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    New Findings Reported from Rush University Medical Center Describe Advances in A rtificial Intelligence (Artificial Intelligence and Point-of-care Ultrasound: Be nefits, Limitations, and Implications for the Future)

    75-75页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Artificial Intelligen ce have been presented. According to news reporting from Chicago, Illinois, by N ewsRx journalists, research stated, “The utilization of artificial intelligence (AI) in medical imaging has become a rapidly growing field as a means to address contemporary demands and challenges of healthcare.” The news correspondents obtained a quote from the research from Rush University Medical Center, “Among the emerging applications of AI is point -ofcare ultrasou nd (POCUS), in which the combination of these two technologies has garnered rece nt attention in research and clinical settings.” According to the news reporters, the research concluded: “In this Controversies paper, we will discuss the benefits, limitations, and future considerations of A I in POCUS for patients, clinicians, and healthcare systems.”
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    Studies from Nanyang Technological University Further Understanding of Machine L earning (Analysis of Machine Learning Application in Campus Network Traffic Anom aly Detection)

    76-77页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting out of Nanyang Technological Universit y by NewsRx editors, research stated, “In this paper, machine learning algorithm s are first utilized to extract features of campus network traffic, and then the multi-attention mechanism is introduced to fuse the massive features extracted at different scales.” Our news reporters obtained a quote from the research from Nanyang Technological University: “Unsupervised learning is used to propose a method for detecting ne twork traffic anomalies, and simulation experiments are conducted to verify the model’s performance. The results show that the detection rates of machine learni ng algorithms are all above 80%, the false alarm rate basically sta ys below 10%.”
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    Reports Outline Machine Learning Findings from SRM Institute of Science and Tech nology (Human Activity Recognition In Cyberphysical Systems Using Optimized Mac hine Learning Techniques)

    77-78页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting out of Ghaziabad, India, by NewsRx editors, research stated, “Human Activity Recognition (HAR) is an active research topic as it finds use in many real-world applications such as health mo nitoring and biometric user identification. Smart wearables which form an integr al part of the Internet of Medical Things (IoMT) and Cyber-Physical Systems can provide information about human activities on a daily basis, which may be used a s soft biometrics for user identification.” Financial support for this research came from King Saud University. Our news journalists obtained a quote from the research from the SRM Institute o f Science and Technology, “Over the last few years, one of the popular problem-s olving approaches for HAR has been in the form of artificial intelligence method s. Since security is related to robustness, our primary aim is to solve the prob lem with better model capabilities. In this study, we consider machine learning algorithms like Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (k- NN)(and deep learning algorithms such as Convolutional Neural Networks (CNN), Lo ng Short Term Memory (LSTM), and Gated Recurrent Units (GRU)) for the purpose of HAR. In order to improvise the model performance, we introduce optimization tec hniques along with CNN, LSTM, and GRU. We rely on Stochastic Gradient Descent (S GD), and optimizers Adam and RMSProp, and evaluate the strength of the models us ing Accuracy and F-1 score. Moreover, the study has been carried out on three da tasets that incorporate several human activities.”
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    Eotvos Lorand University (ELTE) Researchers Describe Advances in Intelligent Sys tems (Cluster-based oversampling with area extraction from representative points for class imbalance learning)

    78-79页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on intelligent systems are presented in a new report. According to news reporting from Budapest, Hungary, b y NewsRx journalists, research stated, “Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class.” The news reporters obtained a quote from the research from Eotvos Lorand Univers ity (ELTE): “Resampling methods have been used to adjust the class distribution, but they often have limitations for small disjunct minority subsets. This paper introduces AROSS, an adaptive cluster-based oversampling approach that addresse s these limitations. AROSS utilizes an optimized agglomerative clustering algori thm with the Cophenetic Correlation Coefficient and the Bayesian Information Cri terion to identify representative areas of the minority class. Safe and half-saf e areas are obtained using an incremental k-Nearest Neighbor strategy, and overs ampling is performed with a truncated hyperspherical Gaussian distribution.” According to the news editors, the research concluded: “Experimental evaluations on 70 binary datasets demonstrate the effectiveness of AROSS in improving class imbalance learning performance, making it a promising solution for mitigating c lass imbalance challenges, especially for small disjunct minority subsets.”
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    Researchers from Shandong Women’s University Discuss Findings in Computational I ntelligence (Feamix: Feature Mix With Memory Batch Based On Self-consistency Lea rning for Code Generation and Code Translation)

    79-79页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning - Comp utational Intelligence is now available. According to news originating from Jina n, People’s Republic of China, by NewsRx correspondents, research stated, “Data augmentation algorithms, such as back translation, have shown to be effective in various deeplearning tasks. Despite their remarkable success, there has been a hurdle to applying data augmentation algorithms to code-related tasks since cod e consists of discrete tokens with uniqueness and certainty.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Shandong Women’s Un iversity, “In this work, we propose FeaMix, a novel yet simple data augmentation approach designed for the feature mix with memory batch based on self-consisten cy learning. FeaMix has a couple of uniqueness. First, it specially selects the samples to be mixed by memory batch to guarantee that the generated features are in the same spatial distribution as the mixed features. Second, it extends the self-consistency learning technique to optimize the language model for code-rela ted tasks. With extensive experiments, we empirically validate that our method o utperforms several baseline models and traditional data augmentation methods on code generation and code translation.”
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    Investigators from Zhejiang University of Technology Zero in on Machine Learning (Accelerating the Screening of Modified Ma2z4 Catalysts for Hydrogen Evolution Reaction By Deep Learning-based Local Geometric Analysis)

    80-81页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news originating from Hangzhou, People’s Republic o f China, by NewsRx correspondents, research stated, “Machine learning (ML) integ rated with density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of single-atom catalysts (SACs) by establ ishing deep structure-activity relationships. The traditional ML models are alwa ys difficult to identify the structural differences among the single-atom system s with different modification methods, leading to the limitation of the potentia l application range.” Financial supporters for this research include National Natural Science Foundati on of China, National Basic Research Program of China.
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    Studies from Gadjah Mada University Reveal New Findings on Machine Learning (Imp act of landslide on geoheritage: Opportunities through integration, geomorpholog ical classification and machine learning)

    81-81页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Yogy akarta, Indonesia, by NewsRx correspondents, research stated, “Landslides are wi dely understood to cause damage to the geological features and the surrounding e nvironment. Our study focuses on the northern region of the Karangsambung-Karang bolong Geopark (KKNG), characterized by diverse lithology and multi-phase tecton ics.” The news correspondents obtained a quote from the research from Gadjah Mada Univ ersity: “This study aims to explore (i) landslide susceptibility assessment, (ii ) geomorphological characteristics and distribution of landslide susceptibility, and (iii) identification of landslide impacts on geosites. We mapped morphogene sis, morphology, materials, and processes to understand the geomorphological con text, identifying three primary landforms: structural, pediments, and fluvial. F or landslide susceptibility mapping, we used the XGBoost algorithm with cross-va lidation and utilized the area under the receiver operating characteristic curve (AUROC) for model validation. The XGBoost model revealed a high susceptibility classification for 10 geosite points. Landslides have negative impacts, such as Olistoliths of coral limestones, Exotic-blocks of chert, and calcareous red clay stone that change landforms and damage outcrops. Nevertheless, some landslides h ave positive impacts on the geosite, such as Exotic-blocks of phyllites, and Exo tic-blocks of pillow lava and radiolarian chert, because landslides can reveal f resher outcrops and rock structures, and the outcrop area becomes larger. Landsl ide mapping successfully identified geosites that are highly vulnerable and have adverse impacts, especially those with certain lithological characteristics. Th is research on viewing disaster as a harmful process has evolved into a more hol istic view of the disaster.”
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    Findings from University of Santiago de Compostela Provide New Insights into Art ificial Intelligence (Are Artificial Intelligence Chatbots a Reliable Source of Information About Contact Lenses?)

    82-82页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Artificial Intelligen ce have been presented. According to news originating from Santiago de Compostel a, Spain, by NewsRx correspondents, research stated, “Artificial Intelligence (A I) chatbots are able to explain complex concepts using plain language. The aim o f this study was to assess the accuracy of three AI chatbots answering common qu estions related to contact lens (CL) wear.” Funders for this research include Xunta de Galicia, Maria Zambrano contract at U SC - European Union-NextGenerationEU, MCIN/AEI, ESF Investing in your future. Our news journalists obtained a quote from the research from the University of S antiago de Compostela, “Three open access AI chatbots were compared: Perplexity, Open Assistant and ChatGPT 3.5. Ten general CL questions were asked to all AI c hatbots on the same day in two different countries, with the questions asked in Spanish from Spain and in English from the U.K. Two independent optometrists wit h experience working in each country assessed the accuracy of the answers provid ed. Also, the AI chatbots’ responses were assessed if their outputs showed any b ias towards (or against) any eye care professional (ECP). The answers obtained b y the same AI chatbots were different in Spain and the U.K. Also, statistically significant differences were found between the AI chatbots for accuracy. In the U.K., ChatGPT 3.5 was the most and Open Assistant least accurate (p <0.01). In Spain, Perplexity and ChatGPT were statistically more accurate than O pen Assistant (p <0.01). All the AI chatbots presented bia s, except ChatGPT 3.5 in Spain. AI chatbots do not always consider local CL legi slation, and their accuracy seems to be dependent on the language used to intera ct with them.”
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    Studies from Universidad Autonoma de Guadalajara Have Provided New Data on Machi ne Learning (Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation)

    83-83页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting from Guadalaja ra, Mexico, by NewsRx journalists, research stated, “The integration of artifici al intelligence (AI) models in the classification of electromyographic (EMG) sig nals represents a significant advancement in the design of control systems for p rostheses.” Funders for this research include National Science And Technology Council. Our news reporters obtained a quote from the research from Universidad Autonoma de Guadalajara: “This study explores the development of a portable system that c lassifies the electrical activity of three shoulder muscles in real time for act uator control, marking a milestone in the autonomy of prosthetic devices. Utiliz ing low-power microcontrollers, the system ensures continuous EMG signal recordi ng, enhancing user mobility. Focusing on a case study-a 42-year-old man with lef t shoulder disarticulation- EMG activity was recorded over two days using a speci fically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devi ces. The first day was dedicated to a training session with 150 repetitions spre ad across 30 trials and three different movements. Based on these data, the seco nd day tested the AI model’s ability to classify EMG signals in new movement exe cutions in real time.”
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    Report Summarizes Artificial Intelligence Study Findings from School of Economic s and Management (Supply Chain Management and Artificial Intelligence Improve th e Microstructure and Economic Evaluation of Composite Materials)

    84-84页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ar tificial Intelligence. According to news originating from Shandong, People’s Rep ublic of China, by NewsRx correspondents, research stated, “In the current study , we aim to evaluate both microstructural characteristics and economic benefits of composite structures from supply chain utilizing AI -based method. In this re gard, the various aspects of microstructure of composite materials along with th e features of supply chain are discussed and quantified.” Financial support for this research came from Key Funding Project of Green Devel opment Research Fund of Higher Education Ministry: Research on Intelligent Energ y Conservation Strategy Based on Big Data (Educational Development). Our news journalists obtained a quote from the research from the School of Econo mics and Management, “In addition, the final economic aspects of the composite m aterials and are also presented. Based on available data, a designed artificial neural network is utilized for prediction of both microstructure and economical feature of the composite material.”
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