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    University of Liverpool Reports Findings in Machine Learning (Diagnostic utility of clinicodemographic, biochemical and metabolite variables to identify viable pregnancies in a symptomatic cohort during early gestation)

    19-20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 th e subject of a report. According to news reporting originating from Liverpool, U nited Kingdom, by NewsRx correspondents, research stated, "A significant number of pregnancies are lost in the first trimester and 1-2% are ectopi c pregnancies (EPs). Early pregnancy loss in general can cause significant morbi dity with bleeding or infection, while EPs are the leading cause of maternal mor tality in the first trimester."Funders for this research include Wellbeing of Women, SRI/Bayer, Wellcome Trust, Liverpool Women's Hospital NHS Foundation Trust, NIHR, MRC.
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    Studies in the Area of Pattern Recognition and Artificial Intelligence Reported from Taizhou University (Tool Wear Prediction Based on LSTM and Deep Residual Ne twork)

    20-21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 pattern recognition a nd artificial intelligence have been presented. According to news reporting orig inating from Zhejiang, People's Republic of China, by NewsRx correspondents, res earch stated, "To improve the accuracy and efficiency of tool wear predictions, this study proposes a tool wear prediction model called LSTM_ResNet which is based on the long short-term memory (LSTM) network and the Residual Ne twork (ResNet)."The news editors obtained a quote from the research from Taizhou University: "Th e model utilizes LSTM layers for processing, where the first block and loop bloc ks serve as the core modules of the deep residual network. The model employs a s eries of methods including convolution, batch normalization (BN), and Rectified Linear Unit (ReLU) to enhance the model's expression and prediction capabilities . The performance of the LSTM_ResNet model was evaluated using expe rimental data from the PHM2010 datasets and two different depths (64 and 128 lay ers), training both LSTM_ResNet models for 200 epochs. The 64-layer model's root mean square error (RMSE) values are 3.36, 4.35, and 3.59, and the mean absolute error (MAE) values are 2.42, 2.85, and 2.21; using 128 layers, the RMSE values are 3.66, 3.99, and 3.77, and the MAE values are 2.49, 2.73, and 3. 01."
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    Second Affiliated Hospital Reports Findings in Gout (Interpretable machine learn ing framework to predict gout associated with dietary fiber and triglyceride-glu cose index)

    21-22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 Musculoskeletal Diseas es and Conditions - Gout is the subject of a report. According to news reporting originating in Zhejiang, People's Republic of China, by NewsRx journalists, res earch stated, "Gout prediction is essential for the development of individualize d prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict go ut."Financial support for this research came from Wenzhou Basic Scientific Research Project of China.
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    Valahia University Reports Findings in Artificial Intelligence (Comparative stud y on the results of orthodontic diagnostics by using algorithms generated by Art ificial Intelligence and simple algorithms)

    22-23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 su bject of a report. According to news originating from Targoviste, Romania, by Ne wsRx correspondents, research stated, "Artificial intelligence (AI) is computergenerated intelligence, as opposed to the natural intelligence of humans and som e animals. Kaplan and Haenlein define AI as ‘the ability of a system to correctl y interpret external data, to learn from such data and use what it has learned t o achieve specific goals and tasks through a flexible adaptation'."Our news journalists obtained a quote from the research from Valahia University, "The term ‘artificial intelligence' is used colloquially to describe machines t hat mimic the ‘cognitive' functions that people associate with other human minds . One of the areas where technological advances have brought significant changes is orthodontics, especially in terms of diagnosis and orthodontic prediction. o f this study is to conduct a comparative analysis between the results obtained b y using the complete algorithms that define Artificial Intelligence and the simp le algorithms of classical medical software, used in the detection of the positi on and shape of teeth in various orthodontic anomalies. A group of 45 patients w ith maxillary-dento anomalies Angle Class I (DDM with crowding and deviation of the superior inter-incisive line) was studied. Two types of algorithms were used in the study group: modern type I algorithms and simple algorithms used in clas sical software to detect the position of the frontal teeth. Through the symmetri cal points of the face the facial axes were determined, and after the detection of the contour of each tooth the incisional curve was calculated. The median lin e was analyzed against the vertical axis of the face, and the incisional curve t owards the horizontal axis. The study shows that AI algorithms offer an increase d level of tooth position detection, compared to traditional softwares. Complex algorithms, specific to Artificial Intelligence, showed superior detection, and more stability in the analysis."
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    Study Results from Manchester Metropolitan University Update Understanding of Ar tificial Intelligence (Artificial Intelligence in Political Campaigns)

    23-23页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting from Mancheste r, United Kingdom, by NewsRx journalists, research stated, “Modern political cam paigns are in constant flux and are influenced by numerous factors.” The news reporters obtained a quote from the research from Manchester Metropolit an University: “Voters are constantly on the move, making segmentation significa ntly challenging. Simultaneously, literacy and education levels among the electo rate are increasing every day, leading to a more critical attitude towards the p ersuasion process. In addition, changes in technology and the development of inf or- mation and communication systems directly and drastically impact the shaping and management of modern campaigns. Artificial intelligence, machine learning, and deep learning have been influenc- ing the creation of modern political campa igns for a decade.”
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    University of Southern Denmark Reports Findings in Brain-Based Devices (A brain machine interface framework for exploring proactive control of smart environment s)

    24-25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 Brain-Based Devices is the subject of a report. According to news reporting originating from Odense, D enmark, by NewsRx correspondents, research stated, "Brain machine interfaces (BM Is) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous becau se it requires issuing commands sequentially."Financial support for this research came from Horizon 2020 Framework Programme.
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    Researchers at Ohio State University Have Published New Data on Machine Learning (Controlling chaos using edge computing hardware)

    24-24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 artificial intelligen ce have been presented. According to news reporting from Ohio State University b y NewsRx journalists, research stated, "Machine learning provides a data-driven approach for creating a digital twin of a system - a digital model used to predi ct the system behavior."Financial supporters for this research include United States Department of Defen se | U.S. Air Force. Our news journalists obtained a quote from the research from Ohio State Universi ty: "Having an accurate digital twin can drive many applications, such as contro lling autonomous systems. Often, the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedd ed computing hardware that can operate without a cloud-computing connection. Her e, we show that a nonlinear controller based on next-generation reservoir comput ing can tackle a difficult control problem: controlling a chaotic system to an a rbitrary time-dependent state. The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array typically found in embedded devi ces."
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    Research on Machine Learning Described by Researchers at University of Johannesb urg (Machine Learning Approaches for Power System Parameters Prediction: A Syste matic Review)

    25-26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 Johannesburg, South Africa, by NewsRx editors, research stated, "Prediction in the power syste m network is very crucial as expansion is needed in the network."The news editors obtained a quote from the research from University of Johannesb urg: "Several methods have been used to predict the load on a network, from shor t to long time load prediction, to ensure adequate planning for future use. Sinc e the power system network is dynamic, other parameters, such as voltage and fre quency prediction, are necessary for effective planning against contingencies. A lso, most power systems are interconnected networks; using isolated variables to predict any part of the network tends to reduce prediction accuracy. This revie w analyzed different machine learning approaches used for load, frequency, and v oltage prediction in power systems and proposed a machine learning predictive ap proach using network topology behavior as input variables to the model. The anal ysis of the proposed model was tested using a regression model, Decision tree re gressor, and long short-term memory. The analysis results indicate that with net work topology behavior as input to the model, the prediction will be more accura te than when isolated variables of a particular Bus in a network are used for pr ediction."
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    Study Findings from Chongqing Normal University Provide New Insights into Suppor t Vector Machines (Absolute Value Inequality SVM for the PU Learning Problem)

    26-27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 support vector machine s is the subject of a new report. According to news reporting from Chongqing, Pe ople's Republic of China, by NewsRx journalists, research stated, "Positive and unlabeled learning (PU learning) is a significant binary classification task in machine learning; it focuses on training accurate classifiers using positive dat a and unlabeled data."Financial supporters for this research include Chongqing Municipal Government. Our news correspondents obtained a quote from the research from Chongqing Normal University: "Most of the works in this area are based on a two-step strategy: t he first step is to identify reliable negative examples from unlabeled examples, and the second step is to construct the classifiers based on the positive examp les and the identified reliable negative examples using supervised learning meth ods. However, these methods always underutilize the remaining unlabeled data, wh ich limits the performance of PU learning. Furthermore, many methods require the iterative solution of the formulated quadratic programming problems to obtain t he final classifier, resulting in a large computational cost. In this paper, we propose a new method called the absolute value inequality support vector machine , which applies the concept of eccentricity to select reliable negative examples from unlabeled data and then constructs a classifier based on the positive exam ples, the selected negative examples, and the remaining unlabeled data."
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    Recent Findings from Florida A&M University Provides New Insights i nto Artificial Intelligence (Artificial Intelligence for Water Consumption Asses sment: State of the Art Review)

    27-28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 Artificial Intelligence is now available. According to news reporting originating in Tallahassee, Flori da, by NewsRx journalists, research stated, "In recent decades, demand for fresh water resources has increased the risk of severe water stress. With the growing prevalence of artificial intelligence (AI), many researchers have turned to it a s an alternative to linear methods to assess water consumption (WC)."Financial support for this research came from National Institute of Food and Agr iculture. The news reporters obtained a quote from the research from Florida A& M University, "Using the PRISMA (Preferred Reporting Items for Systematic Review s and Meta-Analyses) framework, this study utilized 229 screened publications id entified through database searches and snowball sampling. This study introduces novel aspects of AI's role in water consumption assessment by focusing on innova tion, application sectors, sustainability, and machine learning applications. It also categorizes existing models, such as standalone and hybrid, based on input , output variables, and time horizons. Additionally, it classifies learnable par ameters and performance indexes while discussing AI models' advantages, disadvan tages, and challenges. The study translates this information into a guide for se lecting AI models for WC assessment. As no one-size-fits-all AI model exists, th is study suggests utilizing hybrid AI models as alternatives. These models offer flexibility regarding efficiency, accuracy, interpretability, adaptability, and data requirements. They can address the limitations of individual models, lever age the strengths of different approaches, and provide a better understanding of the relationships between variables."
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