University students stress level and brainwave balancing index: Comparison between early and end of study semester

Adnan, Nadia; Murat, Zunairah Hj; Kadir, Ros Shilawani S. Abdul; Yunos, Noradibah Hj Mohamad

doi:10.1109/scored.2012.6518608

Cited by 15 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While for the entertainment, sport and dormitory area, the percentage of negative Tweets dropped at the end of semester. This could mean that negative feelings might be associated with exam induced stress and study workload (Adnan et al 2012).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

European Handbook of Crowdsourced Geographic Information

2016

View full text Add to dashboard Cite

Part II: Quality: Criteria and methodologiesChapter 5. Handling quality in crowdsourced geographic information (Laura Criscuolo, Paola Carrara, Gloria Bordogna, Monica Pepe, Francesco Zucca, Roberto Seppi, Alessandro Oggioni and Anna Rampini) 57Chapter 6. Data quality in crowdsourcing for biodiversity research: issues and examples (Clemens Jacobs) 75Chapter 7. Semantic Challenges for Volunteered Geographic Information (Andrea Ballatore) 87Chapter 8. Quality analysis of the Parisian OSM toponyms evolution (Vyron Antoniou, Guillaume Touya and Ana-Maria Raimond) 97Chapter 9. Tackling the thematic accuracy of areal features in OpenStreetMap (Ahmed Loai Ali) 113iv European Handbook of Crowdsourced Geographic InformationFive years later, in 2012, the ENERGIC action started exploring new VGI sources, sharing and developing data retrieval software, assessing VGI quality, defining standardization criteria for interoperability with other datasets, identifying applications and transferring them for business implementation (market analysis, risk management, advertising, etc. 1 ).The action is based on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet. People now consume and produce geographic information on the go via platforms such as Facebook, Twitter, Flickr, Instagram and others. The availability of cheap GPS allows everyone to survey and map and contribute to projects like Wikimapia and OpenStreetMap. The exploitation, integration and application of these sources, termed crowdsourced or user generated information, offer to multidisciplinary scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales.The most popular definition of such content that possesses a geographic reference data is Volunteered Geographic Information ( The growing volume of scientific production on the topic cover multiple domains but some major threads may be identified, although intertwined and often coexisting, to build a narrative on the development of VGI. After an initial phase concerned mostly with conceptualizing and defining the new phenomenon (Coleman 2010; Elwood,2008; Capineri & Rondinone 2011;See et al. 2016;Sui et al. 2012) In conclusion, a very wide panorama which proves the broad penetration of VGI in the scientific community and the diverse research paths.The collection of the papers in this book touch on many of these threads. The Book StructureThe book includes peer-reviewed chapters, organised in six parts, which try to address some PART I: Theoretical and social aspectsPart I deals with the nature and features of crowdsourced geographic information: the different sources and typologies of VGI, the fundamental aspect of participation. Capineri (Chapter 2) discusses the main features of crowdsourced geographic information by focusing on the components of such data: the geographical reference, the contents and the producers' profile in order to show the potentialities and crit...

show abstract

Section: Discussionmentioning

confidence: 99%

“…• Temporal difference Adnan et al (2012) conducted research on student stress levels at the beginning and the end of the semester and found the stress level of university students varies throughout a semester. Generally speaking, students felt more stressful at the end of the semester than at the beginning of the semester.…”

Section: Research Hypothesismentioning

confidence: 99%

European Handbook of Crowdsourced Geographic Information

2016

View full text Add to dashboard Cite

show abstract

“…The system involves feature extraction from the EEG data and the development of precise predictive models for emotion recognition to detect stress and anxiety [5], [6]. Adnan et al [7] studied the stress levels and brainwave balance index of university students at the beginning and end of a semester. The perceived stress scale questionnaire developed by Sheldon Cohen was used to quantify stress levels while the EEG signals were captured using g-Mobilab from Guger Technologies.…”

Section: Introductionmentioning

confidence: 99%

Stress and anxiety detection: deep learning and higher order statistic approach

Joshi,

Kulkarni,

Uke

2024

IJEECS

View full text Add to dashboard Cite

<p>Today's teenagers are dealing with anxiety and stress. Anxiety, depression, and suicide rates have increased in recent years because of increased social rivalry. The research is focused on detecting anxiety in students due to exam pressure to reduce the potential harm to a person's wellness. Research is performed on databases for anxious states based on psychological stimulation (DASPS) and our own database. The measured signal is divided into sub bands that correspond to the electroencephalogram (EEG) rhythms using the Butterworth sixth-order order filter. In higher dimensional space, the nonlinearities of each sub-band signal are analyzed using higher order statistics third-order cumulants (TOC). We have classified stress and anxiety using the support vector machine (SVM), K-nearest neighbor (K-NN), and deep learning bidirectional long short-term memory (BiLSTM) network. In comparison to previous techniques, the proposed system's performance using BiLSTM is quite good. The best accuracy in this analysis was 87% on the DASPS database and 98% on the own database. Finally, subjects with high stress levels had more gamma activity than subjects with little stress. This could be an important attribute in the classification of stress.</p>

show abstract

“…The mental stress of university students can come in different ways. It is critical to investigate the current degree of mental stress among university students 4,7 . Mental stress has evolved into social problems and may contribute to a functional impairment at a regular job.…”

Section: Introductionmentioning

confidence: 99%

University student's mental stress detection using machine learning

Firoz,

Islam,

Shidujaman

et al. 2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

View full text Add to dashboard Cite

University students stress level and brainwave balancing index: Comparison between early and end of study semester

Cited by 15 publications

References 13 publications

European Handbook of Crowdsourced Geographic Information

European Handbook of Crowdsourced Geographic Information

Stress and anxiety detection: deep learning and higher order statistic approach

University student's mental stress detection using machine learning

Contact Info

Product

Resources

About