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Akpur, Akın, Burhanettin Zengin, and Tuna Çakar. "Havayolu Reklamlarında İzleyicilerin Duygu Ölçümü: Microsoft Azure Face API ile Yüz Kodlama Uygulaması." İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi 12, no. 1 (2025): 190–202. https://doi.org/10.17336/igusbd.1434670.
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Amaç: Havayolu işletmeleri, müşterilerini uçuşlara çekmek ve marka imajlarını güçlendirmek için sık sık reklam kampanyaları düzenlerler. Ancak, reklam kampanyalarının etkinliği geleneksel yöntemlerle ölçüldüğünde sınırlı kalabilmektedir. Bu çalışmanın amacı yüz kodlama teknolojisinin havayolu reklamlarında duygu ölçüm performansının değerlendirilmesidir. Yöntem: Bu çalışma deneysel bir tasarıma sahip olup laboratuvar ortamında 40 denek ile yapılmıştır. Microsoft Azure Face API uygulamasından elde edilen sayısal duygu verileri istatistik paket program aracılığı ile analiz edilmiştir. Bulgular: Araştırmada final sahnesi olan reklamlar anlamlı şekilde final sahnesi olmayan reklama göre daha fazla mutluluk duygusu oluşturduğu tespit edilmiştir. Sonuç: Bu çalışma kapsamında kullanılan yüz kodlama yazılımının elde edilen çıktılar çerçevesinde ölçümlemede başarılı olduğu görülmüştür.
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Domínguez-Ramírez, Omar Arturo, and Arturo Austria-Cornejo. "Sistema de Reconocimiento de Patrones de Rostros en la Nube." Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI 7, no. 13 (2019): 54–61. http://dx.doi.org/10.29057/icbi.v7i13.3540.
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El presente trabajo comprende la implementación de algoritmos de alto desempeño para reconocimiento y detección de rostros, con interactividad en la Nube empleando la plataforma Microsoft Azure. Para su implementación, se analizan técnicas biométricas utilizadas hoy en día para el reconocimiento de patrones de rostros y se plantea de manera general considerar la existencia de ruido en las imágenes a analizar al compararlas con las bases de datos tomando en cuenta la alineación, normalización y escalado de cada una de las imágenes probadas. Para ello, se han llevado a cabo experimentos diferenciados de cada una de las fases del desarrollo del proyecto, de modo que se pudieron evaluar fortalezas y debilidades de la aplicación en la Nube. El análisis de desempeño centra en verificar exactitud, eficiencia y rapidez del servicio; con este propósito se realizó un estudio antropométrico como base experimental para realizar un análisis más exhaustivo del rostro, considerando la detección de atributos y el reconocimiento facial. El desarrollo del proyecto tiene dos líneas principales de trabajo: i) se implementó un servicio basado en las librerías de la API Face de Microsoft Azure para reconocimiento facial en lenguaje C#, cuyo rendimiento fue evaluado con una base de datos local y posteriormente en el Cloud de Microsoft, posteriormente se adaptó y se mejoró el diseño e implementación para su funcionamiento en tiempo real; y ii) el enfoque experimental, llevando a cabo pruebas diferenciadas del servicio en cada una de las etapas de desarrollo, donde se pudo realizar una evaluación de forma detallada. Los experimentos se enfocaron en el estudio de las etapas más relevantes para el análisis de la exactitud, rendimiento y rapidez en las funciones de: agrupación, detección, comprobación, identificación y comparación de rostros y reconocimientos de emociones. Este proyecto finaliza con la implementación del sistema de análisis de rostros con la integración de los servicios Microsoft Azure Face API
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Dorfman, Robert, Irene Chang, Sean Saadat, and Jason Roostaeian. "Making the Subjective Objective: Machine Learning and Rhinoplasty." Aesthetic Surgery Journal 40, no. 5 (2019): 493–98. http://dx.doi.org/10.1093/asj/sjz259.
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Abstract Background Machine learning represents a new frontier in surgical innovation. The ranking Convolutional Neural Network (CNN) is a novel machine learning algorithm that helps elucidate patterns and features of aging that are not always appreciable with the human eye. Objectives The authors sought to determine the impact of aesthetic rhinoplasty on facial aging employing a multidimensional facial recognition and comparison software. Methods A retrospective chart review and subsequent analysis was carried out on all female patients who underwent open rhinoplasty with the senior author from 2014 through 2018 and had postoperative photos at 12 or more weeks follow-up. All photos were analyzed with Microsoft Azure Face API (Redmond, WA), which estimates patients’ age by cropping the face from a photograph and then extracting a CNN-based prediction through multiple deep neural networks. Results A total of 100 patients ultimately met full inclusion criteria. The average post-surgical follow up for this cohort was 29 weeks (median, 14 weeks; range, 12-64 weeks). Patients ranged from 16 to 72 years old (mean, 32.75 years; median, 28.00 years; standard deviation, 12.79 years). The ranking CNN algorithm on average estimated patients preoperatively to be 0.03 years older than their actual age. The correlation coefficient between actual age and predicted preoperative age was r = 0.91. On average, patients were found to look younger post-open rhinoplasty (−3.10 vs 0.03 years, P < 0.0001). Conclusions The ranking CNN algorithm is both accurate and precise in estimating human age before and after cosmetic rhinoplasty. Given the resulting data, the effects of open rhinoplasty on reversing signs of facial aging should be revisited. Level of Evidence: 4
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Gómez Fernández, Juan F., Eduardo Candón Fernández, and Adolfo Crespo Márquez. "A Structured Data Model for Asset Health Index Integration in Digital Twins of Energy Converters." Energies 18, no. 12 (2025): 3148. https://doi.org/10.3390/en18123148.
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A persistent challenge in digital asset management is the lack of standardized models for integrating health assessment—such as the Asset Health Index (AHI)—into Digital Twins, limiting their extended implementation beyond individual projects. Asset managers in the energy sector face challenges of digitalization such as digital environment selection, employed digital modules (absence of an architecture guide) and their interconnection, sources of data, and how to automate the assessment and provide the results in a friendly decision support system. Thus, for energy systems, the integration of Asset Assessment in virtual replicas by Digital Twins is a complete way of asset management by enabling real-time monitoring, predictive maintenance, and lifecycle optimization. Another challenge in this context is how to compound in a structured assessment of asset condition, where the Asset Health Index (AHI) plays a critical role by consolidating heterogeneous data into a single, actionable indicator easy to interpret as a level of risk. This paper tries to serve as a guide against these digital and structured assessments to integrate AHI methodologies into Digital Twins for energy converters. First, the proposed AHI methodology is introduced, and after a structured data model specifically designed, orientated to a basic and economic cloud implementation architecture. This model has been developed fulfilling standardized practices of asset digitalization as the Reference Architecture Model for Industry 4.0 (RAMI 4.0), organizing asset-related information into interoperable domains including physical hierarchy, operational monitoring, reliability assessment, and risk-based decision-making. A Unified Modeling Language (UML) class diagram formalizes the data model for cloud Digital Twin implementation, which is deployed on Microsoft Azure Architecture using native Internet of Things (IoT) and analytics services to enable automated and real-time AHI calculation. This design and development has been realized from a scalable point of view and for future integration of Machine-Learning improvements. The proposed approach is validated through a case study involving three high-capacity converters in distinct operating environments, showing the model’s effective assistance in anticipating failures, optimizing maintenance strategies, and improving asset resilience. In the case study, AHI-based monitoring reduced unplanned failures by 43% and improved maintenance planning accuracy by over 30%.
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Dimić, Slađana. "SISTEM ZA BEZBEDNU KOMUNIKACIJU DVA KORISNIKA PUTEM RAZMENE PORUKA." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 35, no. 04 (2020): 794–97. http://dx.doi.org/10.24867/07oi01dimic.
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Cilj ovog rada jeste da se obezbedi sigurna komunikacija u vidu razmene mail-a dva korisnika preko Web aplikacije i Microsoft Outlook-a, klijenta Microsoft Exchange-a. Za ovu komunikaciju potrebno je registrovati novu aplikaciju na Microsoft Azure portalu koja ima identifikatore neophodne za dobavljanje Bearer tokena. Bearer token sluzi da se klijent, u ovom slučaju razvijena Web aplikacija, autentifikuje servisu. Klijent je tada u mogućnosti da, preko Microsoft Graph RESTful Web API-a, potraži sve korisnike određene grupe registrovane na Azure Active Directory-u i pošalje mail odabranom korisniku.
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Olimova, Muxlisa Vohidjon qizi. "SUN'IY INTELLEKT YORDAMIDA ISHLAB CHIQILGAN ZAMONAVIY TEXNOLOGIYALARNING TAHLILI." Innovative Development in Educational Activities 2, no. 15 (2023): 113–22. https://doi.org/10.5281/zenodo.8249653.
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<em>Sun’iy intellekt (SI) bizning yashash, ishlash sharoitimizda va o‘zaro munosabatlarimizda inqilob yasadi. SI texnologiyasi rivojlanishda davom etar ekan, uning nima ekanligini, turli SI tizimlari qanday ishlashini va ularning maqsadlari, ijobiy va salbiy tomonlarini tushunish juda muhimdir. Algoritmlar sehrli kuchga ega emas va inson aralashuvisiz yo‘q joydan qaror qabul qila olmaydi. Agar siz yuqori sifatli moslashtirilgan ma’lumotlarni taqdim qilmasangiz, hatto eng yetuk algoritm ham sizga mukammal natijani bermaydi. Ushbu maqolada sun’iy intellekt dunyosi va turli toifadagi bir nechta mashhur sun’iy intellekt xizmatlarini, jumladan, tabiiy tilni qayta ishlash (natural language processing), computer vision va sog‘liqni saqlashda sun’iy intellekt vositalari keltiriladi.</em>
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Chode, Balaji. "AI-Driven Risk-Adaptive Authorization for Multi-Tenant Cloud APIs - Microsoft Azure." International Journal of Computer Science and Data Engineering 2, no. 3 (2025): 1–11. https://doi.org/10.55124/csdb.v2i3.254.
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Application Programming Interfaces (APIs) have become the nervous system of modern financial-services platforms; yet fractured authorization logic remains a dominant breach vector, exposing organizations to data exfiltration, fraudulent transactions and regulatory fines. We introduce a cloud-native Contextual Authorization Framework (CAF) embedded in an enterprise Shared Services Platform (SSP) that supports more than 200 customers facing and internal micro-services worldwide. CAF sits behind Azure API Management, authenticates callers via OAuth 2.0 / OpenID Connect, and merges an ensemble machine learning risk score—Isolation Forest, GRU auto-encoder and XGBoost—with attribute-based rules expressed as policy-as-code in Open Policy Agent. A twelve-month evaluation covering 2.1 billion production requests demonstrates that CAF increases attack-detection recall by 42 % and precision by 18 % compared with a signature Web Application Firewall and static RBAC baseline, while adding only 8 ms to the p95 gateway latency—well inside the 15 ms service-level objective required for real-time quote, billing and claim APIs. Operational metrics show a 60 % reduction in security-integration effort and a net annual benefit of $7.8 million due to prevented fraud and lower SOC triage workload. We have proven datasets, Azure ML notebooks and policy templates, demonstrating that latency-bounded, AI-augmented authorization is both technically feasible and economically compelling for enterprises pursuing Zero-Trust maturity. Keywords: API security, Zero Trust Architecture, Contextual Authorization, Cloud-native access control, Policy-as-Code, Open Policy Agent (OPA), OAuth 2.0, OpenID Connect, Machine Learning for security, Risk-adaptive authorization, XGBoost, GRU auto encoder, Isolation Forest, Azure API Management, Real-time access control, Shared Services Platform, Financial services cyber security, AI-driven fraud prevention, Authorization latency optimization, Security integration automation
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Wahyutama, Aria Bisma, and Mintae Hwang. "Auto-Scoring Feature Based on Sentence Transformer Similarity Check with Korean Sentences Spoken by Foreigners." Applied Sciences 13, no. 1 (2022): 373. http://dx.doi.org/10.3390/app13010373.
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This paper contains the development of a training service for foreigners to help them increase their ability to speak Korean. The service developed in this paper is implemented in the form of a mobile application that shows specific Korean sentences to the user for them to record themselves speaking the sentence. The objective is to generate the score automatically based on how similar the recorded voice with the actual sentence using Speech-To-Text (STT) engines and Sentence Transformers. The application is developed by selecting the four most commonly known STT engines with similar features, which are Google API, Microsoft Azure, Naver Clova, and IBM Watson, which are put into a Rest API along with the Sentence Transformer. The mobile application will record the user’s voice and send it to the Rest API. The STT engines will transcribe the file into a text and then feed it into a Sentence Transformer to generate the score based on their similarity. After measuring the response time and consistency as the performance evaluation by simulating a scenario using an Android emulator, Microsoft Azure with 1.13 s is found to be the fastest STT engine and Naver Clova is found to be the least consistent engine with nine different transcribe results.
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Савчук, Т. О., та І. П. Пастух. "РОЗПІЗНАВАННЯ ЕМОЦІЙ УЧАСНИКІВ ВІДЕОКОНФЕРЕНЦІЙ В MICROSOFT TEAMS". Таврійський науковий вісник. Серія: Технічні науки, № 6 (13 лютого 2023): 18–24. http://dx.doi.org/10.32851/tnv-tech.2022.6.3.
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Запропоновано інформаційну технологію розпізнавання емоцій учасників відеоконференцій у Microsoft Teams за рахунок використання нейронної згорткової мережі та засобів технології Azure, а також розроблено її структуру та структуру компонентів платформи Azure. Проаналізовано засоби-аналоги та виявлено їх переваги і недоліки. Проаналізовано узагальнений метод розпізнавання емоцій учасників відеоконфернецій, визначено його недоліки та запропоновано удосконалений метод розпізнавання емоцій учасників відеоконференцій у Microsoft Teams, що дало можливість автоматизувати роботу користувача та отримувати дані про емоційні реакції учасників відеоконференцій на певні новини, оголошення, теми дискусій, для оцінки здібностей ораторів, виявлення емоційно негативних моментів відеоконференцій та причин їх виникнення за рахунок використання бібліотеки Graph API та засобів хмарної технології Azure. Запропонований удосконалений метод ліг в основу відповідної інформаційної технології. Проведено експерименти, кожен з яких передбачав різну кількість учасників відеоконференцій, різні сценарії, різні комбінації ввімкнення та вимкнення камер та різні емоції. Аналіз результатів функціонування показав, що розроблена інформаційна технологія потребує одноразового запуску для того, щоб обробляти будь-яку кількість відеоконференцій, в той час як засоби-аналоги потребують окремого запуску на кожну відеоконференцію. Розроблена технологія надає можливість розпізнати 94% емоцій учасників відеоконференцій на відміну від засобів-аналогів, так як вона підтримує велику кількість учасників одночасно без погіршення якості їх зображень, а також ідентифікувати усіх учасників відеоконференції та їх емоційний стан, що неможливо при використанні сучасних програмних засобів.
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Chode, Balaji. "AI-DRIVEN CENTRAL AUTHORIZATION FRAMEWORK FOR ZERO-TRUST API SECURITY ON MICROSOFT AZURE." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN MANAGEMENT 14, no. 1 (2023): 1–18. https://doi.org/10.34218/ijaiap_04_01_001.
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Wyman, Austin, and Zhiyong Zhang. "API Face Value." Journal of Behavioral Data Science 3, no. 1 (2023): 1–11. http://dx.doi.org/10.35566/jbds/v3n1/wyman.
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Emotion recognition application programming interface (API) is a recent advancement in computing technology that synthesizes computer vision, machine-learning algorithms, deep-learning neural networks, and other information to detect and label human emotions. The strongest iterations of this technology are produced by technology giants with large, cloud infrastructure (i.e., Google, and Microsoft), bolstering high true positive rates. We review the current status of applications of emotion recognition API in psychological research and find that, despite evidence of spatial, age, and race bias effects, API is improving the accessibility of clinical and educational research. Specifically, emotion detection software can assist individuals with emotion-related deficits (e.g., Autism Spectrum Disorder, Attention Deficit-Hyperactivity Disorder, Alexithymia). API has been incorporated in various computer-assisted interventions for Autism, where it has been used to diagnose, train, and monitor emotional responses to one's environment. We identify AP's potential to enhance interventions in other emotional dysfunction populations and to address various professional needs. Future work should aim to address the bias limitations of API software and expand its utility in subfields of clinical, educational, neurocognitive, and industrial-organizational psychology.
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Syeda, Zeba Kauser. "Evaluating Image Processing Capabilities in the Cloud: A Comparative Study of Microsoft Azure and Google Cloud." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 778–86. https://doi.org/10.22214/ijraset.2025.66452.
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Cloud computing is a transformative paradigm that enables the distribution of processing power, application execution, and storage across networks of remote computer systems. This model allows for the flexible allocation and release of IT resources over the internet, offering an affordable solution for both businesses and individuals. Through cloud services, users can access a variety of offerings, such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Desktop as a Service (DaaS), with pricing based on actual usage. In an increasingly competitive market with diverse service options, selecting a long-term cloud provider can be challenging. Dominant providers like Microsoft Azure and Google Cloud lead this market. This paper provides an in-depth evaluation of the image processing services offered by these providers, focusing on Azure Custom Vision, Azure Computer Vision, Azure Cognitive Services, Google Cloud Vision API, and AutoML Vision. The analysis explores the performance and capabilities of these services, emphasizing their strengths and leadership in cloud technology. The primary goal of this study is to offer a comparative analysis of Azure and Google Cloud, helping organizations and users make informed decisions that align with their long-term objectives. In addition, the paper examines the security measures implemented for Integration Platform as a Service (iPaaS) on both platforms, providing a detailed review of their security features and protective mechanisms. The study also highlights key parameters such as performance, scalability, usability, cost, and security to assist organizations in choosing the most appropriate platform for their specific requirements. Case studies and emerging trends in cloud-based image processing are also covered
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Venkata, Raman Immidisetti. "Seamless VMware Workload Migration to Microsoft Azure Using Zerto: A Hypervisor-Based Replication Approach." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 8, no. 5 (2022): 1–6. https://doi.org/10.5281/zenodo.14944996.
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The adoption of hybrid cloud architectures is strategic for enterprises integrating on-premises VMware environments with Microsoft Azure for enhanced scalability, flexibility, and disaster recovery (DR). Organizations face challenges migrating workloads to different Azure regions due to conflicts between Azure Site Recovery (ASR) and Azure Migrate, which use agent-based replication. These conflicts cause operational inefficiencies, replication failures, and data inconsistencies, complicating cloud migration efforts. This study investigates Zerto as an efficient alternative for workload migration and disaster recovery in hybrid cloud environments. Unlike agent-based tools, Zerto's hypervisor-based replication eliminates the need for multiple agents, ensuring a seamless migration process. Through block-level replication, journal-based recovery, and consistency grouping, Zerto enables near-zero Recovery Point Objectives (RPOs) and minimal Recovery Time Objectives (RTOs), ensuring high data integrity and reduced downtime. The paper presents a methodology for migrating VMware workloads to Azure using Zerto, including pre-migration assessment, deployment of replication components, data synchronization, failover, and post-migration validation. A case study highlights Zerto's efficacy over agent-based solutions, demonstrating its ability to reduce complexity, mitigate migration risks, and enhance performance. By leveraging Zerto's unified disaster recovery and migration capabilities, enterprises can achieve seamless cloud transitions while maintaining business continuity, operational efficiency, and cost-effectiveness
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Sethi, KrishnaKanta, and Prof Subhashree Shukla. "Android Application for Hospital Executive." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51320.
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- Hospitals are complex systems requiring timely, data-driven decision-making to manage operations, resources, and patient care efficiently. This thesis presents the development of a secure and real-time Android application for hospital executives. The system integrates HL7 message feeds, SQL databases, and RESTful APIs hosted on Microsoft Azure. Through this mobile app, executives can access key performance indicators such as patient admissions, discharges, census data, and diagnosis trends. The application ensures secure role-based access and real-time synchronization. Performance testing revealed a 98% login accuracy, <2s API response time, and 92% user satisfaction. This project modernizes healthcare management and provides a foundation for future integration with AI and IoT technologies Keywords- Hospital Management System, HL7, Android App, REST API, Real-time Monitoring, Cloud Computing, Role-Based Access, Health Informatics.
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Issa, Reda, Mohamed M. Badr, Omar Shalash, et al. "A Data-Driven Digital Twin of Electric Vehicle Li-Ion Battery State-of-Charge Estimation Enabled by Driving Behavior Application Programming Interfaces." Batteries 9, no. 10 (2023): 521. http://dx.doi.org/10.3390/batteries9100521.
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Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs’ SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study’s results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications.
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Buja, Atdhe. "PROACTIVE MONITORING FOR SMEs USING APPINSIGHT." International Journal of Advanced Research in Computer Science 13, no. 5 (2022): 1–8. http://dx.doi.org/10.26483/ijarcs.v13i5.6901.
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Data Security is a worldwide concern mostly for small medium enterprise (SMEs) and frameworks, approaches, methods are constantly evolving that has a connection with cloud computing, information systems, artificial intelligence, blockchain. Many developers, administrators or product teams running blind. Those are not knowing of problems with their application or do not have the information to fix the problems. The things which can go wrong with web and mobile applications or services is unlimited like dependency failures, resources, and crashes. Main argument is an evaluation of benefits by using Cloud as infrastructure and application on proactive monitoring called Azure Application Insights (AppInsight) towards target like web application, web API, PKI etc. The findings, demonstration of the study should reveal and support our main hypothesis that there is direct link between the proactive monitoring and the main factors that affects utilizing the cloud services. To address this need, in this paper, we introduce AppInsight, the best practice and a model of proactive approach to monitor different targets using Microsoft technology on Azure Cloud services. AppInsight – a model of proactive monitoring includes several functionalities: (1) identifying availability, (2) failures dependencies, (3) performance and (4) using telemetry data generates ad-hoc solution to fix potential failure of web application, web API etc. AppInsight a feature of Azure Monitor used to monitor live applications. AppInsight will automatically detect performance anomalies, and includes powerful analytics tools to help you diagnose issues. You will get a range of telemetry data of analytics of your target which is monitored by AppInsight. To evaluate this tool, we conduct an empirical evaluation by comparing data from actual live monitoring of Y target. Demo Video: https://www.youtube.com/watch?v=q7R8-c0ge7M
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Ravinder Ramidi. "Cloud-Based API Gateways for Seamless Multi-Platform Integration." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2466–503. https://doi.org/10.32628/cseit25112723.
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Cloud-based API gateways offer vital solutions for integrating systems across diverse environments in today's multi-cloud landscape. These gateways provide centralized control for managing, securing, and optimizing API traffic across distributed systems, addressing challenges organizations face when operating across multiple platforms. Key gateway functions include request routing, protocol translation, authentication, rate limiting, caching, lifecycle management, and monitoring capabilities. Major providers—AWS, Azure, and Google Cloud—offer solutions with unique strengths suitable for different business scenarios. Effective integration patterns for customer data, payment processing, and analytics implementations complement optimization techniques spanning performance, security, and fault tolerance. Advanced approaches including serverless functions, microservices patterns, and edge computing enhancements receive particular attention, while a phased implementation roadmap ensures successful adoption across enterprises seeking to modernize their integration infrastructure.
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Горман, Д. О., та Т. О. Коротєєва. "Адаптування користувацьких сервісів для зберігання даних у процесі розроблення програмного забезпечення". Scientific Bulletin of UNFU 33, № 6 (2023): 62–68. http://dx.doi.org/10.36930/40330608.
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Подано розробку та результати оцінювання нової бібліотечної архітектури для зберігання даних у форматі ключ-значення, яка використовує зовнішні кінцеві точки користувацьких API. Це дасть змогу полегшити та здешевити процес збереження даних і конфігурації сховища завдяки можливості безоплатного використання публічних API та мінімізації взаємодії з складною інфраструктурою хмарних провайдерів. Розроблена архітектура дає змогу зберегти модульність внутрішніх компонент, забезпечити безпеку збережених даних, оптимізувати процес зберігання даних і спростити налаштування для розробників. Проаналізовано попередні дослідження щодо спрощення взаємодії зі сховищами ключ-значення, з основним акцентом на підвищенні ефективності та зменшенні витрат. Під час аналізу акцентовано увагу на деталях щодо оптимізації конфігурації та ефективності використання систем ключ-значення. Окреслено ключові особливості цих досліджень та подано недоліки проаналізованих рішень для покращення користувацького досвіду використання сховищ. Наведено результати аналізу популярних рішень для зберігання ключів і значень на підставі хмарних платформ, зокрема Amazon Web Services (AWS) і Microsoft Azure. Встановлено особливості, переваги та недоліки використання цих хмарних рішень, а також особливості як безкоштовних, так і платних планів. Досліджено інтеграцію API GitHub, як системи зберігання ключів і значень, визначено основні функції та можливості цього методу зберігання, який ґрунтується на Git і функціях GitHub. Проаналізовано результати тестів інтеграції API, акцентуючи увагу на продуктивності та ефективності цього рішення. Проведено порівняння сховища "ключ-значення" на підставі інтеграції з GitHub API із традиційними хмарними рішеннями для зберігання. На підставі аналізу та порівняння сформульовано висновок щодо випадків використання та доцільності впровадження дослідженого рішення в розробленні програмного забезпечення.
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Yang, Jeong, and Anoop Abraham. "Analyzing the Features, Usability, and Performance of Deploying a Containerized Mobile Web Application on Serverless Cloud Platforms." Future Internet 16, no. 12 (2024): 475. https://doi.org/10.3390/fi16120475.
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Serverless computing services are offered by major cloud service providers such as Google Cloud Platform, Amazon Web Services, and Microsoft Azure. The primary purpose of the services is to offer efficiency and scalability in modern software development and IT operations while reducing overall costs and operational complexity. However, prospective customers often question which serverless service will best meet their organizational and business needs. This study analyzed the features, usability, and performance of three serverless cloud computing platforms: Google Cloud’s Cloud Run, Amazon Web Service’s App Runner, and Microsoft Azure’s Container Apps. The analysis was conducted with a containerized mobile application designed to track real-time bus locations for San Antonio public buses on specific routes and provide estimated arrival times for selected bus stops. The study evaluated various system-related features, including service configuration, pricing, and memory and CPU capacity, along with performance metrics such as container latency, distance matrix API response time, and CPU utilization for each service. The results of the analysis revealed that Google’s Cloud Run demonstrated better performance and usability than AWS’s App Runner and Microsoft Azure’s Container Apps. Cloud Run exhibited lower latency and faster response time for distance matrix queries. These findings provide valuable insights for selecting an appropriate serverless cloud service for similar containerized web applications.
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Abdelghany, Ahmed, Mohamed Gamal, Dr Tarek Aly, and Prof Dr Mervat Gheith. "Governance for Azure and AWS Cloud Services." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3185–97. http://dx.doi.org/10.22214/ijraset.2024.60595.
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Abstract: These days, cloud computing services are utilized in all business, academic, and governmental domains to build IT infrastructure. Cloud computing services are one IT solution that is becoming more and more popular among different types of enterprises. Cloud computing services face many challenges, like how to protect and raise user knowledge. These problems and difficulties also pertain to cloud computing services, which could result in a number of important risk areas. One of the problems is not being aware of the hazards associated with cloud computing services and the controls that are required to reduce these risks. Because governance is difficult to execute in the cloud computing environment and has the responsibility of assessing performance and adherence to predetermined goals and objectives, it hinders the adoption of cloud computing services. This study aims to develop a model that ensures that the required controls are applied in cloud services offered by Microsoft Azure and Amazon Web Services (AWS) and assesses the risk if the controls are not applied.
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Srinivas, Adilapuram. "Seamlessly Connecting Mainframes to the Cloud for Scalable, Agile and Future-Ready Solutions." European Journal of Advances in Engineering and Technology 7, no. 3 (2020): 63–69. https://doi.org/10.5281/zenodo.14631287.
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Mainframes handle critical operations but often face challenges. They lack scalability and struggle with agility. Modern cloud analytics remains largely untapped. These limitations reduce their effectiveness in today’s data-driven environment. Hybrid IT architecture offers a solution. By connecting mainframes to platforms like AWS, Azure, and GCP, enterprises can unlock their full potential. Secure connectivity enables seamless data exchange. Tools like IBM z/OS Connect facilitate API enablement. Cloud-native services provide scalable compute, data lakes, and AI/ML integration. This article looks at the technical challenges and solutions for connecting mainframes to the cloud.
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Hiden, Hugo, Simon Woodman, Paul Watson, and Jacek Cala. "Developing cloud applications using the e-Science Central platform." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1983 (2013): 20120085. http://dx.doi.org/10.1098/rsta.2012.0085.
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This paper describes the e-Science Central (e-SC) cloud data processing system and its application to a number of e-Science projects. e-SC provides both software as a service (SaaS) and platform as a service for scientific data management, analysis and collaboration. It is a portable system and can be deployed on both private (e.g. Eucalyptus) and public clouds (Amazon AWS and Microsoft Windows Azure). The SaaS application allows scientists to upload data, edit and run workflows and share results in the cloud, using only a Web browser. It is underpinned by a scalable cloud platform consisting of a set of components designed to support the needs of scientists. The platform is exposed to developers so that they can easily upload their own analysis services into the system and make these available to other users. A representational state transfer-based application programming interface (API) is also provided so that external applications can leverage the platform's functionality, making it easier to build scalable, secure cloud-based applications. This paper describes the design of e-SC, its API and its use in three different case studies: spectral data visualization, medical data capture and analysis, and chemical property prediction.
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Mbaya, Emmanuel Baldwin, Babatunde Alao, Philip Ewejobi, Innocent Nwokolo, Victoria Oguntosin, and Emmanuel Adetiba. "NaijaCovidAPI: an application programming interface for retrieval of COVID19 data from the Nigerian Center for Disease Control web platform." F1000Research 10 (December 2, 2021): 1227. http://dx.doi.org/10.12688/f1000research.74998.1.
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Background: In this work, a COVID19 Application Programming Interface (API) was built using the Representational State Transfer (REST) API architecture and it is designed to fetch data daily from the Nigerian Center for Disease Control (NCDC) website. Methods: The API is developed using ASP.NET Core Web API framework using C# programming language and Visual Studio 2019 as the Integrated Development Environment (IDE). The application has been deployed to Microsoft Azure as the cloud hosting platform and to successfully get new data from the NCDC website using Hangfire where a job has been scheduled to run every 12:30 pm (GMT + 1) and load the fetched data into our database. Various API Endpoints are defined to interact with the system and get data as needed, data can be fetched from a single state by name, all states on a particular day or over a range of days, etc. Results: The results from the data showed that Lagos and Abuja FCT in Nigeria were the hardest-hit states in terms of Total Confirmed cases while Lagos and Edo states had the highest death causalities with 465 and 186 as of August 2020. This analysis and many more can be easily made as a result of this API we have created that warehouses all COVID19 Data as presented by the NCDC since the first contracted case on February 29, 2020. This system was tested on the BlazeMeter platform, and it had an average of 11Hits/s with a response time of 2905milliseconds. Conclusions: The extension of NaijaCovidAPI over existing COVID19 APIs for Nigeria is the access and retrieval of previous data. Our contribution to the body of knowledge is the creation of a data hub for Nigeria's COVID-19 incidence from February 29, 2020, to date
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Solovei, Olha, Tetiana Honcharenko, and Anatolii Fesan. "Technologies to manager big data of urban building projects." Management of Development of Complex Systems, no. 60 (November 29, 2024): 121–28. https://doi.org/10.32347/2412-9933.2024.60.121-128.
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The transformation of the construction industry according to the Construction 4.0 concept is possible with the availability of technology for managing big data of construction projects, where the task of managing big data includes tasks: collection; processing; renewal; backup and save data. Today, the information technologies of urban construction projects are a complex of integrated software complexes, and the data of construction projects remain stored in various data repositories, which makes it difficult, and sometimes impossible, to use them for project implementation. The choice of big data management technologies, including, depends on the types of big data that are characteristic of the project. The purpose of this work is to define a list of technologies for managing various types of data of urban construction projects to enable their use for the automation of urban construction projects. To achieve the goal, the work analyzed the types and formats of data information systems of urban construction projects, namely: business process management systems; systems of interaction with interested parties; labor protection and risk management systems in construction; operation management systems; systems for designing and creating models of spatial objects; systems of augmented reality (VR/AR); systems for engineering analysis. Based on the analysis of data types and formats, it is determined that the data belongs to the following categories: structured, semi-structured and unstructured data types. Studying the developments of scientists regarding big data management technologies of certain types made it possible to compile a list of technologies for data management of urban construction projects, namely: for data collection – Apache Kafka, Apache Hbase, Apache Spark, Apache Hadoop, Stream Analytics, Scrapy, Twitter API, Facebook Graph API; for data processing – technologies "Intelligent analysis of texts", "Computer vision", machine and deep learning; 3) for data storage – AWS S3, AWS RDS SQL, Azure Data Lake, HDFS, Redi, CosmosDB, MongoDB, Azure Blob Storage; 4) for backup – AWS Backup, Google Cloud Storage, Microsoft Azure Backup, MongoDB Backup, Cassandra Backup; to update – Apache Kafka/Flink/Spark Streaming, SQL. Further research will consist in conducting an analysis of the effectiveness of the methods of the specified technologies for solving the tasks of data management of construction projects, depending on the nature of the data input.
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Mihail Mateev. "Comparative Analysis on Implementing Embeddings for Image Analysis." Journal of Information Systems Engineering and Management 10, no. 17s (2025): 89–102. https://doi.org/10.52783/jisem.v10i17s.2710.
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This research explores how artificial intelligence enhances construction maintenance and diagnostics, achieving 95% accuracy on a dataset of 10,000 cases. The findings highlight AI's potential to revolutionize predictive maintenance in the industry. The growing adoption of image embeddings has transformed visual data processing across AI applications. This study evaluates embedding implementations in major platforms, including Azure AI, OpenAI's GPT-4 Vision, and frameworks like Hugging Face, Replicate, and Eden AI. It assesses their scalability, accuracy, cost-effectiveness, and integration for multimodal applications. Image embeddings convert visual data into numerical representations for tasks such as object detection and anomaly identification. GPT-4 Vision excels in object recognition and retrieval-augmented generation (RAG), while cost-effective variants like GPT-4o support large-scale applications. Azure AI Vision enhances text-image integration for media curation and content moderation. Third-party frameworks, such as Hugging Face's ImageBind, Replicate, and Eden AI's API aggregation, offer customization and cost efficiency. Hybrid embedding solutions using decomposition techniques, such as Separation of concerns (SoC) and digital twins (DT), optimize predictive analytics workflows. Practical applications include construction defect detection with 99.4% accuracy, security anomaly detection, medical diagnostics, and e-commerce personalization. This comparative analysis underscores the transformative potential of image embeddings in AI applications. Integrating multimodal technologies, hybrid solutions, and cost-efficient strategies positions image embeddings as a cornerstone of modern AI systems. Future research should explore automated decomposition for complex tasks, expand hybrid models, and maximize API aggregation platforms like Eden AI for embedding generation
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Malighetti, Clelia, Simona Sciara, Alice Chirico, and Giuseppe Riva. "Emotional Expression of #body on Instagram." Social Media + Society 6, no. 2 (2020): 205630512092477. http://dx.doi.org/10.1177/2056305120924771.
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Our aim was to explore emotions in Instagram images marked with hashtags referring to body image–related components using an artificial intelligence–based discrete emotional analysis. A total of 500 Instagram photos marked by specific hashtags related to body image components were analyzed and specific discrete emotions expressed in each picture were detected using the Emotion application program interface API from Microsoft Azure Cognitive Service. Results showed that happiness and neutrality were the most intense and recognizable emotions expressed in all images. Happiness intensity was significantly higher in images with #bodyimage and #bodyconfidence and higher levels of neutral emotion were found in images tagged with #body, #bodyfitness, and #thininspirational. This study integrated a discrete emotional model with the conventional dimensional one, and offered a higher degree of granularity in the analysis of emotions–body link on Instagram through an artificial intelligence technology. Future research should deepen the use of discrete emotions on Instagram and the role of neutrality in body image representation.
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Holilah, Holilah, Husyen Ali Alhabsy, Tb Muhammad Farhan Adnan, et al. "Aplikasi Kebugaran Defense In Depth Untuk Aplikasi Kebugaran Perusahaan Start-Up Menggunakan Microsoft Azure." Jurnal Ilmiah Sains dan Teknologi 9, no. 1 (2025): 10–19. https://doi.org/10.47080/saintek.v9i1.3867.
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A start-up company in the fitness and health sector has developed an innovative virtual training platform. The platform allows users to take live fitness classes via their computer, tablet or smartphone, leveraging artificial intelligence (AI) and high-quality video streaming for an interactive and personalized training experience. The platform consists of three main components: the frontend layer (web and mobile applications), the backend layer (application servers), and the data storage layer (cloud database). As the popularity of these platforms increases, companies face challenges in managing on-premises resources, ensuring data security, and complying with strict data protection regulations. This research aims to design a comprehensive cyber security strategy by utilizing Microsoft Azure services to overcome these challenges. This strategy includes identity and access management, data encryption, threat detection and response, and regulatory compliance.
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Nonaka, Fumiaki, Shinya Kawashiri, and Atsushi Kawakami. "Next-generation rheumatoid arthritis specialized telemedicine enabled by IoT and AI." Impact 2021, no. 8 (2021): 58–60. http://dx.doi.org/10.21820/23987073.2021.8.58.
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Ageing populations in developed countries place strain on healthcare systems and when ageing populations live far away from the specialists they require to treat their chronic diseases, the logistics can be challenging to say the least. This is a particularly acute issue in Japan, which is made up of small islands. The COVID-19 pandemic has served to emphasise the need for better systems for remote medical consultations. Dr Fumiaki Nonaka at Goto Chuou Hospital and Professor Atsushi Kawakami and Dr Shinya Kawashiri at Nagasaki University Hospital have collaborated with Microsoft to create the first remote consultation systems for rheumatoid arthritis (RA) that uses mixed reality. Early detection is crucial to managing RA but it is often difficult to make an accurate diagnosis and treatment of the disease in remote islands. This issue became particularly pressing in the context of COVID-19. Working with Microsoft, the researchers sought to develop a method of viewing and rendering the joints in 3D. Microsoft adapted their Azure Kinect DK cameras into a rig covering three different angles of any joint and an array of seven microphones was also installed. Using a reality headset called HoloLens2, the images of the joint can be rendered a 3D hologram to the user, enabling doctors to freely observe the joint from any direction. Combining this with Microsoft's Teams, a face-t-face consultation is facilitated.
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K., Edison, and Sani U. "Students’ Attendance Management in Higher Institutions Using Azure Cognitive Service and Opencv Face Detection & Recognition Attendance System." British Journal of Computer, Networking and Information Technology 5, no. 1 (2022): 43–55. http://dx.doi.org/10.52589/bjcnit-alqqmeee.
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This research aimed at studying the current methods of attendance used at higher institutions of learning in Uganda and the feasibility of using facial biometrics as a new method of capturing attendance. Facial biometrics is distinct from other biometrics because it can be carried out without the consent of the person involved. As a result, the researcher developed a face recognition attendance system using OpenCV and Microsoft Azure CS. Questionnaires, interviews, and observations were used to capture data for the research. The data were analyzed using SPSS to get the requirements and systems functionalities. Object-Oriented Design tools were used to model the architecture of the system. Data Flow Diagram, Use-Case Diagram, Activity Diagram, and Flow Chart were used for processing whereas Entity Relation Diagram was used for data modeling. The system was designed to facilitate attendance management of a large number of attendees with ease. Efficiency and reliability were essential features of the system. Data visualization was provided to help management make informed and timely decisions on management matters that are related to attendance. The system was developed using python Tkinter, OpenCV, and Azure CS as mentioned above. The data (images) used by the system were stored in the cloud for accessibility by multiple users. The system was tested thoroughly using various testing types to uncover and fix errors and to minimize the severity of failures.
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Chandra, T. Bharath. "Bridging the Visual Gap: Integrating Vision Language Models (VLM) and Artificial Intelligence (AI) with Enterprise Resource Planning (ERP) Software System." International Research Journal of Innovations in Engineering and Technology 09, Special Issue ICCIS (2025): 200–205. https://doi.org/10.47001/irjiet/2025.iccis-202532.
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Abstract - Businesses generate vast visual data (e.g., quality check photos, warehouse snapshots, invoices, customer images), but traditional Enterprise Resource Planning (ERP) systems, built for structured data, cannot process it. This study explores integrating Vision Language Models (VLMs), AI combining computer vision and language processing, with ERPs to automate tasks like quality control, inventory monitoring, and document processing. We assess integration feasibility with Microsoft Dynamics 365 Business Central, Salesforce, and SAP S/4HANA, proposing an API-driven system architecture. VLMs face precision challenges, and ERP readiness varies: Microsoft Dynamics needs custom development, Salesforce offers flexible APIs, and SAP S/4HANA is robust but complex. Strategic planning and leveraging VLM strengths enable AI-enhanced enterprise systems.
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Roe, Charlotte, Madison Lowe, Benjamin Williams, and Clare Miller. "Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis." International Journal of Environmental Research and Public Health 18, no. 24 (2021): 13028. http://dx.doi.org/10.3390/ijerph182413028.
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Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter’s Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.
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Ravichandra Mulpuri. "Comprehensive Review of Multi-cloud Architecture for Salesforce in Enterprise Environments." International Journal of Science and Research Archive 15, no. 3 (2025): 1825–38. https://doi.org/10.30574/ijsra.2025.15.3.1967.
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The adoption of multicloud architectures is rapidly increasing as enterprises seek flexibility, scalability, and optimization across diverse workloads. This paper examines the integration of Salesforce, a leading cloud-based CRM platform, within multicloud environments, particularly in large enterprise settings. It explores how Salesforce can operate seamlessly alongside platforms such as AWS, Microsoft Azure, and Google Cloud to support real-time data synchronization, resource allocation, and system interoperability. Key integration techniques discussed include API-based communication (REST, OData, GraphQL), data virtualization, and ETL processes to ensure data consistency across cloud platforms. The paper also addresses performance optimization strategies, including the distribution of workloads across specialized cloud services and the use of DevOps practices like CI/CD pipelines and monitoring tools. Security and compliance considerations are explored in the context of regulations such as GDPR, HIPAA, and CCPA, emphasizing the importance of unified governance, encryption, and access controls. Containerization technologies like Docker and Kubernetes are highlighted for their role in managing consistent deployments across multicloud ecosystems. Overall, this review provides a comprehensive analysis of the opportunities and challenges in integrating Salesforce within a multicloud strategy, offering insights into achieving operational efficiency, regulatory compliance, and scalable CRM performance.
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Ravi Teja Balla. "Oracle Fusion Cloud: Empowering Intelligent Integration for Digital-First Enterprises." Global Journal of Engineering and Technology Advances 23, no. 3 (2025): 264–70. https://doi.org/10.30574/gjeta.2025.23.3.0199.
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This article presents a comprehensive overview of Oracle Fusion Cloud as a strategic integration backbone for digital-first enterprises. In today's interconnected economy, organizations face challenges with siloed operations and fragmented data architectures that hinder visibility, efficiency, and innovation. The article explores how Oracle Fusion Cloud addresses these challenges through a modular ecosystem that enables holistic integration, enterprise extensibility, business process automation, and actionable intelligence. It examines enterprise-grade integration patterns, including event-driven integration, Integration Platform as a Service (iPaaS), and API-driven architecture, and hybrid data loads while outlining a framework for scalable, secure implementation. The discussion emphasizes modern iPaaS solutions such as Oracle Integration Cloud (OIC), Azure Logic Apps, and MuleSoft that have revolutionized enterprise integration through cloud-native capabilities. A case study demonstrates how integration between ERP and CRM systems streamlines the Quote-to-Cash process, highlighting tangible business value through improved efficiency, accuracy, and customer satisfaction.
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M, Dr Ramakrishna, and Akshay Bharadhwaj. "Tracking Realness Using Smart Technology." International Journal of Innovative Research in Information Security 11, no. 02 (2025): 153–56. https://doi.org/10.26562/ijiris.2025.v1102.14.
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Computer vision technologies along with deep learning algorithms have boosted the creation of deepfakes by making manipulated multimedia data more authentic. Extremely believable forged media objects now enable scammers to distribute deceptive information while executing financial schemes. This paper presents Tracking Realness Using Smart Technology as a complete image and video authentication system which operates at scale. Our system follows successive steps beginning with video-to-image conversion through discrete frame extraction followed by MTCNN and Azure Vision API face detection for dominant facial identification followed by dataset development for EfficientNetB0 CNN binary classification training. The deployed model operates through a web application constructed with Django programming together with Docker container distribution features for time-sensitive operations. Results of Deepfake Detection Challenge, FaceForensics++ and Celeb-DF datasets validate high accuracy of detection of the system with low power consumption. A solid foundation for enhancing automated approaches to deep fake detection and multimedia security systems.
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Rehman, Rizwan Ur, Uzair Khaleeq uz Zaman, Shahid Aziz, et al. "Process Parameter Optimization of Additively Manufactured Parts Using Intelligent Manufacturing." Sustainability 14, no. 22 (2022): 15475. http://dx.doi.org/10.3390/su142215475.
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Additive manufacturing is the technique of combining materials layer by layer and process parameter optimization is a method used popularly for achieving the desired quality of a part. In this paper, four input parameters (layer height, infill density, infill pattern, and number of perimeter walls) along with their settings were chosen to maximize the tensile strength for a given part. Taguchi DOE was used to generate an L27 orthogonal array which helped to fabricate 27 parts on the Ender 3 V2 fused deposition modeling (FDM) printer. The ultimate testing machine was used to test all 27 samples to generate the respective tensile strength values. Next, the Microsoft Azure ML database was used to predict the values of the tensile strength for various input parameters by using the data obtained from Taguchi DOE as the input. Linear regression was applied to the dataset and a web service was deployed through which an API key was generated to find the optimal values for both the input and output parameters. The optimum value of tensile strength was 22.69 MPa at a layer height of 0.28 mm, infill density of 100%, infill pattern of honeycomb, and the number of perimeter walls as 4. The paper ends with the conclusions drawn and future research directions.
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Dr., Juby Mathew, Sen Easow Neil, Shankar Rajalakshmi, Babu Nandhu, and Pratap Singh Rudra. "Career Finder: AI powered career guider." International Journal on Emerging Research Areas (IJERA) 05, no. 01 (2025): 174–77. https://doi.org/10.5281/zenodo.15187120.
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This paper discusses the overall design, development, and deployment of an AI-based career recommendation system, organized into four interdependent modules: User Interface (UI) Design and Development, Backend Development and API Management, AI Model Integration and Recommendation Engine, and Database and Deployment. The platform leverages cutting-edge technologies such as React.js for a dynamic front-end, Flask for robust backend API development, OpenAI GPT-based models (or alternatives like Hugging Face Transformers or LLaMA) for personalized career insights, and MongoDB for scalable data storage. The UI module prioritizes creating an intuitive and responsive user experience, incorporating features like dynamic forms and skill gap analysis dashboards. The Backend module focuses on developing secure and efficient RESTful APIs to handle user data processing and AI model communication. The AI Model Integration module delves into natural language processing (NLP) techniques to analyze user inputs, match skills, and generate tailored career recommendations. The Database and Deployment module is focused on data management that scales and is secure on cloud systems such as AWS, Azure, or Google Cloud, with authentication through Firebase and CI/CD through GitHub Actions. The project focuses on team collaboration, with tools such as Jira and GitHub used for easy integration between modules and effective development practices. This paper lays out the process of development, challenges encountered, improvements in the future of the platform and possible improvements in the future of the platform. Keywords— Career recommendation, AI, React.js, Flask, OpenAI GPT, MongoDB, user interface, backend development, machine learning, NLP, cloud deployment, REST API, skill gap analysis.Introduction.
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Macedo, Eric, Liu Haoran, Zihao Fan, Marcus Navarro, José Guilherme Peixoto, and Romain Coulon. "Building a Radionuclide Metrology Algorithm Comparison Platform (NuCodeComP): Insights from Rapid Integration with Microsoft PowerApps." EPJ Web of Conferences 323 (2025): 11003. https://doi.org/10.1051/epjconf/202532311003.
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An essential step in radionuclide activity measurement comparisons is the counting signal processing performed by laboratory-specific software. This has led to initiatives comparing software performance using a unique dataset from a list-mode data structure, as defined in IEC 63047:2021. A virtual Radionuclide Metrology Algorithm Comparison Platform was developed at BIPM to ensure a FAIR approach and facilitate broader laboratory participation. Beyond presenting the final product, this work highlights key insights from its development. The platform was designed to be reliable, adaptable, and future-proof, aligning with the D-SI framework to ensure machine-readable, structured, and interoperable data. A strategic decision was made to investigate Microsoft 365, leveraging its infrastructure to real-test software operations before investing in scalability. However, scalability limitations arise, requiring Azure solutions like SQL Server for big data applications. The drag-and-drop interface simplifies the platform’s development in interface design while utilizing SharePoint tables for data structuring and access control. However, performance constraints exist, particularly with SharePoint’s row-number limitation. Furthermore, non-M365 users face access barriers mitigated through basic M365 plans or per-app licensing. This case study serves as a model for digital metrology projects, balancing cost-effective IT solutions with scalability and accessibility challenges while ensuring alignment with SI Digital transformation.
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Chaudhari, V. J. "Currency Recognition App." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 435–37. http://dx.doi.org/10.22214/ijraset.2021.34982.
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Visually Impaired & foreign people are those people who have vision impairment or vision loss. Problems faced by visually impaired in performing daily activities are in great number. They also face a lot of difficulties in monetary transactions. They are unable to recognize the paper currencies due to similarity of paper texture and size between different categories. This money detector app helps visually impaired patients to recognize and detect money. Using this application blind people can speak and give command to open camera of a smartphone and camera will click picture of the note and tell the user by speech how much the money note is. This Android project uses speech to text conversion to convert the command given by the blind patient. Speech Recognition is a technology that allows users to provide spoken input into the systems. This android application uses text to speech concept to read the value of note to the user and then it converts the text value into speech. For currency detection, this application uses Azure custom vision API using Machine learning classification technique to detect currency based on images or paper using mobile camera.
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Lorido-Botran, Tania, and Muhammad Khurram Bhatti. "ImpalaE: Towards an optimal policy for efficient resource management at the edge." Journal of Edge Computing 1, no. 1 (2022): 43–54. http://dx.doi.org/10.55056/jec.572.
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Edge computing is an extension of cloud computing where physical servers are deployed closer to the users in order to reduce latency. Edge data centers face the challenge of serving a continuously increasing number of applications with a reduced capacity compared to traditional data center. This paper introduces ImpalaE, an agent based on Deep Reinforcement Learning that aims at optimizing the resource usage in edge data centers. First, it proposes modeling the problem as a Markov Decision Process, with two optimization objectives: reducing the number of physical servers used and maximize number of applications placed in the data center. Second, it introduces an agent based on Proximal Policy Optimization, for finding the optimal consolidation policy, and an asynchronous architecture with multiple workers-shared learner that enables for faster convergence, even with reduced amount of data. We show the potential in a simulated edge data center scenario with different VM sizes based on Microsoft Azure real traces, considering CPU, memory, disk and network requirements. Experiments show that ImpalaE effectively increases the number of VMs that can be placed per episode and that it quickly converges to an optimal policy.
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Santos, Marcela, and Reinaldo Gomes. "Strengthening Trust in Virtual Trusted Platform Modules: Integrity-Based Anchoring Mechanism for Hyperconverged Environments." Applied Sciences 15, no. 10 (2025): 5698. https://doi.org/10.3390/app15105698.
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Virtual Trusted Platform Modules (vTPMs) are widely adopted in commercial cloud platforms such as VMware Cloud, Google Cloud, Microsoft Azure, and Amazon AWS. However, as software-based components, vTPMs do not provide the same security guarantees as hardware TPMs. The existing solutions attempt to mitigate this limitation by anchoring vTPMs to physical TPMs, but such approaches often face challenges in heterogeneous environments and in failure recovery or migration scenarios. Meanwhile, the evolution of data center architectures toward hyperconverged infrastructures introduces new opportunities for security mechanisms by integrating compute, storage, and networking into a single solution. This work proposes a novel mechanism to securely anchor vTPMs in hyperconverged environments. The proposed approach introduces a unified software layer capable of aggregating and managing the physical TPMs available in the data center, establishing a root of trust for vTPM anchoring. It supports scenarios where hardware TPMs are not uniformly available and enables anchoring replication for critical systems. The solution was implemented and evaluated in terms of its performance impact. The results show low computational overhead, albeit with an increase in anchoring time due to the remote anchoring process.
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Vamsi Krishna, M., A. Bhargav Reddy, and V. Sandeep. "Facial recognition enabled smart door unlock system." International Journal of Engineering & Technology 7, no. 2.7 (2018): 183. http://dx.doi.org/10.14419/ijet.v7i2.7.10289.
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To verify that our daily life is going in a secure way. Lot of research programmers are going on in this entire society. The turning point comes through the internet of things, industry has been emerged with the lots of elements provided from IOT. We can able to connect our daily life things or objects with this had successfully evolved lots of things. This Facial recognition door unlock system is a process is which will detect the face and identifies the among people. People are having different types of face cut, in that particularly there are many unique faces which are different from each other which inspired us, from that concept this process has been established. Our main aim to create the smart door system to a house, that will secure the house and all your personal things at your home. In this concept of our system we have been used alive web camera in the front side of the door, along with the display monitor. this web camera shows the owner/particular viewer the whom the house is his control, this shows the person who stood front of the door, the system is setup the voice output is being processed by the processor that which is used to show the answers/instructions as the output on the screen. We are using a stepper motor that which is used to lock/open then the by sliding method, so that a normal person stand in front of the door and access it. This process is done through this Microsoft face API application. The display is being operated on a Microsoft Visual Studio application.
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Narendra Kumar Reddy Choppa, John Wesley Sajja, and Govindaraja Babu Komarina. "From complexity to clarity: Generative AI in data analytics." World Journal of Advanced Research and Reviews 26, no. 3 (2025): 349–61. https://doi.org/10.30574/wjarr.2025.26.3.2071.
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The rapid pace of digital transformation has established data analytics as a critical driver of organizational success, yet traditional methods often face challenges in complexity, scalability, and accessibility. This article explores how Generative AI, augmented by AI agents, transforms data analytics by streamlining workflows, enhancing decision-making, and delivering personalized analytics experiences across the analytical lifecycle. Leveraging cloud-native architectures, edge computing, and integration with enterprise platforms like SAP S/4HANA, Microsoft Fabric, Power BI, and Azure AI Foundry, Generative AI and AI agents automate data preparation, enable natural language querying, generate predictive and prescriptive insights, and enhance visualization and narrative storytelling. AI agents drive autonomous tasks, such as real-time anomaly detection and workflow orchestration, amplifying analytical agility. Empirical evidence demonstrates significant quantitative benefits—reduced time-to-insight by 63% and increased analytics adoption by 210% alongside qualitative gains in decision quality and cross-functional collaboration. The article highlights transformative outcomes, including cost efficiency, organizational agility, and democratized data strategies, while addressing challenges like data governance, ethical AI frameworks, and performance optimization. Open-source GenAI contributions further enrich innovation. Looking forward, it proposes research into real-time analytics, multimodal AI, agent-driven domain adaptations, personalized analytics, and standardized governance, providing a roadmap for next-generation analytics that balances innovation with ethical and organizational imperatives.
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A., Jangili*1 S. Ramakrishnan2 S. Seth3. "Harnessing Data Analytics for Improving Management Information Systems (MIS) in Healthcare." International Journal of Pharmaceutical Sciences 3, no. 1 (2025): 1787–95. https://doi.org/10.5281/zenodo.14709903.
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Data-driven strategies are being used more and more by healthcare systems to boost patient outcomes, increase operational effectiveness, and optimize resource management. Finding inefficiencies, forecasting patient needs, and assisting with evidence-based decision-making have all been made possible by data analytics. However, small and medium-sized healthcare facilities face barriers such as limited financial resources, inadequate technical infrastructure, and insufficient expertise that prevent them from leveraging these advancements. The above problems require a proposal to design light cloud-based analytics frameworks for small facilities. It makes use of open-source tools such as Python and R while leveraging cloud infrastructure, like AWS and Microsoft Azure, to cut costs on infrastructure. Furthermore, the paper calls for the implementation of interoperable solutions aligned with international standards such as HL7 and FHIR to promote the seamless flow of data among fractured systems. This paper integrates ethical considerations, including data privacy and mitigating algorithmic bias, to ensure fair and secure implementation. Finally, it proposes cost-benefit analyses and economic models, such as ROI and Total Cost of Ownership (TCO), for justification of long-term value analytics adoption. With practical and scalable solutions, this study empowers healthcare organizations of all sizes to realize the transformative power.
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Didok, V., and M. Pan. "IMPLEMENTATION OF DIGITAL TWINS BASED ON GPT-3.5 FOR ENHANCING ENGLISH LANGUAGE LEARNING IN HIGHER EDUCATION INSTITUTIONS." Municipal economy of cities 6, no. 187 (2024): 8–11. https://doi.org/10.33042/2522-1809-2024-6-187-8-11.
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This paper presents the development of an interactive web-based platform aimed at enhancing English language learning through the utilization of digital twins, powered by the GPT-3.5 model. The platform integrates three primary services essential for language learning: vocabulary acquisition, grammar checking, and conversational practice via simulated interlocutors, offering a dynamic, personalized learning experience. Detailed technical aspects of the system’s architecture are discussed, highlighting the implementation of the Model-View-Controller (MVC) framework, which ensures modularity and scalability. The platform’s backend is developed using C# for reliability and performance, while the frontend leverages HTML, CSS, JavaScript, and the Bootstrap framework to create a responsive, user-friendly interface that adapts to various screen sizes and devices. To support large volumes of user data, a Microsoft Azure SQL Database is employed for robust data management, enabling efficient storage and retrieval of user information, interaction histories, and progress logs. Integration with GPT-3.5 via OpenAI's API facilitates real-time query processing and response generation, making the platform a powerful tool. The platform uses advanced personalization algorithms to adjust learning content based on user preferences, progress, and interaction history. This personalized approach increases student engagement and promotes more effective learning outcomes by adapting content to individual needs. Security and ethical considerations are addressed through encryption protocols and authentication mechanisms. In addition, OpenAI’s built-in content filtering systems ensure that inappropriate or harmful content is blocked, safeguarding users while maintaining high ethical standards. Furthermore, a detailed cost calculation model for token usage is presented, which allows for precise tracking of operational costs, ensuring the platform’s long-term sustainability. By integrating cutting-edge technology with pedagogical principles, this platform demonstrates the potential to revolutionize English language learning in higher education institutions, making it more interactive, personalized, and effective.
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Guillot, Jordan, Brenda Miao, Arvind Suresh, et al. "Constructing adverse event timelines for patients receiving CAR-T therapy using large language models." Journal of Clinical Oncology 42, no. 16_suppl (2024): 2555. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.2555.
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2555 Background: Chimeric antigen receptor T (CAR-T) therapy is associated with a high risk of severe adverse events often only detailed in clinical notes. Monitoring them demands significant time and effort for manual chart review. Recent developments in large language modeling (LLMs) show promise for large-scale information extraction from clinical text. We performed a pilot study to evaluate the capability of the GPT-4 LLM to extract adverse events documented in the progress reports. Methods: We extracted progress notes within 30 days of any CAR-T administration from the UCSF deidentified clinical data warehouse. GPT-4, accessed through a HIPAA compliant Microsoft Azure Studio API, was used to extract CAR-T adverse events resulting in clinical intervention. A random sample of adverse events from 10% of patient notes were evaluated by a clinical reviewer (JG, PharmD). Topic modeling using BERTopic was used to cluster all adverse events to identify trends over time. Results: We identified 4183 clinical notes written within 30 days of CAR-T administration from 253 patients (39.1% women, 60.9% men). Mean age was 60.6 (SD:17.7). Manual validation of clinical notes from 10% of patients with CAR-T therapies (n=25) demonstrated that GPT4 was able to extract CAR-T related adverse events with 64% accuracy. We used BERTopic to cluster all extracted adverse events into 19 topics. Clusters with key terms “hyponatremia, leukocytosis, encephalopathy, toxicities, and neurologic” occurred most often (n=277), and primarily documented 12.9 days after CAR-T administration (Table). Conclusions: Although limited by use of de-identified data and absence of prompt engineering, this pilot supports the further investigation of LLMs for extraction of adverse events from unstructured clinical text. [Table: see text]
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Dierks, Ludwig, Ian Kash, and Sven Seuken. "On the Cluster Admission Problem for Cloud Computing." Journal of Artificial Intelligence Research 71 (May 5, 2021): 1–40. http://dx.doi.org/10.1613/jair.1.12346.
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Cloud computing providers face the problem of matching heterogeneous customer workloads to resources that will serve them. This is particularly challenging if customers, who are already running a job on a cluster, scale their resource usage up and down over time. The provider therefore has to continuously decide whether she can add additional workloads to a given cluster or if doing so would impact existing workloads’ ability to scale. Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low efficiency (i.e., low average utilization of the cluster). We propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization. We first introduce the cluster admission problem and formalize it as a constrained Partially Observable Markov Decision Process (POMDP). As it is infeasible to solve the POMDP optimally, we then systematically design admission policies that estimate moments of each workload’s distribution of future resource usage. Via extensive simulations grounded in a trace from Microsoft Azure, we show that our admission policies lead to a substantial improvement over the simple threshold policy. We then show that substantial further gains are possible if high-quality information is available about arriving workloads. Based on this, we propose an information elicitation approach to incentivize users to provide this information and simulate its effects.
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Alam, Md Azad. "FACIAL RECOGNITION ATTENDANCE SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29448.
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Colleges have historically faced a great deal of difficulty with student attendance, necessitating a large time and effort investment from staff in manual tracking. Even though they are in place, the existing biometric attendance systems are not entirely automated, which causes delays in processing fingerprints, maintenance issues, and inefficiencies in time. Given that almost everyone has a smartphone and is continuously online in this day and age, a more simplified method is necessary. This study suggests using sophisticated object identification algorithms to check attendance using faculty members' smartphones. Because of its effectiveness in face detection and the addition of Microsoft Azure's face API for database recognition, YOLO V3 (You Only Look Once) is the preferred option among these. One special feature of the system is that it takes pictures of the classroom at the start and finish of every class to make sure everyone is present. After determining the number of students in each photograph, YOLO V3 separates the faces that are known and those that are unknown, creating distinct spreadsheets. Monthly email reminders are also sent to teachers, parents, and students. The system that has been put into place shows strong real-time performance in counting and detecting jobs, with excellent facial recognition accuracy and overall efficiency. Keywords:, OpenCV, Local Binary Pattern Histogram (LBPH), Real-time Tracking, Facial Analysis, You Only Look Once (YOLO V3), Firebase Database.
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Nath, Yamuna, Ashok Nath Yogi, Bibechana Paudel, Rabita Bhandari, Ashok k. Yadav, and Suraksha Acharya. "PROFITABILITY AND PRODUCTIVITY OF POTATO IN DARCHULA DISTRICT OF NEPAL." Agribusiness Management In Developing Nations 8, no. 2 (2024): 78–87. https://doi.org/10.26480/amdn.02.2024.78.87.
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Potato is considered a staple food in the Hill region of Nepal. It is considered a major crop (in cropping pattern) of the households in Api-himal RM, Naugadh VDC, Marma RM of Darchula District. The potato produced in these areas has a good quality reputation and is the major source of income. There are very few research studies that have assessed the profitability of potatoes in these areas. So, this study aimed to assess the profitability and productivity of potatoes in Darchula District of Nepal. The three areas were purposively selected as they have much better potato production in comparison to others in the district. With random sampling techniques, household respondents were collected. The respondents were interviewed using the face-to-face interview method in the month of February 2022. Altogether, 102 samples (50 from Api-himal RM, 31 from Marma RM, and 21 from Naugadh VDC) were selected. The necessary result was obtained by analyzing socio-economic and demographic characteristics, benefit-cost ratio, and production function using SPSS, Stata, and Microsoft Excel. The average productivity was found low due to disease infestation on crops. The per ha total cost of production was NRs.13, 008.75 with a total income of NRs.14, 832. The cost of FYM (31.06%) followed by harvesting cost and planting cost. The per hectare profit for potato production was NRs.1, 823.25 and per hectare total income from potatoes was found at NRs.14832 with a B/C ratio of 1.14 in the study area. The low production and productivity were due to the infestation of disease on standing crops. The technical and managerial skills in cultivation practices and provision of technical knowledge to control the disease as well as proper allocation of inputs and available resources would help to increase the profitability and productivity of potatoes. It is suggested to use disease-resistance-improved varieties and follow appropriate recommended cultural practices.
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Kosarevskyi, Bohdan, and Artem Tetskyi. "Modern approaches to deploying the infrastructure of mobile intelligent systems." INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, no. 2(32) (June 30, 2025): 33–48. https://doi.org/10.30837/2522-9818.2025.2.033.
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Subject matter: The infrastructure of mobile intelligent systems (MIS) for monitoring critical assets using groups of unmanned aerial vehicles (UAVs), integrating edge and cloud computing, load balancing methods, and cybersecurity mechanisms. Goal: To investigate the efficiency of automated resource management in MIS through adaptive scaling, heuristic optimization, and failure prediction methods to enhance system reliability and performance. Tasks To design an MIS architecture with hybrid distribution of computations between edge and cloud components; to evaluate the impact of resource balancing mechanisms under variable load conditions; to assess the effectiveness of multi-channel communication technologies in the event of primary link failure; and to implement cybersecurity methods to ensure uninterrupted system operation. Methods: Theoretical analysis of existing approaches, modeling and simulation to assess performance and reliability. The study involves the use of genetic algorithms, swarm intelligence, and artificial potential fields for UAV trajectory management. Results: Experimental evaluations conducted in Microsoft Azure and CoppeliaSim EDU environments confirmed the effectiveness of the proposed approaches. Average latency was reduced by 35%, energy consumption was optimized, and uninterrupted data transmission was ensured in 92% of connection failure scenarios. Reliability analysis showed the benefits of component redundancy and predictive failure detection, reducing the probability of critical faults by 22% and shortening recovery time by 42%. Conclusions: Automated resource management in MIS ensures operational stability and continuity for UAV groups even under dynamic operating conditions. Computational optimization and adaptive scaling enhance system performance, reduce transmission delays, and improve energy efficiency. The developed cybersecurity approaches ensure data protection and infrastructure resilience in the face of external threats and network attacks.
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Martseniuk, Y. V. "EXPLORING THE EFFECTIVENESS OF USING CENTRALIZED CONFIGURATION STORAGE TO SECURELY MANAGE CLOUD SERVICES INFRASTRUCTURE." Computer systems and network 7, no. 1 (2025): 218–34. https://doi.org/10.23939/csn2025.01.218.
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In the current context of widespread adoption of cloud technologies such as AWS, GCP, and Azure, organizations face challenges in centralized management of cloud resources, including ensuring security standards, monitoring service metrics, optimizing costs, and managing configurations. The main issue lies in the differences in the architecture of services provided by various cloud vendors, which complicates the integration and standardization of processes in multi-cloud environments. This article focuses on analyzing the issues of centralized configuration management using the Configuration Management Database (CMDB) as a single source of truth. The study examines methods of organizing and managing CMDB in public cloud environments, with an emphasis on access management, organizational structures, subscriptions, and cloud resource inventory. Particular attention is paid to developing recommendations for optimizing management processes to improve overall efficiency and security. The practical part of the study involves the integration of the Cherwell system as a CMDB with automated data collection through the Prisma API. This integration allows for the automation of resource inventory, reducing the risk of human errors, improving data accuracy, and ensuring compliance with security standards. Additionally, by centralizing data and analyzing it in Power BI, the study demonstrated the effectiveness of the approach in the context of a multi-cloud environment. The purpose of this study is to develop a scientifically grounded approach to centralized configuration management of cloud infrastructure based on the use of a single data repository for configurations (CMDB). The study includes a detailed analysis of the challenges of cloud configuration management, the features of major cloud providers' services, and their integration into a unified informational model. The primary focus is on developing recommendations for building an efficient configuration management system that considers multi-cloud environments, security requirements, and operational processes. The practical aspect of the study is based on the integration of the Cherwell system as a CMDB with Prisma API to automate data collection in a multi-cloud environment. This integration demonstrated significant advantages, including improved data accuracy, reduced manual work, enhanced information security, and optimized management processes. Thus, the aim of the study is not only to provide a theoretical justification of centralized management methods for cloud resources but also to develop practical recommendations to improve the efficiency and security of configuration management in multi-cloud environments. Keywords: Public cloud environments, configuration management, automation, integration.