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    Children's Hospital of Fudan University Reports Findings in Neuroblastomas (Application of Machine Learning and Deep EfficientNets in Distinguishing Neonatal Adrenal Hematomas From Neuroblastoma in Enhanced Computed Tomography Images)

    19-20页
    查看更多>>摘要：New research on Oncology - Neuroblastomas is the subject of a report. According to news originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, “The aim of the study was to employ a combination of radiomic indicators based on computed tomography (CT) imaging and machine learning (ML), along with deep learning (DL), to differentiate between adrenal hematoma and adrenal neuroblastoma in neonates. A total of 76 neonates were included in this retrospective study (40 with neuroblastomas and 36 with adrenal hematomas) who underwent CT and divided into a training group (n = 38) and a testing group (n = 38).” Our news journalists obtained a quote from the research from the Children's Hospital of Fudan University, “The regions of interest (ROIs) were segmented by two radiologists to extract radiomics features using Pyradiomics package. ML classifications were done using support vector machine (SVM), AdaBoost, Extra Trees, gradient boosting, multi-layer perceptron (MLP), and random forest (RF). EfficientNets was employed and classified, based on radiometrics. The area under curve (AUC) of the receiver operating characteristic (ROC) was calculated to assess the performance of each model. Among all features, the least absolute shrinkage and selection operator (LASSO) logistic regression selected nine features. These radiomics features were used to construct radiomics model. In the training cohort, the AUCs of SVM, MLP and Extra Trees models were 0.967, 0.969 and 1.000, respectively. The corresponding AUCs of the test cohort were 0.985, 0.971 and 0.958, respectively. In the classification task, the AUC of the DL framework was 0.987.”
      原文链接:
    • NETL
    • NSTL

    New Artificial Intelligence Study Findings Reported from St Mary's Hospital (Chatgpt Versus Clinician: Challenging the Diagnostic Capabilities of Artificial Intelligence In Dermatology)

    20-21页
    查看更多>>摘要：Investigators discuss new findings in Artificial Intelligence. According to news originating from Portsmouth, United Kingdom, by NewsRx correspondents, research stated, “ChatGPT is an online language-based platform designed to answer questions in a human-like way, using deep learning -technology.Objectives To examine the diagnostic capabilities of ChatGPT using real-world anonymized medical dermatology cases.Methods Clinical information from 90 consecutive patients referred to a single dermatology emergency clinic between June and December 2022 were examined. Thirty-six patients were included.” Our news journalists obtained a quote from the research from St Mary's Hospital, “Anonymized clinical information was transcribed and input into ChatGPT 4.0 followed by the question 'What is the most likely diagnosis?' The suggested diagnosis made by ChatGPT was then compared with the diagnosis made by dermatology.Results After inputting clinical history and examination data obtained by a dermatologist, ChatGPT made a correct primary diagnosis 56% of the time (n = 20). Using the clinical history and cutaneous signs recorded by nonspecialists, it was able to make a correct diagnosis 39% of the time (n = 14). This was similar to the diagnostic rate of nonspecialists (36%; n = 13), but it was much lower than that of dermatologists (83%; n = 30). There was no differential offered by referring sources 28% of the time (n = 10), unlike ChatGPT, which provided a differential diagnosis 100% of the time. Qualitative analysis showed that ChatGPT offered responses with caution, often justifying its reasoning.Conclusions This study illustrates that while ChatGPT has a diagnostic capability, in its current form it does not significantly improve the diagnostic yield in primary or secondary care. Artificial intelligence is a rapidly evolving field within medicine. ChatGPT is a new language-based platform that uses deep learning to answer questions in a human-like way.”
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Researchers at University of British Columbia Release New Data on Machine Learning (A Generalizable Method for Capacity Estimation and Rul Prediction In Lithium-ion Batteries)

    21-22页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting out of Vancouver, Canada, by NewsRx editors, research stated, “Data-driven methods have attracted much attention in capacity estimation and remaining useful life (RUL) prediction of lithium-ion batteries. However, existing studies rely on complex machine learning models (e.g., Gaussian process regression, neural networks, and so on.) that are applicable to specific observed operating conditions, and the prediction accuracy can be affected by different usage scenarios.” Financial supporters for this research include CGIAR, Natural Sciences and Engineering Research Council of Canada (NSERC), Natural Sciences and Engineering Research Council of Canada (NSERC). Our news journalists obtained a quote from the research from the University of British Columbia, “This paper proposes to adopt a linear and robust machine learning technique, partial least-squares regression, for battery capacity estimation, and RUL prediction based on the partial incremental capacity curve. The features can be easily obtained by interpolation of the measured charging profiles without data smoothing, and the bootstrapping technique is used to give confidence intervals of the predictions, which helps to evaluate the robustness and reliability of the model. The proposed method is validated on three battery data sets with different operating conditions provided by NASA. We train the model on one battery and test its performance on the other two batteries without changing the model weights.”
      原文链接:
    • NETL
    • NSTL
    • Amer Chemical Soc

    Researchers at Shanghai University of Finance and Economics Target Machine Learning (SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System)

    22-23页
    查看更多>>摘要：Current study results on artificial intelligence have been published. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, “Recommendation systems play a pivotal role in delivering user preference information.” The news journalists obtained a quote from the research from Shanghai University of Finance and Economics: “However, they often face the challenge of information cocoons due to repeated content delivery, particularly prevalent in stock recommendations that are susceptible to investor sentiment. In response to the information cocoons, we propose the Sentiment and Price Combined Model (SPCM), which leverages sentiment features and price factors to predict stock price movements. This novel framework combines collective sentiment analysis with state-of-the-art BERT transformer models and advanced machine learning techniques. Over a three-year period, we collected 40 million stock comments from the Guba platform, extracting investor sentiment conveyed in text information and investigating the impact of metrics such as homophily on stock recommendations. Experimental results indicate that both the volume of posts and the agreement index affect the effectiveness of investor sentiment, while homophily reduces the accuracy of participants' stock price judgments. The recognition accuracy of the BERT-based sentiment analysis model reaches an impressive 84.12%, and the portfolio constructed by SPCM yields a cumulative return four times that of the industry benchmark.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    University of Texas Tyler Researchers Publish Findings in Robotics [Redesign of Leg Assembly and Implementation of Reinforcement Learning for a Multi-Purpose Rehabilitation Robotic Device (RoboREHAB)]

    23-23页
    查看更多>>摘要：Research findings on robotics are discussed in a new report. According to news reporting out of Tyler, Texas, by NewsRx editors, research stated, “Patients who are suffering from neuromuscular disorders or injuries that impair motor control need to undergo rehabilitation to regain mobility.” Financial supporters for this research include Capstone Senior Design Teams in The Department of Mechanical Engineering At The University of Texas. The news reporters obtained a quote from the research from University of Texas Tyler: “Gait training is commonly prescribed to patients to regain muscle memory. Automated-walking training devices were created to aid this process; while these devices establish accurate ankle-path trajectories, the knee and hip movements are inaccurate. In this work, a redesign of the leg assembly in a multi-purpose rehabilitation robotic device (RoboREHAB) was explored to improve hip- and knee-movement accuracy by adding an extra link and rollers to the assembly. Motion analysis was employed to test feasibility, reinforcement learning was utilized to train the new leg assembly to walk, and the joint motions achieved with the redesign were compared to those achieved by motion-capture (mocap) data. As a key result, the motion analysis showed an improvement in the knee- and hip-path trajectories due to the added roller/joint segment. The redesigned leg assembly, under the reinforcement-learning policy, showed a 5% deviation from the motioncapture joint trajectories with a maximum deviation of 51.177 mm but maintained a similar profile to the mocap trajectory data.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Faculty of Computer Science Researcher Highlights Research in Machine Learning (Automated Text Annotation Using a Semi- Supervised Approach with Meta Vectorizer and Machine Learning Algorithms for Hate Speech Detection)

    24-25页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting out of Krakow, Poland, by NewsRx editors, research stated, “Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has various drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness.” The news journalists obtained a quote from the research from Faculty of Computer Science: “In addition, annotators may annotate ambiguous data. Therefore, we have developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation to detect hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and new data of 2245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available (based on self-learning). Ultimately, this enhances its ability to handle real-world scenarios with scarce annotated information.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    University of Macau Reports Findings in Robotics (Design of an automated robotic microinjection system for batch injection of zebrafish embryos and larvae)

    25-25页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating from Macau, People's Republic of China, by NewsRx correspondents, research stated, “This paper presents the design of a vision-based automated robotic microinjection system for batch injection of both zebrafish embryos and larvae. A novel visual recognition algorithm based on an automatic threshold and excessive dilatation is introduced to accurately identify the center of zebrafish embryos and larval yolks.” Financial supporters for this research include National Natural Science Foundation of China, Fundo para o Desenvolvimento das Ciencias e da Tecnologia, Universidade de Macau. Our news editors obtained a quote from the research from the University of Macau, “A corresponding software system is developed using the producer-consumer model as the framework structure, and a friendly user interface is designed to allow operators to choose from a range of desired functions according to their different needs. In addition, a novel microstructural agarose device is designed and fabricated to simultaneously immobilize mixed batches of embryos and larvae. Moreover, a prototype microinjection system is fabricated by integrating hardware devices with visual algorithms. An experimental study is conducted to verify the performance of the robotic microinjection system. The results show that the reported system can accurately identify zebrafish embryos and larvae and efficiently complete batch microinjection tasks of the mixtures with an injection success rate of 92.05% in 13.88 s per sample.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Reports from Diponegoro University Describe Recent Advances in Machine Learning (Identifying Potential Students as College Customers Using Machine Learning: A Literature Review)

    26-27页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting originating from Diponegoro University by NewsRx correspondents, research stated, “College customers begin with prospective students who are important asset opportunities.” Our news correspondents obtained a quote from the research from Diponegoro University: “Potential prospective students can be identified from academic score data while in SMA / SMK / MAN, achievement data, data on the number of siblings, and data on parents' income. After being a student, their potential can be identified from GPA scores, non-academic achievement data and length of study in college. When becoming an alumnus, college customers can be identified for their potential from the waiting time for alumni to get a job, place of work, and type of alumni work. The use of CRM (Customer Relationship Management) is expected to be able to recognize the potential of college customers to benefit universities. This article contains a systematic literature review of several research themes that have been carried out related to the utilization of machine learning for CRM using several existing machine learning algorithms, The goal of this article is to find new ideas that are better at implementing CRM in college by using machine-based learning methods.”
      原文链接:
    • NETL
    • NSTL

    Researcher at Chonnam National University Publishes Research in Robotics (Acoustic Actuators for the Manipulation of Micro/ Nanorobots: State-of-the-Art and Future Outlooks)

    26-26页
    查看更多>>摘要：Investigators publish new report on robotics. According to news originating from Gwangju, South Korea, by NewsRx editors, the research stated, “Compared to other actuating methods, acoustic actuators offer the distinctive capability of the contactless manipulation of small objects, such as microscale and nanoscale robots.” Financial supporters for this research include Ministry of Health & Welfare, Republic of Korea. Our news journalists obtained a quote from the research from Chonnam National University: “Furthermore, they have the ability to penetrate the skin, allowing for the trapping and manipulation of micro/nanorobots that carry therapeutic agents in diverse media. In this review, we summarize the current progress in using acoustic actuators for the manipulation of micro/nanorobots used in various biomedical applications. First, we introduce the actuating method of using acoustic waves to manipulate objects, including the principle of operation and different types of acoustic actuators that are usually employed.” According to the news reporters, the research concluded: “Then, applications involving manipulating different types of devices are reviewed, including bubble-based microrobots, bubble-free robots, biohybrid microrobots, and nanorobots. Finally, we discuss the challenges and future perspectives for the development of the field.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Fudan University Obstetrics and Gynecology Hospital Reports Findings in Bronchopulmonary Dysplasia (Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning ...)

    27-28页
    查看更多>>摘要：New research on Lung Diseases and Conditions - Bronchopulmonary Dysplasia is the subject of a report. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, “Bronchopulmonary dysplasia (BPD) is often seen as a pulmonary complication of extreme preterm birth, resulting in persistent respiratory symptoms and diminished lung function. Unfortunately, current diagnostic and treatment options for this condition are insufficient.” Our news journalists obtained a quote from the research from Fudan University Obstetrics and Gynecology Hospital, “Hence, this study aimed to identify potential biomarkers in the peripheral blood of neonates affected by BPD. The Gene Expression Omnibus provided the expression dataset GSE32472 for BPD. Initially, using this database, we identified differentially expressed genes (DEGs) in GSE32472. Subsequently, we conducted gene set enrichment analysis on the DEGs and employed weighted gene co-expression network analysis (WGCNA) to screen the most relevant modules for BPD. We then mapped the DEGs to the WGCNA module genes, resulting in a gene intersection. We conducted detailed functional enrichment analyses on these overlapping genes. To identify hub genes, we used 3 machine learning algorithms, includ- ing SVM-RFE, LASSO, and Random Forest. We constructed a diagnostic nomogram model for predicting BPD based on the hub genes. Additionally, we carried out transcription factor analysis to predict the regulatory mechanisms and identify drugs associated with these biomarkers. We used differential analysis to obtain 470 DEGs and conducted WGCNA analysis to identify 1351 significant genes. The intersection of these 2 approaches yielded 273 common genes. Using machine learning algorithms, we identified CYYR1, GALNT14, and OLAH as potential biomarkers for BPD. Moreover, we predicted flunisolide, budesonide, and beclomethasone as potential anti-BPD drugs. The genes CYYR1, GALNT14, and OLAH have the potential to serve as diagnostic biomarkers for BPD.”
      原文链接:
    • NETL
    • NSTL