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检索条件"主题词=Filtering algorithms"

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Enhancing E-Commerce Experience Through Intelligent Recommendation System

Enhancing E-Commerce Experience Through Intelligent Recommen...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Nancy Tomar Krish Sahu Vishal Kumar Anubhava Srivastava School of enginnering and technology Sharda University Greator Noida India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

With the rise of smartphones, online shopping has grown quickly. Researchers have proposed applying several methodologies such as rules of association, collaborative filtering, Markov chains, and recurrent neural network techniques to offer optimal products to customers during their shopping experience. In this paper, the authors begin by explaining the general layout of an e-commerce system for recommendations. They also provide an overview of the key features functional modules and system architecture of the e-commerce recommendation system. The paper then discusses the e-commerce system's recommendation mechanism. Additionally, it enhances the system by utilizing convolutional in nature neural networks a kind of advanced AI, to generate better recommendations. Finally, using data from Alibaba, this paper examines and contrasts the performance of three widely used recommendation systems. The outcomes of the experiment show that the suggested recommendation method, which is based on deep learning and uses a convolutional neural network, surpasses the other two algorithms. It is also broadly applicable and has strong practical value. The use of e-commerce is increasing as smart computing and the internet grow. The goods that are offered on e-commerce platforms should be taken considered since they have the power to influence a user's purchasing choice. The aim of this paper is to develop a structure for e-commerce websites that makes product recommendations based on customer needs. This study uses the Slope One algorithm, where the product's domain ontology is used to determine the input rating. Product relationships are represented using domain ontology.

关键词： Machine learning algorithms Collaborative filtering Computational modeling Smart contracts Ontologies filtering algorithms Information filters Blockchains Electronic commerce Recommender systems

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Fall Detection by 1D-CNN Based on Public Dataset Using a Wrist-Worn Smartwatch

Fall Detection by 1D-CNN Based on Public Dataset Using a Wri...

引用

Automation, Robotics and Computer Engineering (ICARCE), International Conference on

作者： Songsheng Li Hong Kong College of Technology Hong Kong China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

Falls among the elderly population pose a significant threat, often leading to severe injuries, loss of independence, and even fatality. To address this critical issue, researchers have been exploring various approaches to develop effective fall detection systems. In this study, we present a novel fall detection algorithm that leverages the WEDA-FALL dataset, a comprehensive dataset that includes data from wrist-worn sensors capturing both simulated and real-world fall incidents involving elderly individuals. The key insight behind our approach is the observation that falls often exhibit distinct patterns in sensor data. To capture these patterns, we employ a one-dimensional convolutional neural network (1D-CNN) architecture, which has been shown to be well-suited for time-series data analysis. By training our model on the WEDA-FALL dataset, we were able to achieve an impressive accuracy of 94% in fall detection, surpassing the commonly reported accuracy levels of around 90% in the existing literature. The high performance of our algorithm can be attributed to the use of the WEDA-FALL dataset and the effectiveness of the 1D-CNN architecture in extracting and learning the characteristic patterns in the sensor data. Notably, our algorithm offers the key features of being quick, highly accurate, and cost-effective, making it a promising solution for practical deployment in real-world settings to improve the safety and well-being of the aging population.

关键词： Training Wearable Health Monitoring Systems Accuracy Smart homes filtering algorithms Feature extraction Fall detection Older adults Kernel Standards

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Harnessing Advanced NLP Techniques: Empirical Study on Automated Text Summarization via SpaCy Word Embeddings and Cosine Similarity

Harnessing Advanced NLP Techniques: Empirical Study on Autom...

引用

Information Technology (OCIT), OITS International Conference on

作者： Janki Kanakia Sachi Shah Esha Shah Tvisha Modi Harsh Kanakia Ishani Saha Payal Mishra Dept. of Computer Engineering NMIMS University Mumbai India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

As legal professionals and researchers deal with the ever-expanding volume of legal information, the methodology presented in this research paper serves as a valuable tool for enhancing document comprehension and faster information retrieval. The research presented goes deep into the efficacy of an extractive summarization algorithm designed for the nuanced landscape of legal document summarization. Demonstrating the ability to filter key information from a diverse array of legal texts, the algorithm adheres meticulously to predetermined word counts, showcasing its effectiveness in navigating the intricacies of summarization within the legal domain. One unique aspect of this methodology is the use of word embeddings, which are particularly suited for this task and are specifically harnessed to augment the model's capacity in capturing intricate semantic relationships inherent in legal terminology, leading to a higher rogue score value. Results show that as user-requested word limits increase, ROUGE scores increase, highlighting the importance of evaluating summary comprehensiveness based on word limits.

关键词： Law Terminology Navigation Semantics Text summarization filtering algorithms Information filters Natural language processing Data mining Information technology

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Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models

Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estim...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Madhu Vankadari Samuel Hodgson Sangyun Shin Kaichen Zhou Andrew Markham Niki Trigoni University of Oxford Oxford UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Self-supervised depth estimation algorithms rely heavily on frame-warping relationships, exhibiting substantial performance degradation when applied in challenging circumstances, such as low-visibility and nighttime scenarios with varying illumination conditions. Addressing this challenge, we introduce an algorithm designed to achieve accurate selfsupervised stereo depth estimation focusing on nighttime conditions. Specifically, we use pretrained visual foundation models to extract generalised features across challenging scenes and present an efficient method for matching and integrating these features from stereo frames. Moreover, to prevent pixels violating photometric consistency assumption from negatively affecting the depth predictions, we propose a novel masking approach designed to filter out such pixels. Lastly, addressing weaknesses in the evaluation of current depth estimation algorithms, we present novel evaluation metrics. Our experiments, conducted on challenging datasets including Oxford RobotCar and MultiSpectral Stereo, demonstrate the robust improvements realized by our approach.

关键词： Measurement Visualization Matched filters Estimation Lighting Focusing filtering algorithms

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Enhancing Movie Recommendations: A Content Based Approach Using TF-IDF Weighted Word2Vec and Cosine Similarity

Enhancing Movie Recommendations: A Content Based Approach Us...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Mayura Chibb Priyanka Vashisht Anvesha Katti Ashima Nrang Department of Computer Science and Engineering Amity University Haryana Gurugram India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

An essential component of online video streaming platforms is their recommendation system. These systems analyze user preferences, viewing history, and demographic data to recommend customized content. These systems can be categorized into three different types on the basis of the underlying algorithms - Content based recommendation systems, Collaborative filtering based recommendation systems and Hybrid recommendation systems. These recommendation systems often struggle to offer variety in recommendations. This causes users to be stuck in a loop where they are suggested content which is very similar to what they have already watched. This restricts their exposure to new and diverse content and leads to bad user experience. This study presents an innovative approach for improving the accuracy and pertinence of movie recommendation systems. The core methodology involves the creation of a word soup by combining different attributes of movies, vectorizing the word soup using the TF-IDF weighted Word2Vec algorithm and performing recommendations based on the cosine similarity scores of the movies. The proposed method offers nuanced and personalized recommendations enhancing the user experience. The experimental results demonstrate that the proposed methodology is efficient because the generated recommendations are contextually relevant and aligned with user preferences.

关键词： Accuracy Collaborative filtering filtering algorithms Streaming media Motion pictures User experience History Recommender systems

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Exploring Non-Convexity Characteristics of Active Trap Filter Based on Local Optimal Control

Exploring Non-Convexity Characteristics of Active Trap Filte...

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IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG)

作者： Boyuan Cui Chao Gao Liang Huang Wenlong Ding Poh Chiang Loh Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong China Department of Energy Technology Aalborg University Aalborg Denmark School of Control Engineering Shandong University Jinan China

ISBN: (数字)9798350361001

ISBN: (纸本)9798350361018

The active trap filter (ATF) plays a pivotal role in manipulating high frequency switching harmonics with superior flexibility. However, theoretical calculation value of controller's parameter is not accurate and weather the parameter is optimal has not been confirmed. To address this issue, this article starts with single targeted switching frequency, proposing a local optimal control parameter tuning algorithm to locate the local optimal value of each controller. What is more, after each local optimal parameter is found, when all controllers working together, controllers' interactions and the non-convexity characteristic of the active trap filter are observed based on the global system. The effectiveness of the algorithm and the system non-convexity characteristic are verified by simulations.

关键词： Switching frequency Optimal control Switches filtering algorithms Active filters Power electronics High frequency

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Design of a Low-Power Sigma-Delta ADC Digital Filter

Design of a Low-Power Sigma-Delta ADC Digital Filter

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Manyi Li Kun Qin Bofan Jiacheng Tang Huiminliu Engineering Research Center of Optoelectronic Devices and Communication Technology Tianjin University of Technology Ministry of Education School of Integrated Circuit Science and Engineering Tianjin University of Technology China Meraki Integrated Corpline Shenzhen China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

With the development of science and technology, Sigma-Delta (∑-A) ADCs are widely used in various fields of electronics with their flexible advantages. As an essential component of a Sigma-Delta ADC, digital filters directly and significantly impact the ADC's accuracy, delay, and power consumption. This paper presents a multistage digital filter for 18-bit ∑-A ADCs; the filter uses a cascade of multiple filters, including cosine-like (CL), cascaded integral comb (CIC), interpolated second-order polynomial (ISOP), and half-band (HB) filters, to enhance the ADC's accuracy. The filter's order and downsampling multiplier are designed using canonical signed digit (CSD) and common subexpression (CSE) to reduce hardware resource consumption and improve computational performance. The ADC is synthesized using 0.18 µm CMOS technology, achieving an output SNDR of 96.26 dB, ENOB of 15.73 bits, total power of 518.35 uW, and a Digital filter power of 82.93 uW, which effectively reduces the power consumption while improving the accuracy.

关键词： Sigma-delta modulation Power demand Accuracy Frequency modulation Software packages filtering algorithms Distortion

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Incremental Strategy Based ANC Using Hybrid Adaptive Nonlinear Filters

Incremental Strategy Based ANC Using Hybrid Adaptive Nonline...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Kutcharlapati Sai Prasad Varma Rajapantula Kranthi Vasundhara Department of Electronics and Communication Engineering National Institute of Technology Hanumakonda Warangal

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The prevalence of nonlinearities in adaptive filters poses a challenging issue for noise reduction. Several methods are introduced to enhance the effectiveness of distributed active noise control (DANC) systems while dealing with nonlinearities. In this context, DANC algorithms utilizing Mish functions are being proposed to address noise reduction challenges. The Mish function addresses the issue of the vanishing gradient problem and enhances the speed at which convergence occurs by leveraging the smoothness and unboundedness characteristics. Considering this, the present manuscript introduces a collection of nonlinear methods for the DANC system that employs the Mish framework into an incremental strategy for filtered-x least mean fourth (IMF-xLMF) and filtered-x least mean square (IMF-xLMS). The proposed approaches entail designing weight update equations specific to the developed algorithms and performing a comprehensive convergence analysis. The suggested methods yield a quantitatively significant gain of 1–4 dB in noise reduction, effectively handling nonlinearities and reference signals, all while converging faster than previous techniques.

关键词： Prevention and mitigation Noise Noise reduction Adaptive filters Signal processing algorithms Nonlinear filters filtering algorithms Mathematical models Topology Convergence

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Transformer Based Hybrid Bernoulli GSF Tracking Algorithm

Transformer Based Hybrid Bernoulli GSF Tracking Algorithm

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International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Caden Sweeney Du Yong Kim Branko Ristic Brian Cheung RMIT University Melbourne VIC Australia Defence Science and Technology Group Edinburgh SA Australia

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

Target tracking in the maritime domain is a challenging problem due to several factors such as fluctuating and spiky sea clutter that is non-Gaussian with low target signal-to-interference ratios (SIR). The authors propose a method which uses machine learning to improve the representation of targets in a cluttered background. Tracking is then performed on point measurements obtained from the machine learning algorithm. The tracking algorithm selected is the Bernoulli Gaussian Sum filter. The error performance of the proposed method is presented and compared to an existing tracking algorithm applied to the maritime domain.

关键词： Target tracking Machine learning algorithms Sea measurements Radar Machine learning filtering algorithms Radar tracking Transformers Prediction algorithms Signal to noise ratio

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Efficient Infrared Image Super-Resolution Reconstruction via Guided Filter Coefficients Estimation with Parallax Attention Mechanism

Efficient Infrared Image Super-Resolution Reconstruction via...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qingyao Wu Bosheng Chen Chen Li Xiaotong Tu Xinghao Ding Yue Huang Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China National Key Laboratory of Infrared Detection Technologies Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the spectral range mismatch between the images, building an efficient infrared (IR) image super-resolution algorithm suitable for embedded devices remains a significant challenge. Given that visible images possess more abundant high-frequency information compared to infrared images, we utilize the visible light to guide infrared image super-resolution reconstruction. Specifically, we transfer the reconstruction task to a guided filter learning process, whose coefficients are estimated by joint learning of visible and infrared image to complete the reconstruction through homologous constraints. In order to efficiently predict guided filter coefficients, we design a lightweight network which incorporates reparameterized differential convolution blocks and a feature fusion strategy. Striving to enhance the fusion strategy performance, we utilize parallax attention mechanism to solve the non-pixel registration problem between infrared and visible images. Extensive experiments on two challenging IR image datasets show that our method performs SOTA in terms of PSNR, SSIM and LPIPS as compared to current state-of-the-art approaches while showing its effectiveness and practicality in the edge platform of RK3588.

关键词： Measurement Attention mechanisms Convolution Image edge detection Superresolution Network architecture filtering algorithms Information filters Speech processing Image reconstruction

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