Pattern extraction and modelling of the behavior of Web users

İnternetin yaygınlaşması ve her alanda bilgi sağlaması günlük yaşantımıza hızla girmesine neden olmuştur. Haber, ekonomi, kültür, eğitim, sağlık hizmetler ve reklam gibi bir çok alanda bilgi kaynağı olan İnternet ortamında, kullanıcı kendisi için gerekli bilgileri bulmakta çoğu zaman zorlanmaktadır. Bunun nedeni sorgulama araçlarının kısıtlı olması ve bilgilerin fazlalığı olarak görülmektedir. Bu çalışmada kullanıcının bir sonraki istek yapacağı sayfayı öngörerek hızlı ve yüksek oranda doğru öneri yapabilen bir yöntem önerilmiştir. Model tabanlı demetleme yönteminden yaralanarak, kullanıcı oturumları aynı demette bulunan oturumlardaki ortak sayfalarda benzer süreler geçirilmesine göre demetlenmiştir. Ortaya çıkan demetler yeni kullanıcılar için öneri kümesi oluşturmak için kullanılmıştır.Anahtar Kelimeler: Web kullanım madenciliği, kullanıcı örüntüleri, model tabanlı demetleme, Poisson dağılımı. Making recommendation requires predicting what is of interest to a user at a specific time. Even the same user may have different desires at different times. It is important to extract the aggregate interest of a user from his or her navigational path through the site in a session. In this paper, we present a new model that uses only the visiting time and visiting frequencies of pages without considering the access order of page requests in user sessions. The resulting model has lower run-time computation and memory requirements, while providing predictions that are at least as precise as previous proposals. Our objective in this paper is to assess the effectiveness of non-sequentially ordered pages in predicting navigation patterns. The key idea behind this work is that user sessions can be clustered according to the similar amount of time that is spent on similar pages within a session. We first partition user sessions into clusters such that only sessions which represent similar aggregate interest of users are placed in the same cluster. We employ a model-based clustering approach and partition user sessions according to similar amount of time in similar pages. In particular, we cluster sessions by learning a mixture of Poisson odels using Expectation Maximization algorithm. The resulting clusters are then used to recommend pages to a user that are most likely contain the information which is of interest to that user at that time.Keywords: Web usage mining, usage patterns, model based clustering, Poisson distribution

Four payment models for the multi-mode resource constrained project scheduling problem with discounted cash flows

In this paper, the multi-mode resource constrained project scheduling problem with discounted cash flows is considered. The objective is the maximization of the net present value of all cash flows. Time value of money is taken into consideration, and cash in- and outflows are associated with activities and/or events. The resources can be of renewable, nonrenewable, and doubly constrained resource types. Four payment models are considered: Lump sum payment at the terminal event, payments at prespecified event nodes, payments at prespecified time points and progress payments. For finding solutions to problems proposed, a genetic algorithm (GA) approach is employed, which uses a special crossover operator that can exploit the multi-component nature of the problem. The models are investigated at the hand of an example problem. Sensitivity analyses are performed over the mark up and the discount rate. A set of 93 problems from literature are solved under the four different payment models and resource type combinations with the GA approach employed resulting in satisfactory computation times. The GA approach is compared with a domain specific heuristic for the lump sum payment case with renewable resources and is shown to outperform it

International competitiveness power and human development of countries

Human development should be the ultimate objective of human activity and its aim should be healthier, longer, and fuller lives. It is expected that if the competitiveness of a country is suitably managed, human welfare will be enhanced as a consequence. The research described here seeks to explore the relationship between the competitiveness of a country and its use for human development. For this purpose, 45 countries were evaluated using data envelopment analysis, where the global competitiveness indicators are taken as input variables and the human development index indicators as output variables. A detailed analysis is also conducted for the emerging economies

Kaynak kısıtlı proje çizelgelemede indirgenmiş nakit akışı maksimizasyonu için bir genetik algoritma yaklaşımı

Bu çalısmada kaynak kısıtlı proje çizelgelemede indirgenmis nakit akısını ençoklamak için gelistirilen bir genetik algoritma sunulmaktadır. Problem hem yenilenebilir hem de yenilenemez kaynaklar göz önüne alınarak tanımlanmaktadır. Kaynakların uygulanmasında sonlu sayıda mod söz konusudur. Genetik algoritmada, çok-bilesenli, düzgün, sıralama temelli bir çaprazlama operatörü kullanılmıstır. Bu çaprazlama operatörünün öncüllük kısıtlarını ihlal etmeyisi önemli bir avantaj sağlamaktadır. Genetik algoritmanın parametrelerinin saptanması için bir meta-seviye genetik algoritma uygulanmıstır. Önerilen algoritmanın sınanması için teknik yazında mevcut 93 problemlik bir test problem kümesi kullanılmıstır. Ayrıca, salt yenilenebilir kaynaklar problemi için, özel amaçlı bir algoritma ile karsılastırma yapılmıs ve önerilen algoritmanın özellikle büyük boyutlu problemlerde basarılı olduğu gösterilmistir

A decision support system to evaluate the competitiveness of nations

The measurement of competitiveness and strategy development is an important issue for policy makers. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices as well as to conduct a detailed analysis on the ongoing performance of nations’ competitiveness. For this purpose, a methodology composed of three steps is used. To start, a combined clustering analysis methodology is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient to define the stage of competitiveness a country belongs. In the proposed methodology, 135 criteria are used for a proper classification of the countries. Relationships between the criteria and classification of the countries are determined using Artificial Neural Networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in the third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one. As a final analysis, the dynamic change of the rank of the countries over years has also been investigated

How to improve the innovation level of a country? A Bayesian net approach

This study aims to provide strategic guidelines to policy makers who are developing strategies to improve their country’s innovativeness. In this paper, we claim that innovation cannot be related only to some factors inherent in the environment of a country, nor is it a single entity to be managed without any linkages to the rest of the actors comprising the competitiveness of a country. Hence, a comprehensive study on innovation should cover the interaction between competitiveness indicators and innovation. For this purpose, the innovation performance of 148 countries is analyzed using an integrated cluster analysis and a Bayesian network framework. These countries are first clustered based on the average values of their competitiveness indicators representing 12 pillars and several sub-pillars adopted from the Global Competitiveness Reports of World Economic Forum for the 2009-2012 period. As a result, five appropriate clusters emerge: Leaders, Followers, Runners Up, Developing Ones, and Laggers. A factor analysis is then conducted to reveal the main characteristics of each cluster in terms of competitiveness indicators. Subsequently, a Bayesian network is constructed and sensitivity analyses are performed to reveal important policies for each cluster

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

The basic competitiveness factors shaping the innovation performance of countries

The goal of this research is to use Bayesian Networks to discover the relations among the components of competitiveness and the innovation level of countries.For this purpose, initially the competitiveness performance of 148 countries is analyzed using an integrated cluster analysis and factor analysis framework. This facilitates the basic areas where each cluster group demonstrates a good performance and those where they need improvements relative to the othergroups in order to increase their competition level. Subsequently; a Bayesian Network is constructed using WinMine software based on competitiveness indicators drawn from WEF pillars and sub-pillars. This analysis, in its turn,investigates whether the competitiveness stage to which a country belongs has an important impact on its innovation performance and highlights, which of the basic competitiveness variables has a significant impact in shaping its innovation level

Real-world efficacy and safety of Ledipasvir plus Sofosbuvir and Ombitasvir/Paritaprevir/Ritonavir +/- Dasabuvir combination therapies for chronic hepatitis C: A Turkish experience

Background/Aims: This study aimed to evaluate the real-life efficacy and tolerability of direct-acting antiviral treatments for patients with chronic hepatitis C (CHC) with/without cirrhosis in the Turkish population.Material and Methods: A total of 4,352 patients with CHC from 36 different institutions in Turkey were enrolled. They received ledipasvir (LDV) and sofosbuvir (SOF)+/- ribavirin (RBV) ombitasvir/paritaprevir/ritonavir +/- dasabuvir (PrOD)+/- RBV for 12 or 24 weeks. Sustained virologic response (SVR) rates, factors affecting SVR, safety profile, and hepatocellular cancer (HCC) occurrence were analyzed.Results: SVR12 was achieved in 92.8% of the patients (4,040/4,352) according to intention-to-treat and in 98.3% of the patients (4,040/4,108) according to per-protocol analysis. The SVR12 rates were similar between the treatment regimens (97.2%-100%) and genotypes (95.6%-100%). Patients achieving SVR showed a significant decrease in the mean serum alanine transaminase (ALT) levels (50.90 +/- 54.60 U/L to 17.00 +/- 14.50 U/L) and model for end-stage liver disease (MELD) scores (7.51 +/- 4.54 to 7.32 +/- 3.40) (p<0.05). Of the patients, 2 were diagnosed with HCC during the treatment and 14 were diagnosed with HCC 37.0 +/- 16.0 weeks post-treatment. Higher initial MELD score (odds ratio [OR]: 1.92, 95% confidence interval [CI]: 1.22-2.38; p=0.023]), higher hepatitis C virus (HCV) RNA levels (OR: 1.44, 95% CI: 1.31-2.28; p=0.038), and higher serum ALT levels (OR: 1.38, 95% CI: 1.21-1.83; p=0.042) were associated with poor SVR12. The most common adverse events were fatigue (12.6%), pruritis (7.3%), increased serum ALT (4.7%) and bilirubin (3.8%) levels, and anemia (3.1%).Conclusion: LDV/SOF or PrOD +/- RBV were effective and tolerable treatments for patients with CHC and with or without advanced liver disease before and after liver transplantation. Although HCV eradication improves the liver function, there is a risk of developing HCC.Turkish Association for the Study of The Liver (TASL