Diabetic retinopathy (DR), which is a leading cause of adult blindness, primarily affects individ... more Diabetic retinopathy (DR), which is a leading cause of adult blindness, primarily affects individuals with diabetes. The manual diagnosis of DR, with the assistance of an ophthalmologist, has proven to be a time-consuming and challenging process. Late detection of DR is a significant factor contributing to the progression of the disease. To address this issue, the present study utilizes deep learning (DL) and transfer learning algorithms to analyze different stages of DR and precisely detect the condition. Using a large dataset comprising approximately 60,000 images, this study employs ResNet-101, DenseNet121, InceptionResNetV2, and EfficientNetB0 DL models to automatically assess the progression of DR. Images of patients' eyes are inputted into the models, and the DL architectures are adapted to extract relevant features from the eye images. The study's findings demonstrate that DenseNet121 outperforms ResNet-101, InceptionResNetV2, and EfficientNetB0 in accurately classifying the five stages of DR. The accuracy of the models was 97%, 96%, 95%, and 94%, respectively. These results underscore the effectiveness of DL in achieving an accurate and comprehensive classification of retinitis pigmentosa. By enabling accurate and timely diagnosis of DR, the application of DL techniques significantly contributes to the field of ophthalmology, facilitating improved treatment decisions for patients.
International Journal of Advanced Computer Science and Applications, 2023
Continued global economic instability and uncertainty is causing difficulties in predicting sales... more Continued global economic instability and uncertainty is causing difficulties in predicting sales. As a result, many sectors and decision-makers are facing new, pressing challenges. In supply chain management, the food industry is a key sector in which sales movement and the demand forecasting for food products are more difficult to predict. Accurate sales forecasting helps to minimize stored and expired items across individual stores and, thus, reduces the potential loss of these expired products. To help food companies adapt to rapid changes and manage their supply chain more effectively, it is a necessary to utilize machine learning (ML) approaches because of ML's ability to process and evaluate large amounts of data efficiently. This research compares two forecasting models for confectionery products from one of the largest distribution companies in Saudi Arabia in order to improve the company's ability to predict demand for their products using machine learning algorithms. To achieve this goal, Support Vectors Machine (SVM) and Long Short-Term Memory (LSTM) algorithms were utilized. In addition, the models were evaluated based on their performance in forecasting quarterly time series. Both algorithms provided strong results when measured against the demand forecasting model, but overall the LSTM outperformed the SVM.
The 5th International Conference on Future Networks & Distributed Systems, Dec 15, 2021
This paper presents how to utilize deep learning to extract knowledge from Arabic printed documen... more This paper presents how to utilize deep learning to extract knowledge from Arabic printed document images. The fundamental goal of deep learning is automatically extracting significant features from images, eliminating the need for a classic feature extraction method. We describe how to extract high-quality and coherent knowledge from Arabic printed document images using deep learning. This system is constructed on keywords used to classify Arabic document images according to these keywords. We used A questionnaire to choose valuable words according to historical, scientific, or religious documents. The evaluation of the proposed system is applied to Arabic printed document images to extract keywords. The accuracy of the proposed deep learning extraction approach is hugely affected by image preprocessing and image quality. The proposed system has a higher level of accuracy while extracting keywords. We achieve a 3.78% character error rate in the proposed system and a 15.46% word error rate.
International Journal of Advanced Computer Science and Applications, 2021
In this paper, we show how to classify Arabic document images using a convolutional neural networ... more In this paper, we show how to classify Arabic document images using a convolutional neural network, which is one of the most common supervised deep learning algorithms. The main goal of using deep learning is its ability to automatically extract useful features from images, which eliminates the need for a manual feature extraction process. Convolutional neural networks can extract features from images through a convolution process involving various filters. We collected a variety of Arabic document images from various sources and passed them into a convolutional neural network classifier. We adopt a VGG16 pre-trained network trained on ImageNet to classify the dataset of four classes as handwritten, historical, printed, and signboard. For the document image classification, we used VGG16 convolutional layers, ran the dataset through them, and then trained a classifier on top of it. We extract features by fixing the pre-trained network's convolutional layers, then adding the fully connected layers and training them on the dataset. We update the network with the addition of dropout by adding after each max-pooling layer and to the fourteen and the seventeenth layers which are the fully connected layers. The proposed approach achieved a classification accuracy of 92%.
This paper aims to study the problem of cooperation in the extended enterprise case. This study f... more This paper aims to study the problem of cooperation in the extended enterprise case. This study focuses on the presentation of agent-oriented extended enterprise as a solution allowing enterprises to overcome the cooperation problem, in general, and conflict resolution, in particular. In this regard, this objective is defined by three aspects: organizational, reasoning and application. The organizational and reasoning aspects are presented through an overview of designing the extended enterprise by an agent-oriented approach via the proposed MAIS-E2 model (Multi-Agent Information System for an Extended Enterprise) and the proposed argument-based negotiation framework R2-IBN (Relationship-Role and Interest Based Negotiation) defined for MAIS-E2 model, respectively. Then, we focus mainly on the application aspect through the instantiation of MAIS-E2 model to the port application. These aspects are consolidated by the experimental validation via the developed prototype using JADE platform and experimental results with different cases in the port area.
The execution traces generated by an application contain information that the developers believed... more The execution traces generated by an application contain information that the developers believed would be useful in debugging or monitoring the application, it contains application states and significant events at various critical points that help them gain insight into failures and identify and predict potential problems before they occur. Despite the ubiquity of these traces universally in almost all computer systems, they are rarely exploited because they are not readily machine-parsable. In this paper, we propose a Multi-Agents approach for prediction process using Blockchain technology, which allows automatically analysis of execution traces and detects early warning signals for system failure prediction during executing. The proposed prediction approach is constructed using a four-layer Multi-Agents system architecture. The proposed prediction approach performance is based on data prepossessing and supervised learning algorithms for prediction. Blockchain was used to coordinate collaboration between agents, and to synchronize prediction between agents and the administrators. We validated our approach by applying it to real-world distributed systems, where we predicted problems before they occurred with high accuracy. In this paper we will focus on the Architecture of our prediction approach.
Nowadays, the integration of Cloud Computing and the Internet of Things (Cloud-IoT) has drawn att... more Nowadays, the integration of Cloud Computing and the Internet of Things (Cloud-IoT) has drawn attention as new technologies in the Future Internet. Cloud-IoT accommodates good solutions to address real-world problems by offering new services in real-life scenarios. Nonetheless, the traditional Cloud-IoT will be probably not going to give suitable service to the user as it handles enormous amounts of data at a single server. Furthermore, the Cloud-IoT shows huge security and privacy problems that must be solved. To address these issues, we propose an integrated Fog Cloud-IoT architecture based on Multi-Agents System and Blockchain technology. Multi-Agents System has proven itself in decision-making aspects, distributed execution, and its effectiveness in acting in the event of an intrusion without user intervention. On the other side, we propose Blockchain technology as a distributed, public, authentic ledger to record the transactions. The Blockchain represents a great advantage to the next generation computing to ensures data integrity and to allows low latency access to large amounts of data securely. We evaluated the performance of our proposed architecture and compared it with the existing models. The result of our evaluation shows that performance is improved by reducing the response time.
Today vast and diverse event records of applications exist for almost every scientific domain, ma... more Today vast and diverse event records of applications exist for almost every scientific domain, making their integration and intelligent exploitation challenging. Indeed, complex data require expressive data representation models. This work deals with the intention mining that is a very active and promising research area. Performance, flexibility, and adaptation are the biggest challenges for intention mining. Those issues have been illustrated from research on method engineering and guidance. Intention mining is the ability to predict a user's goals. Knowing the user's intention can support the decision-making of the network administrators. In addition, the user-friendliness is another big challenge to the intention mining in this paper. For this, based on the communication and coordination of intelligent Agents, a new multi-Agents based System approach is gathered in order to discover an intentional process model and provide specific recommendations. In addition, the input of intention mining is the trace of activities, which is an unstructured file because the activities are distributed from different sources. However, the dataset that will be used in intention mining must be well structured and filtered, so often some efforts are required to filter the relevant data. The main input of all algorithm used to discover intentional process model is the log file (traces activities), which is unstructured dataset and not ready to be feed as-is to machine learning algorithm. Therefore, this paper aims to describe the data preprocessing steps, which transform the unstructured log file to a structured one.
Distributed Denial of Service attacks (DDoS) are one of the most prevalent attacks threatening sy... more Distributed Denial of Service attacks (DDoS) are one of the most prevalent attacks threatening systems and their security. In this paper, various models to categorize these attacks are presented, analyzed and compared on regards of their effectiveness for DDoS detection. Machine learning (ML) algorithms for classification are used after pre-processing DDoS dataset to classify network traffic. After analyzing the results of Naïve bayes, Decision Tree, Support Vector Machine, and Random Forest classifiers, we conclude that the most accurate results appeared when using the Random Forest classifier.
Advances in intelligent systems and computing, 2017
This paper aims to improve the argument based negotiation framework \(R^{2}\)-IBN to overcome the... more This paper aims to improve the argument based negotiation framework \(R^{2}\)-IBN to overcome the cooperation problem within the extended enterprise by the agent-oriented model named MAIS-\(E^{2}\). In order to overcome the problem of cooperation in general and conflict resolution which prevent this practice in particular, we propose as a solution the integration of game theory foundations into two modules of \(R^{2}\)-IBN framework which are: “Argument Evaluation” module and “Argument selection” module. Finally, we expose the evaluation and the validation of our work through the use of a port application.
Cette these a pour but l’etude du probleme de la cooperation dans le cadre de l'entreprise et... more Cette these a pour but l’etude du probleme de la cooperation dans le cadre de l'entreprise etendue. Cette etude est focalisee sur la presentation du concept d'entreprise etendue orientee agents comme solution permettant aux entreprises de surmonter les problemes de la cooperation, en general, et la resolution de conflits, qui freinaient ce genre de pratique, en particulier. A cet egard, cet objectif est presente selon trois aspects : organisationnel, raisonnement et interaction. L'aspect organisationnel est concretise par le biais d'une restructuration de l'entreprise etendue selon une approche orientee agents, qui a donne lieu a un modele nomme MAIS-E². MAIS-E² est un SMA cooperatif compose d'agents cognitifs et sociaux. Il repose sur l'existence d'une societe d'agents Mediateur. L'aspect raisonnement est concretise a travers la proposition d'un framework de negociation par argumentation nomme R²-IBN dedie au modele MAIS-E². R²-IBN est caracterise par l'analyse des trois modules fondamentaux caracterisant un agent argumentateur : Evaluation, Generation et Selection d'arguments. De plus, nous avons propose un modele de Confiance et de Reputation possedant une contribution majeure pour R²-IBN. L'aspect interaction est specifie a travers la presentation des differents types de dialogues adoptes, la definition du langage de communication et la presentation des protocoles adequats pour R²-IBN et MAIS-E². En guise de synthese des trois aspects pris en consideration, nous avons mis en relief nos contributions par une hybridation d'approches : intelligente distribuee, intelligente floue, mathematique, statistique et a base de raisonnement non monotone. MAIS-E² ainsi que R²-IBN ont ete consolides par la presentation de l'aspect applicatif a travers une validation experimentale dans le domaine portuaire.
Intelligent software based on agent technology emerges to improve system design, and to increase ... more Intelligent software based on agent technology emerges to improve system design, and to increase enterprise competitive position as well. The competitiveness is based on the cooperation. Thus, within this cooperation, conflicts may arise. Argumentation theory has become an important topic in the field of Multi-Agent Systems and especially in the negotiation problem. Moreover, research on trust and reputation is a
Engineering, Technology & Applied Science Research
Event logs are essential in many software systems’ maintenance and development, as detailed runti... more Event logs are essential in many software systems’ maintenance and development, as detailed runtime information is recorded in them, allowing support engineers and developers to monitor systems, understand behaviors, and identify errors. With the increasing size and complexity of modern software systems, parsing their logs by the traditional (manual) method is cumbersome and useless. For this reason, recent studies have focused on automatically parsing log files. This paper presents the Hierarchical Clustering Log Parsing method, called HCLPars, for automatically parsing log files, consisting of 3 steps: parameter removal according to acquired knowledge in order to avoid errors, grouping similar raw log messages, and getting the set of keys that make up the log. Experiments were run on 16 real system log data, and the performance of the proposed algorithm was compared with the one of other 14 algorithms. It was shown that the HCLPars outperformed the other log parsers in terms of ac...
International Journal of Advanced Computer Science and Applications
Continued global economic instability and uncertainty is causing difficulties in predicting sales... more Continued global economic instability and uncertainty is causing difficulties in predicting sales. As a result, many sectors and decision-makers are facing new, pressing challenges. In supply chain management, the food industry is a key sector in which sales movement and the demand forecasting for food products are more difficult to predict. Accurate sales forecasting helps to minimize stored and expired items across individual stores and, thus, reduces the potential loss of these expired products. To help food companies adapt to rapid changes and manage their supply chain more effectively, it is a necessary to utilize machine learning (ML) approaches because of ML's ability to process and evaluate large amounts of data efficiently. This research compares two forecasting models for confectionery products from one of the largest distribution companies in Saudi Arabia in order to improve the company's ability to predict demand for their products using machine learning algorithms. To achieve this goal, Support Vectors Machine (SVM) and Long Short-Term Memory (LSTM) algorithms were utilized. In addition, the models were evaluated based on their performance in forecasting quarterly time series. Both algorithms provided strong results when measured against the demand forecasting model, but overall the LSTM outperformed the SVM.
The 5th International Conference on Future Networks & Distributed Systems
This paper presents how to utilize deep learning to extract knowledge from Arabic printed documen... more This paper presents how to utilize deep learning to extract knowledge from Arabic printed document images. The fundamental goal of deep learning is automatically extracting significant features from images, eliminating the need for a classic feature extraction method. We describe how to extract high-quality and coherent knowledge from Arabic printed document images using deep learning. This system is constructed on keywords used to classify Arabic document images according to these keywords. We used A questionnaire to choose valuable words according to historical, scientific, or religious documents. The evaluation of the proposed system is applied to Arabic printed document images to extract keywords. The accuracy of the proposed deep learning extraction approach is hugely affected by image preprocessing and image quality. The proposed system has a higher level of accuracy while extracting keywords. We achieve a 3.78% character error rate in the proposed system and a 15.46% word error rate.
Diabetic retinopathy (DR), which is a leading cause of adult blindness, primarily affects individ... more Diabetic retinopathy (DR), which is a leading cause of adult blindness, primarily affects individuals with diabetes. The manual diagnosis of DR, with the assistance of an ophthalmologist, has proven to be a time-consuming and challenging process. Late detection of DR is a significant factor contributing to the progression of the disease. To address this issue, the present study utilizes deep learning (DL) and transfer learning algorithms to analyze different stages of DR and precisely detect the condition. Using a large dataset comprising approximately 60,000 images, this study employs ResNet-101, DenseNet121, InceptionResNetV2, and EfficientNetB0 DL models to automatically assess the progression of DR. Images of patients' eyes are inputted into the models, and the DL architectures are adapted to extract relevant features from the eye images. The study's findings demonstrate that DenseNet121 outperforms ResNet-101, InceptionResNetV2, and EfficientNetB0 in accurately classifying the five stages of DR. The accuracy of the models was 97%, 96%, 95%, and 94%, respectively. These results underscore the effectiveness of DL in achieving an accurate and comprehensive classification of retinitis pigmentosa. By enabling accurate and timely diagnosis of DR, the application of DL techniques significantly contributes to the field of ophthalmology, facilitating improved treatment decisions for patients.
International Journal of Advanced Computer Science and Applications, 2023
Continued global economic instability and uncertainty is causing difficulties in predicting sales... more Continued global economic instability and uncertainty is causing difficulties in predicting sales. As a result, many sectors and decision-makers are facing new, pressing challenges. In supply chain management, the food industry is a key sector in which sales movement and the demand forecasting for food products are more difficult to predict. Accurate sales forecasting helps to minimize stored and expired items across individual stores and, thus, reduces the potential loss of these expired products. To help food companies adapt to rapid changes and manage their supply chain more effectively, it is a necessary to utilize machine learning (ML) approaches because of ML's ability to process and evaluate large amounts of data efficiently. This research compares two forecasting models for confectionery products from one of the largest distribution companies in Saudi Arabia in order to improve the company's ability to predict demand for their products using machine learning algorithms. To achieve this goal, Support Vectors Machine (SVM) and Long Short-Term Memory (LSTM) algorithms were utilized. In addition, the models were evaluated based on their performance in forecasting quarterly time series. Both algorithms provided strong results when measured against the demand forecasting model, but overall the LSTM outperformed the SVM.
The 5th International Conference on Future Networks & Distributed Systems, Dec 15, 2021
This paper presents how to utilize deep learning to extract knowledge from Arabic printed documen... more This paper presents how to utilize deep learning to extract knowledge from Arabic printed document images. The fundamental goal of deep learning is automatically extracting significant features from images, eliminating the need for a classic feature extraction method. We describe how to extract high-quality and coherent knowledge from Arabic printed document images using deep learning. This system is constructed on keywords used to classify Arabic document images according to these keywords. We used A questionnaire to choose valuable words according to historical, scientific, or religious documents. The evaluation of the proposed system is applied to Arabic printed document images to extract keywords. The accuracy of the proposed deep learning extraction approach is hugely affected by image preprocessing and image quality. The proposed system has a higher level of accuracy while extracting keywords. We achieve a 3.78% character error rate in the proposed system and a 15.46% word error rate.
International Journal of Advanced Computer Science and Applications, 2021
In this paper, we show how to classify Arabic document images using a convolutional neural networ... more In this paper, we show how to classify Arabic document images using a convolutional neural network, which is one of the most common supervised deep learning algorithms. The main goal of using deep learning is its ability to automatically extract useful features from images, which eliminates the need for a manual feature extraction process. Convolutional neural networks can extract features from images through a convolution process involving various filters. We collected a variety of Arabic document images from various sources and passed them into a convolutional neural network classifier. We adopt a VGG16 pre-trained network trained on ImageNet to classify the dataset of four classes as handwritten, historical, printed, and signboard. For the document image classification, we used VGG16 convolutional layers, ran the dataset through them, and then trained a classifier on top of it. We extract features by fixing the pre-trained network's convolutional layers, then adding the fully connected layers and training them on the dataset. We update the network with the addition of dropout by adding after each max-pooling layer and to the fourteen and the seventeenth layers which are the fully connected layers. The proposed approach achieved a classification accuracy of 92%.
This paper aims to study the problem of cooperation in the extended enterprise case. This study f... more This paper aims to study the problem of cooperation in the extended enterprise case. This study focuses on the presentation of agent-oriented extended enterprise as a solution allowing enterprises to overcome the cooperation problem, in general, and conflict resolution, in particular. In this regard, this objective is defined by three aspects: organizational, reasoning and application. The organizational and reasoning aspects are presented through an overview of designing the extended enterprise by an agent-oriented approach via the proposed MAIS-E2 model (Multi-Agent Information System for an Extended Enterprise) and the proposed argument-based negotiation framework R2-IBN (Relationship-Role and Interest Based Negotiation) defined for MAIS-E2 model, respectively. Then, we focus mainly on the application aspect through the instantiation of MAIS-E2 model to the port application. These aspects are consolidated by the experimental validation via the developed prototype using JADE platform and experimental results with different cases in the port area.
The execution traces generated by an application contain information that the developers believed... more The execution traces generated by an application contain information that the developers believed would be useful in debugging or monitoring the application, it contains application states and significant events at various critical points that help them gain insight into failures and identify and predict potential problems before they occur. Despite the ubiquity of these traces universally in almost all computer systems, they are rarely exploited because they are not readily machine-parsable. In this paper, we propose a Multi-Agents approach for prediction process using Blockchain technology, which allows automatically analysis of execution traces and detects early warning signals for system failure prediction during executing. The proposed prediction approach is constructed using a four-layer Multi-Agents system architecture. The proposed prediction approach performance is based on data prepossessing and supervised learning algorithms for prediction. Blockchain was used to coordinate collaboration between agents, and to synchronize prediction between agents and the administrators. We validated our approach by applying it to real-world distributed systems, where we predicted problems before they occurred with high accuracy. In this paper we will focus on the Architecture of our prediction approach.
Nowadays, the integration of Cloud Computing and the Internet of Things (Cloud-IoT) has drawn att... more Nowadays, the integration of Cloud Computing and the Internet of Things (Cloud-IoT) has drawn attention as new technologies in the Future Internet. Cloud-IoT accommodates good solutions to address real-world problems by offering new services in real-life scenarios. Nonetheless, the traditional Cloud-IoT will be probably not going to give suitable service to the user as it handles enormous amounts of data at a single server. Furthermore, the Cloud-IoT shows huge security and privacy problems that must be solved. To address these issues, we propose an integrated Fog Cloud-IoT architecture based on Multi-Agents System and Blockchain technology. Multi-Agents System has proven itself in decision-making aspects, distributed execution, and its effectiveness in acting in the event of an intrusion without user intervention. On the other side, we propose Blockchain technology as a distributed, public, authentic ledger to record the transactions. The Blockchain represents a great advantage to the next generation computing to ensures data integrity and to allows low latency access to large amounts of data securely. We evaluated the performance of our proposed architecture and compared it with the existing models. The result of our evaluation shows that performance is improved by reducing the response time.
Today vast and diverse event records of applications exist for almost every scientific domain, ma... more Today vast and diverse event records of applications exist for almost every scientific domain, making their integration and intelligent exploitation challenging. Indeed, complex data require expressive data representation models. This work deals with the intention mining that is a very active and promising research area. Performance, flexibility, and adaptation are the biggest challenges for intention mining. Those issues have been illustrated from research on method engineering and guidance. Intention mining is the ability to predict a user's goals. Knowing the user's intention can support the decision-making of the network administrators. In addition, the user-friendliness is another big challenge to the intention mining in this paper. For this, based on the communication and coordination of intelligent Agents, a new multi-Agents based System approach is gathered in order to discover an intentional process model and provide specific recommendations. In addition, the input of intention mining is the trace of activities, which is an unstructured file because the activities are distributed from different sources. However, the dataset that will be used in intention mining must be well structured and filtered, so often some efforts are required to filter the relevant data. The main input of all algorithm used to discover intentional process model is the log file (traces activities), which is unstructured dataset and not ready to be feed as-is to machine learning algorithm. Therefore, this paper aims to describe the data preprocessing steps, which transform the unstructured log file to a structured one.
Distributed Denial of Service attacks (DDoS) are one of the most prevalent attacks threatening sy... more Distributed Denial of Service attacks (DDoS) are one of the most prevalent attacks threatening systems and their security. In this paper, various models to categorize these attacks are presented, analyzed and compared on regards of their effectiveness for DDoS detection. Machine learning (ML) algorithms for classification are used after pre-processing DDoS dataset to classify network traffic. After analyzing the results of Naïve bayes, Decision Tree, Support Vector Machine, and Random Forest classifiers, we conclude that the most accurate results appeared when using the Random Forest classifier.
Advances in intelligent systems and computing, 2017
This paper aims to improve the argument based negotiation framework \(R^{2}\)-IBN to overcome the... more This paper aims to improve the argument based negotiation framework \(R^{2}\)-IBN to overcome the cooperation problem within the extended enterprise by the agent-oriented model named MAIS-\(E^{2}\). In order to overcome the problem of cooperation in general and conflict resolution which prevent this practice in particular, we propose as a solution the integration of game theory foundations into two modules of \(R^{2}\)-IBN framework which are: “Argument Evaluation” module and “Argument selection” module. Finally, we expose the evaluation and the validation of our work through the use of a port application.
Cette these a pour but l’etude du probleme de la cooperation dans le cadre de l'entreprise et... more Cette these a pour but l’etude du probleme de la cooperation dans le cadre de l'entreprise etendue. Cette etude est focalisee sur la presentation du concept d'entreprise etendue orientee agents comme solution permettant aux entreprises de surmonter les problemes de la cooperation, en general, et la resolution de conflits, qui freinaient ce genre de pratique, en particulier. A cet egard, cet objectif est presente selon trois aspects : organisationnel, raisonnement et interaction. L'aspect organisationnel est concretise par le biais d'une restructuration de l'entreprise etendue selon une approche orientee agents, qui a donne lieu a un modele nomme MAIS-E². MAIS-E² est un SMA cooperatif compose d'agents cognitifs et sociaux. Il repose sur l'existence d'une societe d'agents Mediateur. L'aspect raisonnement est concretise a travers la proposition d'un framework de negociation par argumentation nomme R²-IBN dedie au modele MAIS-E². R²-IBN est caracterise par l'analyse des trois modules fondamentaux caracterisant un agent argumentateur : Evaluation, Generation et Selection d'arguments. De plus, nous avons propose un modele de Confiance et de Reputation possedant une contribution majeure pour R²-IBN. L'aspect interaction est specifie a travers la presentation des differents types de dialogues adoptes, la definition du langage de communication et la presentation des protocoles adequats pour R²-IBN et MAIS-E². En guise de synthese des trois aspects pris en consideration, nous avons mis en relief nos contributions par une hybridation d'approches : intelligente distribuee, intelligente floue, mathematique, statistique et a base de raisonnement non monotone. MAIS-E² ainsi que R²-IBN ont ete consolides par la presentation de l'aspect applicatif a travers une validation experimentale dans le domaine portuaire.
Intelligent software based on agent technology emerges to improve system design, and to increase ... more Intelligent software based on agent technology emerges to improve system design, and to increase enterprise competitive position as well. The competitiveness is based on the cooperation. Thus, within this cooperation, conflicts may arise. Argumentation theory has become an important topic in the field of Multi-Agent Systems and especially in the negotiation problem. Moreover, research on trust and reputation is a
Engineering, Technology & Applied Science Research
Event logs are essential in many software systems’ maintenance and development, as detailed runti... more Event logs are essential in many software systems’ maintenance and development, as detailed runtime information is recorded in them, allowing support engineers and developers to monitor systems, understand behaviors, and identify errors. With the increasing size and complexity of modern software systems, parsing their logs by the traditional (manual) method is cumbersome and useless. For this reason, recent studies have focused on automatically parsing log files. This paper presents the Hierarchical Clustering Log Parsing method, called HCLPars, for automatically parsing log files, consisting of 3 steps: parameter removal according to acquired knowledge in order to avoid errors, grouping similar raw log messages, and getting the set of keys that make up the log. Experiments were run on 16 real system log data, and the performance of the proposed algorithm was compared with the one of other 14 algorithms. It was shown that the HCLPars outperformed the other log parsers in terms of ac...
International Journal of Advanced Computer Science and Applications
Continued global economic instability and uncertainty is causing difficulties in predicting sales... more Continued global economic instability and uncertainty is causing difficulties in predicting sales. As a result, many sectors and decision-makers are facing new, pressing challenges. In supply chain management, the food industry is a key sector in which sales movement and the demand forecasting for food products are more difficult to predict. Accurate sales forecasting helps to minimize stored and expired items across individual stores and, thus, reduces the potential loss of these expired products. To help food companies adapt to rapid changes and manage their supply chain more effectively, it is a necessary to utilize machine learning (ML) approaches because of ML's ability to process and evaluate large amounts of data efficiently. This research compares two forecasting models for confectionery products from one of the largest distribution companies in Saudi Arabia in order to improve the company's ability to predict demand for their products using machine learning algorithms. To achieve this goal, Support Vectors Machine (SVM) and Long Short-Term Memory (LSTM) algorithms were utilized. In addition, the models were evaluated based on their performance in forecasting quarterly time series. Both algorithms provided strong results when measured against the demand forecasting model, but overall the LSTM outperformed the SVM.
The 5th International Conference on Future Networks & Distributed Systems
This paper presents how to utilize deep learning to extract knowledge from Arabic printed documen... more This paper presents how to utilize deep learning to extract knowledge from Arabic printed document images. The fundamental goal of deep learning is automatically extracting significant features from images, eliminating the need for a classic feature extraction method. We describe how to extract high-quality and coherent knowledge from Arabic printed document images using deep learning. This system is constructed on keywords used to classify Arabic document images according to these keywords. We used A questionnaire to choose valuable words according to historical, scientific, or religious documents. The evaluation of the proposed system is applied to Arabic printed document images to extract keywords. The accuracy of the proposed deep learning extraction approach is hugely affected by image preprocessing and image quality. The proposed system has a higher level of accuracy while extracting keywords. We achieve a 3.78% character error rate in the proposed system and a 15.46% word error rate.
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Papers by Lobna Hsairi