Md. Nurul Ahad Tawhid

Victoria University Melbourne | VU · Institute for Sustainable Industries and Liveable Cities

The burden of neurological disorders is huge on global health and recognized as major causes of death and disability worldwide. There are more than 600 neurological diseases, but there is no unique automatic standard detection system yet to identify multiple neurological disorders using a single framework. Hence, this study aims to develop a common...

The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neurologists visually analyze to identify and understa...

Mining large scale brain signal data using artificial intelligence offers an unparalleled chance to investigate the dynamics of the brain in neurological disorders diagnosis. Electroencephalography (EEG) produces a multi-channel time-series large scale brain signal data recorded from scalp and visually analyzed by expert clinicians for abnormality...

Epilepsy is a severe neurological disorder characterized by recurrent seizures, which increases the risk of death three times more than normal. Currently Electroencephalography (EEG) has emerged as a highly promising technique for the diagnosis of epilepsy. The majority of current EEG-based epilepsy detection research have employed a variety of dee...

Software visualization helps to comprehend the system by providing a vivid illustration. The developers, as well as the analysts, can have a glance over the total system to understand the basic changes over time from a high-level point of view through this technique. In recent years, many tools are proposed to visualize software based on different...

Program comprehension is one of the most important activities in developing and maintaining software. Although existing studies have examined aspects of Go such as design patterns, code smells and comment density, the comprehensibility of Go has not been explored yet. This study analyzes the comprehensibility of Go by comparing it with Java based o...

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its l...

Analysis of brain signal data like Electroencephalography (EEG) plays an important role in efficient diagnosis of neurological disorders and treatment. EEG records electrical activity of the brain and contains huge volume of multi-channel time-series data that are visually analyzed by neurologists to identify abnormalities within the brain, which i...

A major aspect of maintaining the quality of software systems is the management of bugs. Bugs are commonly fixed in a corrective manner; detected after the code is tested or reported in production. Analyzing Fix-Inducing Changes (FIC) — developer code that introduces bugs — provides the opportunity to estimate these bugs proactively. This study ana...

Autism is a type of neurodevelopment disorder in which individuals often have difficulties in social relationship, communication, expressing and controlling emotions and poor eye contact, among other symptoms. Currently, electroencephalography (EEG) is the most popular tool to investigate the presence of autism biomarkers. Generally, EEG recordings...

Non-Functional Requirements (NFR), a set of quality attributes, required for software architectural design. Which are usually scattered in SRS and must be extracted for quality software development to meet user expectations. Researchers show that functional and non-functional requirements are mixed together within the same SRS, which requires a mam...

Random forest is one of the most popular supervised learning methods which is a collection of multiple decision trees. It is computationally fast, easy to use and gives reasonable performances over diversified applications. In a random forest, outcomes from multiple trees are aggregated to make the final decision where all trees get the same import...

Automatic gender classification from facial image has become an attractive research area in the field of machine learning. Various methods have already been proposed for gender recognition in both controlled and uncontrolled situations. Problem arises in uncontrolled situation when there are high rate of noises, lack of illumination etc. To mitigat...

Copying code fragments and then reusing those by pasting with or without modifications or adaptations are common activities in software development. This type of reuse approach of existing code is called code cloning and the pasted code fragment is called a clone of the original. Several studies show that about 5% to 20% of software systems can con...

Automatic garments design class identification for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several methods, based on Hand-Engineered feature coding exist for identifying garments desig...

Automatic identification of garment design class might play an important role in the garments and fashion industry. To achieve this, essential initial works are found in the literature. For example, construction of a garment database, automatic segmentation of garments from real life images, categorizing them into the type of garments such as shirt...

Path planning and navigation in unknown environment is one of the most challenging tasks for autonomous mobile robots. Decomposition of the state space is vital for avoiding obstacles and generating an efficient trajectory. For the purpose of localization and building an efficient map in an unknown environment, decomposition of this area is equally...

The aim of this paper is to develop a real time vision-based facial expression recognition and adaptation system for human-computer interaction. Major objective of this research is to detect face, to identify and recognize user's facial expression using face image in real time and to be able to adapt with new user's facial expression. It also works...

This paper proposes an intensity and size invariant real time computer vision-based face recognition approach. With this method, human facial area(s) are first detected automatically from real-time captured images. The images are then normalized using histogram equalization and contrast stretching. Finally face is recognized using eigenfaces method...