2024-01

Science Journal of University of Zakho (Issue : 1) (Volume : 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.

2023-10

Digital Scholarship in the Humanities (Issue : 66) (Volume : 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.

2023-08

Science Journal of University of Zakho (Issue : 3) (Volume : 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.

2023-02

BAR-Brazilian Administration Review (Issue : 2023) (Volume : 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.

2023-01

Jurnal Informatika (Issue : 1) (Volume : 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.

2022-11

Science Journal of University of Zakho (Issue : 4) (Volume : 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.

2020-07

IEEE Computer Graphics and Applications (Issue : 1111) (Volume : 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.