iosr-jmca   Volume-12 ~ Issue-3 ~ May-Jun. 2025

ABSTRACT: This paper explores the cross-platform software development, focusing on the approaches and challenges faced when creating single code capable of running across different operating system and device environments. The main objective is to analyze the techniques used to ensure the compatibility and efficiency of the software on various platforms, such as Windows, macOS, Linux, and mobile devices. Aspects such as the choice of programming languages......

Keywords: Multiplatform software, single code, diverse environments, compatibility, performance

[1] Goh, H.-A.; Ho, C.-K.; Abas, F. S. Front-End Deep Learning Web Apps Development And Deployment: A Review. Applied Intelligence, V. 53, N. 12, P. 15923-15945, 2023.
[2] Lai, Chiu-Lin; Tu, Yun-Fang. Roles, Strategies, And Research Issues Of Generative Ai In The Mobile Learning Era. International Journal Of Mobile Learning And Organisation, V. 18, N. 4, P. 516-537, 2024.[3] Segun-Falade, Osinachi Deborah Et Al. Developing Cross-Platform Software Applications To Enhance Compatibility Across Devices And Systems. Computer Science & It Research Journal, V. 5, N. 8, 2024.K. Elissa, "Title Of Paper If Known," Unpublished.
[4] Beňo, Lukáš; Kučera, Erik; Bašista, Matej. Smart Sleep Monitoring: An Integrated Application For Tracking And Analyzing Babies' Sleep—Babycare. Electronics, V. 13, N. 21, P. 4210, 2024.
[5] Alvarez, Oscar Danilo Gavilánez Et Al. Comparative Analysis Of Cross-Platform Frameworks. Journal Of Namibian Studies: History Politics Culture, V. 33, P. 2465-2486, 2023.

ABSTRACT: Summary This article discusses the challenges of housekeeping teams in the hotel industry and how the implementation of an application can contribute positively to the optimization of tasks and processes. With main objective of developing a prototype of mobile system to standardize routines and improve communication between employees from different sectors, mainly housekeeping staff, supervisors, and receptionists. The applied methodology was based on bibliographic review, analysis of existing......

[1] Ataíde, M. Rotatividade No Setor De Governança Hoteleira: Um Estudo De Caso. Monografia (Especialização) — Universidade Federal De Minas Gerais, Belo Horizonte, 2011. Disponível Em:
Https://Repositorio.Ufmg.Br/Bitstream/1843/54704/1/Mono_Final.Pdf Accessed On: April 08, 2025
[2] Dantas, M. Abc De Housekeeping: Um Método De Alta Performance Para A Governança Hoteleira. Disponível Em:
<Https://Abgnacional.Com.Br/Wp-Content/Uploads/2020/04/Ebook-Abc-Do-Housekeeping.Pdf>. Accessed On: February 25, 2025.
[3] Dawson, M. Sistema De Gestão De Propriedades Hoteleiras - Siteminder. Disponível Em:
<Https://Www.Siteminder.Com/Pt/Tir/Sistema-De-Gestao-De-Propriedades-Hoteleiras>. Accessed On: March 16, 2025.
[4] Flexkeeping. Hotel Housekeeping Software - Flexkeeping. Disponível Em: <Https://Flexkeeping.Com/Products/Housekeeping-Software>. Accessed On: March 11, 2025.
[5] Galvão, R. Hospedagens Memoráveis: Como Criar Estadias Inesquecíveis Na Era Digital E Da Experiência. Disponível Em:
<Https://Plataforma.Bvirtual.Com.Br>. Accessed On: February 20, 2025.

ABSTRACT: In today’s fast-paced business world, companies really need to tap into the power of data to make smart decisions. This paper dives into how machine learning (ML)-powered dashboards can help create a culture that’s all about data. By integrating predictive models into user-friendly visualization tools like Tableau and Power BI—along with programming languages such as Python, R, or ML APIs—businesses can turn raw data into valuable insights. These smart......

Keywords: Data-Driven Culture, Machine Learning, Predictive Analytics, Dashboards, Tableau, Power BI, Python, R, Embedded Analytics, Data Literacy, Business Intelligence, Cross-Functional Collaboration

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ABSTRACT: Federated Learning (FL) is an emerging machine learning paradigm with transformative potential in the field of cybersecurity. By enabling decentralized model training across distributed devices without transferring raw data, FL addresses critical privacy concerns while supporting real-time threat detection. This review synthesizes recent advances in the application of FL to cybersecurity, focusing on its deployment in domains such as the Internet of Things (IoT), intrusion detection systems, and threat intelligence networks. The review highlights key......

Keywords: Federated Learning, Cybersecurity, Intrusion Detection, Privacy-Preserving Machine Learning, Internet of Things (IoT) Security, Threat Intelligence

[1]. Alazab, M., Rm, S. P., Maddikunta, P. K. R., Gadekallu, T. R., & Pham, Q. V. (2021a). Federated learning for cybersecurity: Concepts, challenges, and future Transactions Informatics, directions. on IEEE Industrial 18(5), 3501–3509. https://ieeexplore.ieee.org/abstract/d ocument/9566732/
[2]. Alazab, M., Rm, S. P., Maddikunta, P. K. R., Gadekallu, T. R., & Pham, Q. V. (2021b). Federated learning for cybersecurity: Concepts, challenges, and future Transactions Informatics, directions. on IEEE Industrial 18(5), 3501–3509. https://ieeexplore.ieee.org/abstract/d ocument/9566732/
[3]. Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021). Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis. IEE Access, 9, 138509–138542. https://ieeexplore.ieee.org/abstract/d ocument/9562531/
[4]. Ghimire, B., & Rawat, D. B. (2022). Recent advances on federated learning for cybersecurity and cybersecurity for federated learning for internet of things. IEEE Internet of Things Journal, 9(11), 8229–8249. https://ieeexplore.ieee.org/abstract/d ocument/9709603/
[5]. Katari, P., Bonam, V. S. M., Bojja, S. G. R., Ravi, Venkataramanan, Decentralized C. S. S., & (2021). Cybersecurity: Implementing Federated Learning in Threat Intelligence Networks. Journal of Informatics Education and Research, 1, 29–40. https://www.researchgate.net/profile/ Srinivasan-Venkataramanan-2/public ation/389660179_Decentralized_Cyb ersecurity_Implementing_Federated_.