Asiful Arefeen

Asiful Arefeen
Arizona State University | ASU · Department of Biomedical Informatics

Bachelor of Engineering

About

9
Publications
3,936
Reads
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65
Citations
Education
February 2015 - April 2019
Bangladesh University of Engineering and Technology
Field of study
  • Electrical and Electronic Engineering

Publications

Publications (9)
Conference Paper
Full-text available
Of late, usage of neural network in the field of disease detection has been on advanced stage. Hence, ocular disease diagnosis has also been under the influence of machine learning. Human eye is very prone to disorders like cataract, glaucoma, myopia etc. and with the passage of time, these diseases get more and more complex and the vision of human...
Conference Paper
Full-text available
Speech is considered as the widest and most natural medium of communication. Speech can convey a plethora of information regarding one's mental, behavioral, emotional traits. Besides, speech-emotion recognition related work can aid in averting cyber crimes. Research on speech-emotion recognition exploiting concurrent machine learning techniques has...
Conference Paper
Full-text available
Usage of Electric Vehicle (EV) around the world has been on the increase rapidly. As a result, their integration to the grid, dependency, reliability have raised the question of feasibility. Hence, numerous researches regarding their charging, fault analysis, propulsion, architecture are on the advanced level. Since thousands of EVs are getting inc...
Conference Paper
Full-text available
Rapid diagnosis of heart disease has become a staple of Cardiology. The electrocardiogram (ECG) is an effective diagnostic tool which provides significant signals to detect heart abnormalities. In this context, this paper presents a novel and economical approach to the quick approximation of hearts overall condition of a person. Using low cost ECG...
Conference Paper
Full-text available
The increasing demand of smart security systems has enhanced the demand for the proper identification and verification of a person. In this context, accurate estimation of age as well as proper identification of gender is highly significant. Therefore, in this work, we have implemented two separate methods with satisfactory runtime and efficiency t...
Conference Paper
With rapid growth in unhealthy diet behavior, implementing strategies that improve healthy eating is becoming increasingly important. One approach to improve diet behavior is to monitor dietary intake (e.g., calorie intake) and continuously provide educational, motivational, and recommendation feedback. Although technologies based on wearable senso...
Conference Paper
Postprandial hyperglycemia (PPHG) is detrimental to health and increases risk of cardiovascular diseases, reduced eyesight, and life-threatening conditions like cancer. Detecting PPHG events before they occur can potentially help with providing early interventions. Prior research suggests that PPHG events can be predicted based on information about...
Conference Paper
Automatic lying posture tracking is an important factor in human health monitoring. The increasing popularity of the wrist-based trackers provides the means for unobtrusive, affordable, and long-term monitoring with minimized privacy concerns for the end-users and promising results in detecting the type of physical activity, step counting, and slee...
Preprint
Full-text available
Inter-beat interval (IBI) measurement enables estimation of heart-rate variability (HRV) which, in turns, can provide early indication of potential cardiovascular diseases. However, extracting IBIs from noisy signals is challenging since the morphology of the signal is distorted in the presence of the noise. Electrocardiogram (ECG) of a person in h...

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Projects

Projects (2)
Archived project
Of late, usage of neural network in the field of disease detection has been on advanced stage. Hence, ocular disease diagnosis has also been under the influence of machine learning. Human eye is very prone to disorders like cataract, glaucoma, myopia etc. and with the passage of time, these diseases get more and more complex and the vision of human eye gradually decays. As a matter of fact, their early detection is mandatory in order to preclude complete blindness. Several diagnosis tests like visual acuity test, retinal exam, ocular tonometry are undertaken in real life but these are undoubtedly time consuming and frustrating for the patient as well. In this paper, a unique method for detecting eight types of ocular diseases using convolutional neural network (CNN) has been presented and its performance is evaluated. The affected regions for some diseases can also be detected. Some conventional pre-processing are performed and the data is sent to the network for rigorous classification. The model has been trained and tested with high-end graphics processing unit (GPU) on a brand-new dataset. Our developed model has achieved a cogent F-score of approx. 85%, Kappa score of 31% and an AUC value of 80.5%. Since this is the first "real-life" (i.e. plausible for a clinical scenario of patients including camera variation) prediction of multiple diseases in an eye based on this dataset, there is hardly any analogous task to compare with. So, our model has also been performed on other dataset and it has excelled with convincing F-score, Kappa score and an AUC value.
Project
Part of this project aims at developing an offline diet scheduler that would drive the user to his/her desired nutrition goal within desired time duration. Being user friendly, it prioritizes users' flexibility and accuracy at the same time. Details are being added to make it more realistic.