Dunhong Yao^1,2,3,*, Yu Chen⁴

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 59-65, 2020, DOI:10.32604/jihpp.2020.010223

Abstract With the rapid development of the Internet, many enterprises have launched their network platforms. When users browse, search, and click the products of these platforms, most platforms will keep records of these network behaviors, these records are often heterogeneous, and it is called log data. To effectively to analyze and manage these heterogeneous log data, so that enterprises can grasp the behavior characteristics of their platform users in time, to realize targeted recommendation of users, increase the sales volume of enterprises’ products, and accelerate the development of enterprises. Firstly, we follow the process of big data collection, storage, analysis, and… More >

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 (APL). In order to detect… More >