IUT Institutional Repository

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Communities in IUT Institutional Repository

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Now showing 1 - 5 of 12

Recent Submissions

Foodie Haven
(Department of Technical and Vocational Education(TVE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2025-10-25) Kobra, Khadiza-Tul-; Khatun, Shiha; Babajo, Ahmad
The Food Delivery Platform helps users, restaurant owners, and delivery personnel manage food orders efficiently. It provides customers with an easy way to user registration and login, browse restaurant and food, view menus, place orders and payment. The Food Delivery Platform is an application developed using Tailwind CSS, React Native, Node.js, Express.js, TypeScript, MySQL database. Since it is platform independent, it can be used on almost any device with internet access. Furthermore, the system is adaptable to the unique requirements of various user groups or eateries
Smart Academic Forecasting Using Data-Driven Approaches
(Department of Technical and Vocational Education(TVE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2025-10-25) Smita, Most.Nosin Nahar; Lipi, Farhana Akter; Shrabonti, Sultana Razia; Nayeem, Mishkatul
Proper prediction of student academic performance is critical to supporting timely interventions and improving learning results. The article is an evaluation of clas sical machine learning models, including Logistic Regression, Decision Tree, Ran dom Forest, K-Nearest Neighbors, and Support Vector Machine, and a transformer based foundation model based on benchmark academic performance datasets. Transformer-based approach had the best predictive accuracy, whereas Decision Tree provided a good compromise between predictive accuracy and Interpretability. Be sides model benchmarking, the study was aimed at exploring the importance of fea tures in addition to analysing Principal Component Analysis (PCA) and Linear Dis criminant Analysis (LDA). These methods pointed out that behavioral attributes like participation, resource use and absence days were the strongest predictors of perfor mance. The consistency of these results was validated on another dataset and the strength of the feature analysis was established. On the whole the study shows that predictive modeling combined with dimensionality reduction gives reliable and ex plainable information that can be used to build trustworthy academic prediction sys tems.
Smart Infant/Baby Incubator
(Department of Technical and Vocational Education(TVE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2025-10-25) Kabir, Md. Humayun; Ceesay, Sait; Duganda, Isatou
Machine Learning Based Emotion Detection
(Department of Technical and Vocational Education(TVE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2025-10-25) Khatun, Topy; Hosen, Kamal; Abusina, Md.; Hossen, Ikram
The detection of emotion in speech has become relevant as social media becomes more popular and audio-based attacks become more frequent. This project aims to identify five emotions, including happiness, sadness, threat, panic, and neutrality, in the Bangla speech. The 1,106 five second audio samples dataset was formed by using different sources and was also extended to 6,636 samples by inserting various types of noise. The MFCC, Melspectrogram, Spectral Centroid, and LPC features had been extracted and used by both machine learning and deep learning models. Random Forest yielded the best results among conventional algorithms, with a 1D Convolutional Neural Network (CNN) performing better with the larger dataset, achieving 83 percent test accuracy and large ROC values. Gender-based testing was more accurate in female voices (88.07%) and male voices (81.2%). Further overfitting was suppressed and accuracy stabilized by the use of higher-order statistics, including the Teager Energy Operator. Finally, the project was able to construct a Bangla speech sentiment database and create an effective CNN-based classifier. The findings prove to have potential uses in security, psychological evaluation, and social media surveillance.
Feasibility Analysis of Solid Waste into Energy conversion in Gazipur City Corporation
(Department of Technical and Vocational Education(TVE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2025-10-25) Mamun, Abdullah Al; Ahmed, Likhon
The study evaluates the potential of implementing Waste-to-Energy (WtE) technology in Gazipur in terms of calorific energy generation and the overall technical, economic, and environmental feasibility of a proposed incinerator plant. The analysis shows that plastic has the highest calorific value of 30 MJ kg⁻¹, next comes wood (19.735 MJ kg⁻¹), leaf (16.78 MJ kg⁻¹), paper (15.75 MJ kg⁻¹), and textiles (17 MJ kg⁻¹). Such measurements suggest that plastic, paper, and wood should serve as suitable feedstock’s to be incinerated, and leaf waste seems to be more suitable to be digested anaerobically. The total daily energy potential of the waste streams chosen is calculated as 3279.55 MWh/day -1, with an electrical output of 655.91 MWh/day -1 to 983.865 MWh/day - according to the efficiency of incineration. The technological analysis indicates that the incineration of WtE is feasible, since the mixture of waste in Gazipur provides enough calorific values to warrant this type of technology. However, the efficiency of plants depends both on the features of the waste feedstock and the operation technologies that are used. In terms of the money, the project faces severe challenges that are occasioned by high capital and operating costs. The negative Net Present Value (NPV) of = -60.59 Additionally, 15.44 years and a 6.47 per cent payback horizon and internal rate of return (IRR) indicate that the project needs more performance and cost reduction to make it economically viable. Regarding environmental impacts, the incineration plant is estimated to have a net negative CO₂ footprint, with the emissions being higher than the CO₂ offset of the energy generated. The Net CO₂ Impact value of -887 630.4 kg/day determined is negative to indicate that the plant cannot have a significant impact on the environment unless the emission-controlling technologies are optimized and the efficiency of the plants is improved. In conclusion, despite the strong technical potential of the incineration plant, the encountered economic and environmental barriers should be overcome to achieve the long term sustainability and success of the incineration plant.