English العربية کوردی
01001010

د. Ismael Ali Ali

Lecturer
كلية التربية / التربية وعلم النفس
07507116889
ismael.ali@uoz.edu.krd
عدد المنشورات: 8
1
جوائز
8
البحوث العلمية
2
الاطاريح
2
الدورات التدريبية

0
الكتب
7
المؤتمرات العلمية
0
العروض التقديمية
0
ورش العمل
2018
Doctor of Philosophy

Kent State University
Kent State University

2010
Master

University of Mosul
University of Mosul

2007
Bachelor

University of Mosul
University of Mosul

Section Editor at Scientific Journal of UoZ

Director at the Directorate of Scientific and Postgraduate Affairs of UoZ

Director of General Directorate of the ICT and Statistics of the UoZ

Director of General Directorate of the Journals of the UoZ

Project Manager of E-Campus Software Development Project of UoZ

Personal website
Linkedin
Ccnp research lab
Uoz semantic web research lab
Google scholar

2017

Professional

Association for Computing Machinery (ACM)

2017-01-15,current
Professional

Association for Computing Machinery (ACM) USA

2017-01-15,current
Lecturer

تاريخ: 2019-03-24

Assistant Lecturer

تاريخ: 2010-12-07

2012

Ministry of Higher Education and Scientific Research

Full PhD Scholarship Award to Study Abroad

2012-06

2025

THE COGNITIVE NEUROSCIENCE OF RELIGIOUS BELIEF FROM THE PERSPECTIVE OF SAM HARRIS: A CRITIQUE
Jurnal Ilmiah Islam Futura (القضية : 1) (الحجم : 25)
This research aims to criticize the perspective of Sam Harris on which he built his philosophical and intellectual framework in his cognitive neuroscience-based interpretation of multiple philosophical concepts such as belief, disbelief, morality, and free will in his base research paper titled (Functional Neuroimaging of Belief, Disbelief, and Uncertainty). This research relied on description, analysis, and criticism. It describes Sam Harris’s core thoughts in his study on neuroimaging, and then analyzes them and states their purposes and implications. Then it criticizes his ideas, relying on two elements. First, the logical and philosophical and Kalam argumentations to criticize his ideas and to explain the flaws in his knowledge foundations. Second, relying on the neuroscientists’ views contradicting the theses in Harris’ paper besides general scientific criticism. The research concluded that consciousness precedes matter and not the opposite, and that belief is innate in the human mind from birth and is one of the so-called necessary-knowledge that cannot be refuted by material experiences. Thus, faith is an innate awareness and not a purely materialistic cognitive state that can be reduced, or abstracted, and its level cannot be measured by simply conducting tests through belief, disbelief, or uncertainty about written prepositions.
2025-02

2024

MINDBOT: DESIGN AND IMPLEMENTATION OF A MIND-CONTROLLED EDUCATIONAL ROBOT TOY FOR DISABLED CHILDREN
Science Journal of University of Zakho (القضية : 1) (الحجم : 12)
The mindBot robot is a new educational robot toy that can be controlled by brain signals and voice commands. It was evaluated with children with disabilities as well as healthy children as the potential users. The most significant challenge was the size of the used Emotiv Insight electroencephalogram headset when adjusting it on the children’s’ heads. Despite all the challenges, the mindBot robot is a promising technology that could be fun and educational for disabled children. The 11 participants took 36 minutes to finish all tasks on average. This includes the time they spent setting up the robot for the first time, putting on the headset, learning how to use the robot, and using the main educational features. The System Usability Scale usability score for the robot is 71.13, which is considered to be the score of good. The future stages of improving the mindBot includes adding more mobility capabilities and adding the feature of educational assessment.
2024-01

2023

A hybrid part-of-speech tagger with annotated Kurdish corpus: advancements in POS tagging
Digital Scholarship in the Humanities (القضية : 66) (الحجم : 66)
With the rapid growth of online content written in the Kurdish language, there is an increasing need to make it machine-readable and processable. Part of speech (POS) tagging is a critical aspect of natural language processing (NLP), playing a significant role in applications such as speech recognition, natural language parsing, information retrieval, and multiword term extraction. This study details the creation of the DASTAN corpus, the first POS-annotated corpus for the Sorani Kurdish dialect. The corpus, containing 74,258 words and thirty-eight tags, employs a hybrid approach utilizing the bigram hidden Markov model in combination with the Kurdish rule-based approach to POS tagging. This approach addresses two key problems that arise with rule-based approaches, namely misclassified words and ambiguity-related unanalyzed words. The proposed approach’s accuracy was assessed by training and testing it on the DASTAN corpus, yielding a 96% accuracy rate. Overall, this study’s findings demonstrate the effectiveness of the proposed hybrid approach and its potential to enhance NLP applications for Sorani Kurdish.
2023-10
INTELLIGENT HOME: EMPOWERING SMART HOME WITH MACHINE LEARNING FOR USER ACTION PREDICTION
Science Journal of University of Zakho (القضية : 3) (الحجم : 11)
Smart homes is an emerging technology that is transforming the way people live and interact with their homes. These homes are equipped with various devices and technologies that allow the homeowner to control, monitor, and automate various aspects of their home. This can include lighting, heating and cooling, security systems, and appliances. However, to enhance the efficiency of these homes, machine learning algorithms can be utilized to analyze the data generated from the home environment and adapt to user behaviors. This paper proposes a smart home system empowered by machine learning algorithms for enhanced user behavior prediction and automation. The proposed system is composed of three modes, including manual, automatic, and intelligent, with the objectives of maximizing security, minimizing human effort, reducing power consumption, and facilitating user interaction. The manual mode offers control and monitoring capabilities through a web-based user interface, accessible from anywhere and at any time. The automatic mode provides security alerts and appliances control to minimize human intervention. Additionally, the intelligent mode employs machine learning classification algorithms, such as decision tree, K-nearest neighbors, and multi-layer perceptron, to track and predict user actions, thereby reducing user intervention and providing additional comfort to homeowners. Experiments conducted employing the three classifiers resulted in accuracies of 97.4%, 97.22%, and 97.36%, respectively. The proposed smart home system can potentially enhance the quality of life for homeowners while reducing energy consumption and increasing security.
2023-08
Good News from Mass Media Induces More Investments in the Equity Crowdfunding Market
BAR-Brazilian Administration Review (القضية : 2023) (الحجم : 20)
This study verifies the association between the text sentiment of news items and the value of capital investments in the equity crowdfunding market. It also analyzes the influence of geographic attributes on the investments made. Based on data for 736 investments made in different ventures in the largest equity crowdfunding platform in one of the main emerging markets, this study’s results indicate that the attributes of ventures can affect the amount of capital invested in them. In addition, published mass media news items that have a greater quantity of positive words can stimulate larger investments in these ventures. On the other hand, the geographic distance between the entrepreneur and the investor can negatively affect the value of these investments. These results are relevant since they can contribute to the definition of fundraising strategies on the part of entrepreneurs and platform managers.
2023-02
Towards a Complete Kurdish NLP Pipeline: Challenges and Opportunities
Jurnal Informatika (القضية : 1) (الحجم : 17)
With the rapid growth of Kurdish language content on the web, there is a high demand for making this information readable and processable by machines. In order to accomplish this, the Kurdish Natural Language Processing (KNLP) pipeline is required. Computers that can process human language use the field of Natural Language Processing (NLP). In its efforts to bridge the communication gap between humans and computers, NLP draws from a wide range of fields, including computer science and computational linguistics. There have been some notable efforts made toward creating the KNLP pipeline. However, it does not support the complete NLP tasks needed to enable semantic web and text mining applications. This paper surveys the work done in the field of NLP for the Kurdish language, its applications, and linguistic challenges.
2023-01

2022

Smart Homes for Disabled People: A Review Study
Science Journal of University of Zakho (القضية : 4) (الحجم : 10)
The field of smart homes has gained notable attention from both academia and industry. The majority of the work has been directed at regular users, and less attention has been placed on users with special needs, particularly those with mobility problems or quadriplegia. Brain computer interface has started the mission of helping people with special needs in smart homes by developing an environment that allows them to make more independent decisions. This study investigates the efforts made in the literature for smart homes that have been established to manage and control home components by disabled people and makes a comparison between the reviewed papers, in terms of the controlled devices, the central controller, the people with disabilities the system is meant for, whether or not machine learning was used in the system, and the system's command method. In the field of machine learning-based smart homes for disabled people, the limitations have been pointed out and talked about. Current challenges and possible future directions for further progress have also been given.
2022-11

2020

CLEVis: A Semantic Driven Visual Analytics System for Community Level Events
IEEE Computer Graphics and Applications (القضية : 1111) (الحجم : 1111)
Community-level event (CLE) datasets, such as police reports of crime events, contain abundant semantic information of event situations and descriptions in a geospatial-temporal context. They are critical for frontline users, such as police officers and social workers, to discover and examine insights about community neighborhoods. We propose CLEVis, a neighborhood visual analytics system for CLE datasets, to help frontline users explore events for insights at community regions of interest (CROIs), namely fine-grained geographical resolutions such as small neighborhoods around local restaurants, churches, and schools. CLEVis fully utilizes semantic information by integrating automatic algorithms and interactive visualizations. The design and development of CLEVis are conducted with solid collaborations with real world community workers and social scientists. Case studies and user feedback are presented with real world datasets and applications.
2020-07

2018

PhD in Computer Science: Using and Improving Computational Cognitive Models for Graph-Based Semantic Learning and Representation from Unstructured Text with Applications
In the era of data-driven industry, the unstructured text, which is generated by human cognition skills, remains the main data format with a massive amount being generated from different sources of technology. The problem we are handling in our work is: How can machine more efficiently learn, represent, and grow semantics from unstructured text, as the written form of natural language? We propose a cognitive model, ICAN-2, inspired by human cognition skills, to learn/extract and represent semantics from text. ICAN-2 is an improved version of the ICAN cognitive model of semantic memory. The ICAN model is from Incremental Construction of an Associative Network model, and it aims at computationally modeling the development of semantic associations in the human semantic memory. Both the ICAN and ICAN-2 models use semantic-graphs to represent semantics. The traditional and yet widely used text representation model is the Vector Space Model (VSM), also known as Bag-Of-Words (BOW) model, in which documents are represented simply by n dimensional feature-vectors. The most widely used term weighting scheme in VSM model is called Term-Frequency/Inverse-Document-Frequency (TF/IDF), which is also used in the latent semantic analysis (LSA) model of semantic learning and representation. Both VSM-based approaches of the TF/IDF and LSA have some notable limitations such as neglecting the word order and other dependency relations among the terms appearing in the original text documents. The ICAN-2 model is an alternative cognitive-graph based model for the traditional VSM model of text representation. After a detailed survey of related works, the performance of the ICAN-2 model is compared against the two most closely related models of semantic modeling in the literature: (1) the LSA model as a cognitive model that has been applied in different text-mining tasks and is an alternative for the TF/IDF technique and (2) the ICAN model which is the seed model for our work and technically the most related model to our model. Experimental results show the ICAN-2 model outperforming both the semantic learning and representation models LSA and ICAN. Then we statistically analyze the cognitive-graphs generated by the ICAN-2 model to explore their structural characteristics and their meanings.
2018-05-24

2026

PhD in Computer Science: Using and Improving Computational Cognitive Models for Graph-Based Semantic Learning and Representation from Unstructured Text with Applications
PhD in Computer Science: Using and Improving Computational Cognitive Models for Graph-Based Semantic Learning and Representation from Unstructured Text with Applications
2018

2023

The Kurdish Language Corpus: State of the Art
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.
2023-02

2022

Smart Homes Powered by Machine Learning: A Review
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.
2022-03

2019

Computational Cognitive-Semantic Based Semantic Learning, Representation and Growth: A Perspective
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.
2019-07
Smart Homes Powered by Machine Learning: A Review
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.
2019-01

2018

Semantic-based text document clustering using cognitive semantic learning and graph theory
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.
2018-01

2017

Using text comprehension model for learning concepts, context, and topic of web content
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.
2017-01

2011

Design and implementation of artificial immune system for detecting flooding attacks
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.
2011-07

2016

Graduate Professional and Academic Development, Kent State University
Graduate Professional and Academic Development
2016-02-11,2016-03-24

2010

College Teaching: Psychology and Its Methods, University of Zakho
College Teaching: Psychology and Its Methods
2010-07-15,2010-09-25