Browsing by Author "Kabir, Tasnim"

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Phytochemical profiling, antioxidant potential, and antimicrobial assessment of the essential oil extracted from Moringa oleifera
(BRAC University, 2025-11) Amir, Nafisa; Kabir, Tasnim; Opsory, Munia Haque; Nafisa, Nusrat Jahan; Turna, Noorjahan Islam; Hossain, Mohammed Mahboob
Since antibiotic resistance is still on the rise, the search for safe plant-based bioactives keeps growing in importance. The "Miracle Tree," Moringa oleifera, also has immense potential since it contains a vast pool of compounds and is also said to be capable of curing a vast pool of diseases as well. Phytochemical content, antioxidant activity, and antibacterial activity of Moringa oleifera leaf extracts in ethanol, ethyl acetate, and chloroform solvents were investigated comprehensively in this study. Four fractions: crude ethanol, non-partitioned ethyl acetate, organic chloroform, and aqueous chloroform, were subjected to Thin Layer Chromatography (TLC), DPPH antioxidant assay, and Kirby-Bauer antibacterial test. Thin Layer Chromatography (TLC) of Moringa oleifera leaf extracts exhibited solvent-dependent variation in bioactive compound profiles. The most compositionally rich was ethanol extract, with several flavonoid bands (Rf ≈ 0.55–0.93), alkaloid bands (Rf ≈ 0.51–0.88), anthraquinone bands (Rf ≈ 0.57–0.64), phenolics, and proteins, showing efficient extraction of mid-polar and polar compounds. Moderate alkaloids, flavonoids, and anthraquinones bands (Rf ≈ 0.24–0.75) were shown by the organic chloroform fraction, while the ethyl acetate extract showed weak bands of phenolics and alkaloids (Rf ≈ 0.78–0.93). No bands were seen by aqueous chloroform extract, showing low yield of phytochemicals. Overall, ethanol was the most suitable solvent since it had extracted the broadest range of bioactive compounds for the antimicrobial and antioxidant activity of Moringa oleifera leaves. DPPH assay for antioxidant assay showed that extracts had dose-dependent radical scavenging activity. Ethanol extract was superior, with an IC₅₀ value of 56.54 μg/mL. Ethyl acetate, aqueous chloroform, and organic chloroform extracts were inferior with IC₅₀ values 159.40 μg/mL, 300.52 μg/mL, and 580.25 μg/mL, respectively. 5 Compared to the control ascorbic acid (IC₅₀ = 6.1–10.47 μg/mL), the ethanol extract was around 5–9-fold lower. Yet it was greater than 90% scavenging activity at 1000 μg/mL and therefore was highly active against antioxidants owing to the presence of flavonoids and phenolics. No zone of inhibition was found in well diffusion and disk diffusion antibacterial activity against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumoniae, Salmonella typhi, Shigella flexneri, Vibrio cholerae, and Enterococcus faecalis. Inhibition of activity because of small quantity of the extract (30 mg/disc), inability of lipophilic molecules to diffuse via agar, and ultimate break-down of heat labile molecules. Moringa oleifera leaf extracts, particularly the ethanol fraction, were rich in phytochemicals and showed vigorous antioxidant activity with poor antibacterial inhibition under the assay conditions. The research highlights the solvent polarity as a driving force for phytochemical recovery and antioxidant activity and as a foundation for its future optimization and for improved nano-formulation approaches to antimicrobial delivery.
Scalable algorithm for multi-class support vector machine on geo-distributed datasets
(Department of computer Science and Engineering, 2019-07-23) Kabir, Tasnim; Adnan, Dr. Muhammad Abdullah
Training machine learning models on large scale data to efficiently discover valuable information while maintaining the security and privacy of data remains an important research issue. Often many real-life applications such as health-care systems or financial organizations distribute data over many data centers and these data centers may have a different privacy policy. The joint decision over this dataset while not sharing the local information of the data centers is a must and becomes a challenging problem. The State-of-the-art method mainly relies on cryptographic technique to ensure the privacy for data communication between the data centers. This technique alone is not suitable for the large-scale geo-distributed datasets as this is designed for small-scale systems. To solve these problems, we propose a novel approach to achieve privacy-preserving Support Vector Machine (SVM) algorithm where the training set is distributed and each partition can contain large-scale data. We utilize the traditional SVM model to train the dataset of local data centers and with a few parameters sent by those training models, a centralized machine calculates the final result. We show that the proposed model is secure in an adverse environment and use experimental evaluation to demonstrate its correctness and computation speed compared to other parallel SVM training models. Our proposed Distributed SVM (D-SVM) and Time-constrained Distributed SVM (TCD- SVM) algorithms scale the efficiency and speed of the learning network. We conduct exper- iments to compare our algorithms with traditional SVM and state-of-the-art algorithms using data sets collected from the UCI Machine Learning repository. We simulate these algorithms on Amazon Web Service (AWS) EC2 instances and show that using Distributed SVM and Time- constrained Distributed SVM, we can achieve an improvement of 87.5% and 90.67% in task iv completion time, respectively, compared to the traditional SVM. We show that we can achieve accuracy very close to the traditional SVM having great improvement in task completion times.
Will AI technology reduce job opportunities in future?
(Brac University Research For Development Club (BURED), 2024-07-03) Kabir, Tasnim; Saha, Durba
The main focus of this study is to perceive whether artificial intelligence influences different aged individuals along with their opinion regarding this new technology that helps us to know the percentage of agreement and disagreement and of the fact that " Will AI technology reduce job opportunities in future?". Data were collected through discussions in peer member groups, and the research portion was surveyed through a Google Form questionnaire. 35 people participated in the survey and shared their opinions by answering the following questions.