Mahiye Uluyagmur Ozturk^a, Ayse Rodopman Arman^b, Gresa Carkaxhiu Bulut^c, Onur Tugce Poyraz Findik^b, Sultan Seval Yilmaz^d, Herdem Aslan Genc^b, M. Yanki Yazgan^e,f, Umut Teker^a, Zehra Cataltepe^a

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 891-905, 2018, DOI:10.31209/2018.100000058

Abstract Emotion recognition behavior and performance may vary between people with major neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences for early diagnosis and individual treatment purposes. This study represents a methodology by using statistical data analysis and machine learning to provide help to psychiatrists and therapists on the diagnosis and individualized treatment of participants with ASD and ADHD. In this paper we propose an emotion recognition experiment environment and collect eye tracker fixation data together with the application log data More >

J. Zidek^1,2, J. Kucera¹, J. Jancar^1,2

CMC-Computers, Materials & Continua, Vol.28, No.3, pp. 213-230, 2012, DOI:10.3970/cmc.2012.028.213

Abstract The paper presents a combination of worm-like chain numerical models and one with a finite set of nano-particles. The primary objective of the models was to analyze the distribution of space in a system filled by particles. Information on the distribution of space was compared to properties of chains inside the set of particles. The set of nanoparticles was constructed with a tool generating a finite set of particles that is randomly distributed in a given space. The particles have a prescribed volume fraction and uniform size. First, the proportions of chains and particles were… More >

Ramamurty Raju P.¹, Rajesh S.¹, Satyanarayana B.², Ramji K.³

Structural Durability & Health Monitoring, Vol.7, No.1&2, pp. 139-152, 2011, DOI:10.3970/sdhm.2011.007.139

Abstract This paper presents the details of method of sample size determination to estimate the characteristic fatigue life of aluminum alloy, A356.2-T6. The characteristic fatigue life of the alloy has been estimated by assuming log normal distribution model. A step wise procedure is outlined to determine the number of specimens required at predetermined stress amplitude to estimate the fatigue life with an acceptable error at 50% probability and various confidence levels, 90%, 95% and 99%. Maximum percentage of errors has also been calculated for the above probability and confidence levels. Details of generation of S-N curve More >