Implementation Of Framework Research Articles

An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems

Confronting the challenge of intermittent renewables, current unit commitment practices falter, urging the development of novel short-term generation scheduling techniques for enhanced microgrid stability. This study presents an adaptive robust unit commitment approach using machine learning techniques for renewable power systems, computing the Calinski-Harabasz index to identify prediction inaccuracies related to intermittent sources. The uncertainties are subsequently grouped together using the spatial clustering tool, and the average density of the K-means distribution is calculated. The clustering of points in space, considering noise, discrete uncertainty in renewable energy sources, and outliers within the comprehensive uncertainty set, is addressed via a nonparametric algorithm. The implementation of established methodologies and frameworks, in conjunction with density-based spatial clustering of applications with noise, introduces an innovative method for vulnerability clustering. This methodology guarantees that every cluster aligns with data pertaining to vulnerabilities of renewable energy sources. The performance of the suggested method is showcased by conducting experiments on modified IEEE 39-bus and 118-bus test systems that use intermittentwindpower. The results demonstrate that the proposed framework may lower the cost of robustness by 8–48% compared to traditional robust optimization techniques. The results of stochastic programming showed that the optimized system with a stable economic organization would have 75 % faster calculations.

Artificial Intelligence the Future of Cardiology

Artificial Intelligence (AI) essentially refers to various types of machine learning, often involving deep neural networks. It autocompletes our ideas as we write, enables us to communicate with our phones, and supports language translation.1 According to the 2019 Global Burden of Disease Study, the estimated age-standardized incidence of cardiovascular disease (CVD) in Pakistan was 918.8 per 100,000 people (global: 684.33 per 100,000), and the age-standardized death rate was 357.88 per 100,000 (global: 239.85 per 100,000).2 With AI, new analytical and data-driven approaches could lead to significant advances in understanding multimorbid groups of cardiology patients and potentially improve therapeutic strategies.3 AI has been used to interpret echocardiograms and heart rhythms from ECGs, and to detect indicators of heart disease, such as left ventricular dysfunction, from surface ECGs and nuclear cardiology.4-6 It is a misconception that AI will replace cardiologists. Instead, skilled practitioners will be able to expand their clinical capabilities, make more accurate and prompt diagnoses, and improve management decisions in patient care. As with any statistical application, it is important to understand AI's strengths and limitations. To understand the basics of AI, it starts with developing an algorithm based on human expertise. Programmers create relationships between input and output, known as expert systems. In machine learning, a general algorithm, such as a neural network, approximates a mathematical relationship between input data and expected outputs. In unsupervised learning, such as clustering, only the inputs are fed into the algorithm, which then finds insights in the data using its inner structure and statistics. An AI model can discover new relationships in data that have previously eluded human discovery.1 For research purposes, cardiologists using AI may follow these steps: Type and collection of data. Preprocessing of data. Choosing the right machine learning approach. Validating and evaluating methods and results.3 The application of AI techniques in the healthcare system is still in its infancy and requires more understanding through workshops and integrated learning.7 In conclusion, AI represents a new development in the field of medicine, especially cardiology. However, it is susceptible to significant errors in interpretation and raises safety and ethical concerns. References Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, et al. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc. 2020;95(5):1015-39. Samad Z, Hanif B. Cardiovascular Diseases in Pakistan: Imagining a Postpandemic, Postconflict Future. Circulation. 2023;147(17):1261-3. Gill SK, Karwath A, Uh HW, Cardoso VR, Gu Z, Barsky A, et al. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare. Eur Heart J. 2023;44(9):713-25. Cheng LT, Zheng J, Savova GK, Erickson BJ. Discerning tumor status from unstructured MRI reports--completeness of information in existing reports and utility of automated natural language processing. J Digit Imaging. 2010; 23(2):119-32. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016;68(21):2287-95. Garcia EV, Klein JL, Taylor AT. Clinical decision support systems in myocardial perfusion imaging. J Nucl Cardiol. 2014;21(3):427-39. Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res 2022;24:e32215.

Facilitators and Barriers Perceived by German Teachers Considering Basic Life Support Education in School-A Qualitative Study.

This qualitative study aims to analyse the personal qualification, attitudes and the pedagogical concepts of German teachers as experts in their profession regarding basic life support (BLS) education in secondary schools. Thirteen (n = 13) secondary school teachers participated in semi-structured expert interviews and were interviewed for at least 20 to 60 min regarding BLS student education. Interviews were semi-structured with guiding questions addressing (1) personal experience, (2) teacher qualification for BLS and (3) implementation factors (e.g., personal, material and organisational). Audio-recorded interviews were analysed by content analysis, generating a coding system. School teachers provided a heterogeneous view on implementation-related processes in BLS education. Many teachers were educated in first aid, acknowledge its importance, but had no experience in teaching BLS. They want to assure being competent for teaching BLS and need tailored trainings, materials, pedagogical information and the incorporation into the curriculum. Also, the management of time constraints, unwilling colleagues, or young students being overwhelmed were commonly mentioned considerations. Concluding, teachers reported to be willing to teach BLS but a stepwise implementation framework incorporating practice-oriented qualification and educational goals is missing.

Prototyping a Zoomorphic Interactive Robot Companion with Emotion Recognition and Affective Voice Interaction for Elderly People

An aging society paired with a skilled labor shortage, particularly in European countries, requires a rethinking of deprecated structures. Intelligent assistive technologies, specifically socially assistive robots, addressing the gap between caretakers and elderly people in need of care have moved into the focus of debate due to their potentials to reduce costs, improve independence, and eventually raise quality of life. In this work, we outline the potentials of zoomorphic robot companions combining intelligent conversational abilities and emotion recognition. We then describe the prototyping of an emotion-sensing zoomorphic interactive robot companion including the development and implementation of a multimodal emotion recognition framework. This framework uses speech emotion recognition, sentiment analysis, and affective voice interaction based on a large language model. The prototyping has been accompanied by two studies on elderly peoples' design preferences regarding the proposed feature set as well as different embodiments to find the appropriate casing for the robot companion. This work provides valuable insights into the prototyping and can thus support future research endeavors in this area.

Enhancing Digital Transformation: A Comprehensive Review of PT Hutama Karya's Information Technology Management in 2023

This paper provides an in-depth analysis of PT Hutama Karya’s Information Technology (IT) Management Report for the year 2023. The study focuses on the company’s strategic initiatives and achievements in aligning IT functions with its business objectives, particularly in the context of Industry 4.0 readiness. Key areas examined include the evolution of IT architecture, implementation of IT governance frameworks, cybersecurity enhancements, ERP integration, and the development of supporting applications. The findings highlight the significant progress made in digital transformation and the proactive steps taken to ensure the company’s IT infrastructure supports its strategic goals.

Implementation of Inclusive Education Framework in Preservice Teacher Preparation Programs

The study was motivated by the global pursuit of educational equity and equality, and the unique challenges of implementing inclusive education in China, a country with a profound educational legacy and rapid modernization. The study aimed to address the research gap of evaluating the adaptability and effectiveness of the current teacher training framework within the Chinese context, offering guidance and references for educational reforms.The study aimed to evaluate the effectiveness and implementation of the inclusive education framework in pre-service teacher training programs in China, and to answer seven research questions related to the profile, perception, and assessment of pre-service teachers.The study employed a descriptive quantitative research method and adopted a descriptive correlational research design. The study concluded that the pre-service teachers had a moderate level of knowledge, skills, attitudes, and beliefs about inclusive education, and that their training and preparation needed to be improved and tailored to the Chinese context. The study also recommended some innovative measures to enhance the effectiveness and implementation of the inclusive education framework in pre-service teacher training programs.

Indonesia's Digital Economic Policy To Increase Economic Resilience

The growth of information and communication technology (ICT) has changed the way people live their lives and conduct business. The era of the Industrial Revolution 4.0, characterized by the use of digital technology and the Internet of Things (IoT), has created a new economic environment known as the “digital economy." In this context, digital economy policy becomes very important to support sustainable economic growth and improve national economic resilience. This research aims to analyze Indonesia's digital economy policy in developing the digital economy in the context of Society 5.0 and Industrial Revolution 4.0. The main focus of the research is how government policies can strengthen the national economy. This research uses a descriptive-qualitative method with secondary data collected through a systematic review. Data were collected from various scholarly sources, including relevant journals, research publications, reports, and websites. This qualitative approach generates descriptive notes and data that are integrated into the text for further analysis. When policies are implemented correctly, that is, by paying attention to and realizing the various aspects that have an impact on the policy implementation process, it will produce the maximum benefits, including the Integration, Interactivity, Transparency, Control, and Accountability (IITCA) factors. The research shows that digital economy policies have a significant positive impact on economic growth and resilience. The implementation of the Digital Economic Agreement Framework (DEFA) is expected to strengthen the MSME sector, attract investment, encourage innovation, increase productivity, and create high-quality jobs. The establishment of the Digital Innovation and Sustainable Economy Center (DISC) also plays an important role in supporting the transformation of businesses and organizations towards a sustainable digital economy. In addition, the Indonesian government has improved digital infrastructure, such as 4G networks, 5G technology, and low-earth orbit satellites, and launched digital literacy and skills programs to reduce the digital divide and strengthen economic resilience. An effective digital economy policy can be a powerful instrument to strengthen a country's economic resilience. Research results confirm that the implementation of digital economy policies not only drives economic growth but also improves social and financial inclusion. Collaboration between the public sector, the business world, and civil society is essential for the successful implementation of this policy. In addition, transparency and accountability in the implementation of digital policies, such as through the use of blockchain technology and digital payment systems, can reduce corruption and ensure that government assistance and support are well-targeted. Overall, digital economy policies play an important role in strengthening national economic resilience amid the challenges and opportunities of the digital era.

A comprehensive review of it governance: effective implementation of COBIT and ITIL frameworks in financial institutions

A comprehensive review of it governance: effective implementation of COBIT and ITIL frameworks in financial institutions

Turbo-RANS: straightforward and efficient Bayesian optimization of turbulence model coefficients

PurposeIndustrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.Design/methodology/approachThe authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.FindingsThe authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.Originality/valueTo the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

Leveraging Science to Advance Health Equity: Preliminary Considerations for Implementing Health Equity Science at State and Local Health Departments.

In 2021, the Centers for Disease Control and Prevention (CDC) launched CORE, an agency-wide strategy to embed health equity as a foundational component across all areas of the agency's work. The CDC established a definition of health equity science (HES) and principles to guide the development, implementation, dissemination, and use of the HES framework to move beyond documenting inequities to investigating root causes and promoting actionable approaches to eliminate health inequities. The HES framework may be used by state and local health departments to advance health equity efforts in their jurisdictions. Identify implementation considerations and opportunities for providing technical assistance and support to state and local public health departments in advancing HES. A series of implementation consultations and multi-jurisdictional facilitated discussions were held with state and local health departments and community partners in 5 states to gather feedback on the current efforts, opportunities, and support needs to advance HES at the state and local levels. The information shared during these activities was analyzed using inductive and deductive methods, validated with partners, and summarized into themes and HES implementation considerations. Five themes emerged regarding current efforts, opportunities, and support needed to implement HES at state and local health departments. These themes included the following criteria: (1) enhancing the existing health equity evidence base; (2) addressing interdisciplinary public health practice and data needs; (3) recognizing the value of qualitative data; (4) evaluating health equity programs and policies; and (5) including impacted communities in the full life cycle of health equity efforts. Within these themes, we identified HES implementation considerations, which may be leveraged to inform future efforts to advance HES at the state and local levels. Health equity efforts at state and local health departments may be strengthened by leveraging the HES framework and implementation considerations.

Virtual Power Plants: Challenges, Opportunities, and Profitability Assessment in Current Energy Markets

The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in economic gains and improved power grid reliability. Nevertheless, VPPs encounter major challenges when it comes to engaging in energy markets, mainly because there is no all-encompassing policy and regulatory framework specifically designed to accommodate their unique characteristics. This underscores the necessity for research endeavours to develop more advanced methods and structures for the long-term viability of VPPs. To address this concern, the study advocates for the implementation of a multi-aspect framework (MAF) as a systematic approach to thoroughly examine each aspect of virtual power plants (VPPs). A STEEP (social, technological, environmental, economic, and political) analytical tool is utilized to evaluate the challenges, opportunities, and benefits of a VPP in the existing energy markets. The proposed approach highlights important factors and actions that need to be taken to tackle the challenges related to VPP’ entry into energy markets. This study suggests that further support is required to promote the fast and widespread adoption of long-term VPP implementations. For this reason, a more favourable policy and regulatory framework based on social, technological, economic, environmental, and policy considerations is necessary to realize the genuine contributions of VPPs.

A Study on Sarva Shiksha Abhiyan under National Education Policy Act

The right to education has been recognised and treated important in many international legal instruments in the form of conventions, covenants, charters and regional laws. From the adoption of the Universal Declaration of Human Rights (UDHR) the right to education under Article 26 has been reaffirmed in numerous treaties at international and regional level including UNESCO, ICEARD, ICESCR, ICCPR, CEDAW, UNCRc, CRMW, CRPWD. The right to education has also been recognised under the conventions of International Legal Organisations and international humanitarian law. The government of India has inserted Article 21 A in the constitution of India in its constitution (Eighty-sixth Amendment) Act, 2002. The provision made education to be provided in free and compulsory manner for all the children in the age group between six to fourteen years. As we know education is a fundamental right and available for all categories of people there are some needs in the implementation of the quality of education. The new education policy, SWAYAM, Sarva Shikha Abhigyan are the recent schemes passed by the government of India. This paper concentrates on the scheme Sarva shiksha Abhiyan which is a framework for implementation of free and compulsory under Right of children to free and compulsory education act 2009.

Onboarding for liaison librarians: building community and practice

PurposeThis article describes an onboarding program created for liaison librarians at an academic library and details a replicable framework for effective implementation at other institutions.Design/methodology/approachThis article outlines a month-long onboarding program that provides new-to-the-field and experienced librarians with support and structure as they learn the core competencies of liaison work, adjust to a new professional setting and grow in their practice as liaison librarians.FindingsInformation about onboarding practices across academic libraries, liaison librarianship within the library and information science (LIS) curriculum and the need for onboarding programs that provide a comprehensive overview of core liaison librarianship competencies is provided.Originality/valueOnboarding programs specific to liaison librarians is an underexplored topic. This article emphasizes fostering community and skill development throughout the onboarding process to bolster confidence, engagement and retention.

An evaluation of the impact of the Irish Rugby Football Union Coach Education Framework on the coach–athlete dyad across the age and stage spectrum in rugby union

The purpose of this research was to evaluate the implementation of the novel Irish Rugby Football Union Coach Education Framework (CEF) on coach–athlete interactions and perceptions. Participants were coaches (n = 4) and athletes (n = 54) from two rugby union teams. Coaches were observed pre-intervention and post-intervention of an education workshop based on the CEF and had nine training sessions video-recorded and analysed using the Coach Analysis and Intervention System (CAIS). Their perceptions of their relationship with their athletes were measured using the Coach–Athlete Relationship Questionnaire (CART-Q). Athlete perceptions of their coaches’ behaviours were measured using the Coaching Behaviour Scale for Sport (CBS-S). CAIS results revealed coaches increased the use of ‘Management’ and ‘Feedback’ behaviours ( p < 0.05) post-CEF, without altering prominent high-volume behaviours (e.g. ‘Instruction’). Coaches utilised similar time proportions for CAIS ‘Practice’, ‘Playing’ and ‘Management’ states, respectively, with no significant change post-CEF. CART-Q and CBS-S results revealed predominantly positive perceptions between coaches and athletes for both pre-CEF and post-CEF. Coach education is a non-linear learning process requiring consistent application over long periods of time, however, the alteration in coach behaviours post-CEF, coupled with the largely positive perceptions of both agents of the coach–athlete dyad, is an encouraging step forward for the further implementation of the CEF.

Factors Influencing Implementation of the Commission on Cancer’s Breast Synoptic Operative Report (Alliance A20_Pilot9)

BackgroundThe technical aspects of cancer surgery have a significant impact on patient outcomes. To monitor surgical quality, in 2020, the Commission on Cancer (CoC) revised its accreditation standards for cancer surgery and introduced the synoptic operative reports (SORs). The standardization of SORs holds promise, but successful implementation requires strategies to address key implementation barriers. This study aimed to identify the barriers and facilitators to implementing breast SOR within diverse CoC-accredited programs.MethodsIn-depth semi-structured interviews were conducted with 31 health care professionals across diverse CoC-accredited sites. The study used two comprehensive implementation frameworks to guide data collection and analysis.ResultsSuccessful SOR implementation was impeded by disrupted workflows, surgeon resistance to change, low prioritization of resources, and poor flow of information despite CoC’s positive reputation. Participants often lacked understanding of the requirements and timeline for breast SOR and were heavily influenced by prior experiences with templates and SOR champion relationships. The perceived lack of monetary benefits (to obtaining CoC accreditation) together with the significant information technology (IT) resource requirements tempered some of the enthusiasm. Additionally, resource constraints and the redirection of personnel during the COVID-19 pandemic were noted as hurdles.ConclusionsSurgeon behavior and workflow change, IT and personnel resources, and communication and networking strategies influenced SOR implementation. During early implementation and the implementation planning phase, the primary focus was on achieving buy-in and initiating successful roll-out rather than effective use or sustainment. These findings have implications for enhancing standardization of surgical cancer care and guidance of future strategies to optimize implementation of CoC accreditation standards.

Adaptive Approach as a Tool of Library Management in the Conditions of Turbulent External Environment

The purpose of the article is to define the role, place and possibilities of the adaptive approach in library management within the framework of practical implementation of the concept of sustainable innovative development of the library industry; to study the reasons for applying adaptive management methods in libraries. Research methods. The following methods and approaches were used: general scientific methods of cognition, in particular, analysis and synthesis, abstract and logical, monographic and analytical, decomposition and systematic approach. Using these methods ensured the validity of the provisions of this study. The scientific novelty lies in creating a holistic view of the features, trends and factors of the adaptive approach in library management. Conclusions. A system is considered adaptive if it can adapt to changes in internal and external conditions. Such a system remains operational in the event of unforeseen changes in the properties of the managed object, management goals, or environmental conditions by changing its functioning algorithm, behavioural programme, or search for optimal states. The task of adaptive library management is to keep the library as a system and its processes within the specified limits. The adaptive management system assesses the state of the process within the limits of permissible fluctuations and determines the need for corrective adaptation measures. Fluctuations in the processes of a library institution are expressed in terms of library production. Thus, the use of adaptive management methods meets the practical tasks of sustainable innovative development of the library industry and can be used in the library management tools for its implementation. An adaptive approach to library management is possible in the following cases: a) a developed forecasting system for prompt monitoring of the current situation of the library; b) the possibility of promptly changing plans if they no longer correspond to the new reality; c) an effective system of operational management accounting and control.

A Delve into the Implementation of Data Quality Framework on Very Large Databases

In today’s data-driven landscape, the reliability and accuracy of information are paramount for informed decision-making and operational efficiency. A robust data quality framework provides a structured methodology for managing and maintaining the quality of data as-sets throughout their lifecycle. It in-troduces a comprehensive data qual-ity framework encompassing four key pillars: data governance, data profil-ing, data cleansing, and data monitor-ing. The foundation of the framework lies in establishing clear data governance policies and procedures, defining owner-ship, responsibilities, and accountability for data quality across the organization. Data profiling techniques are then em-ployed to assess the quality of data, iden-tifying anomalies, inconsistencies, and inaccuracies. Subsequently, data cleans-ing processes are implemented to rec-tify these issues, employing techniques such as deduplication, standardization, and validation to ensure data integrity. Continuous data monitoring is integral to the framework, enabling proactive de-tection of data quality issues and facil-itating timely corrective actions. Ad-vanced analytics and machine learn-ing algorithms may be leveraged to au-tomate monitoring processes, flagging anomalies and deviations from prede-fined quality thresholds.

NeuronLab: BCI framework for the study of biosignals

Brain–Computer Interfaces (BCIs) allow the acquisition of brain activity using non-invasive techniques such as Electroencephalography (EEG). Since BCI devices do not commonly interpret the acquired EEG signals, external software applications play a critical role in the BCI lifecycle. Despite the literature offering a great variety of platforms and frameworks, they present several limitations, such as implementing old software architectures or needing more functionality to cover all phases of the BCI lifecycle. Based on these limitations, this work proposes the design and implementation of NeuronLab, a secure, multi-platform, standalone, multi-paradigm, and web-based framework that defines all BCI lifecycle phases and provides novel functionality compared to current open-source BCI software, such as sharing experiments between researchers and storing data on the cloud. This framework has been validated in two experiments common in BCI literature: P300 identification and limb movements detection. This verification has been performed based on performance metrics, such as CPU and RAM consumption, highlighting that NeuronLab is a promising solution for BCI scenarios requiring a distributed and collaborative platform for researchers and practitioners.

Respect for the Patient-Oncologist Relationship May Limit Serious Illness Communication by Acute and Postacute Care Clinicians After Discharge to a Skilled Nursing Facility

PURPOSE There is a need to increase palliative care access for hospitalized older adults with cancer discharged to a skilled nursing facility (SNF) at risk of poor outcomes. Assessing and Listening to Individual Goals and Needs (ALIGN) is a palliative care intervention developed to address this gap. This study gathered perspectives from clinicians across care settings to describe perceptions on serious illness communication and care coordination for patients with cancer after discharge to a SNF to guide ALIGN refinements. METHODS We conducted 37 semistructured interviews with clinicians and leaders in hospital medicine (n = 12), oncology (n = 9), palliative care (n = 12), home health care (n = 6), and hospice (n = 4). Some participants had experience working in more than one specialty. The Practical Robust Implementation and Sustainability Model framework was used to develop the interview guide that explored barriers to care, prognosis discussions, and hospice recommendations. Interviews were coded and analyzed using thematic content analysis. RESULTS Analysis identified four themes: (1) discharge to a SNF is recognized as a time of worsening prognosis; (2) care silos create communication and information barriers during a period of increasing palliative care need; (3) family caregiver distress escalates following care transitions; and (4) lack of clarity of roles and respect for the patient-oncologist relationship limits prognostic communication and changes in focus of treatment. CONCLUSION These findings suggest that acute and postacute care clinicians defer serious illness conversations to the oncologist when patients are on a steep trajectory of decline, experiencing multiple care transitions, and may have limited contact with their oncologist. There is a need to clarify roles among nononcology and oncology clinicians in discussing prognosis and recommending hospice for older adults discharged to SNF.

IoT-Based Framework for Implementation of Leaf Color Chart for Nitrogen Monitoring in Crop

Effective nitrogen management is essential in precision agriculture to optimize crop yields and promote environmental sustainability. Conventional techniques for evaluating nitrogen levels, although commonly used, suffer from a lack of accuracy and scalability. This research presents a novel design framework that combines the Leaf Colour Chart (LCC), a well-established agricultural tool for nitrogen evaluation, with modern Internet of Things (IoT) technologies. The proposed system utilizes specialized sensors to gather precise leaf color data, which is subsequently analyzed using cloud-based computer vision techniques to correctly and instantaneously predict nitrogen levels. The framework streamlines data gathering and processing, allowing for accurate nitrogen management. This enables the application of fertilizer to specific areas and minimizes wastage. This article provides an overview of the system architecture, examines the difficulties and resolutions in integrating Internet of Things (IoT) technology in agriculture, and showcases case studies that demonstrate the effectiveness of the framework in practical environments. The incorporation of IoT technology with the Leaf Colour Chart signifies a notable progression in agricultural technology, offering the potential to enhance crop management techniques and support sustainable agriculture endeavors.