Artificial Machine Research Articles

A review of AI and machine learning contribution in business process management (process enhancement and process improvement approaches)

PurposeThe significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning artificial intelligence (AI) and machine learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field.Design/methodology/approachIn this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology to analyze related papers.FindingsIn business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis.Research limitations/implicationsWhile this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024.Originality/valueThis work addresses a significant gap by employing a pioneering approach to introduce challenges in BPM alongside AI/ML techniques and integrated tools. Hence, it offers comprehensive guidelines that elucidate the alignment between ML methods and solutions to current challenges across the BPM life-cycle, including process enhancement and process improvement. Additionally, by detailing various aspects of the life-cycle phases and highlighting ML technique characteristics, this research demonstrates potential approaches for future exploration, thereby enhancing applicability for both process analysts and researchers in this context.

A Comprehensive Review on Accident Detection Using Artificial Intelligence and Machine Learning

This paper reviews the role of Artificial Intelligence (AI) and Machine Learning (ML) in accident detection systems, focusing on the application of image processing techniques and Convolutional Neural Networks (CNNs) to enable detection. Through a comprehensive analysis of recent methodologies, challenges, limitations, and future research directions, this paper aims to provide a detailed understanding of how AI and ML are advancing the field of accident detection

The Role of Technology in Enhancing the Effectiveness of Integrated Performance Appraisal Frameworks

The role of technology in enhancing the effectiveness of integrated performance appraisal frameworks has become increasingly important in modern organizational management. As businesses strive for improved productivity, engagement, and employee satisfaction, traditional performance appraisal methods have shown limitations in providing real-time, accurate, and constructive feedback. This research explores how technological innovations such as artificial intelligence (AI), machine learning, and cloud-based human resource management software contribute to the enhancement of performance appraisal systems. By integrating these technologies, organizations can automate data collection, reduce bias, offer personalized feedback, and continuously monitor employee progress. This study investigates the impact of such technological integration on the accuracy, efficiency, and overall effectiveness of performance appraisals, ultimately linking these improvements to organizational performance, employee development, and retention. The findings aim to provide organizations with actionable insights into adopting and leveraging technology to create more robust, fair, and dynamic performance appraisal systems.

Application of artificial intelligence and machine learning for risk stratification acute kidney injury among hematopoietic stem cell transplantation patients: PCRRTICONIC AI Initiative Group Meeting Proceedings.

Acute kidney injury (AKI) is a frequent, severe complication of hematopoietic stem cell transplantation (HSCT) and is associated with an increased risk of morbidity and mortality. Recent advances in artificial intelligence (AI) and machine learning (ML) have showcased their proficiency in predicting AKI, projecting disease progression, and accurately identifying underlying etiologies. This review examines the central aspects of AKI post-HSCT, veno-occlusive disease (VOD) in HSCT recipients, discusses present-day applications of artificial intelligence in AKI, and introduces a proposed ML framework for the early detection of AKI risk.

Systematic Review of AI-Assisted MRI in Prostate Cancer Diagnosis: Enhancing Accuracy Through Second Opinion Tools

Background: Prostate cancer is a leading cause of cancer-related deaths in men worldwide, making accurate diagnosis critical for effective treatment. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown promise in improving the diagnostic accuracy of prostate cancer. Objectives: This systematic review aims to evaluate the effectiveness of AI-based tools in diagnosing prostate cancer using MRI, with a focus on accuracy, specificity, sensitivity, and clinical utility compared to conventional diagnostic methods. Methods: A comprehensive search was conducted across PubMed, Embase, Ovid MEDLINE, Web of Science, Cochrane Library, and Institute of Electrical and Electronics Engineers (IEEE) Xplore for studies published between 2019 and 2024. Inclusion criteria focused on full-text, English-language studies involving AI for Magnetic Resonance Imaging (MRI) -based prostate cancer diagnosis. Diagnostic performance metrics such as area under curve (AUC), sensitivity, and specificity were analyzed, with risk of bias assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results: Seven studies met the inclusion criteria, employing various AI techniques, including deep learning and machine learning. These studies reported improved diagnostic accuracy (with AUC scores of up to 97%) and moderate sensitivity, with performance varying based on training data quality and lesion characteristics like Prostate Imaging Reporting and Data System (PI-RADS) scores. Conclusions: AI has significant potential to enhance prostate cancer diagnosis, particularly when used for second opinions in MRI interpretations. While these results are promising, further validation in diverse populations and clinical settings is necessary to fully integrate AI into standard practice.

Artificial intelligence, medications, pharmacogenomics, and ethics.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

Transforming Patient Outcomes: Cutting-Edge Applications of AI and ML in Predictive Healthcare

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has emerged as a transformative force, offering unprecedented potential to enhance patient outcomes. This paper explores the cutting-edge applications of AI and ML in predictive healthcare, focusing on how these technologies are revolutionizing early detection, personalized treatment, and patient management. Through advanced algorithms and data-driven insights, AI and ML are enabling healthcare providers to predict disease progression, optimize therapeutic interventions, and improve patient monitoring in real-time. Key applications discussed include predictive modeling for chronic disease management, AI-powered diagnostic tools, and the use of machine learning in precision medicine. The paper also addresses the challenges associated with these technologies, including data privacy concerns, algorithmic bias, and the need for robust validation. By examining current case studies and future trends, this work aims to highlight the transformative impact of AI and ML on patient care, ultimately driving a shift toward more proactive, tailored, and efficient healthcare systems.

Economics and Equity of Large Language Models: Health Care Perspective.

Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway. This perspective discusses 3 LLM implementation pathways-training from scratch pathway (TSP), fine-tuned pathway (FTP), and out-of-the-box pathway (OBP)-as potential onboarding points for health systems while facilitating equitable adoption. The choice of a particular pathway is governed by needs as well as affordability. Therefore, the risks, benefits, and economics of these pathways across 4 major cloud service providers (Amazon, Microsoft, Google, and Oracle) are presented. While cost comparisons, such as on-demand and spot pricing across the cloud service providers for the 3 pathways, are presented for completeness, the usefulness of managed services and cloud enterprise tools is elucidated. Managed services can complement the traditional workforce and expertise, while enterprise tools, such as federated learning, can overcome sample size challenges when implementing LLMs using health care data. Of the 3 pathways, TSP is expected to be the most resource-intensive regarding infrastructure and workforce while providing maximum customization, enhanced transparency, and performance. Because TSP trains the LLM using enterprise health care data, it is expected to harness the digital signatures of the population served by the health care system with the potential to impact outcomes. The use of pretrained models in FTP is a limitation. It may impact its performance because the training data used in the pretrained model may have hidden bias and may not necessarily be health care-related. However, FTP provides a balance between customization, cost, and performance. While OBP can be rapidly deployed, it provides minimal customization and transparency without guaranteeing long-term availability. OBP may also present challenges in interfacing seamlessly with downstream applications in health care settings with variations in pricing and use over time. Lack of customization in OBP can significantly limit its ability to impact outcomes. Finally, potential applications of LLMs in health care, including conversational artificial intelligence, chatbots, summarization, and machine translation, are highlighted. While the 3 implementation pathways discussed in this perspective have the potential to facilitate equitable adoption and democratization of LLMs, transitions between them may be necessary as the needs of health systems evolve. Understanding the economics and trade-offs of these onboarding pathways can guide their strategic adoption and demonstrate value while impacting health care outcomes favorably.

Smart Medicine: The Role of Artificial Intelligence and Machine Learning in Next-Generation Healthcare Innovation

The paper focuses on big data and healthcare pharmaceuticals, which pose both great promise and challenge as they can contribute to well-being and deliver innovation and knowledge to next-generation healthcare products. This paper provides an overview of the benefits that machine learning and big data technology contribute to healthcare systems. Realities such as patient and healthcare professional demands, the number of diseases, insufficient drugs, regulations, legal and ethical guideline restrictions, modus operandi, and technology development take place in the healthcare sector. In addition, big data opportunities and challenges for healthcare companies, research, and healthcare infrastructure are discussed. Finally, a vision of health innovation through personalized medicines as well as smart medicine is presented as a step closer to patients through regulatory, legal, and ethical guidelines, applications, and opportunities that need to be regulated and resolved.

Human-centric lighting asset management for LED bulbs: a context-driven approach on prognostics and maintenance strategy development in public libraries

ABSTRACT Traditional asset management of lighting systems typically focuses on functionality, cost, and lifespan. In contrast, a human-centric approach prioritizes social sustainability and user well-being by ensuring lighting assets “provide the right light at the right time” for diverse activities. Light-emitting diode (LED) bulbs, known for energy efficiency and longevity, have become a preferred choice, yet public libraries often struggle to manage these assets sustainably, remaining in a reactive “fix/replace when it breaks” stage. Current predictive methods, such as artificial intelligence and machine learning, rely on laboratory data that often overlook real-world contexts, leading to performance gaps. This paper presents a context-driven, human-centric methodology for LED prognosis and maintenance strategies in public libraries, employing limited degradation data from LED testing. Advanced analytical techniques, including Markov Chain Monte Carlo (MCMC) and Deviance Information Criterion (DIC), support a shift from function-based to performance-based reliability assessment. By incorporating Mean Time of Exposure (MTOE) and Critical Integrated Levels (CILs), the approach defines optimal maintenance inspection intervals. This research enhances sustainable LED lighting management in public libraries, offering a framework adaptable to broader applications and aligned with human-centric goals..

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

QTc interval prolongation impact on in-hospital mortality in acute coronary syndromes patients using artificial intelligence and machine learning.

Prediction of mortality in hospitalized patients is a crucial and important problem. Several severity scoring systems over the past few decades and machine learning models for mortality prediction have been developed to predict in-hospital mortality. Our aim in this study was to apply machine learning (ML) algorithms using QTc interval to predict in-hospital mortality in ACS patients and compare them to the validated conventional risk scores. This study was retrospective, using supervised learning, and data mining. Out of a cohort of 500 patients admitted to a tertiary care hospital from September 2018 to August 2020, who presented with ACS. Prediction models for in-hospital mortality in ACS patients were developed using 3 ML algorithms. We employed the ensemble learning random forest (RF) model, the Naive Bayes (NB) model and the rule-based projective adaptive resonance theory (PART) model. These models were compared to one another and to two conventional validated risk scores; the Global Registry of Acute Coronary Events (GRACE) risk score and Thrombolysis in Myocardial Infarction (TIMI) risk score. Out of the 500 patients included in our study, 164 (32.8%) patients presented with unstable angina, 148 (29.6%) patients with non-ST-elevation myocardial infarction (NSTEMI) and 188 (37.6%) patients were having ST-elevation myocardial infarction (STEMI). 64 (12.8%) patients died in-hospital and the rest survived. Performance of prediction models was measured in an area under the receiver operating characteristic curve (AUC) ranged from 0.83 to 0.93 using all available variables compared to the GRACE score (0.9 SD 0.05) and the TIMI score (0.75 SD 0.02). Using QTc as a stand-alone variable yielded (0.67 SD 0.02) with a cutoff value 450 using Bazett's formula, whereas using QTc in addition to other variables of personal and clinical data and other ECG variables, the result was 0.8 SD 0.04. Results of RF and NB models were almost the same, but PART model yielded the least results. There was no significant difference of AUC values after replacing the missing values and applying class balancer. The proposed method can effectively predict patients at high risk of in-hospital mortality early in the setting of ACS using only clinical and ECG data. Prolonged QTc interval can be used as a risk predictor of in-hospital mortality in ACS patients.

AI-Enhanced Predictive Analytics for Proactive Healthcare Management: Leveraging Machine Learning to Improve Patient Care and Operational Efficiency

As healthcare changes quickly, combining artificial intelligence (AI) and machine learning (ML) technologies has become a major force that can change things. This is because it improves predictive analytics for proactive healthcare management. This essay looks at how AI-powered predictive analytics might be able to help healthcare systems provide better care to patients and run more smoothly. Machine learning algorithms can find patterns and trends in huge amounts of patient data that traditional analysis methods might miss. This lets healthcare workers make smart decisions before big problems happen. The study looks into many uses of AI-enhanced prediction analytics, such as figuring out which patients are at the highest risk, finding diseases early, and making treatment plans work better. Using past patient data and real-time tracking, predictive models can figure out how likely it is that a person will develop a chronic condition. This lets doctors act quickly and make personalized care plans for each person. This cautious method not only improves the health of patients, but it also makes healthcare systems less busy by cutting down on hospital readmissions and trips to the emergency room. The paper also looks at how predictive analytics can help healthcare groups streamline their working processes. Healthcare facilities can better arrange staff and tools, making sure that resources are available where and when they are needed, by predicting how many patients will be admitted and how resources will be used. This speed makes patients happier, cuts down on wait times, and raises the standard of care generally. The study also looks at the problems that come with using AI and predictive analytics in healthcare, such as worries about data privacy, integrating new systems with old ones, and the need for strong algorithms that can handle a wide range of patients. It stresses how important it is for lawmakers, healthcare workers, and data scientists to work together to make ethical rules for the use of AI in healthcare situations.

Diagnosis of heart disease using an advanced triple hybrid algorithm combining machine learning techniques

Purpose This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care. Design/methodology/approach This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation. Findings The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector. Originality/value The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.

DecTree: a physics-based geochemical surrogate for surface complexation of uranium on clay

Abstract. Geochemistry is usually the computational bottleneck in coupled reactive transport simulations, which hampers the complexity of the systems and of the processes they can investigate. In recent years, promising speedups have been obtained by substituting the numerical solution of geochemical models with approximated surrogates borrowed from artificial intelligence and machine learning (AI/ML). In the framework of the DONUT/EURAD project a set of benchmarks were defined to assess the performance and the accuracy of different surrogate approaches in settings relevant to the safety assessment of nuclear waste repositories, such as the surface complexation and exchange of U(VI) on clay. In this context, this work introduces am original surrogate modelling approach based on recursive partitioning of parameter space, which exploits prior domain knowledge for the training. The surrogate, which can be represented as a decision tree, hence the DecTree name, performs dimensionality reduction by identifying functional relationships between outputs and input variables using a straightforward non-monotonic extension of the Spearman's rank correlation coefficient. New predictions are then interpolated from the partitioned training data. Applied to a low-dimensional geochemical model, DecTree shows virtually no training time and excellent accuracy, ensuring a throughput of around 500 000 predictions per second on a single CPU core.

Abstract 4115654: Assessing Diagnostic Performance: A Systematic Review and Meta-Analysis of Machine Learning-Based Fractional Flow Reserve versus Traditional Invasive Fractional Flow Reserve for Coronary Artery Stenosis Evaluation

Introduction: Fractional Flow Reserve (FFR) is a stenosis-specific index for coronary artery stenosis (CAS) detection and its effect on myocardial perfusion. Visualizing stenosis through an invasive coronary angiography (iFFR) lacks consistency and is highly subjective. Recently, non-invasive techniques have been developed using artificial intelligence (AI) and Machine learning (ML) to measure FFR (ML-FFR). We aimed to analyze the diagnostic performance of ML-FFR in comparison to iFFR in the detection of CAS. Methods: A search was carried out in PubMed, IEEE Xplore digital library, and clinicaltrials.gov where studies evaluating the risk of CAS in patients undergoing ML-FFR and iFFR were considered in terms of FFR values from 2014 to 2024. OpenMetaAnalyst was used to evaluate the diagnostic performance in terms of pooled Sensitivity(SN), Specificity(SP), Negative Likelihood Ratio(NLR), Positive Likelihood Ratio(PLR), and Diagnostic Odds Ratio(DOR) at 95% confidence interval (CI). P<0.05 was considered significant. Results: A total of 9 studies including 1358 vessel lesions were analyzed. The pooled SN, SP, NLR, and PLR were 81.1%(74.3%-86.4,p=0.014), 91.8%(84.9%-95.7%, p<0.001), 0.187 (0.151-0.232, p=0.575) and 9.898(5.492-17.839,p<0.001), respectively with >75% heterogeneity in SP and PLR. After excluding two studies, a “leave-out” analysis showed pooled SN, SP, NLR, and PLR as 79.9% (71.4%-86.3%, p=0.007), 94.4% (90.2%-96.9%, p=0.002), 0.187 ( 0.140-0.250, p=0.540) and 14.307 (8.094-25.289, p=0.004) with <70% heterogeneity. The pooled DOR was 53.42 (28.26-100.99, p=0.005) at random effect model. Conclusion: ML-FFR showed significant diagnostic values signifying that it can be used as a potential non-invasive alternative to iFFR for detecting lesion severity and guiding clinical decision-making in patients with suspected CAD, especially for those who are considered high-risk or ineligible for invasive angiography.

Leveraging Industry 4.0 for marketing strategies in the medical device industry of emerging economies.

This research investigates the impact of the technologies through an integrated Fuzzy Analytic Hierarchy Process (F-AHP) and VIKOR model to determine their viability as a marketing strategy for business growth in the Prosthetics and Orthotics (P&O) medical device industry. Based on expert data, the study evaluates the integration of Industry 4.0 technologies and ranks them by priority for marketing performance: "Artificial Intelligence & Machine Learning" (0.3295), "Internet of Things" (0.4325), "Virtual Reality & Augmented Reality" (0.5000), and "Big Data" (0.5704). The findings confirm that the proposed model effectively Leverage the industry 4.0 technologies to enhance marketing strategies, using Pakistan as a case study. This research serves as a blueprint for addressing the integration of next-generation marketing tools, technological advancements, and business growth challenges in the P&O industry. It contributes to the growing body of knowledge on the role of technology in improving healthcare marketing and business performance in emerging markets. Future research should explore the broader impact of Industry 4.0 technologies on healthcare marketing, focusing on strategic decision-making, efficient marketing resource allocation, and performance measurement to ensure sustained success in the P&O medical device sector. The study concludes that prioritizing Industry 4.0 technologies, however, limitations such as sample size, data collection constraints, and model complexity may Leverage the generalizability of the results. Future research should address these limitations by expanding the scope of expert involvement and exploring the application of this model in other emerging economies, to further validate the findings and assess the broader implications for healthcare marketing strategies globally.

Abstract 4113047: Discovering novel biomarkers and predicting cardiovascular disease using AI/ML techniques for precision medicine

Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients (Figure 1). After robust gene expression data pre-processing, we utilized three statistical tests to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, a classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers. After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy (Figure 2). Additionally, we cross-validated our results with clinical records collected from patients in our cohort (Figure 3). The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.

Gaussian Mixture Model Based Voronoi Partitioning Scheme in Multi-Beam Satellites

With advances in technology and growing user needs, satellite communication is changing over time. Efficiently managing resources across diverse satellite coverage areas can be significantly enhanced through the application of Artificial Intelligence (AI) and Machine Learning (ML). The intended project seeks to employ fundamental machine learning algorithms, including Weighted k-means and Gaussian Mixture Model (GMM), to cluster user demands within a designated geographical region. This approach aims to optimize the utilization of satellite bandwidth in a multi-beam satellite system. A comparison of the results obtained using two techniques indicates improved performance of the GMM in terms of allocating the user demands. To ensure effective coverage, the clusters are transformed into shapes that match the coverage areas of the satellite. A technique called Voronoi Tessellation is used to create irregular polygons, and then these polygons are approximated as ellipses to cover the clusters more effectively.

Exploring Artificial Intelligence and Machine Learning in Precision Agriculture: A Pathway to Improved Efficiency and Economic Outcomes in Crop Production

This review analyzes secondary data from academic databases, research articles, and case studies to explore the role of new technologies for precision agriculture (PA) and investigates the value addition that Artificial Intelligence (AI) and Machine Learning (ML) provide to resource use, crop yield, and economic performance. Accordingly, the most of the key applications of AI in PA were related to crop yield prediction, disease detection, and effective water usage. Operating models through AI will analyze much data in real time, thus providing insight into informed decision making by farmers for proactive action against crop challenges like drought or pest attack. Furthermore, IoT devices and remote sensing support continuous monitoring in the delivery of correct data about optimizations of resources with minimal environmental impact. AI-driven robotics further automates all tasks related to planting, harvesting, and pesticide application, improving labor productivity and operational efficiency. This would involve in other issues like implementation costs, data privacy, and general unawareness among farmers of developing areas. Equally important will be ethical issues like ownership of data and loss of jobs. Various case studies in India, China, the United States, and Africa reveal how AI could transform the future of agriculture if integrated into agricultural systems properly to gain higher productivity and sustainability. Improvements in data quality and ethical issues, and increased access by smallholder farmers, will also be part of future research. Eventually, integrating AI with IoT, robotics, and big data analytics could provide high potential to meet global food demand in a sustainable manner.