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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Elham Mohammed Thabit A. Alsaadi
University of Kerbala
Computer Science & IT

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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.
LPCNN: convolutional neural network for link prediction based on network structured features Asia Mahdi Naser Alzubaidi; Elham Mohammed Thabit A. Alsaadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 6: December 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i6.22990

Abstract

In a social network (SN), link prediction (LP) is the process of estimating whether a link will exist in the future. In prior LP papers, heuristics score techniques were used. Recent state-of-the-art studies, like Wesfeiler-Lehman neural machine (WLNM) and learning from subgraphs, embeddings, and attributes for link prediction (SEAL), have demonstrated that heuristics scores may increase LP model accuracy by employing deep learning and sub-graphing techniques. WLNM and SEAL, on the other hand, have some limitations and perform poorly in some kinds of SNs. The goal of this research is to present a new framework for enhancing the effectiveness of LP models throughout various types of social networks while overcoming the constraints of earlier techniques. We present the link prediction based convolutional neural network (LPCNN) framework, which uses deep learning techniques to examine common neighbors and predict relations. Adapts the LP task into an image classification issue and classifies the links using a convolutional neural network. On 10 various types of real-work networks, tested the suggested LP model and compared its performance to heuristics and state-of-the-art approaches. Results revealed that our model outperforms the other LP benchmark approaches with an average area under curved (AUC) above 99%.
Co-Authors Asia Mahdi Naser alzubaidi Huda Hallawi Sabah Mohammed Fayadh