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1
x, Rajdeep. "Mathematics Model Used in Artificial Intelligence (AI) and Machine Learning (ML)." International Journal of Science and Research (IJSR) 13, no. 12 (2024): 1773–77. https://doi.org/10.21275/sr241227144834.
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Praveen, Halingali, Kumar Santosh, Desai Sanket, and S. Alagoudar Punith. "Survey on Applications of Machine Learning." Journal of Research and Review: Machine Learning 1, no. 2 (2025): 29–35. https://doi.org/10.5281/zenodo.14922864.
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<em>Machine learning (ML) has transformed research methodologies across technical disciplines by enabling data-driven decision-making and predictive analytics. This paper explores ML’s core principles, issues and future developments, emphasizing its impact on optimizing research processes in fields such as engineering, materials science, and telecommunications. Key ML paradigms, including supervised, unsupervised, and reinforcement learning, are analyzed in the context of technical applications. Additionally, this study addresses challenges such as data quality and model interpretability while discussing advancements in explainable AI and federated learning. The findings underscore ML’s role in accelerating innovation and enhancing research precision.</em>
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Mistry, Het. "Mastering Model Selection for AI/ML Models." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 55–67. https://doi.org/10.37745/ejcsit.2013/vol13n145567.
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This article presents a comprehensive framework for mastering model selection in artificial intelligence and machine learning applications across diverse domains. The article addresses the fundamental challenge of selecting models that optimally balance complexity with generalization capability, navigating the classic bias-variance tradeoff that underpins predictive performance. Beginning with theoretical foundations of regularization approaches and complexity measures, the article proceeds through data-driven selection strategies, including cross-validation techniques and advanced hyperparameter optimization methods. The article incorporates robust evaluation metrics for both classification and regression tasks, emphasizing the importance of multi-metric assessment in capturing various performance dimensions. The article extends beyond initial model selection to address the critical yet often overlooked dimension of post-deployment maintenance, including concept drift detection and retraining strategies that ensure sustained model performance over time. The article demonstrates the practical application of these principles in high-stakes environments with domain-specific constraints. The article's integrated framework offers decision support for strategy selection based on data characteristics, with implementation guidance across common machine learning platforms. By synthesizing theoretical insights with practical considerations, this article provides researchers and practitioners with a structured approach to model selection throughout the complete machine learning lifecycle, ultimately enhancing the reliability and sustainability of AI applications in production environments.
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Chittibala, Dinesh Reddy, and Srujan Reddy Jabbireddy. "Security in Machine Learning (ML) Workflows." International Journal of Computing and Engineering 5, no. 1 (2024): 52–63. http://dx.doi.org/10.47941/ijce.1714.
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Purpose: This paper addresses the comprehensive security challenges inherent in the lifecycle of machine learning (ML) systems, including data collection, processing, model training, evaluation, and deployment. The imperative for robust security mechanisms within ML workflows has become increasingly paramount in the rapidly advancing field of ML, as these challenges encompass data privacy breaches, unauthorized access, model theft, adversarial attacks, and vulnerabilities within the computational infrastructure.
 Methodology: To counteract these threats, we propose a holistic suite of strategies designed to enhance the security of ML workflows. These strategies include advanced data protection techniques like anonymization and encryption, model security enhancements through adversarial training and hardening, and the fortification of infrastructure security via secure computing environments and continuous monitoring.
 Findings: The multifaceted nature of security challenges in ML workflows poses significant risks to the confidentiality, integrity, and availability of ML systems, potentially leading to severe consequences such as financial loss, erosion of trust, and misuse of sensitive information.
 Unique Contribution to Theory, Policy and Practice: Additionally, this paper advocates for the integration of legal and ethical considerations into a proactive and layered security approach, aiming to mitigate the risks associated with ML workflows effectively. By implementing these comprehensive security measures, stakeholders can significantly reinforce the trustworthiness and efficacy of ML applications across sensitive and critical sectors, ensuring their resilience against an evolving landscape of threats.
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Shandilya, Ayush. "ML Model for Stock Classification." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5230–39. http://dx.doi.org/10.22214/ijraset.2024.61136.
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Abstract: This study examines how effectively volatility indices can forecast stock market movements by applying different machine learning techniques. It uses a dataset from Kaggle for the TCS Company, including variables such as 'Date,' 'Symbol,' 'Series,' 'Prev Close,' 'Open,' 'High,' 'Low,' 'Last,' 'Close,' 'VWAP,' 'Volume,' 'Turnover,' 'Deliverable Volume,' and '%Deliverable.' Various models were tested and evaluated based on metrics like the AUC-ROC Curve, Accuracy, Precision, Recall, F1-Score, Cross-Validation Accuracy, and the Confusion Matrix. The findings reveal that the Linear Regression (Classification model) surpasses other models in forecasting stock market directions with the highest levels of accuracy across all evaluation metrics. This highlights the potential of machine learning methods in leveraging volatility indices to accurately predict stock market trends
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Gaur, Aditya. "ML Based Macroeconomic Model Simulator." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45585.
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Abstract - This AutoFi, a cutting-edge AI-driven macroeconomic simulation model, provides a robust framework for forecasting financial markets. Financial markets are deeply influenced by macroeconomic indicators such as GDP, unemployment rates, inflation, and interest rates. Accurate forecasting of these indicators is critical for optimizing investment strategies. This research presents AutoFi, a machine learning-based macroeconomic simulation model that predicts key financial indicators and asset performance over time. The proposed system incorporates historical macroeconomic data, time series forecasting models, and regression-based asset prediction to estimate the future value of investments. Additionally, an interactive financial assistant provides investment insights, enhancing accessibility and decision-making. The system enables investors to input their capital allocation preferences across different asset classes and receive projected portfolio values. The results demonstrate the potential of AI-driven economic modeling to improve financial decision-making and risk assessment. Key Words: Macroeconomic forecasting, financial markets, AI-driven investment, AutoFi, machine learning, economic simulation, portfolio management, asset performance prediction
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ARAVIND ,, P. "Brain Stroke detection Using AI/ML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem42994.
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Brain stroke is a leading cause of disability and mortality worldwide, requiring rapid and accurate diagnosis for effective treatment. Traditional stroke diagnosis methods, such as CT and MRI scans, often rely on expert interpretation, which can be time-consuming and prone to human error. This study explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance the accuracy and efficiency of brain stroke detection. We propose a deep learning-based model that utilizes medical imaging data, such as CT and MRI scans, to automatically detect stroke occurrences. The model is trained on a dataset of annotated stroke images and leverages convolutional neural networks (CNNs) for feature extraction and classification. Additionally, we explore machine learning algorithms such as Random Forest, Support Vector Machines (SVM), and XGBoost for predictive stroke risk assessment based on clinical and demographic data. Our results demonstrate that AI-driven stroke detection can achieve high accuracy and assist healthcare professionals in early diagnosis, leading to improved patient outcomes. The study also highlights the potential for real-time stroke detection using AI-powered cloud platforms and mobile applications. Future work will focus on optimizing model performance, integrating multi-modal data sources, and enhancing explainability for clinical adoption.
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Chang, Chaokun, Eric Lo, and Chunxiao Ye. "Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines." Proceedings of the VLDB Endowment 17, no. 10 (2024): 2631–40. http://dx.doi.org/10.14778/3675034.3675052.
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Machine learning inference pipelines commonly encountered in data science and industries often require real-time responsiveness due to their user-facing nature. However, meeting this requirement becomes particularly challenging when certain input features require aggregating a large volume of data online. Recent literature on interpretable machine learning reveals that most machine learning models exhibit a notable degree of resilience to variations in input. This suggests that machine learning models can effectively accommodate approximate input features with minimal discernible impact on accuracy. In this paper, we introduce Biathlon, a novel ML serving system that leverages the inherent resilience of models and determines the optimal degree of approximation for each aggregation feature. This approach enables maximum speedup while ensuring a guaranteed bound on accuracy loss. We evaluate Biathlon on real pipelines from both industry applications and data science competitions, demonstrating its ability to meet real-time latency requirements by achieving 5.3× to 16.6× speedup with almost no accuracy loss.
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Dhanwate, Prof P. "Multiple Disease Prediction System Using ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3612–17. http://dx.doi.org/10.22214/ijraset.2024.59642.
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Abstract: The increasing prevalence of diverse diseases presents a challenge to global healthcare systems, underscoring the need for innovative and efficient methods for early detection and preventive measures. This paper explores the application of machine learning algorithms in multiple disease prediction to enhance diagnostic accuracy and enable timely intervention. Leveraging diverse health-related data sources, including medical records and genomic information, comprehensive predictive models are developed. A multi-faceted machine learning approach integrates support vector machines, decision trees, neural networks, and ensemble learning methods to analyze complex data patterns. Feature selection and dimensionality reduction techniques optimize model performance and interpretability. The development of a predictive system involves essential steps such as data collection, preprocessing, and model training, followed by evaluation using metrics like accuracy and recall. Integration of Flask for web application development facilitates user interaction and prediction functionality. Deployment, testing, debugging, and ongoing maintenance ensure system efficiency and compliance with regulatory requirements for healthcare data security and privacy.
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Abhishek, Shivanna. "Framework for Implementing Experiment Tracking in Machine Learning Development." Journal of Scientific and Engineering Research 11, no. 10 (2024): 118–23. https://doi.org/10.5281/zenodo.14273552.
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Machine learning (ML) projects often involve numerous experiments that need to be tracked, compared, and reproduced to ensure consistent results and effective collaboration. This paper explores the significance of experiment tracking in ML workflows, discusses best practices, and addresses challenges in implementation. We present a comprehensive framework for experiment tracking that enhances reproducibility, accountability, and collaboration within ML teams. This paper emphasizes on how systematic tracking can optimize workflows, accelerate model development, and improve the overall quality of machine learning projects.
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Aaryan, Arora, and Basu Nirmalya. "Machine Learning in Modern Healthcare." International Journal of Advanced Medical Sciences and Technology (IJAMST) 3, no. 4 (2023): 12–18. https://doi.org/10.54105/ijamst.D3037.063423.
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<strong>Abstract: </strong>Traditional healthcare systems have long struggled to meet the diverse needs of millions of patients, leading to inefficiencies and suboptimal outcomes. However, the advent of machine learning (ML) has introduced a transformative paradigm shift towards value-based treatment, enabling healthcare providers to deliver personalized and highly effective care.Modern healthcare equipment and devices now incorporate internal applications that gather and store comprehensive patient data, presenting a valuable resource for ML-driven predictive models. In this research article, we delve into the profound impact of ML on modern healthcare, highlighting its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, harnessing the power of extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and Py Torch (accuracy: 0.7337662337662337), to determine the best-performing model. The achieved accuracies demonstrate the effectiveness of these ML techniques in disease prediction and showcase the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.
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Yamikov, Rustem Raficovich, and Karen Albertovich Grigorian. "Analysis and Development of the MLOps Pipeline for ML Model Deployment." Russian Digital Libraries Journal 25, no. 2 (2022): 177–96. http://dx.doi.org/10.26907/1562-5419-2022-25-2-177-196.
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The growth in the number of IT products with machine-learning features is increasing the relevance of automating machine-learning processes. The use of MLOps techniques is aimed at providing training and efficient deployment of applications in a production environment by automating side infrastructure issues that are not directly related to model development.
 In this paper, we review the components, principles, and approaches of MLOps and analyze existing platforms and solutions for building machine learning pipelines. In addition, we propose an approach to build a machine learning pipeline based on basic DevOps tools and open-source libraries.
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Roy, Urmimala, Tanmoy Pramanik, Subhendu Roy, Avhishek Chatterjee, Leonard F. Register, and Sanjay K. Banerjee. "Machine Learning for Statistical Modeling." ACM Transactions on Design Automation of Electronic Systems 26, no. 3 (2021): 1–17. http://dx.doi.org/10.1145/3440014.
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We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼500 hours of computation. On the other hand, if 10 6 samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken ∼250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.
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Choudhary, Laxmi, and Jitendra Singh Choudhary. "Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence." Journal of Scientific Research and Reports 30, no. 11 (2024): 865–75. http://dx.doi.org/10.9734/jsrr/2024/v30i112614.
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The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.
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Meenal, R., Prawin Angel Michael, D. Pamela, and E. Rajasekaran. "Weather prediction using random forest machine learning model." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1208–15. https://doi.org/10.11591/ijeecs.v22.i2.pp1208-1215.
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The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many researchers recommended that the machine learning models can produce sensible weather predictions in spite of having no precise knowledge of atmospheric physics. In this work, global solar radiation (GSR) in MJ/m2/day and wind speed in m/s is predicted for Tamil Nadu, India using a random forest ML model. The random forest ML model is validated with measured wind and solar radiation data collected from IMD, Pune. The prediction results based on the random forest ML model are compared with statistical regression models and SVM ML model. Overall, random forest machine learning model has minimum error values of 0.750 MSE and R2 score of 0.97. Compared to regression models and SVM ML model, the prediction results of random forest ML model are more accurate. Thus, this study neglects the need for an expensive measuring instrument in all potential locations to acquire the solar radiation and wind speed data.
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Ghufran, Hamid Zghair, and Shaheed Al-Azzawi Dheyaa. "Comparing emotion classification: machine learning algorithms and hybrid model with support vector machines." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3671–85. https://doi.org/10.11591/ijai.v13.i3.pp3671-3685.
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Recently, the use of artificial intelligence techniques has become widespread, having been adopted in brain-computer interfaces (BCIs) with electroencephalograms (EEGs). BCIs allow direct communication between a person's brain and a computer, and have various uses ranging from assistive technology to neuroscientific study. This paper provides an introductory overview of BCIs and EEG. We adopted the use of machine learning (ML) algorithms, including K-nearest neighbors (KNN), logistic regression, decision trees, random forests, and support vector machine (SVM). Additionally, we proposed a hybrid model of deep learning (DL) and ML by combining convolutional neural networks (CNNs) and SVMs. Our achieved 98% accuracy. The goal is to classify EEG signals into three emotional states: happy, normal, and sad. The study aims to achieve a comprehensive understanding of the effectiveness of these algorithms in accurately classifying emotional states based on EEG data. By comparing the performance of traditional ML methods and the proposed hybrid model, we seek to identify the most robust and accurate approach to sentiment classification.
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Spelda, Petr, and Vit Stritecky. "Human Induction in Machine Learning." ACM Computing Surveys 54, no. 3 (2021): 1–18. http://dx.doi.org/10.1145/3444691.
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As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, then an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The article asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach “elsewhere” in space and time or deploy ML models in non-benign environments. The article argues that the only viable version of the contract can be based on optimality (instead of on reliability, which cannot be justified without circularity) and aligns this position with Schurz's optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (“elsewhere” and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full.
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Meenal, R., Prawin Angel Michael, D. Pamela, and E. Rajasekaran. "Weather prediction using random forest machine learning model." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1208. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1208-1215.
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The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many researchers recommended that the machine learning models can produce sensible weather predictions in spite of having no precise knowledge of atmospheric physics. In this work, global solar radiation (GSR) in MJ/m2/day and wind speed in m/s is predicted for Tamil Nadu, India using a random forest ML model. The random forest ML model is validated with measured wind and solar radiation data collected from IMD, Pune. The prediction results based on the random forest ML model are compared with statistical regression models and SVM ML model. Overall, random forest machine learning model has minimum error values of 0.750 MSE and R2 score of 0.97. Compared to regression models and SVM ML model, the prediction results of random forest ML model are more accurate. Thus, this study neglects the need for an expensive measuring instrument in all potential locations to acquire the solar radiation and wind speed data.
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Raval, Ohm Milindkumar. "Scalable ML Model Deployment on AWS SageMaker." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 1309–15. https://doi.org/10.22214/ijraset.2025.66568.
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This research aims to understand how to build scalable machine learning models on AWS SageMaker, specifically about preparing datasets, training data in a distributed manner, searching hyperparameters and efficiently using resources. Besides, this study can show a way to high-performance also cost-effective solution by deploying the model in real-time inference using the auto-scaling abilities of the SageMaker’s platform along with the monitoring tool. These findings emphasize SageMaker's ability to manage large-scale datasets while ensuring model accuracy, yet they also reveal areas that need improvement in terms of model interpretability, drift, and long-term adaptation. The study highlights the need for scalable, flexible solutions, particularly for real-world applications in fields such as healthcare and finance. The challenges present opportunities for future research in areas like model explainability and continuous learning. A comprehensive understanding of this work will not only help to build scalable, real-time solutions for ensuring deployment for cloud-based machine learning but also provide practical insights into its industry applications.
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Studer, Stefan, Thanh Binh Bui, Christian Drescher, et al. "Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology." Machine Learning and Knowledge Extraction 3, no. 2 (2021): 392–413. http://dx.doi.org/10.3390/make3020020.
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Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the form of risks. The methodology is drawn from practical experience and scientific literature, and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.
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Friedman, Joseph I., Prathamesh Parchure, Fu-Yuan Cheng, et al. "Machine Learning Multimodal Model for Delirium Risk Stratification." JAMA Network Open 8, no. 5 (2025): e258874. https://doi.org/10.1001/jamanetworkopen.2025.8874.
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ImportanceAutomating the identification of risk for developing hospital delirium with models that use machine learning (ML) could facilitate more rapid prevention, identification, and treatment of delirium. However, there are very few reports on the performance of ML models for delirium risk stratification in live clinical practice.ObjectiveTo report on development, operationalization, and validation of a multimodal ML model for delirium risk stratification in live clinical practice and its associations with workflow and clinical outcomes.Design, Setting, and ParticipantsThis quality improvement study developed an ML model supported by automated electronic medical records to stratify the risk of non–intensive care unit delirium in live clinical practice using the Confusion Assessment Method as the diagnostic reference standard, with an iterative model update method. Data from patients aged at least 60 years admitted to non–intensive care units at Mount Sinai Hospital between January 2016 and January 2020 were used to train and test the ML model presented. The model was validated in live clinical practice from March 2023 to March 2024. Analysis of the model’s associations with workflow and clinical outcomes was conducted retrospectively in 2024, comparing hospitalized patients prior to deployment of any model version (pre-ML cohort) and during model clinical deployment (post-ML cohort).Main Outcomes and MeasuresOutcomes of interest were area under the receiver operating characteristic curve, monthly delirium detection rates, median length of hospital stay, and daily doses of opiate, benzodiazepine, and antipsychotic medications administered.ResultsThe overall sample included 32 284 inpatient admissions (mean [SD] age, 73.56 (9.67) years, 15 157 [46.9%] women). A total of 25 261 inpatient admissions of older patients with both medical and surgical primary diagnoses represented the combined model testing and training cohort (median age, 73.37 [66.42-81.36] years) and live clinical deployment validation cohort (median [IQR] age, 72.11 [62.26-78.97] years), while 7023 inpatient admissions of older patients with both medical and surgical primary diagnoses represented the combined pre-ML (median [IQR] age, 74.00 [68.00-81.00] years) and post-ML (median [IQR] age, 75.33 [68.34-82.91] years) cohorts. The model presented is a fusion of electronic medical record patient data features and clinical note features processed by natural language processing. The results of model validation in live clinical practice included an area under the curve of 0.94 (95% CI, 0.93-0.95). Median (IQR) monthly delirium detection rates of inpatients assessed for delirium with the Confusion Assessment Method increased from 4.42% (95% CI, 3.70%-5.14%) in the pre-ML cohort to 17.17% (95% CI, 15.54%-18.80%) in the post-ML cohort (P &amp;lt; .001). Post-ML vs pre-ML cohorts received lower daily doses of benzodiazepines (median [IQR] 0.93 [0.42-2.28] diazepam dose equivalents vs 1.60 [0.66-4.27] diazepam dose equivalents; P &amp;lt; .001) and olanzapine (median [IQR], 1.09 [0.38-2.46] mg vs 2.50 [1.17-6.65] mg; P &amp;lt; .001).Conclusions and RelevanceThis quality improvement study demonstrates the feasibility of a novel multimodal ML model to automate delirium risk stratification in live clinical practice. The model demonstrated acceptable performance in live clinical practice and may facilitate resource allocation to enhance delirium identification and care.
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Banegas-Luna, Antonio Jesús, and Horacio Pérez-Sánchez. "SIBILA: Automated Machine-Learning-Based Development of Interpretable Machine-Learning Models on High-Performance Computing Platforms." AI 5, no. 4 (2024): 2353–74. http://dx.doi.org/10.3390/ai5040116.
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As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. This paper presents SIBILA, an AutoML approach designed for HPC environments, focusing on the interpretation of ML models. SIBILA simplifies model development by allowing users to set objectives and preferences before automating the search for optimal ML pipelines. Unlike traditional AutoML frameworks, SIBILA is specifically designed to exploit the computational capabilities of HPC platforms, thereby accelerating the model search and evaluation phases. The emphasis on interpretability is particularly crucial when model transparency is mandated by regulations or desired for stakeholder understanding. SIBILA has been validated in different tasks with public datasets. The results demonstrate that SIBILA consistently produces models with competitive accuracy while significantly reducing computational overhead. This makes it an ideal choice for practitioners seeking efficient and transparent ML solutions on HPC infrastructures. SIBILA is a major advancement in AutoML, addressing the rising demand for explainable ML models on HPC platforms. Its integration of interpretability constraints alongside automated model development processes marks a substantial step forward in bridging the gap between computational efficiency and model transparency in ML applications. The tool is available as a web service at no charge.
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Gondara, Lovedeep, and Ke Wang. "PubSub-ML: A Model Streaming Alternative to Federated Learning." Proceedings on Privacy Enhancing Technologies 2023, no. 2 (2023): 464–79. http://dx.doi.org/10.56553/popets-2023-0063.
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Federated learning is a decentralized learning framework where participating sites are engaged in a tight collaboration, forcing them into symmetric sharing and the agreement in terms of data samples, feature spaces, model types and architectures, privacy settings, and training processes. We propose PubSub-ML, Publish-Subscribe for Machine Learning, as a solution in a loose collaboration setting where each site maintains local autonomy on these decisions. In PubSub-ML, each site is either a publisher or a subscriber or both. The publishers publish differentially private machine learning models and the subscribers subscribe to published models in order to construct customized models for local use, essentially benefiting from other sites' data by distilling knowledge from publishers' models while respecting data privacy. The term “model streaming” comes from the extension of PubSub-ML to decentralized data streams with concept drift. Our extensive empirical evaluation shows that PubSub-ML outperforms federated learning methods by a significant margin.
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Balkrishna, Rasiklal Yadav. "Machine Learning Algorithms: Optimizing Efficiency in AI Applications." International Journal of Engineering and Management Research 14, no. 5 (2024): 49–57. https://doi.org/10.5281/zenodo.14005017.
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Machine learning (ML) is an AI technology that creates programs and data models that can perform tasks without being instructed. It has three major types: guided learning, uncontrolled learning, and reinforcement learning. ML is essential for big projects like real-time decision-making systems and self-driving cars, robots, and drones. It improves AI systems by making it easier to create models, work with data, and run algorithms. ML algorithms have different types of learning, require different amounts of data and training times, and can be improved by tuning hyperparameters. Techniques like feature selection, dimensionality reduction, model editing, and compression can improve performance and accuracy in various fields. In the real world, making AI apps more efficient can lead to more options, lower prices, and faster processing. Key techniques like model compression, transfer learning, and edge computing are needed to achieve these goals.
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Jeevana, P., T. Nandini, D. Srilekha, G. Dinesh, and Mrs Archana. "Diabetic Prediction using ML Techniques." YMER Digital 21, no. 04 (2022): 585–93. http://dx.doi.org/10.37896/ymer21.04/59.
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In today's world, diabetes is a huge problem. Diabetes can cause blood sugar levels to rise, which can contribute to strokes and heart attacks. One of the most rapidly spreading diseases is this one. After speaking with a doctor and receiving a diagnosis, patients are normally required to receive their reports. Because this procedure is time-consuming and costly, we were able to fix the problem utilizing machine learning techniques. In medical organizations, many machine learning applications are both exciting and important. Machine learning is being more widely used in the medical field. Our study aims to create a system that can better predict a patient's diabetic risk level. The medical data set is put to many different uses. in order to develop an artificial intelligence model for disease prediction The National Institute of Diabetes and Digestive and Kidney Diseases provided the data. Among the items on the list are blood pressure, age, insulin level, BMI, and glucose. Models are created using classification methods such as Ada Boost, Gradient Boost, XG Boost, and Cat Boost. The outcomes reveal that the processes are extremely precise. According to the findings, the prediction made with the use the prediction utilizing the Gradient Boosting model had the highest accuracy, according to the findings. Our investigation covers a wide range of machine learning topics as well as the numerous types of prediction models available. We go over the different sorts of models that can be used to create predictions, as well as the characteristics of machine learning.
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Nikose, Archana, Harsh Kuite, Kalyani Mude, Aniruddha Polke, and Nikita Nanhe. "GlycoDetect a Diabetic Prediction Model using ML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem37782.
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This project focuses on using machine learning to predict a patient’s risk of developing diabetes based on a test. This database, compiled by the National Institute of Diabetes and Digestive and Kidney Diseases, contains health indicators for Pima Indian patients. The project involves several key steps: initial data, selecting a feature, selecting a model, updating the hyperparameter, and deploying it via the Flask web application. In the data preprocessing phase, feature scaling and normalization are used to standardize the dataset, while missing values and outliers are handled to ensure data integrity. Feature selection uses correlation matrix and recursive feature elimination (RFE) to reduce dimensionality and improve model efficiency. To ensure the model is optimized for latent data, the dataset is split into two parts: 66% for training and 34% for testing. Various machine learning algorithms are evaluated, including logistic regression, naive Bayes, K-nearest neighbours, decision trees, and support vector classifiers. Logistic regression was selected as the final model due to its accuracy on the test data (80.53%). The model uses grid search for hyperparameter tuning to improve its performance. The training model is embedded in the Flask web application, allowing users to access health metrics and get real-time estimates of blood pressure. The system is designed to be user-friendly and scalable, providing a practical tool for early diagnosis of diabetes. All methods ensure that the model is accurate, reliable, and capable of making real-world predictions. Keywords: prediction, diabetes, glucoses, insulin, machine learning, logistic regression, naive bayes, k- nearest neighbours, decision tree, support vector classifier.
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Yanjun Li, Yanjun Li, Huan Huang Yanjun Li, Qiang Geng Huan Huang, Xinwei Guo Qiang Geng, and Yuyu Yuan Xinwei Guo. "Fairness Measures of Machine Learning Models in Judicial Penalty Prediction." 網際網路技術學刊 23, no. 5 (2022): 1109–16. http://dx.doi.org/10.53106/160792642022092305019.
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<p>Machine learning (ML) has been widely adopted in many software applications across domains. However, accompanying the outstanding performance, the behaviors of the ML models, which are essentially a kind of black-box software, could be unfair and hard to understand in many cases. In our human-centered society, an unfair decision could potentially damage human value, even causing severe social consequences, especially in decision-critical scenarios such as legal judgment. Although some existing works investigated the ML models in terms of robustness, accuracy, security, privacy, quality, etc., the study on the fairness of ML is still in the early stage. In this paper, we first proposed a set of fairness metrics for ML models from different perspectives. Based on this, we performed a comparative study on the fairness of existing widely used classic ML and deep learning models in the domain of real-world judicial judgments. The experiment results reveal that the current state-of-the-art ML models could still raise concerns for unfair decision-making. The ML models with high accuracy and fairness are urgently demanding.</p> <p>&nbsp;</p>
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Sahoo, Abhilipsa, and Kaushika Patel. "Machine Learning-based Inverse Design Model of a Transistor." Indian Journal Of Science And Technology 17, no. 7 (2024): 617–24. http://dx.doi.org/10.17485/ijst/v17i7.3076.
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Objectives: To develop an inverse design model for transistors, utilizing machine learning algorithms to predict key design parameters specifically, the length and width based on specified gain and bandwidth requirements. And to conduct a comprehensive comparative analysis with existing literature, evaluating the efficacy and novelty of the proposed model in the context of semiconductor engineering challenges and methodologies. Methods: The comprehensive dataset, comprising 30,000 values generated through LTspice simulations, forms the basis for training the machine learning model. Utilizing a Random Forest regressor as the base model and a multi-output regressor as the main model, the project involves extensive data analysis, model development, and iterative fine-tuning. Findings: The outcomes demonstrate the efficacy of the developed model in accurately predicting transistor dimensions. Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared, highlight the precision of the model in fulfilling the specified objectives. Novelty: This study introduces a novel approach to semiconductor device design optimization, showcasing the potential of machine learning to streamline the inverse design process. The use of a multi-output regressor, feature engineering, and fine-tuning through log transformation contribute to the innovative nature of the developed model. Keywords: Machine Learning (ML) model, Random Forest regressor, multioutput regressor, Feature engineering, Finetuning
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Rico, Kurniawan, Utomo Budi, N. Siregar Kemal, et al. "Hypertension prediction using machine learning algorithm among Indonesian adults." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 776–84. https://doi.org/10.11591/ijai.v12.i2.pp776-784.
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Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machinelearning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic area under the curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.
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Researcher. "MACHINE LEARNING FEATURE STORES: A COMPREHENSIVE OVERVIEW." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 82–91. https://doi.org/10.5281/zenodo.13711230.
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This article presents a comprehensive examination of Machine Learning (ML) Feature Stores, their role in modern ML infrastructures, and their impact on the efficiency and scalability of ML operations. We explore the key roles of Feature Stores, including centralization of feature management, ensuring consistency between training and serving environments, promoting feature reusability, enhancing governance, and improving overall efficiency in ML workflows. A detailed reference architecture is proposed, outlining essential components such as data ingestion, feature engineering, storage, serving, metadata management, monitoring, integration, and governance layers. The article discusses the significant benefits of implementing Feature Stores, including improved data consistency, enhanced collaboration across teams, accelerated model development and deployment, and better compliance with data governance requirements. We also address the challenges and considerations organizations face when adopting Feature Stores, such as integration with existing ML infrastructure, performance optimization for real-time serving, scalability concerns, and data privacy implications. Case studies from large tech companies illustrate the practical impact of Feature Stores on ML workflow efficiency and model performance. Finally, we explore future trends and developments in the field, including advanced feature discovery systems, integration with AutoML platforms, and enhanced support for federated learning. This comprehensive analysis provides valuable insights for organizations seeking to optimize their ML operations and leverage the full potential of their data and models through the implementation of Feature Stores.
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Kajal, Singh, and Mukherjee Anukriti. "Reliable Algorithms for Machine Learning Models: Implementation Research in Data Science." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 6 (2022): 102–6. https://doi.org/10.35940/ijrte.F6871.0310622.
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<strong>Abstract: </strong>Machine Learning generates programs that make predictions and informed decisions about complex problems in an efficient and reliable way. These ML programs autonomously test solutions against the dataset to find the best fit for the problem. The paper aims to review the ML algorithms that develop prediction models by utilizing training dataset and known output. The paper also focuses on ML principles, algorithms, approaches, and applications for Supervised, Unsupervised, and Reinforcement learning that can perform tasks without being explicitly programmed for it. Completely opposite to rule-based programming, the machine learning paradigm uses examples of real data sets and pre-process it before providing the desired outputs based on these examples. In the case of more involved and complex tasks, it can be challenging for humans to explicitly program the models. On the other hand, it can be more effective to help the machines develop the algorithms for advanced tasks. This paper will also present the trending real-world applications of Machine Learning in Image Recognition and Biomedicine. Additionally, it will provide a background analysis of machine learning and related fields of data science.
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Liu, Xinran, James Anstey, Ron Li, Chethan Sarabu, Reiri Sono, and Atul J. Butte. "Rethinking PICO in the Machine Learning Era: ML-PICO." Applied Clinical Informatics 12, no. 02 (2021): 407–16. http://dx.doi.org/10.1055/s-0041-1729752.
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Abstract Background Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.
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Gawande, Sunad D., Likhit Y. Shende, Vedant R. Dhajekar, Shrushti A. Mankar, Krutika D. Wankhade, and Prof. Jaicky R. Sancheti. "MEDINTEL: Disease Prediction and Drug Recommendation System Using ML." International Journal of Ingenious Research, Invention and Development (IJIRID) 4, no. 2 (2025): 256–62. https://doi.org/10.5281/zenodo.15206426.
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<em>MedIntel: Disease Prediction & Drugs Recommendation System For Health Care Using Machine Learning. MedIntel is an advanced healthcare solution designed to enhance disease prediction and drug recommendations using machine learning. Its primary objectives include improving early disease detection, providing personalized treatment recommendations, and increasing healthcare accessibility. By leveraging cutting-edge AI technologies, MedIntel aims to enhance diagnostic precision, minimize treatment delays, and support medical professionals in making informed, data-driven decisions. The system follows a structured methodology that involves collecting and preprocessing diverse patient data, including demographics, medical history, and diagnostic reports. Disease prediction is carried out using machine learning algorithms such as decision trees, random forests, and deep learning models, which analyze complex patterns in patient data to identify potential health risks. For drug recommendations, collaborative filtering and natural language processing (NLP) techniques are employed to assess patient profiles, drug interactions, and clinical guidelines. The model is trained on large, real-world datasets to ensure its reliability, scalability, and adaptability to various healthcare environments.</em>
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Sari, Mustika, Mohammed Ali Berawi, Teuku Yuri Zagloel, and Rizka Wulan Triadji. "Machine learning model for green building design prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1525. http://dx.doi.org/10.11591/ijai.v11.i4.pp1525-1534.
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<div align="left">Green building (GB) is a design concept that implements sustainable processes and green technologies in the building's life cycle. However, the design process of GB tends to take longer than conventional buildings due to the integration of various green requirements and performances into the building design. Advanced artificial intelligence (AI) methods such as machine learning (ML) are widely used to help designers do their jobs faster and more accurately. Therefore, this study aims to develop a GB design predictive model utilizing ML techniques that consider four GB design criteria: energy efficiency, indoor environmental quality, water efficiency, and site planning. A dataset of GB projects collected from a private construction company based in Jakarta was used to train and test the ML model. The accuracy of the models was evaluated using mean square error (MSE). The comparison of MSE values of the conducted experiments showed that the combination of the artificial neural network (ANN) method with the IF-ELSE algorithm created the most accurate ML model for GB design prediction with an MSE of 1.3.</div>
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Mustika, Sari, Ali Berawi Mohammed, Yuri Zagloel Teuku, and Wulan Triadji Rizka. "Machine learning model for green building design prediction." International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1525–34. https://doi.org/10.11591/ijai.v11.i4.pp1525-1534.
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Green building (GB) is a design concept that implements sustainable processes and green technologies in the building's life cycle. However, the design process of GB tends to take longer than conventional buildings due to the integration of various green requirements and performances into the building design. Advanced artificial intelligence (AI) methods such as machine learning (ML) are widely used to help designers do their jobs faster and more accurately. Therefore, this study aims to develop a GB design predictive model utilizing ML techniques that consider four GB design criteria: energy efficiency, indoor environmental quality, water efficiency, and site planning. A dataset of GB projects collected from a private construction company based in Jakarta was used to train and test the ML model. The accuracy of the models was evaluated using mean square error (MSE). The comparison of MSE values of the conducted experiments showed that the combination of the artificial neural network (ANN) method with the IF-ELSE algorithm created the most accurate ML model for GB design prediction with an MSE of 1.3.
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Khade, Omkar, Yash Kadam, Ashish Ruke, and Suyash Yeolekar. "College NIRF Rank Predictor using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 34–41. http://dx.doi.org/10.22214/ijraset.2023.50023.
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Abstract: The National Institutional Ranking Framework (NIRF) is an annual ranking system initiated by the Indian government to rank higher education institutions based on several parameters such as teaching, research, and outreach activities. In this project, we propose to develop a machine learning model that can predict the NIRF rank of an institution. Here we have used 2020 NIRF ranking dataset from Kaggle. Then based on the score of previous years, we predict the rank by giving the performance indicators to the model. The paper focuses on the use of Random Forest Regressor based Machine learning technique to predict NIRF rank. Factors considered are Teaching, Learning and Resources (TLR) score, Research and Professional Practice (RPC) score, Graduation Outcome (GO) score, Outreach and Inclusivity (OI) score and Perception Score for particular college. The model is evaluated using standard strategic indicator: Root Mean Square Error. The low value of this indicator show that the model is efficient in predicting NIRF rank. We got score of 93% and RMSE of 15.47. We have completed ML model save and load operations using Joblib. We have created a flask server for model deployment and deployed on Render as web service. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for College NIRF rank prediction.
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N Thrimoorthy, Dr. "Customer Service Chatbot With ML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40893.
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In the modern business landscape, customer support plays a critical role in shaping customer experiences and building brand loyalty. With the growing demand for instant responses and 24/7 availability, organizations are increasingly exploring artificial intelligence (AI) solutions to enhance the efficiency and effectiveness of their customer service operations. This work aims to design and develop an intelligent customer support chatbot that leverages machine learning (ML) and natural language processing (NLP) techniques to deliver automated, yet personalized, customer assistance. The core objective is to build a machine learning-based chatbot capable of understanding and responding to a wide array of customer queries in real-time. Traditional customer service models often require significant human resources, leading to high operational costs and long response times. By integrating an AI powered chatbot into the customer support process, businesses can streamline operations, reduce response time, and provide customers with timely, accurate information. Keywords: · Natural Language Understanding (NLU): The chatbot uses NLTK and Speech recognition to process and understand the user's input in natural language. It can identify the intent and extract key details from user queries to provide relevant responses[6]. · Integration in Flask Backend Flask supports the light-weight fast backend with requests that routes those to the correct machine learning model. It manages all the interactions with the users by processing inputs and passing on[4]. · Model Training of Machine Learning Model: The OpenAI models are trained on labelled data for the improvement in recognizing intents of the chatbot. user and giving suitable responses of the model keeps learning and evolving as it processes new data of the users[3]. · Real-Time Error Handling: The system handles errors such as unrecognized queries or technical issues efficiently. It logs errors and provides feedback to the user, ensuring a smooth experience[2]. · Sentiment Analysis: Based on NLTK, it analysis the sentiment of all user inputs to establish possible emotions such as frustration and satisfaction. This will provide the chatbot with means to respond in addition to adjusting its tone[4].
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Unang Achlison, Dendy Kurniawan, Toni Wijanarko Adi Putra, and Siswanto Siswanto. "EXPLORATION OF AMPLITUDE CODING CAPACITIES FOR Q-ML MODEL." Journal of Engineering, Electrical and Informatics 2, no. 3 (2022): 34–49. http://dx.doi.org/10.55606/jeei.v2i3.916.
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Quantum computing implements computation adopting environmental phantasm and the foundation of quantum mechanics to clear up the issues. This design of calculation has been demonstrated to serve the acceleration of some modern processing issues. Current evolution in quantum technology is emerging, and the application of learning design to this current instrument is developing. With enough prospects, the application of quantum development in the area of Machine Learning has come clear.
 This research develops a TensorFlow Quantum (TF-Q) software framework model for machine learning functions. The two models advanced the application of material coding techniques from amplitude coding to constructing a case in the quantum learning model. This study aimed to explore the scope of amplitude coding to serve enhanced case establishment in learning techniques and in-depth investigation of data sets that bring insight into the practice data adopting the “Variational Quantum Classifier” (VQ-C). The emergence of this current method raises the investigation of how best this tool can be adopted, the aim is to provide several analysis explanations for the element of quantum machine learning that can be applied given the constraints of the actual device.
 The results of this study indicate there are clear advantages to adopting amplitude coding over another technique as demonstrated by adopting the combination of quantum-humanistic neural networks in TF-Q. In addition, the different preprocessing steps can generate more aspect-affluent data while using VQ-C the no-charge lunch assumption dominance for quantum learning technique for humanistic models. The material even though conceal in quantum by unadvanced data preparation steps but involves new ways of understanding and appreciating these new methods. Future studies will lack expansion into multi-type of analysis models that are sufficiently advanced to be relevant in work similar to this.
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Gui, Chloe, and Victoria Chan. "Machine learning in medicine." University of Western Ontario Medical Journal 86, no. 2 (2017): 76–78. http://dx.doi.org/10.5206/uwomj.v86i2.2060.
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Machine learning (ML) is a powerful and flexible tool that can be used to analyze and predict outcomes from biological and clinical data. ML models have the potential to improve healthcare efficiency in a number of ways. Algorithms that predict prognosis empower healthcare officials to allocate resources optimally and physicians to select better treatment options for patients. Diagnostic models can be used in screening, in risk stratification, and to recommend appropriate testing and treatment. This would decrease the burden on physicians, increase and expedite patient access to care, save resources, and reduce costs. However, despite the research advances of ML in medicine, its role in the clinic is currently limited. Model building and validation may require large amounts of high-quality data that can be difficult and expensive to obtain, and diagnostic models must be individually built for each disease, a lengthy process. The psychological aspect of trusting black box algorithms may also be challenging to accept. Continued ML research, however, may enable the use of smaller datasets and the development of more transparent models. Careful trials in the clinic will need to be conducted before the more impactful uses of ML, such as diagnosis, can be implemented.
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Journal, IJSREM. "MONITORING OF INFANT CRYING USING ML." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26392.
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This study focuses on the creation of a system for machine learning model designed, monitor, detect, and classify the underlying reasons behind infant crying. Leveraging acoustic features, the model will differentiate between various causes, including hunger, discomfort, pain, and more, with the goal of providing real- time insights for caregivers and healthcare professionals. This innovative technology aims to enhance infant care by facilitating early detection and appropriate responses to the infant's needs, ultimately contributing to the well-being and overall health of newborns and reducing caregiver stress. Keywords: Machine learning, Acoustic analysis, Infant cry, MFCC, KNN, python_speech_features, Django, MySQL.
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Sepulveda, Natalia Espinoza, and Jyoti Sinha. "Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines." Machines 8, no. 4 (2020): 66. http://dx.doi.org/10.3390/machines8040066.
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Artificial intelligence (AI)-based machine learning (ML) models seem to be the future for most of the applications. Recent research effort has also been made on the application of these AI and ML methods in the vibration-based faults diagnosis (VFD) in rotating machines. Several research studies have been published over the last decade on this topic. However, most of the studies are data driven, and the vibration-based ML (VML) model is generally developed on a typical machine. The developed VML model may not predict faults accurately if applied on other identical machines or a machine with different operation conditions or both. Therefore, the current research is on the development of a VML model by optimising the vibration parameters based on the dynamics of the machine. The developed model is then blindly tested at different machine operation conditions to show the robustness and reliability of the proposed VML model.
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Gupta, Adhyayan. "Machine Learning and Deep Learning: A Comprehensive Overview." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1620–26. https://doi.org/10.22214/ijraset.2025.72470.
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Machine Learning (ML) and Deep Learning (DL) are two core areas of Artificial Intelligence (AI) that have significantly transformed technology and research. Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cyber security, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into blackbox machines that hamper development at the standard level. This paper presents a comprehensive overview of ML and DL, their theoretical foundations, methodologies, applications, and current trends. The paper aims to clarify the distinctions and synergies between ML and DL and provide insights into their practical implications in various domains such as healthcare, finance, robotics, and computer vision.
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Rahu, Mushtaque Ahmed, Abdul Fattah Chandio, and Syed Mazhar Ali. "Machine Learning Overview in Agriculture." Journal of Applied Engineering & Technology (JAET) 6, no. 1 (2022): 28–39. http://dx.doi.org/10.55447/jaet.06.01.93.
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Today's agriculture industry makes extensive use of the promising field of machine learning (ML). There is not enough labour available for agriculture, and there are not enough skilled farmers. It is difficult to identify and stop crop diseases without a thorough understanding of the current situation. It is also frequently used in many aspects of agriculture, including managing soils, yields, water, diseases, and weather. The ML models allow rapid and actual decision-making. To anticipate correctness of the output, ML model uses training and testing. Species management, Disease detection, yield prediction, crop quality, water management, weed identification, increased productivity and better management of soil categorization are all aided by an application of ML in agriculture. By highlighting benefits and drawbacks of various ML methodologies put forth in the last five years, this article seeks to provide comprehensive information on them. Additionally, it contrasts several ML techniques employed in contemporary agriculture.
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Khalate, Vaishnavi, and Prof Borate Sukeshkumar. "Diabetes Prediction Using Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1963–69. http://dx.doi.org/10.22214/ijraset.2024.59256.
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bstract: Diabetes, a chronic metabolic disorder affecting millions worldwide, requires early detection and management to mitigate its complications. Machine learning (ML) techniques have emerged as promising tools for predictive analytics in healthcare, offering the potential to improve diagnostic accuracy and patient outcomes. This paper presents a comprehensive review of ML algorithms applied to diabetes prediction, encompassing diverse methodologies and datasets. The study evaluates the performance of various ML algorithms, including but not limited to logistic regression, decision trees, support vector machines, random forests, and deep learning approaches, in predicting the onset or progression of diabetes. Additionally, feature selection techniques and data pre-processing methods are explored to enhance model robustness and interpretability. Furthermore, this review highlights the significance of dataset characteristics such as size, imbalance, and feature diversity in influencing model performance. Challenges associated with model interpretability, scalability, and deployment in clinical settings are also discussed, alongside potential strategies to address these issues. The findings suggest that ML algorithms demonstrate promising capabilities in diabetes prediction, with many studies reporting high accuracy, sensitivity, and specificity. However, there remains a need for standardized evaluation metrics and benchmark datasets to facilitate comparisons across studies. Moreover, efforts to enhance model interpretability and address data privacy concerns are crucial for promoting the adoption of ML-based predictive models in healthcare practice. In conclusion, this review underscores the potential of ML techniques in diabetes prediction and emphasizes the importance of interdisciplinary collaboration between data scientists, clinicians, and healthcare stakeholders to leverage these advancements for improved patient care and disease management
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Lopes, Bárbara Gabrielle C. O., Liziane Santos Soares, Raquel Oliveira Prates, and Marcos André Gonçalves. "Contrasting Explain-ML with Interpretability Machine Learning Tools in Light of Interactive Machine Learning Principles." Journal on Interactive Systems 13, no. 1 (2022): 313–34. http://dx.doi.org/10.5753/jis.2022.2556.
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The way Complex Machine Learning (ML) models generate their results is not fully understood, including by very knowledgeable users. If users cannot interpret or trust the predictions generated by the model, they will not use them. Furthermore, the human role is often not properly considered in the development of ML systems. In this article, we present the design, implementation and evaluation of Explain-ML, an Interactive Machine Learning (IML) system for Explainable Machine Learning that follows the principles of Human-Centered Machine Learning (HCML). We assess the user experience with the Explain-ML interpretability strategies, contrasting them with the analysis of how other IML tools address the IML principles. To do so, we have conducted an analysis of the results of the evaluation of Explain-ML with potential users in light of principles for IML systems design and a systematic inspection of three other tools – Rulematrix, Explanation Explorer and ATMSeer – using the Semiotic Inspection Method (SIM). Our results generated positive indicators regarding Explain-ML and the process that guided its development. Our analyses also highlighted aspects of the IML principles that are relevant from the users’ perspective. By contrasting the results with Explain-ML and SIM inspections of the other tools we were able to identify common interpretability strategies. We believe that the results reported in this work contribute to the understanding and consolidation of the IML principles, ultimately advancing the knowledge in HCML.
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Gomaa, Ibrahim, Hoda M. O. Mokhtar, Neamat El-Tazi, and Ali Zidane. "SML-AutoML: A Smart Meta-Learning Automated Machine Learning Framework." Advances in Artificial Intelligence and Machine Learning 04, no. 04 (2024): 3071–96. https://doi.org/10.54364/aaiml.2024.44176.
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In recent years, Machine Learning (ML) and Automated Machine Learning (Auto-ML) have attracted significant attention. The ML pipeline involves repetitive tasks such as data preprocessing, feature engineering, model selection, and hyperparameter tuning. Developing a machine learning model demands considerable time for development, stress testing, and numerous experiments. Additionally, constructing a model with a limited search space of pipeline steps and various algorithms can take hours. As a result, Auto-ML has become widely adopted to reduce the time and effort required for these tasks. However, most current Auto-ML frameworks primarily concentrate on algorithm selection and hyperparameter optimization, known as CASH, while overlooking other critical ML pipeline steps like data preprocessing and feature engineering. This limited focus often results in suboptimal pipelines for specific datasets. Moreover, a significant number of frameworks overlook the integration of meta-learning, resulting in the promotion of high-performing pipelines customized for individual tasks rather than a universally optimal solution. Consequently, this deficiency necessitates the quest for a new pipeline tailored to each unique task, further underscoring the importance of a more comprehensive approach in Auto-ML frameworks. Additionally, while some Auto-ML frameworks address the entire pipeline, they often overlook the challenges posed by imbalanced datasets. To address these issues, we propose a novel and efficient meta-learning Auto-ML framework that effectively manages imbalanced datasets. The proposed framework outperforms state-of-the-art results in terms of accuracy, precision, recall, and time, demonstrating, on average, more than 5% improvement compared to the existing auto-ML frameworks. This paper also illustrates how our proposed framework outperforms current state-of-the-art solutions.
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Gupta, Bhupesh Kumar, Srishti Tiwari, Shubhi Bhatnagar, Shalu, Yuvraj Singh, and Tanya Ranjan. "Stock Price Prediction using ML algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 5426–32. http://dx.doi.org/10.22214/ijraset.2023.52944.
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Abstract: Stock Price Prediction using ML( Machine Learning) helps to determine the unborn value of stocks of any fiscal means traded on an exchange. The entire generality of prognosticating stock prices is to gain significant gains by minimizing losses vaticinating the stock request price is always challenging for numerous business judges and experimenters. vaticination of the stock request plays a vital part in the stock business. The compass gradationally expanded. The time series model cannot contribute to the non-linear part of the stock data and is therefore hamstrung for the long term, and LSTM neural network makes better use of non-sequential data and has better use of sequence data. Useful information in the long term which makes the root mean square error of the vaticination result, the LSTM neural network needs lower than the time series model, showing LSTM is a better stock price soothsaying system. Machine Learning approach to predict or sense the behavior tracking of the stock market Sensex. Random Forest is the Machine Learning model implemented effectively in predicting the stock prices anddefining the activity between the exchanges the securities between the buyers and sellers.
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Arora, Aaryan, and Nirmalya Basu. "Machine Learning in Modern Healthcare." International Journal of Advanced Medical Sciences and Technology 3, no. 4 (2023): 12–18. http://dx.doi.org/10.54105/ijamst.d3037.063423.
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Traditional healthcare systems have long struggled to meet the diverse needs of millions of patients, leading to inefficiencies and suboptimal outcomes. However, the advent of machine learning (ML) has introduced a transformative paradigm shift towards value-based treatment, enabling healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now incorporate internal applications that gather and store comprehensive patient data, presenting a valuable resource for ML-driven predictive models. In this research article, we delve into the profound impact of ML on modern healthcare, highlighting its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, harnessing the power of extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and Py Torch (accuracy: 0.7337662337662337), to determine the best-performing model. The achieved accuracies demonstrate the effectiveness of these ML techniques in disease prediction and showcase the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.
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Bangar Raju Cherukuri and Senior Web Developer. "Scalable machine learning model deployment using serverless cloud architectures." World Journal of Advanced Engineering Technology and Sciences 5, no. 1 (2022): 087–101. https://doi.org/10.30574/wjaets.2022.5.1.0025.
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Implementations of the developed ML models are related to important questions of scale, resource, and management. This work investigates the use of serverless cloud models to address these issues and enhance and optimize the deployment, scalability, and maintenance of ML models. Reviewing main serverless platforms and their compatibility with different ML development and usage stages, the research compares the effectiveness, cost, and adaptability to the common application deployment practices. The study in this paper employs case and performance analysis to show and explain how serverless solutions can cut infrastructure costs and reduce the need for scaling and maintenance, among others. This paper outlines guidelines for implementing serverless technologies in ML applications and areas of concern that organizations might expect. Consequently, this research adds to the existing literature on deploying ML-based applications in the cloud while providing useful findings for developers and organizations interested in efficient, cost-effective solutions.
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Yue, Zhu, Zhang Xiaoyi, and Zhang Yuechen. "LLM Machine Learning for Predicting Cardiovascular Mortality in Patients." Applied Science and Biotechnology Journal for Advanced Research 3, no. 5 (2024): 31–36. https://doi.org/10.5281/zenodo.14015999.
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Patients with chronic kidney disease (CKD) face a high risk of cardiovascular death, yet accurately predicting this risk remains challenging. This study aims to develop an interpretable machine learning (ML) model to predict 10-year cardiovascular mortality in CKD patients using SHAP explainers. [1]Six ML models were created and tested, with the best model selected for prediction and patient categorization. Survival rates were analyzed using log-rank tests on Kaplan-Meier curves, and Cox regression was employed to explore the relationship between ML-predicted risk scores and mortality. The chosen autoencoder (AE) model demonstrated superior performance, with higher ML scores[2] significantly correlating with increased cardiovascular mortality risk. Key determinants such as age, high blood pressure, C-reactive protein, and serum creatinine were identified. The ML-driven tool showcased high accuracy in determining the 10-year cardiovascular mortality risk for CKD patients, offering valuable insights for individual risk assessments.