the core of design lies in the acquisition and application of knowledge. knowledge push technology can effectively improve the utilization efficiency of knowledge by designers, thus enabling more efficient task comple...
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To obtain the electromagnetic scattering characteristics of transmission lines, we conducted a multi base tower scaled model experiment in an outdoor open experimental field at the China Shipbuilding Research and Desi...
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the parallel connection of converters facilitates the modularization of the operating system, making the internal structure of the system more flexible and variable. However, due to the low damping and low inertia cha...
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In this study, the IDB2 (acute lymphoblastic leukemia-image database) dataset is used to develop an automated method for distinguishing between leukemia and leukemoid reactions from blood smear images. the approach in...
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the alpine regions are characterized by low temperatures and abundant sunlight, such as Northwest China, most forms of energy storage (ES) are not suitable, like electrochemical ES. this paper proposes a method for op...
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Organizations strive to enhance their operational efficiency, addressing a key challenge faced by companies in the plastics industry: the limited availability of equipment, particularly in relation to molding processe...
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Manual engineering of high-performance implementations typically consumes many resources and requires in-depthknowledge of the hardware. Compilers try to address these problems;however, they are limited by design in ...
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ISBN:
(纸本)9798350322637
Manual engineering of high-performance implementations typically consumes many resources and requires in-depthknowledge of the hardware. Compilers try to address these problems;however, they are limited by design in what they can do. To address this, we present CryptOpt, an automatic optimizer for long stretches of straightline code. Experimental results across eight hardware platforms show that CryptOpt achieves a speed-up factor of up to 2.56 over current off-the-shelf compilers.
the structure of knowledge graph is basically flat and lack of hierarchies to make knowledge graph too general to deal with concrete reasoning problems. there are few work about hierarchical knowledge graph. In this p...
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作者：
Josiah Samuel Raj, J.Anitha, G.Saveetha School of Engineering
Saveetha Institute of Medical and Technical Sciences Saveetha University Centre for Applied Research Department of Electronics and Communication Engineering Tamil Nadu Chennai India
An innovative antenna design that has been specifically engineered for 28 GHz communication systems is presented in this study. this design was developed to fulfill the demand for high-performance, wideband antennas t...
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Improving movie recommendations is vital in personalized content delivery. We propose a novel approach that combines Adaptive K-nearest neighbors (KNN) and Matrix Factorization (MF) to tackle recommendation challenges...
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ISBN:
(纸本)9798400716713
Improving movie recommendations is vital in personalized content delivery. We propose a novel approach that combines Adaptive K-nearest neighbors (KNN) and Matrix Factorization (MF) to tackle recommendation challenges. the Adaptive KNN method adjusts its neighborhood size based on user interactions, addressing data sparsity issues. It incorporates contextual information and user similarities to refine suggestions. Meanwhile, MF uncovers hidden patterns within user-item interactions, enhancing recommendation accuracy by understanding preferences more deeply. Using a MovieLens 25M dataset, we evaluated our approach. Comparative analyses against baseline methods confirmed our method's superiority in accuracy, scalability, and adaptability. Cross-validation and sensitivity analysis affirmed its stability and versatility across diverse user preferences and dataset sizes. Our findings emphasize the synergy between Adaptive KNN and Matrix Factorization, surpassing the limitations of traditional systems. this work contributes to recommendation algorithm advancements, offering a promising framework for movie recommendations.
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