The conversational agents is one of the most interested topics in computer science field in the r... more The conversational agents is one of the most interested topics in computer science field in the recent decade. Which can be composite from more than one subject in this field, which you need to apply Natural Language Processing Concepts and some Artificial Intelligence Techniques such as Deep Learning methods to make decision about how should be the response. This paper is dedicated to discuss the system architecture for the conversational agent and explain each component in details.
The conversational agents is one of the most interested topics in computer science field in the r... more The conversational agents is one of the most interested topics in computer science field in the recent decade. Which can be composite from more than one subject in this field, which you need to apply Natural Language Processing Concepts and some Artificial Intelligence Techniques such as Deep Learning methods to make decision about how should be the response. This paper is dedicated to discuss the system architecture for the conversational agent and explain each component in details.
2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), 2021
Internet of Things (IoT) networks have developed tremendously over the past years. The main conce... more Internet of Things (IoT) networks have developed tremendously over the past years. The main concept behind this technology is to facilitate information exchange between devices without human intervention. However, the eccentric and heterogeneous nature of this type of network demands certain security requirements and algorithms that differ from those implemented in traditional networks. Recently, several studies have explored the use of Machine Learning and Deep Learning methodologies to overcome the security problems in IoT networks and preserve data privacy. This paper explores the diverse security threats and challenges existing in IoT networks. It reviews some learning-based Intrusion Detection Systems that are proposed as countermeasures to many consequent security breaches in IoT environments such as Denial of Service, spoofing, or eavesdropping attacks.
Proceedings of the 2020 The 6th International Conference on Frontiers of Educational Technologies, 2020
The field of research in ontology engineering appears to be mature, considering the vast number o... more The field of research in ontology engineering appears to be mature, considering the vast number of contemporary methods and instruments for the formalization and application of knowledge representation models. However, the evolutionary aspects of ontologies are still little understood and supported. This is especially important in distributed and collaborative settings like the Semantic web, where ontologies naturally co-operate with their user communities. Various organizations and teams are building common ground in this context. Ontology is instrumental in this process through the formal description of shared knowledge. Such semanticity constitutes a sound basis for defining, sharing (business) objectives and interests and eventually developing useful collaborative services and systems. In this "complex" and dynamic environment, a collaborative model for process change requires more powerful methodologies for engineering, argumentation and negotiation. Software Engineering provides teamwork, team management, feedback management, versioning, merging, and evolving software artifacts with a wealth of techniques and tools. Many of these techniques can be used again in an ontology engineering environment. This paper examines how this problem can be resolved using Scrum and Nexus frameworks, which are among the most robust models for software development.
Proceedings of the 2020 The 6th International Conference on Frontiers of Educational Technologies, 2020
The Semantic Web is an incomplete dream so far, but a revolutionary platform as Blockchain could ... more The Semantic Web is an incomplete dream so far, but a revolutionary platform as Blockchain could be the solution to this, not optimal reality. There is still little understanding of, and support for, the evolutionary aspects of ontologies. This is particularly crucial in distributed and collaborative settings such as the Semantic Web, where ontologies naturally co-evolve with their communities of use. In this setting, different organizations and teams collaboratively build a common ground of the domain. In this "complex" and dynamic setting, a collaborative change process model requires more powerful engineering, argumentation and negotiation methodologies. Blockchain offers a robust framework for teams' collaboration and ontology versioning globally between an infinite number of teams. Blockchain is an example of a distributed computing system with high Byzantine fault tolerance. This makes blockchains potentially suitable for the recording of evolution events, ontolo...
Intelligent techniques have been used in the marketing and sales sectors of business toimprove an... more Intelligent techniques have been used in the marketing and sales sectors of business toimprove analysis, increase revenues and save time. In customer-centric institutions, one of the areas inwhich intelligent techniques and data mining algorithms have been used is the personalization forenhanced CRM (customer relationship management) performance. However, with a growing number ofcustomers, the diversity of products on offer, the complex behavior of customer groups and thecontinuous change of personalization parameters, the production of a tailored personalizedrecommendation and the prediction of future needs are a challenging task. Within these institutions,personalization that is more true to the customer needs leads to better targeted marketing campaignsand enhances customer satisfaction with the ultimate aim of increasing the rates of customer retention,and improving competitive advantage. Intelligent techniques and data mining algorithms have beenused to produce a more accuratel...
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), 2017
The semantic representation of different medical information enables the medical information syst... more The semantic representation of different medical information enables the medical information system to understand the meaning of medical terms. Ontology is used as a semantic representation technique that organizes and represents the semantic relations between the medical terms. Rheumatoid disease is one of the common autoimmune diseases; it has several clinical findings and several treatments to control its activity. This paper presents a new ontology building methodology that enables the ontology engineer to represent all types of knowledge and the intervention of experts from earlier stages. In addition, the paper presents the development of rheumatoid ontology that covers the major clinical aspects of the disease.
Ontology evolution is defined as the process of updating the ontology according to the changes in... more Ontology evolution is defined as the process of updating the ontology according to the changes in the domain. Current ontology evolution techniques in the medical domain focus on the consistency after the ontology evolution and ignore it during the evolution process. This paper presents a novel evolution system that takes into account the ontology consistency. It relies on the use of standard medical resources that reflect the changes that are occurred in the medical domain. Moreover, the system makes use of a database that contains the scientific and Egyptian commercial names of medicine used in autoimmune diseases.
2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), 2019
This work presents an architecture for localizing interesting target events within long sequences... more This work presents an architecture for localizing interesting target events within long sequences of untrimmed videos. Mainly, we focus on finding temporal boundaries of target visual actions and bypassing irrelevant events of other actions. Both the appearance and motion information are crucial for discriminating between different actions. Based on this, we propose a trainable fused two-stream 3D Convolution neural network framework, integrated with a bi-directional Long Short-Term Memory sequence model (2-stream 3DCNN+ LSTM) for learning. The two stream CNN enables us to model features from both RGB and optical flow short video-clips of resolution $\delta=16$ frames, extracted from the long input video sequence. This framework produces a sequence of class probability scores at each video-clip. Simple low-cost mean, average and max filters are used to localize and classify each relevant action instance and to label the whole video. Such architecture utilized the power of (1) two streams CNN architecture, (2) the spatiotemporal processing of 3D convolution network for capturing spatial and motion patterns, (3) temporal orderings and long-range dependencies of the sequence model for obtaining robust classifications at each time step. We evaluate our framework using THUMOS'15 dataset, attaining 98.9% accuracy and 35.8 % mAP in the video level classification and relevant action detection tasks, respectively.
The amount of data that produced is increased day after day especially data as a text, so with th... more The amount of data that produced is increased day after day especially data as a text, so with this massive production it would be difficult to analyze or extract information to discover the patterns from the unstructured text. Text mining is used for availing the massive amount of knowledge that is in the text and deriving high quality information from the text automatically. This Process would save effort and time. Text mining considered as a subset of data mining where data mining is more generic. This paper proposes a methodology of mining a text for a case study related to publication papers. Some of text mining approaches will be introduced for mining the publication papers using machine learning (ML) and natural language processing (NLP) techniques. Describing each phase as following: First phase is keywords extraction using natural language processing techniques, second phase named entity recognition and last phase is document classification. The last two phases are using th...
The growth of medical applications that work with ontologies in health enterprises creates the ne... more The growth of medical applications that work with ontologies in health enterprises creates the need to ensure the integration and semantic homogeneity between them. In order to provide the reliable knowledge (in terms of new treatments, new clinical findings, new exercises), medical ontologies should be evolved. The adaptation of ontology according to the changes in domain of interest is called ontology evolution. This paper investigates ontology evolution approaches applied in medical domain. It also presents a framework proposal for comparative study that demonstrates the difference between the approaches. Keywords— Ontology engineering, Ontology evolution, Ontology enrichment, Ontology population.
The conversational agents is one of the most interested topics in computer science field in the r... more The conversational agents is one of the most interested topics in computer science field in the recent decade. Which can be composite from more than one subject in this field, which you need to apply Natural Language Processing Concepts and some Artificial Intelligence Techniques such as Deep Learning methods to make decision about how should be the response. This paper is dedicated to discuss the system architecture for the conversational agent and explain each component in details.
The conversational agents is one of the most interested topics in computer science field in the r... more The conversational agents is one of the most interested topics in computer science field in the recent decade. Which can be composite from more than one subject in this field, which you need to apply Natural Language Processing Concepts and some Artificial Intelligence Techniques such as Deep Learning methods to make decision about how should be the response. This paper is dedicated to discuss the system architecture for the conversational agent and explain each component in details.
2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), 2021
Internet of Things (IoT) networks have developed tremendously over the past years. The main conce... more Internet of Things (IoT) networks have developed tremendously over the past years. The main concept behind this technology is to facilitate information exchange between devices without human intervention. However, the eccentric and heterogeneous nature of this type of network demands certain security requirements and algorithms that differ from those implemented in traditional networks. Recently, several studies have explored the use of Machine Learning and Deep Learning methodologies to overcome the security problems in IoT networks and preserve data privacy. This paper explores the diverse security threats and challenges existing in IoT networks. It reviews some learning-based Intrusion Detection Systems that are proposed as countermeasures to many consequent security breaches in IoT environments such as Denial of Service, spoofing, or eavesdropping attacks.
Proceedings of the 2020 The 6th International Conference on Frontiers of Educational Technologies, 2020
The field of research in ontology engineering appears to be mature, considering the vast number o... more The field of research in ontology engineering appears to be mature, considering the vast number of contemporary methods and instruments for the formalization and application of knowledge representation models. However, the evolutionary aspects of ontologies are still little understood and supported. This is especially important in distributed and collaborative settings like the Semantic web, where ontologies naturally co-operate with their user communities. Various organizations and teams are building common ground in this context. Ontology is instrumental in this process through the formal description of shared knowledge. Such semanticity constitutes a sound basis for defining, sharing (business) objectives and interests and eventually developing useful collaborative services and systems. In this "complex" and dynamic environment, a collaborative model for process change requires more powerful methodologies for engineering, argumentation and negotiation. Software Engineering provides teamwork, team management, feedback management, versioning, merging, and evolving software artifacts with a wealth of techniques and tools. Many of these techniques can be used again in an ontology engineering environment. This paper examines how this problem can be resolved using Scrum and Nexus frameworks, which are among the most robust models for software development.
Proceedings of the 2020 The 6th International Conference on Frontiers of Educational Technologies, 2020
The Semantic Web is an incomplete dream so far, but a revolutionary platform as Blockchain could ... more The Semantic Web is an incomplete dream so far, but a revolutionary platform as Blockchain could be the solution to this, not optimal reality. There is still little understanding of, and support for, the evolutionary aspects of ontologies. This is particularly crucial in distributed and collaborative settings such as the Semantic Web, where ontologies naturally co-evolve with their communities of use. In this setting, different organizations and teams collaboratively build a common ground of the domain. In this "complex" and dynamic setting, a collaborative change process model requires more powerful engineering, argumentation and negotiation methodologies. Blockchain offers a robust framework for teams' collaboration and ontology versioning globally between an infinite number of teams. Blockchain is an example of a distributed computing system with high Byzantine fault tolerance. This makes blockchains potentially suitable for the recording of evolution events, ontolo...
Intelligent techniques have been used in the marketing and sales sectors of business toimprove an... more Intelligent techniques have been used in the marketing and sales sectors of business toimprove analysis, increase revenues and save time. In customer-centric institutions, one of the areas inwhich intelligent techniques and data mining algorithms have been used is the personalization forenhanced CRM (customer relationship management) performance. However, with a growing number ofcustomers, the diversity of products on offer, the complex behavior of customer groups and thecontinuous change of personalization parameters, the production of a tailored personalizedrecommendation and the prediction of future needs are a challenging task. Within these institutions,personalization that is more true to the customer needs leads to better targeted marketing campaignsand enhances customer satisfaction with the ultimate aim of increasing the rates of customer retention,and improving competitive advantage. Intelligent techniques and data mining algorithms have beenused to produce a more accuratel...
2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), 2017
The semantic representation of different medical information enables the medical information syst... more The semantic representation of different medical information enables the medical information system to understand the meaning of medical terms. Ontology is used as a semantic representation technique that organizes and represents the semantic relations between the medical terms. Rheumatoid disease is one of the common autoimmune diseases; it has several clinical findings and several treatments to control its activity. This paper presents a new ontology building methodology that enables the ontology engineer to represent all types of knowledge and the intervention of experts from earlier stages. In addition, the paper presents the development of rheumatoid ontology that covers the major clinical aspects of the disease.
Ontology evolution is defined as the process of updating the ontology according to the changes in... more Ontology evolution is defined as the process of updating the ontology according to the changes in the domain. Current ontology evolution techniques in the medical domain focus on the consistency after the ontology evolution and ignore it during the evolution process. This paper presents a novel evolution system that takes into account the ontology consistency. It relies on the use of standard medical resources that reflect the changes that are occurred in the medical domain. Moreover, the system makes use of a database that contains the scientific and Egyptian commercial names of medicine used in autoimmune diseases.
2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), 2019
This work presents an architecture for localizing interesting target events within long sequences... more This work presents an architecture for localizing interesting target events within long sequences of untrimmed videos. Mainly, we focus on finding temporal boundaries of target visual actions and bypassing irrelevant events of other actions. Both the appearance and motion information are crucial for discriminating between different actions. Based on this, we propose a trainable fused two-stream 3D Convolution neural network framework, integrated with a bi-directional Long Short-Term Memory sequence model (2-stream 3DCNN+ LSTM) for learning. The two stream CNN enables us to model features from both RGB and optical flow short video-clips of resolution $\delta=16$ frames, extracted from the long input video sequence. This framework produces a sequence of class probability scores at each video-clip. Simple low-cost mean, average and max filters are used to localize and classify each relevant action instance and to label the whole video. Such architecture utilized the power of (1) two streams CNN architecture, (2) the spatiotemporal processing of 3D convolution network for capturing spatial and motion patterns, (3) temporal orderings and long-range dependencies of the sequence model for obtaining robust classifications at each time step. We evaluate our framework using THUMOS'15 dataset, attaining 98.9% accuracy and 35.8 % mAP in the video level classification and relevant action detection tasks, respectively.
The amount of data that produced is increased day after day especially data as a text, so with th... more The amount of data that produced is increased day after day especially data as a text, so with this massive production it would be difficult to analyze or extract information to discover the patterns from the unstructured text. Text mining is used for availing the massive amount of knowledge that is in the text and deriving high quality information from the text automatically. This Process would save effort and time. Text mining considered as a subset of data mining where data mining is more generic. This paper proposes a methodology of mining a text for a case study related to publication papers. Some of text mining approaches will be introduced for mining the publication papers using machine learning (ML) and natural language processing (NLP) techniques. Describing each phase as following: First phase is keywords extraction using natural language processing techniques, second phase named entity recognition and last phase is document classification. The last two phases are using th...
The growth of medical applications that work with ontologies in health enterprises creates the ne... more The growth of medical applications that work with ontologies in health enterprises creates the need to ensure the integration and semantic homogeneity between them. In order to provide the reliable knowledge (in terms of new treatments, new clinical findings, new exercises), medical ontologies should be evolved. The adaptation of ontology according to the changes in domain of interest is called ontology evolution. This paper investigates ontology evolution approaches applied in medical domain. It also presents a framework proposal for comparative study that demonstrates the difference between the approaches. Keywords— Ontology engineering, Ontology evolution, Ontology enrichment, Ontology population.
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