Integration Of APIs Research Articles

Study Mate using AI-ML

The advent of modern technologies like Machine Learning (ML) and Artificial Intelligence (AI) has paved the way for innovative solutions across various domains, including education. This project presents a comprehensive web application built using the MERN (MongoDB, Express.js, React.js, Node.js) stack. It aims to simplify academic resource sharing and enhance the learning experience for students, particularly engineering students, by integrating AI-powered chatbots and NLP-enhanced semantic search capabilities. The platform enables secure access to study materials, facilitates peer-to-peer collaboration, and supports document exchange and stationery transactions among students. Email API integration is used for real-time notifications to keep users updated with the latest uploads and interactions on the platform. The project focuses on ensuring security through JWT-based authentication and the use of cloud storage for document management. This abstract encapsulates the project’s innovative use of MERN stack technologies alongside AI/ML techniques, showcasing a platform that promotes resource-sharing while ensuring a secure and user-friendly experience

Revolutionizing financial inclusion through strategic API integration and innovation

Revolutionizing financial inclusion through strategic API integration and innovation

Location Based Blood Donor Finding System

Abstract: This research paper delineates the creation and execution of an application based on serverless architecture, aimed at simplifying blood donation by enabling location-based donor searches. The system primarily uses cloud services such as API Gateway, Lambda Functions, and DynamoDB on popular cloud platforms. Google Maps API integration provides a dynamic mapping solution for a better user experience. This application allows a donor and blood bank to register their details including her GPS coordinates, thus providing users with an efficient and real-time way to find a donor nearby. Serverless architecture ensures cost efficiency, scalability, and seamless management of resources. API Gateway and Lambda functions are us ed to create RESTful APIs and facilitate communication between applications and databases. The DynamoDB database is used to store donor information, ensuring data persistence, reliability, and easy retrieval. Users can use the application to search for donors based on their current location and view the results on an interactive map. The system optimizes the blood donation process by calculating and displaying important information such as distance to the donor’s location, direction, and estimated time of arrival.

Enhancing business intelligence in e-commerce: Utilizing advanced data integration for real-time insights

This paper explores how advanced data integration techniques and real-time insights can significantly enhance BI in the e-commerce sector. By leveraging methods such as ETL, ELT, data virtualization, API integration, and streaming data integration, e-commerce businesses can achieve a comprehensive, unified view of their data. The integration of real-time data enables dynamic pricing, personalized customer experiences, efficient inventory management, proactive fraud detection, and improved customer support. Despite challenges such as data silos, data quality issues, scalability, security, and technical complexity, solutions like data governance, cloud computing, and advanced analytics tools can address these obstacles. Future trends, including artificial intelligence, machine learning, edge computing, block chains, and data fabric, promise to further transform BI in e-commerce. Ultimately, advanced data integration and real-time insights are essential for e-commerce companies to stay competitive, optimize operations, and enhance customer satisfaction. In the fast-paced and competitive world of e-commerce, the ability to make quick, informed decisions is crucial for success. Business Intelligence (BI) plays a vital role by transforming vast amounts of data into actionable insights that drive strategic decisions and operational efficiency. This paper explores how advanced data integration techniques and real-time insights can significantly enhance BI in the e-commerce sector. By leveraging methods such as ETL, ELT, data virtualization, API integration, and streaming data integration, e-commerce businesses can achieve a comprehensive, unified view of their data. The integration of real-time data enables dynamic pricing, personalized customer experiences, efficient inventory management, proactive fraud detection, and improved customer support. Despite challenges such as data silos, data quality issues, scalability, security, and technical complexity, solutions like data governance, cloud computing, and advanced analytics tools can address these obstacles. Future trends, including artificial intelligence, machine learning, edge computing, block chain, and data fabric, promise to further transform BI in e-commerce. Case studies from leading e-commerce companies illustrate the practical benefits of implementing these advanced techniques, showcasing significant improvements in operational efficiency and customer satisfaction. Keywords: Business Intelligence, E-commerce Analytics, Data Integration, Real-Time Insights.

ALVoyage – An AI Travel Planner

Abstract: The document discusses the ALVoyage AI travel planner, which integrates with Gemini and Weather APIs and utilizes Firebase authentication. ALVoyage offers personalized travel planning by leveraging AI technology to curate itineraries based on user preferences. It seamlessly integrates with the Gemini API to generate informative content and incorporates real-time weather data from the OpenWeatherMap API. Firebase authentication ensures user security and privacy. The document also highlights the benefits and key features of Gemini and OpenWeatherMap APIs. The methodology section outlines the system architecture and components of ALVoyage, including user authentication, UI/UX design, Gemini API integration, Weather API integration, and user reviews. The results and discussion section emphasizes the system's performance, user feedback, and the future scope of the project. The conclusion highlights the potential of AI-powered travel planning apps and the need for further research and development in the field.

Empowering E-Commerce: Revolutionizing Inventory Management with Seamless API Integration

In today's dynamic e-commerce markets, effective inventory management is crucial for businesses to thrive. This paper explores the transformative potential of integrating Inventory Management APIs with e-commerce websites to streamline operations, enhance customer satisfaction, and maximize profitability. Beginning with an overview of the challenges posed by traditional inventory management methods in the digital age, we delve into the motivations behind adopting Inventory Management APIs and their alignment with the objectives of modern enterprises. Through a comprehensive literature review, we highlight the key dimensions of consumer behavior in online shopping and their implications for inventory management strategies. The methodology section outlines a systematic approach to developing an e-commerce website seamlessly integrated with an Inventory Management API, emphasizing agile principles and iterative development. Challenges and limitations associated with API reliability, data synchronization, scaling, and security are discussed, alongside strategies for mitigating these challenges. The results section showcases the tangible outcomes of the integration, including improved user experience, operational efficiency, and inventory accuracy. Looking ahead, the paper identifies future directions for geographical expansion, enhanced personalization, community features, mobile app optimization, integration of emerging technologies, and advanced analytics. In conclusion, the integration of Inventory Management APIs with e-commerce websites marks a significant milestone in redefining the standards for online retail, offering a comprehensive solution that combines user-friendly interfaces with robust inventory management capabilities. The findings underscore the importance of a holistic approach to e-commerce, addressing both customer-facing aspects and back-end complexities to drive sustainable growth in the digital marketplace.

WEATHER APP

In today's world, the challenge of instantly accessing accurate weather updates for specific locations is significant. Weather forecasting involves complex skills, requiring the observation and processing of vast amounts of data. From small, short-lived thunderstorms to large-scale weather systems spanning thousands of miles and lasting for days, the variability of weather phenomena adds to the difficulty of obtaining precise forecasts. This often leads to inconvenience and challenges for individuals. However, technology offers a solution to this problem. Through the development of an Android app using tools like Android Studio and APIs, users can receive real-time weather updates for their chosen locations. By leveraging technology effectively, this app aims to address the challenges associated with weather forecasting, providing users with timely and accurate information. In the modern era, the challenge of accessing precise and timely weather updates for specific locations is significant. Weather forecasting necessitates intricate skills involving the observation and analysis of extensive datasets. The spectrum of weather phenomena, ranging from brief thunderstorms to sprawling weather systems, further complicates the task of generating accurate forecasts. Consequently, individuals often encounter inconvenience and hurdles in planning their activities. Nevertheless, technology presents a viable solution to this issue. Key Words: Weather forecast,Real-time updates,Android app,Weather data,Location-based, Meteorology,Mobile application,Weather conditions,API integration,Android Studio,Weather alerts,Forecast accuracy

Malware Analysis Using Transfer Learning

Abstract: The evolution of malware threats demands continuous innovation in detection techniques to safeguard systems. This research project introduces a comprehensive solution aimed at unveiling malware detection capabilities through the integration of cutting-edge methodologies and APIs. At its core, our tool utilises transfer learning techniques to analyse files for potential malicious content, providing a first line of defence against emerging threats. Building upon this foundation, the integration of the Virustotal API enhances enumeration capabilities, utilising the collective intelligence of numerous antivirus engines to validate and quantify detected threats. Then it calculates a malware score based on this enumeration, So everyone can easily understand how malicious that file is.

Modified bi-directional long short-term memory and hyperparameter tuning of supervised machine learning models for cardiovascular heart disease prediction in mobile cloud environment

In recent decades, cardiovascular heart disease (CHD) has emerged as the predominant cause of mortality on a global scale. Numerous risk factors for cardiovascular disease necessitate prompt access to accurate, dependable, and efficient early diagnosis and disease management strategies. The timely and precise identification of cardiac disease holds significant importance in the prevention and treatment of heart failure. The reliability of diagnosing heart disease by standard medical history has been widely questioned. Medical imaging, including angiography, is the most widely used method for diagnosing CAD. Nevertheless, angiography is renowned for its high cost and its correlation with many adverse consequences. Noninvasive techniques, such as machine learning, demonstrate reliability and efficiency in the classification of individuals as either healthy or afflicted with cardiac disease. In this research, a new efficient real-time diagnostic method for CHD was designed via hyperparameter tuning of supervised machine learning (SML). This study consists of five significant components: the data preprocessing stage, classification with and without hyperparameter tuning, model integration and clinical decision, intervention and patient engagement, and lastly, model deployment and API integration on Amazon Cloud SageMaker. The data was preprocessed, which included finding outliers using the interquartile range (IQR), normalizing the data, and using the synthetic minority oversampling method (SMOTE) to fix the imbalance in the data. Following this, we propose a new adaptation of the Bi-Directional Long-Short Term Memory (BiLSTM) model, incorporating an attention mechanism (AM) to facilitate feature selection. The feature relevance was determined by calculating the coefficient scores for the top ten (10) features in each of the SMLs, namely the Catboost, extreme gradient boosting (XgBoost), random forest (RF), and linear discriminant analysis (LDA). Both with and without the inclusion of hyperparameter tuning, the grid search process further improved the models. We deployed our proposed models at scale by outsourcing them to the SageMaker cloud environment. The detailed experimental analysis showed that the BiLSTM + AM-XgBoost model worked best when tuned for hyperparameters via the grid search method. Specifically, this model achieved an accuracy of 99.99%, recall of 99.65%, precision of 99.68%, and AUC of 88.16%. The BiLSTM + AM-XgBoost model, without any hyperparameter adjustment, achieved an accuracy of 97.58%, recall of 96.32%, precision of 97.32%, and AUC of 87.32%. The tuning of hyperparameters affects the performance of SML in terms of accuracy, recall, precision, and AUC. In addition, we conducted a comparative analysis of our proposed models against the current state-of-the-art approaches. The findings of our evaluation indicate that our models exhibit a level of performance that outperforms the existing methods. The proposed model can function as a decision support system that employs SML techniques and seeks to enhance the efficacy of cardiac patient diagnostics for healthcare professionals. The simplicity of the suggested model is attributed to its reduced computational complexity. The findings demonstrate the potential application of our method in the field of telemedicine, particularly in the context of mobile cloud device environments for CHD.

Fashion Finder : Ai-Powered Image Analysis and Online Shopping Integration App

The proposed mobile application aims to revolutionize the fashion industry by providing users with a seamless way to discover and purchase clothing items through image recognition technology. The app allows users to either capture images of clothing items through their device's camera or upload images from their stored files. Leveraging machine learning and computer vision algorithms, the app identifies the corresponding clothing items within the images. Upon identification, the app interfaces with e-commerce platforms such as Amazon and Flipkart to search for similar items available for purchase. The development process involves extensive research, design, and implementation of features including image recognition, camera/file upload functionality, API integration with e-commerce platforms, user authentication, and additional user-friendly features. Through rigorous testing, deployment on major app stores, and continuous maintenance, the app aims to offer a seamless and engaging experience for fashion enthusiasts while adhering to user privacy and data security standards.

Integration of Smart Cane with Social Media: Design of a New Step Counter Algorithm for Cane

This research introduces an innovative smart cane architecture designed to empower visually impaired individuals. Integrating advanced sensors and social media connectivity, the smart cane enhances accessibility and encourages physical activity. Three meticulously developed algorithms ensure accurate step counting, swing detection, and proximity measurement. The smart cane’s architecture comprises the platform, communications, sensors, calculation, and user interface layers, providing comprehensive assistance for visually impaired individuals. Hardware components include an audio–tactile interaction module, input command module, microphone integration, local storage, step count module, cloud integration, and rechargeable battery. Software v1.9.7 components include Facebook Chat API integration, Python Facebook API integration, fbchat library integration, and Speech Recognition library integration. Overall, the proposed smart cane offers a comprehensive solution to enhance mobility, accessibility, and social engagement for visually impaired individuals. This study represents a significant stride toward a more inclusive society, leveraging technology to create meaningful impact in the lives of those with visual impairments. By fostering socialization and independence, our smart cane not only improves mobility but also enhances the overall well-being of the visually impaired community.

The impact of Web Api technology on the efficiency of freight transportation

In the contemporary world, where logistics plays a critical role in ensuring the continuity and optimization of freight transportation, WEB API technologies become a crucial tool for enhancing the efficiency of this process. This topic explores the influence of utilizing WEB API on various aspects of logistic processes in freight transportation. It analyzes the role of this technology in increasing speed, accuracy, and simplifying interactions among different participants in the logistic chain. The research covers the impact of WEB API integration on monitoring processes, cargo tracking, route planning, inventory management, and other key logistics aspects. The analysis results can serve as a foundation for developing optimization strategies and implementing innovative solutions in the field of freight transportation. Beyond its operational benefits, the study delves into the challenges and opportunities associated with the adoption of WEB API in the freight industry. Issues such as data security, seamless system integration, and the overall transformation of logistics practices are scrutinized. The analysis not only identifies potential hurdles but also provides strategic insights into mitigating risks and maximizing the benefits of WEB API technology. The implementation of WEB API facilitates precise route planning, optimizing transit times and resource utilization. Furthermore, it streamlines inventory management, offering a dynamic and responsive system that adapts to changing demands. In conclusion, the research findings serve as a comprehensive foundation for developing optimization strategies and implementing innovative solutions in the field of freight transportation. In conclusion, the research findings serve as a comprehensive foundation for developing optimization strategies and implementing innovative solutions in the field of freight transportation. The transformative potential of WEB API technology is evident in its ability to reshape traditional logistic practices, contributing to the evolution of a more efficient, agile, and technologically advanced logistics ecosystem.

PROTO – A Customized Assistant To Optimize Personal Tasks

In today's interconnected world, the combination of technology and AI has become an essential part of daily life. AI-powered voice assistants have evolved into indispensable tools, seamlessly integrating technology into everyday tasks. This project demonstrates the development of a Customised Artificial Intelligence Assistant in Python, which was chosen for its extensive library set suitable for building AI applications. This assistant is designed to make computer-related tasks easier and more convenient for users. The assistant improves functionality and user experience by combining voice recognition, natural language processing, and external API integration, such as the OpenAI API. What distinguishes this assistant is its high level of customisation and extensibility, which allows users to tailor its behaviour and features to their specific requirements. These intelligent programmes use voice commands to understand human natural languages and complete tasks for the user. It can perform a variety of basic tasks, including launching applications, conducting searches without a web browser, playing music, and more, all by responding to voice commands. Furthermore, the assistant works seamlessly with popular tools and services, providing a user-friendly experience for both beginners and experts. The goal of this project is to improve users' productivity and efficiency in their daily computer tasks by combining accessibility, automation, and customisable features.

GPT-4 TURBO: EXPANDING THE BOUNDARIES OF ARTIFICIAL INTELLIGENCE THROUGH API INTEGRATION

GPT-4 Turbo represents a new milestone in the development of artificial intelligence (AI), providing unique opportunities for API integration into proprietary developments. This algorithm, based on the GPT-4 architecture, not only improves the quality of natural language, but also provides high performance and efficiency in a wide variety of tasks. In this article, we will look at the key characteristics of the GPT-4 Turbo, as well as consider the prospects for its implementation through the API in various areas.

Implementation and evaluation of an oncological case management system among public and private healthcare providers in Chile

This article presented the implementation, results, and usability evaluation of a software solution designed to manage oncological cases between healthcare centers. The software was developed to facilitate the exchange of clinical and administrative data for patients referred to the Arturo López Peréz Foundation (FALP) through a charitable program. The software underwent iterative development and included features such as user roles, patient list, progress tracking, document upload and viewer, chat, DICOM viewer, sharing, download, and API integration. The usability of the software was evaluated using the System Usability Scale (SUS) questionnaire, which showed high levels of usability and user satisfaction. The software proved successful in facilitating the coordination and continuity of care for patients referred to FALP and received positive feedback from users. The results of this study highlight the effectiveness and value of the software solution in improving case management and information exchange in the Chilean healthcare system. Future plans include expanding the software for internal patient management at FALP and extending its use to other institutions

Android-based REST API Integration for Counseling Services

Android-based REST API Integration for Counseling Services

CLOUD BASED E-COMMERCE PLATFORM

The emergence of cloud-based technologies has revolutionized the e-commerce sector, facilitating innovative features for enhanced user experiences. This study addresses the need for a more sophisticated e-commerce platform by incorporating store selection and price comparison features within a cloud-based framework. Current e-commerce platforms lack seamless integration of store choices and comprehensive price comparisons, leading to suboptimal consumer decision-making. This research aims to bridge this gap and create a more informed shopping experience for users.The existing e-commerce landscape falls short in providing users with a unified platform that seamlessly integrates store selection and price comparison features. Shoppers are often required to navigate through multiple websites and apps to assess store options and compare prices, leading to inefficiencies and confusion.The proposed cloud based e-commerce platform leverages a combination of web scraping and API integrations to collect data from various online stores. This data includes product prices, availability, and store details. A user-friendly interface is developed to present users with a unified view of store options and price comparisons. The platform's backend operates on cloud infrastructure, ensuring scalability and responsiveness.The implemented cloud-based e-commerce platform successfully integrates store selection and price comparison features. Key findings reveal that users can efficiently explore different stores, leading to more informed purchase decisions. Price variations across stores are presented clearly, aiding users in making cost-effective choices. The discussion highlights how the platform's cloud architecture contributes to its scalability and adaptability, accommodating future expansions and enhancements.Incorporating store selection and price comparison features within a cloud-based e-commerce platform addresses the limitations of existing solutions. The platform empowers users with the ability to make well informed purchasing decisions and enhances their shopping experience. By leveraging cloud infrastructure, the platform is positioned for future growth and improvements, ensuring continued relevance in the dynamic e-commerce landscape. Keywords:Cloud-based e-commerce, store selection, price comparison, user experience,API integration.

Employee Education and Training Recommendations using the Apriori Algorithm

The Ministry of Finance (MoF) aims to enhance employee performance through suitable education and training opportunities. Based on the data on the implementation of education and training in 2022 in the MoF Central ICT Department, only 27.35% of the employees participated in education and training according to the proposed needs for both positions and individuals. This is partly due to mandatory training that must be attended by some or all employees, urgent needs in the current year, or substitute participants who are not from the same team or function. To address this issue, the association method of data mining techniques can be utilized to analyze historical data of employees. The study used the a priori algorithm to analyze historical data on employee positions, organizations, and education and training from 2011 to 2021. This research involved comparing various minimum support values, assuming that employees attended at least 2, 3, and 4 training courses, to calculate the corresponding minimum support values. The evaluation results of the model show that the best rules are generated with a minimum support value of 0.013 and a minimum confidence value of 0.6, which is a total of 10 rules. One of the training recommendations is that if an employee has taken the Enterprise Service Bus (ESB)-API Management training, they will take the ESB API Integration Platform training. Furthermore, it can be used by the Human Resources Unit to provide education and training aligned with organizational needs and improve employee competency in line with their duties and functions, leading to better overall organizational performance.

Analysis of Learner's Response to Process Automation Class Design Using Web Content and Python Library

The spread of artificial intelligence along with the Fourth Industrial Revolution is bringing about changes in future occupations. Therefore, AI education is emerging as an essential subject in universities, and curriculums are being designed and researched through various approaches. This study introduces a case involving process automation classes that can be applied to university SW and AI liberal arts education by utilizing various learning resources of web content that are encountered in daily life. At a time when artificial intelligence education is emerging as important, the AI pre-processing process is also important. Thus, procedural and experiential learning that can select the tools necessary to collect, process, and analyze appropriate data for problem solving is also important. Therefore, in designing the SW⋅AI liberal arts curriculum, it is meaningful in various aspects to design an education that quickly collects and processes life-linked data based on web content by combining the highly utilized Python library and RPA concept. Through related research, cases where process automation implementation learning can be designed and applied, areas of interest among learners regarding classification were derived through learner responses. Furthermore, web content designed through qualitative responses were shown to be excellent practical resources for process automation learning. In the designed learning of classification, interest in ‘data collection using data crawling and scraping’ ranked the highest, followed by ‘open API integration and utilization’. Based on the research results, improvement points and implications are derived, and research in various directions is suggested in order for us to develop this course into a basic subject of artificial intelligence education in the future.

Technology as a Catalyst for Tourism Entrepreneurship: A Critical Insight

The purpose of the present article is to highlight the importance of technological advancement and the role of travel technology providers in the tourism industry. The present study also highlighted the current technological changes and trends in the tourism and hospitality industry. A systematic study of the existing and emerging technologies in the tourism sector and how travel technology providers help entrepreneurs to organize and automate the travel business is presented in the form of a viewpoint. Travel technology providers help tourism enterprises by providing services like API integration, customer relationship management (CRM), portal and website designing, and more. Covid-19 has scaled up the technological changes and boosted digital entrepreneurship worldwide, bringing various technological trends to the tourism and hospitality industry. To meet the needs of modern-day travelers, travel agents and tour operators are considering taking their travel business online and opting for innovative travel technologies. The present study contributes to the existing literature and helps tourism practitioners to remain competitive and cater to the ever-changing demands of today’s travelers.