
Dipankar ChakiUNSW Sydney | UNSW · School of Computer Science and Engineering
Dipankar Chaki
Doctor of Engineering
About
30
Publications
41,348
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303
Citations
Introduction
Currently, I am working as a Research Associate (i.e., Postdoctoral Fellow) at the University of New South Wales (UNSW). I completed my Ph.D. from The University of Sydney. Before joining as a Ph.D. student, I served BRAC University, Bangladesh, as a Lecturer. I have a vast interest in Software Security, IoT Service Mining, and Service Recommendations using statistics and machine learning. Outside of profession, I am very passionate about sports, especially an avid fan of cricket.
Additional affiliations
January 2014 - February 2019
October 2016 - September 2017
Education
March 2019 - February 2023
January 2015 - December 2016
January 2009 - December 2013
Publications
Publications (30)
Predicting society's reaction to a new product in the sense of popularity and adaption rate has become an emerging field of data analysis. The motion picture industry is a multi-billion-dollar business, and there is a massive amount of data related to movies is available over the internet. This study proposes a decision support system for movie inv...
Keyword extraction is an automated process that collects a set of terms, illustrating an overview of the document. The term is defined how the keyword identifies the core information of a particular document. Analyzing huge number of documents to find out the relevant information, keyword extraction will be the key approach. This approach will help...
Bangladesh government has created a Facebook group named “Public Service Innovation Bangladesh” to encourage the spread of ideas and interaction among experienced senior civil servants and junior officers. Currently, this group has around 14500 members. This peer-supported and mentor-ship platform will host innovative ideas and ambitions of governm...
The classification of heart disease patients is of great importance in cardiovascular disease diagnosis. Numerous data mining techniques have been used so far by the researchers to aid health care professionals in the diagnosis of heart disease. For this task, many algorithms have been proposed in the previous few years. In this paper, we have stud...
Universities require a fast and reliable system to provide academic advising to its students, register them into different courses, and to manage change requests. Currently in Bangladesh, academic advising is done mostly on paper, as seen in many of the public and private universities. In this paper, we propose a unique online-based system that wou...
We propose a novel impact conflict detection framework for IoT services in multi-resident smart homes. The proposed impact assessment model is developed based on the integral of a signal deviation strategy. We mine the residents' previous service usage records to design a robust preference estimation model. We design an impact conflict detection ap...
We propose a novel resident identification framework to identify residents in a multi-occupant smart environment. The proposed framework employs a feature extraction model based on the concepts of positional encoding. The feature extraction model considers the locations of homes as a graph. We design a novel algorithm to build such graphs from layo...
As the adoption of IoT-based smart homes continues to grow, the importance of addressing potential conflicts becomes increasingly vital for ensuring seamless functionality and user satisfaction. In this survey, we introduce a novel conflict taxonomy, complete with formal definitions of each conflict type that may arise within the smart home environ...
Habit extraction is essential to automate services and provide appliance usage insights in the smart home environment. However, habit extraction comes with plenty of challenges in viewing typical start and end times for particular activities. This paper introduces a novel way of identifying habits using an ensemble of unsupervised clustering techni...
We propose a non-intrusive, and privacy-preserving occupancy estimation system for smart environments. The proposed scheme uses thermal images to detect the number of people in a given area. The occupancy estimation model is designed using the concepts of intensity-based and motion-based human segmentation. The notion of difference catcher, connect...
We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. The proposed framework employs a preference extraction model based on a temporal proximity strategy. We design a preference aggregation model using a matrix factorization-based approach (i.e., singular value decomposition). The concepts of current resid...
We propose a non-intrusive, and privacy-preserving occupancy estimation system for smart environments. The proposed scheme uses thermal images to detect the number of people in a given area. The occupancy estimation model is designed using the concepts of intensity-based and motion-based human segmentation. The notion of difference catcher, connect...
We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. The proposed framework employs a preference extraction model based on a temporal proximity strategy. We design a preference aggregation model using a matrix factorization-based approach (i.e., singular value decomposition). The concepts of current resid...
We propose a novel conflict resolution framework
for IoT services in multi-resident smart homes. An adaptive
priority model is developed considering the residents’ contextual
factors (e.g., age, illness, impairment). The proposed
priority model is designed using the concept of the analytic
hierarchy process. A set of experiments on real-world datas...
We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. An adaptive priority model is developed considering the residents' contextual factors (e.g., age, illness, impairment). The proposed priority model is designed using the concept of the analytic hierarchy process. A set of experiments on real-world datas...
We propose a novel conflict detection framework
for IoT services in multi-resident smart homes. A fine-grained
conflict model is developed considering the functional and non-functional properties of IoT services. The proposed conflict
model is designed using the concept of entropy and information
gain from information theory. We use a novel algorit...
We propose a novel framework to detect conflicts
among IoT services in a multi-resident smart home. A novel
IoT conflict model is proposed considering the functional
and non-functional properties of IoT services. We design a
conflict ontology that formally represents different types of
conflicts. A hybrid conflict detection algorithm is proposed by...
We propose a novel framework to detect conflicts among IoT services in a multi-resident smart home. A fine-grained conflict model is proposed considering the functional and non-functional properties of IoT services. The proposed conflict model is designed using the concept of entropy and information gain from information theory. We use a novel algo...
We propose a novel framework to detect conflicts among IoT services in a multi-resident smart home. A novel IoT conflict model is proposed considering the functional and non-functional properties of IoT services. We design a conflict ontology that formally represents different types of conflicts. A hybrid conflict detection algorithm is proposed by...
Machine learning is massively used in the prediction of cognitive and psychological features in recent times. This research aims to find the predictability between leading disorders like Internet addiction, depression, and low self-esteem. For this purpose, 461 undergraduate students have been selected arbitrarily from several educational instituti...
With the help of globalization, fast food has become very popular in Bangladesh as it is concerned with the taste and habit of the people. Based on the customers’ choice, there are many factors associated with it such as rating, reviews, environment, publicity, and so on. Hence, it is vital to evaluate customers’ opinion to observe the reasons behi...
Understanding the current health status of a population is a requirement to develop public health strategies, and the prevalence of more fast food businesses affect public health slowly and negatively. To comment on the public health status of a country, a large populations' data on many health-related aspects required. This research aims to invest...
Keywords extraction is one of the significant fields in natural language processing. The main aspect of this process is to retrieve a set of important terms that represent the core information of a document in details, which is directly related to the document context. In this research, an original approach is presented for keywords extraction tech...
The traffic is growing exponentially and expected to be
more congested by thousands of millions of vehicles in near
future and will become an inescapable problem across the world.
It is highly necessary to find the probable reasons for traffic
congestion in a traffic domain in order to minimize the hours
wasted on the streets. Existing centrali...
Traffic congestion severely affects many cities around
the world causing various problems like fuel wastage, increased
stress levels, delayed deliveries and monetary losses. Therefore, it
is urgent to make an accurate prediction of traffic jams to
minimize these losses. But forecasting is a real
challenge to obtain
promising results for vibran...
Movie industry is a multi-billion-dollar industry and now there is a huge amount of data available on the internet related to movie industry. Researchers have developed different machine learning methods which can make good classification models. In this paper, various machine learning classification methods are implemented on our own movie dataset...
In order to take better care and to ensure better facilities to the inpatients, predicting length of stay serves a great importance. Since, the resources and the doctors are limited in the hospital, especially in a developing country like Bangladesh, it is quite difficult to provide proper healthcare to the inpatients. Not only the hospital resourc...
The categorization of chronic kidney disease patients is of extraordinary significance as it is related to increased mortality, morbidity, more significant chance of cardiovascular disease and stroke. Researchers have developed different machine learning techniques to support health care professionals to diagnose chronic kidney disease. In this pap...