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 artificial intelligence. According to news reporting originating from the Univer sity of Economics by NewsRx correspondents, research stated, "Aim/purpose-This pilot study explores the main obstacles hindering the effective implementation of Artificial Intelligence (AI) in small and medium-sized companies (SMEs)." Our news editors obtained a quote from the research from University of Economics : "By thoroughly understanding these barriers, organizations can develop customi zed strategies and interventions to overcome them, facilitating smoother and mor e successful AI adoption. The paper's primary goal is to help organizations unde rstand the barriers to AI adoption to develop tailored strategies and interventi ons to overcome these challenges, leading to a more efficient and successful int egration of AI."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on robotics. Acc ording to news originating from Buraydah, Saudi Arabia, by NewsRx editors, the r esearch stated, "This research paper proposes a hybrid fuzzy logic controller fo r achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm." The news reporters obtained a quote from the research from Qassim University: "T he proposed controller employs kinematic modelling to determine the mobile robot 's path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controll er, while the FLC is responsible for obstacle avoidance. The effectiveness of th e proposed controller has been analyzed, and a comparative study has been carrie d out with optimization techniques like particle swarm optimization (PSO) and cu ckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) a nd the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO."
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 availab le. According to news reporting out of Huainan, People's Republic of China, by N ewsRx editors, research stated, "PurposeThe purpose of this study is to reduce t he residual stress in welded workpieces, optimize the vibratory stress relief tr eatment process through the use of a vibration generator and enhance the durabil ity and longevity of the workpiece by developing a vibratory stress relief robot that incorporates a multi-manipulator multi-manipulator combination work is designed so that each manipulator is depl oyed according to the requirements of vibration stress relief work. Each manipul ator works independently and coordinates with others to achieve multi-dimensiona l vibratory stress relief of the workpiece." Financial supporters for this research include Anhui Province Graduate Education Quality Project, Anhui Province Key Research and Development Plan Project.
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 from Milano, Italy,by NewsRx journalists, research stated, "We study equilibrium approximation in extensive-form adversarial team games, in which two teams of rational players c ompete in a zero-sum interaction." Our news correspondents obtained a quote from the research from Polytechnic Univ ersity Milan: "The suitable solution concept in these settings is the Team-Maxmi n Equilibrium with Correlation (TMECor), which naturally arises when the team pl ayers play ex-ante correlated strategies. While computing such an equilibrium is APX-hard, recent techniques show that scalability beyond toy instances is possi ble. However, even compact representations of the team's strategy space, such as that exploiting Directed Acyclic Graphs (DAGs), have exponential size prohibiti ng solving large instances. In the present paper, we show that Monte Carlo sampl ing for regret minimization in adversarial team games can provide an important a dvancement. In particular, we design a DAG Monte Carlo Counterfactual Regret Min imization algorithm that performs outcome sampling with O ( d ) time complexity per iteration, where d is the depth of the DAG, and with a convergence rate boun d of O ( b kd ),where b is the branching factor and k is the maximum number of private states in each public state of the team. We empirically evaluate our al gorithms with a standard testbed of games, showing their performance when approx imating equilibria."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news originating from Seoul, South Kore a, by NewsRx correspondents, research stated, "IntroductionCurrently, using mach ine learning methods for precise analysis and improvement of swimming techniques holds significant research value and application prospects. The existing machin e learning methods have improved the accuracy of action recognition to some exte nt." The news correspondents obtained a quote from the research from Sangmyung Univer sity: "However, they still face several challenges such as insufficient data fea ture extraction, limited model generalization ability, and poor real-time perfor mance. MethodsTo address these issues, this paper proposes an innovative approac h called Swimtrans Net: A multimodal robotic system for swimming action recognit ion driven via Swin-Transformer. By leveraging the powerful visual data feature extraction capabilities of Swin- Transformer, Swimtrans Net effectively extracts swimming image information. Additionally, to meet the requirements of multimodal tasks, we integrate the CLIP model into the system. Swin-Transformer serves as the image encoder for CLIP, and through fine-tuning the CLIP model, it becomes c apable of understanding and interpreting swimming action data, learning relevant features and patterns associated with swimming. Finally, we introduce transfer learning for pre-training to reduce training time and lower computational resour ces, thereby providing real-time feedback to swimmers."
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-Carcinomas is the subject of a report. According to news reporting out of Quzhou, People's Republic of China, by NewsRx editors, research stated, "Nuclear grading of clear cell renal cell carcinoma (ccRCC) is crucial for its diagnosis and treatment. T o develop and validate a machine learning model for preoperative assessment of c cRCC nuclear grading using CT radiomics." Our news journalists obtained a quote from the research from the Quzhou Affiliat ed Hospital of Wenzhou Medical University, "This retrospective study analyzed 14 6 ccRCC patients who underwent surgery between June 2016 and January 2022 at two hospitals (the Quzhou Affiliated Hospital of Wenzhou Medical University with 11 7 cases and the Affiliated Cancer Hospital of University of Chinese Academy of S ciences with 29 cases). Radiomic features were extracted from preoperative abdom inal CT images. Features reduction and selection were carried out using intracla ss correlation efficient (ICCs), Spearman rank correlation coefficientsand and t he Least Absolute Shrinkage and Selection Operator (LASSO) regression method. Ra diomics and clinical models were developed utilizing Support Vector Machine (SVM ), Extremely Randomized Trees (Extra Trees), Light Gradient Boosting Machine (Li ghtGBM), Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. Subsequent ly, the radiomics nomogramwas developed incorporating independent clinical predi ctors and Rad_signature. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity, with decisio n curve analysis (DCA) assessing its clinical utility. We extracted 1834 radiomi c features from each CT sequence, with 1320 features passing through the ICCs sc reening process. 480 radiomics features were screened by Spearson correlation co efficient. Then, 15 radiomic features with non-zero coefficient values were dete rmined by Lasso dimensionality reduction technique. The five machine learning me thods effectively distinguished nuclear grades. The radiomics nomogram outperfor med clinical radiological models and radiomics feature models in predictive perf ormance, with an AUC of 0.936 (95% CI 0.885-0.986) for the trainin g set and 0.896 (95% CI 0.716-1.000) for the external verification set. DCA indicated potential clinical applicability of the nomogram. The radiom ics nomogram, developed by integrating clinically independent risk factors and a nd Rad_signature, demonstrated robust performance in preoperative c cRCC grading."
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 in telligence have been published. According to news originating from Beijing, Peop le's Republic of China, by NewsRx correspondents, research stated, "Amidst the r apid advancement of artificial intelligence and information technology, the emer gence of big data and machine learning provides a new research paradigm for mine ral exploration." Funders for this research include National Key R&D Program of China ; National Natural Science Foundation of China; Fundamental Research Funds For T he Central Universities. Our news reporters obtained a quote from the research from China University of G eosciences: "Focusing on the Tethyan metallogenic domain, this paper conducted a series of research works based on machine learning methods to explore the criti cal geochemical element signals that affect the metallogenic potential of porphy ry deposits and reveal the metallogenic regularity. Binary classifiers based on random forest, XGBoost, and deep neural network are established to distinguish z ircon fertility, and these machine learning methods achieve higher accuracy, exc eeding 90%, compared with the traditional geochemical methods. Base d on the random forest and SHapley Additive exPlanations (SHAP) algorithms, key chemical element characteristics conducive to magmatic mineralization are reveal ed."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented. According to news reporting originating from Guangdong, People's Republ ic of China, by NewsRx correspondents, research stated, "Magnetorheological elas tomers are active materials which can be actuated by the applied magnetic field. Hard magnetic soft (HMS) materials, a type of magnetorheological elastomers, sh ow great potential in the fields of biomedical engineering and soft robotics, du e to their short response time, remote operation, and shape programmability." Funders for this research include National Natural Science Foundation of China ( NSFC), Science, Technology and Innovation Commission of Shenzhen Municipality.
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 have been published. According to news reporting out of Campinas, Brazil, by NewsRx editors, research stated, "Ultrasonic nondestructive techniques can be u seful tools in the microstructural classification of metallic alloys. Among the most common techniques for characterizing materials, backscattered signal analys is stands out because it does not require a back surface echo." Financial supporters for this research include Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Conselho Nacional de Desenvolvimento Cientif ico e Tecnologico (CNPQ), Fundacao de Amparo a Pesquisa do Estado de Minas Gerai s (FAPEMIG).
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. According to news reporting originating in Wuhan, People's Repub lic of China, by NewsRx journalists, research stated, "Simulation of watershed s treamflow is essential for the prevention and control of flood and drought disas ters. To improve streamflow simulation, a coupled SWAT-LSTM model was constructe d by combining a conceptual processbased hydrological model-Soil and Water Asse ssment Tool (SWAT)-with a machine learning model-Long Short-Term Memory (LSTM)." Financial supporters for this research include National Key R&D Pro gram of China, Hubei Provincial Key Laboratory of Construction and Management in Hydropower Engineering, Three Gorges University, China, Science and Technology Plan Projects of Tibet Autonomous Region.
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