Welcome New Students!

This week stands as a key moment as we celebrated a significant milestone with immense pride: the warm and enthusiastic welcome extended to our newest cohort of students joining our Bachelor and Master degree programs across our vibrant campuses in both Sydney and Melbourne! This milestone signifies not just the beginning of their academic pursuits but also the start of a transformative journey towards personal and professional growth.

We are absolutely thrilled to introduce this cohort of future professionals who are eagerly gearing up to embark on their #GoNextLevel journey with us. Their enthusiasm and determination serve as a testament to their readiness to tackle challenges head-on and seize every opportunity for success.

As we wish all our students the best, we hope they excel in their studies. May this mark the beginning of a rewarding chapter, full of valuable experiences, meaningful relationships, and numerous accomplishments. Let’s approach the journey ahead with curiosity, resilience, and a commitment to ongoing growth.

Explore our comprehensive range of programs we offer at AIH

Congratulations, Master of Business Information System Students!

AIH’s Master of Business Information System (MBIS) team of students who participated in the MBIS5015 Capstone Project unit in 2022 had the very first Student Conference Publication with the paper “Skin Disease Detection as Unsupervised-Classification with Autoencoder and Experience-Based Augmented Intelligence (AI),”¬†Kushal Pokhrel, Suman Giri, Sudip Karki, and Cesar Sanin.
Congratulations to all the authors for this remarkable inaugural achievement.
Here is the abstract for those who’d like to know more.


In this paper, we propose an Artificial Neural Network using an auto-encoder trained with fewer images but increases accuracy based on experience Augmented Intelligence. Most neural network systems use a large number of training sets to achieve a well-performing model and spend great efforts on pre-processing and training times to create a static model. In our case, we propose a system that uses just 4% images per class training set compared to most models and learns with each iteration of being used, interacting with the user, and acquiring experience to increase the accuracy. The average accuracy rate is increased at a 1.33% rate per every 20 user experiences. The proposed model offers advantages in creating dynamic experience-based augmented intelligence models.

Congratulations to Dr. Md Rafiqul Islam!!

AIH Senior Lecturer, Dr. Md Rafiqul Islam and his co-authors have been awarded the Best Paper Award at the 27th International Conference on Information Visualisation (IV 2023), hosted by Tampere University, Finland for their research paper “SIDVis: Designing Visual Interactive System for Analyzing Suicide Ideation Detection”.

Congratulations Rafiqul on such a wonderful achievement!

Here is the abstract for those who’d like to know more –


Suicide is a critical global issue that demands a comprehensive examination of factors such as mental illness, substance abuse, financial stress, and trauma. Effectively identifying individuals at risk is vital for intervention and prevention efforts. However, distinguishing suicidal ideation (SID) from non-suicidal language poses challenges. Existing research has addressed this issue, but limited attention has been given to visually interpretable and interactive systems tailored for SID.

This study contributes to responsible AI by leveraging deep learning and machine learning techniques to enhance SID detection, enabling proactive interventions and support. In this paper, we introduce SIDVis, an interactive visualisation system that improves performance and interpretability at the same time.

The rigorous evaluation demonstrates that SIDVis not only outperforms existing methods in terms of accuracy but also provides an explanation for the responsible use of the underlying AI approach, demonstrating its potential to improve SID detection and intervention strategies.