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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 paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]

A new report from the University of Florence in Italy provides an extensive review of monitoring methods for electrical power lines, with a focus on high-voltage and medium-voltage systems. The objective of these techniques is to prevent catastrophic failures by detecting partial damage or deterioration of components and allowing for organized maintenance operations. The paper discusses the coordination of protection devices and the implementation of artificial intelligence algorithms to improve the reliability of the network. It also highlights diagnostic techniques, protection devices, and prognostic methods, emphasizing the role of artificial intelligence and offering guidelines for choosing between different approaches. [Extracted from the article]

This article discusses the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cancer research. It highlights how these technologies are transforming the diagnosis, treatment, and prevention of cancer by providing sophisticated analytical capabilities. The paper explores the mathematical foundations and practical implementations of AI and ML techniques in various aspects of cancer research, including detection, diagnosis, prognosis, and personalized treatment strategies. It also reviews influential algorithms such as Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and clustering techniques, and their roles in improving diagnostic accuracy and treatment efficacy. Please note that this preprint has not been peer-reviewed. [Extracted from the article]

Purpose: This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach: The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on "how exactly the matching process is done for evaluating the document." Finally, the e-learners are classified based on their grades. Findings: On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value: This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors' knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners. [ABSTRACT FROM AUTHOR]

This paper compares AI (artificial intelligence) algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy, sensitivity, and specificity. Keywords: Algorithms; Artificial Intelligence; Cancer; Emerging Technologies; Gastric Cancer; Gastroenterology; Health and Medicine; Machine Learning; Oncology EN Algorithms Artificial Intelligence Cancer Emerging Technologies Gastric Cancer Gastroenterology Health and Medicine Machine Learning Oncology 427 427 1 03/23/23 20230321 NES 230321 2023 MAR 21 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- A new study on artificial intelligence is now available. [Extracted from the article]

This article investigates artificial intelligence (AI) fairness in decision-making specifically examining the limitations of this process. The article discusses eight inherent limitations in AI fairness including lack of fairness in human decision-making used to train the AI model, lack of a universal fairness definition, and limited power over the complete decision-making process.

The article discusses Explainable artificial intelligence (XAI) and the expectation of artificial intelligence (AI) end users to understand how AI works, including how algorithmic decision making occurs. An overview of AI end users' desire for information on AI methodology on reaching decisions, including in regard to the ethical nature of AI, is provided.

This article discusses the recent areas of focus at the National Institute of Informatics (NII) in Japan. First, a look at its academic services including SINET6, a backbone network service across Japan and its data platform service, the NII Research Data Cloud. The article also details NII research in computer science with projects in innovative computer vision and engineerable artificial intelligence, as well as applied research projects including a COVID-19 pneumonia diagnosis tool.

This article discusses how the combination of humans and artificial intelligence could target misinformation and polarization, by examining two scenarios- one where computing methods can assist human fact-checkers and one where human search methods can assist in search engine algorithms for personalization. Topics include the Australian Communication and Media Authority research on misinformation in Australia, whether filter bubbles are created by personalization attempts of algorithms, and the various computational methods that can assist fact-checking to prevent misinformation.

A research report from Soochow University in Suzhou, China, introduces a new algorithm called Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MASDSC) for brain tumor clustering in medical imaging. The algorithm addresses the limitations of traditional unsupervised deep subspace clustering algorithms by incorporating a semi-supervised learning framework and a multi-scale feature extraction mechanism. It also employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters. The MAS-DSC algorithm achieves excellent clustering results on standard datasets and demonstrates robustness and enhanced clustering performance on a brain tumor dataset. The research has been peer-reviewed and published in Computers Materials & Continua. [Extracted from the article]

The article examines the evolving role of Generative AI (Gen AI) in healthcare, contrasting it with traditional artificial intelligence. It states that traditional AI has been used for specific tasks like predictive modeling and decision-making based on structured data. It mentions that Gen AI generates new content and analyzes both structured and unstructured data, showing potential for significant improvements in various healthcare areas, including coding accuracy, and claims management.

For more information on this research see: Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology. Keywords: Algorithms; Artificial Intelligence; Cancer; Cancer Detection; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Pathology; Prostate Cancer; Prostatic Neoplasms; Technology EN Algorithms Artificial Intelligence Cancer Cancer Detection Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Pathology Prostate Cancer Prostatic Neoplasms Technology 575 575 1 09/04/23 20230905 NES 230905 2023 SEP 5 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Investigators discuss new findings in artificial intelligence. [Extracted from the article]

A new advanced artificial intelligence (AI) algorithm developed by researchers at Penn State College of Medicine may improve predictions and therapies for autoimmune diseases. The algorithm analyzes the genetic code underlying these conditions to accurately model gene expression and regulation, as well as identify additional genes of risk. The algorithm outperforms existing methodologies and identified 26% more novel gene and trait associations. The researchers also used the algorithm to identify potential therapeutics for autoimmune diseases, pointing to drug compounds that could reverse gene expression in specific cell types associated with these diseases. [Extracted from the article]

A recent study conducted by researchers at the University of California San Diego has shown that AI algorithms, combined with a modified pacifier, can determine if newborns are nursing properly. The study measured the strength and regularity of babies' suckling patterns using eight independent parameters. The results provide objective data that can improve standard assessments and potentially prevent surgical interventions. The study also found that the device-generated data could improve the subjective evaluation of infants' suckling ability by clinicians. The researchers hope that their findings will support parents struggling with breastfeeding and improve long-term health outcomes. [Extracted from the article]

The researchers, part of a group of computer-savvy historians who call themselves the Decrypt Project, have spent the past decade poring over manuscripts from European archives, trying to solve hidden messages. (Bloomberg Businessweek) -- More than 400 years ago, Mary, Queen of Scots was imprisoned in an English castle on charges that she'd conspired against Queen Elizabeth I, her cousin. [Extracted from the article]

The new tool relies on an advanced artificial intelligence (AI) algorithm that helps the AI better identify uncertainty when faced with unexpected data in real-world situations. Keywords: Algorithms; Computer Engineering; Engineering; Government Agencies Offices and Entities; North Carolina State University; Technology EN Algorithms Computer Engineering Engineering Government Agencies Offices and Entities North Carolina State University Technology 5024 5024 1 05/02/23 20230505 NES 230505 2023 MAY 5 (NewsRx) -- By a News Reporter-Staff News Editor at Respiratory Therapeutics Week -- A team of engineering and health researchers has developed a tool that improves the ability of electronic devices to detect when a human patient is coughing, which has applications in health monitoring. [Extracted from the article]

The article presents the discussion on Artificial intelligence (AI) and machine learning (ML) increasingly used as technologies. Topics include used for tackling environmental challenges arising from global warming and climate change; and society preparing for climate change adaptation as well as mitigation through actions like reducing the carbon footprint of technology infrastructure.

Keywords: Algorithms; Artificial Intelligence; Biomarkers; Diagnostics and Screening; Emerging Technologies; Eye Diseases and Conditions; Health and Medicine; Machine Learning; Macular Degeneration; Macular Edema; Retinal Degeneration; Retinal Diseases and Conditions EN Algorithms Artificial Intelligence Biomarkers Diagnostics and Screening Emerging Technologies Eye Diseases and Conditions Health and Medicine Machine Learning Macular Degeneration Macular Edema Retinal Degeneration Retinal Diseases and Conditions 222 222 1 03/27/23 20230327 NES 230327 2023 MAR 27 (NewsRx) -- By a News Reporter-Staff News Editor at Diabetes Week -- A new study on artificial intelligence is now available. This paper aims to validate an AI algorithm for identifying and quantifying different major optical coherence tomography (OCT) biomarkers in DME eyes by comparing the algorithm to human expert manual examination.". [Extracted from the article]

Keywords: Mansoura; Egypt; Africa; Algorithms; Artificial Intelligence; DNA Viruses; Data Mining; Diagnostics and Screening; Emerging Technologies; Health and Medicine; Infectious Diseases and Conditions; Information Technology; Information and Data Mining; Machine Learning; Monkeypox; Orthopoxvirus; Poxviridae Infections; Selection Algorithm; Zoonosis; Zoonotic EN Mansoura Egypt Africa Algorithms Artificial Intelligence DNA Viruses Data Mining Diagnostics and Screening Emerging Technologies Health and Medicine Infectious Diseases and Conditions Information Technology Information and Data Mining Machine Learning Monkeypox Orthopoxvirus Poxviridae Infections Selection Algorithm Zoonosis Zoonotic 2023 FEB 17 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- New research on Infectious Diseases and Conditions - Monkeypox is the subject of a report. Our news editors obtained a quote from the research from Mansoura University, "Through this paper, a new detection strategy based on artificial intelligence techniques is provided to early detect monkeypox patients. [Extracted from the article]

The article focuses on the search for algorithms that can reduce the time needed for multiplication problems within the fields of machine learning (ML), deep learning (DL), and artificial intelligence (AI). Topics discussed include the history of matrix multiplication and the data science group DeepMind and their ML approach AlphaTensor.

The article presents three principles concerning five challenges to the establishment of trustworthy artificial intelligence (AI), including the development computer languages and algorithms for data modeling, trustworthy-by-construction design, and complex machine learning (ML) representations.

The article examines trust in artificially intelligent (AI) systems as well as their regulation by various governments including those of the European Union, U.S., and China. The author predicates trust in AI on its perceived safety and reliability and calls for the continued human oversight of AI systems ("human-in-the-loop" or "society-in-the-loop"). Applications of artificial intelligence are outlined including military strategy, law enforcement, and healthcare.

The article utilizes the adaptive learning computer system AlphaZero in order to examine how variations of the game of chess would proceed at what is referred to as a superhuman level. According to the article, AlphaZero marks a significant milestone in the field of artificial intelligence (AI) and the use of algorithms in computer chess.

The article discusses the concept of topic novelty and its impact upon accuracy in the news and journalism, and whether artificial intelligence (AI) algorithms can counter fake news. The article discusses AI, accuracy, and fake news during the COVID-19 pandemic and an experiment performed on Amazon Mechanical Turk (AMT).

The article looks at the developments in experimental quantum physics through the use of the MELVIN machine-learning algorithm system and the THESEUS artificial intelligence (AI) system. Topics discussed include the role of an experiment on quantum states of photons entangled in a particular manner in the creation of MELVIN by quantum physicist Mario Krenn, rare solution found by MELVIN with higher entanglement compared with the rest of the states, and advantages of THESEUS over MELVIN.

This article focuses on algorithmic transparency under EU law, specifically exploring the ‘right to explanation’. It addresses the legal framework concerning Artificial Intelligence (AI) algorithms from the above – mentioned perspective, focusing on remedies which ensure transparency and accountability, given the black box effect of algorithms. Proprietary information, and transparency- performance trade-offs are analysed, as well as the ‘right to explanation’ within the General Data Protection Regulation (GDPR) and in light of the EU ‘AI Act’, which is expected to be adopted. The legal remedies under EU law are examined and assessed in terms of their efficiency. [ABSTRACT FROM AUTHOR]

The article discusses the development and relative effectiveness of anti-surveillance cosmetic makeup designed to elude facial-recognition technology. It also examines the rapid pace of computer vision development, facial-detection algorithms, and how law enforcement agencies utilize facial recognition systems.

Commonsense reasoning remains a major challenge in AI, and yet, recent progresses on benchmarks may seem to suggest otherwise. In particular, the recent neural language models have reported above 90% accuracy on the Winograd Schema Challenge (WSC),22 a commonsense benchmark originally designed to be unsolvable for statistical models that rely simply on word associations. This raises an important question--whether these models have truly acquired robust commonsense capabilities or they rely on spurious biases in the dataset that lead to an overestimation of the true capabilities of machine commonsense. To investigate this question, we introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) large-scale crowdsourcing, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations. Our experiments demonstrate that state-of-the-art models achieve considerably lower accuracy (59.4%-79.1%) on WinoGrande compared to humans (94%), confirming that the high performance on the original WSC was inflated by spurious biases in the dataset. Furthermore, we report new state-of-the-art results on five related benchmarks with emphasis on their dual implications. On the one hand, they demonstrate the effectiveness of WinoGrande when used as a resource for transfer learning. On the other hand, the high performance on all these benchmarks suggests the extent to which spurious biases are prevalent in all such datasets, which motivates further research on algorithmic bias reduction. [ABSTRACT FROM AUTHOR]

For years public concern about technological risk has focused on the misuse of personal data. But as firms embed more and more artificial intelligence in products and processes, attention is shifting to the potential for bad or biased decisions by algorithms—particularly the complex, evolving kind that diagnose cancers, drive cars, or approve loans. Inevitably, many governments will feel regulation is essential to protect consumers from that risk. This article explains the moves regulators are most likely to make and the three main challenges businesses need to consider as they adopt and integrate AI. The first is ensuring fairness. That requires evaluating the impact of AI outcomes on people’s lives, whether decisions are mechanical or subjective, and how equitably the AI operates across varying markets. The second is transparency. Regulators are very likely to require firms to explain how the software makes decisions, but that often isn’t easy to unwind. The third is figuring out how to manage algorithms that learn and adapt; while they may be more accurate, they also can evolve in a dangerous or discriminatory way. Though AI offers businesses great value, it also increases their strategic risk. Companies need to take an active role in writing the rulebook for algorithms. [ABSTRACT FROM AUTHOR]

Bias within algorithms may lead to inaccurate healthcare outcomes and exacerbate health disparities when results derived from these biased algorithms are applied to health interventions. Keywords: Irvine; State:California; United States; North and Central America; Algorithms; Artificial Intelligence; Emerging Technologies; Health and Medicine; Machine Learning; Public Health EN Irvine State:California United States North and Central America Algorithms Artificial Intelligence Emerging Technologies Health and Medicine Machine Learning Public Health 2145 2145 1 07/24/23 20230728 NES 230728 2023 JUL 28 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Data detailed on Artificial Intelligence have been presented. [Extracted from the article]

The article criticizes the use of artificial intelligence in employee hiring. The algorithms utilized to screen resumes of applicants are characterized as inadequate and even discriminatory. The design of recruitment software is examined along with the alleged drive for "hyperefficiency" among human resource departments.

Keywords: Algorithms; Apnea; Emerging Technologies; Fuzzy Logic; Health and Medicine; Intelligent System; Machine Learning; Obstructive Sleep Apnea; Respiration Disorders; Respiratory Tract Diseases and Conditions; Risk and Prevention; Sleep Apnea; Sleep Disorders EN Algorithms Apnea Emerging Technologies Fuzzy Logic Health and Medicine Intelligent System Machine Learning Obstructive Sleep Apnea Respiration Disorders Respiratory Tract Diseases and Conditions Risk and Prevention Sleep Apnea Sleep Disorders 1217 1217 1 06/12/23 20230616 NES 230616 2023 JUN 12 (NewsRx) -- By a News Reporter-Staff News Editor at Respiratory Therapeutics Week -- Investigators discuss new findings in obstructive sleep apnea. The news editors obtained a quote from the research from University of Vigo: "Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). According to the news editors, the research concluded: "This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. [Extracted from the article]

Algorithms, Artificial Intelligence, Emerging Technologies, Health and Medicine, Machine Learning, Ophthalmology Keywords: Algorithms; Artificial Intelligence; Emerging Technologies; Health and Medicine; Machine Learning; Ophthalmology EN Algorithms Artificial Intelligence Emerging Technologies Health and Medicine Machine Learning Ophthalmology 1281 1281 1 05/22/23 20230526 NES 230526 2023 MAY 26 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- New research on artificial intelligence is the subject of a new report. [Extracted from the article]

Keywords for this news article include: ELTE Eotvos Lorand University, Budapest, Hungary, Europe, Cyborgs, Algorithms, Machine Learning, Emerging Technologies. Keywords: Algorithms; Cyborgs; Emerging Technologies; Machine Learning EN Algorithms Cyborgs Emerging Technologies Machine Learning 1123 1123 1 04/17/23 20230421 NES 230421 2023 APR 23 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- Current study results on artificial intelligence have been published. [Extracted from the article]

As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain." Keywords: Algorithms; Artificial Intelligence; COVID-19 Model; Cyborgs; Disease Model; Emerging Technologies; Epidemiology; Investment and Finance; Legal Issues; Machine Learning; Risk and Prevention; Support Vector Machines; Technology EN Algorithms Artificial Intelligence COVID-19 Model Cyborgs Disease Model Emerging Technologies Epidemiology Investment and Finance Legal Issues Machine Learning Risk and Prevention Support Vector Machines Technology 678 678 1 03/23/23 20230320 NES 230320 2023 MAR 26 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx COVID-19 Weekly -- Research findings on artificial intelligence are discussed in a new report. [Extracted from the article]

The article presents a study conducted at APJ Abdul Kalam Technological University focusing on a new approach for liver tumor segmentation and classification. Topics discussed include the application of a deep learning model with a hybrid optimization technique, the use of advanced image processing methods, and the improvements in accuracy, specificity, sensitivity, and precision achieved with the proposed model.

A study conducted by researchers from various institutions, including the University of North Carolina School of Medicine and Duke University, has found that nearly half of the AI medical devices approved by the U.S. Food and Drug Administration (FDA) lack reported clinical validation data. The researchers analyzed over 500 medical AI devices and discovered that approximately 43% of the FDA-approved devices did not have published clinical validation data. The study highlights the need for the FDA and industry to conduct clinical validation studies on these technologies and make the results publicly available. The researchers also recommend that the FDA clearly distinguish between different types of clinical validation studies in its recommendations to manufacturers. [Extracted from the article]

The article focuses on research from Sikkim Manipal University regarding a novel machine learning methodology for imaging intervertebral disc degeneration. Topics include the integration of microscopic histopathological features with macroscopic MRI signal intensities, the development of a hybrid imaging tool for improved assessment, and the use of machine learning algorithms to predict degenerative changes.

Researchers at Dalian Ocean University in Dalian, China have developed a new machine learning-based artificial intelligence method for predicting air pollution levels. The method, called the Hybrid Integration (HIG) algorithm, combines data pre-processing, time series decomposition, signal decomposition, a prediction module, a matching strategy, and a hybrid integrated optimization algorithm. The researchers found that the HIG-RNN and HIG-LSTM models outperformed traditional methods in terms of accuracy. This new method could be a valuable tool for managing air pollution and informing policy decisions. [Extracted from the article]

Researchers at the National Institute for Mathematical Sciences in South Korea have developed a fully automatic registration method for merging dental cone-beam computed tomography (CBCT) and face scan data. The method addresses the challenges of merging these two types of data, which have different image acquisition methods and limited areas of correspondence. The researchers used a pre-trained facial landmark detection algorithm and a novel mathematical algorithm to identify paired 3D landmarks from 2D projection images. The proposed method achieved an average mean surface distance error of 0.74 mm for three pairs of CBCT and face scan datasets. [Extracted from the article]

A newly-discovered technique combines wireless EM monitoring and AI algorithms to "read" text on a victim's screen, and it's already being used in the wild. [ABSTRACT FROM AUTHOR]

A report from Opole University of Technology in Poland explores the integration of numerical algorithms, artificial intelligence (AI), and expert methodologies in the field of nephrology. The study focuses on automation and decision support, examining the impact of numerical algorithms on kidney function evaluation and the potential of AI in early disease detection and personalized treatment strategies. The report also addresses the collaborative role of computer-based tools and expert-driven approaches in enhancing diagnoses. The research concludes that these innovative methodologies have promising prospects for reshaping nephrology research and patient care. [Extracted from the article]

A new report from the Chinese Academy of Sciences discusses the use of artificial intelligence (AI) in recognizing atrial fibrillation (AF) in electrocardiogram (ECG) signals. The researchers investigated the accuracy of AI recognition for AF when ECGs were contaminated with motion artifacts (MAs) and processed by an adaptive filtering algorithm (ADF). They found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%. This research suggests that AI combined with ADF could be a valuable tool for early detection of AF. [Extracted from the article]

Algorithms, Psychology, Mental Health, Neural Networks, Machine Learning, Health and Medicine, Emerging Technologies, Artificial Intelligence, Columbia University School of Engineering and Applied Science Keywords: Algorithms; Psychology; Mental Health; Neural Networks; Machine Learning; Health and Medicine; Emerging Technologies; Artificial Intelligence; Columbia University School of Engineering and Applied Science EN Algorithms Psychology Mental Health Neural Networks Machine Learning Health and Medicine Emerging Technologies Artificial Intelligence Columbia University School of Engineering and Applied Science Robots are all around us, from drones filming videos in the sky to serving food in restaurants and diffusing bombs in emergencies. [Extracted from the article]

The article examines the use of machine learning- and deep learning-based artificial intelligence tools to safeguard user privacy on computer networks of the future. Compliance with data privacy regulations such as the CCPA and GDPR is scrutinized and the application of AI-based approaches to telephone company networks is discussed.

A report from Anshan Normal University in Liaoning, China discusses the application of artificial intelligence image algorithms in personalized medicine, specifically in the field of sports and human health. The researchers used computer vision and image processing technology, along with deep learning algorithms and pattern recognition, to analyze and evaluate the posture, movement, and body state of athletes. The method accurately identified and analyzed these factors, providing personalized health advice and guidance to improve training effectiveness, prevent sports injuries, and enhance the overall quality of sports. The research findings suggest that the use of artificial intelligence image algorithms can have significant benefits in the field of personalized medicine. [Extracted from the article]