Search results for: random PWM algorithm.

Authors: Ayman A. AbuBaker, Rami S.Qahwaji, Musbah J. Aqel, Mohmmad H. Saleh

Abstract:

Two algorithms are proposed to reduce the storage requirements for mammogram images. The input image goes through a shrinking process that converts the 16-bit images to 8-bits by using pixel-depth conversion algorithm followed by enhancement process. The performance of the algorithms is evaluated objectively and subjectively. A 50% reduction in size is obtained with no loss of significant data at the breast region.

Keywords: Breast cancer, Image processing, Image reduction, Mammograms, Image enhancement

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Authors: J. Cheeneebash, V. Beeharry, A. Gopaul

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The aim of this paper is to express the input-output matrix as a linear ordering problem which is classified as an NP-hard problem. We then use a Tabu search algorithm to find the best permutation among sectors in the input-output matrix that will give an optimal solution. This optimal permutation can be useful in designing policies and strategies for economists and government in their goal of maximizing the gross domestic product.

Keywords: Input-Output matrix, linear ordering problem, Tabusearch.

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Authors: Kasthurirangan Gopalakrishnan, Naga Shashidhar

Abstract:

The mechanical properties of granular solids are dependent on the flow of stresses from one particle to another through inter-particle contact. Although some experimental methods have been used to study the inter-particle contacts in the past, preliminary work with these techniques indicated that they do not have the necessary resolution to distinguish between those contacts that transmit the load and those that do not, especially for systems with a wide distribution of particle sizes. In this research, computer simulations are used to study the nature and distribution of contacts in a compact with wide particle size distribution, representative of aggregate size distribution used in asphalt pavement construction. The packing fraction, the mean number of contacts and the distribution of contacts were studied for different scenarios. A methodology to distinguish and compute the fraction of load-bearing particles and the fraction of space-filling particles (particles that do not transmit any force) is needed for further investigation.

Keywords: Computer simulation, three-dimensional particlepacking, coordination number, asphalt concrete, aggregates.

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Authors: Afrooz Moatari Kazerouni

Abstract:

In a competitive production environment, critical decision making are based on data resulted by random sampling of product units. Efficiency of these decisions depends on data quality and also their reliability scale. This point leads to the necessity of a reliable measurement system. Therefore, the conjecture process and analysing the errors contributes to a measurement system known as Measurement System Analysis (MSA). The aim of this research is on determining the necessity and assurance of extensive development in analysing measurement systems, particularly with the use of Repeatability and Reproducibility Gages (GR&R) to improve physical measurements. Nowadays in productive industries, repeatability and reproducibility gages released so well but they are not applicable as well as other measurement system analysis methods. To get familiar with this method and gain a feedback in improving measurement systems, this survey would be on “ANOVA" method as the most widespread way of calculating Repeatability and Reproducibility (R&R).

Keywords: Analysis of Variance (ANOVA), MeasurementSystem Analysis (MSA), Part-Operator interaction effect, Repeatability and Reproducibility.

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Authors: N. Phumchusri, K. Roekdethawesab, M. Lohatepanont

Abstract:

Overbooking is an approach of selling more goods or services than available capacities because sellers anticipate that some buyers will not show-up or may cancel their bookings. At present, many airlines deploy overbooking strategy in order to deal with the uncertainty of their customers. Particularly, some airlines sell more cargo capacity than what they have available to freight forwarders with beliefs that some of them will cancel later. In this paper, we propose methods to find the optimal overbooking level of volume and weight for air cargo in order to minimize the total cost, containing cost of spoilage and cost of offloaded. Cancellations of volume and weight are jointly random variables with a known joint distribution. Heuristic approaches applying the idea of weight and volume independency is considered to find an appropriate answer to the full problem. Computational experiments are used to explore the performance of approaches presented in this paper, as compared to a naïve method under different scenarios.

Keywords: Air cargo overbooking, offloaded capacity, optimal overbooking level, revenue management, spoilage capacity.

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Authors: Seyedsamad Tahani, Samira Ghorbanpour, Sekyung Han

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The global warming crisis forced humans to consider their place in the world and the earth's future. In this regard, Electric Vehicles (EVs) are a significant step towards protecting the environment. By identifying factors that influence people's behavior intentions toward using EVs, we proposed a theoretical model by extending the Technology Acceptance Model (TAM), including three more concepts, Subjective Norm (SN), Self-Efficacy (SE), and Perceived Behavior Control (PBC). The study was conducted in South Korea, and a random sample was taken at a specific time. In order to collect data, a questionnaire was created in a Google Form and sent via Kakao Talk, a popular social media application used in Korea. There were about 220 participants in this survey. However, 201 surveys were completely done. The findings revealed that all factors in the TAM model and the other added concepts such as SNs, SE and PBC significantly affect the behavioral intention of using EVs.

Keywords: Electric vehicles, behavioral intention, subjective norm, self-efficacy, perceived behavior control.

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Authors: Kong Fah Tee, Lutfor Rahman Khan, Hongshuang Li

Abstract:

An advanced Monte Carlo simulation method, called Subset Simulation (SS) for the time-dependent reliability prediction for underground pipelines has been presented in this paper. The SS can provide better resolution for low failure probability level with efficient investigating of rare failure events which are commonly encountered in pipeline engineering applications. In SS method, random samples leading to progressive failure are generated efficiently and used for computing probabilistic performance by statistical variables. SS gains its efficiency as small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment. It is hoped that the development work can promote the use of SS tools for uncertainty propagation in the decision-making process of underground pipelines network reliability prediction.

Keywords: Underground pipelines, Probability of failure, Reliability and Subset Simulation.

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Authors: A. M. Azad, M. Alam, C. M. Hussain

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In this paper, we have presented the effect of varying time-delays on performance and stability in the single-channel multirate sampled-data system in hard real-time (RT-Linux) environment. The sampling task require response time that might exceed the capacity of RT-Linux. So a straight implementation with RT-Linux is not feasible, because of the latency of the systems and hence, sampling period should be less to handle this task. The best sampling rate is chosen for the sampled-data system, which is the slowest rate meets all performance requirements. RT-Linux is consistent with its specifications and the resolution of the real-time is considered 0.01 seconds to achieve an efficient result. The test results of our laboratory experiment shows that the multi-rate control technique in hard real-time operating system (RTOS) can improve the stability problem caused by the random access delays and asynchronization.

Keywords: Multi-rate, PID, RT-Linux, Sampled-data, Servo.

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Authors: Vishal Raj, Noorhan Abbas

Abstract:

Ambiguity in NLP (Natural Language Processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a method to create an affinity matrix to calculate the affinity between any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an algorithm to create the sense clusters of tokens using affinity matrix under hierarchy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contextual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and challenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.

Keywords: Word Sense Disambiguation, WSD, Contextual SenSe Model, Most Frequent Sense, part of speech, POS, Natural Language Processing, NLP, OOV, out of vocabulary, ELMo, Embeddings from Language Model, BERT, Bidirectional Encoder Representations from Transformers, Word2Vec, lemma_POS, Algorithm.

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Authors: Yu-Mi Song, Sung-Ah Kim, Dongyoun Shin

Abstract:

Cities are complex systems of diverse and inter-tangled activities. These activities and their complex interrelationships create diverse urban phenomena. And such urban phenomena have considerable influences on the lives of citizens. This research aimed to develop a method to reveal the causes and effects among diverse urban elements in order to enable better understanding of urban activities and, therefrom, to make better urban planning strategies. Specifically, this study was conducted to solve a data-recommendation problem found on a Korean public data homepage. First, a correlation analysis was conducted to find the correlations among random urban data. Then, based on the results of that correlation analysis, the weighted data network of each urban data was provided to people. It is expected that the weights of urban data thereby obtained will provide us with insights into cities and show us how diverse urban activities influence each other and induce feedback.

Keywords: Big data, correlation analysis, data recommendation system, urban data network.

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Authors: Padmanabhan Balasubramanian, C. Hari Narayanan

Abstract:

This paper discusses a new, systematic approach to the synthesis of a NP-hard class of non-regenerative Boolean networks, described by FON[FOFF]={mi}[{Mi}], where for every mj[Mj]∈{mi}[{Mi}], there exists another mk[Mk]∈{mi}[{Mi}], such that their Hamming distance HD(mj, mk)=HD(Mj, Mk)=O(n), (where 'n' represents the number of distinct primary inputs). The method automatically ensures exact minimization for certain important selfdual functions with 2n-1 points in its one-set. The elements meant for grouping are determined from a newly proposed weighted incidence matrix. Then the binary value corresponding to the candidate pair is correlated with the proposed binary value matrix to enable direct synthesis. We recommend algebraic factorization operations as a post processing step to enable reduction in literal count. The algorithm can be implemented in any high level language and achieves best cost optimization for the problem dealt with, irrespective of the number of inputs. For other cases, the method is iterated to subsequently reduce it to a problem of O(n-1), O(n-2),.... and then solved. In addition, it leads to optimal results for problems exhibiting higher degree of adjacency, with a different interpretation of the heuristic, and the results are comparable with other methods. In terms of literal cost, at the technology independent stage, the circuits synthesized using our algorithm enabled net savings over AOI (AND-OR-Invert) logic, AND-EXOR logic (EXOR Sum-of- Products or ESOP forms) and AND-OR-EXOR logic by 45.57%, 41.78% and 41.78% respectively for the various problems. Circuit level simulations were performed for a wide variety of case studies at 3.3V and 2.5V supply to validate the performance of the proposed method and the quality of the resulting synthesized circuits at two different voltage corners. Power estimation was carried out for a 0.35micron TSMC CMOS process technology. In comparison with AOI logic, the proposed method enabled mean savings in power by 42.46%. With respect to AND-EXOR logic, the proposed method yielded power savings to the tune of 31.88%, while in comparison with AND-OR-EXOR level networks; average power savings of 33.23% was obtained.

Keywords: AOI logic, ESOP, AND-OR-EXOR, Incidencematrix, Hamming distance.

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Authors: Itgel Bayarsaikhan, Jimin Lee, Sejong Oh

Abstract:

Classification is one of the primary themes in computational biology. The accuracy of classification strongly depends on quality of a dataset, and we need some method to evaluate this quality. In this paper, we propose a new graphical analysis method using 'Membership-Deviation Graph (MDG)' for analyzing quality of a dataset. MDG represents degree of membership and deviations for instances of a class in the dataset. The result of MDG analysis is used for understanding specific feature and for selecting best feature for classification.

Keywords: feature, classification, machine learning algorithm.

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Authors: Dhany Saputra, Dayang R. A. Rambli, Oi Mean Foong

Abstract:

In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use sufficient data structure for Seq-Tree framework and separator database to reduce the execution time and memory usage. Thus, with I-PrefixSpan there is no in-memory database stored after index set is constructed. The experimental result shows that using Java 2, this method improves the speed of PrefixSpan up to almost two orders of magnitude as well as the memory usage to more than one order of magnitude.

Keywords: ArrayList, ArrayIntList, minimum support, sequence database, sequential patterns.

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Authors: M. Imran Akram, Ahmed Pasha, Nabeel Iqbal

Abstract:

In this paper a real-time trajectory generation algorithm for computing 2-D optimal paths for autonomous aerial vehicles has been discussed. A dynamic programming approach is adopted to compute k-best paths by minimizing a cost function. Collision detection is implemented to detect intersection of the paths with obstacles. Our contribution is a novel approach to the problem of trajectory generation that is computationally efficient and offers considerable gain over existing techniques.

Keywords: dynamic programming, graph search, path planning.

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Authors: Monjed Ezzeddinne, Nicolas Lefebvre, Régis Lengellé

Abstract:

This paper presents a new version of the SVM mixture algorithm initially proposed by Kwok for classification and regression problems. For both cases, a slight modification of the mixture model leads to a standard SVM training problem, to the existence of an exact solution and allows the direct use of well known decomposition and working set selection algorithms. Only the regression case is considered in this paper but classification has been addressed in a very similar way. This method has been successfully applied to engine pollutants emission modeling.

Keywords: Identification, Learning systems, Mixture ofExperts, Support Vector Machines.

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Authors: Seyed Saeed Changiz Rezaei, Babak Hossein Khalaj

Abstract:

As the demand for higher capacity in a cellular environment increases, the cell size decreases. This fact makes the role of suitable handoff algorithms to reduce both number of handoffs and handoff delay more important. In this paper we show that applying the grey prediction technique for handoff leads to considerable decrease in handoff delay with using a small number of handoffs, compared with traditional hystersis based handoff algorithms.

Keywords: Cellular network, Grey prediction, Handoff.

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Authors: Z. Abdullah, A. R. Hamdan

Abstract:

Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the area in data mining and it can be classified into partition, hierarchical, density based and grid based. Therefore, in this paper we do survey and review four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems as well as deriving more robust and scalable algorithms for clustering.

Keywords: Clustering, method, algorithm, hierarchical, survey.

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Authors: Jatinderkumar R. Saini

Abstract:

E-mail has become a key mechanism of electronic communication. This is true for professional organizations that like to communicate with their subjects online and are slowly shifting to paper-less office. The current paper focuses specifically on academic institutions offering Engineering course in Gujarat state and attempts for textual analysis of the usernames of the institutional e-mail addresses. We found that the institutions tend to design the username segment of their e-mail addresses by choosing words or combination of words from specific categories. The paper also highlights the use of special characters, digits and random words in designing the usernames. On the sidelines, the paper lists the style of employing department names and designations for the design process. To the best of our knowledge, this is the first formal attempt to analyze the selection of words employed for designing username segment of e-mail addresses of engineering institutions.

Keywords: E-mail address, Institute, Engineering, Username.

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Authors: Muhammad Wajid Tahir, Rubina Kauser, Majid Ali Tahir

Abstract:

Pakistani doctors (MBBS) are emigrating towards developed countries for professional adjustments. This study aims to highlight causes and consequences of doctors- brain drain from Pakistan. Primary data was collected from Mayo Hospital, Lahore by interviewing doctors (n=100) through systematic random sampling technique. It found that various socio-economic and political conditions are working as push and pull factors for brain drain of doctors in Pakistan. Majority of doctors (83%) declared poor remunerations and professional infrastructure of health department as push factor of doctors- brain drain. 81% claimed that continuous instability in political situation and threats of terrorism are responsible for emigration of doctors. 84% respondents considered fewer opportunities of further studies responsible for their emigration. Brain drain of doctors is affecting health sector-s policies / programs, standard doctor-patient ratios and quality of health services badly.

Keywords: Brain Drain, Emigration, Remuneration, Politicalinstability, MBBS doctors

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Authors: B. Farhadinia

Abstract:

Although so far, many methods for ranking fuzzy numbers have been discussed broadly, most of them contained some shortcomings, such as requirement of complicated calculations, inconsistency with human intuition and indiscrimination. The motivation of this study is to develop a model for ranking fuzzy numbers based on the lexicographical ordering which provides decision-makers with a simple and efficient algorithm to generate an ordering founded on a precedence. The main emphasis here is put on the ease of use and reliability. The effectiveness of the proposed method is finally demonstrated by including a comprehensive comparing different ranking methods with the present one.

Keywords: Ranking fuzzy numbers, Lexicographical ordering.

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Authors: M. Raseen, A.Assi, P.W. C. Prasad, A. Harb

Abstract:

Binary Decision Diagrams (BDDs) are useful data structures for symbolic Boolean manipulations. BDDs are used in many tasks in VLSI/CAD, such as equivalence checking, property checking, logic synthesis, and false paths. In this paper we describe a new approach for the realization of a BDD package. To perform manipulations of Boolean functions, the proposed approach does not depend on the recursive synthesis operation of the IF-Then-Else (ITE). Instead of using the ITE operation, the basic synthesis algorithm is done using Boolean NOR operation.

Keywords: Binary Decision Diagram (BDD), ITE Operation, Boolean Function, NOR operation.

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Authors: Eduardo Sant'Ana da Silva, Andre Luiz Pires Guedes, Eduardo Todt

Abstract:

Independent spanning trees (ISTs) provide a number of advantages in data broadcasting. One can cite the use in fault tolerance network protocols for distributed computing and bandwidth. However, the problem of constructing multiple ISTs is considered hard for arbitrary graphs. In this paper we present an efficient algorithm to construct ISTs on hypercubes that requires minimum resources to be performed.

Keywords: Hypercube, Independent Spanning Trees, Networks On Chip, Systems On Chip.

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Authors: L. N. A. Chandana Jayawardena, Ales Gregar

Abstract:

Occupational Self Efficacy (OSE) reflects the conviction of a person’s ability to fulfill his job related behavior at a perfectly acceptable level to the employer. Transformational leadership improves followers’ commitment by influencing their needs, values, and self-esteem. Employees also develop a dyadic relationship with their immediate superiors. Study was conducted amongst one hundred and twenty two (122) bank managers in Sri Lanka. They were selected based on multi-stage (seniority in the hierarchy, gender, department-wise etc.) stratified random sampling. Major objectives of this study were to analyze the impact of Transformational leadership style, and OSE along with Sociodemographic factors, and Career, Job and Organizational experience, to the Career satisfaction of managers. SPSS software was used for parametric and non-parametric statistical analyses. Career satisfaction had positive impacts with their Transformational leadership style, and their relationships with the immediate superior. Impact of sociodemographic factors, and career exposure to career satisfaction was assessed.

Keywords: Career success, Relationship with immediate superior, Transformational leadership.

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Authors: Miklos Palfia

Abstract:

An iterative definition of any n variable mean function is given in this article, which iteratively uses the two-variable form of the corresponding two-variable mean function. This extension method omits recursivity which is an important improvement compared with certain recursive formulas given before by Ando-Li-Mathias, Petz- Temesi. Furthermore it is conjectured here that this iterative algorithm coincides with the solution of the Riemann centroid minimization problem. Certain simulations are given here to compare the convergence rate of the different algorithms given in the literature. These algorithms will be the gradient and the Newton mehod for the Riemann centroid computation.

Keywords: Means, matrix means, operator means, geometric mean, Riemannian center of mass.

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Authors: Mohammad Al-Fayoumi, Soumya Banerjee, Jr., P. K. Mahanti

Abstract:

A social network is a set of people or organization or other social entities connected by some form of relationships. Analysis of social network broadly elaborates visual and mathematical representation of that relationship. Web can also be considered as a social network. This paper presents an innovative approach to analyze a social network using a variant of existing ant colony optimization algorithm called as Clever Ant Colony Metaphor. Experiments are performed and interesting findings and observations have been inferred based on the proposed model.

Keywords: Social Network, Ant Colony, Maximum Clique, Sub graph, Clever Ant colony.

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Authors: Aki Happonen, Adrian Burian, Erwin Hemming

Abstract:

Fixed-point simulation results are used for the performance measure of inverting matrices using a reconfigurable processing element. Matrices are inverted using the Cholesky decomposition algorithm. The reconfigurable processing element is capable of all required mathematical operations. The fixed-point word length analysis is based on simulations of different condition numbers and different matrix sizes.

Keywords: Cholesky Decomposition, Fixed-point, Matrixinversion, Reconfigurable processing.

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Authors: Miloje S. Radenkovic, Tamal Bose

Abstract:

This paper presents the convergence analysis of a prediction based blind equalizer for IIR channels. Predictor parameters are estimated by using the recursive least squares algorithm. It is shown that the prediction error converges almost surely (a.s.) toward a scalar multiple of the unknown input symbol sequence. It is also proved that the convergence rate of the parameter estimation error is of the same order as that in the iterated logarithm law.

Keywords: Adaptive blind equalizer, Recursive leastsquares, Adaptive Filtering, Convergence analysis.

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Authors: S. S. Mishra

Abstract:

In this paper, an attempt is made to compute the total optimal cost of interdependent queuing system with controllable arrival rates as an important performance measure of the system. An example of application has also been presented to exhibit the use of the model. Finally, numerical demonstration based on a computing algorithm and variational effects of the model with the help of the graph have also been presented.

Keywords: Computing, Controllable arrival rate, Optimum performance measure.

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Authors: Nino Abesadze, Marine Mindorashvili, Nino Paresashvili

Abstract:

The main purpose of the article is to make complex statistical analysis of tourist expenses of foreign visitors. We used mixed technique of selection that implies rules of random and proportional selection. Computer software SPSS was used to compute statistical data for corresponding analysis. Corresponding methodology of tourism statistics was implemented according to international standards. Important information was collected and grouped from the major Georgian airports. Techniques of statistical observation were prepared. A representative population of foreign visitors and a rule of selection of respondents were determined. We have a trend of growth of tourist numbers and share of tourists from post-soviet countries constantly increases. Level of satisfaction with tourist facilities and quality of service has grown, but still we have a problem of disparity between quality of service and prices. The design of tourist expenses of foreign visitors is diverse; competitiveness of tourist products of Georgian tourist companies is higher.

Keywords: Tourist, expenses, methods, statistics, analysis.

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Authors: A. Appe, B. Poluparthi, L. Kasivajjula, U. Mv, S. Bagadi, P. Modi, A. Singh, H. Gunupudi, S. Troiano, J. Paul, J. Stovall, J. Yamamoto

Abstract:

The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data are considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP (SHapley Additive exPlanations), to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since it is data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for e.g., quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP, a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: Competition, DAGs, hospital, healthcare, machine learning, market share, random forest, SHAP.

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