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• Machine learning techniques in biomedical data analysis: A scoping review of biostatistical perspectives
  Background Machine learning (ML) is increasingly applied in biomedical research to analyse complex datasets from imaging, omics, clinical records, and other sources. However, the extent to which biostatistical methods are integrated into ML applications remain…
  crossref A. Usha, Noel George, K. Janagi, Pradyuman Verma 2026-04-17T15:51:58Z 置信度 0.70
• Machine learning for survival prediction after brain tumor stereotactic radiosurgery: A systematic review and meta-analysis
  Brain tumors remain a significant global health burden, contributing to substantial morbidity and mortality worldwide [1,2]. These neoplasms encompass a wide spectrum of conditions, including primary brain tumors, such as gliomas, and brain metastases [3,4]. G…
  crossref Anna Volodina, Aline Pinto Veiga, Imran Noorani 2026-05-26T10:14:45Z 置信度 0.70
• Comment on “Prediction of venous thromboembolism after spontaneous intracerebral hemorrhage based on machine learning”
  crossref Sadhana U. Adhyapak, Abhishek Kumar Upadhyay, S.R.V. Prasad Reddy 2026-05-18T06:02:59Z 置信度 0.70
• Relevance of Machine Learning to Cardiovascular Imaging
  Artificial intelligence (AI) broadly concerns analytical algorithms that iteratively learn from big datasets, allowing computers to find concealed insights. These encompass a range of operations comprising several terms, including machine learning(ML), cogniti…
  crossref Sumesh Sasidharan, M. Yousuf Salmasi, Selene Pirola, Omar A. Jarral 2020-08-06T09:19:37Z 置信度 0.70
• Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application
  Abstract An accurate identification of schizophrenia spectrum disorder (SSD) at early stage could potentially allow for treating SSD with appropriate intervention to potentially prevent future deterioration. Despite mounting studies found neuroimaging combined…
  crossref Chao Li, Fei Wang, Xiaowei Jiang, Ji Chen 等 2020-06-03T14:51:57Z 置信度 0.70
• Machine learning predictive model based on the corneal biomechanics of clinical data
  Objective To develop and validate a machine learning model for predicting postoperative corneal ectasia risk after refractive surgery by integrating multidimensional preoperative and postoperative biomechanical parameters. Methods A retrospective cohort of 200…
  crossref Tianyu Shi 2025-10-18T16:23:43Z 置信度 0.70
• Machine Learning in oncology: A clinical appraisal
  Machine learning (ML) is a branch of artificial intelligence centered on algorithms which do not need explicit prior programming to function but automatically learn from available data, creating decision models to complete tasks. ML-based tools have numerous p…
  crossref Renato Cuocolo, Martina Caruso, Teresa Perillo, Lorenzo Ugga 等 2020-04-03T20:35:52Z 置信度 0.70
• Accurate Heart Disease Prediction Using Machine Learning Techniques on Clinical Data
  Heart disease is still one of the main causes of mortality in the world, and its early diagnosis represents an important part of timely treatment and prevention.The purpose of this work is to create a stable and accurate Machine Learning (ML) model for predict…
  crossref Sunanda Budihal, Sheetalrani R. Kawale 2025-12-18T19:14:01Z 置信度 0.70
• The faces of sleep apnea in the age of machine learning
  crossref Ofer Jacobowitz, Stuart MacKay 2020-03-17T15:44:36Z 置信度 0.70
• Comment on “Interpretable machine learning model for predicting refeeding syndrome after colorectal cancer surgery”
  crossref P S L Narasimharao Davuluri, Avinash Reddy Segireddy, Goutham Kumar Sheelam 2026-05-18T23:35:00Z 置信度 0.70
• The Use of Artificial Intelligence and Machine Learning in Clinical Research and Health Care
  Conference Education Session Report Speaker J. Kelly Byram, MS, MBA, ELS, Founder and CEO, Duke City Consulting, LLC, Albuquerque, NM
  crossref Noelle Ochotny 2023-03-02T14:46:48Z 置信度 0.70
• Figure 10: Differences between (A) traditional machine learning and (B) transfer learning.
  crossref 2025-01-14T03:30:26Z 置信度 0.70
• Machine Learning and Deep Learning in Quantitative Finance
  <span>This paper systematically reviews the technological evolution and practical applications of machine learning and deep learning in key areas such as stock price prediction, market timing, factor investing, and portfolio optimization. By leveraging a…
  crossref Fanqun Mo 2026-07-29T13:20:29Z 置信度 0.70
• The Price Prediction of Sneakers Based on Machine Learning
  Abstract-Sneaker culture is a self-organizing culture spontaneously formed by teenagers. In the Internet and digital age, sports shoes are endowed with appreciation and financial attributes through manual speculation, which has triggered a wave of sports shoes…
  crossref WeiJia Yan 2022-04-29T20:07:24Z 置信度 0.70
• Machine Learning for Information Management: Some Promising Directions
  Management of personal information such as email messages, calendar entries, to-do items, and workstation documents is one of the most highly visible current uses of computer technology. I will present experimental evidence that machine learning techniques can…
  crossref Dr. William Cohen 2008-04-28T13:34:00Z 置信度 0.70
• Architecture of a Machine Learning IDS
  crossref Emmanuel Tsukerman 2020-10-07T16:04:31Z 置信度 0.70
• Machine Learning in Chinese Courts
  Abstract This chapter focuses on core machine learning (ML) technologies such as Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) technology in the context of how Chinese courts have sought to improve court efficiency and reduce the p…
  crossref Nyu Wang, Michael Yuan Tian 2024-07-18T15:54:17Z 置信度 0.70
• Machine learning-based real-time detection of residual wall thickness in industrial pipelines
  Far-field eddy current detection technology is a special eddy current technology that utilizes electromagnetic effect to detect the pipe through the wall. This paper introduces the principle of the far-field eddy current detection technology, including the two…
  crossref 2024-07-14T08:57:24Z 置信度 0.70
• Quality Control Method of Exploration and Development Data Based on Machine Learning
  Because of increased exploration depth and increasingly complex geological environment survey area, makes the actual acquired seismic exploration data contains a lot of noise, the noise composition is serious interference signal effectively, affect the signal …
  crossref 2023-02-06T01:45:11Z 置信度 0.70
• Machine Learning Algorithms
  Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of mac…
  crossref Namrata Dhanda, Stuti Shukla Datta, Mudrika Dhanda 2022-07-08T11:31:23Z 置信度 0.70
• Receiving information on machine learning-based clinical decision support systems in psychiatric services increases staff trust in these systems: A randomized survey experiment
  Abstract Background Clinical decision support systems based on machine learning (ML) models are emerging within psychiatry. To ensure their successful implementation, healthcare staff needs to trust these systems. Here, we investigated if providing staff with …
  crossref Erik Perfalk, Martin Bernstorff, Andreas Aalkjær Danielsen, Søren Dinesen Østergaard 2024-09-10T19:05:20Z 置信度 0.70
• Advancing Biomedical Engineering With Artificial Intelligence and Machine Learning: A Systematic Review
  The inclusion of artificial intelligence (AI) and machine learning (ML) in biomedical engineering opens new frontiers of innovation and better decision‐making and allows the art to cross new thresholds in healthcare technologies. This review focuses on the pri…
  crossref Abebe Belay Adege 2025-07-29T07:48:38Z 置信度 0.70
• Support Vector Machine
  crossref Zhi-Hua Zhou 2021-08-20T19:23:05Z 置信度 0.70
• Machine Learning-Driven Adaptive Communication Systems and Beyond-6G Network Intelligence
  The transition toward beyond-6G communication networks introduces unprecedented challenges associated with ultra-low latency, massive connectivity, and highly dynamic service environments, demanding a paradigm shift from conventional static architectures to in…
  crossref R. Jegadeesan, Prerana Arun Wankhede 2026-05-13T04:03:28Z 置信度 0.70
• Research on Automated Risk Detection Methods in Machine Learning Integrating Privacy Computing
  As the digitalization process of enterprises accelerates, various application interfaces have become key hubs for system interconnection and data exchange.At the same time, they may also serve as entry points for attackers to obtain sensitive information.Witho…
  crossref 2025-11-21T09:43:36Z 置信度 0.70
• Responsible Use of Machine Learning in Sports Science
  crossref Fabian Wunderlich 2025-07-21T23:08:52Z 置信度 0.70
• Machine Learning for Detecting Malware in PE Files
  The increasing number of sophisticated malware poses a significant cybersecurity threat. Portable executable (PE) files are a common vector for such malware. We review and evaluate machine learning-based PE malware detection techniques in this work. Using a la…
  crossref Collin Connors, Dilip Sarkar 2024-03-19T18:08:18Z 置信度 0.70
• Research on Vehicle Logistics Optimization Distribution Based on Machine Learning
  In order to solve the problems of low efficiency, unscientific management and large amount of manual work in the process of logistics distribution of enterprise products, a vehicle mounted logistics optimization distribution model is designed based on machine …
  crossref Ronghu Zhou 2023-03-07T18:42:09Z 置信度 0.70
• Elizabeth Degefe Discusses Research Findings, Improving, and Resources for Future Research Using Machine Learning to Study Racism
  crossref 2023-03-24T08:31:51Z 置信度 0.70
• Optimization Basics: A Machine Learning View
  crossref Charu C. Aggarwal 2020-05-12T22:02:28Z 置信度 0.70
• Using Background Knowledge to Build Multistrategy Learners
  crossref Claude Sammut 2002-12-22T04:48:21Z 置信度 0.70
• Machine Learning Fundamentals
  Machine learning covers a large group of computational methods built upon past data and experience. This chapter serves as a gentle introduction to machine learning fundamentals. It covers key concepts of machine learning tasks, methods, and applications. The …
  crossref Yan Yan 2022-10-21T21:26:07Z 置信度 0.70
• Determining Student's Engagement in Synchronous Online Classes Using Deep Learning (Compute Vision) and Machine Learning
  In 2020 Scaled-YOLOv4 was introduced. It is one of the best object detection models outclassing its peers in MS COCO test-dev. In this study, the proponents used Scaled-YOLOv4 as their object detection model. The model will be used in the environment of Pasig …
  crossref John Paul Quilingking Tomas, Daniel Oliver Ancheta, Carl Andrei Aquino, Aerol Ortiz 2023-03-06T12:14:02Z 置信度 0.70
• Supplemental Information 7: Machine Learning Code.
  crossref 2026-02-10T08:19:16Z 置信度 0.70
• Understanding the core algorithms behind machine learning: Key learning algorithms explained
  Machine learning is a branch of artificial intelligence. It enables computers to automatically learn and improve from experience without being explicitly programmed [1]. Machine learning algorithms are usually classified into supervised, unsupervised, neural n…
  crossref Priyambada Swain 2025-08-16T18:24:01Z 置信度 0.70
• Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle
  crossref Dana Angluin, Donna K. Slonim 2003-04-04T16:55:36Z 置信度 0.70
• Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries
  crossref Thomas R. Hancock, Mostefa Golea, Mario Marchand 2003-04-04T16:55:36Z 置信度 0.70
• Machine Learning in Molecular Dynamics Applications in Medicinal Chemistry
  Machine learning (ML) with molecular dynamics (MD) simulations represents a transformative approach in medicinal chemistry and computational biology, offering unprecedented insights into the dynamic behavior of molecular systems. The advent of ML has further r…
  crossref Sk Abdul Amin, Supratik Kar, Stefano Piotto 2026-07-22T08:41:26Z 置信度 0.70
• Machine Learning for Mask Synthesis
  crossref Seongbo Shim, Suhyeong Choi, Youngsoo Shin 2019-03-15T12:32:24Z 置信度 0.70
• Research on Integration and Optimization Strategies of Cross-platform Machine Learning Services
  In diverse operating system environments, cross platform machine learning techniques are increasingly becoming the core means of enhancing data processing capabilities and model performance.This study aims to explore the integration and optimization path of cr…
  crossref 2026-01-12T09:54:59Z 置信度 0.70
• Transforming Pharma with Data Science, AI and Machine Learning
  This chapter provides an overview of transformative changes occurring in pharma engendered by big data and artificial intelligence (AI) and discusses the importance of data strategy in realizing full potential of these technology advances in the lifecycle of d…
  crossref Harry Yang 2022-08-12T20:20:36Z 置信度 0.70
• Recent Advances in Drug Design With Machine Learning
  Machine learning (ML) methods have been of special attention, since they can be utilized in numerous steps of the drug discovery process, for example, investigating biological activity of new candidates through construction of model, prediction of target struc…
  crossref Muhammad Faisal 2022-10-07T07:21:48Z 置信度 0.70
• Monetizing Finance Machine Learning
  In this chapter, I am going to put forward some innovative ideas that can be monetized using machine learning in the financial world. I will also show you some examples where a similar approach has been used and has succeeded. For some of them, there may not b…
  crossref Puneet Mathur 2018-12-12T14:05:50Z 置信度 0.70
• Machine Learning in Cardiovascular Disorders
  crossref Shyamasree Ghosh, Rathi Dasgupta 2022-05-04T17:03:22Z 置信度 0.70
• Improving Tuberculosis Prognosis with Benchmarked Machine Learning Models
  Tuberculosis remains a considerable cause of morbidity and mortality in several poor and middle-income countries. When a patient is diagnosed with tuberculosis, healthcare providers must select most appropriate treatment tailored to patient's unique situation …
  crossref Muhammad Azhar Javaid 2026-01-03T12:36:06Z 置信度 0.70
• Machine Learning for High Sigma Analog Designs (Invited)
  Monte Carlo simulations have been the gold standard for assessing parametric yields of analog, mixed signal, and RF circuits as they offer one of the most direct representations of the variation induced by semiconductor manufacturing. However, Monte Carlo anal…
  crossref Srinivas Jallepalli 2024-11-06T18:37:40Z 置信度 0.70
• Machine Learning Molecular Trends
  The parameterization process will require much data, either from experiments or from ab initio calculations, as input for the simulation model. Generating billions of system configurations during a simulation requires evaluating an even more significant number…
  crossref Caroline Desgranges, Jerome Delhommelle 2023-12-13T13:35:51Z 置信度 0.70
• Choice of Basis for Laplace Approximation
  crossref David J.C. MacKay 2002-12-22T05:04:10Z 置信度 0.70
• Determining Arguments of Invariant Functional Descriptions
  crossref Mieczyslaw M. Kokar 2003-04-04T16:57:10Z 置信度 0.70
• Semantic Clone Detection Using Machine Learning
  If two fragments of source code are identical to each other, they are called code clones. Code clones introduce difficulties in software maintenance and cause bug propagation. In this paper, we present a machine learning framework to automatically detect clone…
  crossref Abdullah Sheneamer, Jugal Kalita 2017-02-07T20:39:53Z 置信度 0.70
• Machine Learning Approaches for Adaptive Forecasting Systems
  Adaptive forecasting systems have become essential for analyzing dynamic and uncertain real-world data, where traditional static forecasting models often fail due to changing patterns, concept drift, and non-stationary conditions. Machine learning techniques, …
  crossref Yuki Nakamura 2026-07-27T10:58:38Z 置信度 0.70
• Uncovering Gaps: Dietary Influence and Machine Learning in Hypertension and Comorbidities
  Dear Editor, We read with great interest the recent article by Zhang et al., “Diabetes Mellitus and Hyperlipidemia Status Among Hypertensive Patients in the Community and Influencing Factors Analysis of Blood Pressure Control,” which provides valuable insights…
  crossref Javeria Akhter, Javed Iqbal 2025-05-23T12:59:17Z 置信度 0.70
• Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients
  Objective Diagnosis of mild traumatic brain injury (mTBI) is challenging despite its high incidence, due to the unspecificity and variety of symptoms and the frequent lack of structural imaging findings. There is a need for reliable and simple-to-use diagnosti…
  crossref Juho Aaltonen, Verna Heikkinen, Hanna Kaltiainen, Riitta Salmelin 等 2023-06-30T11:47:27Z 置信度 0.70
• Machine Learning and the Role of IoT in Health Informatics
  The innovations brought by Industry 4.0 to health informatics and the challenges encountered are discussed. Industry 4.0 initiates a new era in the healthcare sector through the integration of technologies like IoT, machine learning, cloud computing, and big d…
  crossref Burak Taşci 2024-10-02T15:32:01Z 置信度 0.70
• What Is Machine Learning?
  The first Checkers program from machine learning pioneer Arthur Samuel debuted in 1956, demonstrating artificial “intelligence” capabilities. Since then, not only has the application of artificial intelligence grown, there is now a velocity, volume, and variet…
  crossref Arjun Panesar 2019-02-04T05:24:47Z 置信度 0.70
• Create Prediction Model using Microsoft Azure Machine Learning Studio
  crossref 2021-03-19T05:40:31Z 置信度 0.70
• Quantitative Trading Method based on Neural Network Machine Learning
  Quantitative trading plays an essential role in the investment field with its advanced mathematical models for computer-aided trading of investment strategies. The artificial neural network algorithm is the trading algorithm with the largest amount of funds ma…
  crossref Weinan Weng 2022-08-19T19:38:41Z 置信度 0.70
• "Personalized Medicine: Predictive Analytics for Drug Response Using Machine Learning Models"
  Personalized medicine represents a transformative approach in healthcare, enabling tailored therapeutic interventions based on individual patient characteristics. Predictive analytics powered by machine learning (ML) has emerged as a cornerstone of this paradi…
  crossref Diganta Bhattacharyya 2026-06-18T11:06:12Z 置信度 0.70
• Comprehensive Study of Machine Learning-Based Systems for Early Warning of Clinical Deterioration
  crossref 2022-12-28T05:58:15Z 置信度 0.70
• Clinical diagnosis of overlapping symptoms in COVID-19 based on machine learning model
  The global pandemic COVID-19 erupted and infected an estimated 10% of the world’s population. Since vaccinations greatly reduced hospitalization rates, most countries removed the restrictive policies implemented to combat the virus. It has become a rather comm…
  crossref Jingxuan Zhang 2024-02-05T22:43:04Z 置信度 0.70
• Early detection of diabetes with quantum machine learning and causal inference techniques
  Diabetes has become one of the most common chronic diseases in the United States. It is also the one that remains either undiagnosed or misdiagnosed over the years, leading a patient to life threatening conditions. The main reasons behind this are- lack of awa…
  crossref Paramita Basak Upama, Md Munirul Haque, Kazi Shafiul Alam, Sheikh Iqbal Ahamed 2026-04-14T17:07:03Z 置信度 0.70
• MACHINE LEARNING BASED CLINICAL DECISION SUPPORT SYSTEM TO PREDICT FETAL HYPOXIA IN WOMEN DURING ANTENATAL CHECK-UP.
  BACKGROUND:Most under-five deaths occur within the first month after birth and intrapartum complications are a major contributor to the cause of death. These defects can be easily identified during the ante-natal check-up by use of a non-stress test. Due to th…
  crossref Sajal Baxi 2021-05-08T03:36:39Z 置信度 0.70
• Monte Carlo Peaks: Simulated Datasets to Benchmark Machine Learning Algorithms for Clinical Spectroscopy
  crossref Jaume Béjar-Grimalt, Ángel Sánchez-Illana, Guillermo Quintas, Hugh James Byrne 等 2025-06-23T20:18:22Z 置信度 0.70
• Machine Learning-Based Predictive Modeling of Postpartum Depression
  Postpartum depression is a serious health issue beyond the mental health problems that affect mothers after childbirth. There are no predictive tools available to screen postpartum depression that also allow early interventions. We aimed to develop predictive …
  crossref Dayeon Shin, Kyung Ju Lee, Temidayo Adeluwa, Junguk Hur 2020-09-08T09:03:48Z 置信度 0.70
• Machine learning versus human expertise: The case of sleep stage classification in disorders of consciousness. Response to Wislowska et al.
  crossref Boris Kotchoubey, Yuri G. Pavlov 2018-10-24T23:22:12Z 置信度 0.70
• Using Machine Learning to Analyze Sudanese Opinions for Political Decision Making
  Sentiment Analysis (SA) is the application of text mining techniques for extraction and identification of subjective opinions from textual data.SA has many applications ranging from financial analysis to political decision-making domains.Although a lot of work…
  crossref 2022-03-29T09:36:37Z 置信度 0.70
• What Is Machine Learning?
  The first Checkers program from machine learning pioneer Arthur Samuel debuted in 1956, demonstrating artificial “intelligence” capabilities [68].
  crossref Arjun Panesar 2020-12-15T14:05:55Z 置信度 0.70
• Machine Learning with R: Classification
  crossref 2019-01-20T19:25:06Z 置信度 0.70
• Fake Review Detection using Machine Learning
  Online reviews have become increasingly important in the world of e-commerce, serving as a powerful tool to establish a business's reputation and attract new customers. However, the rise of fake reviews has become a growing concern as they can skew the reputat…
  crossref Gayathri M, Y.S.N Siva Teja, K.Ajay Sharma 2023-12-21T09:54:33Z 置信度 0.70
• Problems with Machine Learning and Knowledge Acquisition
  The early overhyped expectations about expert systems were not fulfilled. There are now enormous expectations for machine learning. For supervised machine learning, obtaining sufficient high-quality data for learning is difficult. There is a particular problem…
  crossref Paul Compton, Byeong Ho Kang 2021-04-13T08:05:14Z 置信度 0.70
• Proceedings of the 2004 International Conference on Machine Learning and Applications
  crossref 2011-06-01T09:01:21Z 置信度 0.70
• Physics-Informed Sparse Machine Learning of Geotechnical Monitoring Data
  Advancements in geotechnical monitoring provide continuous streams of data, which may originate from multiple sources and increase constantly in quantity over time. However, monitoring data from a specific project are often obtained at limited locations only a…
  crossref Yu Wang, Hua-Ming Tian 2026-07-30T18:37:51Z 置信度 0.70
• Classification with Bayesian MARS
  crossref C.C. Holmes, D.G.T. Denison 2003-03-15T08:37:24Z 置信度 0.70
• Applying Machine Learning to Augment the Design and Assessment of Immersive Learning Experience
  crossref Chih-Pu Dai 2024-02-24T19:01:58Z 置信度 0.70
• Evolution and Opportunities for Transactional Machine Learning in Almost Every Industry
  We have discussed several areas that make TML effective in the application of AutoML to data streams for greater business insights and impact. What underlies TML opportunities is the belief that fast data will require fast machine learning for fast decision-ma…
  crossref Sebastian Maurice 2021-05-19T10:03:18Z 置信度 0.70
• Voice spoofing detection for multiclass attack classification using deep learning
  Voice biometric authentication is increasingly gaining adoption in organizations with high-volume identity verifications and for providing access to physical and other virtual spaces. In this form of authentication, the user’s identity is verified with their v…
  crossref Jason Boyd, Muhammad Fahim, Oluwafemi Olukoya 2023-10-11T01:49:32Z 置信度 0.70
• Editorial Exploratory research in machine learning
  crossref Thomas G. Dietterich 2004-10-31T01:43:51Z 置信度 0.70
• Embedded Machine Learning
  crossref Ian H. Witten, Eibe Frank, Mark A. Hall 2011-01-18T04:13:07Z 置信度 0.70
• The Design of Discrimination Experiments
  crossref Shankar A. Rajamoney 2003-04-04T16:57:10Z 置信度 0.70
• Machine Learning Model for Breast Cancer Detection and Classification
  Breast cancer is still one of the prevalent diseases affecting females globally. Machine learning (ML) has come in handy in breast cancer detection by assisting doctors and other healthcare workers with accurate diagnosis in a short time period. The research a…
  crossref Neeru Mago, Rajinder Singh 2026-02-12T16:16:06Z 置信度 0.70
• Solutions Using Machine Learning for Diabetes
  Diabetes mellitus, a chronic disease that has a significant influence on human lives, families, and communities globally, has reached alarming levels and is therefore a leading economic challenge of the twenty-first century. The International Diabetes Federati…
  crossref Jabar H. Yousif, Kashif Zia, Durgesh Srivastava 2022-08-26T18:05:38Z 置信度 0.70
• Machine Learning–Based VLSI Test and Verification
  To test Integrated Chips, test pattern generation and fault simulation are vital. Testing verifies a circuit's accuracy regarding gates and connections between them. The fundamental purpose of testing is to model the circuit's various activities. Several Elect…
  crossref Jyoti Kandpal 2023-06-25T00:47:53Z 置信度 0.70
• Machine Learning, Data Mining for IoT-Based Systems
  This chapter will addresses challenges with the internet of things (IoT) and machine learning (ML), how a bit of the trouble of machine learning executions are recorded here and should be recalled while arranging the game plan, and the decision of right figuri…
  crossref Ramgopal Kashyap 2022-07-08T11:31:23Z 置信度 0.70
• Explainable AI in drought forecasting
  Droughts are one of the disastrous natural hazards which has severe impacts on agricultural production, economy, and society. One of the critical steps for effective drought management is developing a robust forecasting model and understanding how the variable…
  crossref Abhirup Dikshit, Biswajeet Pradhan 2021-10-25T21:48:08Z 置信度 0.70
• Clause Learning
  crossref 2010-12-29T17:42:10Z 置信度 0.70
• Study on the Prognostic Prediction Model and Clinical Application Value of Machine Learning-based Approach for Septic Children in PICU
  Objective: To explore the application value of a machine learning-based prediction model in assessing the prognosis of septic children in the pediatric intensive care unit (PICU) and provide data support for clinical decision-making. Methods: A total of 180 se…
  crossref Li Yu 2025-10-23T07:14:35Z 置信度 0.70
• Counter-story in machine learning
  crossref Esperanza Huerta Espinosa 2023-07-06T17:12:50Z 置信度 0.70
• Steganographic Capacity of Selected Machine Learning and Deep Learning Models
  As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a steganographic based attack, information would be hidden in a learning model, whi…
  crossref Lei Zhang 2023-06-15T15:53:00Z 置信度 0.70
• Machine Learning and Precision Farming
  crossref Shyamasree Ghosh, Rathi Dasgupta 2022-05-04T17:03:22Z 置信度 0.70
• Machine Learning and Plant Sciences
  crossref Shyamasree Ghosh, Rathi Dasgupta 2022-05-04T17:03:22Z 置信度 0.70
• Introduction to Machine Learning
  crossref Elena Bellodi, Riccardo Zese, Fabrizio Riguzzi, Evelina Lamma 2022-05-25T18:03:53Z 置信度 0.70
• Classification of Fetal Arrhythmia using Electrocardiogram Signal Processing and Machine learning
  Cardiac Arrhythmia, a condition characterized by irregular deviations from the standard cardiovascular cycle, poses a serious threat to fetal well-being and accounts for a significant portion of unexpected infant deaths. The objective of this study is to devel…
  crossref Nav Malhotra 2025-05-23T17:02:44Z 置信度 0.70
• Sociology and Machine Learning
  Abstract This chapter serves as an introduction to the Oxford Handbook of the Sociology of Machine Learning. It outlines the volume’s principal objective: To provide a comprehensive, state-of-the-art overview and forward-looking discussion of three pivotal the…
  crossref Christian Borch, Juan Pablo Pardo-Guerra 2024-07-18T15:54:17Z 置信度 0.70
• Classifier Systems and the Animat Problem
  crossref Stewart W. Wilson 2003-04-04T16:55:36Z 置信度 0.70
• Machine learning–enabled IoT for biomedical applications
  The biomedical field is one of the sectors in which the Internet of Things (IoT) has had an impact and applications in recent decades. Applications for computational methods like data science and machine learning (ML) have also been used in the biomedical fiel…
  crossref Hashmat Usmani, Renu Rani 2024-10-04T11:24:27Z 置信度 0.70
• Machine Learning for Real-time Detection of Complications during Neurosurgery
  crossref David Miller 2025-11-26T05:54:48Z 置信度 0.70
• Toward a unified science of machine learning
  crossref Pat Langley 2004-11-01T03:05:07Z 置信度 0.70
• Introduction to Machine Learning Architecture
  crossref Mohammad Reza Mahdiani 2026-04-30T22:15:43Z 置信度 0.70
• Proceedings 2021 3rd International Conference on Applied Machine Learning [Title page i]
  crossref 2022-02-18T20:34:52Z 置信度 0.70
• Learning Locally and Globally: Maxi-Min Margin Machine
  crossref 2008-09-22T16:10:34Z 置信度 0.70