2023-02

Science Journal of University of Zakho

The notable growth of the digital communities and different online news streams led to the growing availability of online natural language content. However not all natural languages have the enough attention of being made readable and comprehendible to machines. Among these less resourced and paid attention languages is the Kurdish language. Creating the machine-readable text is the first step toward applications of text mining and semantic web, such as translation, information retrieval and recommendation systems. With the de facto challenges in the Kurdish language, such as the scarcity of linguistic sources and not having unified orthography rules, this language has a lack of the language processing tools. However, to overcome the mentioned challenges and enable intelligent applications the well organized and annotated Kurdish text corpora is needed. This review paper investigates the available textual corpora in the Kurdish language and its dialects and then determined challenges are discussed, open problems are listed and future directions suggested.

2022-03

International Conference on Computer Science and Software Engineering (CSASE 2022)

The Internet of Things (IoT) has attracted significant attention from researchers and companies as it covers a wide range of applications, including industrial control, healthcare, transportation, smart cities and homes, and agriculture. As the pioneering IoT application, smart homes aim to increase the quality of residents' lives by making home appliances automated by remotely controlling or automatically operating them far from home. However, not only remotely controlling should take into consideration when building a smart home system, but also making the home environment as smart as possible by incorporating the value and power of Machine Learning (ML) techniques to it. Adding such value is crucial to make the system adaptive to users' activities toward predicting user behavior to reduce power consumption further, enhance security levels, and improve usability experiences. This study reviews popular machine learning techniques in smart home applications with their benefits and limitations. The paper also provides a comprehensive comparison among the available systems in the literature which integrate smart homes with the intelligence of the ML algorithms. Finally, after discussing the results, the open problems and suggested directions are presented, and the challenges and perspectives of future development of smart homes systems are presented and discussed.

2019-07

IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC 2019)

In this era of data-analytics, the unstructured text remains the main data format. The vector space model is commonly used in representing and modeling text semantics; however, it has some limitations. The main alternative for the vector space model is the graph model from graph theory. Then, the question is: On what basis should text semantics be modeled using graph modeling? Using semantic-graphs, cognitive-semantics tries to answer this question, as it models underlying mechanisms of our human cognition modules in learning, representing and expanding semantics. The fact that textual data is produced in the form of human natural language by human cognition skills means that a reverse-engineering methodology could be promising to extract back semantics from text. In this paper, we present a systematic perspective of the main computational graph-based cognitive-semantic models of human memory, that have been used for the semantic processing of unstructured text. The applications, strengths, and limitations of each model are described. Finally, open problems, future work and conclusions are presented.

2019-01

International Conference on Computer Science and Software Engineering (CSASE 2022)

The Internet of Things (IoT) has attracted significant attention from researchers and companies as it covers a wide range of applications, including industrial control, healthcare, transportation, smart cities and homes, and agriculture. As the pioneering IoT application, smart homes aim to increase the quality of residents' lives by making home appliances automated by remotely controlling or automatically operating them far from home. However, not only remotely controlling should take into consideration when building a smart home system, but also making the home environment as smart as possible by incorporating the value and power of Machine Learning (ML) techniques to it. Adding such value is crucial to make the system adaptive to users' activities toward predicting user behavior to reduce power consumption further, enhance security levels, and improve usability experiences. This study reviews popular machine learning techniques in smart home applications with their benefits and limitations. The paper also provides a comprehensive comparison among the available systems in the literature which integrate smart homes with the intelligence of the ML algorithms. Finally, after discussing the results, the open problems and suggested directions are presented, and the challenges and perspectives of future development of smart homes systems are presented and discussed.

2018-01

IEEE 12th International Conference on Semantic Computing (ICSC 2018)

Semantic-based text document clustering aims to group documents into a set of topic clusters. We propose a new approach for semantically clustering of text documents based on cognitive science and graph theory. We apply a computational cognitive model of semantic association for human semantic memory, known as Incremental Construction of an Associative Network (ICAN). The vector-based model of Latent Semantic Analysis (LSA), has been a leading computational cognitive model for semantic learning and topic modeling, but it has well-known limitations including not considering the original word-order and doing semantic reduction with neither a linguistic nor cognitive basis. These issues have been overcome by the ICAN model. ICAN model is used to generate document-level semantic-graphs. Cognitive and graph-based topic and context identification methods are used for semantic reduction of ICAN graphs. A corpus-graph is generated from ICAN graphs, and then a community-detection graph algorithm is applied for the final step of document clustering. Experiments are conducted on three commonly used datasets. Using the purity and entropy criteria for clustering quality, our results show a notable outperformance over the LSA-based approach.

2017-01

IEEE 11th International Conference on Semantic Computing (ICSC 2017)

Concepts in web ontologies help machines to understand data through the meanings they hold. Furthermore, learning contexts and topics of web documents also have helped in better semantic-oriented structuring and retrieval of data on the web. In this short paper we present a novel approach for domain-independent open learning of domain concepts, context and topic of any given Web document. Our approach is based on a computational version of the Construction-Integration (CI) model of text comprehension. Our proposed system mimics the way humans learn the meanings of textual units and identify domain concepts, contexts and topics in the form of semantic networks. We apply our system on a number of web documents with a range of topics and domains. The resulting semantic networks provide a quantitative and qualitative insights into the nature of the given web documents.

2011-07

International Conference on High Performance Computing & Simulation (2011)

The network based denial of service attacks (DoS) are still the big challenge to the researchers in the field of network security. This paper handles the popular DoS attack called TCP-SYN flood attack, and presents the design and implementation of an Artificial Immune system for Syn flood Detection, abbreviated by AISD, based on the Dendritic Cell Algorithm (DCA). The AISD system is able to detect the generated SYN flood attack and response to its generator in a real-time. Performance and accuracy of the system have been evaluated through five experiments. Results of the experiments showed the precision of intrusion detection process to the ratio of 100%, with a notable response speed, and this is shows the benefit and suitability of using artificial immune systems to the network security problems.