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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Huda Hallawi
University of Kerbala
Computer Science & IT Engineering

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Application of smartphone in recognition of human activities with machine learning Sabah Mohammed Fayadh; Elham Mohammed Thabit A. Alsaadi; Huda Hallawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp860-869

Abstract

The aim of activity recognition is to determine the physical action being performed by one or more users based on a series of observations made during the user's actions in the relevant environment. Significant advancements in the field of human activity have resulted in the creation of novel ways for supporting elderly persons in doing their tasks independently. Using ambient computing, this type of service will be manageable. Many of services are provided by ambient technology, involving home automation tools, monitoring the behaviour of diseased individuals, and utility management. Numerous academics are focusing their efforts on computer software architectures, system infrastructure, and distributed applications utilising sensor devices. Aim of this project is to develop an algorithm that can perform human activity recognition (HAR) better than the existing state-of-the-art approach. Several tasks must be done to achieve this goal. To compete with an existing HAR system, this study will rely on secondary data from the cutting-edge experiment; no new data will be collected. The central experiment will be used to quantitatively identify the best classifier based on prediction accuracy. The current study entails monitoring and assessing existing literature in order to generate hypotheses that may be tested via experiment.
User identification based on short text using recurrent deep learning Huda Hallawi; Huda Ragheb Kadhim; Zahraa Najm Abdullah; Noor D. AL-Shakarchy; Dhamyaa A. Nasrawi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1812-1820

Abstract

Technological development is a revolutionary process by this time, it ismainly depending on electronic applications in our daily routines like(business management, banking, financial transfers, health, and other essentialtraits of life). Identification or approving identity is one of the complicatedissues within online electronic applications. Person’s writing style can beemployed as an identifying biological characteristic in order to recognize theidentity. This paper presents a new way for identifying a person in a socialmedia group using comments and based on the Deep Neural Network. Thetext samples are short text comments collected from Telegram group in Arabiclanguage (Iraqi dialect). The proposed model is able to extract the person'swriting style features in group comments based on pre-saved dataset. Theanalysis of this information and features forms the identification decision.This model exhibits a range of prolific and favorable results, the accuracy thatcomes with the proposed system reach to 92.88% (+/- 0.16%).
Co-Authors Dhamyaa A. Nasrawi Elham Mohammed Thabit A. Alsaadi Huda Ragheb Kadhim Noor D. Al-Shakarchy Sabah Mohammed Fayadh Zahraa Najm Abdullah