Suchergebnisse

E-mail is an efficient and reliable data exchange service. Spams are undesired e-mail messages which are randomly sent in bulk usually for commercial aims. Obfuscated image spamming is one of the new tricks to bypass text-based and Optical Character Recognition (OCR)-based spam filters. Image spam detection based on image visual features has the advantage of efficiency in terms of reducing the computational cost and improving the performance. In this paper, an image spam detection schema is presented. Suitable image processing techniques were used to capture the image features that can differentiate spam images from non-spam ones. Weighted k-nearest neighbor, which is a simple, yet powerful, machine learning algorithm, was used as a classifier. The results confirm the effectiveness of the proposed schema as it is evaluated over two datasets. The first dataset is a real and benchmark dataset while the other is a real-like, modern, and more challenging dataset collected from social media and many public available image spam datasets. The obtained accuracy was 99.36% and 91% on benchmark and the proposed dataset, respectively.

Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the most effective parameter, particularly when Age<49.5. Whereas Ki67 appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum error rate, and the test maximum accuracy for K_value selection with an accuracy of 86.24%. Where the distance metric has been assigned using the Euclidean approach. From previous models, it seems that Breast Cancer Grade2 is the most prevalent type. For the future perspective, a comparative study could be performed to compare the supervised and unsupervised data mining algorithms.

Kartun adalah gambar dengan penampilan lucu yang mempresentasikan suatu peristiwa. Kartun editorial atau kartun politis biasanya ditujukan untuk menyatakan pandangan politik atau sosial dengan cara menyindir. Untuk menilai dan menentukan klasifikasi kumpulan kartun editorial dalam jumlah besar dibutuhkan sebuah sistem yang dapat mengklasifikasikan jenis kartun editorial kedalam dua klasifikasi negatif atau positif dengan menerapkan pengolahan citra digital dan OCR Tesseract yang dikombinasikan dengan algoritma K-Nearest Neighbour (KNN). OCR Tesseract diperlukan untuk mengenali teks yang terdapat pada sebuah gambar, sedangkan Algoritma K-Nearest Neighbour (KNN) sebagai metode klasifikasinya Sentimen posi- tif menyatakan pemberian nilai yang baik, sentimen negatif menyatakan nilai yang buruk pada konteks berbentuk teks. Dengan nenghitung kemiripan antar dokumen menggunakan cosine similarity. Berdasarkan jumlah dokumen sebanyak 8 data citra uji, dengan data usul sebanyak 7 dokumen dan data hsil berjumlah 6 maka akurasi terbaik did a pat sebesar 85,7% dengan k=3.

AbstractDespite a large literature on the importance of childhood neighbourhoods for life course transitions, there is a lack of fertility studies combining a life‐course perspective with detailed neighbourhood measures. Addressing this gap, we use longitudinal data in which the entire population of a Swedish town is geocoded at the address‐level, 1939–1967, and linked to national registers from 1968 to 2015. We study how social neighbourhoods in childhood influence fertility outcomes by constructing individual neighbourhoods at the address level to measure the social class of nearby childhood neighbours. We analyse the age at first and last birth, children ever born, birth spacing and childlessness. Growing up in higher‐status neighbourhoods is associated with delayed fertility for both men and women, but no association is found for the number of children ever born or for childlessness. Associations are stable over time, and later ages of neighbourhood exposure matter more, especially for men. Contrary to prior literature's focus on the lower classes, our results are driven by higher‐status individuals growing up in distinctly white‐collar neighbourhoods.

Not only have high-speed rail (HSR) services stimulated the economy of many countries, but they have also significantly uplifted quality of lives (QoL) of countless people. For many decades, the aspiration for HSR network development has dramatically risen, and HSR networks have inevitably become an icon of civilisation. However, only a few successful HSR networks globally can truly generate socio-economic impacts on their societies. This research aims to understand the impact of HSR networks on social and economic impacts and to provide recommendations for success. This study is the world's first to examine the benefits of HSR across all community demographic groups, including young and elderly people. The findings will illustrate the QoL, economic, and educational elements' advantages in explicit terms. It has established two interconnected models via Python to codify a novel customised model for socio-economic evaluation. 'Pearson correlation coefficient' and 'K-Nearest Neighbour' techniques are applied to bolster the reliability of the research findings. The outcomes have been reviewed by 30 international HSR specialists. The benchmarking exhibits that socio-economic impacts apparently occur across vast areas. The insight stemming from this benchmarking also offers policy implications and empirical data for long-term HSR improvement, assisting the government in developing new methods for sustainable communities.

The k-Nearest Neighbour method is a popular nonparametric technique for solving classification and regression problems without having to make potentially restrictive a priori assumptions about the functional form of the statistical relationship under investigation. The purpose of this paper was to demonstrate that the scope of this method can be extended in a way that enables the simultaneous consideration of continuous, ordered discrete, and unordered discrete explanatory variables. An exemplary application to a publicly available dataset demonstrated the feasibility of the proposed approach.

Der Verfasser entwickelt eine Methode der Ableitung, die sowohl auf einem multivariaten Regressionsmodell als auch auf einem "nearest neighbour hot decking" beruht. Er wendet diese Methode erfolgreich auf eine ratioskalierte Variable an, die aus einer hohen Zahl unbekannter Nullwerte besteht. Die so erhaltenen Ergebnisse werden mit Ergebnissen verglichen, die vermittels random hot decking berechnet wurden. Der Verfasser versucht darüber hinaus, Varianzen zu schätzen, die die Tatsache berücksichtigen, dass einige Daten abgeleitet sind. Diese Methode führt zu einer zusätzlichen Komponente der Varianz, die als Ableitungsvarianz bezeichnet wird. Im ersten Teil des Beitrags werden Ableitungsmethoden und -strategien im allgemeinen diskutiert. Der Verfasser entwickelt auch einen diagnostischen Test für die Qualität von Ableitungen. Dieser Test überprüft, wie oft dieselbe Quelle zur Ableitung fehlender Werte verwandt wird. (ICEÜbers)

Abstract. This paper presents two avalanche forecasting applications NXD2000 and NXD-REG which were developed at the Swiss Federal Institute for Snow and Avalanche Re-search (SLF). Even both are based on the nearest neighbour method they are targeted to different scales. NXD2000 is used to forecast avalanches on a local scale. It is operated by avalanche forecasters responsible for snow safety at snow sport areas, villages or cross country roads. The area covered ranges from 10 km2 up to 100 km2 depending on the climatological homogeneity. It provides the forecaster with ten most similar days to a given situation. The observed avalanches of these days are an indication of the actual avalanche danger. NXD-REG is used operationally by the Swiss avalanche warning service for regional avalanche forecasting. The Nearest Neighbour approach is applied to the data sets of 60 observer stations. The results of each station are then compiled into a map of current and future avalanche hazard. Evaluation of the model by cross-validation has shown that the model can reproduce the official SLF avalanche forecasts in about 52% of the days.

With a rapid growth of Internet community making a practical usage of numbers of application used in many areas, i.e., research, commercial, industry, and even in military, there are millions of reports on attacks and attempts to invade the system online; and that phenomenon has led the essential of intrusion detection system (IDS). Data mining is one of the promising approaches to deal with large scale dataset including attack detection and recognition based on attack traces as an example from KDD CUP 1999. However, one of its key limitations is the computational complexity, and thus, this research investigates the possibility to integrate parallel processing to enhance the detection speed-up implemented on NVIDIA CUDA GPU. Several proposals have focused on kNearest Neighbour (KNN) as one of the promising approaches due to its key advantage of simplicity and high precision; however, in addition to KNN evaluation, this research also proposes the integration of a simplified neighborhood classification (Neighborhood) using the percentage instead of group ranking resulting in higher accuracy gain with insignificantly increase of computational complexity trade-off.