World Wide Web Research Articles

Towards a semantic blockchain: A behaviouristic approach to modelling Ethereum

Decentralised ledgers are gaining momentum following the interest of industries and people in smart contracts. Major attention is paid to blockchain applications intended for trading assets that exploit digital cryptographic certificates called tokens. Particularly relevant tokens are the non-fungible tokens (NFTs), namely, unique and non-replicable tokens used to represent the cryptographic counterpart of assets ranging from pieces of art through to licenses and certifications. A relevant consequence of the hard-coded nature of blockchains is the hardness of probing, in particular when advanced searchers involving the capabilities of the smart contracts or the assets digitised by NFTs are required. For this purpose, a formal representation for the operational semantics of smart contracts and of tokens has become particularly urgent, especially in economy and finance, where blockchains become increasingly relevant. Hence, we feel the need to tailor Semantic Web technologies to achieve that semantic representation at least for NFTS. This article reports on an ontology that leverages the Ontology for Agents, Systems, and Integration of Services (“OASIS”) towards the semantic representation of smart contracts responsible for managing ERC721-compliant NFTs and running on the Ethereum blockchain. Called Ether-OASIS, the proposed ontology adopts OASIS and tailors its behaviouristic approach to the Ethereum blockchain by conceiving smart contracts as agents running on the blockchain and, consequently, smart contract interactions as agent commitments. Smart contracts are represented in terms of their actions, purposes and tokens that they manage, thus realising a blockchain that is more usable both by users and automated applications. The ontology is evaluated using standard ontological metrics and applied on a case study concerning the minting and transferring of NFTs that digitise batches of wheat.

Uniqueness meets Semantics: A Novel Semantically Meaningful Bag-of-Words Approach for Matching Resumes to Job Profiles

In an increasingly competitive world, automated screening of resumes of candidates is the need of the hour given the large numbers of such resumes in career portals on the World Wide Web. Resume classification is a subset of the document classification problem in which the keywords extracted from the resume play a significant role in determining the job profile. In this paper, we explore the novel combination of uniqueness in terms of the number of occurrences of a keyword in a resume class as compared to the other resume classes, and the concept of semantics by representing the filtered keywords using word embeddings that can be used to find semantic similarities between resume documents. The principle of maximum entropy partitioning is used to find the keywords unique to a particular class. The aim is to use semantic representations of only those keywords that occur more frequently in one class more than in any other class; these are then passed as input to a Bidirectional long short-term memory (LSTM) for classification. Our experiments on a benchmark dataset proves that the proposed approach outperforms the state of the art in text classification by a significant margin proving the efficacy of our approach.

Real Text Summarizer Extension

The advent of the World Wide Web, with a wealth of information, required easily accessible information, so databases and search engines were created to access that information. This information system is updated whenever new data is generated, since the volume of electronic information rises everyday, and no one can promise that manual analysis will provide the full picture anymore. Accordingly, the need for automated summarization systems is high. Automatic summary reads few papers as input and produces a summarized version and hence saves both information and time. The research was performed in a single paper and produced multiple papers. In this report, we are Specifically focusing on frequency-based approach for text summarization Key Words: Automatic summarization, Extractive, Node package manager (npm), javascript, frequency-based.

APPLICATION OF WEB -BASED LEARNING MEDIA TO IMPROVE SANTRI COMMUNICATION SKILLS

This research aims to evaluate the application of web-based learning media in improving Santri's communication skills. This study focuses on the effectiveness of using web technology in improving students' ability to communicate. The research method uses an experimental approach, involving a control group and an experimental group. identified as research subjects. The use of web platforms for learning is used in a structured manner and integrated into the curriculum to help students develop their communication skills. The results showed a significant improvement in the communication skills of students involved in web-based learning compared to the control group. Quantitative and qualitative data analysis provides a clear picture of the progress achieved by students through the use of web-based learning media. The findings from this research indicate that web-based learning media is effective in improving the communication skills of class VII students. The implications of this research highlight the importance of integrating web technology in an educational context to strengthen students' communication skills. This research provides a basis for further development in the application of web-based learning media to improve the communication skills of students, not only students, but also in the broader educational context Keywords : Approach Ethno -STEM, Skills Think Critical , Student Interest

Optimized continuous homecare provisioning through distributed data-driven semantic services and cross-organizational workflows

BackgroundIn healthcare, an increasing collaboration can be noticed between different caregivers, especially considering the shift to homecare. To provide optimal patient care, efficient coordination of data and workflows between these different stakeholders is required. To achieve this, data should be exposed in a machine-interpretable, reusable manner. In addition, there is a need for smart, dynamic, personalized and performant services provided on top of this data. Flexible workflows should be defined that realize their desired functionality, adhere to use case specific quality constraints and improve coordination across stakeholders. User interfaces should allow configuring all of this in an easy, user-friendly way.MethodsA distributed, generic, cascading reasoning reference architecture can solve the presented challenges. It can be instantiated with existing tools built upon Semantic Web technologies that provide data-driven semantic services and constructing cross-organizational workflows. These tools include RMLStreamer to generate Linked Data, DIVIDE to adaptively manage contextually relevant local queries, Streaming MASSIF to deploy reusable services, AMADEUS to compose semantic workflows, and RMLEditor and Matey to configure rules to generate Linked Data.ResultsA use case demonstrator is built on a scenario that focuses on personalized smart monitoring and cross-organizational treatment planning. The performance and usability of the demonstrator’s implementation is evaluated. The former shows that the monitoring pipeline efficiently processes a stream of 14 observations per second: RMLStreamer maps JSON observations to RDF in 13.5 ms, a C-SPARQL query to generate fever alarms is executed on a window of 5 s in 26.4 ms, and Streaming MASSIF generates a smart notification for fever alarms based on severity and urgency in 1539.5 ms. DIVIDE derives the C-SPARQL queries in 7249.5 ms, while AMADEUS constructs a colon cancer treatment plan and performs conflict detection with it in 190.8 ms and 1335.7 ms, respectively.ConclusionsExisting tools built upon Semantic Web technologies can be leveraged to optimize continuous care provisioning. The evaluation of the building blocks on a realistic homecare monitoring use case demonstrates their applicability, usability and good performance. Further extending the available user interfaces for some tools is required to increase their adoption.

An Efficient Approach to Extract and Store Big Semantic Web Data Using Hadoop and Apache Spark GraphX

The volume of data is growing at an astonishingly high speed. Traditional techniques for storing and processing data, such as relational and centralized databases, have become inefficient and time-consuming. Linked data and the Semantic Web make internet data machine-readable. Because of the increasing volume of linked data and Semantic Web data, storing and working with them using traditional approaches is not enough, and this causes limited hardware resources. To solve this problem, storing datasets using distributed and clustered methods is essential. Hadoop can store datasets because it can use many hard disks for distributed data clustering; Apache Spark can be used for parallel data processing more efficiently than Hadoop MapReduce because Spark uses memory instead of the hard disk. Semantic Web data has been stored and processed in this paper using Apache Spark GraphX and the Hadoop Distributed File System (HDFS). Spark's in-memory processing and distributed computing enable efficient data analysis of massive datasets stored in HDFS. Spark GraphX allows graph-based semantic web data processing. The fundamental objective of this work is to provide a way for efficiently combining Semantic Web and big data technologies to utilize their combined strengths in data analysis and processing. First, the proposed approach uses the SPARQL query language to extract Semantic Web data from DBpedia datasets. DBpedia is a hugely available Semantic Web dataset built on Wikipedia. Secondly, the extracted Semantic Web data was converted to the GraphX data format; vertices and edges files were generated. The conversion process is implemented using Apache Spark GraphX. Third, both vertices and edge tables are stored in HDFS and are available for visualization and analysis operations. Furthermore, the proposed techniques improve the data storage efficiency by reducing the amount of storage space by half when converting from Semantic Web Data to a GraphX file, meaning the RDF size is around 133.8 and GraphX is 75.3. Adopting parallel data processing provided by Apache Spark in the proposed technique reduces the required data processing and analysis time. This article concludes that Apache Spark GraphX can enhance Semantic Web and Big Data technologies. We minimize data size and processing time by converting Semantic Web data to GraphX format, enabling efficient data management and seamless integration.

The effect of social activities on the alienation and family resilience of Chinese caregivers for children with autism: a latent class analysis.

Caregivers of children with autism spectrum disorder (ASD) in China often experience alienation due to societal stigma. While this alienation detrimentally impacts their mental well-being, family resilience serves as a protective factor. Previous research has predominantly examined the social support derived from social activities but has neglected to delve into the specific patterns of these activities. The primary objective of this study was twofold: firstly, to gain insights into the various social activities engaged in by caregivers of children with autism in China, and secondly, to ascertain the influence of these social activities on alienation and family resilience. Between June and August 2023, a cross-sectional survey was carried out across multiple cities in Jilin Province, aiming to gather data from a total of 205 Chinese caregivers of children with autism. Data collection was conducted through the utilization of a structured questionnaire. The assessment of social activity involved the completion of 12 questionnaires, while alienation was evaluated using the Generalized Alienation Scale (GSAS), and family resilience was gauged through the Chinese version of the Family Resilience Scale (FaRE). The classification of social activities was conducted through latent class analysis (LCA), while the impact of these social activities on alienation and family resilience was examined using linear regression analysis. The findings revealed that social activities can be categorized into five types (Low, Self-Recreation, Communication, Web Surfing, High). Communication social activities were found to reduce family resilience(β=.332, p<0.01), while high social activities were associated with reduced alienation(β=-.349, p<0.05) and increased family resilience(β=.417, p<0.01). Supporting these particular types of social activities has the potential to reduce alienation and bolster family resilience among caregivers for children with autism in China.

INNOVATIVE WAYS OF USING VIDEO RESOURCES IN DEVELOPING STUDENTS’ CREATIVE THINKING DURING ONLINE EDUCATION IN THE CONDITIONS OF WAR IN UKRAINE

The evolution of the concept of “online learning” reflects the state of technical capabilities having been used in higher educational institutions. Online education proves the growth of psychological influence on the personal development of the student contributes to the development of his/her creative thinking. A student will be focused on online learning even when computers and other digital devices near him/her are turned off. Online learning is amplified in the consciousness of each individual who has been in the role of a user, in the form of ideas about his/her own virtual profile, niche, etc. It has been proven that the development of creative thinking of students during the organization of online training in the conditions of war in Ukraine is possible if online training includes the following elements: the inclusion of cyberspace– a virtual environment that denotes the amplified phenomenon of the Internet; inclusion of the World Wide Web– spheres with special laws that do not correspond to a real world; inclusion of hypertext– combinations of texts in a certain language with the computer’s ability to use interactive transitions or dynamic explication of non-linear text material that cannot be printed in a traditional way and presented on paper. Hypertext is a model of electronic text organization, characterized by a specific structure and a branched system of programmatically amplified intratextual and intertextual transitions into a single system.

An effective DDoS attack mitigation strategy for IoT using an optimization-based adaptive security model

The Internet of Things enables the creation of transmitted use cases for interconnected devices and complementary channels. The varied structure of it creates additional security needs and problems. In particular, the safeguards used in the IoT should adjust to the changing environment. One of the major dangers to the World Wide Web (WWW) things is Distributed Denial of Service (DDoS). Therefore, in this work, an intelligent Game Theory-based Adaptive security (GT-AS) mathematical model was developed to maximize the effectiveness of DDoS attack mitigation. Moreover, this strategy can strongly derive the five parameters such as energy channel, memory, intruder, and hybrid. These all can achieve a stronger defense posture against DDoS attacks from the newly designed IoT. Consequently, the Recurrent Bat (RB) framework is developed to classify the nodes into two classes such as trusted node and malicious node. In addition, the proposed frameworks analyze how protection effectiveness and energy consumption interact when evaluating adaptive security techniques. To analyze the effectiveness of the suggested paradigm, researchers also give the outcomes of simulation experiments. Researchers demonstrate that, in comparison to existing models, the developed approach has increased the lifespan of the connected objects by 47 %. Also, the developed strategy has attained better accuracy and lower error rates when comparing traditional strategies. Moreover, the packet delivery ratio is 60 KB, energy consumption is 116 KJ, Mean Location Error is 0.078 and resource usage is 148.

Financial Institution's Role in Supply Chain Risk Management: Strengthening Resilience and Mitigating Financial Risks

One of the most important problems that multinational organizations are dealing with in risk-oriented supply chains is efficiency assessment and choosing the best low-risk financial institution for financial assistance. We provide an integrated approach for evaluating the unpredictable, dangerous, and fragile arrangement of the financial sector, which directly affects the supply chain network's processing stages. This model is supported by fuzzy-soft tools. The real economy is currently under pressure from both the domestic economic crisis and the decline in international commerce. President Xi Jinping has often suggested stepping up initiatives to assist the real economy's high-quality growth. In this regard, the development of the model for supply chain financing offers a fresh approach to resolving the funding issues faced by businesses. To tackle the financial issues facing the marketplace, we should use the chain of custody finance model, but we also need to thoroughly understand the risks associated with it and how to manage them. False trade orders and insufficient control over core businesses are difficulties that the present logistics finance risk management must deal with. The World Wide Web, big data, and block chain technologies are all blooming, opening new avenues for risk management in block chain finance. As a result, the goal of this essay is to examine the features of supply chain finance based on the technology of block chain, examine the risks associated with financing supply chains, and debate how to use block chain innovation for supply chain banking risk management.[4] Risk Financing: To assist organizations in transferring or reducing supply chain risks, financial institutions offer risk financing solutions including insurance &amp; alternative risk transfer methods. The creation of novel risk financing products, evaluation of their performance, and comprehension of the effects on the resilience of supply chains can all be the subject of research. Financial institutions provide supply chain financing options like factoring, discounted invoices, and supply link finance programmed that help businesses manage their working capital, improve cash flow, and lower the risk of supplier failure to pay or insolvency. The effects of different financing choices on supplier relationships, supply chain stability, and overall risk reduction may be studied. Financial institutions collaborate with businesses to detect and classify different supply chain risks. An extensive investigation of the supply network environment, encompassing suppliers, transportation, logistics, and market dynamics, is required in this phase. Data gathering techniques used may include questionnaires, interviews, &amp; site visits. In the above rank one is axis, and the last one is Punjab national bank. Financial Institution in Supply Chain Risk Management In the above rank one is axis, and the last one is Punjab national bank.

The Impact of Cloud Systems on Enhancing Organizational Performance through Innovative Business Models in the Digitalization Era

Abstract Information technology has massively transformed the world of business over the past fifty years - first individual functional areas within companies (“first wave”), later increasingly also cross-divisional value-added processes and trade (“second wave”), advent of the internet and the World Wide Web marked a transformative shift in how information is accessed, shared, and distributed (“third wave”), mobile computing and smartphone (“fourth wave”) and cloud computing that has emerged as a paradigm for delivering computing resources and services over the internet on-demand, providing scalability, flexibility, and cost-effectiveness (“fifth wave”). This paper investigates the transformative impact of cloud systems on increasing organizational performance through innovative business models in the age of digitalization. By employing a mixed methods approach, the study integrates qualitative and quantitative techniques to provide a comprehensive understanding of the research phenomenon.

Excessive internet use as a risk factor for suicide ideation among university students in Malawi: A cross-sectional study.

Covid 19 has fast-paced the use of technological innovations, mainly the internet. However, Internet use can lead to several behavioral and psychological conditions, such as cyberbullying and distorted relationships, which could lead to suicide ideation. Suicide is the second leading cause of death among young adults. To assess the association between Internet addiction and suicide ideation among university students in Malawi. Furthermore, to assess the factors associated with suicidal thoughts among Malawian college students who surf the World Wide Web. This cross-sectional study utilized secondary research and used data available from https://data.mendeley.com/drafts/xbfbcy5bhv. Internet addiction was measured using the Internet Addiction Test. The dependent variable includes suicide ideation. Binary logistic regression was used to analyze the relationship between the dependent and independent variables. The value of p < 0.05 was considered statistically significant. Out of the 620 participants, 514 (82.2%) were aged between 15 and 24. The majority were males 401 (64.7%). The mean IAT score was 46.08 (SD = 14.60). The IAT score was 44.81 (SD = 13.85) among males and 48.40 (SD = 15.65) among females (p = 0.003). About 341 (55%) of students use the internet excessively. Suicide ideation was prevalent among 101 (16.3%) of the students. Suicide ideation was significantly associated with internet addiction. (p < 0.001). The odds of developing suicidal thoughts increased about 3 times among excessive internet users compared to average users (OR = 2.91, 95% CI = 1.213-7.018). However, age, gender, discipline, and year of study were not associated with suicide ideation. The study suggests that internet addiction affects suicide ideation mainly through distorting social relationships. School settings should increase awareness regarding the safe use of the internet to ensure a balance between online and real-life interactions and curb suicide.

School Real time Chat Application System

Abstract— The emergence of new technologies has brought about significant changes in the way people communicate with each other. One of the most popular ways of communication in today’s digital age is through messaging applications. To facilitate this need, several chat applications have been developed. In this thesis, we introduce a chat application built using the MEAN stack, which is a popular technology stack used for building web applications. This application provides users with the ability to create accounts, join chat rooms, and send messages to other users in real-time. With the use of web sockets and Angular’s two-way binding, the application allows users to see messages as soon as they are sent. Moreover, the application also includes features such as user authentication and authorization to ensure secure access to the chat rooms. Through this project, we aim to demonstrate the feasibility and effectiveness of building real-time chat applications using the Mern stack. IndexTerms – Educational Management System, Modern Web Technologies, Real Time Chat , Technical Architecture, MERN Stack..

Real Estate Website

Abstract: The motivation to develop Real Estate Web Application comes from my urge to learn Visual Studio .NET 2005 for building the business logic of the application, SQL server 2005 for database designing and using new web technologies like AJAX, Java Script for website designing. The most influential factor for selecting this application is to add some innovative features to the search engine of a Real Estate Website which can make the task of a property buyer easy to search for property listings. The most significant feature in these websites is the interactive search criterion which lets the buyer specify their requirements to get the correct set of records from the database. The search tool should be strong enough to include all the required features which a buyer may desire. Also, each search requires a post back call to the database to retrieve some set of records but making the website AJAX enabled prevents the annoying post backs. Thus, each call refreshes just the data grid to display the listings rather the whole web page. Some other new features like drag and drop tool to save the listings, retrieving related results for each search, etc make this application more feature rich.

Evaluating Machine Learning Techniques for Web Robot Detection

A web robot, also known as a web crawler, is an automated script that systematically browses the World Wide Web in a methodical manner. Web robots are commonly used by search engines to index web pages, gather information, and update search engine databases. They follow hyperlinks from one web page to another, collecting data and information for various purposes such as indexing, data mining, and website monitoring. Web robot detection is the process of identifying and distinguishing between human users and web robots on websites, which are a major source of web traffic. This process is vital to prevent malicious web robots from having a negative effect on web servers’ traffic and their users’ privacy. Companies such as Imperva, Inc. use machine learning models to identify malicious bots and prevent them from having unauthorized access to an organization’s server. To understand the benefit that machine learning models bring to web robot detection, we used pre-published server log data to construct machine learning models on Orange (a Data Mining Software) and Python that can distinguish between malicious and benign web robots. We evaluated the performance of three well-known machine learning algorithms: kNN, neural network, and decision tree. Based on our experimental results, the neural network gains the highest precision and the lowest false-positive and false-negative percentage of web robots. However, the neural network requires more time to generate the desired output.

FiReS: A semantic model for advanced querying and prediction analysis for first responders in post-disaster response plans

Natural disasters have emerged as a recurring and severe menace to the sustainability of countries, resulting in compromised environmental and infrastructural integrity, human fatalities, and significant economic repercussions. Prompt and effective response by first responders is essential in Disaster Risk Management (DRM) to reduce individuals' vulnerability and minimize environmental and infrastructural damages. However, disaster-related information is often generated by heterogeneous sources, making the first responders’ decision-making process complex and time-consuming. To address these challenges, a common conceptual model is imperative to improve interoperability among diverse organizations and software systems, enabling effective collaboration. Semantic Web technologies offer a promising solution for integrating heterogeneous data and providing well-defined meaning in the representation and exchange of DRM-related knowledge. In this context, this study introduces FiReS (First Responders System), an ontological model designed to enhance data interoperability among first responders in post-disaster response plans for advanced data analysis and machine learning prediction. The validation of FiReS is conducted through a series of case studies exploring various aspects of disaster response, such as the response time of emergency services and the volume and classification of emergency calls. This approach facilitates streamlined access, thorough analysis, and seamless exchange of information, empowering stakeholders to strengthen their disaster response strategies and foster resilience within communities.

Toward Efficient Legislative Processes: Analysis of Chilean Congressional Bill Votes Using Semantic Web Technologies

Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner.

Online Blogging System for an Organization

Abstract: Organizations understand the critical significance of encouraging communication and collaboration among their members in today's digitally connected environment. The conception and creation of an online blogging system designed especially for usage by organizations are described in this abstract. The suggested system seeks to transform the dynamics of internal communication by giving staff members a centralized forum on which to exchange knowledge, experiences, and insights in an organized and easily accessible way. By utilizing modern web technologies, the system provides an easy-to-use interface with features that are intuitively designed to make content creation, publication, and interaction smooth. Personalized user profiles, a hierarchical topic classification system, real-time commenting, and content moderation tools are some of the main features. Additionally, cross-platform connectivity is improved and data consistency is guaranteed by integration with current corporate databases and communication channels. By encouraging a transparent and cooperative atmosphere. The suggested system seeks to transform the dynamics of internal communication by giving staff members a centralized forum on which to exchange knowledge, experiences, and insights in an organized and easily accessible way. By utilizing modern web technologies, the system provides an easy-to-use interface with features that are intuitively designed to make content creation, publication, and interaction smooth. Personalized user profiles, a hierarchical topic classification system, real-time commenting, and content moderation tools are some of the main features. Additionally, cross-platform connectivity is improved and data consistency is guaranteed by integration with current corporate databases and communication channels. The Online Blogging System encourages employees to share their knowledge, participate in productive discussions, and develop an innovative and open culture within the company by creating an environment that is open and collaborative. Additionally, the technology makes it easier for knowledge to be retained and shared, allowing insightful.

Smart Slot Assignment for Urban EV Charging Using Folium API Integration

Abstract: Efficient management of electric vehicle (EV) charging distribution in urban areas is crucial amid increasing demand and varying traffic conditions. This study proposes a novel web-based solution that integrates Folium Map API for realtime location tracking and Python Flask backend with MySQL database storage to allocate charging demands. By leveraging real-time traffic information from the Folium Map API, the proposed model optimizes charging resource allocation across multiple stations and road networks. The web interface, built using HTML, CSS, and JavaScript, allows users to interact with the system seamlessly. Charging allocation is dynamically adjusted based on real-time data, prioritizing maxi- mum returns. Additionally, an emergency charge counter facility is introduced to cater to unforeseen charging needs, enhancing user flexibility. The system offers two interfaces and a dashboard for both station administrators and users, en- abling pre-booking services for travelers during their journey, facilitating access to nearby charging stations, and providing a facility for prebooking. These features contribute to improved station revenue and pile utilization, highlighting the efficacy of the proposed web-based approach. By leveraging advanced web technologies and APIs, this comprehensive strategy enhances charging infrastructure efficiency, fosters the widespread adoption of electric vehicles, and contributes to sustainable urban mobility management.

Blood Link

Abstract: In the face of medical emergencies, the rapid and reliable delivery of blood to hospitals can be a matter of life and death. This project presents "BloodLink," a comprehensive solution that leverages modern web technologies and drone delivery systems to streamline the blood supply chain between blood banks and hospitals. The front-end of the system is developed using HTML, CSS, and JavaScript, ensuring a user-friendly interface for hospital staff to post blood group requirements. The backend is powered by the Flask framework of Python, facilitating robust and scalable server-side operations. Utilizing the Google Maps API, our platform dynamically identifies and displays the nearest blood banks in response to hospital requests, ensuring timely and accurate information. For critical situations requiring immediate action, we have integrated an innovative drone delivery system designed to expedite the transportation of blood to the requesting hospitals. The software development phase of the project is complete, and efforts are currently focused on the practical design and implementation of the drone system. This paper details the architecture, development process, and potential impact of "BloodLink" in revolutionizing emergency blood delivery and hospital blood bank coordination.