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    Shandong University Researcher Furthers Understanding of Artificial Intelligence (Influence of artificial intelligence on modern book design)

    48-49页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New study results on artificial intell igence have been published. According to newsreporting originating from Shandon g University by NewsRx correspondents, research stated, "The studyaddresses the necessity to investigate the influence of artificial intelligence (AI) on book design in a varietyof socio-cultural contexts."The news editors obtained a quote from the research from Shandong University: "T he objective isto conduct a cross-cultural analysis of contemporary AI applicat ions in book design in the USA, Finland,and China. The research employs a cross -cultural, systemic, and structural approach, utilizing theoreticalmethods such as analysis, synthesis, and comparison. The study identifies the current design trends, whichinclude space aestheticization, communication, and AI integration . The study examines the relationship between AI and book design, with a particu lar focus on the role of the creator's subjectivity and thepotential for enhanc ed creative output. The necessity of a comparative approach to the analysis of A Idrivenbook design is underscored. The particulars of AI utilization in Americ an book design are examined,emphasizing the infusion of cultural value, communi cation of environmental concerns, and the creation ofheritage-based works."
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    Findings from Southwest Jiaotong University Reveals New Findings on Machine Lear ning (Tftsvm: Near Color Recognition of Polishing Red Lead Via Svm Based On Thre shold and Feature Transform)

    49-50页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Data detailed on Machine Learning have been presented. According to news reportingout of Chengdu, People's Republic o f China, by NewsRx editors, research stated, "With the extensiveapplication ofm achine vision in themanufacturing industry, target region recognition in complex industrialscenes is becoming a vital research territory. In the automatic poli shing of molds, polishing red lead, asan auxiliary tool for polishing positioni ng, can intuitively determine the areas to be polished."Financial support for this research came from Sichuan Science and Technology Pro gram.Our news journalists obtained a quote from the research from Southwest Jiaotong University, "Its brightcolor information are very suitable for vision-based rec ognition. Due to the interference of the near color inthe polishing environment , the traditional color recognition method has the appearance of over-segmentation. In this paper, we propose a novel near-color recognition method via SVM base d on threshold andfeature transform (TFTSVM) to improve the identification accu racy of polishing red lead. Specifically, thismethod adopts a threshold-based c olor recognition algorithm to extract two kinds of color features of redlead co lor and its near color in HSV color space and skillfully finds it is distinguish able in three dimensions.To reduce the computational complexity, a machine lear ning segmentation model is constructed, whichrealizes dimension reduction by in tegrating the feature transformation method of sample transformationand project ion transformation to achieve the best segmentation effect. Experimental results on selfestablisheddataset demonstrate that the proposed method has an excelle nt identification effect on thered lead area in the field polishing environment and also shows good robustness under the condition thatthere are reflections o n the mold surface. It meets the requirements of mechanical arm polishing and im proves the safety and reliability of automatic polishing. In addition, we also c ompare different machinelearning algorithms and advanced studies to verify the correctness of the algorithm."
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    Researchers from Duy Tan University Describe Findings in Machine Learning (Analy sis of 27 Supervised Machine Learning Models for the Co-gasification Assessment of Peanut Shell and Spent Tea Residue In an Open-core Downdraft Gasifier)

    50-51页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Machine Learning is th e subject of a report. According to newsreporting from Da Nang, Vietnam, by New sRx journalists, research stated, "The producer gas (PG)obtained through thermo chemical processing of the various renewable biomass types contributes to Sustainable Development Goal-7 (SDG-7). Hence, this study examined 27 supervised and m ultipleinput singleoutputML models in terms of performance metrics and accurac y to predict CO, H2, CH4, and CO2compositions, as well as PG's HHV in the co-ga sification of peanut shell (PS) and spent tea residue(STR)."The news correspondents obtained a quote from the research from Duy Tan Universi ty, "Multiple trialexperiments were tested with various equivalence ratios (ERs ) and mixing ratios (MRs) on a 90 m3/h gas-yield open-core, down-draft gasifier with air as the gasification medium. About 18 models werechosen after evaluati ng their performance metrics in estimating relevant parameters. The accuracy ofthose models is assessed based on 12 sample runs with varying ER and MR. The fin dings revealed that16 models from the linear, neural network, support vector ma chine, Gaussian process, tree, and ensemblecategories were reliable ML models. The chosen 16 models' R-2 values for CO, H-2, CH4, CO2, andHHVPG are above 0.93 1, 0.937, 0.962, 0.806, and 0.971, respectively, with the exception of TRE."
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    COMSATS University Islamabad Researchers Add New Data to Research in Machine Lea rning (Active-Darknet: An Iterative Learning Approach for Darknet Traffic Detect ion and Categorization)

    51-52页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators publish new report on ar tificial intelligence. According to news reportingoriginating from Islamabad, P akistan, by NewsRx correspondents, research stated, "Darknet refers to asignifi cant portion of the internet that is hidden and not indexed by traditional searc h engines."Funders for this research include Deanship of Research And Graduate Studies, Kin g Khalid University,Through The Small Group Research Project.The news correspondents obtained a quote from the research from COMSATS Universi ty Islamabad:"It is often associated with illicit activities such as the traffi cking of illicit goods, such as drugs, weapons,and stolen data. To keep our onl ine cyber spaces safe in this era of rapid technological advancement andglobal connectivity, we should analyse and recognise darknet traffic. Beyond cybersecur ity, this attentionto detail includes safeguarding intellectual property, stopp ing illegal activity, and following the law. Inorder to improve accuracy and pr ecision in identifying illicit activities, this study presents a novel approachnamed Active-Darknet that uses an active learning-based machine learning model f or detecting darknettraffic. In order to guarantee high-quality analysis, our m ethodology includes extensive data preprocessing,such as numerically encoding c ategorical labels and improving the representation of minority classes usingdat a balancing. In addition to machine learning models, we also use Deep Neural Net works (DNN),Bidirectional Long Short-Term Memory (BI-LSTM) and Flattened-DNN fo r experimentation."
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    Researchers from University of Birmingham Report on Findings in Robotics (An Ada ptive Framework for Trajectory Following In Changing-contact Robot Manipulation Tasks)

    52-52页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Data detailed on Robotics have been pr esented. According to news reporting originatingin Birmingham, United Kingdom, by NewsRx journalists, research stated, "We describe an adaptive controlframewo rk for changing-contact robot manipulation tasks that require the robot to make and breakcontacts with objects and surfaces. The piecewise continuous interacti on dynamics of such tasks make itdifficult to construct and use a single dynami cs model or control strategy."Funders for this research include Honda Research Institute EU, Engineering & Physical Sciences ResearchCouncil (EPSRC).The news reporters obtained a quote from the research from the University of Bir mingham, "Also, thenonlinear dynamics during contact changes can damage the rob ot or the domain objects. Our frameworkenables the robot to incrementally impro ve its prediction of contact changes in such tasks, efficiently learnmodels for the piecewise continuous interaction dynamics, and to provide smooth and accura te trajectorytracking based on a task-space variable impedance controller."
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    Affiliated Hospital of Inner Mongolia Medical University Reports Findings in Art ificial Intelligence (CT coronary fractional flow reserve based on artificial in telligence using different software: a repeatability study)

    53-53页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Artificial Intelligenc e is the subject of a report. According tonews reporting originating from Inner Mongolia, People's Republic of China, by NewsRx correspondents,research stated , "This study aims to assess the consistency of various CT-FFR software, to dete rmine thereliability of current CT-FFR software, and to measure relevant influe nce factors. The goal is to build asolid foundation of enhanced workflow and te chnical principles that will ultimately improve the accuracyof measurements of coronary blood flow reserve fractions."Our news editors obtained a quote from the research from the Affiliated Hospital of Inner MongoliaMedical University, "This improvement is critical for assessi ng the level of ischemia in patients with coronaryheart disease. 103 participan ts were chosen for a prospective research using coronary computed tomographyang iography (CCTA) assessment. Heart rate, heart rate variability, subjective pictu re quality, objectiveimage quality, vascular shifting length, and other factors were assessed. CT-FFR software including Ksoftware and S software are used for CT-FFR calculations. The consistency of the two software is assessedusing pair ed-sample t-tests and Bland-Altman plots. The error classification effect is use d to constructthe receiver operating characteristic curve. The CT-FFR measureme nts differed significantly between theK and S software, with a statistical sign ificance of P<0.05. In the Bland-Altman plot, 6% of the points(14 out of 216) fell outside the 95% consistency lev el. Single-factor analysis revealed that heart ratevariability, vascular disloc ation offset distance, subjective image quality, and lumen diameter significantl yinfluenced the discrepancies in CT-FFR measurements between two software progr ams (P <0.05). TheROC curve shows the highest AUC for the vessel shifting length, with an optimal cut-off of 0.85 mm.CT-FFR measurements vary among software from different manufacturers, leading to potential misclassification of qualitative diagnostics."
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    New Machine Learning Study Findings Reported from Shanghai Normal University (Re alized Volatility Forecasting for Stocks and Futures Indices With Rolling Ceemda n and Machine Learning Models)

    54-54页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators discuss new findings in Machine Learning. According to news reportingout of Shanghai, People's Republic of China, by NewsRx editors, research stated, "As an essential indexfor measur ing market risk, realized volatility (RV) possesses mixed features and volatilit y aggregation,which makes it difficult for machine learning (ML) models to iden tify its features and trends directly foraccurate prediction. Hence, this study first uses the rolling CEEMDAN (complete ensemble empirical modedecomposition with adaptive noise) method to decompose the original RV sequence of the major s tockmarket indices as well as the bean and the metal futures indices."Financial support for this research came from Shanghai Planning Project of Philo sophy and SocialScience.
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    Researcher from Sam Houston State University Reports Details of New Studies and Findings in the Area of Machine Learning (Identifying Tampered Radio-Frequency T ransmissions in LoRa Networks Using Machine Learning)

    55-55页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on artificial intelligenc e is the subject of a new report. According tonews originating from Huntsville, Texas, by NewsRx correspondents, research stated, "Long-range networks,renowne d for their long-range, low-power communication capabilities, form the backbone of many Internetof Things systems, enabling efficient and reliable data transmi ssion."Financial supporters for this research include King Saud University, Riyadh, Sau di Arabia.Our news correspondents obtained a quote from the research from Sam Houston Stat e University:"However, detecting tampered frequency signals poses a considerabl e challenge due to the vulnerabilityof LoRa devices to radio-frequency interfer ence and signal manipulation, which can undermine both dataintegrity and securi ty. This paper presents an innovative method for identifying tampered radio freq uencytransmissions by employing five sophisticated anomaly detection algorithms -Local Outlier Factor, IsolationForest, Variational Autoencoder, traditional Au toencoder, and Principal Component Analysis within theframework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applyingimage-based tampered frequency techniques with these algorithms, offering a new perspective on securingLoRa transmissions. We generated a dataset of over 26,0 00 images derived from real-world experimentswith both normal and manipulated f requency signals by splitting video recordings of LoRa transmissionsinto frames to thoroughly assess the performance of each algorithm."
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    Findings from Chinese Academy of Sciences Provide New Insights into Nanocomposit es (From Processing To Properties: Enhancing Machine Learning Models With Micros tructural Information In Polymer Nanocomposites)

    56-56页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-Investigators discuss new findings in Nanotechnol ogy - Nanocomposites. According to newsreporting originating from Changchun, Pe ople's Republic of China, by NewsRx correspondents, researchstated, "For polyme rs and their composites, processing conditions and the resultant microstructures arecrucial in determining their properties. Traditional machine learning (ML) methods typically focus onestablishing direct relationships between processing parameters and material properties, often overlookingthe critical intermediate step of how processing influences microstructure, limiting the predictive accuracy."Funders for this research include National Key R&D Program of China , National Natural ScienceFoundation of China (NSFC), Network and Computing Cen ter in Changchun Institute of Applied Chemistry.Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "In thisstudy, we introduce an approach that first establishes a deta iled relationship between processing parametersand the resultant microstructure , and then uses transfer learning and feature fusion to integrate thisrelations hip into the prediction of material properties. Using carbon black-reinforced ru bber composites(CRC) as an example, we compared ML models in predicting mechani cal properties from processingdata. A multi-task deep neural network performed best achieving an R-2 of 0.763 with only processingdata as input. When incorpor ating transfer learning and feature fusion, the R-2 improved to 0.852 and0.878, respectively. Shapley explanation analysis validated our approach, highlighting the importance ofintegrating processing, microstructure, and properties in ML models."
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    National University Health System Reports Findings in Robotics (An Assessment of an Inpatient Robotic Nurse Assistant: A Mixed-Method Study)

    57-57页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Robotics is the subjec t of a report. According to news reportingout of Singapore, Singapore, by NewsR x editors, research stated, "The worldwide nursing shortage hasled to the explo ration of using robotics to support care delivery and reduce nurses' workload. I n thisobservational, mixed-method study, we examined the implementation of a ro botic nurse assistant (RNA)in a hospital ward to support vital signs measuremen ts, medication, and item delivery."Financial support for this research came from National Robotics and Research Pro gramme Office.Our news journalists obtained a quote from the research from National University Health System,"Human-robot interaction was assessed in four domains: usability , social acceptance, user experience,and its societal impact. Patients in a gen eral medicine ward were recruited to participate in a one-timetrial with the RN A and a post-trial 75-question survey. Patients' interactions with the RNA were videorecorded for analysis including patients' behaviours, facial emotions, and visual attention. Focus groupdiscussions with nurses elicited their perception s of working with the RNA, areas for improvement, andscalability. Sixty-seven p atients aged 21-79 participated in the trial. Eight in 10 patients reported positive interactions with the RNA. When the RNA did not perform to expectations, on ly 25% of patientsattributed fault to the RNA. Video analysis sho wed patients at ease interacting with the RNA despitesome technical problems. N urses saw potential for the RNA taking over routine tasks. However, theywere sc eptical of real time savings and were concerned with the RNA's ability to work w ell with olderpatients. Patients and nurses suggested greater interactivity bet ween RNA and patients. Future studiesshould examine potential timesaving and wh ether time saved translated to nurses performing higher valueclinical tasks."
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