Cryptanalysis of an Encryption Scheme Based on Blind Source Separation

Recently Lin et al. proposed a method of using the underdetermined BSS (blind source separation) problem to realize image and speech encryption. In this paper, we give a cryptanalysis of this BSS-based encryption and point out that it is not secure against known/chosen-plaintext attack and chosen-ciphertext attack. In addition, there exist some other security defects: low sensitivity to part of the key and the plaintext, a ciphertext-only differential attack, divide-and-conquer (DAC) attack on part of the key. We also discuss the role of BSS in Lin et al.'s efforts towards cryptographically secure ciphers.Comment: 8 pages, 10 figures, IEEE forma

Pre-classification module for an all-season image retrieval system

Author name used in this publication: Zheru ChiAuthor name used in this publication: Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization

Energy operations and schedules are significantly impacted by load and energy forecasting systems. An effective system is a requirement for a sustainable and equitable environment. Additionally, a trustworthy forecasting management system enhances the resilience of power systems by cutting power and load-forecast flaws. However, due to the numerous inherent nonlinear properties of huge and diverse data, the classical statistical methodology cannot appropriately learn this non-linearity in data. Energy systems can appropriately evaluate data and regulate energy consumption because of advanced techniques. In comparison to machine learning, deep learning techniques have lately been used to predict energy consumption as well as to learn long-term dependencies. In this work, a fusion of novel multi-directional gated recurrent unit (MD-GRU) with convolutional neural network (CNN) using global average pooling (GAP) as hybridization is being proposed for load and energy forecasting. The spatial and temporal aspects, along with the high dimensionality of the data, are addressed by employing the capabilities of MD-GRU and CNN integration. The obtained results are compared to baseline algorithms including CNN, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). The experimental findings indicate that the proposed approach surpasses conventional approaches in terms of accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RSME).</p> </abstract&gt

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

������ ������ ��������� ������ ��������� ��������� ��������� ������ ��������������� ������

Big data analysis and machine learning are rising analytical tools in data analysis. Big data is an area that collects and maintains a huge amount of raw data for field-specific data analysis. Machine learning is the main analytical tool for handling such data. This study investigates the applicability of keyword search volume, and develops an ANN (Artificial Neural Network) model using panel data to analyze electricity demand and forecast prices.There is no analysis using keyword search volume in econometrics, especially energy economics. Therefore, this study intends to build a new electricity demand model. In addition, since there is no model building study that applies panel data, this study constructs a novel panel ANN model. This study consists of two essays: panel analysis model development and panel ANN model development. In the first essay, this analysis derives the relationship between US household electricity consumption and renewable energy. For this purpose, keyword search volume is used to present new influential factors in analyzing economic indicators. The model considers three keywords related to electricity consumption: “renewable,” “weather forecast,” and “temperature.” Furthermore, there has been no way to quantify household renewable energy consumption, no studies have analyzed the correlation between renewable energy and US household electricity consumption. Such consumption is difficult to estimate and it is more difficult to grasp than other major sectors including commerce and industry because of issues related to personal information collection and the cost of measurement. This study therefore analyzes the correlation with household electricity consumption by constructing a model including interest in renewable energy using keyword search volume. The model, which analyzes the impact of these keywords is constructed using three regression equations based on the static energy demand model, and analyze the impact of these keywords. In the household sector, although a variety of renewable energy is used, it is difficult to derive the economic implications of such use as it is not converted into a quantifiable value. Therefore, this study uses the search keyword “renewable” to estimate the impact of renewable energy. “Weather forecast” and “temperature” were also selected as Internet search keywords. These keywords are used because temperature is one of the important factors in determining household electricity consumption. As a result, all the variables are stationary and the Hausman test indicates that the fixed effects estimation is more robust than the random effects estimation. In the case of the model using the keyword “renewable” as an explanatory variable, all the variables except the price variable are statistically significant at the 1% level; this search term has a negative correlation with household electricity consumption. Household electricity consumption decreases by 16.017 million kWh for every one unit increase in the keywords search using “renewable.” “Temperature” also has a negative coefficient, which is similar to heating degree days. The correlation between the two variables, which intuitively appear to be unrelated, could have significant meaning. When one searches for “renewable” in the context of their household, they probably have a clear purpose. In the event that excessive electricity is consumed or electricity bills are high, households will search for alternatives to reduce electricity consumption. In the case of households equipped with renewable energy facilities, the power consumption will decrease in proportion to the capacity, and the results of the estimation can be seen. This study finds that the correlation coefficient of the “renewable” variable is the highest, and the “temperature” variable also has a significant correlation with household electricity consumption. The “renewable” keyword has a large negative correlation with household electricity consumption, which can be estimated as being a result of the growing interest in renewable energy. Although the electricity consumption patterns of households are influenced by many variables, this study suggests that interest in renewable energy should also be included as a major factor influencing such consumption. In the second essay, this study predicts electricity price using ANN, which have already been used as tools for prediction in various fields. In general, ANN have been used for short-term forecasting in many economic analysis studies. On the other hand, as the forecast point increases, the accuracy of prediction decreases sharply. The forecasting accuracy in long-term forecasting is greater than that of short-term forecasting in the same dataset. Therefore, this study uses panel data to compensate for the decline in ANN forecasting accuracy in long-term forecasts in the same dataset. The panel data contains information that time series data does not have. It has trend information of time series data as well as state or country characteristics. However, there are very few studies in economics that have used panel data for prediction using ANN. Existing studies use panel data without differentiating between entities in the model structure. The panel ANN studies did not differentiate between state and national data or have independent learning such as the pooled OLS method. Therefore, this study constructs a panel ANN structure using the advantages of panel data and analyzes its accuracy according to the change of forecasting periods. The model intends to improve the accuracy of predicted values by learning the unobserved heterogeneity contained in panel data from each state. The analysis is conducted on the assumption that it would be possible to learn not only time series information but also country or state information. The panel analysis removes the cross-sectional dependence in the unobserved heterogeneity of the panel data. Unlike panel analysis, this study constructs a model structure to learn the unobserved heterogeneity of such data. The learning is conducted separately for each state, and two or three hidden layers are inserted. After 6, 12, 18 and 24 months forecasting, total RMSE and MAPE are estimated and the optimal model is selected. For empirical analysis, this study uses panel data of US electricity prices by state. Natural gas prices are also predicted for additional model verification. For the electricity price forecasting model, the accuracy of the result using time series data in 6 and 12 months forecasts is higher than using panel data. On the other hand, the results of 18 and 24 months indicate that the results of panel data are much better. In the case of natural gas Citygate prices, the results of the model using time series data for only 6-month predictions are better while other predictions show that the panel data model has high accuracy. A noteworthy point is that panel data models tend to be more accurate as the forecast period increases. Although the timing of improvement in accuracy differs, both models show an improvement of the panel data forecasting model in long-term predictions. According to the results, when estimating a small number of predicted values, the trend of the time-series data greatly influences the result and a time-series model produces better predictions. On the other hand, the longer the forecast period, the better the panel data model that learns from unobserved heterogeneity of the states rather than from the trends. Since weights are updated without affecting each layer, it can be said that the model learns by considering the heterogeneity of each state. In comparison to a time series model in which only the trend is learned, the panel data model utilizes more information to improve accuracy by learning the trends and heterogeneity of each state. In this study, electricity consumption is analyzed using panel data and electricity price prediction is performed. The electricity consumption analysis suggests a new approach based on the model considered in household electricity consumption literature that incorporates data drawn from keyword search volume. This study used keyword search volume as a substitute variable to analyze the phenomenon that was impossible to explain due to the lack of quantitative data. This study shows that variables that have not been used hitherto, as they are not quantifiable or statistically significant, can be analyzed through keyword search volume. In the electricity price forecasting analysis, a novel panel ANN model is proposed to compensate for the decrease in forecasting accuracy when the forecasting period increases Panel ANN is a model that can be applied from day-to-day and hourly forecasts to longterm trends of several years depending on the type of panel data. In analyzing the longterm trends, a neural network model that can replace the large-scale simulation models such as NEMS (National Energy Modeling System) and WEM (World Energy Model) can also be constructed. Therefore, this model can be applied in various fields ranging from the hourly price forecast of the next day's electricity market to the long-term trend of CO2 emissions. ��� ��������� ������ ������������ ������������ ������ ������������ ������������ ������ ������ ��������� ���������������. ������ ������������ ������ ��������� ��������� ������ ������ ������������ ��������� ��������� ������������ ��������� ��������� ��������� ������������ ��������� ��������� ������������ ������. ��� ������ ������ ������ ������ ��� ��������� ������������������ ������ ��������� ������ ������������ ��������� ������������ ������������ ������������ ������������ ������ ��������� ��� ��������� ��������� ��� ��������������� ��������� ������������ ������ ��������� ������������ ���������������. ��������������� ��� ��������� ������������ ������������ ������������ ������������ ������������������ ��������������� ������������ ��������� ��������� ������������ ������ ������ ��� ��� ��������� ������������.Docto

Kalite iyileştirmede veri madenciliği kullanımı ve geliştirilmesi

TÜBİTAK MAG30.06.2009Bu projede amaç, sanayi kuruluşlarında ürün ve süreçlerin kalitesini iyileştirmeye yönelik veri madenciliği (VM) yaklaşımlarını belirlemek ve daha etkili yaklaşımlar geliştirmektir. Projede imalat sanayi kuruluşlarının ürün ve süreçlerinin kalitesini iyileştirme ile ilgili kalitenin tanımlanması, tahmin edilmesi, sınıflandırılması ve parametrelerinin optimizasyonu problemleri ele alınmıştır. Bu problemlerin çözümü için veri hazırlama ve önişlemenin yanısıra kümeleme, tahmin etme, sınıflandırma, birliktelik analizi ve optimizasyon VM işlevlerinin gerekli olabileceği belirlenmiştir. Bu kapsam dahilinde geniş bir literatür taraması yapılmış ve değişik imalat sektörlerinde etkinlik gösteren altı kuruluş ziyaret edilmiştir. Bunlardan üçünün sağladığı veriler üzerinde uygun VM metotları uygulanmış ve sonuçlar karşılaştırılmıştır. Bu karşılaştırma sonucunda belli VM işlevleri için kalite iyileştirme amaçlarına en uygun VM metotları belirlenmiş ve uygulayıcılara önerilmiştir. Projenin yöntem geliştirme kısmında ise uygulama aşamasında karşılaşılan bazı problemlerin giderilmesi ve mevcut yöntemlerin kullanım kolaylığı ve/veya etkililiğinin artırılması yönünde çalışmalar gerçekleştirilmiştir. Sonuçta, kalite verilerinin yeniden örneklenmesi için bir yöntem; parametrik olmayan alternatif bir regresyon yaklaşımı (CMARS); ikili sınıflandırmada kullanımı kolay olan Mahalanobis Taguchi Sistemi metodunun çok sınıf ve ayrıca parametre optimizasyonu için uyarlamalar; bulanık sınıflandırmada kalite verilerine uygun alternatif yaklaşımlar (bulanık regresyona dayalı modeller) ve parametrik olmayan bulanık tahmin etme ve sınıflandırma fonksiyonları; parametre optimizasyonunda çekicilik fonksiyonlarının optimizasyonu için alternatif yaklaşımlar ve birliktelik kurallarının seçimi için bir yöntem geliştirilmiştir. Bu sonuçların ve metotların kalite iyileştirme alanında uygulayıcıların çalışmalarına yön vermesi ve bunların kullanım kolaylığı ile etkililiğini artırması beklenmektedir.The objective of this project is to identify the data mining (DM) approaches that can effectively improve product and process quality in industrial organizations, and to develop more effective approaches. In the project, quality definition, prediction, classification and parameter optimization problems associated with product and process quality improvement in manufacturing industries are considered. For the solution of these problems, clustering, prediction, classification, association and optimization functions of DM as well as data preparation and preprocessing are determined as relevant. A comprehensive literature survey has been performed and six manufacturing companies operating in different sectors have been visited, within this context. Appropriate DM methods are applied on data sets obtained from three of these companies, and the results are compared. As a result, the most appropriate DM methods are suggested for specific DM functions and quality improvement purposes. In the method development part of the project, studies are performed to overcome some problems encountered during the applications, and to increase ease of use and effectiveness of the VM methods. As a result, a resampling method for quality data; an alternative nonparametric approach (CMARS) for regression; adaptations of an easy to use binary classification method, Mahalanobis Taguchi system, to multiple classes and also to parameter optimization; alternative approaches for fuzzy classification of quality data (models based on fuzzy regression) and nonparametric fuzzy functions; alternative approaches for optimization of desirability functions in parameter optimization; and a method for reduction of association rules are developed. It is expected that these results and approaches guide practitioners in quality improvement area, and incease the ease of use and effectiveness of them

Training issues and learning algorithms for feedforward and recurrent neural networks

Ph.DDOCTOR OF PHILOSOPH