Gender Identification of Mobile Phone Users based on Internet Usage Pattern

Negaresh, F.; Kaedi, M.; Zojaji, Z.

doi:10.5829/ije.2023.36.02b.13

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…However, this result implies that males and females' difference are not affect on the mobile phone usage. Even though in past studies interpreted as (Negaresh et al, 2023;Andone et al, 2016): females spend more time on phones compare to males. The finding showed no significant differences on using mobile phone among this group of respondents.…”

Section: Factors That Contributing In Influencing Mobile Phone Addict...mentioning

confidence: 96%

“…As a result, these devices contain a wide variety of data about users and are great devices for gaining a better knowledge of the person (Daneshvar & Inkpen, 2018). Various people use mobile phones in different way such as age, gender, education level and occupation (Negaresh et al, 2023).…”

Section: Factors That Contributing In Influencing Mobile Phone Addict...mentioning

confidence: 99%

“…Based on prior studies, the result indicated gender influences the way of users engage with mobile phones (Negaresh et al, 2023). There is ample evidence that males and females have distinct preferences on mobile phone use.…”

Section: Factors That Contributing In Influencing Mobile Phone Addict...mentioning

confidence: 99%

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Factors that Contributing in influencing Mobile Phone Usage among University Students

Munusamy,

Ahmad Ghazali

2023

IJARBSS

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Mobile phone technology has gradually become integrated into the higher education experience and nearly every member of the university community uses or owns a mobile phone to connect with others. According to past research there is a strong association between gender, self-esteem, social anxiety and mobile phone use. However, there has very few researches on self-esteem, and social anxiety towards mobile phone usage. Regardless of the distance between people, mobile phones allow them to interact and integrate on a regular basis. Even though these benefits are present, mobile phone has been identified as global problem, particularly among teenagers. Therefore, this study was examine the factors that influence on mobile phone usage. The purpose of this study was to determine the impact of gender, self-esteem and social anxiety towards mobile phone addiction among public university students in Klang Valley. In this study, a survey method was used. There were 400 teenagers from four public universities took part in this survey. The findings conclude that gender differences, self-esteem and social anxiety not influencing on the mobile phone usage among teenagers. Mobile phones are an inevitable part of modern life and it would be an indispensable tool in our society. However, over use of mobile phone has resulted in the problem of mobile addiction. Hence, need to identify main causes that influencing mobile phone use among teenagers before it's affect the student's academic performance and their productivity.

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Section: Factors That Contributing In Influencing Mobile Phone Addict...mentioning

confidence: 96%

Section: Factors That Contributing In Influencing Mobile Phone Addict...mentioning

confidence: 99%

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Factors that Contributing in influencing Mobile Phone Usage among University Students

Munusamy,

Ahmad Ghazali

2023

IJARBSS

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“…The random forest algorithm is used to train the model with the dataset which contains a combination of symptoms and the corresponding diseases [22]. The driving force behind using the random forest algorithm is that it has the capacity to handle data sets with continuous variables, as in regression, and categorical variables, as in classification [21,23]. It produces superior results with regard to classification problems.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

Human Disease Prediction using Machine Learning Techniques and Real-life Parameters

Gaurav,

Kumar,

Singh

et al. 2023

IJE

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Disease prediction of a human means predicting the probability of a patient's disease after examining the combinations of the patient's symptoms. Monitoring a patient's condition and health information at the initial examination can help doctors to treat a patient's condition effectively. This analysis in the medical industry would lead to a streamlined and expedited treatment of patients. The previous researchers have primarily emphasized machine learning models mainly Support Vector Machine (SVM), K-nearest neighbors (KNN), and RUSboost for the detection of diseases with the symptoms as parameters. However, the data used by the prior researchers for training the model is not transformed and the model is completely dependent on the symptoms, while their accuracy is poor. Nevertheless, there is a need to design a modified model for better accuracy and early prediction of human disease. The proposed model has improved the efficacy and accuracy model, by resolving the issue of the earlier researcher's models. The proposed model is using the medical dataset from Kaggle and transforms the data by assigning the weights based on their rarity. This dataset is then trained using a combination of machine learning algorithms: Random Forest, Long Short-Term Memory (LSTM), and SVM. Parallel to this, the history of the patient can be analyzed using LSTM Algorithm. SVM is then used to conclude, the possible disease. The proposed model has achieved better accuracy and reliability as compared to state-of-the-art methods. The proposed model is useful to contribute towards development in the automation of the healthcare industries.

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“…The increasing demand for accuracy in measuring and analyzing nanoparticles within materials, and because the SEM technique is inaccurate way to measure the particle size and requires time (15,16). The computer vision is a subfield of Artificial Intelligence (AI) (17,18), which uses some algorithms to detect the details of images by using a computer called image processing (19). Image processing (20,21) is the process by which one can obtain useful information about any image.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Particle Size in Composite Materials Using Image Processing

Alabdali,

Alkalid

2024

IJE

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Composite materials are the most important materials in materials science and engineering, which contain two or more materials. In materials engineering, the scanning electron microscopy (SEM) technique is an approach to measure the material's particle size. A new procedure was used instead of SEM is called Artificial Intelligence (AI). Artificial Intelligence (AI) is an interdisciplinary science and branch of computer science that involves solving problems that require human intelligence and capabilities. The computer vision is a subfield of AI, which uses some algorithms to detect the details of images by using computer called image processing. Detecting the particles and measuring the size of materials scanned by SEM is an essential task that helps to describe their feature, traditionally, the size is calculated manually by adding mesh to an SEM image or by drawing a diagonal line in an arbitrary particle. In this paper, a new model based on Artificial Intelligence (AI) is proposed using computer vision to analyze the size of all particles. This model is used to detect the particle size of additives in composite materials like graphene flakes and measure the size of them depending on the reference size fixed on the scanning electron microscope (SEM). The model was used based on the Open-source Computer Vision (OpenCV) library, utilizing multi-layers of canny edge detection, Sobel filter, Brightness and contrast algorithms, using Python 3. The results have achieved very satisfied indication with a very low process time = 0.2 mili-seconds.

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Gender Identification of Mobile Phone Users based on Internet Usage Pattern

Cited by 6 publications

References 24 publications

Factors that Contributing in influencing Mobile Phone Usage among University Students

Factors that Contributing in influencing Mobile Phone Usage among University Students

Human Disease Prediction using Machine Learning Techniques and Real-life Parameters

Analysis of Particle Size in Composite Materials Using Image Processing

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