Data-Driven decision making in agriculture and business: The role of advanced analytics

Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(7), С. 1565 - 1575

Опубликована: Июль 7, 2024

Advanced analytics has revolutionized decision-making processes in agriculture and business by harnessing data-driven insights to optimize operations, manage risks, drive innovation. This paper explores the transformative role of advanced these sectors, highlighting key benefits, challenges, future directions. In agriculture, enables precision farming integrating AI, IoT sensors, satellite imagery. Predictive models forecast crop yields, irrigation, enhance soil management practices, improving productivity sustainability. Similarly, supports strategic analyzing consumer behavior, predicting market trends, optimizing supply chain operations. However, adopting faces challenges such as data quality, technical expertise, cost constraints, ethical considerations. Addressing requires investments infrastructure, talent development, regulatory compliance ensure secure usage. Emerging trends include AI-driven automation, blockchain for transparency, augmented democratizing access. Recommendations stakeholders investing capabilities, fostering collaborative partnerships, promoting a culture decision making. conclusion, offers profound opportunities efficiency, inform making, sustainable growth business. Embracing technologies is essential organizations seeking thrive economy. Keywords: Analytics, Precision Farming, Data-driven Decision Making, Business Intelligence.

Язык: Английский

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AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

Опубликована: Янв. 24, 2024

Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between intelligence and innovation as foundational elements in digital framework sustained growth operational excellence. provides holistic perspective cultivation pillars AI-powered highlighting their pivotal role revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, agriculture. The work emphasizes key essential fostering monitoring performance measurement to use power present, continuous learning data analytics insights, predictive analytics, innovative product development. investigates how these serve foundation groundbreaking advancements, driving efficiency, enhancing decision-making processes, creativity within organizations. explores significance learning, interdisciplinary collaboration, industry partnerships nurturing thriving ecosystem. By understanding harnessing fundamental elements, businesses can navigate complexities age, that not only optimizes processes but also enhances overall human experience, ushering new era technological excellence societal progress.

Язык: Английский

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AI Capability and Sustainable Performance: Unveiling the Mediating Effects of Organizational Creativity and Green Innovation with Knowledge Sharing Culture as a Moderator

Sustainability, Год журнала: 2024, Номер 16(17), С. 7466 - 7466

Опубликована: Авг. 29, 2024

The purpose of this study is to investigate the role AI capability (AIC) on organizational creativity (OC), green innovation (GI), and sustainable performance (SP). It also aims mediating roles OC GI, as well moderating knowledge sharing culture (KNC). This used quantitative methodology utilized a survey collect data from 421 employees in different organizations Bangladesh. We structural equation modeling (SEM) technique analyze data. finds that significantly influences OC, SP. GI work mediators, KNC serves moderator among suggested relationships. notable for its novelty examining multiple unexplored aspects current body research. research provides valuable insights policymakers practitioners regarding effective integration enhance competitiveness.

Язык: Английский

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Towards a Digital Transformation Hyper-Framework: The Essential Design Principles and Components of the Initial Prototype

Applied Sciences, Год журнала: 2025, Номер 15(2), С. 611 - 611

Опубликована: Янв. 10, 2025

To cope with the complexity, digital transformation of cyber-physical and socio-technology systems demands utilization heterogeneous tailorable development environments dynamic configuring ability transparent integration independently developed dedicated frameworks. The essential design principles component-based architecting initial prototype hyper-framework represent this research target. These are derived from broad scope analysis projects, methods, tools glued to proposed virtual twin hyper-document. critical domain influenced formulation five hypotheses that frame transformation, as second goal article. Armed a meta-modeling layer, incremental hybrid architecture instances focuses on meta-models their transformations into functional, interpretable environments. applicability aspects formulated hypothesis verified throughout architecture, meta-configuration, handling information resources segments version evolution prototype. detailed illustration horizontal vertical interoperability framework is illustrated by Life Cycle Modeling component creatively integrates System, Software, Operation Engineering hyper-framework. capabilities discussed in context contemporary ecosystem. Specification additional frameworks, compliance specified generative mechanisms, directing further refinements

Язык: Английский

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Trends and Applications of Artificial Intelligence in Project Management

Electronics, Год журнала: 2025, Номер 14(4), С. 800 - 800

Опубликована: Фев. 19, 2025

The integration of artificial intelligence (AI) into project management (PM) transforms how projects are planned, executed, and monitored. main objective this study is to provide a comprehensive bibliometric analysis exploring trends, thematic areas, future directions in AI applications by examining publications from the last decade. This research uncovers dominant themes such as machine learning, decision making, information management, resource optimization. findings highlight growing use enhance efficiency, accuracy, innovation PM processes, with recent trends favoring data-driven approaches emerging technologies like generative AI. Geographically, China, India, United States lead publications, while Kingdom Australia show high citation impact. landscape, including AI-enhanced decision-making frameworks cost analysis, demonstrates diversity PM. An increased interest its impact on managers was observed. contributes field offering structured overview defining challenges opportunities for integrating practices perspectives technologies.

Язык: Английский

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Reliable Process Tracking Under Incomplete Event Logs Using Timed Genetic-Inductive Process Mining

Systems, Год журнала: 2025, Номер 13(4), С. 229 - 229

Опубликована: Март 27, 2025

Process mining facilitates the discovery, conformance, and enhancement of business processes using event logs. However, incomplete logs complexities concurrent activities present significant challenges in achieving accurate process models that fulfill completeness condition required mining. This paper introduces a Timed Genetic-Inductive Mining (TGIPM) algorithm, novel approach integrates strengths Genetic (TGPM) Inductive (IM). TGPM extends traditional (GPM) by incorporating time-based analysis, while IM is widely recognized for producing sound precise models. For first time, these two algorithms are combined into unified framework to address both missing activity recovery structural correctness discovery. study evaluates scenarios: sequential approach, which executed independently sequentially, TGIPM where integrated framework. Experimental results real-world from health service Indonesia demonstrate achieves higher fitness, precision, generalization compared slightly compromising simplicity. Moreover, algorithm exhibits lower computational cost more effectively captures parallelism, making it particularly suitable large datasets. research underscores potential enhance outcomes, offering robust efficient discovery driving innovation across industries.

Язык: Английский

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A Deep Learning-Based Ensemble Framework to Predict IPOs Performance for Sustainable Economic Development

Sustainability, Год журнала: 2025, Номер 17(3), С. 827 - 827

Опубликована: Янв. 21, 2025

Addressing resource scarcity and climate change necessitates a transition to sustainable consumption circular economy models, fostering environmental, social, economic resilience. This study introduces deep learning-based ensemble framework optimize initial public offering (IPO) performance prediction while extending its application processes, such as recovery waste reduction. The incorporates advanced techniques, including hyperparameter optimization, dynamic metric adaptation (DMA), the synthetic minority oversampling technique (SMOTE), address challenges class imbalance, risk-adjusted enhancement, robust forecasting. Experimental results demonstrate high predictive performance, achieving an accuracy of 76%, precision 83%, recall 75%, AUC 0.9038. Among methods, Bagging achieved highest (0.90), outperforming XGBoost (0.88) random forest (0.75). Cross-validation confirmed framework’s reliability with median 0.85 across ten folds. When applied scenarios, model effectively predicted sustainability metrics, R² values 0.76 for both reduction low mean absolute error (MAE = 0.11). These highlight potential align financial forecasting environmental objectives. underscores transformative learning in addressing challenges, demonstrating how AI-driven models can integrate goals. By enabling IPO predictions enhancing outcomes, proposed aligns Industry 5.0’s vision human-centric, data-driven, industrial innovation, contributing resilient growth long-term stewardship.

Язык: Английский

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Generative Artificial Intelligence in Adaptive Social Manufacturing: A Pathway to Achieving Industry 5.0 Sustainability Goals

Processes, Год журнала: 2025, Номер 13(4), С. 1174 - 1174

Опубликована: Апрель 12, 2025

As manufacturing transitions from Industry 4.0 to 5.0, a critical challenge emerges in integrating Generative Artificial Intelligence (GAI) into adaptive social achieve sustainability goals. This transition reflects paradigmatic shift technology-centric model focused on automation and efficiency toward more holistic framework that embeds human-centricity environmental responsibility industrial systems. Whereas emphasizes digital innovation productivity, 5.0 seeks align technological advancement with broader ecological societal objectives. Despite advancements digitalization, existing frameworks lack structured approach leveraging GAI for environmental, social, economic sustainability. study explores the transformative role of manufacturing, addressing gap frameworks. Employing multi-method research design, including content analysis, expert-driven validation, system dynamics modeling, identifies nine key dimensions maps them 17 functions. The findings reveal significantly enhances by optimizing resource efficiency, promoting inclusivity, supporting ethical governance. System analysis highlights complex interdependencies between GAI-driven functions outcomes, underscoring need balance human values. provides novel industries seeking implement sustainable production systems, bridging theoretical insights practical applications. Additionally, it offers actionable strategies address challenges such as workforce adaptation, AI governance, adoption barriers, ultimately facilitating 5.0’s

Язык: Английский

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Analysing the Impact of Generative AI in Arts Education: A Cross-Disciplinary Perspective of Educators and Students in Higher Education

Informatics, Год журнала: 2024, Номер 11(2), С. 37 - 37

Опубликована: Июнь 3, 2024

Generative AI refers specifically to a class of Artificial Intelligence models that use existing data create new content reflects the underlying patterns real-world data. This contribution presents study aims show what current perception arts educators and students education is with regard generative Intelligence. It qualitative research using focus groups as collection technique in order obtain an overview participating subjects. The design consists two phases: (1) generation illustrations from prompts by students, professionals tool; (2) (N = 5) artistic education. In general, coincides usefulness tool support illustrations. However, they agree human factor cannot be replaced AI. results obtained allow us conclude can used motivating educational strategy for

Язык: Английский

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Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization

Sustainability, Год журнала: 2024, Номер 16(12), С. 5095 - 5095

Опубликована: Июнь 14, 2024

This study focuses on examining the shift of an application system from a traditional monolithic architecture to cloud-native microservice (MSA), with specific emphasis impact this transition resource efficiency and cost reduction. In order evaluate whether artificial intelligence (AI) performance management (APM) tools can surpass methods in enhancing operational performance, these advanced technologies are integrated. The research employs refactor/rearchitect methodology framework, aiming validate enhanced capabilities AI optimizing cloud resources. main objective is demonstrate how AI-driven strategies facilitate more sustainable economically efficient computing environments, particularly terms managing scaling Moreover, aligns model-based approaches that prevalent systems engineering by structuring transformation through simulation-supported frameworks. It synergy between endogenous integration within processes overarching goals Industry 5.0, which emphasize sustainability not only benefit technological advancements but also enhance stakeholder engagement human-centric environment. exemplifies technology contribute resilient adaptive industrial service systems, furthering objectives initiatives.

Язык: Английский

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