Scope: This special session of the IHCI2021: International Conference on Intelligent Human-Computer Interaction is devoted to “Theory & Application of Intelligent Systems in Modelling, Simulation, and Automation”. Papers are being solicited for this session. Topics of this special session include, but are not limited to, the following area: Intelligent Business Systems, Intelligent Control
Intelligent Systems in Automation, Adaptation and learning for agents, Human and computer interaction, Virtual agent-based marketplaces, Intelligent systems for personalization and privacy issues, Automated shopping and trading agents, Intelligent systems for modeling and Simulations, Intelligent systems in software engineering, Intelligent systems in social media
Intelligent Systems & E-commerce applications, Intelligent systems in logistics issues
Dr. Mohd Helmy Abd Wahab
Universiti Tun Hussein Onn Malaysia
Dr. Masoud Mohammadian
University of Canberra, Australia
Scope : The increasing use of Information and Communication Technologies (ICT) for delivering education has been posing new challenges to educators and creating opportunities for research. The adoption of ICT for education is generating data at an exponential rate. This data needs to be analyzed to get insights about learner’s behavior and to improve learning outcomes. The multidisciplinary field of Cognitive learning analytics concerns about developing theories, frameworks and technologies for the efficient analysis of educational data. The purpose of this special session on HCI Data- learning analytics is to bring together the researchers working towards addressing the challenges faced by educators due to the increasing deployment of ICT in educational institute. In this session, we seek the HCI – learning analytics of submission to enhance dialogue among researchers.
Dr. Arvind W Kiwelekar
Dr Babasaheb Ambedkar Technological University-Lonere, India
Dr. Julio Ariel Hurtado Alegria
University of Cauca, Colombia
Dr. Roopak Tamboli
Saarland University, Germany
Scope : The domain Brain-computer interfaces (BCIs) aim to enable people to interact with the external world through an alternative, nonmuscular communication channel, that uses brain signal responses to complete specific cognitive tasks. BCIs have been growing rapidly during the past few years, with most of the BCI research focusing on system performance, such as improving accuracy or information transfer rate. Despite these advances, BCI research and development is still in its infancy and requires further consideration to significantly affect human experience in most real-world environments. Exploration of this domain through Artificial Intelligence is the scope of this special session.
Dr. Surya Kanth V Gangasetty
KL University, Andhra Pradesh State, India
– Issues, Trends, and Exemplar Projects –
Scope : This session explores the innovations and challenges of Human-Computer Interfaces in E-Health Monitoring and Management in either completely virtual spaces or in virtual spaces associated with aiding physical spaces. This should appeal to scholars, practitioners, and entrepreneurs wanting to share their insights on how to adapt HCI for e-health, defined as self-monitoring and digital sharing of data on (1) tracking or diagnosing physical health (sleep/motion analysis, weight, blood sugar, breath/cough audio or chemical analysis, etc.), (2) monitoring, diagnosing, or improving psychological problems (of addiction, depression, anxiety, impulsiveness, hopelessness, etc.), or (3) tracking medical adherence and ongoing evaluation of patients, drugs, or treatments. We accept case studies or comparative analysis of such platforms and projects that involve medical diagnosis or ongoing treatments whether they are websites, cloud drives, and/or mobile apps. The analysis of successes, failures, or ongoing improvements of such platforms are for learning better routes for constructing HCI may involve hospitals, disaster/refugee camps, or self-help app tools and how they are all used commonly for health/behavioral monitoring and for diagnostic aids. Analysis of different kinds of data inputs, data processing, or data outputs of such platforms are welcome. Analysis of benefits or drawbacks of using different tactics for e-health and self-help are of interest that include: chatbots, AI, natural language processing, machine learning, biofeedback from users, wearables, smartphone-peripherals, virtual communities, questionnaires, audio analysis, visual analysis, gait analysis, sleep pattern analysis, eye-tracking, diagnostic algorithms, gamification, concerns of data sharing/privacy, etc.
Scope:Music has been an integral part of mankind. It is not just a performing art to entertain the world but also has many facets to it in terms of Culture that is always making our lives vibrant, Educating society, Cognitive and Physiological connections, and Technological advancements. These are some important interdisciplinary subjects that are related to music that help us progress positively in various aspects.
A lot of work across the globe has been happening and still, there is a huge scope for innovation and research in the above-mentioned areas. 1. Music education and technological innovations to support teaching and learning processes by developing suitable tools and methods by using technology. 2. Music as a therapeutic tool as part of human support systems in handling stress, and other psychophysiological problems in the healthcare industry. Though it is a common experience that music is an effective tool for relaxation, substantial empirical-based research especially in Indian contexts is yet to take off with greater intensity. 3. Reading music-related cognitive reflections and reactions in the human brain. 4. Development of automated recommendation systems in music by using Machine learning and Deep learning concepts as Artificial Intelligence opens multi-dimensional research with the integration of music and technology.
5. Solving problems related to music recognition by developing useful tools by using signal processing concepts.
In this connection, we are happy to call for papers in the areas of Music Education, Music and Human Brain, Music Therapy and Psychology, Music Cognition, Music Machine Learning, and Deep Learning, Music Signal Processing areas across the globe.
Scope: Emotional intelligence is the ability to recognize and respect one’s true feelings, to make genuinely understandable decisions, and to control impulses, and to control the emotions that cause stress, such as anxiety and anger.
EQ refers to the ability to understand one’s own and others’ emotions and the ability to control them in ways that enrich their lives. People with high EQ have the ability to analyze conflict situations and recognize their own situations accurately. Demonstrates empathic understanding of others while restraining emotional responses.
In order for AI to cultivate such emotional intelligence, it is necessary to quantitatively measure and quantify its ability, and in this process, we need emotional quantization. Regarding quantization of emotions, there is a method of inferring emotions through human facial expressions or gestures, which are visual information, a method of inferring emotions by measuring biological signals, or a method of inferring emotions by measuring hormonal changes, which are chemical information.
In this session, we are conducting a study on a method for quantitatively quantifying emotions in a convergence using AI for these emotion measurement methods