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    Research Data from University of Chinese Academy of Sciences Update Understandin g of Machine Learning (Machine Learning- Based Research for Predicting Shale Gas Well Production)

    67-68页
    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 Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “The estimated ul timate recovery (EUR) of a single well must be predicted to achieve scale-effect ive shale gas extraction.”Funders for this research include Study on Technical Policy For Beneficial Devel opment of Deep Shale Gas in Chongqing Shale Gas Company.
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    Investigators from Beihang University Report New Data on Field Robotics (Energy- consumption Model for Rotary-wing Drones)

    68-69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 - Field Robotic s are presented in a new report. According to news reporting originating from Be ijing, People’s Republic of China, by NewsRx correspondents, research stated, “W ith technological advancement, the use of drones in delivery systems has become increasingly feasible. Many companies have developed rotary-wing drone (RWD) tec hnologies for parcel delivery.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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    Zhejiang University of Technology Reports Findings in Tissue Engineering [Hybrid machine learning model based predictions for properties of poly(2-hydroxy ethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacteri al ...]

    69-70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 Biomedical Engineering - Tissue Engineering is the subject of a report. According to news reporting or iginating from Hangzhou, People’s Republic of China, by NewsRx correspondents, r esearch stated, “Supermacroporous composite cryogels with enhanced adjustable fu nctionality have received extensive interest in bioseparation, tissue engineerin g, and drug delivery. However, the variations in their components significantly impactfinal properties.” Our news editors obtained a quote from the research from the Zhejiang University of Technology, “This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-p oly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-P VA-BC) based on their compositions. By considering the ratios of HEMA (1.0-22.0 wt%), PVA (0.2-4.0 wt%), poly(ethylene glycol) diacryl ate (1.0-4.5 wt%), BC (0.1-1.5 wt%), and water (68.0-9 6.0 wt%) as investigational variables, overlay sampling uniform des ign (OSUD) was employed to construct a high-quality dataset for model developmen t. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate th ree-class classification of preparation conditions. Among the four models, the G BRT model exhibited the best predictive performance of the basic properties, wit h the mean absolute percentage error of 16.04 %, 0.85 % , and 2.44 % for permeability, effective porosity, and height of t heoretical plate (1.0 cm/min), respectively. Characterization results of the rep resentative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 x 10 m, and a range of height of theoret ical plate between 0.40-0.49 cm at flow velocities of 0.5-3.0 cm/min. These indi cate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropore s, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2-3.5 wt% ) and BC (0.1-1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties.”
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    Investigators at Xi’an University of Architecture and Technology Report Findings in Machine Learning (Enhancing Typical Meteorological Year Generation for Diver se Energy Systems: a Hybrid Sandia-machine Learning Approach)

    70-71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 Shaanxi, People’s Republic of China, by NewsRx correspondents, research stated, “Accurate performance asses sment of energy systems heavily relies on Typical Meteorological Year (TMY) data . The Sandia method, commonly used for TMY generation, is limited by default wei ghting criteria for meteorological parameters, restricting its suitability for d iverse energy system analyses.” Funders for this research include National Natural Science Foundation of China ( NSFC), National Key R &D Program of China.
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    Studies in the Area of Artificial Intelligence Reported from Indian Institute of Information Technology (Efficient Paddy Grain Quality Assessment Approach Utili zing Affordable Sensors)

    71-72页
    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 reporting originating from Allahab ad, India, by NewsRx correspondents, research stated, “Paddy (Oryza sativa) is o ne of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much eff ort and expertise.” The news correspondents obtained a quote from the research from Indian Institute of Information Technology: “The grain quality of the product is heavily affecte d by the weather conditions, irrigation frequency, and many other factors. Howev er, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for ric e grain quality assessment, noting that the key characteristics of paddy and ric e are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliab le ML-based IoT paddy grain quality assessment system utilizing affordable senso rs. It involves a specific data collection procedure followed by image processin g with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios.”
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    Reports Outline Social Engineering Study Results from Gebze Technical University (Using Machine Learning Algorithms To Predict Individuals’ Tendency To Be Victi m of Social Engineering Attacks)

    72-73页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Engineering - Social Engineering is the subject of a report. According to news reporting out of Kocaeli, Turkey, by NewsRx editors, research stated, “In information security context, social eng ineering is defined as malicious activities caused by cybercriminals by means of human interactions. It is mainly a psychological manipulation technique which g ets benefit of human error to reach private information.” Financial support for this research came from Turkiye Bilimsel ve Teknolojik Ara stirma Kurumu - TUBITAK The Scientific and Technological Research Council of Turk iye.
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    New Findings Reported from Northeastern University Describe Advances in Machine Learning (Prioritizing Environmental Attributes to Enhance Residents’ Satisfacti on in Post-Industrial Neighborhoods: An Application of Machine Learning-Augmente d ...)

    73-74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 ar tificial intelligence. According to news originating from Shenyang, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Post-industrial neig hborhoods are valued for their historical and cultural significance but often co ntend with challenges such as physical deterioration, social instability, and cu ltural decay, which diminish residents’ satisfaction.” Funders for this research include National Natural Science Foundation of China. The news journalists obtained a quote from the research from Northeastern Univer sity: “Leveraging urban renewal as a catalyst, it is essential to boost resident s’ satisfaction by enhancing the environmental quality of these areas. This stud y, drawing on data from Shenyang, China, utilizes the combined strengths of grad ient boosting decision trees (GBDTs) and asymmetric impact-performance analysis (AIPA) to systematically identify and prioritize the built-environment attribute s that significantly enhance residents’ satisfaction. Our analysis identifies tw elve key attributes, strategically prioritized based on their asymmetric impacts on satisfaction and current performance levels. Heritage maintenance, property management, activities, and heritage publicity are marked as requiring immediate improvement, with heritage maintenance identified as the most urgent.”
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    Huazhong University of Science and Technology Researcher Provides Details of New Studies and Findings in the Area of Machine Learning (Deep Learning for Code In telligence: Survey, Benchmark and Toolkit)

    74-75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 originating from W uhan, People’s Republic of China, by NewsRx correspondents, research stated, “Co de intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming.” The news correspondents obtained a quote from the research from Huazhong Univers ity of Science and Technology: “Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engi neering, machine learning, data mining, natural language processing, and program ming languages. In this paper, we conduct a comprehensive literature review on d eep learning for code intelligence, from the aspects of code representation lear ning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-sourc e toolkit tailored for the rapid prototyping of deep-learning-based code intelli gence models. In particular, we inspect the existing code intelligence models un der the basis of code representation learning, and provide a comprehensive overv iew to enhance comprehension of the present state of code intelligence.”
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    Wroclaw University of Science and Technology Researcher Details Research in Mach ine Learning (IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data)

    75-76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 Wroclaw, Po land, by NewsRx editors, research stated, “This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room.” Our news reporters obtained a quote from the research from Wroclaw University of Science and Technology: “The analysis was based on several air parameters-tempe rature, relative humidity, CO2 concentration, and TVOC-recorded in five apartmen ts. Multiple input-multiple output prediction models were built. Linear (multipl e linear regression and multilayer perceptron (MLP)) and nonlinear (decision tre es, random forest, k-nearest neighbors, and MLP) methods were investigated. Five -fold cross-validation was applied, where four apartments provided data for mode l training and the remaining one was the source of the test data. The models wer e compared using performance metrics (R2, MAPE, and RMSE). The naive approach wa s used as the benchmark. This study showed that linear MLTs performed best. In t his case, the coefficients of determination were highest: R2 = 0.94 (T), R2 = 0. 94 (RH), R2 = 0.63 (CO2), R2 = 0.84 (TVOC, based on the SGP30 sensor), and R2 = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air pa rameters was not equally effective. Based on the lowest percentage error, best p redictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41% ).”
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    Researchers from Polytechnic University Milan Detail Findings in Machine Learnin g (Radiometric Estimation of Tropospheric Attenuation: a Mixed Physically Based/ machine Learning Approach)

    76-77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 originating from Milan, Italy, by NewsRx correspondents, research stated, “A mixed physically based/machine learning (ML ) approach to measure tropospheric attenuation A in all-weather conditions by me ans of microwave radiometers (MWRs) is proposed. The key idea is to combine the advantages originating from the accurate radiometric A retrievals, provided by t he well-established Cosmic background (CB) approach in clear-sky conditions, wit h the benefits coming from ML techniques.” Financial support for this research came from European Space Agency (ESA) throug h “WRADCharacterization of W-Band Propagation Channel Through Ground-Based Obse rvations (Expro Plus)” under ESTEC.
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