The Telecare Medicine Information System (TMIS) revolutionizes healthcare delivery by integrating medical equipment and sensors, facilitating proactive and cost-effective services. Accessible online, TMIS empowers pat...
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Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user *** address this,our study presents a Personalized Adaptive Multi-Prod...
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Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user *** address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited ***-GRU captures both spatial and sequential dependencies in user-item *** innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant *** approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item *** model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional *** study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.
To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentat...
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To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentation,lane detection,and traffic object ***,in the encoding stage,features are extracted,and Generalized Efficient Layer Aggregation Network(GELAN)is utilized to enhance feature extraction and gradient ***,in the decoding stage,specialized detection heads are designed;the drivable area segmentation head employs DySample to expand feature maps,the lane detection head merges early-stage features and processes the output through the Focal Modulation Network(FMN).Lastly,the Minimum Point Distance IoU(MPDIoU)loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes,facilitating model training *** results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union(mIoU)of 92.2%,lane detection accuracy and intersection over union(IoU)of 75.3%and 26.4%,respectively,and traffic object detection recall and mAP of 89.7%and 78.2%,*** detection performance surpasses that of other single-task or multi-task algorithm models.
With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing *** Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerab...
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With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing *** Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research ***,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this *** algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for *** problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing ***,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its *** KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in *** effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.
Deep learning-based character recognition of Tamil inscriptions plays a significant role in preserving the ancient Tamil language. The complexity of the task lies in the precise classification of the age-old Tamil let...
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Computing-aware routing is the core technology of Computing Power Network. Through independently determining the forwarding target in each router, computingaware routing aims at scheduling the computing task to an opt...
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Blockchain technology provides a technical solution for the challenges faced by e-government, such as low efficiency, excessive energy consumption, and lack of trust mechanisms. It can promote the establishment of a m...
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In modern society,an increasing number of occasions need to effectively verify people's *** is the most ef-fective technology for personal *** research on automated biometrics recognition mainly started in the 196...
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In modern society,an increasing number of occasions need to effectively verify people's *** is the most ef-fective technology for personal *** research on automated biometrics recognition mainly started in the 1960s and *** the following 50 years,the research and application of biometrics have achieved fruitful *** 2014-2015,with the successful applications of some emerging information technologies and tools,such as deep learning,cloud computing,big data,mobile communication,smartphones,location-based services,blockchain,new sensing technology,the Internet of Things and federated learning,biometric technology entered a new development ***,taking 2014-2015 as the time boundary,the development of biometric technology can be divided into two *** addition,according to our knowledge and understanding of biometrics,we fur-ther divide the development of biometric technology into three phases,i.e.,biometrics 1.0,2.0 and *** 1.0 is the primary de-velopment phase,or the traditional development *** 2.0 is an explosive development phase due to the breakthroughs caused by some emerging information *** present,we are in the development phase of biometrics *** 3.0 is the future development phase of *** the biometrics 3.0 phase,biometric technology will be fully mature and can meet the needs of various *** 1.0 is the initial phase of the development of biometric technology,while biometrics 2.0 is the advanced *** this paper,we provide a brief review of biometrics ***,the concept of biometrics 2.0 is defined,and the architecture of biometrics 2.0 is *** particular,the application architecture of biometrics 2.0 in smart cities is *** challenges and perspectives of biometrics 2.0 are also discussed.
The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been *** previous studies,it is common to set ...
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The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been *** previous studies,it is common to set up a one-to-one correspondence between the nodes of a multi-layer network,ignoring the more complex situations in *** the present work,we explore this situation by setting up a partially coupled model of a two-layer network and investigating the impact of asymptomatic infected individuals on *** propose a self-discovery mechanism for asymptomatic infected individuals,taking into account situations such as nucleic acid testing in the community and individuals performing self-antigen testing during the *** these factors together,through the microscopic Markov chain approach(MMCA)and extensive Monte Carlo(MC)numerical simulations,we find that the greater the coupling between the networks,the more information dissemination is *** order to control the epidemics,more asymptomatic infected individuals should be made aware of their *** adoption of nucleic acid testing and individual adoption of antigenic self-testing can help to contain epidemic ***,the epidemic threshold of the proposed model is derived,and then miscellaneous factors affecting the epidemic threshold are also *** results are conducive to devising the prevention and control policies of pandemics.
Accurate detection of gastrointestinal (GI) diseases is critical for effective medical intervention. Existing methods often lack accuracy and efficiency, emphasizing the need for more advanced approaches. The complexi...
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