Search

The American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America have announced the finalists for the paper of the year for each society through their flagship journals. The finalists were selected based on the number of citations and full-text views, and represent less than 1% of the submitted papers. The winning papers will be acknowledged at an awards ceremony during the 2024 ASA, CSSA, and SSSA International Annual Meeting in San Antonio, Texas. The article provides a list of the finalists for each society's award, along with links to their respective papers. [Extracted from the article]

The Food Allergy Institute (FAI) reported that a peer reviewed paper authored by its founder Inderpal Randhawa, MD, has been published by the clinical journal PLOS ONE. The FAI said [...]

The article evaluates six cutting-edge Artificial Intelligence (AI) research tools using the REACT framework, assessing their relevancy, ease of use, Diversity, Equity, Inclusion, and Accessibility (DEIA) commitment, currency, and transparency. It provides detailed reviews of citation-based literature mapping tools (Litmaps, Connected Papers, ResearchRabbit) and text-extraction tools (Elicit, scite, Consensus), offering insights into their effectiveness and usability for academic research.

This article discusses a research paper titled "How to Bring AI-Enabled Valuations to Meet Professional Duty of Care Standards?" The paper focuses on real estate valuations but raises important issues that are relevant to the business valuation community. It emphasizes the need for consistency, objectivity, and transparency in AI-driven methodologies to ensure accountability and public trust. The authors examine existing standards from the Royal Institution of Chartered Surveyors (RICS) and suggest the use of Explainable Artificial Intelligence (XAI) to enhance transparency in automated valuation models (AVMs). The paper also provides a hypothetical checklist for AI-enabled valuation reporting. The authors stress the importance of aligning AI-driven valuations with professional and legal standards for the continued trust and reliability of the valuation process in a digital world. [Extracted from the article]

Researchers from COMSATS University in Lahore, Pakistan have developed a cellulose paper-based aptasensing protocol for the detection of the cancer biomarker mucin 1 (MUC1). The paper surface was modified using polydopamine modified carbon nanotubes (PDA@CNTS) to create a strong adhesion and binding interface. The researchers used dynamic laser speckle (DLS) analysis to analyze the speckle patterns generated from the cellulose-based paper surface, providing sensitive and accurate analysis of the target biomarker. The paper-based aptasensor demonstrated sensitive and selective detection of MUC1, making it a promising tool for low-cost and portable biological applications. [Extracted from the article]

Readers can get a preview of the International Journal ofMetalcastings wealth of technical knowledge-sharing through open access papers, like 'Improving Manufacturing Applications of Machine Learning by Understanding Defect Classification and [...]

A new research commentary by the Artificial Intelligence in Sleep Medicine Committee of the American Academy of Sleep Medicine explores the potential of artificial intelligence (AI) in the field of sleep medicine. The committee highlights three critical areas where AI can revolutionize care: clinical applications, lifestyle management, and population health. However, the paper also warns about the challenges and pitfalls of integrating AI into sleep medicine, including issues of data privacy, security, accuracy, and potential biases. The authors emphasize that AI should complement, not replace, the expertise of sleep medicine professionals, and call for comprehensive validation and standardization protocols to ensure the responsible and ethical implementation of AI technologies in healthcare. [Extracted from the article]

Researchers at Vancouver Island University have developed a new method for detecting chronic kidney disease (CKD) using paper spray mass spectrometry (PS-MS) combined with machine learning. The protocol is rapid and simple, based on analyzing metabolic profile changes in urine. The study found that the PS-MS combined with machine learning approach had a diagnostic performance of over 96% accuracy for CKD. This research suggests that PS-MS combined with machine learning could be a valuable tool for the rapid and simple diagnosis of CKD. [Extracted from the article]

A recent article in Blood Weekly highlights the growing importance of advanced robotics and tissue engineering in the field of biomedical research. Robotic systems have traditionally played a role in nurturing tissue growth in controlled environments, but recent advancements in soft robots and biomimetic robots offer new possibilities for enhancing tissue engineering. These robots can provide realistic mechanical stimulation to grafts and implants, potentially accelerating their clinical implementation and deepening our understanding of biological tissues. The article calls for researchers to share their latest breakthroughs in this field, with topics ranging from smart scaffolds to biohybrid and biomimetic robots. [Extracted from the article]

A recent study from the Icahn School of Medicine at Mount Sinai analyzed the top-cited papers on artificial intelligence (AI) in health and life sciences over the past three years. The study found that AI is playing a significant role in clinical settings, particularly in diagnostics, telemedicine, and medical education, with the COVID-19 pandemic accelerating its adoption. AI is also impacting biology by helping to analyze complex genomic and proteomic data, leading to advancements in personalized medicine strategies. However, the study emphasizes the need for ethical and regulatory considerations to ensure the responsible integration of AI into healthcare and research. [Extracted from the article]

Researchers at Mayo Clinic in Rochester, Minnesota have published an educational paper on the use of machine learning (ML) in orthopedic surgery. ML, a type of artificial intelligence, is well-suited for processing large amounts of data, making it valuable for orthopedic research and translation into clinical practice. The paper discusses the challenges of ML, such as the reliance on good data and the potential for overfitting, and provides examples of successfully published models. The research was supported by the American Joint Replacement Research-Collaborative (AJRR-C) and has been peer-reviewed. [Extracted from the article]

This patent application discusses the development of a paper-based microfluidic diagnostic device that aims to improve the accuracy and efficiency of point-of-collection (POC) analyses. The device consists of a top and bottom panel with cut regions that form diagnostic wells, which contain a diagnostic paper layer with components for analyte assessment. The device allows for the vertical flow of fluid samples through the diagnostic paper layer, eliminating the need for specialized instrumentation. The invention also includes methods for detecting and quantifying analytes using the device, as well as selecting machine learning models for predicting diagnostic test results. The patent application describes various implementations of microfluidic diagnostic devices and methods for their rapid processing, with potential applications in healthcare diagnostics. [Extracted from the article]

Purpose: With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research. Design/methodology/approach: The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods. Findings: The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms. Originality/value: The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods. [ABSTRACT FROM AUTHOR]

A report from Diponegoro University discusses the use of artificial intelligence (AI) and deep learning in medical imaging to identify kidney stones. The research highlights that deep learning is more commonly used than traditional machine learning in this field. The aim of the study was to review existing literature on deep learning in medical imaging for detecting kidney stones. The research concludes that using the appropriate AI model with high accuracy can assist radiologists in accurately detecting kidney stones. This information may be useful for those interested in AI applications in medical diagnostics. [Extracted from the article]

Keywords: Artificial Intelligence; Cardiology; Cardiovascular; Cardiovascular Diseases and Conditions; Cardiovascular Research; Emerging Technologies; Health Information Technology; Health and Medicine; Machine Learning; Telecardiology; Telemedicine EN Artificial Intelligence Cardiology Cardiovascular Cardiovascular Diseases and Conditions Cardiovascular Research Emerging Technologies Health Information Technology Health and Medicine Machine Learning Telecardiology Telemedicine 1176 1176 1 09/04/23 20230908 NES 230908 2023 SEP 4 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- Investigators publish new report on telemedicine. For more information on this research see: The Italian Society of Cardiology and Working Group on Telecardiology and Informatics 2023 updated position paper on telemedicine and artificial intelligence in cardiovascular disease. [Extracted from the article]

Keywords: Artificial Intelligence; Emerging Technologies; Machine Learning; Mergers and Acquisitions; Paper; Technology EN Artificial Intelligence Emerging Technologies Machine Learning Mergers and Acquisitions Paper Technology 428 428 1 03/27/23 20230328 NES 230328 2023 MAR 28 (NewsRx) -- By a News Reporter-Staff News Editor at TB & Outbreaks Week -- Paper(TM), the leading Educational Support System (ESS), announced that it has acquired Readlee, an innovative learning tool that uses artificial intelligence (AI) and speech recognition technology to help students improve their reading skills. Artificial Intelligence, Emerging Technologies, Machine Learning, Mergers and Acquisitions, Paper, Technology. [Extracted from the article]

Researchers at the University of Florida have developed a new technique using artificial neural networks to enhance pathogen identification in cheese. The method, called the artificial neural network-driven paper chromogenic array sensor (ANN-PCA), allows for the nondestructive and simultaneous detection of Salmonella Enteritidis and Escherichia coli O157:H7 in shredded cheddar cheese. The technique has shown promising potential for integration into a digitalized, smart, and resilient nondestructive surveillance system for real-time pathogen detection in food supply chains. The research was funded by the US Army and the University of Florida, among others. [Extracted from the article]

Patient agility constitutes the capabilities that enable hospitals to sense the health service needs of their patients and respond to the changing health service demands of these patients. Keywords: Emerging Technologies; Health and Medicine; Hospitals; Machine Learning; Predictive Analytics EN Emerging Technologies Health and Medicine Hospitals Machine Learning Predictive Analytics 3786 3786 1 11/06/23 20231110 NES 231110 2023 NOV 10 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Current study results on agriculture have been published. [Extracted from the article]

The research focus in the field of artificial intelligence in DR has transitioned from AI applications in disease detection and grading to a more concentrated exploration of AI-assisted diagnostic systems. Keywords: Artificial Intelligence; Convolutional Network; Diabetic Angiopathies; Diabetic Retinopathy; Emerging Technologies; Eye Diseases and Conditions; Health and Medicine; Machine Learning; Networks; Neural Networks; Nutritional and Metabolic Diseases and Conditions; Ophthalmology; Retinal Diseases and Conditions; Retinopathy; Vascular Diseases and Conditions EN Artificial Intelligence Convolutional Network Diabetic Angiopathies Diabetic Retinopathy Emerging Technologies Eye Diseases and Conditions Health and Medicine Machine Learning Networks Neural Networks Nutritional and Metabolic Diseases and Conditions Ophthalmology Retinal Diseases and Conditions Retinopathy Vascular Diseases and Conditions 193 193 1 11/06/23 20231106 NES 231106 2023 NOV 6 (NewsRx) -- By a News Reporter-Staff News Editor at Diabetes Week -- Researchers detail new data in artificial intelligence. [Extracted from the article]

Researchers from the University of Malaya in Kuala Lumpur, Malaysia, have developed a colorimetric biosensor based on microfluidic paper that can diagnose diseases and predict prognosis. This biosensor is the first to simultaneously quantify cardiac troponin and lipid biomarkers, including high-density lipoprotein and low-density lipoprotein. The researchers used an advanced machine learning technique called CatBoost to achieve accurate diagnosis and prognosis of acute myocardial infarction. The biosensor also employs an electrophoretic method to overcome challenges posed by nonspecific adsorption. The study demonstrated that the proposed machine learning framework produced better results than conventional statistical analysis. The research has been peer-reviewed and published in Sensors and Actuators B: Chemical. [Extracted from the article]

The CNN showed potential for detecting tremors in scanned images of paper seismogram records from the past, facilitating downstream tasks such as the creation of new tremor catalogs." Keywords: Tokyo; Japan; Asia; Convolutional Network; Dyskinesias; Emerging Technologies; Machine Learning; Musculoskeletal Diseases and Conditions; Networks; Neural Networks; Tremors EN Tokyo Japan Asia Convolutional Network Dyskinesias Emerging Technologies Machine Learning Musculoskeletal Diseases and Conditions Networks Neural Networks Tremors 264 264 1 04/10/23 20230414 NES 230414 2023 APR 10 (NewsRx) -- By a News Reporter-Staff News Editor at Pain & Central Nervous System Week -- Researchers detail new data in Musculoskeletal Diseases and Conditions - Dyskinesias. Keywords for this news article include: Tokyo, Japan, Asia, Asia, Convolutional Network, Dyskinesias, Emerging Technologies, Japan, Machine Learning, Musculoskeletal Diseases and Conditions, Networks, Neural Networks, Tremors, University of Tokyo. [Extracted from the article]

This article discusses two papers published in the Annals of Family Medicine that explore the impact of technology on medical practice and the adaptation of doctors. The first paper examines the use of artificial intelligence (AI) in monitoring asthma exacerbations in patients of all ages, including children under 5. The researchers found that AI analysis of home stethoscope recordings alone can efficiently detect asthma exacerbation. The second paper focuses on the psychological well-being of family physicians during the COVID-19 pandemic and the shift to virtual care. It found that family physicians who had little choice in using virtual care had lower well-being, while satisfaction of their workplace psychological needs was associated with higher well-being. [Extracted from the article]

A patent application titled "Compact Robotic Cleaner For Hazardous Environments" has been published online by the US Patent and Trademark Office. The patent application, filed by inventors Michael J. Arcediano, Troy Johnson, and Timothy Patrick Kelly II, describes a robotic cleaner designed to remove material from tanks and other vessels in industries such as oil and gas, chemical production, transportation, manufacturing, and more. The robotic cleaner features a chassis, elongated track members, a cleaning head with an inlet for debris, and optional components such as a vacuum hose, pump, head guard, and sensor support. The invention aims to improve the efficiency and safety of tank cleaning operations. The assignee for this patent application is Ecoserv Technologies LLC. [Extracted from the article]

Keywords: Chicago; State:Illinois; United States; North and Central America; Algorithms; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning EN Chicago State:Illinois United States North and Central America Algorithms Cyborgs Emerging Technologies Health and Medicine Machine Learning 380 380 1 05/22/23 20230526 NES 230526 2023 MAY 28 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Devices & Surgical Technology Week -- Fresh data on Machine Learning are presented in a new report. Machine learning algorithms may be used to compare the risk of failure of specific patient-procedure combinations in the treatment of cartilage defects of the knee. [Extracted from the article]

Keywords: Fuzhou; People's Republic of China; Asia; Cloud Computing; Deep Learning; Diagnostics and Screening; Genetics; Information Technology; Machine Learning; Testing EN Fuzhou People's Republic of China Asia Cloud Computing Deep Learning Diagnostics and Screening Genetics Information Technology Machine Learning Testing 1039 1039 1 05/02/23 20230505 NES 230505 2023 MAY 7 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- New research on Information Technology - Cloud Computing is the subject of a report. Keywords for this news article include: Fuzhou, People's Republic of China, Asia, Cloud Computing, Deep Learning, Diagnostics and Screening, Genetics, Information Technology, Machine Learning, Testing, Fuzhou University. [Extracted from the article]

The article discusses several troubling trends within machine-learning scholarship and researchers as of June 2019 that could mislead the public and hinder future research. Topics include trends of failure to distinguish between explanation and speculation, failure to identify sources of empirical gains, using mathematics that confuses or impresses rather than clarifies, and misusing language.

As part of a larger effort to digitize its global supply chain, Kimberly-Clark is putting a combination of machine learning and optimization algorithms to work to optimize its transportation networks-- [...]

Fast-growing scientific papers pose the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Citation recommendation is an indispensable technique to overcome this obstacle. In this paper, we propose a citation recommendation approach via mutual reinforcement on a three-layered graph, in which each paper, author or venue is represented as a vertex in the paper layer, author layer, and venue layer, respectively. For personalized recommendation, we initiate the random walk separately for each query researcher. However, this has a high computational complexity due to the large graph size. To solve this problem, we apply a three-layered interactive clustering approach to cluster related vertices in the graph. Personalized citation recommendations are then made on the subgraph, generated by the clusters associated with each researcher’s needs. When evaluated on the ACL anthology network, DBLP, and CiteSeer ML data sets, the performance of our proposed model-based citation recommendation approach is comparable with that of other state-of-the-art citation recommendation approaches. The results also demonstrate that the personalized recommendation approach is more effective than the nonpersonalized recommendation approach. [ABSTRACT FROM AUTHOR]

A patent application titled "System And Method Of Providing Accessibility In Use Of Digital Ink" has been published online by the US Patent and Trademark Office. The patent application, filed by Mrinal Kumar Sharma, describes a device and method for improving accessibility in the use of digital ink. The device would detect physical disabilities, such as hand tremors, and automatically apply corrections to the digital ink input to improve legibility. The invention aims to make digital ink input more accessible for users with disabilities. The assignee for this patent application is Microsoft Technology Licensing LLC. [Extracted from the article]

The article explores how deep learning models have evolved from relying on hand-crafted operator libraries to utilizing compiler-based approaches for optimization, especially with the growing diversity of hardware platforms. It highlights the Apache TVM project, which allows machine learning engineers to compile models for specific hardware targets, optimizing performance without altering model accuracy. The article points to an accompanying paper that proposes a unified transformation approach that optimizes model architectures through program transformations, avoiding expensive retraining and achieving significant performance gains without compromising accuracy.

The regulatory uses are real: In 2021, more than 100 drug and biologic applications submitted to the FDA included AI/ML components. Keywords: Algorithms; Artificial Intelligence; Bioengineering; Biologics; Biotechnology; Cybersecurity; Cyborgs; Drug Development; Drug Manufacturing; Drugs and Therapies; Emerging Technologies; FDA; Genetic Engineering; Genetically-Engineered Proteins; Government Agencies Offices and Entities; Health and Medicine; Machine Learning; Office of the FDA Commissioner; Public Health; Technology; U.S. Food and Drug Administration EN Algorithms Artificial Intelligence Bioengineering Biologics Biotechnology Cybersecurity Cyborgs Drug Development Drug Manufacturing Drugs and Therapies Emerging Technologies FDA Genetic Engineering Genetically-Engineered Proteins Government Agencies Offices and Entities Health and Medicine Machine Learning Office of the FDA Commissioner Public Health Technology U.S. Food and Drug Administration 497 497 1 05/22/23 20230523 NES 230523 2023 MAY 22 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Trials Week -- By: Patrizia Cavazzoni, M.D., Director of the Center for Drug Evaluation and Research Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work. [Extracted from the article]

The article discusses data quality and considers the building and scaling of data through open-source models. It mentions information duplication, degraded model quality, and data filtering in large language models (LLMs). Several research papers are cited including one titled "Textbooks are all you need" which focuses on Phi1, a transformer-based model which synthetically generates high-quality textbooks using output from sources such as GPT-3.5.

This article discusses the increasing popularity of data-driven methods for predicting heat transfer and fluid flow behavior in complex physical systems. The special issue focuses on innovative research that expands the boundaries of this field, showcasing advancements in methodologies and their applications across various domains. The article introduces 16 papers featured in the special issue, highlighting their contributions and findings. The overall aim of this special issue is to highlight the potential of data-driven methods in heat transfer and fluid flow, paving the way for more efficient and accurate solutions in engineering and scientific applications. [Extracted from the article]

Purpose: This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool. Design/methodology/approach: This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated. Findings: The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it. Originality/value: The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS. [ABSTRACT FROM AUTHOR]

According to the news editors, the research concluded: "On the eve of widespread use of AI in imaging practice, the group invites the radiology community to align and join Image IntelliGently (www. imageintelligently.org) to ensure that the use of AI is safe, reliable, and effective for children." Keywords: Houston; State:Texas; United States; North and Central America; Artificial Intelligence; Emerging Technologies; Health and Medicine; Machine Learning; Pediatrics; Radiology EN Houston State:Texas United States North and Central America Artificial Intelligence Emerging Technologies Health and Medicine Machine Learning Pediatrics Radiology 452 452 1 11/06/23 20231110 NES 231110 2023 NOV 11 (NewsRx) -- By a News Reporter-Staff News Editor at Pediatrics Week -- Fresh data on Artificial Intelligence are presented in a new report. [Extracted from the article]

Kimberly-Clark Corporation (KMB) announced the appointment of Zack Hicks in the newly created role of Chief Digital and Technology Officer, effective July 26, 2022. He is a member of the [...]

According to a white paper from Swiss Re, the insurance industry ranks sixth in terms of current AI risk and seventh in terms of future risk. The paper highlights the opportunities for insurers to cover AI risks in various industries and suggests that providing AI risk solutions is a significant opportunity for the insurance industry. The analysis shows that AI risks will grow in sectors such as health care and transportation, while the IT sector currently exhibits the most favorable ranking changes. The paper also provides detailed information on specific risks related to AI use in different industries. [Extracted from the article]

A patent has been issued for a device and method to assess hand flexion tone. The device, called the Isometric Force Pillow (IFP), measures static flexion force from the fingers to provide a quantitative measure of involuntary finger grip due to spastic hypertonia. The IFP can be used to evaluate hand flexion caused by muscle tone, hypertonia, spastic hypertonia, the flexion synergy, or contractures. It consists of an inflatable pillow that is wrapped around the hand and connected to an air pressure sensor, which converts the air pressure changes into therapeutic meaningful values. The device could be used to assess the effectiveness of interventions for relieving hypertonia. [Extracted from the article]

Keywords: Antibodies; Biological Structures; Blood Proteins; Cyborgs; Emerging Technologies; Engineering; Immunoglobulins; Immunology; Indiana University; Machine Learning; Proteins; Software EN Antibodies Biological Structures Blood Proteins Cyborgs Emerging Technologies Engineering Immunoglobulins Immunology Indiana University Machine Learning Proteins Software 118 118 1 08/07/23 20230812 NES 230812 2023 AUG 12 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Katy Borner's team from the Luddy School of Informatics, Computing, and Engineering's Cyberinfrastructure for Network Science Center has made significant contributions to constructing a Human Reference Atlas and has led or co-authored six research articles in a just-released HuBMAP package in Nature. Antibodies, Biological Structures, Blood Proteins, Cyborgs, Emerging Technologies, Engineering, Immunoglobulins, Immunology, Indiana University, Machine Learning, Proteins, Software. [Extracted from the article]

Purpose: Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain. Design/methodology/approach: The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment. Findings: The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information. Practical implications: The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises. Originality/value: The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain. [ABSTRACT FROM AUTHOR]

Purpose: The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues. Design/methodology/approach: Based on complex network theory and machine learning method, this paper constructs the structure of "three-layer coupling network" (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data. Findings: From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis. Originality/value: Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters. [ABSTRACT FROM AUTHOR]

Algorithms, Diagnostics and Screening, Emerging Technologies, Fibromyalgia, Health and Medicine, Linear Discriminant Analysis, Machine Learning, Muscular Diseases and Conditions, Musculoskeletal Diseases and Conditions, Neuromuscular Diseases and Conditions, Rheumatic Diseases and Conditions Keywords: Algorithms; Diagnostics and Screening; Emerging Technologies; Fibromyalgia; Health and Medicine; Linear Discriminant Analysis; Machine Learning; Muscular Diseases and Conditions; Musculoskeletal Diseases and Conditions; Neuromuscular Diseases and Conditions; Rheumatic Diseases and Conditions EN Algorithms Diagnostics and Screening Emerging Technologies Fibromyalgia Health and Medicine Linear Discriminant Analysis Machine Learning Muscular Diseases and Conditions Musculoskeletal Diseases and Conditions Neuromuscular Diseases and Conditions Rheumatic Diseases and Conditions 158 158 1 04/10/23 20230413 NES 230413 2023 APR 13 (NewsRx) -- By a News Reporter-Staff News Editor at Hematology Week -- Research findings on fibromyalgia are discussed in a new report. [Extracted from the article]

Purpose: With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more splendid profitability and is one of the essential key performance indicators (KPI) used in customer segmentation. Thus, this paper proposes a stacked ensemble learning method, a combination of multiple machine learning methods, for CLV prediction. Design/methodology/approach: In order to utilize customers' behavioral features for predicting the value of each customer's CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared with several popular predictive methods named deep neural networks, bagging support vector regression, light gradient boosting machine, random forest and extreme gradient boosting. Findings: Empirical results indicate that the regression performance of the stacked ensemble learning method outperformed other methods in terms of normalized rooted mean squared error, normalized mean absolute error and coefficient of determination, at 0.248, 0.364 and 0.848, respectively. In addition, the prediction capability of the proposed method improved significantly after optimizing its hyperparameters. Originality/value: This paper proposes a stacked ensemble learning method as a new method for accurate CLV prediction. The results and comparisons support the robustness and efficiency of the proposed method for CLV prediction. [ABSTRACT FROM AUTHOR]

Precision agriculture tools like decision support systems increasingly use machine‐learning algorithms and other types of artificial intelligence (AI) to analyze large quantities of agricultural data and provide recommendations to producers and crop advisers. However, several barriers threaten adoption of these tools. Three papers in the recent Agronomy Journal special section, "Machine Learning in Agriculture," explore this phenomenon and offer solutions and opportunities for building trust in these technologies. [ABSTRACT FROM AUTHOR]

The article discusses the deployment of machine learning technology in production environments. The authors describe the algorithms which constitute and shape machine learning protocols with a focus on papers on subjects including the MauveDB project to integrate machine learning technology into relational databases, scalable response prediction for online advertising, and optimization of neural network queries for object detection in video streams.