BRAC University Institutional Repository
Preserving Knowledge, Advancing Research, Sharing Scholarship
A digital platform for collecting, preserving, and sharing BRAC University’s scholarly, academic, and institutional outputs.
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Faculty members and students are invited to submit their research publications, theses, dissertations, and scholarly works to increase visibility, access, and long-term preservation.
Recent Submissions
Cobalt ferrite nanoparticle’s safety in biomedical and agricultural applications: A review of recent progress
(Bentham Science Publishers, 2023-05-01) Shakil, Md Salman; Bhuiya M.S.; Morshed M.R.; Babu G.; Niloy M.S.; Hossen M.S.; Islam M.A.; Department of Mathematics and Natural Sciences
Cobalt ferrite nanoparticles (CFN) have drawn attention as a theranostic agent. Unique physicochemical features of CFN and magnetic properties make CFN an outstand-ing candidate for biomedical, agricultural, and environmental applications. The extensive use of CFN may result in intentional inoculation of humans for disease diagnosis and therapeutic purposes or unintentional penetration of CFN via inhalation, ingestion, adsorption, or other means. Therefore, understanding the potential cytotoxicity of CFN may pave the way for their future biomedical and agricultural applications. This review scrutinized CFN biocompatibility, possible effects, and cytotoxic mechanisms in different biological systems. Literature indicates CFN toxicity is linked with their size, synthesizing methods, coating materials, exposure time, route of administration, and test concentrations. Some in vitro cytotoxicity tests showed misleading results of CFN potency; this might be due to the interaction of CFN with cytotoxicity assay regents. To date, published research indicates that the biocompatibility of CFN outweighed its cytotoxic effects in plant or animal models, but the opposite outcomes were observed in aquatic Zebrafish.
Disease X: Combating the next pandemic needs the nifty wastewater-based epidemiology tool
(Lippincott Williams and Wilkins, 2023-11-01) Mohd I.; Gohil N.V.; MohanaSundaram A.S.; Gurajala S.; Fuentes Gandara F.; Islam, Md Rabiul; School of Pharmacy
Insights on melatonin as an active pharmacological molecule in cancer prevention: What’s new?
(Bentham Science Publishers, 2019-01-01) Bjørklund G.; Rajib, Samiul Alam; Saffoon N.; Pen J.J.; Chirumbolo S.; School of Pharmacy
Along with playing an important role in circadian rhythm, melatonin is thought to play a significant role in preventing cells from damage, as well as in the inhibition of growth and in triggering apoptosis in malignant cells. Its relationship with circadian rhythms, energetic homeostasis, diet, and metabolism, is fundamental to achieve a better comprehension of how melatonin has been considered a chemopreventive molecule, though very few papers dealing with this issue. In this article, we tried to review the most recent evidence regarding the protective as well as the antitumoral mechanisms of melatonin, as related to diet and metabolic balance. From different studies, it was evident that an intracellular antioxidant defense mechanism is activated by upregulating an antioxidant gene battery in the presence of high-dose melatonin in malignant cells. Like other broad-spectrum antioxidant molecules, melatonin plays a vital role in killing tumor cells, preventing metastasis, and simultaneously keeping normal cells protected from oxidative stress and other types of tissue damage.
Classification of depression, internet addiction and prediction of self-esteem among university students
(Institute of Electrical and Electronics Engineers Inc., 2019-12-01) Rubaiyat, Nadia; Apsara, Anika Islam; Chaki D.; Arif, Hossain; Israt, Lamiah; Kabir, Lamiya ; Rabiul Alam, Md. Golam; Department of Computer Science and Engineering
Machine learning is massively used in the prediction of cognitive and psychological features in recent times. This research aims to find the predictability between leading disorders like Internet addiction, depression, and low self-esteem. For this purpose, 461 undergraduate students have been selected arbitrarily from several educational institutions of Dhaka city and voluntarily completed a standard questionnaire that was prepared based on the self-reported measures concerning the disorders mentioned above. Different standard psychometric scales such as Internet Addiction Test (IAT) by Dr. Kimberly Young, Self-esteem Scale by M. Rosenberg, PROMIS Emotional Distress Depression short-scale by PROMIS Health Organization have been used in the correlational survey. The internal consistency of the data has been proven by Cronbach alpha. Subsequently, the Shapiro-Wilk Normality test revealed the data to be non-parametric. Several essential features have been extracted to reduce the redundancy from the data using minimum-redundancy-maximum-reduction (mRMR) and Chi-square test. A prediction model has been devised using Logistic Regression, Naive Bayes, Random Forest, C4.5 Decision Tree, and k-Nearest Neighbors. The experimental result shows that Internet addiction and depression are interconnected with self-esteem, and thereby, the prediction model can be built to reduce the severity of these disorders.
LSTM based approach for diabetic symptomatic activity recognition using smartphone sensors
(Institute of Electrical and Electronics Engineers Inc., 2019-12-01) Bahadur E.H.; Kadar Muhammad Masum A.; Barua A.; Rabiul Alam, Md. Golam; Zaman Chowdhury M.A.U.; Alam M.R.; Department of Computer Science and Engineering
Being concerned about the rising rate of the usage level of smartphone for last few years, researchers are striving to let out the disguised assets of smartphones. Regarding this phrase, the embedding of a variety of sensors such as accelerometer sensor, gyroscope sensor, humidity sensor etc. has attained the considerable attention of researchers for facilitating the human activity recognition task employing the sensor's applications. In this paper, we practiced the Long Short-Term Memory a.k.a. LSTM deep learning model with an aim to recognize thirteen human activities stated as walking, walking upstairs, walking downstairs, sitting, standing, jogging, squatting in the toilet, fallen down, lying, cycling, drinking, eating and genital itching. We opted for these activities with an intention of early diagnosing of diabetes in near future. We amassed data from four sensors stated accelerometer sensor, gyroscope sensor, humidity sensor and temperature sensor subjecting ten volunteers implying a frequency of 10Hz. The data was attained using an android application which was developed by us for the purpose of accumulation of sensor data from the smartphone.