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    Findings from Dalian Maritime University Broaden Understanding of Machine Learni ng (A Machine Learning-based Adaptive Heuristic for Vessel Scheduling Problem Un der Uncertainty Via Chanceconstrained Programming)

    134-134页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting out of Dalian, People's Republic of China,by NewsRx editors, research stated, "Efficient vessel scheduling in port is cr itical for enhancing navigational efficiency. However, it faces substantial chal lenges due to unforeseeable events." Funders for this research include National Natural Science Foundation of China ( NSFC), Dalian Science and Technology Innovation Fund. Our news journalists obtained a quote from the research from Dalian Maritime Uni versity, "In this context, this paper addresses the vessel scheduling problem wi th stochastic sailing times in port. The problem is formulated into a chance-con strained programming (CCP) model and then transformed into an equivalent determi nistic programming problem. A novel approach utilizing a machine learning-based adaptive differential evolution algorithm (MLDE) is proposed to address this mod el. In MLDE, a Kmeans clustering method is employed to generate initial populat ion, aiming to enhance the population's quality and diversity while mitigating t he impact of random interference. Throughout the mutation and crossover stages, we introduce a parameter adaption strategy based on Q-learning, which is establi shed as a Markov decision process (MDP) model. The model effectively defines the state, action, and reward functions to guide the population toward selecting th e optimal scaling factor and crossover probability parameters. Numerical experim ents based on different instance sizes are conducted at the Comprehensive port. The obtained results reveal the superior performance of the MLDE algorithm in co mparison to existing metaheuristic algorithms and traditional differential evolu tion (DE) variants."
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    Data from Jouf University Broaden Understanding of Artificial Intelligence (Does the Impact of Artificial Intelligence on Unemployment Among People With Disabil ities Differ by Educational Level? A Dynamic Panel Threshold Approach)

    135-135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 from Sakaka, Saudi Ar abia, by NewsRx journalists, research stated, "Artificial intelligence (AI) tech nologies can significantly influence the employment and career development of pe ople with disabilities, presenting both opportunities and challenges." Funders for this research include King Salman Center For Disability Research. The news reporters obtained a quote from the research from Jouf University: "On the one hand, AI can enhance accessibility, create new job opportunities, and as sist in skill development. On the other hand, it can lead to potential job displ acement and exacerbate barriers if not implemented inclusively. The objective of this research is to explore the implications of different artificial intelligen ce measures (artificial intelligence, machine learning, data science, big data) and unemployment among disabled people with different educational levels (primar y, secondary, tertiary). The first difference generalized method of moments with threshold model confirms the nonlinear linkages between AI and unemployment. Mo re specifically, AI reduces the unemployment rate among people with disability o nly in the upper regime, particularly for those with secondary and tertiary educ ational levels. For unemployed disabled people with primary education, there is little evidence of the role of AI in lowering unemployment in both regimes."
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    Reports from Beijing Key Laboratory Highlight Recent Research in Robotic Systems (A lightweight color and geometry feature extraction and fusion module for end- to-end 6D pose estimation)

    136-136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 ro botic systems. According to news reporting originating from Beijing, People's Re public of China, by NewsRx correspondents, research stated, "Although advancemen ts in red-green-blue-depth (RGB-D)-based six degree-of-freedom (6D) pose estimat ion methods, severe occlusion remains challenging." Financial supporters for this research include National Natural Science Foundati on of China. The news editors obtained a quote from the research from Beijing Key Laboratory: "Addressing this issue, we propose a novel feature fusion module that can effic iently leverage the color and geometry information in RGB-D images. Unlike prior fusion methods, our method employs a two-stage fusion process. Initially, we ex tract color features from RGB images and integrate them into a point cloud. Subs equently, an anisotropic separable set abstraction network-like network is utili zed to process the fused point cloud, extracting both local and global features, which are then combined to generate the final fusion features. Furthermore, we introduce a lightweight color feature extraction network to reduce model complex ity. Extensive experiments conducted on the LineMOD, Occlusion LineMOD, and YCB- Video datasets conclusively demonstrate that our method significantly enhances p rediction accuracy, reduces training time, and exhibits robustness to occlusion. "
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    University of Jaffna Researchers Have Provided New Study Findings on Machine Lea rning (Prediction of moisture content of cementstabilized earth blocks using so il characteristics, cement content, and ultrasonic pulse velocity)

    136-137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 reporting originating from the Universi ty of Jaffna by NewsRx correspondents, research stated, "This article investigat es the importance of moisture content in cement-stabilized earth blocks (CSEBs) and explores methods for their prediction using machine learning." The news journalists obtained a quote from the research from University of Jaffn a: "A key aspect of the research is the development of accurate moisture content prediction models. The study compares the performance of various machine learni ng models, and XGBoost emerges as the most promising model, demonstrating superi or accuracy in predicting moisture content based on factors like soil properties,cement content, and ultrasonic pulse velocity (UPV). The study employs SHAP (S Hapley Additive exPlanations) to understand how these features influence the mod el's predictions. UPV is the most significant factor affecting predicted moistur e content, followed by cement content and soil properties like uniformity coeffi cient. Also, the study explores the possibility of using a reduced set of featur es for moisture content prediction."
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    New Machine Learning Study Results from First Affiliated Hospital of Zhengzhou U niversity Described [Machine learning approaches for predicti ng frailty base on multimorbidities in US adults using NHANES data (1999-2018)]

    137-138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 new report. According to news originating from First Affiliated Hospi tal of Zhengzhou University by NewsRx correspondents, research stated, "The glob al increase in an aging population has led to more common age-related health cha llenges, particularly multimorbidity and frailty, but there is a significant gap . This cross-sectional study utilized data from the National Health and Nutritio n Examination Survey (1999-2018)." Financial supporters for this research include The First Affiliated Hospital of Zhengzhou University. Our news correspondents obtained a quote from the research from First Affiliated Hospital of Zhengzhou University: "The association between age and frailty was assessed using a restricted cubic spline (RCS) model, while weighted adjusted mu ltivariable logistic regression evaluated the effect of diseases to frailty. And in machine learning process, feature selection for the frailty prediction model involved three algorithms. The model's performance was optimized using nested c ross-validation and tested with various algorithms including decision tree, Logi stic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and R egression Trees, and eXtreme Gradient Boosting (XGBoost). We used areas under th e receiver operating characteristic curve (AUC) and area under the precision-rec all curve (AU-PRC) to evaluate six algorithms, select the optimal model, and tes t the discrimination and consistency of the optimal model. The study included 46 ,187 participants, with 6,009 cases of frailty. RCS analysis showed a non-linear association between age and frailty, with a turning point at 49 years. Key impa cting variables identified are Anemia, Arthritis, Diabetes Mellitus, Coronary He art Disease, and Hypertension. In the machine learning process, we selected the optimal data set by feature selection, including 13 variables. Through nested cr oss-validation, a total of 31,900 models were built using 6 algorithms. And the XGBoost model showed the highest performance (AUC = 0.8828 and AU-PRC = 0.624), and clear proficiency in both discrimination and calibration."
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    Studies from University of Belgrade Yield New Information about Robotics (Multip le Attribute Decision-making Model for Artificially Intelligent Last-mile Delive ry Robots Selection In Neutrosophic Square Root Environment)

    138-139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 Robotics have been pr esented. According to news reporting originating in Belgrade, Serbia, by NewsRx journalists, research stated, "We introduce novel methodological techniques for decision-making with multiple attributes utilizing logarithmic square root neutr osophic vague sets. One important thing is that we improved decision-making by a dding logarithmic square root neutrosophic ambiguous weighted operators." The news reporters obtained a quote from the research from the University of Bel grade, "Logarithmic square root, neutrosophic imprecise weighted averaging, geom etric procedures, and expanded versions of these are some of the data processing methodologies that we explore. The use of Hamming distances and Euclidean dista nces in decisionmaking situations is illustrated by real-world instances. To cla rify the basic properties of these sets, the research uses an algebraic framewor k. Numerous domains make use of neural networks, including translation, medical diagnosis, and picture and speech recognition. Developing multipurpose artificia lly intelligent robots with analytical, functional, visual, interactive, and tex tual capabilities relies heavily on the synergy between computer science and mac hine tool technology. This is especially true when it comes to the evolution of artificial intelligence. The operating procedures, expenses, time, and externali zes of an artificially intelligent robot system should be considered while asses sing its quality. Finding the best answer from a list of possibilities is made e asier with the help of expert views and established criteria. By comparing them to other methods, we verify and show that the suggested models work."
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    Reports Outline Robotics Study Findings from Pusan National University (Path Pla nning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorith m)

    139-140页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on robotics have been published . According to news reporting originating from Pusan National University by News Rx correspondents, research stated, "The artificial potential field algorithm ha s been widely applied to mobile robots and robotic arms due to its advantage of enabling simple and efficient path planning in unknown environments." Financial supporters for this research include Ministry of Trade Industry & Energy. The news reporters obtained a quote from the research from Pusan National Univer sity: "However, solving the local minimum problem is an essential task and is st ill being studied. Among current methods, the technique using the virtual hill c oncept is reliable and suitable for real-time path planning because it does not create a new local minimum and provides lower complexity. However, in the previo us study, the shape of the obstacles was not considered in determining the robot 's direction at the moment it is trapped in a local minimum. For this reason, lo nger or blocked paths are sometimes selected."
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    Humanitas University Reports Findings in Thyroid Nodules (Development of machine learning models to predict papillary carcinoma in thyroid nodules: The role of immunological, radiologic, cytologic and radiomic features)

    140-141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 Thyroid Diseases and C onditions-Thyroid Nodules is the subject of a report. According to news report ing from Milan, Italy, by NewsRx journalists, research stated, "Approximately 30 % of thyroid nodules yield an indeterminate diagnosis through con ventional diagnostic strategies. The aim of this study was to develop machine le arning (ML) models capable of identifying papillary thyroid carcinomas using pre operative variables." The news correspondents obtained a quote from the research from Humanitas Univer sity, "Patients with thyroid nodules undergoing thyroid surgery were enrolled in a retrospective monocentric study. Six 2-class supervised ML models were develo ped to predict papillary thyroid carcinoma, by sequentially incorporating clinic al-immunological, ultrasonographic, cytological, and radiomic variables. Out of 186 patients, 92 nodules (49.5 %) were papillary thyroid carcinomas in the histological report. The Area Under the Curve (AUC) ranged from 0.41 to 0.61 using only clinical-immunological variables. All ML models exhibited an inc reased performance when ultrasound variables were included (AUC: 0.95-0.97). The addition of cytological (AUC: 0.86-0.97) and radiomic (AUC: 0.88-0.97) variable s did not further improve ML models' performance. ML algorithms demonstrated low accuracy when trained with clinicalimmunological data."
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    Studies from Marche Polytechnic University Further Understanding of Machine Lear ning (A Cost Modelling Methodology Based On Machine Learning for Engineered-to-o rder Products)

    141-142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 have been published. According to news reporting from Ancona, Italy, by News Rx journalists, research stated, "Recent scientific studies are targeted at appl ying and assessing the effectiveness of Machine Learning (ML) approaches for cos t estimation during the preliminary design phases. To train ML prediction models,comprehensive and structured datasets of historical data are required." The news correspondents obtained a quote from the research from Marche Polytechn ic University, "This solution is inapplicable when such information is unavailab le or sparse due to the lack of structured datasets. For engineered-to-order pro ducts, the number of historical records is often limited and strongly influenced by different purchasing or manufacturing strategies, thus requiring complex nor malisation of such data. This method overcomes the above limitations by presenti ng an ML-based cost modelling methodology for the conceptual design that is appl icable even when historical data are insufficient to train the prediction algori thms. The training dataset is generated through an analytical and automatic soft ware tool for manufacturing cost estimation. Such a tool, starting from a 3D mod el of a product, can quickly and autonomously assess the related cost in differe nt scenarios. An extensive and structured training dataset can be easily generat ed. The proposed methodology was based on CRISP-DM (Cross Industry Standard Proc ess for Data Mining). Cost engineers of an Oil & Gas company used the method to develop parametric cost models for discs and spacers of an axial c ompressor. The solution guarantees lower error (7% vs 9% ) and significant time-saving (minutes instead of hours) than estimations based on other approaches."
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    Findings from Lulea University of Technology Update Knowledge of Machine Learnin g (Identifying Climate-related Failures In Railway Infrastructure Using Machine Learning)

    142-143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 Machine Learning are pre sented in a new report. According to news reporting from Lulea, Sweden, by NewsR x journalists, research stated, "Climate change impacts pose challenges to a dep endable operation of railway infrastructure assets, thus necessitating understan ding and mitigating its effects. This study proposes a machine learning framewor k to distinguish between climatic and non-climatic failures in railway infrastru cture." Funders for this research include Swedish Research Council Formas, Kempe Foundat ion. The news correspondents obtained a quote from the research from the Lulea Univer sity of Technology, "The maintenance data of turnout assets from Sweden's railwa y were collected and integrated with asset design, geographical and meteorologic al parameters. Various machine learning algorithms were employed to classify fai lures across multiple time horizons. The Random Forest model demonstrated a high accuracy of 0.827 and stable F1-scores across all time horizons. The study iden tified minimum-temperature and quantity of snow and rain prior to the event as t he most influential factors. The 24-hour time horizon prior to failure emerged a s the most effective time window for the classification."
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