Brain dynamics big data is of increasing promise for many applications like epilepsy detection and cognitive understanding, with the advancements of consumer technology. However, the deep-source brain measurement is d...
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Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO...
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Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho
Welcome to the twelfth issue of 2023 in the IEEE/CAA Journal of Automatica Sinica(JAS).In the sixth issue of2023,I systematically addressed the latest development of Meta Vehicles,and sorted out some important contrib...
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Welcome to the twelfth issue of 2023 in the IEEE/CAA Journal of Automatica Sinica(JAS).In the sixth issue of2023,I systematically addressed the latest development of Meta Vehicles,and sorted out some important contributions published in the IEEE/CAA JAS focusing on control,estimation,and optimization of automated vehicles with reliability,security,efficiency,and intelligence.
This work focuses on the temporal average of the backward Euler-Maruyama(BEM)method,which is used to approximate the ergodic limit of stochastic ordinary differential equations(SODEs).We give the central limit theorem...
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This work focuses on the temporal average of the backward Euler-Maruyama(BEM)method,which is used to approximate the ergodic limit of stochastic ordinary differential equations(SODEs).We give the central limit theorem(CLT)of the temporal average of the BEM method,which characterizes its asymptotics in *** the deviation order is smaller than the optimal strong order,we directly derive the CLT of the temporal average through that of original equations and the uniform strong order of the BEM *** the case that the deviation order equals to the optimal strong order,the CLT is established via the Poisson equation associated with the generator of original *** experiments are performed to illustrate the theoretical *** main contribution of this work is to generalize the existing CLT of the temporal average of numerical methods to that for SODEs with super-linearly growing drift coefficients.
In the realm of decision-making for defense and security applications,it is paramount to swiftly and accurately identify the intentions of incoming *** identification methods predominantly focus on single-target appli...
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In the realm of decision-making for defense and security applications,it is paramount to swiftly and accurately identify the intentions of incoming *** identification methods predominantly focus on single-target applications and overlook the perturbations introduced by measurement *** this study,we propose a novel concept:the Dynamic Distribution Probability(DDP)image,which is constructed using the estimated state and its covariance *** grayscale pixel value within the image signifies the probability of the presence of the agent within the *** proposed identification scheme integrates the use of Extended Kalman Filter(EKF),Convolutional Neural Network(CNN),Back Propagation(BP)network,and Gated Recurrent Unit(GRU)***,the DDP image is processed through a CNN to distill the formation characteristics,and the estimated swarm state from EKF is inputted into a BP network to deduce the kinematic *** outputs from both networks are summed and subsequently channeled into a GRU network to capture temporal *** numerical simulations and flight experiments are presented to demonstrate the robust anti-noise capability of the proposed scheme compared with conventional methods,as well as its superior training efficiency.
Complex systems can be more accurately described by higher-order interactions among multiple *** excel at depicting these interactions,surpassing the binary limitations of traditional ***,retrieving valuable informati...
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Complex systems can be more accurately described by higher-order interactions among multiple *** excel at depicting these interactions,surpassing the binary limitations of traditional ***,retrieving valuable information from hypergraphs is often challenging due to their intricate *** address this issue,we introduce a new category of structural patterns,hypermotifs,which are defined as statistically significant local structures formed by interconnected *** propose a systematic framework for hypermotif *** framework features the encoding,census,and evaluation of higher-order patterns,effectively overcoming their inherent complexity and *** experimental results demonstrate that hypermotifs can serve as higher-order fingerprints of real-world hypergraphs,helping to identify hypergraph classes based on network *** motifs potentially represent preferential attachments and key modules in real-world hypergraphs,arising from specific mechanisms or *** work validates the efficacy of hypermotifs in exploring hypergraphs,offering a powerful tool for revealing the design principles and underlying dynamics of interacting systems.
作者：
Krishna, Siram ChaitanyaReddy, Painti NagiKirubanantham, P.School of Computing
SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India School of Computing
College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India
Today, the day-to-day generation of such a large amount of content on social media and other websites makes it impossible to maintain each image manually, so the need for automatically identifying sensitive images and...
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The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gra...
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The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity *** work uses the consistency check method to find an accurate depth map for identifying occluded *** prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for *** improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction *** experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and *** observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain *** this gain,we have created our dataset with occlu-sion using the structured lighting *** proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing *** experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.
Speech enhancement is the task of taking a noisy speech input and pro-ducing an enhanced speech *** recent years,the need for speech enhance-ment has been increased due to challenges that occurred in various applicati...
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Speech enhancement is the task of taking a noisy speech input and pro-ducing an enhanced speech *** recent years,the need for speech enhance-ment has been increased due to challenges that occurred in various applications such as hearing aids,Automatic Speech Recognition(ASR),and mobile speech communication *** of the Speech Enhancement research work has been carried out for English,Chinese,and other European *** a few research works involve speech enhancement in Indian regional *** this paper,we propose a two-fold architecture to perform speech enhancement for Tamil speech signal based on convolutional recurrent neural network(CRN)that addresses the speech enhancement in a real-time single channel or track of sound created by the *** thefirst stage mask based long short-term mem-ory(LSTM)is used for noise suppression along with loss function and in the sec-ond stage,Convolutional Encoder-Decoder(CED)is used for speech *** proposed model is evaluated on various speaker and noisy environments like Babble noise,car noise,and white Gaussian *** proposed CRN model improves speech quality by 0.1 points when compared with the LSTM base model and also CRN requires fewer parameters for *** performance of the pro-posed model is outstanding even in low Signal to Noise Ratio(SNR).
Hybrid power systems need frequent voltage regulation to handle fluctuations in load profile and renewable energy output, ensuring effective voltage management. This increased need for regulation extends the operating...
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