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Enhancing Fuzzy Logic for Pattern Recognition Technique to Recognize Arabic Quran Short Diacritics

Enhancing Fuzzy Logic for Pattern Recognition Technique to Recognize Arabic Quran Short Diacritics

Abstract – A word in Arabic language with different diacritics gives different meaning and pronunciation. It is important to recognize Arabic characters and diacritics for accurate reading; writing and pronunciation. Significant research interest about Arabic text recognition problem was attracted in the past. Recognition problems usually emerged as diacritics and ligature. This paper uses effective recognition approaches that enhanced the recognition of diacritics by developing several algorithms and techniques to classify and recognize Arabic and Quran diacritics. Proposed technique recognizes double, triple, and special diacritics using fuzzy logic for pattern recognition. The technique recognizes all Arabic diacritics including diacritics in many Arabic font types and diacritics in all positions of Arabic text. This paper deal with diacritics as a separate part of the text. Arabic diacritics possibly could be complex, as in the special diacritics for the holy Qur'an. Arabic diacritics are recognized using fuzzy logical for pattern recognition technique which is basically uses strokes which in its turn compost diacritics, in order to calculate the lines angle using polygon formula to be determined as one of the main lines and curves. Afterwards, stores strokes in vectors to represent all diacritics. Then the unknown diacritics vector is compared with all stored vectors to determine the correct diacritics.Recognition results for special and normal diacritics using fuzzy logic for pattern recognition adopted technique scored 97%, and for normal diacritics scored 94% because of the missing of classifying. Over all diacritics recognition scored the result scoring 95.6 %.
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AN EFFICIENT ALGORITHM FOR VARIANT BULK TRANSPORTATION PROBLEM

AN EFFICIENT ALGORITHM FOR VARIANT BULK TRANSPORTATION PROBLEM

The name Lexicographic-search or Lexi-search method implies that the search is made for an optimal solution in a systematic way, just as one search for meaning of a word in a dictionary. When the process of feasibility checking of a partial word becomes difficult, though lower bound computation is easy, Pattern Recognition Technique (Sundara Murthy, 1979) can be used. Lexi-Search algorithms, in general, require less memory, due to the existence of Lexicographic order of partial words. If Pattern Recognition Technique is used, the dimension requirement of the problem can be reduced, since it reduces to the two-dimensional cost array into a linear and the problem can be reduced to a linear form of finding an optimal word of length n (Sundara Murthy, 1979) and hence reduces computational work in getting an optimal solution.
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A Sensorless Speed Estimation For Brushed Dc Motor At Start-Up

A Sensorless Speed Estimation For Brushed Dc Motor At Start-Up

This paper has presented a new sensorless method for estimating the speed of brushed dc motors at starting up. The method uses sensorless techniques based on the current ripple component at starting-up. The method employs active filters and amplifiers to normalize and extract the useful features of the current signal for digital processing. To enable rotor speed measurement and estimation, artificial neural network based pattern recognition technique is employed to detect the periodic current ripple generated in brushed dc motor during commutation. The artificial neural network is trained based on the heights and widths of the ripple pulses. Finally, the trained network can recognize the current ripple pulses for the system to count in order to estimate the position and speed of the brushed DC motor. The experimental results were obtained to validate the proposed method, showing that the method works in a wide range of starting up speeds and in different operating conditions, such as abrupt starting and ramp start up.
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Improved statistical recognition algorithms for oil palm ripeness identification

Improved statistical recognition algorithms for oil palm ripeness identification

current grading process by simulating the grading system using Pattern Recognition technique. It is observed that Statistical Pattern Recognition can offer better solution for this particular problem. This is supported by findings by Jain et al. (2000) due to the availability of Statistical Pattern Recognition in handling large databases and stringent performance requirements (speed, accuracy, and cost).Nureizi and Watada (2009) provided fuzzy evaluation criteria to support the weightage of important features of oil palm grading. By exploiting Distance Measurement in Multiple Features, it is hoped to improve current grading process as mentioned previously.
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Speech Recognition for English Language Pattern Recognition Approach

Speech Recognition for English Language Pattern Recognition Approach

There is statistical method which is widely used for characterizing spectral properties known as Hidden Markov Model. There were two scientist by the name of Baker and Jelinek from Carnegie Mellon University and at IBM respectively, they implement in 1970 for the first time in their speech recognition research [10]. HMM is pattern recognition technique that is very popular during voice recognition system [11]. Basically HMM is the statistical model to present speech pattern.

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Protein Secondary Structure Prediction using Pattern Recognition Neural Network

Protein Secondary Structure Prediction using Pattern Recognition Neural Network

Proteins are key biological molecules with diverse functions. With newer technologies producing more data (genomics, proteomics) than can be annotated manually, in silico methods of predicting their structure and thereafter their function has been christened the Holy Grail of structural bioinformatics. Successful secondary structure prediction provides a starting point for direct tertiary structure modeling; in addition it improves sequence analysis and sequence-structure binding for structure and function determination. Using machine learning and data mining process, we developed a pattern recognition technique based on statistical for predicting protein secondary structure from the component amino acid sequence. By applying this technique, a performance score of Q 8 =72.3% was
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Real Time Traffic Sign Recognition System based on Colour Image Segmentation

Real Time Traffic Sign Recognition System based on Colour Image Segmentation

-symbol recognition. The classifiers based on the image features have a major drawback that, due to the exhaustive search over the feature set, the training time grows with respect to the number of features. Color-based symbol detection relies on color, whereas the grayscale method concentrates on the geometry/shape of the object. Recent works have used both color segmentation and shape recognition to improve the detection rates. First, candidate regions are selected using color features, and then, an edge based method is employed to the perimeter of the regions for the detection step. X. Chen and J. Yang and J. Zhang and A. Waibel [14] presented an approach to detect and recognize Chinese symbols and translated the recognized text into English. The technique embeds multi resolution and multi scale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework to handle text in different sizes, orientations, color distributions, and backgrounds. King Hann Lim, Li-Minn Ang, Kah Phooi Seng[5] discussed new hybrid technique which proposed for traffic sign detection. This system is combination of knowledge based analysis and radial basis function neural classifier (RBFNN). Firstly the traffic signs are detected from the natural image using color image segmentation technique. The extracted signs are then passed to the recognition system for classification. The recognition system consists of three stages: color histogram classification, shape classification and RBF neural classification. Unique color and shape of traffic signs are used to classify them into smaller subclass and then can be easily recognized using RBFNN. In this system traffic sign features are from the image using principle component analysis. Then the most discriminant features are obtained using the Fisher's Linear Discriminant (FLD). Siti Sarah Md Sallah, Fawnizu Azmadi Hussain, and Mohd Zuki Yusoff [24] proposed new method for road sign detection and recognition algorithm for an embedded application. The algorithm uses Hue Saturation
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The Differences of Subjective findings in Pattern Recognition among Experts, Novices and Students: A Quasi-Delphi Technique

The Differences of Subjective findings in Pattern Recognition among Experts, Novices and Students: A Quasi-Delphi Technique

typical syndromes and tactics of management in addition to pathobiological and psychosocial factors (7). Understanding of pattern recognition is essential in successful management because patients with the same nominal pathology can have different contributing factors (7). The effectiveness of diagnosis using pattern recognition in contrast to using hypotheticodeductive reasoning has been stressed (2). This knowledge is applied to new patients in the form of forward reasoning. Differences of clinical reasoning processes between experts and novices have been reported by several authors. Experts use forward reasoning to diagnose accurately whereas non- experts prefer to use backward reasoning (8, 10). Also, an expert has a sophisticated clinical picture to identify a patient’s problem effectively on the basis of professional knowledge, professional craft knowledge, and personal knowledge. On the other hand, clinical reasoning beginners are inefficient in collecting information on patients and in finding their problems. Numerous forms of knowledge, elaborated causal networks, abridged networks, illness scripts and instance scripts are all characterized as belonging to an expert (2, 11).
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Dorsal Palm Vein Pattern Based Recognition System

Dorsal Palm Vein Pattern Based Recognition System

There are plenty of issues associated with the current existing biometric techniques. Fingerprint recognition system, one of the most popular biometric techniques used has a major disadvantage, that it can be replicated easily with the help of a cello tape. Also, if the skin of the finger is damaged due to some injury or skin disease this technique is rendered usless.Retina Recognition System can be easily passed with the help of lenses. Iris Recognition System becomes unreliable as iris changes as a person grows older. Criminals and Imposters could essentially copy the digital code for iris scans and reproduce it whenever they want to unlawfully gain access into a certain security system or secured place. Also the parts of the iris can be hidden easily by eyelashes and eyelids. These scans cause discomfort to the end user. Iris scans also pose a risk of damaging the eyes of an individual due to its intrusive nature. Voice Recognition has a major disadvantage that in a crowded place, the noise cannot be filtered out. This results in the mixing of the various voices in the background which makes distinguishing an individual's voice very difficult. Also if two people have similar voice frequencies, then it becomes a major drawback of the system as its accuracy would decrease. Face Recognition has some major drawbacks too. The face can be blocked by hair falling on a person's face, mufflers and spectacles. Also changes in lighting or facial expressions can throw off the device, thereby reducing accuracy. Also in addition, the individual's facial features changes over time. Hence, constant updating of the change in the person's facial features would be required. In case of twins, the distance between the eyes and jaw line could be the same. This distance is another factor which is used as a parameter in facial recognition systems. So in case of twins, face recognition will fail.
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An Introduction to Pattern Recognition   Michael Alder pdf

An Introduction to Pattern Recognition Michael Alder pdf

compression schemes. I can, and shall, regard a gaussian pdf as simply a compressed way of describing a set of points in . This follows Rissanen's approach, see the references for details. This approach is relatively new, but very appealing to those of us who can almost believe in information theoretic ideas. As has been pointed out by Penttila, another Finn, the Babylonians kept careful records of eclipses and other astronomical events. They also kept careful records of city fires and floods and famines. The records allowed them to eventually become able to predict eclipses but not to predict fires, floods or famines. Thus they only got good results on the things they didn't actually care about. What is done in practice is to follow the example of the Babylonians: you try hard to use the data to make predictions by any method you can devise. If you can't find any pattern at all, you say to yourself `Sod it, the bloody stuff is random'. This is essentially a statement about your intellectual limitations. If I am much cleverer than you, I might be able to figure out a pattern you cannot. What the moron describes as `bad luck', the smarter guy identifies as incompetence. One man's random variable is another's causal system, as Persi Diaconis can demonstrate with a coin.
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CV dwlee(new)

CV dwlee(new)

- Pattern Recognition Letter, International Journal of Pattern Recognition and Artificial Intelli- gence, IE Interface, International Joint Conference on Neural Networks, International J[r]

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Injury affects coelomic fluid proteome of the common starfish, Asterias rubens

Injury affects coelomic fluid proteome of the common starfish, Asterias rubens

Although our findings suggest that BL is distinguished from PW by a set of proteins, the predicted functional terms are too general, hindering recognition of injury-specific physiological processes. This is a second limitation of our work and a general challenge of studies on non-model invertebrates. Therefore, further experiments on recombinant protein expression, tertiary structure determination and functional activity testing are needed to clarify the physiological roles of these proteins. Nevertheless, an overlap of both injury-responsive proteins and general functional terms between injuries could suggest five common processes playing a role in early responses to injury: (1) activation of innate immunity involving defense and non-self- recognition; (2) regulation of proteolysis by a rich repertoire of peptidase inhibitors; (3) activation of proteolysis and degradation of collagenous ECM; (4) regulation of cell adhesion and migration; and (5) activation of regenerative processes. Our assumptions about the activation of regenerative processes is supported by an overlap of injury-responsive proteins with those involved in regeneration in sea cucumbers, namely β -microseminoprotein, serum amyloid A, and ependymin-like and avidin-like proteins. Observed associations of cancer-related terms with such proteins as WAPL, LYSTARs, GRAN, BMSP, CATB, CLTR and ENDL is not surprising, because carcinogenesis involves both cell migration and transdifferentiation, which are thought to be important mechanisms of regeneration in echinoderms (Kalacheva et al., 2017). Therefore, our dataset may represent an important tool for discovery of novel proteins involved in regeneration in echinoderms, suggesting important targets for future studies. Moreover, observed signs of innate immune response call for further studies comparing responses to immune challenge and wounding, and targeting more precise discrimination of regeneration-related components.
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Local Descriptor based Face Recognition System

Local Descriptor based Face Recognition System

Local Binary Pattern (LBP) is a parametric independent visual descriptor used for texture classification. LBP and its various types are implemented for still images and video sequences in the case of facial expression analysis. Many studies will consider visual face data for facial expression analysis and classify the given input expressions in seven states viz. fear, anger, surprise, happy, disgust, sad and neutral. Girish et al. [1] conducted experiment using MBLBP with PCA feature extraction technique and obtained recognition rate of 91.79% on ORL dataset for 3 X 3 operator scale. They again conducted the same experiment on INDIAN FACE dataset with 9 X 9 operator scale and achieved recognition rate of 85.71%. Shirinivas et al. [2] conducted experiment using AR-LBP with SVN feature extraction technique and obtained recognition rate of 84.29% on JAFFE dataset for 3 X 3 operator scale. They again conducted the same experiment on FGNET dataset with 3 X 3 and 15 X 9 operator scale and achieved recognition rate of 71.6% and 79.46% respectively. PCA is the most successfully used technique for image analysis and it is considered as baseline method for face recognition. PCA is less sensitive to different datasets compared to other holistic methods [4], hence it is widely used technique in the area of face recognition. Turk et al. [5] conducted an experiment on PCA using datasets of 2500 images of 16 subjects along with 3 different head scales, 3 different head orientations and 3 lighting conditions and achieved 96%, 85% and 64% recognition rates for lighting, head orientation and head scale variation. Ahonen et al. [6] conducted an experiment on PCA feature extraction with Mahalanobis Cosine distance similarity metric and reported 65%, 85%, 44%, 22% on fc, fb, dup-I, dup-II FERET datasets.
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A New Top Down Context Free Parsing for Syntactic Pattern Recognition

A New Top Down Context Free Parsing for Syntactic Pattern Recognition

In this paper,at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top –down tabular parser known as Earley’s algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problemand for this reasontakes a lots of time,searching in array and grammar parsing,and wasting lots of memory. In order to solve these problems and most important,the cubic time complexity,in this article,a new algorithm has been introduced,which reduces wasting the memory to zero, with using linked list data structure.Also, withthe changes in the implementation and performance of the algorithm,cubic time complexity has transformed into O (n*R) order.
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A Comprehensive Review of Protection Schemes for Distributed Generation

A Comprehensive Review of Protection Schemes for Distributed Generation

A novel wide-area backup protection algorithm based on the fault component voltage distribution is suggested in [84]. The designed scheme is very useful in solving the issues of complex setting and faulty operation under power flow transfer of conventional backup protection. The measured values of voltage and current of faulty component at one terminal of the distribution line are applied to approximate the fault component voltage at the other terminal. Consequently, the fault element can be identified by computing the ratio between the measured and estimated values. Moreover, the speed of fault element identification can be augmented by a faulted area detection scheme. The suggested technique has the benefit of simple settings and flexible requirements for synchronized wide-area data. The scheme is tested on the IEEE 39-bus system. Ten synchronous genera- tors are used as DG sources. Verification of the algorithm is performed by using PSCAD/EMTDC software environment and it considers both symmetrical and unsymmetrical faults. The flaw in the wide area protection technique is that it has only been tested for PV connected distribution grids. Moreover, it does not consider communication failures [72]. A general configuration schematic is shown in Figure 19. In the diagram, SCADA stands for Supervisory Control and Data Acquisition, LBPC stand for Local Backup Protection Centers and SBPC stands for System Backup Protection Center.
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Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study in Muda River Basin

Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study in Muda River Basin

The reduction of variables into a set of factors for further analysis can be observed using chemometric tech- nique, such as the utilization of Factor Analysis. It is seldom that the researcher collects and analyzes data with prior knowledge regarding the relationship of the variables, but through this technique, variables with the big- gest influence in the change of the hydrological modelling in the study area can be compared on a cost effective and quicker manner compared to other techniques (Gorsuch, 1990) [4].

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A Fuzzy Reasoning Technique for Pattern Recognition

A Fuzzy Reasoning Technique for Pattern Recognition

binary image of sixth sample of numeral class two and Fig.8 (b) shows its feature vector. The features vectors of unknown samples are compared with the KB feature vectors of all the classes. Depending on the feature value, a fuzzy reasoning is made and assigned the membership value for their matching. An example, the feature vector of a sample given in Fig.7 (b) is matched with the KB. The membership values are assigned for different classes features are tabulated in the Table1. It is seen that, for most of the features corresponds to numeral class two have high membership value compare to other classes. The algebraic sum of membership values of each class is computed to recognize the unknown sample. The class 2 has highest membership value and therefore its class is assigned to unknown sample. When the unknown sample feature value does not lie within the range of reference class features region, a zero membership value is assigned to them. In Table 1, majority cells have zero values. The cells having zero values corresponds to incorrect classes and cells corresponds to the correct class have non-zero value. When the feature vectors of training samples are classified by the proposed technique, found 100% classification rate. Similarly, the feature vectors of unknown (testing) samples are matched with the KB and recognized by the fuzzy reasoning technique. It is found that all the samples were recognized correctly and found 100% recognition rate.
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Research on Feature Extraction for Character Recognition of NaXi Pictograph

Research on Feature Extraction for Character Recognition of NaXi Pictograph

information collection, information analysis and processing, information classification and discrimination, and so on. Information collection means that gray of characters on paper will be converted into electrical signals, which can be input into computers. Information collection is based on paper feeding mechanisms and photoelectric conversion devices in character recognition reader, flying-spots scanners, video camera, photosensitive components, laser scanners, and other photoelectric conversion devices. Information analysis and processing eliminate the noises and disturbance caused by printing quality, paper quality, writing instruments and other factors. It can normalize size, deflexion, shade and thickness. Information classification and discrimination can remove the noises, normalize the character information, classify the character information and output the recognition results. [2-6]
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Survey of Pattern Recognition Approaches in
          Japanese Character Recognition

Survey of Pattern Recognition Approaches in Japanese Character Recognition

Handwritten text differs due to differences in writing styles, and hence, handwritten character recognition suffers from absorbing variations of the same characters among different writing styles. Also, resolution of the graphical similarity of different characters in Japanese text is another consideration to be taken into account. To overcome the problem, an offline effective algorithm for large scale character recognition for large set characters like Korean and Chinese was proposed by Kim [17]. The algorithm was developed based on template matching and improvement strategies; First, Multi-stage pre-classification that reduces the processing time of the template matching by cutting off a number of recognition target classes [18] is done. It is desirable to cut off as many classes as possible with little or no degradation of recognition accuracy. Second, the pair wise reordering is done to enhance the recognition accuracy by performing a fine detail classification on the recognition candidates generated from the template matching [19].
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Myoelectric control techniques for a rehabilitation robot

Myoelectric control techniques for a rehabilitation robot

There are several directions that can be pursued for the future work of the real time myoelectric control scheme. Using the developed pattern recognition myoelectric control scheme, one could develop robotic devices for rehabilitation exercises. These rehabilitation robotic devices would use the myoelectric control scheme to assist subjects in movement in order to help strengthen their muscles. It could also be used to extend the range of motion for someone like the CCS subject who did not have full mobility of several motions of the arm. Ultimately it would be beneficial to develop rehabilitation robotic devices that could assist in everyday life for those needing assistance at all times. It would be desirable to extend the real time myoelectric control scheme to subjects with other conditions and disabilities besides CCS. CCS is only one condition of the many neuromuscular disabilities that might benefit from applications involving myoelectric control schemes. Other changes could certainly be attempted in order to make the classification accuracy better. This would include testing of other features, classifiers, and windowing schemes. Another approach one could use to increase the accuracy is adding other electrode locations or trying to target different muscles. One could also try to extend the three DOF to four or five DOF by adding motions of the shoulder. In preliminary work, experiments were completed which attempted to run two classifiers in parallel. If this implementation would work, it would allow for movement in multiple DOF. There was no success at accomplishing this, but in the future the parallel classifier concept could be reattempted.
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