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    Studies from University of Milan Reveal New Findings on Machine Learning (Tuning Machine Learning to Address Process Mining Requirements)

    57-57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 out of Milan, Italy, by NewsRx editors, research stated, “Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction.” Our news reporters obtained a quote from the research from University of Milan: “Often, the design of such models is based on some ad-hoc assumptions about the corresponding data distributions, which are not necessarily in accordance with the non-parametric distributions typically observed with process data. Moreover, mainstream machine-learning approaches tend to ignore the challenges posed by concurrency in operational processes. Data encoding is a key element to smooth the mismatch between these assump- tions but its potential is poorly exploited. In this paper, we argue that a deeper understanding of the challenges associated with training machine learning models on process data is essential for establishing a robust integration of process mining and machine learning. Our analysis aims to lay the groundwork for a methodology that aligns machine learning with process mining requirements.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    University of Sheffield Reports Findings in Dementia (Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies)

    58-59页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Neurodegenerative Diseases and Conditions - Dementia is the subject of a report. According to news originating from Sheffield, United Kingdom, by NewsRx correspondents, research stated, “Early diagnosis of dementia diseases, such as Alzheimer’s disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neuropathology features in the brains of dementia patients, it is important to investigate how the selection of features may be impacted and which features are most important for the classification of dementia.” Our news journalists obtained a quote from the research from the University of Sheffield, “We objectively assessed neuropathology features using machine learning techniques for filtering features in two independent ageing cohorts, the Cognitive Function and Aging Studies (CFAS) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). The reliefF and least loss methods were most consistent with their rankings between ADNI and CFAS; however, reliefF was most biassed by feature-feature correlations. Braak stage was consistently the highest ranked feature and its ranking was not correlated with other features, highlighting its unique importance. Using a smaller set of highly ranked features, rather than all features, can achieve a similar or better dementia classification performance in CFAS (60%-70% accuracy with Naive Bayes). This study showed that specific neuropathology features can be prioritised by feature filtering methods, but they are impacted by feature-feature correlations and their results can vary between cohort studies.”
      原文链接:
    • NETL
    • NSTL

    Researchers from School of Electrical Engineering and Informatics Report on Findings in Machine Learning (Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review)

    59-60页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on artificial intelligence have been presented. According to news reporting originat- ing from Bandung, Indonesia, by NewsRx correspondents, research stated, “Predicting university student graduation is a beneficial tool for both students and institutions. With the help of this predictive capacity, students may make well-informed decisions about their academic and career paths, and institutions can proactively identify students who may not graduate and offer tailored support to ensure their success.” Funders for this research include (lembaga Pengelola Dana Pendidikan (Lpdp)/indonesia Endowment Fund For Education Agency) Riset Inovatif Produktif (Rispro) Invitasi; Statistics Indonesia-badan Pusat Statistik. Our news journalists obtained a quote from the research from School of Electrical Engineering and Informatics: “The use of machine learning for predicting university student graduation has drawn more attention in recent years. Large datasets of student academic performance data can be used to train machine learning algorithms to identify patterns that are applicable in predicting future outcomes. In accordance with some studies, this approach predicts student graduation with an accuracy rate as high as 90%. Many systematic literature reviews (SLRs) have been conducted in this field, but there are still limitations, including not discussing the predictive models and algorithms used, a lack of coverage of the machine learning algorithms applied, small database coverage, keyword selection that does not cover all synonyms relevant to the investigation, and less specific data collection transparency. By delving into the limitations of existing SLRs on this topic, this research not only enhances the understanding of machine learning applications in forecasting student graduation but also fills a crucial gap in the literature. The inclusion of weaknesses in current SLRs provides a foundation for justifying the need for this study, emphasizing the necessity of a more nuanced and comprehensive review to advance the field and guide future research efforts in smart learning environments. This research conducts a thorough systematic review of the existing literature on machine learning-based student graduation prediction models from 70 journal articles from 2018 through 2023 that are pertinent. This review includes the various machine learning algorithms that have been implemented, the various academic performance data that was obtained from students, and the effectiveness of the models that have been developed. It also discusses the difficulties and potential advantages of utilizing machine learning to predict student graduation. The review indicates that the most common approach employed is the prediction of students’ academic performance, which relies on data obtained from the Learning Management System and Student Information System. The primary data utilized for prediction purposes consists Student retention and time of academic and behavioral information.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Artificial Intelligence Findings from University of Valencia Reported (Hey Google, I Trust You! the Consequences of Brand Anthropomorphism In Voice-based Artificial Intelligence Contexts)

    60-61页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Artificial Intelligence have been published. According to news originating from Valencia, Spain, by NewsRx correspondents, research stated, “Users’ increasing adoption of voice assistant services is fostering the growth of a novel strand of marketing research on the branding implications of brand anthropomorphism (BA). However, the branding outcomes of brand anthropomorphization in this research area remain underinvestigated.” Financial support for this research came from Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital - Generalitat Valenciana , Spain. Our news journalists obtained a quote from the research from the University of Valencia, “Accordingly, in the name-brand voice assistant (NBVA) interaction field, this study tests a model of the consequences of brand anthropomorphism, outlining the relationships among brand anthropomorphism, brand trust, and multidimensional consumer-brand engagement (CBE), i.e., the relevant cognitive, affective, and behavioral dimensions, as well as the moderating role of perceived privacy risk. A survey of young adults shows that brand anthropomorphism positively affects brand trust as well as the affective and behavioral dimensions of CBE. Furthermore, perceived privacy risk positively moderates the relationship between brand anthro- pomorphism and brand trust. Specifically, the influence of brand anthropomorphism on brand trust is strengthened at higher levels of perceived privacy risk.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Chinese Academy of Sciences Reports Findings in Machine Learning (A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning)

    61-62页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news report- ing originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of pro- teins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connec- tions between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored.” Funders for this research include Fundamental Research Funds for the Central Universities, National Natural Science Foundation of China. The news reporters obtained a quote from the research from the University of Chinese Academy of Sciences, “In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (F ) is identified as the most crucial feature. While, in the further three-class LLPS- Fibrils-Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature-based LLPS-Fibrils-Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Data on Machine Learning Reported by Researchers at University of Stellenbosch (Diagnosis Prediction Using Knowledge Graphs)

    62-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting originating from Stellenbosch, South Africa, by NewsRx correspondents, research stated, “Con- sultations between doctors and patients form the basis of the interaction between both parties, and lay the groundwork for administering appropriate treatment. Advances in machine learning, information, and communication technologies have enabled healthcare practitioners to enhance the manner in which data are captured and analysed during these information-rich meetings.” Our news editors obtained a quote from the research from the University of Stellenbosch, “The true potential of clinical data can only be realised if clinical data sources are synthesised in an appropriate data-representation and modelling approach. One such approach is the so-called knowledge graph (KG). The aim in this paper is to model consultation-related data in a KG and thereafter employ graph machine- learning techniques to identify missing links between entities in the graph through link prediction, thereby providing additional decision support to doctors.” According to the news editors, the research concluded: “A case study data set comprising a list of patients, their respective conditions, and their medications forms the basis of the algorithmic analysis that is carried out.”
      原文链接:
    • NETL
    • NSTL
    • Southern African Inst Industrial Engineering

    Study Data from Southern University of Science and Technology (SUSTech) Update Knowledge of Robotics (A Contact Parameter Estimation Method for Multi-modal Robot Locomotion On Deformable Granular Terrains)

    63-64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 Robotics is now available. According to news reporting originating in Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “In this paper, we consider the problem of contact parameters (slippage and sinkage) estimation for multi-modal robot locomotion on granular terrains. To describe the contact events in the same framework for robots operated at different modes (e.g., wheel, leg), we propose a unified description of contact parameters for multi-modal robots.” Funders for this research include Science, Technology and Innovation Commission of Shenzhen Munic- ipality, National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the Southern University of Science and Technology (SUSTech), “We also provide a parameter estimation method for multi-modal robots based on CNN and DWT (discrete wavelet transformation) techniques and verify its effectiveness over different types of granular terrains. Besides motion modes, this paper also considers the influence of slope angles and the robot’s handing angles over contact parameters. Through comparison and analysis of the prediction results, our method can not only effectively predict the contact parameters of multi-modal robot locomotion on a granular medium (better than $96\%$ accuracy) but also achieves the same or better performance when compared to other (direct) contact measurement methods designed for individual motion modes, that is, single-modal robots such as quadruped robots and mars rovers.”
      原文链接:
    • NETL
    • NSTL
    • Cambridge Univ Press

    Findings from Fuzhou University Update Knowledge of Artificial Intelligence (Memory-processing-display Integrated Hardware With All-in-one Structure for Intelligent Image Processing)

    64-65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 in Fuzhou, People’s Republic of China, by NewsRx journalists, research stated, “Empowering displays with intelligent functions enables their application in intelligent image processing and intelligent interactive displays, which is the combination of future display and artificial intelligence technologies. However, existing display technologies face significant processing and transmission burdens due to their conventional hardware separation architecture, which separates memory from the processor and display module from the processing module.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China. The news reporters obtained a quote from the research from Fuzhou University, “To address these challenges, a highly integrated memory- processing-display intelligent light-emitting hardware (MPDIH) capable of information generation, memory, processing, and visualization is proposed. The MPDIH, based on an organic light-sensitive layer and activated by ultraviolet light irradiation, exhibits unique photoinhibi- tion behavior attributed to the reverse light-induced electric field formed by the directional arrangement of photo-generated excitons. This behavior enables dynamic regulation and autonomous learning during the device training process. Leveraging this phenomenon, intelligent image processing is successfully demon- strated, achieving a significant contrast improvement with a maximum enhancement of 261% compared to unprocessed raw signals. Furthermore, the memory-processing-display intelligent image processing scheme enables fashion MNIST image recognition using an artificial neural network, and achieves noticeably higher recognition accuracy (>89%) compared to the original fuzzy images (<59%).”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Researcher from Tamil Nadu Provides Details of New Studies and Findings in the Area of Pattern Recognition and Artificial Intelligence (Pelican Whale Optimization Enabled Deep Learning Framework for Video Steganography Using Arnold ...)

    65-66页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on pattern recognition and artificial intelligence is the subject of a new report. According to news reporting from Tamil Nadu, India, by NewsRx journalists, research stated, “Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of information sources.” Our news journalists obtained a quote from the research from Department of Computer Science and En- gineering: “Video steganography is superior to image steganography since the videos can hide a substantial quantity of secret messages more than the image. Hence, this research introduced the video stereography technique, Arnold Transform with SqueezeNet-based Pelican Whale Optimization Algorithm (AT[Formula: see text]SqueezeNet_PWOA), for concealing the secret image on the video. To hide the secret image on the video, the proposed method follows three steps: key frame and feature extraction, pixel prediction and embedding. The extraction of the key frame process is carried out by the Structural Similarity Index Measure (SSIM), and then the neighborhood features and convolutional neural network (CNN) features are extracted from the frame to improve the robustness of the embedding process. Moreover, the pixel prediction is completed by the SqueezeNet model, wherein the learning factors are tuned by the PWOA. In addition, the embedding process is completed by applying the Arnold transform on the predicted pixel, and the transformed regions are combined with the secret image using the embedding function. Likewise, the extraction process extracts the secret image from the embedded video by substituting the predicted pixel and Arnold transform on the embedded video. The proposed method is used to hide chunks of secret data in the form of video sequences and it improves the performance.”
      原文链接:
    • NETL
    • NSTL
    • World Scientific

    Investigators from University of Aveiro Release New Data on Machine Learning (A Combined Framework of Biplots and Machine Learning for Real-world Driving Volatility and Emissions Data Interpretation)

    66-67页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting out of Aveiro, Portugal, by NewsRx editors, research stated, “Advanced visualization techniques can be useful for a better understanding of driving behavior and vehicle emissions in real-time. This study used classic and sparse HJ-biplots to examine the relationship between driving behavior, vehicle engine, exhaust emissions, and route type variables.” Financial supporters for this research include Fundacao para a Ciencia e a Tecnologia (FCT), Centro Por- tugal Regional Operational Program (Centro2020) , under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund, Project INTERREG EUROPE PriMaaS, Fundacao para a Ciencia e a Tecnologia (FCT), Fundacao para a Ciencia e a Tecnologia (FCT). Our news journalists obtained a quote from the research from the University of Aveiro, “Different Machine Learning classifiers were applied. Second-by-second vehicle dynamic, engine, and emissions data were collected from three lightduty vehicles (hybrid, diesel, and gasoline) and along three different routes (urban, rural, and highway). The dataset included a sample of 12,150 s of speed, acceleration, vehicular jerk, engine speed, engine load, fuel flow rate, vehicular specific power mode, carbon dioxide and nitrogen oxides emissions. The proposed methodology not only enables the distinction of driving styles, road types, and emissions profiles but also allows for revealing the correlation of variables in a single plot. The Random Forest algorithm showed to present the highest accuracy.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier