Browsing by Author "Ashraf, Mohsena"

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A Survey on Dimensionality Reduction Techniques for Time-Series Data
(IEEE, 2023-04-24) Ashraf, Mohsena; Anowar, Farzana; Setu, Jahanggir H.; Chowdhury, Atiqul I.; Ahmed, Eshtiak
Data analysis in modern times involves working with large volumes of data, including time-series data. This type of data is characterized by its high dimensionality, enormous volume, and the presence of both noise and redundant features. However, the “curse of dimensionality” often causes issues for learning approaches, which can fail to capture the temporal dependencies present in time-series data. To address this problem, it is essential to reduce dimensionality while preserving the intrinsic properties of temporal dependencies. This will help to avoid lower learning and predictive performances. This study presents twelve different dimensionality reduction algorithms that are specifically suited for working with time-series data and fall into different categories, such as supervision, linearity, time and memory complexity, hyper-parameters, and drawbacks.
An Investigation into the Level of Valence Offered By Different Pointing Devices against Challenging Tasks
(International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2020) Ahmed, Eshtiak; Islam, Ashraful; Ashraf, Mohsena; Khan, Md. Ibrahim; Chowdhury, Atiqul Islam; Karim, Asif
Pointing devices are the primary media of interac-tion between humans and computers. The three most popular pointing devices used in computers (both portable and non-portable) are mouse, touchpad and nubs (joystick). They have their different advantages and use cases while being targeted to different user groups. The aim of this study was to investigate whether the aforementioned pointing devices have different effects on human valence. A total of 12 participants were recruited for the experiment. Each participant completed a pointing reaction test with every pointing device aforementioned, where they selected as many randomly appearing circles as possible in a given amount of time. Then, subjective ratings of emotional valence and arousal were collected, and the effects of the pointing device used on these ratings were investigated. Our study shows that the valence rating of using the mouse were significantly higher in challenging scenarios, compared to the likes of touchpad and nub.
hActNET
(Scopus, 2020) Chowdhury, Atiqul Islam; Ashraf, Mohsena; Islam, Ashraful; Ahmed, Eshtiak; Jaman, Md. Saroar; Rahman, Mohammad Masudur
Human activity recognition (HAR) is considered as one of the most difficult and challenging issues now a days. Many experiments are now in progress regarding this problem. Among many human activities, mostly six are considered for research in this area. This activity recognition issue can be measured with the help of smartphones and smartphone sensors, along with the connection of Internet of Things (IoT) devices. In this research, an improved deep learning scheme is proposed for the recognition of human activities. A customized Neural Network (NN) model was designed and tested for the research. The proposed model obtained 96.47% accuracy on the HAR with smartphones dataset that is better than most other analyzed models. Sensors such as accelerometer, gyroscope are focused on the data analysis portion of this research work. This article will give a clear idea of the dataset, Machine Learning algorithms, and the effect of the proposed algorithm.