http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/658 Thu, 09 Apr 2026 18:31:15 GMT 2026-04-09T18:31:15Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14169 Design And Implemantation Iot Based Smart Village Farming With Sun Detecting Solar Powered Renewable Energy. Labib, Farhan; Akter, Mahfuza In order to implement effective and sustainable farming methods, this project suggests developing a smart farming system that makes use of cutting-edge sensor technology and Internet of Things integration. In this project, a PIR motion sensor, an infrared sensor, a rain detection sensor, a soil moisture sensor, and a humidity sensor have been used. For the actuator, a servo motor has been used to control irrigation valves and gates. For the central control system, an ESP8266 microcontroller with Wi-Fi has been used for data collection by mobile app communication, which is powered by solar panels for sustainable operation where sun-detecting solar panels capture renewable energy. In this project, the PIR motion sensor has detected and notified animals, or trespassers. An infrared sensor has been used for automated watering and feeding based on distance and soil moisture, which is useful for monitoring crop and animal positions. A rain detection sensor has been used to measure rainfall data, alert the farmer about impending flooding, and automate the irrigation system based on rain data. A soil moisture sensor has been used to determine soil moisture content, which enables automatic irrigation or notifies the farmer based on soil conditions, and a humidity sensor has also been used to measure air humidity, which gives real-time monitoring and actuator control via a mobile app. This project has been tested several times and has successfully achieved the desired output. This Internet of Things (IoT)-based Smart Village Farming system presents a viable means of advancing sustainability, updating agricultural methods, and providing farmers with data-driven decision-making resources. Project Mon, 05 Feb 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14169 2024-02-05T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14168 Development of Intelligent Traffic Management System Sanji, Tabassum Barka; Roy, Vaskor Traffic management is a major challenge in densely populated countries like Bangladesh, where traffic violations are common and hard to enforce. In this project, we propose an Development of Intelligent Traffic Management System (DITMS) that uses computer vision techniques to automatically detect and record traffic infractions. The DITMS consists of four modules: (1) License Plate Detection using YOLOv8, a deep learning model that can identify and extract license plate numbers from images; (2) Speed Measurement with Kalman Filter, a mathematical method that can estimate the speed of vehicles from consecutive frames; (3) Vehicle Type Detection utilizing a Convolutional Neural Network (CNN), a machine learning model that can classify vehicles into different categories based on their shape and size; and (4) Vehicle Counting through a combination of object detection, tracking, and segmentation algorithms, which can count the number of vehicles passing through a given area. The DITMS can be deployed on roadside cameras or drones to monitor traffic flow and capture evidence of violations such as speeding, overloading, or illegal parking. The DITMS aims to digitize traffic infractions in Bangladesh and contribute to the vision of SMART Bangladesh 2041, a national initiative that seeks to transform the country into a digital and developed nation Project Wed, 31 Jan 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14168 2024-01-31T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14167 Design and Implementation of Eco-Friendly Concentrated Solar Power Akash, Tanvirul Islam; Ekhowan, Rafshan; Kormoker, Arup Kumer The goal of this project is to design and develop a concentrated solar power system that can generate a minimum of 10 W of electricity. Generally, a CSP system can generate heat at temperatures between 300°C and 1000°C (572°F and 1832°F), depending on the specific technology used. For example, parabolic trough systems, which are one of the most common types of CSP technology, can typically generate heat at temperatures between 300°C and 400°C (572°F and 752°F), while tower systems can generate heat at temperatures up to 1000°C (1832°F), which heats a fluid or material, such as molten salt or water. The steam can be used in cooking food. High-temperature heat generated by CSP can be used in various cooking applications, such as baking, roasting, and grilling. This can help reduce the amount of fossil fuels used in cooking and decrease carbon emissions. Also, it can be used to melt metals, extract minerals, or produce hydrogen gas. This can help reduce the carbon footprint of these processes by replacing fossil fuels with renewable energy. The system has been efficient, cost-effectives, and environmentally friendly. The project has aimed to improve the efficiency and performance of CSP technology and explore the potential for integrating CSP with other renewable energy sources such as wind and hydropower. Project Sat, 03 Feb 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14167 2024-02-03T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14166 Malware Detection Using Machine Learning And Deep Learning Rahman, Md Tasnim; Mim, Israt Jahan; Rhidita, Sumiya Benta Salam This research explores the vital field of malware detection, which is a crucial component of modern cybersecurity and deals with threats to workstations, servers, cloud instances, and mobile devices. Utilizing machine learning and deep learning algorithms, the project takes an inventive method to better protect data security, privacy, and overall security by identifying and preventing unwanted activity. The main goal is to use cutting-edge technologies to detect malware with more precision and predictive power. The research employs a thorough approach to examine and assess malware within datasets, acknowledging the ever-changing landscape of both online and offline threats. A paradigm change towards the integration of cutting-edge technology is needed due to the growing diversity and sophistication of malware operations, which exposes the shortcomings of conventional security measures. Algorithms for machine learning and deep learning are regarded as essential technologies because they effectively analyze and identify malware in datasets. The machine learning and deep learning algorithms are carefully analyzed by the project methodology to determine how well they detect malicious behavior. Proper algorithms are tested on a wide range of datasets that represent the complexity of the real world. This project is an example of a forward-thinking approach to cybersecurity, strategically aligned with the need to strengthen security measures against rapidly emerging cyber threats. As a result, the combination of deep learning and machine learning algorithms is a shining example of improved malware detection. It has a positive impact on both academic research and real-world cybersecurity practices by strengthening defenses against malware, which is becoming an increasingly dangerous threat in a variety of digital settings Project Mon, 05 Feb 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14166 2024-02-05T00:00:00Z