant fuzzy sets. The decisions are made using the extended Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method under a hesitant fuzzy environment. Hence, a methodology is based on a trapezoidal valued hesitant fuzzy TOPSIS decision-making model with entropy weights. The ranking of alternatives is performed by the proximity of their distances to both the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS). For this purpose, the weighted hesitant Hamming distance is used. An example of investment decision-making is shown that clearly explains the procedure of the proposed methodology.

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Authors: Rachid Belhachemi, Pierre Rostan, Alexandra Rostan

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This paper develops a discrete-time option pricing model for index options. The model consists of two key ingredients. First, daily stock return innovations are driven by a continuous hidden threshold mixed skew-normal (HTSN) distribution which generates conditional non-normality that is needed to fit daily index return. The most important feature of the HTSN is the inclusion of a latent state variable with a continuum of states, unlike the traditional mixture distributions where the state variable is discrete with little number of states. The HTSN distribution belongs to the class of univariate probability distributions where parameters of the distribution capture the dependence between the variable of interest and the continuous latent state variable (the regime). The distribution has an interpretation in terms of a mixture distribution with time-varying mixing probabilities. It has been shown empirically that this distribution outperforms its main competitor, the mixed normal (MN) distribution, in terms of capturing the stylized facts known for stock returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence. Second, heteroscedasticity in the model is captured by a threeexogenous-factor GARCH model (GARCHX), where the factors are taken from the principal components analysis of various world indices and presents an application to option pricing. The factors of the GARCHX model are extracted from a matrix of world indices applying principal component analysis (PCA). The empirically determined factors are uncorrelated and represent truly different common components driving the returns. Both factors and the eight parameters inherent to the HTSN distribution aim at capturing the impact of the state of the economy on price levels since distribution parameters have economic interpretations in terms of conditional volatilities and correlations of the returns with the hidden continuous state. The PCA identifies statistically independent factors affecting the random evolution of a given pool of assets -in our paper a pool of international stock indices- and sorting them by order of relative importance. The PCA computes a historical cross asset covariance matrix and identifies principal components representing independent factors. In our paper, factors are used to calibrate the HTSN-GARCHX model and are ultimately responsible for the nature of the distribution of random variables being generated. We benchmark our model to the MN-GARCHX model following the same PCA methodology and the standard Black-Scholes model. We show that our model outperforms the benchmark in terms of RMSE in dollar losses for put and call options, which in turn outperforms the analytical Black-Scholes by capturing the stylized facts known for index returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence.

Keywords: continuous hidden threshold, factor models, GARCHX models, option pricing, risk-premium

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Authors: Sepase K. Ivande

Abstract:

Morality and frugality are two of the strongest motivations for pro-environmental behavior. However, formulating interventions based on these motivations requires knowledge of who is likely to be motivated by morality and who by frugality. This study investigated which personality traits make someone predisposed to morality motivation and which to frugality motivation for pro-environmental behavior. Results from a series of multiple regression analyses indicated that openness and agreeableness had a positive association with morality motivation, while conscientiousness had a positive association with frugality motivation. The link of agreeableness to morality motivation was stronger when the individuals were also higher on openness. Furthermore, a pair of Wilcoxon signed-rank tests revealed that individuals high on openness and agreeableness but low on conscientiousness scored higher on morality than frugality motivation. On the other hand, individuals low on openness and agreeableness but high on conscientiousness scored higher on frugality than morality motivation. The results of this study could inform the formulation of personalized interventions based on people’s personal predisposition to morality and frugality motivation for pro-environmental behavior, which could be more effective in getting them to be pro-environmental.

Keywords: agreeableness, conscientiousness, frugality, higher order motivations, morality, openness to experience, personality traits, pro-environmental behavior

Procedia PDF Downloads 100

Authors: Muhammad Shahzad Shifa, Amna Shifa, Muhammad Omar, Aamir Shahzad, Rahmat Ali Khan

Abstract:

Many applications of remote sensing to vegetation and crop response depend on spectral properties of individual leaves and plants. Vegetation indices are usually determined to estimate crop biophysical parameters like crop canopies and crop leaf area indices with the help of remote sensing. Cotton crops assessment is performed with the help of vegetative indices. Remotely sensed images from an optical multispectral radiometer MSR5 are used in this study. The interpretation is based on the fact that different materials reflect and absorb light differently at different wavelengths. Non-normalized and normalized forms of these datasets are analyzed using two complementary data mining algorithms; K-means and K-nearest neighbor (KNN). Our analysis shows that the use of normalized reflectance data and vegetative indices are suitable for an automated assessment and decision making.

Keywords: cotton, condition assessment, KNN algorithm, clustering, MSR5, vegetation indices

Procedia PDF Downloads 323

Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

Abstract:

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: texture classification, texture descriptor, SIFT, SURF, ORB

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Authors: Prasanna Ramachandran, Kareem Graham, Helena Kiefel, Sunit Jain, Todd DeSantis

Abstract:

Microbes that reside inside the mammalian GI tract, commonly referred to as the gut microbiome, have been shown to have therapeutic effects in animal models of disease. We hypothesize that specific proteins produced by these microbes are responsible for this activity and may be used directly as therapeutics. To speed up the discovery of these key proteins from the big-data metagenomics, we have applied machine learning techniques. Using amino acid sequences of known epitopes and their corresponding binding partners, protein interaction descriptors (PID) were calculated, making a positive interaction set. A negative interaction dataset was calculated using sequences of proteins known not to interact with these same binding partners. Using Random Forest and positive and negative PID, a machine learning model was trained and used to predict interacting versus non-interacting proteins. Furthermore, the continuous variable, cosine similarity in the interaction descriptors was used to rank bacterial therapeutic candidates. Laboratory binding assays were conducted to test the candidates for their potential as therapeutics. Results from binding assays reveal the accuracy of the machine learning prediction and are subsequently used to further improve the model.

Keywords: protein-interactions, machine-learning, metagenomics, microbiome

Procedia PDF Downloads 366

Authors: U.V. Suryawanshi, S.S. Chowhan, U.V Kulkarni

Abstract:

Accuracy of segmentation methods is of great importance in brain image analysis. Tissue classification in Magnetic Resonance brain images (MRI) is an important issue in the analysis of several brain dementias. This paper portraits performance of segmentation techniques that are used on Brain MRI. A large variety of algorithms for segmentation of Brain MRI has been developed. The objective of this paper is to perform a segmentation process on MR images of the human brain, using Fuzzy c-means (FCM), Kernel based Fuzzy c-means clustering (KFCM), Spatial Fuzzy c-means (SFCM) and Improved Fuzzy c-means (IFCM). The review covers imaging modalities, MRI and methods for noise reduction and segmentation approaches. All methods are applied on MRI brain images which are degraded by salt-pepper noise demonstrate that the IFCM algorithm performs more robust to noise than the standard FCM algorithm. We conclude with a discussion on the trend of future research in brain segmentation and changing norms in IFCM for better results.

Keywords: image segmentation, preprocessing, MRI, FCM, KFCM, SFCM, IFCM

Procedia PDF Downloads 319

Authors: Masood Ahmad, Ataul Aziz Ikram, Ishtiaq Wahid

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Wireless sensor network (WSN) is the interconnection of mobile wireless nodes with limited energy and memory. These networks can be deployed formany critical applications like military operations, rescue management, fire detection and so on. In flat routing structure, every node plays an equal role of sensor and router. The topology may change very frequently due to the mobile nature of nodes in WSNs. The topology maintenance may produce more overhead messages. To avoid topology maintenance overhead messages, an optimized cluster based mobile wireless sensor network using memetic algorithm is proposed in this paper. The nodes in this network are first divided into clusters. The cluster leaders then transmit data to that base station. The network is validated through extensive simulation study. The results show that the proposed technique has superior results compared to existing techniques.

Keywords: WSN, routing, cluster based, meme, memetic algorithm

Procedia PDF Downloads 475

Authors: M. Munirul Islam, Makhduma Khatun M., Janet M. Peerson, Tahmeed Ahmed, M. Abid Hossain Mollah, Kathryn G. Dewey, Kenneth H. Brown

Abstract:

Information is required on the effects of dietary energy density (ED) and feeding frequency (FF) of complementary foods (CF) on food consumption during individual meals and time expended in child feeding. We evaluated the effects of varied ED and FF of CFs on food intake and time required for child feeding during individual meals. During 9 separate, randomly ordered dietary periods lasting 3-6 days each, we measured self-determined intakes of porridges by 18 healthy, breastfed children 8-11 mo old who were fed coded porridges with energy densities of 0.5, 1.0 or 1.5 kcal/g, during 3, 4, or 5 meals/d. CF intake was measured by weighing the feeding bowl before and after every meal. Children consumed greater amounts of CFs per meal when they received diets with lower ED (p = 0.044) and fewer meals per day (p < 0.001). Food intake was less during the first meal of the day than the other meals. Greater time was expended per meal when fewer meals were offered. Time expended per meal did not vary by ED, but the children ate the lower ED diets faster (p = 0.019). Food intake velocity was also greater when more meals were offered per day (p = 0.005). These results provide further evidence of young children’s ability to regulate their energy intakes, even during infancy; and they convey information on factors that affect the amount of time that caregivers must devote to child feeding.

Keywords: complementary foods, energy density, feeding frequency, young children

Procedia PDF Downloads 456

Authors: Rana I. Abdulghani, Amera I. Melhum

Abstract:

With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

Procedia PDF Downloads 375