Amidst the ever-expanding realm of scientific and mathematical literature, distilling valuable insights from a multitude of articles holds paramount importance. This study introduces a fresh perspective on identifying...
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The existence of concrete defects such as corrosion stains, cracks, efflorescence, exposed bars, and spallation has greatly compromised the safety and convenience of bridge users. These calamities can lead to deaths a...
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In recent years, the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has shown tremendous potential for transformative advancements in healthcare. Traditional diagnostic methods for DR oft...
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Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syn...
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Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.
Open Information Extraction (OIE) is a structured prediction (SP) task in Natural Language Processing (NLP) that aims to extract structured n-ary tuples - usually subject-relation-object triples - from free text. The ...
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With the advent of Industry 4.0(I4.0),predictive maintenance(PdM)methods have been widely adopted by businesses to deal with the condition of their *** the help of I4.0,digital transformation,information techniques,co...
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With the advent of Industry 4.0(I4.0),predictive maintenance(PdM)methods have been widely adopted by businesses to deal with the condition of their *** the help of I4.0,digital transformation,information techniques,computerised control,and communication networks,large amounts of data on operational and process conditions can be collected from multiple pieces of equipment and used to make an automated fault detection and diagnosis,all with the goal of reducing unscheduled maintenance,improving component utilisation,and lengthening the lifespan of the *** this paper,we use smart approaches to create a PdM planning *** five key steps of the created approach are as follows:(1)cleaning the data,(2)normalising the data,(3)selecting the best features,(4)making a decision about the prediction network,and(5)producing a *** the outset,PdM-related data undergo data cleaning and normalisation to get everything in order and within some kind of *** next step is to execute optimal feature selection in order to eliminate unnecessary *** research presents the golden search optimization(GSO)algorithm,a powerful population-based optimization technique for efficient feature *** first phase of GSO is to produce a set of possible solutions or objects at *** objects will then interact with one another using a straightforward mathematical model to find the best feasible *** to the wide range over which the prediction values fall,machine learning and deep learning confront challenges in providing reliable *** is why we recommend a multilayer hybrid convolution neural network(MLH-CNN).While conceptually similar to VGGNet,this approach uses fewer parameters while maintaining or improving classification correctness by adjusting the amount of network modules and *** projected perfect is evaluated on two datasets to show that it can accurately predict the future state of components for upkeep prepara
Over the past two decades, the rise in video streaming has been driven by internet accessibility and the demand for high-quality video. To meet this demand across varying network speeds and devices, transcoding is ess...
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Orthodontic treatment monitoring involves using current images and previous 3D models to estimate the relative position of individual teeth before and after orthodontic *** process differs from image-based object 6D p...
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Orthodontic treatment monitoring involves using current images and previous 3D models to estimate the relative position of individual teeth before and after orthodontic *** process differs from image-based object 6D pose estimation due to the gingiva deformation and varying pose offsets for each tooth during *** by the fact that the poses of molars remain relatively fixed in implicit orthodontics,we design an approach that employs multiview pose evaluation and bidirectional temporal propagation for jaw pose estimation and then employs an iteration-based method for tooth *** handle changes in tooth appearance or location with weak texture across frames,we also introduce an instance propagation module that leverages positional and semantic information to explore instance relations in the temporal *** evaluated the performance of our approach using both the Shining3D tooth pose dataset and the Aoralscan3 tooth registration *** experimental results demonstrate remarkable accuracy improvements compared with existing methods.
Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity co...
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Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.
Plant disease is one prevalent factor that has a significant impact worldwide on food security and production, with plants contributing over 80% of food that is consumed by human, due to the importance of plants for w...
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