13 research outputs found
Sensitivity study for the simulation of Tornado/Nor’wester during pre-monsoon season over Bangladesh using high resolution WRF-ARW model
No full textThis thesis is submitted to the Department of Physics, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Physics, August 2016.Cataloged from PDF Version of Thesis.Includes bibliographical references (pages 121-127).Comprehensive sensitivity analyses on physical parameterization schemes of Weather Research and Forecasting (WRF-ARW V3.5.1) Model have been carried out for the simulation of squall/nor'wester on 07 April 2012, 28 April 2012, 27 April 2014 and 15 May 2014 over Bangladesh. Final Reanalysis (FNL) data (l° x l°) from National Centre for Environment Prediction (NCEP), is used as initial and lateral boundary conditions which is updated at six hourly interval i.e. the model is initialized with 0000, 0600, 1200 and 1800 UTC initial fields of corresponding dates. The NCFP FNL data is interpolated to the model horizontal and vertical grids. The model is configured in a single domain, having 3 km horizontal grid spacing with 173x225 grids in the east-west and north-south directions and 15 vertical levels. By using Grads software, the different meteorological parameters, stability indices and energies are obtained, which are related to Nor'wester/squall line. The six different MP schemes used in this research are Kessler, Lin et al., WSM6, Thompson, SBU and WDM6. The meteorological parameters, which have been analyzed, are Wind Speed at 10m Level, Temperature at 2m Level, Sea Level Pressure (SLP), Relative Humidity (RH), Maximum Reflectivity, Latent Heat, MCAPE, MCIN, Total Total Index ('IT), Vertical Total Index (VT). Cross Total Index (CT), K Index (KI), Lifting Condensation Level (LCI.) and Level of Free Convection (LFC) at Dhaka, Chittagong, Sylhet, Khulna and Barisal stations. Significant changes are found by all the MPs coupling with CPs in all the parameters during the time of occurrence of squall/gusty wind. Significant changes are found in all the parameters for all MPs coupling with CPs at Dhaka after 0500 UTC and at Chittagong after 0900 UTC of 7 April 2012, which is delayed almost one and half hours. BMJ has simulated reflectivity at a particular time but KF simulated reflectivity wide range of time over Dhaka and Chittagong on 7 April 2012. Significant changes are found by all the MPs coupling with CPs in all the parameters during the time of occurrence of squall at Barisal and Chittagong on 15 May 2014.Busrat JahanMaster of Science in Physic
Offline optical character recognition (OCR) method: An effective method for scanned documents
No full textA Comprehensive Review on Lane Marking Detection Using Deep Neural Networks
No full textLane marking recognition is one of the most crucial features for automotive vehicles as it is one of the most fundamental requirements of all the autonomy features of Advanced Driver Assistance Systems (ADAS). Researchers have recently made promising improvements in the application of Lane Marking Detection (LMD). This research article has taken the initiative to review lane marking detection, mainly using deep learning techniques. This paper initially discusses the introduction of lane marking detection approaches using deep neural networks and conventional techniques. Lane marking detection frameworks can be categorized into single-stage and two-stage architectures. This paper elaborates on the network’s architecture and the loss function for improving the performance based on the categories. The network’s architecture is divided into object detection, classification, and segmentation, and each is discussed, including their contributions and limitations. There is also a brief indication of the simplification and optimization of the network for simplifying the architecture. Additionally, comparative performance results with a visualization of the final output of five existing techniques is elaborated. Finally, this review is concluded by pointing to particular challenges in lane marking detection, such as generalization problems and computational complexity. There is also a brief future direction for solving the issues, for instance, efficient neural network, Meta, and unsupervised learning
A deep learning approach for lane marking detection applying encode-decode instant segmentation network
No full textA lot of people suffer from disability and death due to unintentional road accidents, which also result in the loss of a significant amount of financial assets. Several essential features of Advanced Driver Assistance Systems (ADAS) are being incorporated into vehicles by researchers to prevent road accidents. Lane marking detection (LMD) is a fundamental ADAS technology that helps the vehicle to keep its position in the lane. The current study employs Deep Learning (DL) methodologies and has several research constraints due to various problems. Researchers sometimes encounter difficulties in LMD due to environmental factors such as the variation of lights, obstacles, shadows, and curve lanes. To address these limitations, this study presents the Encode-Decode Instant Segmentation Network (EDIS-Net) as a DL methodology for detecting lane marking under various environmental situations with reliable accuracy. The framework is based on the E-Net architecture and incorporates combined cross-entropy and discriminative losses. The encoding segment was split into binary and instant segmentation to extract information about the lane pixels and the pixel position. DenselyBased Spatial Clustering of Application with Noise (DBSCAN) is employed to connect the predicted lane pixels and to get the final output. The system was trained with augmented data from the Tusimple dataset and then tested on three datasets: Tusimple, CalTech, and a local dataset. On the Tusimple dataset, the model achieved 97.39% accuracy. Furthermore, it has an average accuracy of 97.07% and 96.23% on the CalTech and local datasets, respectively. On the testing dataset, the EDIS-Net exhibited promising results compared to existing LMD approaches. Since the proposed framework performs better on the testing datasets, it can be argued that the model can recognize lane marking confidently in various scenarios. This study presents a novel EDIS-Net technique for efficient lane marking detection. It also includes the model's performance verification by testing in three different public datasets
An Approach to Detect Suicidal Bengali Posts from Social Media Using Machine Learning Algorithms
No full textIn this modern era, suicide is one of the critical issues. According to the WHO, more than seven million people die
due to suicide every year. Suicide is also the second cause of unnatural death for persons between the ages of 15 and 29.
Youth in nations like Bangladesh struggle with schoolwork, employment, relationships, drug use, and family issues, all of
which are significant or minor contributors on the road to depression. In Bangladesh, people are uncomfortable discussing
this ailment openly and frequently mistake this problem as madness. Many at-risk persons use social platforms to talk about
their issues or get knowledge on related topics. This study aims to prevent suicide by identifying suicidal posts on social
media. We collected suicidal-related data from Kaggle. (We use nine algorithms for three features). The prediction model
achieved good performance. Stochastic Gradient Descent is the best model with the highest accuracy for unigram features,
87.23%. For bigram features, the Multinomial Naive Bayes is the best model with the highest accuracy, 88.69%. The best
model with the highest accuracy for trigram features, 86.13%, is Stochastic Gradient Descent. This research demonstrates the
chance that a machine-learning strategy can reduce the risk of suicide. Hopefully, this model will serve as a guide for
lowering potential suicide risk in the future. The study concludes with a summary of several practical concerns that may be
considered to improve model performanc
A Pronoun Replacement-Based Special Tagging System for Bengali Language Processing (BLP)
No full textBangladeshi crops leaf disease detection using YOLOv8
No full textThe agricultural sector in Bangladesh is a cornerstone of the nation’s economy, with key crops
such as rice, corn, wheat, potato, and tomato playing vital roles. However, these crops are highly
vulnerable to various leaf diseases, which pose significant threats to crop yields and food security
if not promptly addressed. Consequently, there is an urgent need for an automated system that
can accurately identify and categorize leaf diseases, enabling early intervention and management.
This study explores the efficacy of the latest state-of-the-art object detection model, YOLOv8 (You
Only Look Once), in surpassing previous models for the automated detection and categorization
of leaf diseases in these five major crops. By leveraging modern computer vision techniques, the
goal is to enhance the efficiency of disease detection and management. A dataset comprising 19
classes, each with 150 images, totaling 2850 images, was meticulously curated and annotated
for training and evaluation. The YOLOv8 framework, known for its capability to detect multiple
objects simultaneously, was employed to train a deep neural network. The system’s performance
was evaluated using standard metrics such as mean Average Precision (mAP) and F1 score. The
findings demonstrate that the YOLOv8 framework successfully identifies leaf diseases, achieving a
high mAP of 98% and an F1 score of 97%. These results underscore the significant potential of this
approach to enhance crop disease management, thereby improving food security and promoting
agricultural sustainability in Bangladesh
Impact Analysis of Harassment Against Women in Bangladesh Using Machine Learning Approaches
No full textViolence against women is a major threat in Bangladesh, where reports of harassment of women and girls have spread an alarming rate. Unfortunately, despite significant achievements in women’s development and bearing a magnanimous history of women’s movement, incidences of harassment against women is still a burning issue. The majority of women are harassed by their relatives, friends and other people. From the survey we have tried to give an idea about the types, causes and impacts of harassment against women in Bangladesh. This is a survey-based paper and using Apriori algorithm to analyze the impacts of harassment against women and girls of Bangladesh. For these reasons, we have selected 2300 respondents to identify the impact of harassment. This study aims to find out the impact of violence in our society and cohere it with our social norms and values. The impact of sexual harassment on these outcomes (Anxiety, Intense fear, Ongoing fears, Depressions, Disrupted work life, Degradation of performances in study or work, Face difficulties with communication, Intimacy and enjoyment of social activities, Sleep disturbances or Nightmares) among different age’s women/school and college going girls was compared to the outcomes among each other. In this study, according to comparison we find out that teenager, age below 18, is most vulnerable to harassment
A Proposed Method For Recognizing Complex Hand Drawn Graphs Using Digital Geometric Techniques
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