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Offline Handwritten English Alphabet Recognition (OHEAR)
Get PDFIn most pattern recognition models, the accuracy of the recognition plays a major role in the efficiency of those models. The feature extraction phase aims to sum up most of the details and findings contained in those patterns to be informational and non-redundant in a way that is sufficient to fen to the used classifier of that model and facilitate the subsequent learning process. This work proposes a highly accurate offline handwritten English alphabet (OHEAR) model for recognizing through efficiently extracting the most informative features from constructed self-collected dataset through three main phases: Pre-processing, features extraction, and classification. The features extraction is the core phase of OHEAR based on combining both statistical and structural features of the certain alphabet sample image. In fact, four feature extraction portions, this work has utilized, are tracking adjoin pixels, chain of redundancy, scaled-occupancy-rate chain, and density feature. The feature set of 27 elements is constructed to be provided to the multi-class support vector machine (MSVM) for the process of classification. The OHEAR resultant revealed an accuracy recognition of 98.4%
An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept
Get PDFApplications for retrieving similar images from a large collection of images have increased significantly in various fields with the rapid advancement of digital communication technologies and exponential evolution in the usage of the Internet. Content-based image retrieval (CBIR) is a technique to find similar images on the basis of extracting the visual features such as color, texture, and/or shape from the images themselves. During the retrieval process, features and descriptors of the query image are compared to those of the images in the database to rank each indexed image accordingly to its distance to the query image. This paper has developed a new CBIR technique which entails two layers, called bi-layers. In the first layer, all images in the database are compared to the query image based on the bag of features (BoF) technique, and hence, the M most similar images to the query image are retrieved. In the second layer, the M images obtained from the first layer are compared to the query image based on the color, texture, and shape features to retrieve the N number of the most similar images to the query image. The proposed technique has been evaluated using a well-known dataset of images called Corel-1K. The obtained results revealed the impact of exploring the idea of bi-layers in improving the precision rate in comparison to the current state-of-the-art techniques in which achieved precision rate of 82.27% and 76.13% for top-10 and top-20, respectively
Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm: English
No full textAs information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases
Urban Rainwater Harvesting Assessment in Sulaimani Heights District, Sulaimani City, KRG, Iraq
Get PDFRainwater harvesting is the collection of rainwater and runoff from catchment areas such as roofs or other urban surfaces. Collected water has productive end-uses such as irrigation, industry, domestic, and can recharge groundwater. Sulaimani heights have been selected as a study area, which is located in Sulaimani Governorate in Kurdistan Region, North Iraq. The main objective of this study was to estimate the amount of harvested rainwater form Sulaimani heights urban area in Sulaimani City. Three methods for runoff calculation have been compared, the storm water management model (SWMM), the soil conservation service (SCS) method, and the runoff coefficient (RC) using daily rainfall data from 1991 to 2019. The annual harvested runoff results with the three different methods SWMM, SCS, and RC were estimated as 836,470 m3, 508,454 m3, and 737,381 m3, respectively. The results showed that SWMM method has the highest runoff result and could meet 31% of the total demand of the study area and 28% and 19% for RC and SCS methods, respectively
Kurdish Text Segmentation using Projection-Based Approaches
Get PDFAn optical character recognition (OCR) system may be the solution to data entry problems for saving the printed document as a soft copy of them. Therefore, OCR systems are being developed for all languages, and Kurdish is no exception. Kurdish is one of the languages that present special challenges to OCR. The main challenge of Kurdish is that it is mostly cursive. Therefore, a segmentation process must be able to specify the beginning and end of the characters. This step is important for character recognition. This paper presents an algorithm for Kurdish character segmentation. The proposed algorithm uses the projection-based approach concepts to separate lines, words, and characters. The algorithm works through the vertical projection of a word and then identifies the splitting areas of the word characters. Then, a post-processing stage is used to handle the over-segmentation problems that occur in the initial segmentation stage. The proposed method is tested using a data set consisting of images of texts that vary in font size, type, and style of more than 63,000 characters. The experiments show that the proposed algorithm can segment Kurdish words with an average accuracy of 98.6%
Comparison of Different Ensemble Methods in Credit Card Default Prediction
Get PDFCredit card defaults pause a business-critical threat in banking systems thus prompt detection of defaulters is a crucial and challenging research problem. Machine learning algorithms must deal with a heavily skewed dataset since the ratio of defaulters to non-defaulters is very small. The purpose of this research is to apply different ensemble methods and compare their performance in detecting the probability of defaults customer’s credit card default payments in Taiwan from the UCI Machine learning repository. This is done on both the original skewed dataset and then on balanced dataset several studies have showed the superiority of neural networks as compared to traditional machine learning algorithms, the results of our study show that ensemble methods consistently outperform Neural Networks and other machine learning algorithms in terms of F1 score and area under receiver operating characteristic curve regardless of balancing the dataset or ignoring the imbalanc
Knowledge Management Functions Applied in Jordanian Industrial Companies: Study the Impact of Regulatory Overload
Get PDFThis research aims to study the impact of electronic information overload on knowledge management functions in Jordanian industrial companies. The research population included all Jordanian industrial companies listed on the Amman Stock Exchange. A simple random sample of 30% of the research population of 1242 seniors and middle managers in the research population was done to 373 individuals. 206 questionnaires are successfully retrieved to be analyzed. Descriptive and heuristic statistical methods such as simple and multiple regression analysis were applied using SPSS.16 program. The obtained result indicated that there is a statistically significant impact of the electronic information overload (organizational overload) on the knowledge management functions (acquisition, generation, transmission, sharing, and application of knowledge) in Jordanian industrial companies. In the scope of the results, this work made a number of recommendations, including: Adopting an organizational aspect that suits the nature of the tasks that the industrial companies operate in Jordan, in addition to providing technical capabilities to reduce the electronic information overload faced by the industrial companies in Jordan while practicing their tasks
An Efficient Two-layer based Technique for Content-based Image Retrieval
Get PDFThe rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively
Characterization of European Medieval Silver Bars Using Micro X-ray Fluorescence, Conductivity Meter and Scanning Electron Microscopy
Get PDFThe objective of this paper is to use μ-X-ray fluorescence (XRF) analysis to evaluate the fineness and components of European Medieval Silver Bars samples. Conductivity measurements were used to assess the fineness and localization of the faults found in the samples. Because unevenness causes a change in conductivity, the tests were performed on the flattest areas of the Bars. Some rods, such as B3 and B9, have greater conductivity than others. All bars were subjected to the segregation test. In the instance of certain bars, it was not always practicable to categorically state that segregation had happened. There is no diminishing conductivity curve as one moves away from the zero height, as there is for bars B1, B8, and B9. As a result, there may be no solidification on these bars from Obverse to Reverse. A scanning electron microscope was used to record the following bars at various positions on the bars, and quantitative determinations were achieved using energy-dispersed XRF analysis through intensity measurements of the element-specific wavelength
Offline Writer Recognition for Kurdish Handwritten Text Document Based on Proposed Codebook
Get PDFHandwritten text recognition has been an ongoing attractive task to research in the field of document analysis and recognition with applications in handwriting forensics, paleography, document examination, and handwriting recognition. In the present research, an automatic method of writer recognition is presented using digitized images of unconstrained texts. Despite the increasing efforts by prior literature on the different methods used for the same purpose, such methods performance, particularly their accuracy, has not been promising, leaving plenty of room for improvements. This method made use of codebook-based writer characterization, with each writing sample represented by a group of computed features from a primary and secondary codebook. The writings were then represented through the computation of the probability of codebook patterns occurrence, and the probability distribution was employed for each writer’s characterization. Writer identification process involved comparing two writings through the computation of the distances between their respective probability distribution. The study carried out experiments to determine the performance of the implemented method in light of rates of identification with the help of standard datasets, namely, KRDOH and IAM, the former being the most current and largest Kurdish handwritten datasets with 1076 writers, and the latter being a dataset containing 650 writers. The outcome of the experiments was promising with a rate of identification of 94.3%, with the proposed method outperforming the state-of-the-art methods by 2–3%