Showing 3 results for Asemi
Fatemeh Zarmehr, Asefeh Asemi, Mitra Pashootanizade,
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The purpose of the present study was to investigate the opinion of blind and visually impaired of the possibility of employing gadgets in delivering resources and information services to them.
Method: The research method was descriptive - survey and questionnaires were used to collect data. The data was analyzed by descriptive and inferential statistics.
Results: The result showed that respondent approved the use of information resources and services through variety of gadgets and the fact that gadgets are generally more desirable. Based on the agreement in regard to the gadgets used as resources and information services to blind and visually impaired it was found that mobile phone and pocket PC were in the first place (56.7%), Google glasses in second place(55%) e-book readers in third place (54.4%), tablets in fourth place (54.2%) and RFID systems in fifth place (44.1%).
Conclusion: Based on the findings of the study it is concluded that blinds ability to use gadgets in optimal utilization of resources and information services is possible and more than desirable. As a results we can make proper use of the new technology in order to present resources and information services to blinds and visually impaired
Shohreh S. Hoseyni, Asefe Asemi, Ahmad Shabani, Mozafar Cheshmesohrabi,
Volume 4, Issue 1 (6-2017)
Abstract
Background and Aim: The current study aimed to investigate the state of health information supply and demand on prostate cancer among Iranian medical researchers and Iranian web users during 2011 to 2015. The purpose of this study, based on infodemiology indicators, was to investigate the alignment of health information supply and demand on prostate cancer among Iranian medical researchers and Iranian web users.
Methods: A mixed method research was conducted. In qualitative part, a focus group interview was applied to the users to identify their selected keywords searched for prostate cancer in Google. The collected data were analyzed using Open Code software. In quantitative part, data were synthesized using R software in two parts. First, users’ internet information-seeking behavior was analyzed using Google Trends outputs during 2011 to 2015. Second, the scientific publication of Iranian prostate cancer specialists was surveyed using PubMed during the period of the study.
Results: The results showed that the search volume index of preferred keywords on prostate cancer have decreased from 2191 in 2011 to 1798 in 2015. Also, the findings revealed that Iranian scholars had 161 scientific papers on prostate cancer in PubMed during 2011 to 2015. Among these 161 papers, 20 records related to 2011 and 44 records related to 2015. There was no significant relationship between users’ information seeking behavior in Google Trends and the scientific publication behavior of Iranian prostate cancer specialists (p>0.05).
Conclusion: According to the results, when the search volume index of Iranian web users decreased or increased during the period of the study, the number of scientific publications had not been affected by users’ search volume. Thus, it can be mentioned that Iranian scholars had not pay enough attention to the concerns of people toward prostate cancer.
Ms. Maryam Abolghasemi, Dr. Fatemeh Fahimnia,
Volume 8, Issue 4 (2-2022)
Abstract
Background and Aim: In processing large data, scientists have to perform the tedious task of analyzing hefty bulk of data. Machine learning techniques are a potential solution to this problem. In citizen science, human and artificial intelligence may be unified to facilitate this effort. Considering the ambiguities in machine performance and management of user-generated data, this paper aims to explain how machine learning can be combined with the active citizenship concept. In addition, it discusses the necessary conditions for advancing the citizen science and beyond.
Method: The review method and comprehensive systematic study was applied to assess the concept of machine learning, citizen science and human-computer interaction.
Results: Many research problems seem to be computationally insolvable and may demand human cognitive skills. Therefore, due to classification activities which are performed in the majority of large-scale citizenship science projects, in addition to participants who may learn lessons about the science, machines also learn lessons about human and imitate him and slowly its learning capacity enhances over time. Artificial intelligence, particularly machine learning is a debatable topic with related ambiguities and biases which should strongly take into consideration.
Conclusion: The application of machine learning techniques carries many advantages including classification time cut and masterful evaluations in the process of making decisions on big data sets. However, algorithms usually act as a black box where data biases are not observable at first glance. Taking this problem into consideration may mitigate serious risks arising from the application of such techniques.
