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    Guizhou University Researcher Reveals New Findings on Machine Learning (Federated Learning Backdoor Attack Based on Frequency Domain Injection)

    48-49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 reporting from Guiyang, People’s Republic of China, by NewsRx journalists, research stated, “Fed- erated learning (FL) is a distributed machine learning framework that enables scattered participants to collaboratively train machine learning models without revealing information to other participants.” Funders for this research include National Key Research And Development Program of China; National Natural Science Foundation of China; Guizhou Science Contract Plat Talent; Research Project of Guizhou University For Talent Introduction; Cultivation Project of Guizhou University, Pr China; Open Fund of Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Pr China. Our news editors obtained a quote from the research from Guizhou University: “Due to its distributed nature, FL is susceptible to being manipulated by malicious clients. These malicious clients can launch backdoor attacks by contaminating local data or tampering with local model gradients, thereby damaging the global model. However, existing backdoor attacks in distributed scenarios have several vulnerabilities. For example, (1) the triggers in distributed backdoor attacks are mostly visible and easily perceivable by humans; (2) these triggers are mostly applied in the spatial domain, inevitably corrupting the semantic information of the contaminated pixels. To address these issues, this paper introduces a frequency-domain injection-based backdoor attack in FL.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Edge University Reports Findings in Artificial Intelligence (Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence)

    49-50页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the subject of a report. According to news reporting from Izmir, Turkey, by NewsRx journalists, research stated, “In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices.” The news correspondents obtained a quote from the research from Edge University, “This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry.” According to the news reporters, the research concluded: “The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL

    First Affiliated Hospital of Soochow University Reports Findings in Rectal Cancer (Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer)

    50-51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 Oncology - Rectal Cancer is the subject of a report. According to news reporting originating from Suzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Laparoscopic total mesorectal excision (LaTME) is standard surgical methods for rectal cancer, and LaTME operation is a challenging procedure. This study is intended to use machine learning to develop and validate prediction models for surgical difficulty of LaTME in patients with rectal cancer and compare these models’ performance.” Our news editors obtained a quote from the research from the First Affiliated Hospital of Soochow University, “We retrospectively collected the preoperative clinical and MRI pelvimetry parameter of rectal cancer patients who underwent laparoscopic total mesorectal resection from 2017 to 2022. The difficulty of LaTME was defined according to the scoring criteria reported by Escal. Patients were randomly divided into training group (80%) and test group (20%). We selected independent influencing features using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression method. Adopt synthetic minority oversampling technique (SMOTE) to alleviate the class imbalance problem. Six machine learning model were developed: light gradient boosting machine (LGBM); categorical boosting (CatBoost); extreme gradient boost (XGBoost), logistic regression (LR); random forests (RF); multilayer perceptron (MLP). The area under receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity and F1 score were used to evaluate the performance of the model. The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best machine learning model. Further decision curve analysis (DCA) was used to evaluate the clinical manifestations of the model. A total of 626 patients were included. LASSO regression analysis shows that tumor height, prognostic nutrition index (PNI), pelvic inlet, pelvic outlet, sacrococcygeal distance, mesorectal fat area and angle 5 (the angle between the apex of the sacral angle and the lower edge of the pubic bone) are the predictor variables of the machine learning model. In addition, the correlation heatmap shows that there is no significant correlation between these seven variables. When predicting the difficulty of LaTME surgery, the XGBoost model performed best among the six machine learning models (AUROC=0.855). Based on the decision curve analysis (DCA) results, the XGBoost model is also superior, and feature importance analysis shows that tumor height is the most important variable among the seven factors. This study developed an XGBoost model to predict the difficulty of LaTME surgery.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Data from Southeast University Provide New Insights into Machine Learning (Non-contact Vehicle Weight Identification Method Based On Explainable Machine Learning Models and Computer Vision)

    51-52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 Machine Learning have been presented. According to news reporting originating in Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “This paper first explores an alternative non-contact method based on computer vision and explainable machine learning (EML) models to identify and predict vehicle overload cost-effectively. First, 1108 sets of data are extracted from traditional contact measurements, non-contact measurements (Optical Character Recognition and thermal imaging), and literature collection to establish a novel and comprehensive database.” Financial supporters for this research include Transportation Science and Technology Project of Jiangsu Province, Tencent Foundation, Natural Science Foundation of Jiangsu Province, Key Scientific and Tech- nological Projects of Jiangxi Provincial Department of Transportation, State Key Laboratory of Mechanical Behavior. The news reporters obtained a quote from the research from Southeast University, “The missing value imputation and the randomized search are then selected to find the optimal ML model for further analysis. Moreover, two typical theoretical and five ML models are adopted to evaluate the optimal model’s perfor- mance. Finally, the sHapley Additive exPlanations (SHAP) is applied to interpret the influence factors of the optimal ML model. The results indicate that the divided length between the tire and the ground is the most significant input feature, followed by the tire’s inflation pressure, the section height of tire, and the radius.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Steno Diabetes Center Copenhagen Reports Findings in Artificial Intelligence (The use of artificial intelligence to assess diabetic eye disease among the Greenlandic population)

    52-53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 Artificial Intelligence is the subject of a report. According to news reporting originating from Copenhagen, Denmark, by NewsRx correspondents, research stated, “Retina fundus images conducted in Greenland are telemedically assessed for diabetic retinopathy by ophthalmo- logical nurses in Denmark. Applying an AI grading solution, in a Greenlandic setting, could potentially improve the efficiency and cost-effectiveness of DR screening.” Our news editors obtained a quote from the research from Steno Diabetes Center Copenhagen, “We developed an AI model using retina fundus photos, performed on persons registered with diabetes in Greenland and Denmark, using Optos® ultra wide-field scanning laser ophthalmoscope, graded according to ICDR.Using the ResNet50 network we compared the model’s ability to distinguish between different images of ICDR severity levels in a confusion matrix. Comparing images with ICDR level 0 to images of ICDR level 4 resulted in an accuracy of 0.9655, AUC of 0.9905, sensitivity and specificity of 96.6%.Comparing ICDR levels 0,1,2 with ICDR levels 3,4, we achieved a performance with an accuracy of 0.8077, an AUC of 0.8728, a sensitivity of 84.6% and a specificity of 78.8%. For the other comparisons, we achieved a modest performance. We developed an AI model using Greenlandic data, to automatically detect DR on Optos retina fundus images.”
      原文链接:
    • NETL
    • NSTL

    Department of Computer Sciences Reports Findings in Artificial Intelligence (Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications)

    53-54页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Enugu, Nigeria, by NewsRx correspondents, research stated, “The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches.” Our news editors obtained a quote from the research from the Department of Computer Sciences, “The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Findings from Tufts University Provides New Data on Machine Learning (Pixel-based Classification Method for Earthquake-induced Landslide Mapping Using Remotely Sensed Imagery, Geospatial Data and Temporal Change Information)

    54-55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 Machine Learning have been published. According to news reporting out of Medford, Massachusetts, by NewsRx editors, research stated, “A series of earthquakes occurred in Kumamoto, Japan, in April 2016, which caused numerous landslides. In this study, high- resolution pre-event and post-event optical imagery, plus bi-temporal Synthetic Aperture Radar (SAR) data are paired with geospatial data to train a pixel-based machine learning classification algorithm using logistic regression to identify landslides occurred because of the Kumamoto earthquakes.” Financial supporters for this research include United States Geological Survey, National Geospatial Intelligence Agency (NGIA), United States Geological Survey. Our news journalists obtained a quote from the research from Tufts University, “The geospatial data used include a categorical variable (surficial geology), and six continuous variables including elevation, slope, aspect, curvature, annual precipitation, and landslide probability derived by the USGS preferred geospatial model which incorporates ground shaking in the input parameters. Grayscale index change and vegetation index change are also calculated from the optical imagery and used as input variables, in addition to temporal differences in HH (horizontally transmitted and horizontally received polarization) and HV (horizontally transmitted and vertically received polarization) amplitudes of SAR data. A detailed human-drawn landslide occurrence inventory was used as ground-truth for model development and testing. The selection of optimal features was done using a supervised feature ranking method based on the Receiver Operating Characteristic (ROC) curve. To weigh the benefit of combining different types of imagery, temporal change information and geospatial environmental indicators for landslide mapping after earthquakes, five different combinations of features were tested, and the results showed that adding data of selected geospatial parameters (landslide probability, slope, curvature, precipitation, and geology) plus selected change indices (grayscale index change, vegetation index change, and HV amplitude difference of SAR data) to the imagery (post event optical) lead to the highest classification accuracy of 86.5% on class-balanced independent testing data.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    October University for Modern Sciences and Arts Reports Findings in Machine Learning (An integrative approach to medical laboratory equipment risk management)

    55-56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 out of Giza, Egypt, by NewsRx editors, research stated, “Medical Laboratory Equipment (MLE) is one of the most influential means for diagnosing a patient in healthcare facilities. The accuracy and dependability of clinical laboratory testing is essential for making disease diagnosis.” Financial support for this research came from October University for Modern Sciences and Arts. Our news journalists obtained a quote from the research from October University for Modern Sciences and Arts, “A risk-reduction plan for managing MLE is presented in the study. The methodology was initially based on the Failure Mode and Effects Analysis (FMEA) method. Because of the drawbacks of standard FMEA implementation, a Technique for Ordering Preference by Similarity to the Ideal Solution (TOPSIS) was adopted in addition to the Simple Additive Weighting (SAW) method. Each piece of MLE under investigation was given a risk priority number (RPN), which in turn assigned its risk level. The equipment performance can be improved, and maintenance work can be prioritized using the generated RPN values. Moreover, five machine learning classifiers were employed to classify TOPSIS results for appropriate decision-making. The current study was conducted on 15 various hospitals in Egypt, utilizing a 150 MLE set of data from an actual laboratory, considering three different types of MLE. By applying the TOPSIS and SAW methods, new RPN values were obtained to rank the MLE risk. Because of its stability in ranking the MLE risk value compared to the conventional FMEA and SAW methods, the TOPSIS approach has been accepted.”
      原文链接:
    • NETL
    • NSTL

    Findings from Brigham Young University Broaden Understanding of Machine Learning (Integrating Machine Learning and Bayesian Nonparametrics for Flexible Modeling of Point Pattern Data)

    56-57页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news originating from Provo, Utah, by NewsRx correspondents, research stated, “Two common approaches to analyze point pattern (location-only) data are mixture models and log-Gaussian Cox processes. The former provides a flexible model for the intensity surface at the expense of no covariate effect estimates while the latter estimates covariate effects at the expense of computation.” Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from Brigham Young University, “A bridge is built between these two methods that leverages the strengths of both approaches. Namely, Bayesian nonparametrics are first used to flexibly model the intensity surface. The posterior draws of the fitted intensity surface are then transformed into the equivalent representation under the log-Gaussian Cox process approach. Using principles of machine learning, estimates of covariate effects are obtained.” According to the news editors, the research concluded: “The proposed two-step approach results in accurate estimates of parameters, with proper uncertainty quantification, which is illustrated with real and simulated examples.(.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Recent Findings from University of California Berkeley Provides New Insights into Machine Learning (A Voxel-based Machine-learning Framework for Thermo-fluidic Identification of Unknown Objects)

    57-58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 presented in a new report. According to news reporting originating from Berkeley, California, by NewsRx editors, the research stated, “The rapid iden- tification of unknown objects by their thermo-fluid flow field signature is becoming increasingly more important. In this work, a machine-learning framework is developed that efficiently simulates and adapts object geometries in order to match the thermo-flow field signature generated by an unknown object, across a time series of voxel-frames.” Funders for this research include UC Berkeley College of Engineering, USA, Sandia National Labs, USA. Our news editors obtained a quote from the research from the University of California Berkeley, “In order to achieve this, a thermo-fluid model is developed, based on the Navier-Stokes equations and the first law of thermodynamics, using a voxel rendering of the system, which is rapidly solved with a voxel-tailored, temporally-adaptive, iterative solution scheme. This voxel-framework is then combined with a genomic- based machine-learning algorithm to develop a digital-twin (digital-replica) of the system that can run in real-time or faster than the actual physical system.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier