DSpace 7
DSpace is the world leading open source repository platform that enables organisations to:
- easily ingest documents, audio, video, datasets and their corresponding Dublin Core metadata
- open up this content to local and global audiences, thanks to the OAI-PMH interface and Google Scholar optimizations
- issue permanent urls and trustworthy identifiers, including optional integrations with handle.net and DataCite DOI
Join an international community of leading institutions using DSpace.
The test user accounts below have their password set to the name of this software in lowercase.
- Demo Site Administrator = dspacedemo+admin@gmail.com
- Demo Community Administrator = dspacedemo+commadmin@gmail.com
- Demo Collection Administrator = dspacedemo+colladmin@gmail.com
- Demo Submitter = dspacedemo+submit@gmail.com
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Recent Submissions
A Semantic Web Approach: Creating A Tourism Ontology For Chittagong District
(International Islamic University Chittagong, 2023-07) Ullah, Md. Rohmat; Riyad, Suliman Hossain; Rahat, Rakib Hasan
The purpose of this thesis is to utilize the Semantic Web, a web of interconnected mean ing, to develop a comprehensive tourism ontology for Chittagong district. This paper implemented ontology on tourism domain, proposed a general framework for tourism on tology and explained searching mechanism through Chittagong district tourism. Also, it presents different ways of reasoning the ontology. In general, ontology classifies the vari ables in need for some computations and creates interrelationships between them. The introduction of semantic web poses the demands for creating ontology in many domains. We have found that the utilization of ontologies within the tourism domain remains relatively limited. Notably, research into ontologies specifically focused on Chittagong tourism is entirely absent. To address this gap, this study proposed the development of a dedicated Chittagong tourism ontology. In the digital age, tourism thrives on readily available information and efficient organization. This thesis delves into the creation of a robust tourism ontology for Chittagong district, Bangladesh. By formalizing knowledge about tourism resources, attractions, and experiences, we aim to enhance information re trieval, facilitate data integration within the tourism sector. This ontology leverages the expressiveness of Web Ontology Language (OWL) to model the intricate relationships between various tourism entities. We capture details about historical sites, cultural at tractions, natural wonders, transportation networks, foods and accommodation options. The ontology also incorporates relevant concepts like accessibility, sustainability, and cultural sensitivity
Approach to Improving Machine Learning Models for Intrusion Detection System
(International Islamic University Chittagong, 2022-07) Labib, Ahmad Ibtisam; Chy, Shamsuddin Ahmmed; Hossain, Md. Shahriar
In today's digital world, there are several security risks that digital assets must contend with. Systems for detecting intrusions (IDS) are essential security tools that protect digital assets. But their usefulness depends on meeting strict accuracy requirements, and their effectiveness depends on timely alarms. This study offers a novel IDS model that combines deep learning and machine learning methods as a solution to these problems. The study applies several classification techniques, such as Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree, K-Nearest Neighbors (KNN), Soft Voting, and Hard Voting, using the well known KDD Cup-1999 dataset. After a large-scale dataset was processed, the Decision Tree method performed better than the others, with a 99.9% accuracy rate. This study aims to investigate the effects of soft voting and hard voting, a novel application in IDS. Decision Tree proved to be the better performance in spite of these efforts. By offering information about algorithmic efficacy, the research advances the field of intrusion detection and helps decision-makers in the design and deployment of intrusion detection systems. These findings have implications for improving digital asset protection against changing cyber threats.
ScrapVault – Which is a digital marketplace dedicated to buying and selling scrap materials.
(International Islamic University Chittagong, 2022-06) Chowdhury, Md. Ashfiqul Alam; Chowdhury, Alim Ullah; Amin Joy, Shahriar
ScrapVault is an innovative digital marketplace that aims to revolutionize the scrap trading industry by providing a dynamic platform to connect environmentally conscious individuals and businesses. The website has been designed with a user-centric approach, offering an intuitive interface for easy navigation and efficient discovery of a wide range of scrap materials, including metals, plastics, and paper. The platform prioritizes security, offering a robust transactional framework to ensure peace of mind. Advanced geo-location features are integrated to help users locate nearby sources of scrap materials. ScrapVault is unique in its commitment to transparency, providing environmental impact metrics that allow users to track and quantify their contributions to sustainability. In addition to its functional aspects, ScrapVault also focuses on building a community of like-minded individuals and businesses. This community not only supports recycling efforts but also cultivates a sense of responsibility towards the environment. Overall, ScrapVault is a commendable project that contributes to the digital trans formation of the scrap trading industry. By balancing economic considerations with a commitment to sustainability, community engagement, and environmental responsi bility, ScrapVault has emerged as a trailblazing initiative towards a greener and more interconnected future
A Web Based Application For Home Service System
(International Islamic University Chittagong, 2022-07) Obyedullah, A.S.M.; Shanto, Naimul Islam; Afridi, Shahid
Introducing a revolutionary home service system, an innovative web-based application poised to revolutionize the way service seekers connect with providers. This cutting-edge platform not only facilitates seamless discussions about diverse home services but also streamlines the entire booking process, enabling hassle-free online payments for the discerning user. In times of urgency, our system excels at handling quick service requests, sparing you the inconvenience of complicated bookings. Moreover, it opens up exciting job opportunities tailored to the specific needs of service providers. Picture this: users empowered to select their desired service, effortlessly logging into their personalized portals to manage their profiles directly from our website. With the seamless integration of computer and smartphone accessibility, connecting with service providers has never been more intuitive. Embrace the future of home services with our state-of-the-art platform
An E-commerce recommendation system based on LightGBM Machine Learning Algorithm
(International Islamic University Chittagong, 2022-07) Hera, Mohammed Ashrafujjaman; Chowdhury, Md.Injamamul Hoque; Tohidu, Mohammad
In the dynamic realm of e-commerce, recommendation systems play a pivotal role in shaping user experiences and fueling business growth. This study advocates for a novel approach to online shopping recommendations, leveraging the power of the LightGBM machine learning algorithm. By focusing on item-to-item recommendations, our method ology seeks to elevate user satisfaction by swiftly and precisely offering customers highly personalized product choices. At the core of our recommendation system lies the fusion of item-to-item association analysis and user interactions, culminating in the delivery of accurate, real-time rec ommendations. This research contributes significantly to the e-commerce landscape by presenting a practical and scalable method that enriches customer experiences, conse quently amplifying sales and fostering customer loyalty. Through extensive testing and evaluation, our results underscore the transformative potential of the proposed item-to-item e-commerce recommendation system. This inno vative system stands poised to revolutionize digital commerce by providing users with pinpoint-accurate product recommendations. The seamless integration of machine learn ing, coupled with a focus on item-to-item relationships, not only expedites the decision making process for consumers but also cultivates a deeper connection between customers and the digital marketplace. In summary, our study demonstrates the capability of our approach to usher in a new era of precision and effectiveness in digital commerce, promis ing a paradigm shift in the way users discover and engage with products onlin