NSU INSTITUTIONAL REPOSITORY
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Open Access
Faculty Search : Fall 2025 North South University
(প্রথম আলো, 2025-07-02) প্রথম আলো; The Daily Star
ob circular on the daily Bangla newspaper the Prothom Alo and English newspaper the Daily Star. Applications are invited for faculty member positions in the following areas: School of Business & Economics (SBE), School of Engineering & Physical Sciences (SEPS) , School of Humanities & Social Sciences (SHSS), School of Health & Life Sciences (SHLS). Please apply by Sunday, 20 July 2025 with a cover letter, CV, two copies of recent photos and all academic certificates, marksheets/transcripts. (SSC, HSC, Graduate, Master's, Ph.D. etc.)
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Research paper guideline generator (rpgg)
(North South University, 2023) Siaam Ibn Ali; Md. Jafor Sadek; Dr. Mohammad Ashrafuzzaman Khan; 1931863042; 1931469042
Our project presents the development and implementation of the "Research Paper Guideline Generator," a project designed to assist researchers and students in navigating the vast landscape of research papers. The project inputs a research or project paper topic and generates a guideline with a curated list of similar papers, ordered by difficulty level. By leveraging natural language processing techniques and similarity measures, the tool aids in discovering relevant papers and provides a structured learning progression. The paper discusses the background, motivation, purpose, and goals of the project, along with the methodologies employed in topic analysis, similarity assessment, and difficulty level ranking. Additionally, the paper highlights the potential societal impacts, including improved knowledge accessibility, research collaboration, and advancement in various domains. The "Research Paper Guideline Generator" project represents a valuable contribution to enhancing efficiency and organization in the research process, empowering researchers and students with a tool for effective resource discovery and informed decision-making. The project is not yet fully ready, however progress has been made, unigram and bigram analysis, as well as cosine similarity has been used, and distributions were found, along with cosine similarity between unigrams of papers and common english, and also cosine similarity between bigrams of papers and common english. The purpose of using these processes is to be able to rank papers in order of difficulty, according to cosine similarity.
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AI Dekho (Smar t spect acle for visually challenged people)
(North South University, 2023) Shajeda Par vin; MD Shadman Zar if; Dr. Atiqur Rahman; 1931313042; 1931796042
Our study addresses t he hardships experienced by individuals lacking good vision. The cur rent technology t hat helps t he visually impaired people is quite expensive and does not work well for recognizing people’s faces and objects around t hem. The paper introduces a new idea of g lasses t hat can help visually impaired people. As t he wear able market expands, t he primary aim is to enhance people’s lives through t he use of wear able technology. The primary goal of t his project is to provide assistance to visually impaired individuals through t he utilization of g lasses equipped wit h a distinctive camera. The camera helps t hem to identify people and objects, which is important for t heir daily lives. A collection of pictures showing regular people is put together to help wit h identifying t hem. This met hod uses cameras to help people who can’t see well. The main goal is to make special g lasses t hat can recognize faces and objects in real-time using library called OpenCV. The system has two parts, one t hat faces t he user and one t hat connects to a computer. The findings indicate its ability to identify individuals and surroundings, although concerns remain regarding its speed and data transmission methodologies. It aims to redevelop t he wear able wit h more features and cloud computing technologies.
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Stock Market Price Forecasting Service using LSTM
(North-south University, 2019-04-30) Md. Mahabubul Hasan; Pritom Roy; Sabbir Sarker; Dr. Mohammad Monirujjaman Khan; 1421274042; 1430378042; 1420134042
In recent years we’ve seen in Bangladesh that the Stock market is not stable at all. In 2011 Stocks continued to tumble amid jitters over banks' liquidity crisis. After starting the day at 5,710, DSEX, the benchmark index of the Dhaka Stock Exchange, plunged to below 5,700 points in less than half an hour. Eventually, it lost 81.92 points to close the day at 5,623.64. People invest a lot in stock market. Many people just lost their hard-earned money in a blink of an eye because of investing at the wrong place. But we’ve seen the vice versa situation from the stock market as well. Stockify comes in play here. Stockify is dedicated to show accurate predicated prices of market shares. 70% accuracy have been achieved via training the algorithm Using LSTM which is an artificial neural network. Deep learning algorithm library TensorFlow have been implemented to show us predicted price using the closing prices of a day. Currently we are providing free service via our website and application. It would be on subscription based when the site will be more enriched in a business level. Thus, we hope to help people to make a profit in stock market and upraise the stock market of Bangladesh.
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E-learning App with Augmented Reality
(North South University, 2019-04-30) Md Nazmus Shakib; Safin Mahmud; Polash Chakrabarty; Abdullah All Noman Abir; Dr. Shazzad Hosain; 1511336042; 1510903042; 1520579042; ID # 1521047042
Smartphone has become a great tool for teaching kids. As kids nowadays spend a lot of time on a smartphone. Preschool education is considered pivotal for a child’s development. Mobile learning is a new way to access learning content. Mobile learning is very popular among preschool kids. Because it really motivates kids to learn if they can use mobile phones or tablets. That's why we have developed a marker-based AR application, which will help children to learn or study the basics of Alphabets with fun. While using our app, kids will learn interactively with the help of Augmented Reality. Our app will help the parents teach their kids without much effort.
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Chiral and Plasmonic dimers: Broadband reversal of optical binding force
(North South University, 2022-12-31) Missing information; Missing Information
The behavior between the chiral –plasmonic nanoparticles and their optical binding force in near and far field has not been investigated in the literature yet. There is no generic way to reverse the far field optical binding force for chiral and plasmonic (sphere) heterodimers. Also the behavior of Fano resonance and the reversal of far field optical binding force of chiral plasmonic heterodimers with and without plasmonic substrates have not been studied so far. In this article, for chiral and plasmonic heterodimers, we have demonstrated a general way to control the reversal of far field binding force. However, if the chiral-plasmonic nanoparticles are located at different distance, positive and reversal of optical binding force occurs in far field. We have varied the wavelength of the dimers. We have also observed Fano resonance at both near and far field without substrate .Also while applying the same set-up over a plasmonic substrate, stable Fano resonance occurs along with the reversal of far field optical binding force. It is observed that during such Fano resonance, stronger coupling occurs between the dimers and plasmonic substrate. The reversal of optical binding force occurs near the Fano dip position. Notably, for particle clustering and aggregation, controlling the far fled binding force can be a key factor. Our proposed idea can be confirmed by simple experiment.
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Open Access
Sleep Apnea Detection From Raw Ecg Signal Using Deep Learning And Machine Learning
(North South University, 2023) Salem Shamsul Alam; Sumit Saha; Md. Shahriar Hussain; 1931849642; 1931415042
Sleep apnea, a prevalent yet underdiagnosed sleep disorder, necessitates robust and accurate diagnostic tools. In this project, we undertook an in-depth exploration of machine learning (ML) and deep learning (DL) models for sleep apnea detection, specifically utilizing raw electrocardiogram (ECG) signals. Our comparative analysis encompassed a range of ML models, including Random Forest, Logistic Regression, Decision Tree, AdaBoost, and XGBoost, and a specialized 1D-CNN model within the DL domain. Results underscore the exceptional performance of the 1D-CNN model, achieving a remarkable accuracy of 99.56%, sensitivity of 96.05%, and specificity of 99.66%. This outperforms traditional ML models, signifying the prowess of DL in extracting intricate patterns from raw ECG signals for accurate sleep apnea detection. The 1D-CNN model's ability to discern subtle features proves crucial for accurately identifying apnea events. Our study not only emphasizes the effectiveness of the 1D-CNN model for sleep apnea detection and highlights the transformative potential of deep learning in healthcare diagnostics. This research contributes valuable insights into the optimal choice of models for sleep apnea detection, paving the way for enhanced diagnostic accuracy and improved patient care.
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Open Access
Developing a Mobile Application using Deep Learning for Cataract Classification
(North South University, 2023) Tasnia Ishrat Khan; Fatima Ibrahim; Mohammad Monirujjaman Khan; 1911539642; 2121340642
One of the leading global causes of vision loss and blindness is the cataract. The percentage of blind people is around 50%. As a result, early cataract detection and prevention may limit vision loss and blindness. Contrary to cataract, artificial intelligence (AI) has made significant progress in the treatment of glaucoma, macular degeneration, diabetic retinopathy, corneal abnormalities, and age-related eye diseases. However, the vast majority of cataract detection algorithms in use are built using common machine learning techniques. On the other hand, manual extraction of retinal features is a laborious method that needs a skilled ophthalmologist. In order to detect cataracts, we have built the framework of an Android application. We then used algorithms to extract accuracy, graphs, trainable and untrainable parameters, and differentiation of cataract and non-cataract eye images from a gathered dataset. In order to identify the cataract using color fundus images, we presented the VGG19 (Visual Geometry Group), and digital image we presented Inception V3, which is a CNN (convolutional neural network) model. This will be incorporated into an Android application. The results of fundus image, the training procedure demonstrate that the model attained a flawless accuracy of 1.0000 on the training data for epochs 10 to 15. It scored an accuracy of 0.963 on the validation set, which is still quite high. With values ranging from 0.25 to 0.27, the validation loss was similarly largely consistent. The model is doing well and has mastered correctly classifying the photos. On the test data, the model produced a loss of 0.25735 and an accuracy of 0.9241. The result of the digital image, accuracy is 0.973 on the validation set, which is quite high and on the test data, the model produced a loss of 0.26753 and accuracy of 0.93491. The significance of these results is that the model performs effectively, can reliably categorize test photos with high accuracy, and will be trustworthy for patients to utilize.
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Open Access
Augmented Reality Home Decorator
(North South University, 2019) AL-AMIN; SHARIEAZ KAVIER; Atiqur Rahman; 1520664042; 1513192642
This project presents an application of Augmented Reality (AR) for interior design. Due to huge advancements in computer vision algorithms and cheap hardware, Augmented Reality is becoming mainstream. All over the world, most of the sales come from physical stores. Buying furniture from brick-and-mortar shops is cumbersome and time-consuming. AR is changing the furniture industry. In an AR environment, virtual furniture could be placed and manipulated in the physical world in real time, which allows the user to have an interactive experience. Users would be able to visualize exactly how a table would look in their kitchen, dining room, bedroom, or anywhere they want. When people can place an actual couch in the living room or visualize how a bookshelf would look in a Different color. The risk of product return and logistics is drastically reduced. As online stores replace brick-and-mortar shops. AR will play a vital role in the furniture sales. This project provides new ways an individual/enterprise could utilize AR to design interiors.
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Open Access
Under Water Rescue Drone
(North South University, 2020) Al Shakline khan; Fahmida Alam Usha; Mohammad Momin; Mahmudur Rashid Remon; K. M. A. Salam; 1712095043; 1721645643; 1711370645; 1711224043
Remotely operated underwater vehicles (ROVs) are remote-controlled underwater robots driven by an individual on the surface. These robots are tethered by a series of wires that send signals between the operator and the ROV. All ROVs are equipped with a video camera, propulsion system, and lights. Other equipment is added depending on the specifications required. These include a manipulator arm, a water sampler, instruments that measure clarity, light penetration, temperature, and depth. Team Dubori was determined to recreate such an ROV in order to fulfill a specific mission. By using the system, an organization such as the military can explode mines underwater and also take real-time data of the river, which is also very helpful for rescue teams to identify the position of boats after any accident. The system has been designed in such a manner that it takes care of all the needs of a typical rescue team, and it is capable of providing easy and correct information about things underwater.