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Psychology of adolescents: a fuzzy logic analysis

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Abstract

The process of medical diagnosis, like many other fields, has to pass through various stages of uncertainty, especially in cases where the data is mostly available in linguistic format. Under such circumstances of vague data, application of fuzzy logic concepts can play an important role in extracting approximate information which in turn may help in reaching to a particular diagnosis. The present study is devoted to the application of fuzzy logic rules for analyzing the psychology of adolescents with respect to Indian scenario. The objective here is to identify whether the subject requires counselling. Fuzzy logic approach is applied to Global Adjustment Scale, used by mental health clinicians to rate the general functioning of children under the age of 18.

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References

  • Abbasi H, Unsworth CP, Gunn AJ, Bennet L (2014) Superiority of high frequency hypoxic ischemic EEG signals of fetal sheep for sharp wave detection using Wavelet-Type 2 fuzzy classifiers. In: Proceedings of the 36th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Chicago, IL, 26–30 Aug 2014, pp 1893–1896

  • Abbod MF, von Keyserlingk DG, Linkens DA, Mahfouf M (2001) Survey of utilization of fuzzy technology in medicine and healthcare. Fuzzy Sets Syst 120(2):331–349

    Article  Google Scholar 

  • Araujo E (2008) Social relationship explained by fuzzy logic. In: IEEE international conference on fuzzy systems FUZZ-IEEE 2008, Hong Kong, 1–6 June 2008, pp 2129–2134

  • Arief Z, Sato T, Okada T, Kuhara S, Kanao S, Togashi K, Minato K (2010) Radiologist model for cardiac rest period determination based on fuzzy rule. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), Buenos Aires. Aug 31 2010–Sept 4 2010, pp 4092–4095

  • Arslan E, Yildiz S, Köklükaya E, Albayrak Y (2010) Classification of fibromyalgia syndrome by using fuzzy logic method. In: 15th national biomedical engineering meeting (BIYOMUT), Antalya, 21–24 April 2010, pp 1–5

  • Aymerich FX, Sobrevilla P, Montseny E, Rovira A (2011) Fuzzy approach toward reducing false positives in the detection of small multiple sclerosis lesions in magnetic resonance. In: Annual international conference of the IEEE engineering in medicine and biology society, EMBC, Boston, MA, Aug 30 2011–Sept 3 2011, pp 5694–5697

  • Barro S, Marín R (2001) Fuzzy logic in medicine, vol 83. Springer, New York

    MATH  Google Scholar 

  • Dawes RV, Lofquist LH (1984) A Psychological Theory of Work Adjustment. University of Minnesota Press, Minneapolis

  • Devi S, Kumar S, Kushwaha GS (2016) An adaptive neuro fuzzy inference system for prediction of anxiety of students. In: Proceedings of eighth international conference on advanced computational intelligence (ICACI) 2016, Chiang Mai 14–16 Feb 2016, pp 7–13

  • Eisman EM, López V, Castro JL (2009) Controlling the emotional state of an embodied conversational agent with a dynamic probabilistic fuzzy rules based system. Expert Syst Appl 36:9698–9708

    Article  Google Scholar 

  • El-Nasr MS, Yen J (1998) Agents, emotional intelligence and fuzzy logic. In: Conference of the North American fuzzy information processing society: NAFIPS, Pensacola Beach, FL, 20–21 Aug 1998, pp 301–305

  • Gambino O, Daidone E, Sciortino M, Pirrone R, Ardizzone E (2011) Automatic skull stripping in MRI based on morphological filters and fuzzy c-means segmentation. In: Annual international conference of the IEEE engineering in medicine and biology society, EMBC, Boston, MA, Aug 30 2011–Sept 3 2011, pp 5040–5043

  • Gouveia S, Bras S (2012) Exploring the use of fuzzy logic models to describe the relation between SBP and RR values. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), San Diego, Aug 28 2012–Sept 1 2012, pp 2827–2830

  • Hesketh B, Mclachlan K, Gardner D (1992) Work adjustment theory: an empirical test using a fuzzy rating scale. J Vocat Behav 4:318–337

    Article  Google Scholar 

  • Honka AM, van Gils MJ, Parkka J (2011) A personalized approach for predicting the effect of aerobic exercise on blood pressure using a fuzzy inference system. In: Annual international conference of the IEEE engineering in medicine and biology society, EMBC, Boston, MA, Aug 30 2011–Sept 3 2011, pp 8299–8302

  • Jain S, Asawa K (2015) EmET: emotion elicitation and emotion transition model. In: Proceedings of second international conference India 2015, information systems design and intelligent applications, vol 1, pp 209–217

  • Karemore G, Mullick JB, Sujatha R, Nielsen M, Santhosh C (2010) Classification of protein profiles using fuzzy clustering techniques: an application in early diagnosis of oral, cervical and ovarian cancer. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), Buenos Aires, Aug 31 2010–Sept 4 2010, pp 6361–6364

  • Kushwaha GS, Kumar S (2009) Role of the fuzzy system in psychological research. Europe’s J Psychol 2(2009):123–134

    Google Scholar 

  • Liu Z, He SH, Xiong W (2008) A fuzzy logic based emotion model for virtual human. In: International conference on cyberworlds 2008, Hangzhou, 22–24 Sept 2008, pp 284–288

  • Liu R, Xue K, Wang YX, Yang L (2011) A fuzzy-based shared controller for brain-actuated simulated robotic system. In: Annual international conference of the IEEE engineering in medicine and biology society, EMBC, Boston, MA, Aug 30 2011–Sept 3 2011, p 7384

  • Loia V, Senatore S (2014) A fuzzy-oriented sentic analysis to capture the human emotion in Web-based content. Knowl Based Syst 58:75–85

    Article  Google Scholar 

  • Madkour MA, Roushdy M (2004) Methodology for medical diagnosis based on fuzzy logic. Egypt Comput Sci J 26(1):1–9

    Google Scholar 

  • Maeda Y (1999) Fuzzy rule expression for emotional generation model based on subsumption architecture. In: 18th International conference of the North American fuzzy information processing society NAFIPS. New York, NY, Jul 1999, pp 781–785

  • Malhotra VK, Kaur H, Alam MA (2013) A spectrum of fuzzy clustering algorithm and its applications. In: International conference on machine intelligence and research advancement (ICMIRA), Katra, 21–23 Dec 2013, pp 599–603

  • Mandryk RL, Atkins MS (2007) A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. Int J Hum Comput Stud 65:329–347

    Article  Google Scholar 

  • Mirza M, GholamHosseini H, Harrison MJ (2010) A fuzzy logic-based system for anesthesia monitoring. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), Buenos Aires, Aug 31 2010–Sept 4 2010, pp 3974–3977

  • Narasimhan B, Malathi A (2014a) A fuzzy logic system with attribute ranking technique for risk-level classification of CAHD in female diabetic patients. In: International conference on intelligent computing applications (ICICA), Coimbatore, 6–7 March 2014, pp 179–183

  • Narasimhan B, Malathi A (2014b) Fuzzy logic system for risk-level classification of diabetic nephropathy. In: International conference on green computing communication and electrical engineering (ICGCCEE), Coimbatore, 6–8 March 2014, pp 1–4

  • Orrego DA, Becerra MA, Delgado-Trejos E (2012) Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), San Diego, CA, Aug 28 2012–Sept 1 2012, pp 5282–5285

  • Pawade DY, Diwase TS, Pawade TR (2013) Designing and implementation of fuzzy logic based automatic system to estimate dose of anesthesia. In: Confluence 2013: the next generation information technology summit (4th international conference), Noida, 26–27 Sept 2013, pp 95–102

  • Peng K, Martel S (2011) Preliminary design of a SIMO fuzzy controller for steering micro particles inside blood vessels by using a magnetic resonance imaging system. In: Annual international conference of the IEEE engineering in medicine and biology society, EMBC, Boston, MA, Aug 30 2011–Sept 3 2011, pp 920–923

  • Pham TD (2010) Australia brain lesion detection in MRI with fuzzy and geostatisticalmodels. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), Buenos Aires, Aug 31 2010–Sept 4 2010, pp 3150–3153

  • Phuong NH, Kreinovich V (2001) Fuzzy logic and its applications in medicine. Int J Med Inform 62(2):165–173

    Article  Google Scholar 

  • Ponce P, Molina A, Grammatikou D (2016) Design based on fuzzy signal detection theory for a semi-autonomous assisting robot in children autism therapy. Comput Hum Behav 55:28–42

    Article  Google Scholar 

  • Puri J, Yadav SP (2015) A fully fuzzy approach to DEA and multicomponent DEA for measuring fuzzy technical efficiencies in the presence of undesirable outputs. Int J Syst Assur Eng Manag 6(2):157–164

    Article  Google Scholar 

  • Rabbi AF, Aarabi A, Fazel-Rezai R (2010) Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), Buenos Aires, Aug 31 2010–Sept 4 2010, pp 3301–3304

  • Sadiq M, Jain SK (2015) A fuzzy based approach for the selection of goals in goal oriented requirements elicitation process. Int J Syst Assur Eng Manag 6(3):268–285

    Article  Google Scholar 

  • San PP, Ling SH, Nguyen HT (2012) Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), San Diego, CA, Aug 28 2012–Sept 1 2012, pp 6325–6328

  • Schaefer G, Nakashima T (2010) Hybrid cost-sensitive fuzzy classification for breast cancer diagnosis. In: Annual international conference of the IEEE engineering in, medicine and biology society (EMBC), Buenos Aires, Aug 31 2010–Sept 4 2010, pp 6170–6173

  • Schneider M, Adamy J (2014) Towards modelling affect and emotions in autonomous agents with recurrent fuzzy systems. In: IEEE international conference on systems, man, and cybernetics, 5–8 Oct 2014, San Diego, pp 31–38

  • Singh SK, Yadav SP (2015) Efficient approach for solving type-1 intuitionistic fuzzy transportation problem. Int J Syst Assur Eng Manag 6(3):259–267

    Article  Google Scholar 

  • Smithson M (1982) Applications of fuzzy set concepts to behavioral sciences. Math Soc Sci 2(3):257–274

    Article  MathSciNet  MATH  Google Scholar 

  • Szczepaniak PS, Lisoba PJG, Kacprzyk J (2000) Fuzzy Systems in Medicine. Physica, Heidelberg

    Book  Google Scholar 

  • Tsipouras MG, Karvounis EC, Tzallas AT, Goletsis Y, Fotiadis DI, Adamopoulos S, Trivella MG (2012) Automated knowledge-based fuzzy models generation for weaning of patients receiving Ventricular Assist Device (VAD) therapy. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), San Diego, CA, Aug 28 2012–Sept 1 2012, pp 2206–2209

  • Vishwakarma Y, Sharma SP (2016) Uncertainty analysis of an industrial system using linguistic fuzzy set theory. Int J Syst Assur Eng Manag 7(1):73–83

    Article  Google Scholar 

  • Vohra S (2013) Global adjustment scale.[source: psychotronicsbangalore.com/catalogue-2013.pdf]

Download references

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Correspondence to Shilpa Srivastava.

Appendices

Appendix 1: Questionnaire

figure dfigure dfigure d

Appendix 2: Scoring key

figure g

Appendix 3: Fuzzy inference rules

  1. 1.

    If (EMOTIONAL is ENC) and (FAMILY is FNC) then (output is NC).

  2. 2.

    If (EMOTIONAL is ENC) and (FAMILY is FIC) then (output is IC).

  3. 3.

    If (EMOTIONAL is ENC) and (FAMILY is FIGC) then (output is IGC).

  4. 4.

    If (EMOTIONAL is EIC) and (FAMILY is FNC) then (output is IC).

  5. 5.

    If (EMOTIONAL is EIC) and (FAMILY is FIC) then (output is IC).

  6. 6.

    If (EMOTIONAL is EIGC) then (output is IGC).

  7. 7.

    If (FAMILY is FIGC) then (output is IGC).

  8. 8.

    If (HEALTH is HIGC) then (output is IGC).

  9. 9.

    If (EMOTIONAL is ENC) and (FAMILY is FNC) and (HEALTH is HNC) then (output is NC).

  10. 10.

    If (EMOTIONAL is ENC) and (FAMILY is FIC) and (HEALTH is HNC) then (output is IC).

  11. 11.

    If (EMOTIONAL is ENC) and (FAMILY is FNC) and (HEALTH is HIC) then (output is IC).

  12. 12.

    If (EMOTIONAL is EIC) and (FAMILY is FNC) and (HEALTH is HNC) then (output is IC).

  13. 13.

    If (EMOTIONAL is EIC) and (FAMILY is FIC) and (HEALTH is HNC) then (output is IC).

  14. 14.

    If (EMOTIONAL is EIC) and (FAMILY is FNC) and (HEALTH is HIC) then (output is IC).

  15. 15.

    If (EMOTIONAL is EIC) and (FAMILY is FIC) and (HEALTH is HIC) then (output is IC).

  16. 16.

    If (SCHOOL is SIGC) then (output is IGC).

  17. 17.

    If (EMOTIONAL is ENC) and (FAMILY is FNC) and (HEALTH is HNC) and (SCHOOL is SNC) then (output1 is NC).

  18. 18.

    If (EMOTIONAL is ENC) and (FAMILY is FNC) and (HEALTH is HNC) and (SCHOOL is SIC) then (output is IC).

  19. 19.

    If (EMOTIONAL is ENC) and (FAMILY is FNC) and (HEALTH is HIC) and (SCHOOL is SNC) then (output is IC).

  20. 20.

    If (EMOTIONAL is ENC) and (FAMILY is FNC) and (HEALTH is HIC) and (SCHOOL is SIC) then (output is IC).

  21. 21.

    If (EMOTIONAL is ENC) and (FAMILY is FIC) and (HEALTH is HNC) and (SCHOOL is SNC) then (output is IC).

  22. 22.

    If (EMOTIONAL is ENC) and (FAMILY is FIC) and (HEALTH is HNC) and (SCHOOL is SIC) then (output is IC).

  23. 23.

    If (EMOTIONAL is ENC) and (FAMILY is FIC) and (HEALTH is HIC) and (SCHOOL is SNC) then (output is IC).

  24. 24.

    If (EMOTIONAL is ENC) and (FAMILY is FIC) and (HEALTH is HIC) and (SCHOOL is SIC) then (output is IC).

  25. 25.

    If (EMOTIONAL is EIC) and (FAMILY is FNC) and (HEALTH is HNC) and (SCHOOL is SNC) then (output is IC).

  26. 26.

    If (EMOTIONAL is EIC) and (FAMILY is FNC) and (HEALTH is HNC) and (SCHOOL is SIC) then (output is IC).

  27. 27.

    If (EMOTIONAL is EIC) and (FAMILY is FNC) and (HEALTH is HIC) and (SCHOOL is SNC) then (output is IC).

  28. 28.

    If (EMOTIONAL is EIC) and (FAMILY is FNC) and (HEALTH is HIC) and (SCHOOL is SIC) then (output is IC).

  29. 29.

    If (EMOTIONAL is EIC) and (FAMILY is FIC) and (HEALTH is HNC) and (SCHOOL is SNC) then (output is IC).

  30. 30.

    If (EMOTIONAL is EIC) and (FAMILY is FIC) and (HEALTH is HNC) and (SCHOOL is SIC) then (output is IC).

  31. 31.

    If (EMOTIONAL is EIC) and (FAMILY is FIC) and (HEALTH is HIC) and (SCHOOL is SNC) then (output is IC).

  32. 32.

    If (EMOTIONAL is EIC) and (FAMILY is FIC) and (HEALTH is HIC) and (SCHOOL is SIC) then (output is IC).

Appendix 4: System requirements for MATLAB R2016a

Operating System: Windows 2008.

Processor: Any Intel or AMD ×86–64 processor.

Disk Space: 2 GB.

RAM: 4B.

Graphic: No specific graphics card is required.

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Srivastava, S., Pant, M. & Agrawal, N. Psychology of adolescents: a fuzzy logic analysis. Int J Syst Assur Eng Manag 9, 66–81 (2018). https://doi.org/10.1007/s13198-016-0472-9

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