This paper, proposed a classification approach that utilizes the high recognition ability of Hidd... more This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies ( i.e. the context ) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits in converting the raw image data into useful information which achieves high classification accuracy. It is known that other clustering schemes as traditional k-means does not take into account the spatial inter-pixels dependencies. Experiments work has been conducted on a set of 10 multispectral satellite images. Proposed algorithm is verified to simulate images and applied to a selected satellite image processing in the MATLAB environment.
SMS classifying technology has important significance to assist people in dealing with SMS messag... more SMS classifying technology has important significance to assist people in dealing with SMS messages. Although sms classification can be performed with little or no effort by people, it still remains difficult for computers. Machine learning offers a promising approach to the design of algorithms for training computer programs to efficiently and accurately classify short text message data.. In this paper we introduce a weighting method based on statistical estimation of the importance of a word for an SMS categorization problem, which will classify Mobile SMS into predefined classes such as occasions, friendship, sales etc. All sms are converted into text documents. After preprocessing vector space model is prepared and weight is assigned to each term. This weighting method based on statistical estimation of the importance of a word for an SMS categorization problem. The experiments reported in the paper shows that this weighting method improves significantly the classification accur...
A fuzzy expert system for selected Arabic sub-words recognition is presented in this paper. For e... more A fuzzy expert system for selected Arabic sub-words recognition is presented in this paper. For each sub-word pattern, membership values are determined for a number of fuzzy sets defined on the features extracted from the pattern. These sub-words consist of two characters and are written cursively, so, the first step is to segment the sub-words into two objects, main and secondary objects, keeping the two characters connected and making use of the general and special features in the recognition process. Presence or absence of dots in a sub-word and the number of such dots are fuzzy features, since the dot(s) may not appear exactly above or below the related character and they may appear mixed together. Closed loop is a fuzzy feature also. The proposed expert system consists of two main parts. First, the preprocessing part includes the feature extraction step that provides sufficient information to the inference engine. Second, the inference engine which applies the suitable set of fuzzy rules and aggregates them towards the final decision
مجلة الرافدين لعلوم الحاسبات والرياضيات المجلد (7) العدد(2)2010, 2010
Automatic recognition of printed text is of high importance in modern IT applications. Recognitio... more Automatic recognition of printed text is of high importance in modern IT applications. Recognition of text for lateen scripted language is readily in use for a long time. For cursive script languages (such as Arabic language) recognition of text is not available as a robust one with a reliable performance. More improvements still exist to reduce average of incorrect words, rather then no constraints on the limit of words of a specific language. Numerous approaches were tried in recognition of text but recognition of Arabic text based on Hidden Markov model seems to be the most promising one because of its ability to discriminate cursive scripts. This paper provides an off-line system to recognize printed Arabic text by using hidden Markov model with the aid of the algorithm that segment the text lines into connected parts then into characters. By looking on the results given by the designed recognition system it is found that a recognition rate (94.9 %) can be achieved. Such rate is in the same order of rates of recognition researches viewed in previous studies. This rate can still be improved. The language used in building the system is Matlab V7.6 (R2008a).
International Journal of Computer Networks and Communications Security, 2013
SMS classifying technology has important significance to assist people in dealing with SMS messag... more SMS classifying technology has important significance to assist people in dealing with SMS messages. Although sms classification can be performed with little or no effort by people, it still remains difficult for computers. Machine learning offers a promising approach to the design of algorithms for training computer programs to efficiently and accurately classify short text message data.. In this paper we introduce a weighting method based on statistical estimation of the importance of a word for an SMS categorization problem, which will classify Mobile SMS into predefined classes such as occasions, friendship, sales etc. All sms are converted into text documents. After preprocessing vector space model is prepared and weight is assigned to each term. This weighting method based on statistical estimation of the importance of a word for an SMS categorization problem. The experiments reported in the paper shows that this weighting method improves significantly the classification accuracy as measured on many categorization tasks.
International Journal of Computer Networks and Communications Security, 2013
This paper, proposed a classification approach that utilizes the high recognition ability of Hidd... more This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies (i.e. the context) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits in converting the raw image data into useful information which achieves high classification accuracy. It is known that other clustering schemes as traditional k-means does not take into account the spatial inter-pixels dependencies. Experiments work has been conducted on a set of 10 multispectral satellite images. Proposed algorithm is verified to simulate images and applied to a selected satellite image processing in the MATLAB environment.
The amount of the available data increases the ability to analyze and understand. The internet re... more The amount of the available data increases the ability to analyze and understand. The internet revolution has added billions of customer's review data in its depots. This has given an interest in sentiment analysis and opinion mining in the recent years. People have to depend on machines to classify and process the data as there are terabytes of review data in stock of a single product. So that prediction customer sentiments is very important to analyze the reviews as it not only helps in increasing profits but also goes a long way in improving and bringing out better products. In this paper , we present a survey regarding the presently available techniques and applications that appear in the field of opinion mining , such as , economy , security , marketing , spam detection , decision making , and elections expectation.
Second International Conference of Mathematics ICM-Erbil, 2018
In this research, we used a Support Vector Machine (SVM) algorithm to distinguish land appearance... more In this research, we used a Support Vector Machine (SVM) algorithm to distinguish land appearance in satellite images and classify them to desert or not desert areas. So, because of desertification problem which is one of the most monitoring land appearances by ecologicalist in present, it takes a large share of classification programs. A Support Vector Machine (SVM) considered as a suitable algorithm for classification, so it was used to classify images. At first, a training image created from a different desertification. Patches, like barren zones and many types of sand dunes, were taken from band7 of Landsat imagery. After this stage, features matrix containing (Correlation , Homogeneity , Energy , Entropy , Skewness , Kurtosis , mean , standard derivation , contrast) were extracted from that image, the matrix of features was input to SVM to produce the training matrix. This is input in addition to the features matrix of Landsat test image (which is produced by the same way of producing training features matrix). Thus by this operation the regions were classified as desert or not desert. The calculations in this research include the percentage of desertification of the whole image to find the changes through years (2003-2009). This research shows a high accuracy of classification reached to %99.89. The language used in implementation of this system is (Matlab R2011A) under Microsoft Windows 7. [Type text]
International Journal of Computer Networks and Communications Security, 2013
SMS classifying technology has important significance to assist people in dealing with SMS messag... more SMS classifying technology has important significance to assist people in dealing with SMS messages. Although sms classification can be performed with little or no effort by people, it still remains difficult for computers. Machine learning offers a promising approach to the design of algorithms for training computer programs to efficiently and accurately classify short text message data.. In this paper we introduce a weighting method based on statistical estimation of the importance of a word for an SMS categorization problem, which will classify Mobile SMS into predefined classes such as occasions, friendship, sales etc. All sms are converted into text documents. After preprocessing vector space model is prepared and weight is assigned to each term. This weighting method based on statistical estimation of the importance of a word for an SMS categorization problem. The experiments reported in the paper shows that this weighting method improves significantly the classification accuracy as measured on many categorization tasks.
In this paper we benefiting from Satellite imaging to retrieve informations by using its contents... more In this paper we benefiting from Satellite imaging to retrieve informations by using its contents, which is the pixels value of the image and by using the information of groups of pixels like texture, color gradation etc….then analyzing these information to extract spatial and temporal information of this images. Content Based Information Retrieval (CBIR) technique was used to retrieve image contents depending on visual objects of it. Support Vector Machine (SVM) technique was put into use by depending on more than one function like polynomial and RBF, then applying every one of them alone with the training image with different blocks size, then using block size and function that give best result from the training phase to be applied on the test images. The Satellite imaging was classified into two areas, desert and none desert in order to find the desert percentage of each image and comparing increasing of the desert percentages in Al-Hatra Region as a typical desertification area in nenavah governorate on different temporal periods. The language used in building the system is Matlab R2011a.
International Journal of Computer Science and Information Security, 2013
This paper, proposes a new classification method that uses Hidden Markov Models (HMM s) to classi... more This paper, proposes a new classification method that uses Hidden Markov Models (HMM s) to classify remote sensing imagery by exploiting the spatial and spectral information. When applying unsupervised classification to remote sensing images it can provide more useful and understandable information. Experiments shows that other clustering scheme like traditional k-means does not performs well because it does not take into account the spatial dependencies. Experiments are conducted on a set of multispectral satellite images. Proposed algorithm is verified for simulated images and applied for a selected satellite image processing in the MATLAB environment. Index Terms-Hidden Markov Models(HMM), land cover, multispectral satellite images, unsupervised classification.
Fifth Scientific Conferance for Information Technology, 2013
This paper presents a medical application based on digital image processing and Artificial Neural... more This paper presents a medical application based on digital image processing and Artificial Neural Network (ANN), which can recognize three types of Hereditary Hemolytic Anemia (HHA) that affect the Red Blood Cells (RBCs) and change their shape. Three Feed Forward Back Propagation Learning (FFBBL) Neural Networks are used in hierarchical approach to achieve this goal. The essence of this research is to segment each Red Blood Cell in a separate image and then extract some interesting features from each image in order to present them to the neural networks. The latter will, in turn, take the decision whether the RBC is infected or not. The results showed a recognition rate 92.38 %.
The Mobile is a circuit that receives and sends signals through earth stations and satellites s... more The Mobile is a circuit that receives and sends signals through earth stations and satellites so the objective of this research is to build a system that retrieves information about the location of the mobile user. The proposed system gets information of the location of the mobile user within Mosul university depending on the values of longitude and Latitude that have been received from GPS. The system displays on the mobile screen the values of longitude and latitude of the current location, as well as a list of names for the nearest buildings that surrounding the user location. The system works on a mobile model (Nokia) from the third generation and beyond. This mobile has an integrated GPS receiver. The job of this receiver is to receive the incoming signals from the satellites, and they are working out of the buildings (out door). We used the Java 2 Micro Edition (J2ME) Language to write the program for this application, since it is the language that is mostly used to write the mobile applications.
In this research we propose a statistical method and morpho-lexical analysis for correcting Arabi... more In this research we propose a statistical method and morpho-lexical analysis for correcting Arabic words as a post processor for Arabic words output from OCR systems. Dictionaries of words were built for the comparison to the attached word. The present research uses multiple knowledge sources and basing on the Arabic language properties, statistical method, morpho-lexical analysis and dictionary look-up for error detection and correction. Correction of errors in this research depends on the type of possible error, which can be: transposing two adjacent letters, rejection, replacing an incorrect letter, inserting a missing letter, substitution errors, which are most frequently committed by the OCR systems. ﺍﻟ ﻤ ﺴﺘ ﺨ ﻠ ﺹ ﻴﻘﺘﺭ ﺇﺤﺼﺎﺌﻴﺔ ﻁﺭﻴﻘﺔ ﺍﻟﺒﺤﺙ ﻫﺫﺍ ﺡ ﻭﺘﺤﻠﻴل ﻤﻭﺭﻓﻭﻟﻭﺠﻲ ﺍﻟﻌﺭﺒﻴـﺔ ﺍﻟﻜﻠﻤـﺎﺕ ﻟﺘﺼﺤﻴﺢ ﺍﻟﺨﺎﻁﺌﺔ ﺒﻭﺼﻔﻬﺎ ﻭﺴﻴﻠﺔ ﺍﻟـﻀﻭﺌﻲ ﺍﻟﺘﻤﻴﻴـﺯ ﺃﻨﻅﻤـﺔ ﻤﻥ ﺍﻟﻨﺎﺘﺠﺔ ﺍﻟﻌﺭﺒﻴﺔ ﻟﻠﻜﻠﻤﺎﺕ ﻨﻬﺎﺌﻴﺔ ﻤﻌﺎﻟﺠﺔ OCR. ﻴ ﺒﺎﻟﺒﺤﺙ ﻠﺤﻕ ﻗﻭﺍﻤﻴﺱ ﺒﺎ ﻤﻊ ﺍﻟﻤﻌﺎﻟﺠﺔ ﺘﺤﺕ ﺍﻟﻜﻠﻤﺔ ﻤﻘﺎﺭﻨﺔ ﺒﻬﺩﻑ ﻟﻜﻠﻤﺎﺕ ﻤﻁﺎﺒﻘﺘﻬﺎ ﻓـﻲ ﺍﻟﻘﺎﻤﻭﺱ .
ان تعدين البيانات فعالية الحصول على المعرفة لتحقيق هدف أساس وهو اكتشاف الحقائق الخفية (Hidden Fac... more ان تعدين البيانات فعالية الحصول على المعرفة لتحقيق هدف أساس وهو اكتشاف الحقائق الخفية (Hidden Facts) التي تتضمنها قواعد البيانات وذلك من خلال استخدام تقنيات متعددة تشتمل على الذكاء الاصطناعي، التحليلات الاحصائية، تقنيات و نمذجة البيانات ... الخ. فأن عملية تعدين البيانات تولد نماذج وعلاقات واضحة في البيانات والتي تساعد على توقع النتائج في المستقبل. وقد ظهرت العديد من الخوارزميات التي في هذا المجال، وترتب عليها مقارنة بين هذه الخوارزميات لاختبار الخوارزمية المناسبة في الحصول على نتائج أفضل. وقد هدف البحث الى استخدام المصنف C4.5 وربطها مع الشبكة العصبية نوع Back – Propagation (BP) وذلك لتكوين نموذج تصنيف يحمل خواص الطرفين، فضلا عن مقارنة النتائج المستحصلة مع نتائج التصنيف باستخدام الحزمة البرمجية الجاهزة Minitab . وتوصل البحث الى ان المعادلات الخاصة بالمصنف C4.5 كانت أكفأ في الاداء وخاصة بعد ربطها بالشبكة العصبية BP لازالة التناقض والتشويش الموجود في البيانات، كما عززت النتائج من افضلية استخدام لغات البرمجة مقارنة بنتائج التطبيق الجاهزة.
International Journal of Computer Networks and Communications Security, 2013
This paper, proposed a classification approach that utilizes the high recognition ability of Hidd... more This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies (i.e. the context) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits in converting the raw image data into useful information which achieves high classification accuracy. It is known that other clustering schemes as traditional k-means does not take into account the spatial inter-pixels dependencies. Experiments work has been conducted on a set of 10 multispectral satellite images. Proposed algorithm is verified to simulate images and applied to a selected satellite image processing in the MATLAB environment.
A fuzzy system for handwritten numerals recognition using a fuzzy Hough transform technique is pr... more A fuzzy system for handwritten numerals recognition using a fuzzy Hough transform technique is presented. The system is an off-line system since the data processed was written before the time of recognition. A data base of 480 patterns of unconstrained (free) handwritten numerals was used in the proposed system. Membership values are determined as fuzzy sets which are defined on the standard Hough transform vector. Manhattan distance measurement has been used to measure the similarity of an input feature vector to a number of numeral pattern classes. The overall recognition accuracy of the system for the ten numerals is 95%.
This paper, proposed a classification approach that utilizes the high recognition ability of Hidd... more This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies ( i.e. the context ) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits in converting the raw image data into useful information which achieves high classification accuracy. It is known that other clustering schemes as traditional k-means does not take into account the spatial inter-pixels dependencies. Experiments work has been conducted on a set of 10 multispectral satellite images. Proposed algorithm is verified to simulate images and applied to a selected satellite image processing in the MATLAB environment.
SMS classifying technology has important significance to assist people in dealing with SMS messag... more SMS classifying technology has important significance to assist people in dealing with SMS messages. Although sms classification can be performed with little or no effort by people, it still remains difficult for computers. Machine learning offers a promising approach to the design of algorithms for training computer programs to efficiently and accurately classify short text message data.. In this paper we introduce a weighting method based on statistical estimation of the importance of a word for an SMS categorization problem, which will classify Mobile SMS into predefined classes such as occasions, friendship, sales etc. All sms are converted into text documents. After preprocessing vector space model is prepared and weight is assigned to each term. This weighting method based on statistical estimation of the importance of a word for an SMS categorization problem. The experiments reported in the paper shows that this weighting method improves significantly the classification accur...
A fuzzy expert system for selected Arabic sub-words recognition is presented in this paper. For e... more A fuzzy expert system for selected Arabic sub-words recognition is presented in this paper. For each sub-word pattern, membership values are determined for a number of fuzzy sets defined on the features extracted from the pattern. These sub-words consist of two characters and are written cursively, so, the first step is to segment the sub-words into two objects, main and secondary objects, keeping the two characters connected and making use of the general and special features in the recognition process. Presence or absence of dots in a sub-word and the number of such dots are fuzzy features, since the dot(s) may not appear exactly above or below the related character and they may appear mixed together. Closed loop is a fuzzy feature also. The proposed expert system consists of two main parts. First, the preprocessing part includes the feature extraction step that provides sufficient information to the inference engine. Second, the inference engine which applies the suitable set of fuzzy rules and aggregates them towards the final decision
مجلة الرافدين لعلوم الحاسبات والرياضيات المجلد (7) العدد(2)2010, 2010
Automatic recognition of printed text is of high importance in modern IT applications. Recognitio... more Automatic recognition of printed text is of high importance in modern IT applications. Recognition of text for lateen scripted language is readily in use for a long time. For cursive script languages (such as Arabic language) recognition of text is not available as a robust one with a reliable performance. More improvements still exist to reduce average of incorrect words, rather then no constraints on the limit of words of a specific language. Numerous approaches were tried in recognition of text but recognition of Arabic text based on Hidden Markov model seems to be the most promising one because of its ability to discriminate cursive scripts. This paper provides an off-line system to recognize printed Arabic text by using hidden Markov model with the aid of the algorithm that segment the text lines into connected parts then into characters. By looking on the results given by the designed recognition system it is found that a recognition rate (94.9 %) can be achieved. Such rate is in the same order of rates of recognition researches viewed in previous studies. This rate can still be improved. The language used in building the system is Matlab V7.6 (R2008a).
International Journal of Computer Networks and Communications Security, 2013
SMS classifying technology has important significance to assist people in dealing with SMS messag... more SMS classifying technology has important significance to assist people in dealing with SMS messages. Although sms classification can be performed with little or no effort by people, it still remains difficult for computers. Machine learning offers a promising approach to the design of algorithms for training computer programs to efficiently and accurately classify short text message data.. In this paper we introduce a weighting method based on statistical estimation of the importance of a word for an SMS categorization problem, which will classify Mobile SMS into predefined classes such as occasions, friendship, sales etc. All sms are converted into text documents. After preprocessing vector space model is prepared and weight is assigned to each term. This weighting method based on statistical estimation of the importance of a word for an SMS categorization problem. The experiments reported in the paper shows that this weighting method improves significantly the classification accuracy as measured on many categorization tasks.
International Journal of Computer Networks and Communications Security, 2013
This paper, proposed a classification approach that utilizes the high recognition ability of Hidd... more This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies (i.e. the context) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits in converting the raw image data into useful information which achieves high classification accuracy. It is known that other clustering schemes as traditional k-means does not take into account the spatial inter-pixels dependencies. Experiments work has been conducted on a set of 10 multispectral satellite images. Proposed algorithm is verified to simulate images and applied to a selected satellite image processing in the MATLAB environment.
The amount of the available data increases the ability to analyze and understand. The internet re... more The amount of the available data increases the ability to analyze and understand. The internet revolution has added billions of customer's review data in its depots. This has given an interest in sentiment analysis and opinion mining in the recent years. People have to depend on machines to classify and process the data as there are terabytes of review data in stock of a single product. So that prediction customer sentiments is very important to analyze the reviews as it not only helps in increasing profits but also goes a long way in improving and bringing out better products. In this paper , we present a survey regarding the presently available techniques and applications that appear in the field of opinion mining , such as , economy , security , marketing , spam detection , decision making , and elections expectation.
Second International Conference of Mathematics ICM-Erbil, 2018
In this research, we used a Support Vector Machine (SVM) algorithm to distinguish land appearance... more In this research, we used a Support Vector Machine (SVM) algorithm to distinguish land appearance in satellite images and classify them to desert or not desert areas. So, because of desertification problem which is one of the most monitoring land appearances by ecologicalist in present, it takes a large share of classification programs. A Support Vector Machine (SVM) considered as a suitable algorithm for classification, so it was used to classify images. At first, a training image created from a different desertification. Patches, like barren zones and many types of sand dunes, were taken from band7 of Landsat imagery. After this stage, features matrix containing (Correlation , Homogeneity , Energy , Entropy , Skewness , Kurtosis , mean , standard derivation , contrast) were extracted from that image, the matrix of features was input to SVM to produce the training matrix. This is input in addition to the features matrix of Landsat test image (which is produced by the same way of producing training features matrix). Thus by this operation the regions were classified as desert or not desert. The calculations in this research include the percentage of desertification of the whole image to find the changes through years (2003-2009). This research shows a high accuracy of classification reached to %99.89. The language used in implementation of this system is (Matlab R2011A) under Microsoft Windows 7. [Type text]
International Journal of Computer Networks and Communications Security, 2013
SMS classifying technology has important significance to assist people in dealing with SMS messag... more SMS classifying technology has important significance to assist people in dealing with SMS messages. Although sms classification can be performed with little or no effort by people, it still remains difficult for computers. Machine learning offers a promising approach to the design of algorithms for training computer programs to efficiently and accurately classify short text message data.. In this paper we introduce a weighting method based on statistical estimation of the importance of a word for an SMS categorization problem, which will classify Mobile SMS into predefined classes such as occasions, friendship, sales etc. All sms are converted into text documents. After preprocessing vector space model is prepared and weight is assigned to each term. This weighting method based on statistical estimation of the importance of a word for an SMS categorization problem. The experiments reported in the paper shows that this weighting method improves significantly the classification accuracy as measured on many categorization tasks.
In this paper we benefiting from Satellite imaging to retrieve informations by using its contents... more In this paper we benefiting from Satellite imaging to retrieve informations by using its contents, which is the pixels value of the image and by using the information of groups of pixels like texture, color gradation etc….then analyzing these information to extract spatial and temporal information of this images. Content Based Information Retrieval (CBIR) technique was used to retrieve image contents depending on visual objects of it. Support Vector Machine (SVM) technique was put into use by depending on more than one function like polynomial and RBF, then applying every one of them alone with the training image with different blocks size, then using block size and function that give best result from the training phase to be applied on the test images. The Satellite imaging was classified into two areas, desert and none desert in order to find the desert percentage of each image and comparing increasing of the desert percentages in Al-Hatra Region as a typical desertification area in nenavah governorate on different temporal periods. The language used in building the system is Matlab R2011a.
International Journal of Computer Science and Information Security, 2013
This paper, proposes a new classification method that uses Hidden Markov Models (HMM s) to classi... more This paper, proposes a new classification method that uses Hidden Markov Models (HMM s) to classify remote sensing imagery by exploiting the spatial and spectral information. When applying unsupervised classification to remote sensing images it can provide more useful and understandable information. Experiments shows that other clustering scheme like traditional k-means does not performs well because it does not take into account the spatial dependencies. Experiments are conducted on a set of multispectral satellite images. Proposed algorithm is verified for simulated images and applied for a selected satellite image processing in the MATLAB environment. Index Terms-Hidden Markov Models(HMM), land cover, multispectral satellite images, unsupervised classification.
Fifth Scientific Conferance for Information Technology, 2013
This paper presents a medical application based on digital image processing and Artificial Neural... more This paper presents a medical application based on digital image processing and Artificial Neural Network (ANN), which can recognize three types of Hereditary Hemolytic Anemia (HHA) that affect the Red Blood Cells (RBCs) and change their shape. Three Feed Forward Back Propagation Learning (FFBBL) Neural Networks are used in hierarchical approach to achieve this goal. The essence of this research is to segment each Red Blood Cell in a separate image and then extract some interesting features from each image in order to present them to the neural networks. The latter will, in turn, take the decision whether the RBC is infected or not. The results showed a recognition rate 92.38 %.
The Mobile is a circuit that receives and sends signals through earth stations and satellites s... more The Mobile is a circuit that receives and sends signals through earth stations and satellites so the objective of this research is to build a system that retrieves information about the location of the mobile user. The proposed system gets information of the location of the mobile user within Mosul university depending on the values of longitude and Latitude that have been received from GPS. The system displays on the mobile screen the values of longitude and latitude of the current location, as well as a list of names for the nearest buildings that surrounding the user location. The system works on a mobile model (Nokia) from the third generation and beyond. This mobile has an integrated GPS receiver. The job of this receiver is to receive the incoming signals from the satellites, and they are working out of the buildings (out door). We used the Java 2 Micro Edition (J2ME) Language to write the program for this application, since it is the language that is mostly used to write the mobile applications.
In this research we propose a statistical method and morpho-lexical analysis for correcting Arabi... more In this research we propose a statistical method and morpho-lexical analysis for correcting Arabic words as a post processor for Arabic words output from OCR systems. Dictionaries of words were built for the comparison to the attached word. The present research uses multiple knowledge sources and basing on the Arabic language properties, statistical method, morpho-lexical analysis and dictionary look-up for error detection and correction. Correction of errors in this research depends on the type of possible error, which can be: transposing two adjacent letters, rejection, replacing an incorrect letter, inserting a missing letter, substitution errors, which are most frequently committed by the OCR systems. ﺍﻟ ﻤ ﺴﺘ ﺨ ﻠ ﺹ ﻴﻘﺘﺭ ﺇﺤﺼﺎﺌﻴﺔ ﻁﺭﻴﻘﺔ ﺍﻟﺒﺤﺙ ﻫﺫﺍ ﺡ ﻭﺘﺤﻠﻴل ﻤﻭﺭﻓﻭﻟﻭﺠﻲ ﺍﻟﻌﺭﺒﻴـﺔ ﺍﻟﻜﻠﻤـﺎﺕ ﻟﺘﺼﺤﻴﺢ ﺍﻟﺨﺎﻁﺌﺔ ﺒﻭﺼﻔﻬﺎ ﻭﺴﻴﻠﺔ ﺍﻟـﻀﻭﺌﻲ ﺍﻟﺘﻤﻴﻴـﺯ ﺃﻨﻅﻤـﺔ ﻤﻥ ﺍﻟﻨﺎﺘﺠﺔ ﺍﻟﻌﺭﺒﻴﺔ ﻟﻠﻜﻠﻤﺎﺕ ﻨﻬﺎﺌﻴﺔ ﻤﻌﺎﻟﺠﺔ OCR. ﻴ ﺒﺎﻟﺒﺤﺙ ﻠﺤﻕ ﻗﻭﺍﻤﻴﺱ ﺒﺎ ﻤﻊ ﺍﻟﻤﻌﺎﻟﺠﺔ ﺘﺤﺕ ﺍﻟﻜﻠﻤﺔ ﻤﻘﺎﺭﻨﺔ ﺒﻬﺩﻑ ﻟﻜﻠﻤﺎﺕ ﻤﻁﺎﺒﻘﺘﻬﺎ ﻓـﻲ ﺍﻟﻘﺎﻤﻭﺱ .
ان تعدين البيانات فعالية الحصول على المعرفة لتحقيق هدف أساس وهو اكتشاف الحقائق الخفية (Hidden Fac... more ان تعدين البيانات فعالية الحصول على المعرفة لتحقيق هدف أساس وهو اكتشاف الحقائق الخفية (Hidden Facts) التي تتضمنها قواعد البيانات وذلك من خلال استخدام تقنيات متعددة تشتمل على الذكاء الاصطناعي، التحليلات الاحصائية، تقنيات و نمذجة البيانات ... الخ. فأن عملية تعدين البيانات تولد نماذج وعلاقات واضحة في البيانات والتي تساعد على توقع النتائج في المستقبل. وقد ظهرت العديد من الخوارزميات التي في هذا المجال، وترتب عليها مقارنة بين هذه الخوارزميات لاختبار الخوارزمية المناسبة في الحصول على نتائج أفضل. وقد هدف البحث الى استخدام المصنف C4.5 وربطها مع الشبكة العصبية نوع Back – Propagation (BP) وذلك لتكوين نموذج تصنيف يحمل خواص الطرفين، فضلا عن مقارنة النتائج المستحصلة مع نتائج التصنيف باستخدام الحزمة البرمجية الجاهزة Minitab . وتوصل البحث الى ان المعادلات الخاصة بالمصنف C4.5 كانت أكفأ في الاداء وخاصة بعد ربطها بالشبكة العصبية BP لازالة التناقض والتشويش الموجود في البيانات، كما عززت النتائج من افضلية استخدام لغات البرمجة مقارنة بنتائج التطبيق الجاهزة.
International Journal of Computer Networks and Communications Security, 2013
This paper, proposed a classification approach that utilizes the high recognition ability of Hidd... more This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies (i.e. the context) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits in converting the raw image data into useful information which achieves high classification accuracy. It is known that other clustering schemes as traditional k-means does not take into account the spatial inter-pixels dependencies. Experiments work has been conducted on a set of 10 multispectral satellite images. Proposed algorithm is verified to simulate images and applied to a selected satellite image processing in the MATLAB environment.
A fuzzy system for handwritten numerals recognition using a fuzzy Hough transform technique is pr... more A fuzzy system for handwritten numerals recognition using a fuzzy Hough transform technique is presented. The system is an off-line system since the data processed was written before the time of recognition. A data base of 480 patterns of unconstrained (free) handwritten numerals was used in the proposed system. Membership values are determined as fuzzy sets which are defined on the standard Hough transform vector. Manhattan distance measurement has been used to measure the similarity of an input feature vector to a number of numeral pattern classes. The overall recognition accuracy of the system for the ten numerals is 95%.
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Papers by Ghayda Altalib
Numerous approaches were tried in recognition of text but recognition of Arabic text based on Hidden Markov model seems to be the most promising one because of its ability to discriminate cursive scripts.
This paper provides an off-line system to recognize printed Arabic text by using hidden Markov model with the aid of the algorithm that segment the text lines into connected parts then into characters.
By looking on the results given by the designed recognition system it is found that a recognition rate (94.9 %) can be achieved. Such rate is in the same order of rates of recognition researches viewed in previous studies. This rate can still be improved. The language used in building the system is Matlab V7.6 (R2008a).
Manhattan distance measurement has been used to measure the similarity of an input feature vector to a number of numeral pattern classes. The overall recognition accuracy of the system for the ten numerals is 95%.
Numerous approaches were tried in recognition of text but recognition of Arabic text based on Hidden Markov model seems to be the most promising one because of its ability to discriminate cursive scripts.
This paper provides an off-line system to recognize printed Arabic text by using hidden Markov model with the aid of the algorithm that segment the text lines into connected parts then into characters.
By looking on the results given by the designed recognition system it is found that a recognition rate (94.9 %) can be achieved. Such rate is in the same order of rates of recognition researches viewed in previous studies. This rate can still be improved. The language used in building the system is Matlab V7.6 (R2008a).
Manhattan distance measurement has been used to measure the similarity of an input feature vector to a number of numeral pattern classes. The overall recognition accuracy of the system for the ten numerals is 95%.