Browsing by Author "AHMED, ESHTIAK"

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A Survey on Dimensionality Reduction Techniques for Time-series Data
(Independent University, Bangladesh, 2023-06) ASHRAF, MOHSENA; ANOWAR, FARZANA; SETU, JAHANGGIR H.; CHOWDHURY, ATIQUL I.; AHMED, ESHTIAK; ISLAM, ASHRAFUL; MAMUN, ABDULLAH AL
Data analysis in modern times involves working with large volumes of data, including timeseries 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 timeseries data and fall into different categories, such as supervision, linearity, time and memory complexity, hyper-parameters, and drawbacks.
Assessing Early Stage Design of a mHealth App for Gestational Diabetes Mellitus Management in Bangladeshi Women
(The International Diabetes Federation (IDF), 2023, 2023-10) Islam, Ashraful; AHMED, ESHTIAK; Zaman, Marzia; Rangon, Fairy Hasan; Amin, M Ashraful; Islam, Rakibul
There is significant concern over the rising incidence of Gestational Diabetes Mellitus (GDM) among expectant mothers in Bangladesh [1]. Limited healthcare facilities in rural areas hinder prompt diagnosis and efficient management of GDM in Bangladesh. Despite mHealth's benefits, there is a lack of GDM management apps in Bangla, the native language of Bangladeshi citizens. To assess the viability of the first GDM management mHealth app in Bangla, users were asked about its early designs and functionalities. The app features a blood glucose tracker, food diary, medication reminder, educational resources, activity tracker, and personalized recommendations. 30 women with pre-existing GDM who were visiting a clinic in Dhaka, Bangladesh, were freely recruited during July 2023, and participation was anonymized. Participants ranged in age from 24 to 43 years (mean 33.43, SD 5.4). Following a briefing on the app's features and functionalities, participants were shown early sketches of the app. Later, they were prompted with a series of questions to provide feedback on the initial design and features. The majority (n=24) participants exhibited a positive response towards the app, expressing a wish that they had such a tool during their experience with GDM. However, 2 participants viewed the app as an impractical tool, while 3 were uncertain, expressing concerns about the accuracy of the information and guidance provided by the app. Beyond a textual interface, 1 participant suggested the inclusion of voice-based interaction to accommodate users who are illiterate, unfamiliar with using apps or having visual impairments. All participants appreciated the interface's use of the Bangla language and its cultural tailoring. 9 participants specifically highlighted the culturally tailored dietary recommendation feature as particularly praiseworthy. The app can play a crucial role in managing GDM in Bangladesh based on the early-stage evaluation feedback. However, further research is warranted to evaluate the real-world effectiveness and feasibility of it with a high-fidelity prototype for in-situ evaluation with the target users.
Low-cost relay selection in multihop cooperative networks
(Journal of King Saud University - Computer and Information Sciences, Q1, 2023-09) Rahman, Suryaia; Alam, Md Zahangir; Islam, Ashraful; Habib, Md. Tarek; AHMED, ESHTIAK; Hasan, Mahady; Ahmed, Tarem
A best relay selection algorithm for a cooperative multi-hop cross-layer single-input single-output (SISO) amplify-and-forward (AF) wireless relay network is analyzed in this work, with the application where exact channel state information (CSI) is known. We present algorithmic strategies to simplify a multihop parallel SISO relay network into a series multi-hop network by finding the best path having the maximum received signal-to-noise ratio (SNR). The best relay selection by using dynamic programming search entails high computations and large memory requirements, as well as involves the full CSI information, making this approach impractical for large-scale networks. The goal of the proposed low-cost near-optimal routing strategy in this work is to provide close to optimal performance with much less complexity compared to traditional routing. Next, we also propose a low-cost power allocation to further improve system performance over traditional power allocation found in the literature. Computer simulations show excellent performance improvement of our proposed methods in terms of bit error rate (BER) as well as outage probability over the traditional relaying and power allocation algorithms.