Browsing by Author "Hasan, Mehedi"

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A comparative study of object detection models for Real Time Application in Surveillance Systems
(BRAC University, 2022-01) Alam, Saimun; Ahmed, Mahim Uddin; Hasan, Mehedi; Islam, Md. Morshedul; Arnob, Shahed Mehrab; Hossain, Dr. Muhammad Iqbal; Seraj, Mehnaz
In this paper, we attempted to give an overview based on thorough research and test ing of the latest object detection methods with an aim to help developers to build a Real Time Responsive CCTV Camera Model. As we welcome the 5G network worldwide, the coming future will surely be heavily dependent on smart machines and internet-based technologies. Therefore, we can assume that our daily life secu rity will also be managed by smart devices. In this research work, our aim is to do a thorough research on the latest models so that one can be chosen to implement and minimize the existing security system into a one device depended security system. The device we often use for surveillance and security purpose is CCTV camera. However, most of the cameras are not connected to the internet also they are not responsive. Which means, the outputs from the cameras cannot be used for further analysis by machines and can only be saved for manual check by humans. Our re search will help to develop such a system that will make the camera act like more of a security guard itself rather than a video recording device only. As we need to find out the best suited detection method we will check the accuracy, implementation process, power usage, GPU and CPU usage and then choose between previously invented methods such as HOG (Histogram of Oriented Gradients), Viola Jones De tector or the latest inventions such as R-CNN, SSD YOLO. Finally, this research will help the security device developers to choose the best algorithm and build cost efficient systems. Also, the future works of the research will help to create alert for abnormal presence of unknowns under surveillance automatically. Overall, we can say that our research will help to build more affordable, efficient and digitally secured home, offices, schools or any other buildings and even roads and highways in coming days.
A Computer Vision System for the Categorization of Citrus Fruits Using Convolutional Neural Network
(2021 International Symposium on Electronics and Smart Devices (ISESD), IEEE, 2021-08-12) Hasan, Md. Mehedi; Salehin, Imrus; Moon, Nazmun Nessa; Kamruzzaman, T. M.; -Ul-Islam, Baki; Hasan, Mehedi
The recognition of citrus fruit is one of the most challenging and crucial measures in citrus yield mapping. Several artificial vision systems have been proposed to solve the issue of fruits recognition problem with sundry effects. In this study, we developed an automated system to categorize citrus fruit images using Convolutional Neural Network. We categorized two different citrus fruits, Orange (Citrus Sinensis) and Kinnow (Citrus Reticulate). Firstly the images of Orange and Kinnow were collected and preprocessed. Secondly, the fruit images and their background were segmented by image segmentation and edge detection. Four main features of Orange and Kinnow fruit were extracted based on image segmentations such as fruit size, surface color fruit shape and fruit surface defects. These features were examined through Convolutional Neural Network. We implemented three separate Convolutional Neural Network models to further experiment and tested recognition rates for different parameters. We have used the classical measurements including precision, recall, F1 score, ROC and accuracy for performance evaluation. Among the three experimented models, the third model was outperformed by 92.25% percent accuracy.
A Deep Computer Vision Approach to Detect Eggplant Diseases
(Daffodil International University, 2021-09) Hasnat, Abul; Hasan, Mehedi
Farming inputs are very vital, yet they are not always available to farmers. The goal of this research was to construct an intelligence system utilizing the recognition of eggplant diseases utilizing picture treatment techniques in order to educate farmers about eggplant sickness. The lack of data for both disorders encouraged us to develop a standard dataset for two prominent diseases in the laboratory. Pre-trained Eggplant-disease classification Visual Geometry Group 16 (VGG16) resnet50 and inceptionV3 architectures are used in our work. Further, VGG16 was utilized as the 8th convolution layer feature extractor and these features were used to graduate illnesses. An equivalent or in some cases a greater accuracy was shown in the analysis. There have been proposed possible causes for variations in interclass precision and future direction. Our highest accuracy is achieved by VGG16 the accuracy rate is 99.55%.
A review of microplastic threat mitigation in Asian lentic environments
(Scopus, 2024-01-31) Sadia, Moriom Rahman; Hasan, Mehedi; Islam, Abu Reza Md. Towfiqul; Jion, Most. Mastura Munia Farjana; Masud, Md Abdullah Al; Rahman, Md. Naimur
Microplastic (MP) pollution has evolved into a significant worldwide environmental concern due to its widespread sources, enduring presence, and adverse effects on lentic ecosystems and human well-being. The growing awareness of the hidden threat posed by MPs in lentic ecosystems has emphasized the need for more in-depth research. Unlike marine environments, there remain unanswered questions about MP hotspots, ecotoxic effects, transport mechanisms, and fragmentation in lentic ecosystems. The introduction of MPs represents a novel threat to long-term environmental health, posing unresolved challenges for sustainable management. While MP pollution in lentic ecosystems has garnered global attention due to its ecotoxicity, our understanding of MP hotspots in lakes from an Asian perspective remains limited. Hence, the aim of this review is to provide a comprehensive analysis of MP hotspots, morphological attributes, ecotoxic impacts, sustainable solutions, and future challenges across Asia. The review summarizes the methods employed in previous studies and the techniques for sampling and analyzing microplastics in lake water and sediment. Notably, most studies concerning lake microplastics tend to follow the order of China > India > Pakistan > Nepal > Turkey > Bangladesh. Additionally, this review critically addresses the analysis of microplastics in lake water and sediment, shedding light on the prevalent net-based sampling methods. Ultimately, this study emphasizes the existing research gaps and suggests new research directions, taking into account recent advancements in the study of microplastics in lentic environments. In conclusion, the review advocates for sustainable interventions to mitigate MP pollution in the future, highlighting the presence of MPs in Asian lakes, water, and sediment, and their potential ecotoxicological repercussions on both the environment and human health.
A Smart Polluted Water Overload Drainage Detection and Alert System
(2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC),IEEE, 2021-06-09) Salehin, Imrus; Islam, Baki-Ul-; Noman, S. M.; Hasan, Md. Mehedi; Dip, Sadia Tamim; Hasan, Mehedi
A smart city constructed through the Internet of Things is a great and best medium nowadays. In our study, we have designed an advanced and automated device that can identify overloaded polluted drainage, which is responsible for water-borne disease and unexpected floods. We are using a smart ultrasonic sensor with an Ethernet shield integrated Arduino UNO. This proposed model's most vital side is the remote data access system using IP address and the webserver. This research is adequate for city corporations to advance their city, Develop their city to be more delighter, and reduce their fund used for extra human resources of city corporations cleaner. For data access methods, we are using the city Wi-Fi router and a monitoring station. We also proposed a separate web page designed to show the report. Materials used here are very uncomplicated and inexpensive but adequate to make an advanced automation intelligence system. In this modern scientific era, to lead a comfortable life, an automation system has helped develop a city more than the old system.
ALPR: ResNet50 powered Bangla License Plate Detectionand OCR by Root Mean Square Propagation Optimizer and Linear SVM Classifier
(Scopus, 2024-06-10) Chowdhury, Abdulla Nasir; Summit, Samya Pal; Laskar, Md. Fuad Ahmed; Chowdhury, Gulam Mahfuz; Chowdhury, Ishmam Ahmed; Hasan, Mehedi
This paper implements the MATLAB Image Processing Toolbox in detecting the license plate region using several user-defined functions in order to pre-process and process the image up until the point of extraction of characters. The extracted characters were then classified by utilizingResNet50 from the Deep Learning Toolbox of MATLAB, custom training it on above a thousand images of Bangla and English characters and numbers alongside possible categories of noise extracted from the ROI after processing the image which resulted in a datastore of 103 total categories. The output is converted to a string and saved in an excel sheet to be accessed later on. In this ALPR model, the model will scan through the images of vehiclesfrom a folder in a destination specified by the code to identify the license plates and characters and perform necessary actions on them. The aim of this paper is to properly implement the Image Processing Toolbox by MATLAB in order to identify the Region of Interest and study the performance of the Linear SVM(Support Vector Machine) classifier with ResNet50 when it comes to Bangla OCR. The training and validation accuracy achieved by using the Root Mean Square Optimizer was 97.57%. The final accuracies and precision achieved while testing the model on 50% of the image dataset was 99.2%. Moreover, theER (Error Rate) and FPR (False Positive Rate)were limited within 0.02%. The model scored 100% on F1 scores and Matthews Correlation Coefficient for every category of image classified
An Investigation on the Comparative Sensor Performance of Polysilicon Nanowires with Single Crystal Silicon Nanowire
(East West University, 4/30/2017) Shargen, Anamika; Hasan, Mehedi; Sazzad, Salim
We study for the first time the effect of nanowire thickness and doping concentration on the electrical characteristics of single crystal and polycrystalline silicon nanowire biosensors. For nanowire thicknesses of 100 nm and 75 nm, a plausible sub-threshold slope around 100 mV/decade for a viable biosensor operation only achieve if doping concentration is 2×1016/cm3 or below both for single crystal and poly Si nanowires. For a 50nm nanowire thickness a relatively wide doping concentration range with a maximum doping up to 4×1017/cm3 choose for biosensor design while maintaining decent sub-threshold characteristics. The widest range of doping concentrations choose for 25nm and 10nm nanowire thickness with a maximum doping up to 1018/cm3 while maintaining a promising sub-threshold slope around 95 mV/decade for a viable biosensor design using single crystal and polycrystalline silicon nanowires. In general poly Si NW shows inferior characteristics than single crystal Si NW. However, for 10nm Si NW single crystal & poly Si NW show same sub-threshold slopes at all doping densities. Considering the fact that spacer etch process provides the cheapest & mass manufacturable platform for biosensor fabrication using poly Si material in comparison to the available single crystal platforms. It decides from this work that poly Si NW biosensor with Si thickness ≤ 10nm is the possible commercial route of sensor fabrication.
An IoT based Environment Monitoring System
(IEEE, 2021-01-18) Hassan, Mosfiqun Nahid; Islam, Mohammed Rezwanul; Faisal, Fahad; Semantha, Farida Habib; Siddique, Abdul Hasib; Hasan, Mehedi
In recent years, people are getting more conscious of the environment they are living in. This consciousness is driving the need to develop a reliable environmental monitoring system. An environmental air quality monitoring system also has industrial application. In mining or in heavy industry, there is a possibility of air contamination by different harmful gases. In such hazardous situations, an environmental monitoring system can potentially save the life of the workers. In such large-scale sensor deployment, there are data collection, data management, connection, and power consumption issues. IoT technology is specifically suited for this sort of need. This paper presents an IoT based framework that effectively monitors the change in an environment using sensors, microcontroller, and IoT based technology. Users can monitor temperature, humidity, detect the presence of harmful gases both in the indoor and outdoor environment using the proposed module. The data is stored in the web server and the user can access the data anywhere in the world through an internet connection. In the proposed work a web application is developed to provide vital information to the user. The user can also set up a notification for critical changes in the sensor data. In comparison to other closely related systems, the proposed system is a low-cost one, accurate and user friendly. It is also cloud-based and has easy monitoring and data visualization modules. The system has been evaluated in different stages. After testing all the functions in different conditions, it shows a high degree of accuracy and reliability.
Analysis and risk evaluation of soil microplastics in the Rohingya refugee camp area, Bangladesh: A comprehensive study
(Scopus, 2024) Hossain, Aowlad; Adham, Md. Ibrahim; Hasan, Mehedi; Ali, Mir Mohammad; Siddique, Md. Abu Bakar; Senapathi, Venkatramanan; Islam, Abu Reza Md. Towfiqul
The global concern over the pollution-induced by microplastics (MPs) has intensified due to its adverse effects on the environment, particularly in terrestrial ecosystems, where it poses potential threats to soil quality and resident species. However, there is a noticeable research gap regarding soil MPs in dumping sites, specifically within the Rohingya Refugee Camp (RRC), the world's largest humanitarian crisis located in Bangladesh. The main objective of this study is to assess soil MPs' abundance, spatial distribution, and inherent risks in the RRC. The investigation involved extracting MPs from ten soil sampling sites in Kutupalong RRC, home to Rohingya refugees who sought refuge in Bangladesh following the 2017 ethnic atrocities in Northern Rakhine State, Myanmar. Stereomicroscopy and Fourier transform infrared spectroscopy were employed for identification purposes. The concentration of MPs in the study area varied from 67 to 126 (items/kg) (dry weight), with a mean concentration of 103.80 ± 20.671 (items/kg). MPs with sizes <0.5 mm constituted the majority at 83 %, with fragments (68 %) being the prevailing shape, and transparent (63 %) as the most abundant color. Predominant polymers included polyethylene (53 %) and polypropylene (46 %). Negative correlations were observed between MP abundance and pH and moisture content (p<0.05), while a positive correlation was found between MP abundance and organic matter. PCA results suggested that human-induced inappropriate waste and air deposition are the primary sources of soil MP pollution. Contamination factor values suggested moderate pollution with MPs in the study area. According to the geo-accumulation index (Igeo), the area was classified as pollution grade II, signifying 'uncontaminated to moderately contaminated.' However, pollutant load index and potential ecological risk index indicated Hazard Level-I and Pollution Grade-I, respectively. This study illuminates the contamination scenario with MPs, underscoring concerns for eco-environmental safety and providing crucial data for future investigations into MPs in terrestrial dumping habitats.
Analysis and risk evaluation of soil microplastics in the Rohingya refugee camp area, Bangladesh: A comprehensive study
(Regional Studies in Marine Science, 2024-12) Hossain, Aowlad; Adham, Md. Ibrahim; Hasan, Mehedi; Ali, Mir Mohammad; Siddique, Md. Abu Bakar; Senapathi, Venkatramanan; Islam, Abu Reza Md. Towfiqul
The global concern over the pollution-induced by microplastics (MPs) has intensified due to its adverse effects on the environment, particularly in terrestrial ecosystems, where it poses potential threats to soil quality and resident species. However, there is a noticeable research gap regarding soil MPs in dumping sites, specifically within the Rohingya Refugee Camp (RRC), the world's largest humanitarian crisis located in Bangladesh. The main objective of this study is to assess soil MPs' abundance, spatial distribution, and inherent risks in the RRC. The investigation involved extracting MPs from ten soil sampling sites in Kutupalong RRC, home to Rohingya refugees who sought refuge in Bangladesh following the 2017 ethnic atrocities in Northern Rakhine State, Myanmar. Stereomicroscopy and Fourier transform infrared spectroscopy were employed for identification purposes. The concentration of MPs in the study area varied from 67 to 126 (items/kg) (dry weight), with a mean concentration of 103.80 ± 20.671 (items/kg). MPs with sizes <0.5 mm constituted the majority at 83 %, with fragments (68 %) being the prevailing shape, and transparent (63 %) as the most abundant color. Predominant polymers included polyethylene (53 %) and polypropylene (46 %). Negative correlations were observed between MP abundance and pH and moisture content (p<0.05), while a positive correlation was found between MP abundance and organic matter. PCA results suggested that human-induced inappropriate waste and air deposition are the primary sources of soil MP pollution. Contamination factor values suggested moderate pollution with MPs in the study area. According to the geo-accumulation index (Igeo), the area was classified as pollution grade II, signifying 'uncontaminated to moderately contaminated.' However, pollutant load index and potential ecological risk index indicated Hazard Level-I and Pollution Grade-I, respectively. This study illuminates the contamination scenario with MPs, underscoring concerns for eco-environmental safety and providing crucial data for future investigations into MPs in terrestrial dumping habitats.
Analysis and Risk Evaluation of Soil Microplastics in the Rohingya Refugee Camp Area, Bangladesh: A Comprehensive Study
(Elsevier, 2024-12-15) Hossain, Aowlad; Adham, Md. Ibrahim; Hasan, Mehedi; Ali, Mir Mohammad; Siddique, Md. Abu Bakar; Senapathi, Venkatramanan; Islam, Abu Reza Md. Towfiqul
The global concern over the pollution-induced by microplastics (MPs) has intensified due to its adverse effects on the environment, particularly in terrestrial ecosystems, where it poses potential threats to soil quality and resident species. However, there is a noticeable research gap regarding soil MPs in dumping sites, specifically within the Rohingya Refugee Camp (RRC), the world's largest humanitarian crisis located in Bangladesh. The main objective of this study is to assess soil MPs' abundance, spatial distribution, and inherent risks in the RRC. The investigation involved extracting MPs from ten soil sampling sites in Kutupalong RRC, home to Rohingya refugees who sought refuge in Bangladesh following the 2017 ethnic atrocities in Northern Rakhine State, Myanmar. Stereomicroscopy and Fourier transform infrared spectroscopy were employed for identification purposes. The concentration of MPs in the study area varied from 67 to 126 (items/kg) (dry weight), with a mean concentration of 103.80 ± 20.671 (items/kg). MPs with sizes <0.5 mm constituted the majority at 83 %, with fragments (68 %) being the prevailing shape, and transparent (63 %) as the most abundant color. Predominant polymers included polyethylene (53 %) and polypropylene (46 %). Negative correlations were observed between MP abundance and pH and moisture content (p<0.05), while a positive correlation was found between MP abundance and organic matter. PCA results suggested that human-induced inappropriate waste and air deposition are the primary sources of soil MP pollution. Contamination factor values suggested moderate pollution with MPs in the study area. According to the geo-accumulation index (Igeo), the area was classified as pollution grade II, signifying 'uncontaminated to moderately contaminated.' However, pollutant load index and potential ecological risk index indicated Hazard Level-I and Pollution Grade-I, respectively. This study illuminates the contamination scenario with MPs, underscoring concerns for eco-environmental safety and providing crucial data for future investigations into MPs in terrestrial dumping habitats.
Analysis of Learning Environment and Influence of Environment among Problem Solvers
(Daffodil International University, 2020-05) Hasan, Mehedi
As we are growing up in the competitive world of science and technologies. Computer programmers are doing a great job overcoming the progress of science and technologies. The study includes the findings of the hard work, struggles, and back and forth of being a competitive programmer. We focus on the learning environment and the effects of the environment over programmers. The methodologies we used for our research is quite simple. We used questionnaires to collect our qualitative and quantitative data to carry our study. Anaconda, a big framework of python is being used to clean, process, and analyze our data. We used some weighted value on each of the qualitative data fields to transform into quantitative data. Then we build up some relationship among the data field using both manual and scikit learn and matplotlib to visualize our findings. What we found through the research is that every individual programmer has to go through a lot of hard work and struggles over the journey of being a professional programmer. They have to overcome a lot of weaknesses and pull themselves up from a lot of distractions from the environment. Sharing knowledge tendencies among them is very common and they found competitive programming helpful over their journey of being a programmer. We do appreciate the hard work and struggle they have made through their journey and respect their sharing tendency. We should try to create the best learning environment and ensure the best support for them.
Applications of artificial intelligence in drug discovery and pharmacy practice: a review
(BRAC University, 2024-09) Hasan, Mehedi; Raj, Asef
Artificial intelligence (AI) is revolutionizing the pharmaceutical industry, through enhancing drug discovery and improving pharmacy practice, presenting novel solutions for healthcare’s complex challenges. Reviewing everything from target identification to medication excipient and dose form prediction, the paper explains how artificial intelligence is revolutionizing the pharmaceutical industry. Artificial intelligence models have the potential to decrease animal testing by assisting in the identification of disease targets, enhancing virtual screening of drug candidates, and even supporting toxicity prediction and pharmacokinetics, AI is transforming pharmacy practice with applications in dose adjustment, adverse drug reaction monitoring, and patient compliance, thus personalizing and improving patient care. In pharmacy practice, AI enables dose optimization and adverse drug reaction detection as well as personalized medicine along with aiding in medication adherence and provision of tele-pharmacy services. Despite the wide range of benefits AI offers, algorithm bias, data privacy and regulatory questions are challenges that need to be faced for a proper adoption. Finally, the review compares its use in developed countries to expand applications in Bangladesh, underlining how AI can potentially improve efficiency and patient safety as well as outcomes on a global scale. Ultimately, the potential for AI to revolutionize pharmaceutical research, clinical practice and healthcare delivery is immense.
Association between the frequency of television watching and overweight and obesity among women of reproductive age in Nepal: Analysis of data from the Nepal Demographic and Health Survey 2016
(PLOS ONE, 2/10/2020) Gupta, Rajat Das; Haider, Shams Shabab; Hashan, Mohammad Rashidul; Hasan, Mehedi; Sutradhar, Ipsita; Sajal, Ibrahim Hossain; Joshi, Hemraj; Haider, Mohammad Rifat; Sarker, Malabika
Background The prevalence of overweight and obesity, particularly among women, is increasing in Nepal. Previous studies in the South Asia have found television watching to be a risk factor for overweight and obesity among women of reproductive age. However, this association had not been studied in the context of Nepal. This study aims to identify the association between frequency of television watching and overweight and obesity among Nepalese women of reproductive age. Methods This cross-sectional study utilized the Nepal Demographic and Health Survey 2016 (NDHS 2016) data. A total weighted sample of 6,031 women were included in the final analyses. The women were 15–49 years of age and were either not pregnant or had not delivered a child within the two months prior to the survey. Body mass index (BMI) was the primary outcome of this study, which was categorized using an Asia-specific cutoff value. Normal and/ or underweight was defined as a BMI <23.0 kg/m2 , overweight was defined as a BMI between 23.0 kg/m2 and <27.5 kg/m2 , and obesity was defined as a BMI �27.5 kg/m2 . Frequency of watching television was the main independent variable of this study, which was divided into the following three categories: not watching television at all, watching television PLOS ONE | https://doi.org/10.1371/journal.pone.0228862 February 10, 2020 1 / 13 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Das Gupta R, Haider SS, Hashan MR, Hasan M, Sutradhar I, Sajal IH, et al. (2020) Association between the frequency of television watching and overweight and obesity among women of reproductive age in Nepal: Analysis of data from the Nepal Demographic and Health Survey 2016. PLoS ONE 15(2): e0228862. https:// doi.org/10.1371/journal.pone.0228862 Editor: Cindy Gray, University of Glasgow, UNITED KINGDOM Received: July 20, 2019 Accepted: January 25, 2020 Published: February 10, 2020 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0228862 Copyright: © 2020 Das Gupta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The dataset of NDHS 2016 is available at the Demographic and Health less than once a week, and watching television at least once a week. Multilevel ordered logistic regression was conducted to find the factors associated with overweight and obesity. A p-value <0.05 was considered significant in the final model. Results Around 35% of the participants were overweight or obese (overweight: 23.7% and obese: 11.6%). A majority of the study participants was aged between 15 and 24 years (36.5%), and resided in an urban area (63.2%), Province No. 3 (22.3%), and the Terai ecological region (49.5%). Around one-third (34.0%) of the participants received no formal education while an almost similar proportion (35.5%) completed secondary education. Approximately half of the study participants (50.6%) reported watching television at least once a week, whereas more than a quarter (28.7%) of them did not watch television at all. Women who watched television at least once a day had a higher prevalence of overweight and obesity than the other groups (p-value <0.0001). Women who watched television at least once a week were 1.3 times more likely to be overweight or obese in comparison to women who never watched television (Adjusted Odds Ratio (AOR): 1.3, 95% CI: 1.0–1.7; p-value <0.05). In the urban areas, women who watched television at least once a week were 40% more likely to be overweight or obese than those who did not watch television at all (AOR: 1.4, 95% CI: 1.1–1.7; p-value <0.01). No significant association between overweight and obesity and the frequency of viewing television was observed in the rural area. Conclusions Watching television at least once a week is associated with overweight and obesity in women of reproductive age living in the urban areas of Nepal. Public health promotion programs should raise awareness among women regarding harmful health consequences of sedentary lifestyle due to television watching.
Association of frequency of television watching with overweight and obesity among women of reproductive age in India: Evidence from a nationally representative study
(PLOS ONE, 8/29/2019) Gupta, Rajat Das; Haider, Shams Shabab; Sutradhar, Ipsita; Hashan, Mohammad Rashidul; Sajal, Ibrahim Hossain; Hasan, Mehedi; Haider, Mohammad Rifat; Sarker, Malabika
Background For women of reproductive age, overweight and obesity are an established risk factor for several medical complications. To address the increasing rate of obesity in India through public health awareness programs, the association between common behaviors and overweight and obesity needs to be investigated. This study aims to determine whether there is any association between the frequency of television watching and overweight and obesity among women of reproductive age (15–49 years) in India. Methods This is a cross-sectional study that utilized data from the National Family Health Survey (NFHS-4), which utilized a nationally representative sample from all 29 states and 7 union territories of India. The survey itself followed a two-staged stratified random sampling technique. The primary outcome of interest was overweight (23.0 kg/m2 to <27.5 kg/m2) and obesity (≥27.5 kg/m2), measured by using the Asian body mass index cut-off. The major explanatory variable was the frequency of television watching, measured in days per week. Sample weight of NFHS-4 was adjusted during the analysis. Multilevel ordered logistic regression was conducted to identify the factors associated with overweight and obesity. To show the strength of association, both the unadjusted Crude Odds Ratio (COR) and the Adjusted Odds Ratio (AOR) were reported with a 95% confidence interval (CI). A p-value<0.05 was considered statistically significant. Results The analysis included weighted data from 644,006 Indian women of reproductive age (15–49 years). Among the respondents, 33.5% were overweight or obese (BMI ≥23.0 kg/m2). The prevalence of overweight and obesity increased with age (p-value <0.0001) and almost half of the women aged 35–49 years were either overweight or obese (48.6%). The prevalence was significantly higher among those living in an urban area compared to a rural area (urban 46.5% vs. rural 26.5%; p-value <0.001). The prevalence of overweight and obesity increased with the frequency of watching television and was the highest among the individuals who reported watching television almost every day (p-value <0.0001). Women watching television almost every day had 24% (AOR: 1.24, 95% CI: 1.21–1.26; p-value <0.001) increased odds of being overweight and obese compared to their counterparts who never watched television. Conclusions This study found that the likelihood of being overweight and obese significantly increased with the frequency of watching television; likely due to physical inactivity during leisure time. Further studies should examine the physical activity and food habits of this target group. Public health promotion programs in India should raise awareness regarding the harmful effects of the sedentary lifestyle associated with watching television.
Automated Traffic Detection System Based on Image Processing
(Al-Kindi Center for Research and Development, 2020-06-30) Faisal, Fahad; Das, Sumon Kumar; Siddique, Abdul Hasib; Hasan, Mehedi; Sabrin, Samia; Hossain, Chowdhury Akram; Tong, Zhou
This paper proposes a low-cost automated traffic detection system based on image processing. Dhaka is one of the crowded cities in the world with highly challenging traffic system. There is substantial lack of awareness among the drivers of transport system. As a result, citizens do not follow the rules and regulation while driving in Dhaka city. The tendency of violating the traffic regulation is noticeable throughout the country. As a result, the whole traffic system collapses very often and sometimes it ends-up with severe accidents. In recent days, the government has taken different initiatives including enlargement of pedestrian walkways, building new flyovers and foot-over bridges, expansion of existing roads. But, violation still the outcome of all these initiatives could not improve the situation significantly. The proposed system will automatically detect the traffic through live streaming video so that the detected images can be used to detect traffic violation. Later on, the law enforcement agency will be able to take necessary legal steps based on the stored information on the database.
Bangla Speaker Accent Variation Detection by MFCC Using Recurrent Neural Network Algorithm
(Springer, 2020-03-04) Mamun, Rezaul Karim; Abujar, Sheikh; Islam, Rakibul; Been Md. Badruzzaman, Khalid; Hasan, Mehedi
There are a number of languages accent differential applications that detect the different accents in assorted languages. The studies which have done before most of them are based on the English language and different languages throughout the world. A few researches have been performed in Bangla regional language accent differential applications, which is not conclusive for the system to be able to manage Bangla accented speakers. In this paper, we report regional language accent detection experiments of different types of Bangladesh. We demonstrate a strategy to observe Bangladeshi different accents which exploit Mel frequency cepstral coefficient (MFCC) and recurrent neural network (RNN). Listening from the people of different places in Bangladesh creates an accent differentiation results performed by the speakers. This experimental result shows the adaptation of the people to adapt of the regional languages.
BanglaBait: using transformers, neural networks & statistical classifiers to detect clickbaits in New Bangla Clickbait Dataset
(BRAC University, 2022-01) Mahtab, Motahar; Haque, Monirul; Hasan, Mehedi; Akon, Mujtahid Al-Islam; Mostakim, Moin
The art of luring us to click on certain content by exploiting our curiosity is recognized as clickbait. Clickbait might be aggravating at times because it is misleading. Several studies have worked on the detection of clickbait in online platforms as we transition from the Information Age to the Age of AI. Nonetheless, predicting clickbait in Bengali new articles is still a work in progress. Here, we use deep learning, the process of extracting pattern or feature from data using neural networks, to determine whether an online Bengali article is clickbait or not. We scrape data from online Bengali news articles, manually annotate them and employ deep nerural network architectures like CNN, Bi-LSTM,Bi-GRU and pre-trained fine-tuning language representation approaches –i.e. BERT, BanglaBERT, M-BERT to provide inputs for various types of classifiers. Finally, we evaluate the classifiers’ outputs and choose the best outcome to predict clickbait in Bengali news articles.
BGMEA President: Workers will lose jobs if apparel prices not increased
(Dhaka Tribune, 2019-02-09) Hasan, Mehedi
CIP,ETP,WTP Plant, Ammonia plant and distribution department of Lovello Ice-cream factory
(Daffodil International University, 2019-05-21) Hasan, Mehedi
The Internship was conducted at Taufika Foods and Agro Industries Ltd. in Lovello Ice Cream from 09 February, 2019 to 15 March, 2019. This factory mainly manufactures difference types of Ice Cream. To prepare Ice cream is used to milk, sugar, stabilizers, emulsifiers, water, food grade flavor, food grade color, fruit pulp, skim milk powder, butter oil, coconut oil, glucose. After preparing the mixing tank of ice cream pasteurized and homogenize. In Ice cream they mainly check physical, chemical, microbiological test for quality control. Major objective of this report is to identify the actual health hazard and quality control of Lovello Ice cream Ltd and also develop the production and quality control. In this regard, Customers are very important for every business. My report is based on the hazardous free and qualified Lovello Ice cream. The report contains information of the organization itself, Sanitation, hygienic facilities of the overall industries and Collected qualified raw materials. Also involve the raw materials test, safe production, ultimately quality check ofthe final product than marketing. Also I have discussed about safe production and quality control of Lovello Ice cream.