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    Recent Research from Nazarbayev University Highlight Findings in Machine Learnin g (Predicting Disc Cutter Wear Using Two Optimized Machine Learning Techniques)

    75-76页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on Machine Learn ing have been published. According to newsoriginating from Astana, Kazakhstan, by NewsRx correspondents, research stated, “The estimation ofdisc cutter wear ( CW) remains a complex problem in mechanized tunneling using tunnel boring machin es(TBM), despite the development of numerous TBM performance models. This resea rch aimed to estimatethe cutter life index (CLI) as an index to predict the CW by developing predictive models based on twomachine learning algorithms, namely gradient boosting (GB) and random forest (RF), optimized by threeoptimization techniques: particle swarm optimization (PSO), differential evolution (DE), and simulatedannealing (SA).”Funders for this research include Nazarbayev University, Faculty Development Com petitive ResearchGrant program of Nazarbayev University in Kazakhstan.Our news journalists obtained a quote from the research from Nazarbayev Universi ty, “To gain theaim, a dataset consisting of four rock parameters-density (rho) , uniaxial compressive strength, Braziliantensile strength (BTS), and brittlene ss index-with 80 mechanized tunnel cases for each parameter hasbeen utilized by obtaining the sample and then relevant tests on them were conducted in the labo ratory.First, various parameter selection methods, such as mutual information, have been employed to reduce thedimensionality of the problem, and it has been revealed that rho and BTS have been the most influentialparameters to estimate the CLI. Then, by developing six optimized models, including GB-PSO, GB-DE,GB-S A, RF-PSO, RF-DE, and RF-SA, using the two mentioned parameters, their performan ce has beenassessed via three performance evaluation indices of coefficient of determination (r2), root mean squareerror (RMSE), and mean absolute percentage error (MAPE).”
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    University of Bern Reports Findings in Artificial Intelligence (Application of an artificial intelligence for quantitative analysis of endothelial capillary beds in vitro)

    76-77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 to newsreporting originating in Bern, S witzerland, by NewsRx journalists, research stated, “The use of endothelialcell cultures has become fundamental to study angiogenesis. Recent advances in artif icial intelligences(AI) offer opportunities to develop automated assessment met hods in medical research, analyzing largerdatasets.”The news reporters obtained a quote from the research from the University of Ber n, “The aim ofthis study was to compare the application of AI with a manual met hod to morphometrically quantify invitro angiogenesis. METHODS: Co-cultures of human microvascular endothelial cells and fibroblasts wereincubated mimicking e ndothelial capillary-beds. An AI-software was trained for segmentation of endothelial capillaries on anti-CD31-labeled light microscope crops. Number of capilla ries and branches andaverage capillary diameter were measured by the AI and man ually on 115 crops. The crops were analyzedfaster by the AI than manually (3 mi nutes vs 1 hour per crop). Using the AI, systematically more capillaries(mean 4 8/mm2 vs 27/mm2) and branches (mean 23/mm2 vs 11/mm2) were counted than manually .Both methods had a strong linear relationship in counting capillaries and bran ches (r-capillaries = 0.88,r-branches = 0.89). No correlation was found for mea surements of the diameter (r-diameter = 0.15).”
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    Recent Findings from Nankai University Has Provided New Information about Robotics (Simultaneous Source Localization and Formation Via a Distributed Sign Gradie nt-free Algorithm)

    77-78页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - Investigators publish new report on Robotics. Acc ording to news reporting from Tianjin, People’sRepublic of China, by NewsRx jou rnalists, research stated, “This article develops a distributed sign gradientfree algorithm for simultaneous source localization and formation of a multirobot system.”Financial support for this research came from National Natural Science Foundatio n of China (NSFC).The news correspondents obtained a quote from the research from Nankai Universit y, “A distinguishedfeature of the algorithm is that it takes into account robot s’ measure noise as well as ternary communication,which significantly reduces t he communication cost of the overall network. Considering the presenceof noise, the algorithm is designed to be gradient-free.”According to the news reporters, the research concluded: “In addition, an inhere nt connection isestablished between the selection of control parameters and the convergence property of the sign gradientfreealgorithm, as well as signal str ength, noisy intensity, formation radius, and the number of informedrobots.”
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    Data from JiMei University Advance Knowledge in Intelligent Systems (Dual-student Knowledge Distillation for Visual Anomaly Detection)

    78-78页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Researchers detail new data in Machine Learning - Intelligent Systems. According tonews reporting originating from Fu jian, People’s Republic of China, by NewsRx correspondents, researchstated, “An omaly detection poses a significant challenge in the industry and knowledge dist illationconstructed using a frozen teacher network and a trainable student netw ork is the prevailing approachfor detecting suspicious regions. Forward and rev erse distillation are the main ways to achieve anomalydetection.”Financial supporters for this research include Natural Science Foundation of Xia men, National NaturalScience Foundation of China (NSFC), Xiamen, China, US Depa rtment of Education, Fujian Province ofChina, Xiamen Science and Technology.Our news editors obtained a quote from the research from JiMei University, “To d esign an effectivemodel and aggregate detection results, we propose a dual-stud ent knowledge distillation (DSKD) based onforward and reverse distillation. Tak ing advantage of the priority of reverse distillation to obtain high-levelrepre sentation, we combine a skip connection and an attention module to build a rever se distillationstudent network that simultaneously focuses on high-level repres entation and low-level features. DSKDuses a forward distillation network as an auxiliary to allow the student network to preferentially obtain thequery image. For different anomaly score maps obtained by the dual-student network, we use s yntheticnoise enhancement in combination with image segmentation loss to adapti vely learn the weight scores ofindividual maps. Empirical experiments conducted on the MVTec dataset show that the proposed DSKDmethod achieves good performan ce on texture images as well as competitive results on object imagescompared wi th other state-of-the-art methods.”
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    Researcher at Universiti Teknologi PETRONAS Details Research in Machine Learning (Fundamental error in tree-based machine learning model selection for reservoir characterisation)

    79-79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 reportingfrom Seri Iskandar, Malaysia, by NewsRx journalists, research stated, “Over the past two decades, machinelea rning techniques have been extensively used in predicting reservoir properties. While this approachhas significantly contributed to the industry, selecting an appropriate model is still challenging for mostresearchers.”The news journalists obtained a quote from the research from Universiti Teknolog i PETRONAS:“Relying solely on statistical metrics to select the best model for a particular problem may not alwaysbe the most effective approach. This study e ncourages researchers to incorporate data visualization intheir analysis and mo del selection process. To evaluate the suitability of different models in predic tinghorizontal permeability in the Volve field, wireline logs were used to trai n Extra-Trees, Ridge, Bagging, andXGBoost models. The Random Forest feature sel ection technique was applied to select the relevant logsas inputs for the model s. Based on statistical metrics, the Extra-Trees model achieved the highest testaccuracy of 0.996, RMSE of 19.54 mD, and MAE of 3.18 mD, with XGBoost coming in second. However,when the results were visualised, it was discovered that the X GBoost model was more suitable for theproblem being tackled. The XGBoost model was a better predictor within the sandstone interval, whilethe Extra-Trees mode l was more appropriate in non-sandstone intervals. Since this study aims to pred ictpermeability in the reservoir interval, the XGBoost model is the most suitable.”
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    Reports from University of Delhi Add New Study Findings to Research in Machine L earning [Landslide susceptibility analysis in the Bhilangana Basin (India) using GIS-based machine learning methods]

    80-80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 reporting out of Delhi, Indi a, by NewsRx editors, research stated, “Landslides are frequent naturalhazards in mountainous regions, and harshly upset people’s lives and livelihoods.”Our news correspondents obtained a quote from the research from University of De lhi: “In the presentstudy, we have carried out an analysis of seven GIS-based m achine-learning techniques; and asses theirperformance for landslide susceptibi lity mapping (LSM) in the Bhilangana Basin, Garhwal Himalaya. Alandslide invent ory consisting of 423 polygons was prepared using repeated field investigations, and multidatedsatellite images for the periods between 2000 and 2022. The lan dslide dataset was classified intotwo groups: training (70%) and t est dataset (30%), and 12 predictive variables were used for the LSM. The methods used to produce LSM are boosted regression tree (BRT), Fisher dis criminant analysis(FDA), generalized linear model (GLM), multivariate adaptive regression splines (MARS), model-architectanalysis (MDA), random forest (RF) an d support vector machine (SVM). The sensitivity and performanceof these models to predict landslide susceptible areas were carried out using the area under the curve (AUC)method. The RF model (AUC = 0.988) has given the highest precision indicating the best performance.”
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    Chinese Academy of Sciences Reports Findings in Artificial Intelligence (RamanCl uster: A deep clustering-based framework for unsupervised Raman spectral identif ication of pathogenic bacteria)

    80-81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 originating from Shenyang, Peopl e’s Republic of China, by NewsRx correspondents, research stated,“Raman spectro scopy serves as a powerful and reliable tool for the characterization of pathoge nic bacteria. The integration of Raman spectroscopy with artificial intelligence techniques to rapidly identify pathogenicbacteria has become paramount for exp editing disease diagnosis.”Our news journalists obtained a quote from the research from the Chinese Academy of Sciences,“However, the development of prevailing supervised artificial inte lligence algorithms is still constrainedby costly and limited well-annotated Ra man spectroscopy datasets. Furthermore, tackling various highdimensionaland in tricate Raman spectra of pathogenic bacteria in the absence of annotations remai ns aformidable challenge. In this paper, we propose a concise and efficient dee p clustering-based framework(RamanCluster) to achieve accurate and robust unsup ervised Raman spectral identification of pathogenicbacteria without the need fo r any annotated data. RamanCluster is composed of a novel representationlearnin g module and a machine learning-based clustering module, systematically enabling the extraction ofrobust discriminative representations and unsupervised Raman spectral identification of pathogenic bacteria.The extensive experimental resul ts show that RamanCluster has achieved high accuracy on both Bacteria-4and Bact eria-6, with ACC values of 77 % and 74.1 %, NMI value s of 75 % and 73 %, as well as AMIvalues of 74.6 % and 72.6 %, respectively. Furthermore, compared with other state-of -the-art methods,RamanCluster exhibits the superior accuracy on handling variou s complicated pathogenic bacterial Ramanspectroscopy datasets, including situat ions with strong noise and a wide variety of pathogenic bacterialspecies. Addit ionally, RamanCluster also demonstrates commendable robustness in these challeng ingscenarios.”
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    Studies from Zhejiang University in the Area of Machine Learning Reported (A Comparative Evaluation of Clustering Methods and Data Sampling Techniques In the Prediction of Reservoir Landslide Deformation State)

    81-82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 Ma chine Learning. According to news reporting outof Hangzhou, People’s Republic o f China, by NewsRx editors, research stated, “Landslides exhibiting stepwisede formation characteristics are extensively dispersed throughout the Three Gorges Reservoir (TGR) region of China. Predicting the deformation state of landslides in TGR holds paramount significance inlandslide early warning and risk manageme nt.”Financial supporters for this research include Natural Science Foundation of Jia ngsu Province, NationalField Observation and Research Station of Landslides in the TGR Area of the Yangtze River.Our news journalists obtained a quote from the research from Zhejiang University , “Machine learningbasedlandslide deformation state prediction is a combinatio n of clustering and imbalanced classification.This paper compares the efficacy of three prevalent clustering methods, namely K-means, Density-BasedSpatial Clu stering of Applications with Noise (DBSCAN), and Gaussian Mixture Model (GMM), i n theclustering analysis process. Furthermore, the paper evaluates the performa nce of three widely-used datasampling technologies, namely Synthetic Minority O versampling Technique (SMOTE), SMOTE-EditedNearest Neighbors (SMOTE-ENN), and A DAptive SYNthetic Sampling (ADASYN), in the imbalancedclassification process. T he Baijiabao and Bazimen landslides in the TGR region, which exhibit step-wised eformation characteristics, are used as case studies. DBSCAN and GMM exhibit sig nificant advantagesin the clustering process. Meanwhile, the mixture models tha t integrate oversampling technologies andclassification algorithms perform exce ptionally well in imbalanced classification. The aforementionedalgorithms are r ecommended for predicting the deformation states of step-wise landslides in the TGRregion.”
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    Second Affiliated Hospital Zhejiang University School of Medicine Reports Findin gs in Artificial Intelligence (Efficacy of artificial intelligence in reducing miss rates of GI adenomas, polyps, and sessile serrated lesions: a meta-analysis of …)

    82-83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 to newsreporting originating from Hangz hou, People’s Republic of China, by NewsRx correspondents, researchstated, “The aim of this study was to determine if utilization of artificial intelligence (A I) in the course of endoscopic procedures can significantly diminish both the ad enoma miss rate (AMR) and the polyp missrate (PMR) compared with standard endos copy. We performed an extensive search of various databases,encompassing PubMed , Embase, Cochrane Library, Web of Science, and Scopus, until June 2023.”Our news editors obtained a quote from the research from the Second Affiliated H ospital ZhejiangUniversity School of Medicine, “The search terms used were arti ficial intelligence, machine learning, deeplearning, transfer machine learning, computer-assisted diagnosis, convolutional neural networks, gastrointestinal(G I) endoscopy, endoscopic image analysis, polyp, adenoma, and neoplasms. The main study aimwas to explore the impact of AI on the AMR, PMR, and sessile serrated lesion miss rate. A total of 7randomized controlled trials were included in th is meta-analysis. Pooled AMR was markedly lower in theAI group versus the non-A I group (pooled relative risk [RR], .46; 9 5% confidence interval [CI], .36-.59; P<.001). PMR was also reduced in the AI group in contrast with the non-AI control (pooled RR, .43; 95% CI, .27-.69 ; P<.001). The results showed that AI decreased the miss r ate of sessile serrated lesions(pooled RR, .43; 95% CI, .20 to .9 2; P<.05) and diminutive adenomas (pooled RR, .49; 95% CI, .26-.93)during endoscopy, but no significant effect was observed for advanc ed adenomas (pooled RR, .48; 95%CI, .17-1.37; P = .17). The avera ge number of polyps (Hedges’ g = -.486; 95% CI, -.697 to -.274; P= .000) and adenomas (Hedges’ g = -.312; 95% CI, -.551 to -.074; P = .01) detected during the secondprocedure also favored AI. However, AI implem entation did not lead to a prolonged withdrawal time (P> .05). This meta-analysis suggests that AI technology leads to significant reduc tion of miss rates for GIadenomas, polyps, and sessile serrated lesions during endoscopic surveillance.”
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    LMU University Hospital Reports Findings in Prostatectomy [Assessing Stress Induced by Fluid Shifts and Reduced Cerebral Clearance during Robotic-Assisted Laparoscopic Radical Prostatectomy under Trendelenburg Positioning (UroTreND Study)]

    83-84页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Surgery - Prostatectom y is the subject of a report. According to news reporting originating in Munich, Germany, by NewsRx journalists, research stated, “In additionto general anesth esia and mechanical ventilation, robotic-assisted laparoscopic radical prostatec tomy(RALP) necessitates maintaining a capnoperitoneum and placing the patient i n a pronounced downwardtilt (Trendelenburg position). While the effects of the resulting fluid shift on the cardiovascular systemseem to be modest and well to lerated, the effects on the brain and the blood-brain barrier have not beenthor oughly investigated.”The news reporters obtained a quote from the research from LMU University Hospit al, “Previous studiesindicated that select patients showed an increase in the o ptic nerve sheath diameter (ONSD), detectedby ultrasound during RALP, which sug gests an elevation in intracranial pressure. We hypothesize thatthe intraoperat ive fluid shift results in endothelial dysfunction and reduced cerebral clearanc e, potentiallyleading to transient neuronal damage. This prospective, monocentr ic, non-randomized, controlled clinicaltrial will compare RALP to conventional open radical prostatectomy (control group) in a total of 50subjects. The primar y endpoint will be the perioperative concentration of neurofilament light chain (NfL)in blood using single-molecule array (SiMoA) as a measure for neuronal dam age. As secondary endpoints,various other markers for endothelial function, inf lammation, and neuronal damage as well as the ONSDwill be assessed. Perioperati ve stress will be evaluated by questionnaires and stress hormone levels insaliv a samples. Furthermore, the subjects will participate in functional tests to eva luate neurocognitivefunction. Each subject will be followed up until discharge.”
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