Bahareh Nakisa

• PhD student
• PhD Student at Queensland University of Technology

Apologies are a powerful tool used in human-human interactions to provide affective support, regulate social processes, and exchange information following a trust violation. The emerging field of AI apology investigates the use of apologies by artificially intelligent systems, with recent research suggesting how this tool may provide similar value...

Emotion recognition is significantly enhanced by integrating multimodal biosignals and IMU data from multiple domains. In this paper, we introduce a novel multi-scale attention-based LSTM architecture, combined with Squeeze-and-Excitation (SE) blocks, by leveraging multi-domain signals from the head (Meta Quest Pro VR headset), trunk (Equivital Ves...

Emerging research in Pluralistic Artificial Intelligence (AI) alignment seeks to address how intelligent systems can be designed and deployed in accordance with diverse human needs and values. We contribute to this pursuit with a dynamic approach for aligning AI with diverse and shifting user preferences through Multi Objective Reinforcement Learni...

Depression is a globally prevalent mental disorder with potentially severe repercussions if not addressed, especially in individuals with recurrent episodes. Prior research has shown that early intervention has the potential to mitigate or alleviate symptoms of depression. However, implementing such interventions in a real-world setting may pose co...

The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and the dynamic variations. Through the utilization of advanced algorithms, CERS provides profound insights into emotional dynamics, facilitating a nuanced understanding and customized respo...

In human-AI coordination scenarios, human agents usually exhibit asymmetric behaviors that are significantly sparse and unpredictable compared to those of AI agents. These characteristics introduce two primary challenges to human-AI coordination: the effectiveness of obtaining sparse rewards and the efficiency of training the AI agents. To tackle t...

Chronic kidney disease (CKD) poses a major global public health burden, with approximately 7 million affected. Early identification of those in whom disease is likely to progress enables timely therapeutic interventions to delay advancement to kidney failure. This study developed explainable machine learning models leveraging pathology data to accu...

Negative emotions may induce dangerous driving behaviors leading to extremely serious traffic accidents. Therefore, it is necessary to establish a system that can automatically recognize driver emotions so that some actions can be taken to avoid traffic accidents. Existing studies on driver emotion recognition have mainly used facial data and physi...

Performing prescribed physical exercises during home-based rehabilitation programs plays an important role in regaining muscle strength and improving balance for people with different physical disabilities. However, patients attending these programs are not able to assess their action performance in the absence of a medical expert. Recently, vision...

Stress and driving are a dangerous combination which can lead to crashes, as evidenced by the large number of road traffic crashes that involve stress. Therefore, it is essential to build a practical system that can classify driver stress level with high accuracy. However, the performance of such a system depends on hyperparameter optimization choi...

Solar irradiance forecasting is a major priority for the power transmission systems in order to generate and incorporate the performance of massive photovoltaic plants efficiently. As such, prior forecasting techniques that use classical modelling and single deep learning models that undertake feature extraction procedures manually were unable to m...

Biometric authentication is a new technology that verifies an individual’ s identity via their unique physical or behavioural characteristics including Palm Vein or hand geometry. Despite its potential benefits and growing popularity in the public and private sectors, end-user adoption of the technology has been relatively slow. This study aims to...

Whilst the application of artificial intelligence promises immense potential benefits to virtually all disciplines and activities, it also raises new risks, in scope like in scale. This duality is especially relevant with health applications and more particularly mental health. Making decisions in regards to the application of AI in healthcare base...

- Whilst the use of music in health is an ancestral practice, the emergence of new technologies, more particularly artificial intelligence and machine learning, offers new capabilities for music creation and production as well as for diagnostic purposes and for the monitoring of treatment efficacy. It also makes possible completely new modes of col...

Depression is a serious and common psychological disorder that requires early diagnosis and treatment. In severe episodes the condition may result in suicidal thoughts. Recently, the need for building an effective audio-based Automatic Depression Detection (ADD) system has sparked the interest of the research community. To date, most of the reporte...

Emotion plays an increasingly important role in our daily lives and negative emotions can increase dangerous driving behaviors leading to extremely serious traffific accidents. Therefore, it is necessary to establish a system that can automatically recognize emotions to alert drivers to avoid dangerous driving behaviors. To this end, we propose to...

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 contr...

To decrease the number of traffic accidents caused by changes in drivers' physical and mental health conditions and accomplish automatic monitoring and real-time optimization of drivers' health states, a closed-loop feedback system framework for drivers' health states was proposed based on the basic theory of cybernetics. First, a personalized heal...

Stress has been identified as one of major contributing factors in car crashes due to its negative impact on driving performance. It is in urgent need that the stress levels of drivers can be detected in real time with high accuracy so that intervening or navigating measures can be taken in time to mitigate the situation. Existing driver stress det...

Stress and driving are a dangerous combination which can lead to crashes, as evidenced by the large number of road traffic crashes that involve stress. Motivated by the need to address the significant costs of driver stress, it is essential to build a practical system that can classify driver stress level with high accuracy. However, the performanc...

Stress has been identified as one of major contributing factors in car crashes due to its negative impact on driving performance. It is in urgent need that the stress levels of drivers can be detected in real time with high accuracy so that intervening or navigating measures can be taken in time to mitigate the situation. Existing driver stress det...

Stress has been identified as one of major contributing factors in car crashes due to its negative impact on driving performance. It is in urgent need that the stress levels of drivers can be detected in real time with high accuracy so that intervening or navigating measures can be taken in time to mitigate the situation. Existing driver stress det...

Emotion recognition using miniaturised wearable physiological sensors has emerged as a revolutionary technology in various applications. However, detecting emotions using the fusion of multiple physiological signals remains a complex and challenging task. When fusing physiological signals, it is essential to consider the ability of different fusion...

Accurate modeling of municipal solid waste (MSW) generation is vital as a reliable support for decision‐making processes ensuring the success of the future development and management of wastes. The present study aims to forecast monthly and seasonal MSW generation using radial basis function (RBF) neural network and assess the effect of the gender...

Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general...

Recently, emotion recognition using low-cost wearable sensors based on Electroencephalogram (EEG) and Blood Volume Pulse (BVP) has received much attention. Long Short Term Memory (LSTM) networks, a special type of Recurrent Neural Networks (RNN), have been applied successfully to emotion classification. However, the performance of these sequence cl...

Target searching in unknown environment using multi-robot search systems has received increasing attention in recent years. Particle Swarm Optimization (PSO) has applied successfully on multi-robot target searching system. However, this algorithm suffer from premature convergence problem and cannot escape from the local optima. It is, therefore, im...

There is currently no standard or widely accepted subset of features to effectively classify different emotions based on electroencephalogram (EEG) signals. While combining all possible EEG features may improve the classification performance, it can lead to high dimensionality and worse performance due to redundancy and inefficiency. To solve the h...

Typical commercial lease agreements in Australia stipulate 22.5 ± 1.5 °C in summer as the acceptable thermal condition that buildings have to meet, even though the overcooling incurs excessive and unnecessary energy use, gas emissions and financial expense. An argument that backs up this practice asserts that office workers' productivity and comfor...

Particle swarm optimization (PSO), a new population-based algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obs...

Particle swarm optimization (PSO), a new populationbased algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obst...

Recently, more and more researches have been conducted on the multi-robot system by applying bioinspired algorithms. Particle Swarm Optimization (PSO) is one of the optimization algorithms that model a set of solutions as a swarm of particles that spread in the search space. This algorithm has solved many optimization problems, but has a defect whe...

This paper proposes a method based on the Multi-Swarm Particle Swarm Optimization (PSO) with Local Search on the multi-robot search system to find a given target in a Complex environment that contains static obstacles. This method by applying Multi-Swarm with Multi-Best particles on the multi-robot system can overcome the premature convergence prob...

Robot Path Planning (RPP) in dynamic environments is a search problem based on the examination of collision-free paths in the presence of dynamic and static obstacles. Many techniques have been developed to solve this problem. Trapping in a local minima and maintaining a Real-Time performance are known as the two most important challenges that thes...

Multi-robot Search system is one area that attracts many researchers. In the field of multi-robot system one of the problem is to design a system that allow the robot to work within a team to find a target. There are many methods that are used on the multi-robot systems. One of the methods is Particle Swarm Optimization (PSO) that uses a virtual mu...

In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between target and the robot...

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method based on the social behaviors of birds flocking or fish schooling. Although PSO is represented in solving many well-known numerical test problems, but it suffers from the premature convergence. A number of basic variations have been developed d...