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    Mikrotik Router Network Configuration
    (Daffodil International University, 2025-01-11) Hamza, Md Abu
    An outline of my internship assignment at Atova Technology, which concentrated on MikroTik router configuration, is provided in this report. The internship improved my academic knowledge and practical abilities by giving me hands-on experience with network infrastructure and routingtechnologies. The main objective was to become acquainted with the features of the MikroTik router, apply different configurations, and solve networking problems so that I could effectively contribute to actual network management situations. The core concepts of MikroTik routers, such as routerOS, firewall filtering, NAT configuration, and bandwidth control, were learned throughout the internship. The dynamic potential of MikroTik hardware and software was examined, as well as the utilization of tools like Winbox for configuration and monitoring. Routing protocols, such as OSPF, BGP, and VLAN installation, were better understood, and the learning process was aided by the hands-on activities and advice of Atova Technology experts. Additionally, the ability to use networking technology and address practical issues was enhanced. Several network configurations had been successfully established at the project's end, and a comprehensive grasp of the MikroTik router's functionality had been acquired. The usage of MikroTik routers in several contexts will be emphasised, with the aim of mastering network architecture, security, and optimisation. It is also expected that contributions would be made to the creation of innovative technologies that will improve network scalability and reliability, guaranteeing businesses and organisations steady access. Significant advancements in professional growth have been achieved as a result of the internship, during which the technical know-how and self-assurance required to succeed in the fast-paced networking industry were gained.
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    A Platform of Online Education by Using Modern web Development
    (Daffodil International University, 2025-01-25) Saki, Salekin Md Rahagir Miraj
    "BUCKET HEAD" is a web-based platform designed to provide comprehensive musical instrument training. The platform offers a variety of online music courses, ranging from guitar and drums to music theory and DJ mixing. It features a user-friendly interface with sections dedicated to course listings, top instructors, and user profiles. Admins can manage content, monitor user progress, and handle payments for premium courses through a secure back-end system built using PHP and Laravel. The platform’s design ensures ease of navigation, responsiveness across devices, and seamless integration with social media for enhanced user engagement. The primary goal of the project is to create an accessible and efficient platform for users to learn musical instruments, connect with professional instructors, and track their learning journey. This project aims to contribute to the growing demand for online music education and provide a scalable solution for both learners and instructors.
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    IOT Base Soil Nutrient and Fertilizer Monitoring System For Smart Agriculture
    (Daffodil International University, 2025-10-25) Riyad, Md. Shafayet Jamil; Tahmid, Khandaker Ahanaf
    In smart agriculture, accurate monitoring of soil is necessary for the maximization of crop yield and sustainable farming. In this project an Iot Base Soil Nutrient and Fertilizer Monitoring System for Smart Agriculture that can monitor soil remotely on a real time basis is proposed which works on ESP32 microcontroller. The system includes a 3-in-1 NPK (Nitrogen, Phosperous, Potassium), DS18B20 temp sensor and capacitive soil moisture sensor. These sensors acquire environmental parameters, that showed on a 0.96-inch OLED display and also sent at the same time to mobile application designed with MIT App Inventor using MQTT protocol. The RS485 bus is used for communication between ESP32 and the NPK sensor, MP1584 buck converter steps down 12V switching adapter to a safe 5V work voltage. I use the MQTT broker named broker. emqx. io, as it supports low latency light communication for IoT data. The product enables data driven predictive farming, controlling the environmental factors that affects yield and crop quality of yields. Scalable, less expensive and an effective technique for monitoring the agricultural field in real time can provide insightful information to farmers to improve productivity and resource utilization.
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    Implementing Blockchain for Enhancing Cloud Security
    (Daffodil International University, 2025-08-30) Shanto, Mushfiqur Rahman; Sakib, Muktadir Khan; Morshed, Nishat
    As digital data continues to grow rapidly across sectors, ensuring its integrity and authenticity presents a critical challenge—particularly in centralized cloud storage systems, which are vulnerable to unauthorized access, data tampering, and single points of failure. This project proposes a decentralized architecture that integrates Ethereum smart contracts, the InterPlanetary File System (IPFS), and a Flask-based backend to enhance file security, transparency, and traceability. By decentralizing file storage and verification, the system addresses key risks inherent in traditional cloud platforms. Each file is hashed into a unique Content Identifier (CID) via IPFS, and this CID is stored immutably using Ethereum smart contracts. Flask facilitates all backend operations between users, IPFS, and the blockchain, while Twilio is utilized for real-time WhatsApp alerts upon unauthorized access or CID mismatches. The system was rigorously tested using Ganache, Truffle, and Postman. Results demonstrated precise detection of file tampering, instant alerting, and a high level of reliability. The proposed model proves to be scalable, secure, and suitable for sensitive applications in legal, healthcare, and government data management.
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    Design And Implemantation Iot Based Smart Village Farming With Sun Detecting Solar Powered Renewable Energy.
    (DAFFODIL INTERNATIONAL UNIVERSITY, 2024-02-05) 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.
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    Development of Intelligent Traffic Management System
    (DAFFODIL INTERNATIONAL UNIVERSITY, 2024-01-31) 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
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    Design and Implementation of Eco-Friendly Concentrated Solar Power
    (2024-02-03) 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.
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    Malware Detection Using Machine Learning And Deep Learning
    (DAFFODIL INTERNATIONAL UNIVERSITY, 2024-02-05) 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
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    Productivity Analysis of Potato (Solanum tuberosum) Yielding in Cumilla, Bangladesh Based on Soil Chemical Parameters Using Machine Learning Approaches.
    (DAFFODIL INTERNATIONAL UNIVERSITY, 2024-09-24) Hasan, Md. Kamrul; Mia, Anonto; Shohan, Yeasir Arafat
    The aim of this project is to apply machine learning methods to evaluate the productivity of potato yields in Cumilla, Bangladesh, based on soil chemical parameters. Collaborating with the Regional Agricultural Research Station, Bangladesh Agriculture Research Institute (BARI) in Cumilla, a comprehensive dataset spanning crop yields, soil quality, climate conditions, and region-specific agronomic practices has been developed. This dataset was subjected to 277 machine learning classifiers in order to determine the most effective techniques for predicting potato productivity. Based to the analysis, the top 25 classifiers including AdaBoost, GLMBoost, CNN, Simulated Annealing, Bayesian Optimization, GAN, Multi-Layer Perceptron, Support Vector Machine, FDA, Deep-Q-Network, Linear Regression, LDA, Ensemble Model, Autoencoder provided significant insights into the parameters influencing potato yield, with soil chemical characteristics emerging as key influences. Simulated Annealing, Bayesian Optimization, MLP, and CNN exhibit outstanding results with 90%, 89%, 88%, and 87% accuracy, respectively. despite this, GAN approaches and become the best match for the dataset with 99.96% accuracy. The correctness and applicability of the dataset to the regional agricultural environment were validated during the validation procedure. The relationship between crop yield and soil properties has become clearer because of to these discoveries, which have real-world implications for enhancing agricultural practices in Bangladesh. The verified dataset supports data-driven decision-making and sustainable development objectives by bridging a critical gap in the local agricultural research infrastructure
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    Advancing Digital Agriculture: Deep Learning Based A Smart Rose Disease Classifier
    (DAFFODIL INTERNATIONAL UNIVERSITY, 2024-09-25) Islam, Shawn; Gazi, MD Seam; Chowdhury, Asif Hasan
    A Deep Learning-Based Smart Rose Disease Classifier tries to solve the problem of plant illnesses that are getting worse and hurting farming, costing farmers much money. This project is about using deep learning to quickly and accurately diagnose diseases that affect roses so that they can be treated early and less money is lost. Handling rare diseases by hand requires much knowledge, so the process is complex, takes a long time, and needs much work. This project aims to use image processing better to make a dataset from a nearby rose garden. The collection has 24,801 pictures, grouped into seven groups: Rust, Fresh Leaf, Botrytis blight, Cercospora leaf spot, Downy mildew, and Black spot. Three deep learning models—AlexNet, CNN, and MobileNet V2—were used for training and testing. The MobileNet V2 algorithm did the best, with a fantastic accuracy rate of 98.86%