Browsing by Author "Faruk, Md. Omor"

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E-learning Portal
(Daffodil International University, 2020-09-12) Faruk, Md. Omor; Samir, Md. Akib Ibne
E-Learning is changing the usual learning experience. It has become the new standard way of learning and is highly beneficial to students due to its flexibility, low cost, and user experience.Today's worlds changing day-by-day by the touch of technology. In the case of the education sector is one of them. In the previous day for any kind of teach or learn purpose that is mandatory to attend physically. But now-a-days this is changed.Now we can do this by sitting at our home by the use of the E-Learning portal. The previous day that is essential to carry a lot of books to go to school college or university but now-a-days that idea is changed. Maximum off institutes teach students from slide, PDF or e-book so that's why they need not Carry books physically. In our country still maximum education or skill development certificate is institute dependent. This is costly and also difficult but if we use the e-learning portal, that is cost-effective and also much more is easier.The best thing of e-learning portal is many of our students are lives in the rural site. They don't get the best teacher and other materials. But by the use of the e-Learning portal they can get world any teacher or books any other material just on their hand.
Online Buying House Management System
(Daffodil International University, 2018-12) Faruk, Md. Omor; Mahmud, Shah
This project has been developed by thinking on the industrial progression of Bangladesh, particularly Ready Made Garments Industry. To make a strong, steadfast and sophisticated platform for the buyers and sellers from home and abroad to deal with garments products easily. To order or get order both buyers and sellers will be registered merchant of our application. Industries owners/ sellers will be able to post their products here to advertise .Every buyers can see posted products, search products on their choice basis, communicate with the sellers/industries to bargain with the prices, qualities, quantities, colors, sizes etc whatever they exactly need to make the deal of the products .Both buyers and sellers will be star marked basis on their reputations. Buyers could be post a job to hire product makers/ sellers and then worthy people will accept the job based on buyers demands. Financial transaction will be done through us as a guarantee and we will work as a via of two parties. Buyers could easily order from any part of the world to get garments products of Bangladesh. We belief that this application will help our nation to prompt the garments business across the world as buyers could easily buy products and they not need to visit any industry and neither need to be harassed nor cheated by faulty businessman.
SqueezeNet Based Classification for Drone vs Bird for Enhanced Recognition
(Daffodil International University, 2024-07-13) Karim, Anowar; Faruk, Md. Omor
Drone growth in recent years has brought up a number of opportunities as well as challenges in a number of sectors, including aviation safety, security, and animal protection. Accurately identifying and classifying drones from birds is a significant difficulty for efficient airspace surveillance and collision avoidance systems. This paper describes a deep learning-based method that uses the SqueezeNet architecture to accurately categorize birds and drones. The labeled images in the dataset, which was divided into training, validation, and test sets, were taken from Roboflow.Comprehensive data preprocessing methods, such as augmentation and normalization, were used to improve model performance. SqueezeNet and a customized Convolutional Neural Network (CNN) were the two models created and assessed [1]. The SqueezeNet model exceeded the bespoke CNN model, which had an accuracy of 97.51%, with an amazing accuracy of 99.51%. SqueezeNet is a lightweight and efficient model whose outstanding performance highlights its applicability to real-time drone identification applications.To make sure the models were reliable and resilient, a lot of evaluation metrics and visualizations were used[2]. The study comes to the conclusion that the SqueezeNet-based classification method provides an effective way to differentiate drones from birds, boosting surveillance capabilities, safeguarding wildlife, and boosting aviation safety.This study also emphasizes how crucial careful model evaluation and data pretreatment are to getting the best results from deep learning. This technique has ramifications for many different applications, such as improving security standards, aiding in the conservation of species, and reducing aviation hazards. Prospective avenues for investigation encompass broadening the dataset, refining model architectures, executing practical deployments, and tackling moral and legal implications linked to drone detection technology. This work advances drone detection systems and highlights the ability of deep learning models to tackle challenging classification problems in a variety of settings.