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    Semmelweis University Reports Findings in Artificial Intelligence (Validation of Artificial Intelligence Application for Dental Caries Diagnosis on Intraoral Bi tewing and Periapical Radiographs)

    39-40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 Intelligenc e is the subject of a report. According to news originating from Budapest, Hunga ry, by NewsRx correspondents, research stated, "This study aimed to assess the r eliability of AI-based Diagnocat system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independen t observers using the AI-based Diagnocat system." Our news journalists obtained a quote from the research from Semmelweis Universi ty, "The presence or absence of carious lesions was recorded during Phase 1. Aft er 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiograp hic data (Phase 3). Subsequently, data reflecting human disagreements were exclu ded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnost ic accuracy of Diagnocat, were calculated. During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were k=0.66-1, k=0. 58-0.7, and k=0.49-0.7. The Fleiss kappa values were k=0.57-0.8. The sensitivity , specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively. The Diagnocat CNN supports the evaluation of intra oral radiographs for caries diagnosis, as determined by consensus between human and AI system observers."
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    Studies from Anhui University of Technology Provide New Data on Intelligent Syst ems (Adaptive Local Neighborhood Information Based Efficient Fuzzy Clustering Ap proach)

    40-41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 Machine Lea rning - Intelligent Systems. According to news reporting originating in Ma'ansha n, People's Republic of China, by NewsRx journalists, research stated, "The purp ose of clustering is to partition data similar with each other into a same group and partition data dissimilar with each other into different groups." Financial supporters for this research include Educational Commission of Anhui P rovince, Anhui Province Collaborative Innovation Project, National Natural Scien ce Foundation of China (NSFC).
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    Studies from Henan University Further Understanding of Intelligent Systems (A Su rvey On Social Network's Anomalous Behavior Detection)

    41-42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 - Intelligent Systems. According to news reporting originating in Luoyang, People's Republic of China, by NewsRx journalists, research stated, "T he onset of Web 3.0 has catalyzed the rapid advancement of social networking, tr ansforming platforms into essential elements deeply embedded within the fabric o f daily life. Researchers have proposed several methods for detecting anomalous behaviors in various scenarios." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of China (NSFC), Henan P rovince Science Fund for Distinguished Young Scholars.
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    Researchers from Xidian University Detail New Studies and Findings in the Area o f Computational Intelligence (Apr-net Tracker: Attention Pyramidal Residual Netw ork for Visual Object Tracking)

    42-43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 Learn ing - Computational Intelligence have been published. According to news reportin g out of Guangzhou, People's Republic of China, by NewsRx editors, research stat ed, "Visual object tracking has attracted much attention thanks to its remarkabl e capability to identify a moving target accurately in real-world video scenario s. Recently, tracking performance has improved significantly." Financial support for this research came from State Scholarship Fund.
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    New Computational Intelligence Findings from Hebei Agricultural University Discu ssed (A Generative Adversarial Networks Model Based Evolutionary Algorithm for M ultimodal Multi-objective Optimization)

    43-44页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning - Compu tational Intelligence have been published. According to news reporting from Baod ing, People's Republic of China, by NewsRx journalists, research stated, "The ke y to solving multimodal multi-objective optimization problems is to achieve good diversity in the decision space. However, the existing algorithms usually adopt the reproduction operation based on random mechanism, which do not make full us e of the distribution features of promising solutions in the population, resulti ng in the defects of the diversity of the obtained Parteo optimal solution sets. " 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 Hebei Agricultur al University, "In order to solve the above problem, this paper proposes a multi modal multi-objective optimization evolutionary algorithm (MMOEA) based on gener ative adversarial networks (GANs). Specifically, we firstly design a classificat ion strategy to distinguish good solutions from poor solutions. The solutions in the population are classified as real samples and fake samples by non-dominated selection sorting based on special crowding distance, and the training data of GANs are obtained. Secondly, a GANs-based offspring generation method is propose d. Through the adversarial training of GANs, the generator can simulate the dist ribution of promising solutions in the population and generate offspring with go od diversity. Thirdly, an environment selection strategy based on GANs is constr ucted. By sorting the classification probability of the solutions output by the discriminator, the population are selected and updated."
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    University of Debrecen Researcher Releases New Study Findings on Robotics (Image -to-Image Translation-Based Deep Learning Application for Object Identification in Industrial Robot Systems)

    44-44页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on robotics have been presented. Ac cording to news reporting out of Debrecen, Hungary, by NewsRx editors, research stated, "Industry 4.0 has become one of the most dominant research areas in indu strial science today. Many industrial machinery units do not have modern standar ds that allow for the use of image analysis techniques in their commissioning." Funders for this research include National Research, Development, And Innovation Fund of Hungary. The news editors obtained a quote from the research from University of Debrecen: "Intelligent material handling, sorting, and object recognition are not possibl e with the machinery we have. We therefore propose a novel deep learning approac h for existing robotic devices that can be applied to future robots without modi fication. In the implementation, 3D CAD models of the PCB relay modules to be re cognized are also designed for the implantation machine. Alternatively, we devel oped and manufactured parts for the assembly of aluminum profiles using FDM 3D p rinting technology, specifically for sorting purposes. We also apply deep learni ng algorithms based on the 3D CAD models to generate a dataset of objects for ca tegorization using CGI rendering. We generate two datasets and apply image-to-im age translation techniques to train deep learning algorithms."
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    Researcher from China University of Geosciences Publishes Findings in Machine Le arning (A machine learning approach to discrimination of igneous rocks and ore d eposits by zircon trace elements)

    45-45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 intelligenc e is the subject of a new report. According to news reporting out of Beijing, Pe ople's Republic of China, by NewsRx editors, research stated, "The mineral zirco n has a robust crystal structure, preserving a wealth of geological information through deep time. Traditionally, trace elements in magmatic and hydrothermal zi rcon have been employed to distinguish between different primary igneous or meta llogenic growth fluids." The news reporters obtained a quote from the research from China University of G eosciences: "However, classical approaches based on mineral geochemistry are not only time consuming but often ambiguous due to apparent compositional overlap f or different growth environments. Here, we report a compilation of 11 004 zircon trace element measurements from 280 published articles, 7173 from crystals in i gneous rocks, and 3831 from ore deposits. Geochemical variables include Hf, Th, U, Y, Ti, Nb, Ta, and the REEs. Igneous rock types include kimberlite, carbonati te, gabbro, basalt, andesite, diorite, granodiorite, dacite, granite, rhyolite, and pegmatite. Ore types include porphyry Cu-Au-Mo, skarn-type polymetallic, int rusion-related Au, skarn-type Fe-Cu, and Nb-Ta deposits. We develop Decision Tre e, XGBoost, and Random Forest algorithms with this zircon geochemical informatio n to predict lithology or deposit type. The F1-score indicates that the Random F orest algorithm has the best predictive performance for the classification of bo th lithology and deposit type."
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    Reports Outline Machine Learning Study Results from University of Trieste (Emplo yee Turnover In Multinational Corporations: a Supervised Machine Learning Approa ch)

    46-47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 is now available. According to news reporting from Trieste, Italy, by NewsRx journalis ts, research stated, "This research explores the potential of supervised machine learning techniques in transforming raw data into strategic knowledge in the co ntext of human resource management. By analyzing a database with over 205 variab les and 2,932 observations related to a telco multinational corporation, this st udy tests the predictive and analytical power of classification decision trees i n detecting the determinants of voluntary employee turnover." Financial support for this research came from Universit degli Studi di Trieste. The news correspondents obtained a quote from the research from the University o f Trieste, "The results show the determinants of groups of employees who may vol untarily leave the company, highlighting the level of analytical depth of the cl assification tree. This study contributes to the field of human resource managem ent by highlighting the strategic value of the classification decision tree in i dentifying the characteristics of groups of employees with a high propensity to voluntarily leave the firm."
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    Wuhan University of Science and Technology Researcher Illuminates Research in Ro botics (Gas source localization using Dueling Deep Q-Network with an olfactory q uadruped robot)

    46-46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 robotics are presented i n a new report. According to news reporting originating from Wuhan, People's Rep ublic of China, by NewsRx correspondents, research stated, "Gas source localizat ion is one of the most common applications of a gas-sensitive mobile robot." The news reporters obtained a quote from the research from Wuhan University of S cience and Technology: "However, most of the existing work focuses on rule-based algorithms for wheeled robots, which are difficult to apply in complex terrain with obstacles. In this article, we propose an olfactory quadruped robot to perf orm the gas source localization task using the Dueling Deep Q-Network (Dueling D QN) algorithm. For training, we designed a set of environments and imported gas dispersion data from computational fluid dynamics (CFD) software to construct a simulator. The olfactory quadruped robot was trained in this simulator using the Dueling DQN algorithm to learn how to find the gas source. The trained neural n etwork was then deployed on the olfactory quadruped robot."
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    Researchers from TH Koln - University of Applied Sciences Describe Findings in M achine Translation (A Competence Matrix for Machine Translation-oriented Data Li teracy Teaching)

    47-48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 Translation. According to news reporting originating from Cologne, Germa ny, by NewsRx correspondents, research stated, "This article presents a matrix o f competence descriptors aimed at machine translation-oriented data literacy tea ching. This competence matrix constitutes the didactics-facing side of the DataL itMT project, which develops learning resources for teaching relevant components of data literacy in their translation-specific form of professional machine tra nslation (MT) literacy to BA and MA students in translation and specialised comm unication." Funders for this research include Stifterverband, Ministry of Culture and Scienc e of North Rhine- Westphalia, Germany.
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