dhafar hamed - Academia.edu (original) (raw)
Papers by dhafar hamed
Communications in Computer and Information Science, 2020
In the recent years, the number of web logs, and the amount of opinionated data on the World Wide... more In the recent years, the number of web logs, and the amount of opinionated data on the World Wide Web, have been grown substantially. The ability to determine the political orientation of an article automatically can be beneficial in many areas from academia to security. However, the sentiment classification of web log posts (political web log posts in particular), is apparently more complex than the sentiment classification of conventional text. In this paper, a supervised machine learning with two feature extraction techniques Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) are used for the classification process. For investigation, SVM with four kernels for supervised machine learning have been employed. Subsequent to testing, the results reveal that the linear with TF achieved the results in accuracy of 91.935% also with TF-IDF achieved the 95.161%. The linear kernel was deemed the most suitable for our model.
Communications in Computer and Information Science, 2020
Sentiment analysis plays an important role in most of human activities and has a significant impa... more Sentiment analysis plays an important role in most of human activities and has a significant impact on our behaviours. With the development and use of web technology, there is a huge amount of data that represents users opinions in many areas such as politics and business. This paper applied Naive Bayes (NB) to analyse the opinions by exploring categories from a text and classified it to the right class (Reform, Conservative and Revolutionary). It investigates the effect of using two feature extraction i.e. Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods with Naive Bayes classifiers (Gaussian, Multinomial, Complement and Bernoulli) on the accuracy of classifying Arabic articles. Precision, recall, F1-score and number of correct predict have been used to evaluate the performance of the applied classifiers. The results reveal that, using TF with TF-IDF improved the accuracy to 96.77%. The Complement was deemed the most suitable for our model.
2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), 2017
The expert systems and smart devices played a key role in the development of health care in terms... more The expert systems and smart devices played a key role in the development of health care in terms of continuous monitoring of patients treatment and preservation of E-medication system. The basic challenge that patients faced is the fact of difficulty in contacting physicianspecialists. The problem is there was no direct contact with the physician. This paper proposes an intelligent system that can offer self-care and monitoring system that can simulate the patient based on the application installed on his smartphone. The procedure will be whenever a patient sends his information about his blood test and other tests, the expert system will decide whether the situation is critical or not. In non-criticalcondition, the intelligent system will provide the recommendations and treatment directly. Otherwise, it will contact the physician directly to suggest the proper action that the patient should follow. Further expert system will update information regularly with patient information. A machine-learning algorithm was conductto perform the classification process.
2018 1st Annual International Conference on Information and Sciences (AiCIS), 2018
Abstract -Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that... more Abstract -Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that developed for pattern classification. This technique has been employed in many fields such as bioinformatics and with different attributes of datasetsfor instance numeric, nominal or mixed. One of the significant issues that user faces when implementing the SVM is choosing the appropriate kernel function with attributes of datasetto be investigated. This paper studied the behavior of SVM in regarding to the used attributes of dataset with different kernel functions. It analyzed the influence of various datasets descriptions on efficiency of (SVM)classification.SVM with these kernels have been implemented in Matlab. The investigated kernel functions are linear, polynomials, Sigmoid and Radial Based Function (RBF) . The evaluation process shows that the description of dataset with the used kernel function affects the performance of SVM classifier. Generally, SVM with linear and RBF achieved 100% in classification process when Mushroom dataset is used, and 99% when Sickle Cell Disease (SCD) is used.
2017 10th International Conference on Developments in eSystems Engineering (DeSE), 2017
This paper presents the utilisation of dynamical recurrent neural network architectures in the pu... more This paper presents the utilisation of dynamical recurrent neural network architectures in the purpose of classifying the Sickle Cell disorder data. It is indicted that recurrent neural networks such as the Jordan network produce a great improvement with clinical data sets and have helped in acquiring high accuracy. The main aim of this study is to provide a sophisticated model to differentiate applications of dynamical neural networks for medically related problems. We attempt to classify the amount of medications for each patient with Sickle Cell disorder. We use different recurrent neural network architectures in terms of examining performance for each model within this study. The motivation for the classification approach used in this study is to support medical sectors to offer proper therapy advice depending on the former data set. The outcomes yield from different classifiers during our experiments indicated that Elman and hybrid recurrent neural networks produced inferior results when compared to Jordan neural networks. Results have indicated that for the recurrent network models tested, the Jordan architecture was found to yield considerably better results over the range of performance measures that been selected for this research.
Journal of Al-Qadisiyah for computer science and mathematics, 2017
Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a pub... more Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a public issue within the last years because many types of mushrooms are poisonous. Proper determination of mushrooms is one of the key safety issues in picking activities of it, which is widely spread, in countries. This contribution proposes a novel approach to support determination of the mushrooms through using a proposed system with mobile devices. Part of the proposed system is a mobile application that easily used by a user - mushroom picker. Hence, the mushroom type determination process can be performed at any location based on specific attributes of it. The mushroom type determination application runs on Android devices that are widely spread and inexpensive enough to enable wide exploitation by users. This paper developed Mushroom Diagnosis Assistance System (MDAS) that can be used on a mobile phone. Two classifiers are used which are Naive Bays and Decision Tree to classify the m...
Bulletin of Electrical Engineering and Informatics, 2021
Currently, sentiment analysis into positive or negative getting more attention from the researche... more Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the syst...
Bulletin of Electrical Engineering and Informatics, 2021
Currently, sentiment analysis into positive or negative getting more attention from the researche... more Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.
In the recent years, the number of web logs, and the amount of opinionated data on the World Wide... more In the recent years, the number of web logs, and the amount of opinionated data on the World Wide Web, have been grown substantially. The ability to determine the political orientation of an article automatically can be beneficial in many areas from academia to security. However, the sentiment classification of web log posts (political web log posts in particular), is apparently more complex than the sentiment classification of conventional text. In this paper, a supervised machine learning with two feature extraction techniques Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) are used for the classification process. For investigation, SVM with four kernels for supervised machine learning have been employed. Subsequent to testing, the results reveal that the linear with TF achieved the results in accuracy of 91.935% also with TF-IDF achieved the 95.161%. The linear kernel was deemed the most suitable for our model.
Springer, 2020
Sentiment analysis plays an important role in most of human activities and has a significant impa... more Sentiment analysis plays an important role in most of human activities and has a significant impact on our behaviours. With the development and use of web technology, there is a huge amount of data that represents users opinions in many areas such as politics and business. This paper applied Naïve Bayes (NB) to analyse the opinions by exploring categories from a text and classified it to the right class (Reform, Conservative and Revolutionary). It investigates the effect of using two feature extraction i.e. Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods with Naïve Bayes classifiers (Gaussian, Multinomial, Complement and Bernoulli) on the accuracy of classifying Arabic articles. Precision, recall, F1-score and number of correct predict have been used to evaluate the performance of the applied classifiers. The results reveal that, using TF with TF-IDF improved the accuracy to 96.77%. The Complement was deemed the most suitable for our model.
IEEE
This paper presents the utilisation of dynamical recurrent neural network architectures in the pu... more This paper presents the utilisation of dynamical recurrent neural network architectures in the purpose of classifying the Sickle Cell disorder data. It is indicted that recurrent neural networks such as the Jordan network produce a great improvement with clinical data sets and have helped in acquiring high accuracy. The main aim of this study is to provide a sophisticated model to differentiate applications of dynamical neural networks for medically related problems. We attempt to classify the amount of medications for each patient with Sickle Cell disorder. We use different recurrent neural network architectures in terms of examining performance for each model within this study. The motivation for the classification approach used in this study is to support medical sectors to offer proper therapy advice depending on the former data set. The outcomes yield from different classifiers during our experiments indicated that Elman and hybrid recurrent neural networks produced inferior results when compared to Jordan neural networks. Results have indicated that for the recurrent network models tested, the Jordan architecture was found to yield considerably better results over the range of performance measures that been selected for this research.
Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a pub... more Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a public issue within the last years because many types of mushrooms are poisonous. Proper determination of mushrooms is one of the key safety issues in picking activities of it, which is widely spread, in countries. This contribution proposes a novel approach to support determination of the mushrooms through using a proposed system with mobile devices. Part of the proposed system is a mobile application that easily used by a user-mushroom picker. Hence, the mushroom type determination process can be performed at any location based on specific attributes of it. The mushroom type determination application runs on Android devices that are widely spread and inexpensive enough to enable wide exploitation by users. This paper developed Mushroom Diagnosis Assistance System (MDAS) that can be used on a mobile phone. Two classifiers are used which are Naive Bays and Decision Tree to classify the mushroom types. The proposed approach selects the most effective of the already known mushroom attributes, and then specify the mushroom type. The use of specific features in mushroom determination process achieved very accurate results.
IEEE
The expert systems and smart devices played a key role in the development of health care in terms... more The expert systems and smart devices played a key role in the development of health care in terms of continuous monitoring of patients treatment and preservation of E-medication system. The basic challenge that patients faced is the fact of difficulty in contacting physicianspecialists. The problem is there was no direct contact with the physician. This paper proposes an intelligent system that can offer self-care and monitoring system that can simulate the patient based on the application installed on his smartphone. The procedure will be whenever a patient sends his information about his blood test and other tests, the expert system will decide whether the situation is critical or not. In non-criticalcondition, the intelligent system will provide the recommendations and treatment directly. Otherwise, it will contact the physician directly to suggest the proper action that the patient should follow. Further expert system will update information regularly with patient information. A machine-learning algorithm was conductto perform the classification process.
IEEE
Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that developed... more Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that developed for pattern classification. This technique has been employed in many fields such as bioinformatics and with different attributes of datasetsfor instance numeric, nominal or mixed. One of the significant issues that user faces when implementing the SVM is choosing the appropriate kernel function with attributes of datasetto be investigated. This paper studied the behavior of SVM in regarding to the used attributes of dataset with different kernel functions. It analyzed the influence of various datasets descriptions on efficiency of (SVM)classification.SVM with these kernels have been implemented in Matlab. The investigated kernel functions are linear, polynomials, Sigmoid and Radial Based Function (RBF). The evaluation process shows that the description of dataset with the used kernel function affects the performance of SVM classifier. Generally, SVM with linear and RBF achieved 100% in classification process when Mushroom dataset is used, and 99% when Sickle Cell Disease (SCD) is used.
Communications in Computer and Information Science, 2020
In the recent years, the number of web logs, and the amount of opinionated data on the World Wide... more In the recent years, the number of web logs, and the amount of opinionated data on the World Wide Web, have been grown substantially. The ability to determine the political orientation of an article automatically can be beneficial in many areas from academia to security. However, the sentiment classification of web log posts (political web log posts in particular), is apparently more complex than the sentiment classification of conventional text. In this paper, a supervised machine learning with two feature extraction techniques Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) are used for the classification process. For investigation, SVM with four kernels for supervised machine learning have been employed. Subsequent to testing, the results reveal that the linear with TF achieved the results in accuracy of 91.935% also with TF-IDF achieved the 95.161%. The linear kernel was deemed the most suitable for our model.
Communications in Computer and Information Science, 2020
Sentiment analysis plays an important role in most of human activities and has a significant impa... more Sentiment analysis plays an important role in most of human activities and has a significant impact on our behaviours. With the development and use of web technology, there is a huge amount of data that represents users opinions in many areas such as politics and business. This paper applied Naive Bayes (NB) to analyse the opinions by exploring categories from a text and classified it to the right class (Reform, Conservative and Revolutionary). It investigates the effect of using two feature extraction i.e. Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods with Naive Bayes classifiers (Gaussian, Multinomial, Complement and Bernoulli) on the accuracy of classifying Arabic articles. Precision, recall, F1-score and number of correct predict have been used to evaluate the performance of the applied classifiers. The results reveal that, using TF with TF-IDF improved the accuracy to 96.77%. The Complement was deemed the most suitable for our model.
2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), 2017
The expert systems and smart devices played a key role in the development of health care in terms... more The expert systems and smart devices played a key role in the development of health care in terms of continuous monitoring of patients treatment and preservation of E-medication system. The basic challenge that patients faced is the fact of difficulty in contacting physicianspecialists. The problem is there was no direct contact with the physician. This paper proposes an intelligent system that can offer self-care and monitoring system that can simulate the patient based on the application installed on his smartphone. The procedure will be whenever a patient sends his information about his blood test and other tests, the expert system will decide whether the situation is critical or not. In non-criticalcondition, the intelligent system will provide the recommendations and treatment directly. Otherwise, it will contact the physician directly to suggest the proper action that the patient should follow. Further expert system will update information regularly with patient information. A machine-learning algorithm was conductto perform the classification process.
2018 1st Annual International Conference on Information and Sciences (AiCIS), 2018
Abstract -Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that... more Abstract -Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that developed for pattern classification. This technique has been employed in many fields such as bioinformatics and with different attributes of datasetsfor instance numeric, nominal or mixed. One of the significant issues that user faces when implementing the SVM is choosing the appropriate kernel function with attributes of datasetto be investigated. This paper studied the behavior of SVM in regarding to the used attributes of dataset with different kernel functions. It analyzed the influence of various datasets descriptions on efficiency of (SVM)classification.SVM with these kernels have been implemented in Matlab. The investigated kernel functions are linear, polynomials, Sigmoid and Radial Based Function (RBF) . The evaluation process shows that the description of dataset with the used kernel function affects the performance of SVM classifier. Generally, SVM with linear and RBF achieved 100% in classification process when Mushroom dataset is used, and 99% when Sickle Cell Disease (SCD) is used.
2017 10th International Conference on Developments in eSystems Engineering (DeSE), 2017
This paper presents the utilisation of dynamical recurrent neural network architectures in the pu... more This paper presents the utilisation of dynamical recurrent neural network architectures in the purpose of classifying the Sickle Cell disorder data. It is indicted that recurrent neural networks such as the Jordan network produce a great improvement with clinical data sets and have helped in acquiring high accuracy. The main aim of this study is to provide a sophisticated model to differentiate applications of dynamical neural networks for medically related problems. We attempt to classify the amount of medications for each patient with Sickle Cell disorder. We use different recurrent neural network architectures in terms of examining performance for each model within this study. The motivation for the classification approach used in this study is to support medical sectors to offer proper therapy advice depending on the former data set. The outcomes yield from different classifiers during our experiments indicated that Elman and hybrid recurrent neural networks produced inferior results when compared to Jordan neural networks. Results have indicated that for the recurrent network models tested, the Jordan architecture was found to yield considerably better results over the range of performance measures that been selected for this research.
Journal of Al-Qadisiyah for computer science and mathematics, 2017
Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a pub... more Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a public issue within the last years because many types of mushrooms are poisonous. Proper determination of mushrooms is one of the key safety issues in picking activities of it, which is widely spread, in countries. This contribution proposes a novel approach to support determination of the mushrooms through using a proposed system with mobile devices. Part of the proposed system is a mobile application that easily used by a user - mushroom picker. Hence, the mushroom type determination process can be performed at any location based on specific attributes of it. The mushroom type determination application runs on Android devices that are widely spread and inexpensive enough to enable wide exploitation by users. This paper developed Mushroom Diagnosis Assistance System (MDAS) that can be used on a mobile phone. Two classifiers are used which are Naive Bays and Decision Tree to classify the m...
Bulletin of Electrical Engineering and Informatics, 2021
Currently, sentiment analysis into positive or negative getting more attention from the researche... more Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the syst...
Bulletin of Electrical Engineering and Informatics, 2021
Currently, sentiment analysis into positive or negative getting more attention from the researche... more Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.
In the recent years, the number of web logs, and the amount of opinionated data on the World Wide... more In the recent years, the number of web logs, and the amount of opinionated data on the World Wide Web, have been grown substantially. The ability to determine the political orientation of an article automatically can be beneficial in many areas from academia to security. However, the sentiment classification of web log posts (political web log posts in particular), is apparently more complex than the sentiment classification of conventional text. In this paper, a supervised machine learning with two feature extraction techniques Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) are used for the classification process. For investigation, SVM with four kernels for supervised machine learning have been employed. Subsequent to testing, the results reveal that the linear with TF achieved the results in accuracy of 91.935% also with TF-IDF achieved the 95.161%. The linear kernel was deemed the most suitable for our model.
Springer, 2020
Sentiment analysis plays an important role in most of human activities and has a significant impa... more Sentiment analysis plays an important role in most of human activities and has a significant impact on our behaviours. With the development and use of web technology, there is a huge amount of data that represents users opinions in many areas such as politics and business. This paper applied Naïve Bayes (NB) to analyse the opinions by exploring categories from a text and classified it to the right class (Reform, Conservative and Revolutionary). It investigates the effect of using two feature extraction i.e. Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods with Naïve Bayes classifiers (Gaussian, Multinomial, Complement and Bernoulli) on the accuracy of classifying Arabic articles. Precision, recall, F1-score and number of correct predict have been used to evaluate the performance of the applied classifiers. The results reveal that, using TF with TF-IDF improved the accuracy to 96.77%. The Complement was deemed the most suitable for our model.
IEEE
This paper presents the utilisation of dynamical recurrent neural network architectures in the pu... more This paper presents the utilisation of dynamical recurrent neural network architectures in the purpose of classifying the Sickle Cell disorder data. It is indicted that recurrent neural networks such as the Jordan network produce a great improvement with clinical data sets and have helped in acquiring high accuracy. The main aim of this study is to provide a sophisticated model to differentiate applications of dynamical neural networks for medically related problems. We attempt to classify the amount of medications for each patient with Sickle Cell disorder. We use different recurrent neural network architectures in terms of examining performance for each model within this study. The motivation for the classification approach used in this study is to support medical sectors to offer proper therapy advice depending on the former data set. The outcomes yield from different classifiers during our experiments indicated that Elman and hybrid recurrent neural networks produced inferior results when compared to Jordan neural networks. Results have indicated that for the recurrent network models tested, the Jordan architecture was found to yield considerably better results over the range of performance measures that been selected for this research.
Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a pub... more Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a public issue within the last years because many types of mushrooms are poisonous. Proper determination of mushrooms is one of the key safety issues in picking activities of it, which is widely spread, in countries. This contribution proposes a novel approach to support determination of the mushrooms through using a proposed system with mobile devices. Part of the proposed system is a mobile application that easily used by a user-mushroom picker. Hence, the mushroom type determination process can be performed at any location based on specific attributes of it. The mushroom type determination application runs on Android devices that are widely spread and inexpensive enough to enable wide exploitation by users. This paper developed Mushroom Diagnosis Assistance System (MDAS) that can be used on a mobile phone. Two classifiers are used which are Naive Bays and Decision Tree to classify the mushroom types. The proposed approach selects the most effective of the already known mushroom attributes, and then specify the mushroom type. The use of specific features in mushroom determination process achieved very accurate results.
IEEE
The expert systems and smart devices played a key role in the development of health care in terms... more The expert systems and smart devices played a key role in the development of health care in terms of continuous monitoring of patients treatment and preservation of E-medication system. The basic challenge that patients faced is the fact of difficulty in contacting physicianspecialists. The problem is there was no direct contact with the physician. This paper proposes an intelligent system that can offer self-care and monitoring system that can simulate the patient based on the application installed on his smartphone. The procedure will be whenever a patient sends his information about his blood test and other tests, the expert system will decide whether the situation is critical or not. In non-criticalcondition, the intelligent system will provide the recommendations and treatment directly. Otherwise, it will contact the physician directly to suggest the proper action that the patient should follow. Further expert system will update information regularly with patient information. A machine-learning algorithm was conductto perform the classification process.
IEEE
Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that developed... more Recently, the Support Vector Machine (SVM) algorithm becomes very common technique that developed for pattern classification. This technique has been employed in many fields such as bioinformatics and with different attributes of datasetsfor instance numeric, nominal or mixed. One of the significant issues that user faces when implementing the SVM is choosing the appropriate kernel function with attributes of datasetto be investigated. This paper studied the behavior of SVM in regarding to the used attributes of dataset with different kernel functions. It analyzed the influence of various datasets descriptions on efficiency of (SVM)classification.SVM with these kernels have been implemented in Matlab. The investigated kernel functions are linear, polynomials, Sigmoid and Radial Based Function (RBF). The evaluation process shows that the description of dataset with the used kernel function affects the performance of SVM classifier. Generally, SVM with linear and RBF achieved 100% in classification process when Mushroom dataset is used, and 99% when Sickle Cell Disease (SCD) is used.