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检索条件"任意字段=2nd IEEE Conference on Information Technology and Data Science, CITDS 2022"

共 8828 条记录，以下是191-200 订阅

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A Hybrid Recommendation System using Collaborative Filtering with Apache Spark in E-Commerce 2

A Hybrid Recommendation System using Collaborative Filtering...

引用

2nd ieee International conference on data science and Network Security, ICDSNS 2024

作者： Shaik, Sadaf Tabassum G. Naraynamma Institute of Technology and Science Department of Information Technology Hyderabad India

ISBN: (纸本)9798350373110

In the area of product recommender systems, a collaborative recommender system built on a latent factor model has shown notable success. However, there are issues associated with sparsity and its capacity to extract non-linear data features is restricted, which leads to inadequate performance. To overcome the aforementioned challenges, the proposed user-based collaborative filtering creates a social user model for sharing rating patterns to recommend products. The proposed model utilizing Spark architecture and winged hawk optimization (WHO) optimized attention-based CNN is employed for product recommendation. To understand user interests and internet activity, correlation relationships are established using a graph embedding approach, which supports appropriate recommendations for users on e-commerce platforms. The proposed approach on evaluation using the music dataset attains lowest MAE, MSE, and RMSE values as 0.51, 0.55, and 0.74 respectively. The suggested model attained high robustness minimized the computational burden and outperformed other existing models. © 2024 ieee.

关键词： attention layer collaborative filtering convolutional neural network (CNN) hybrid recommendation optimization

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Adaptive QoS Protocol in Systemized Networks with Machine Learning 2

Adaptive QoS Protocol in Systemized Networks with Machine Le...

引用

2nd ieee International conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Nitish Rao, K.P. Amith Kumar, S. Keerthika, K. School of Computing Department of Computer Science Amrita Vishwa Vidyapeetham Mysuru India

ISBN: (纸本)9798350372892

In today's networking world, Quality of Service (QoS) is essential for guaranteeing that data packets are delivered efficiently. Understanding metrics like jitter, throughput, and latency is also important for meeting the needs of different users and applications. As a result, QoS techniques have a direct impact on the network and may be overly inflexible in an operator-centric setting. This article describes a revolutionary approach to network QoS management based on machine learning (ML) algorithms such as KNN, random forest, and decision trees. In this research, NS3 simulations and real-world data are used to train and test the machine learning models on several QoS metrics, demonstrating their automation potential. The findings of this study pave the door for dynamic QoS adjustment, which would enhance service delivery. © 2024 ieee.

关键词： Adversarial machine learning

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Electronic information Professional English Learning System Using Recurrent Neural Network-Collaborative Filtering Recommendation Algorithm 2

Electronic Information Professional English Learning System ...

引用

2nd International conference on data science and information System, ICDSIS 2024

作者： Xu, Xuanwei Chengdu College Of University Of Electronic Science And Technology Of China Chengdu China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381665

E-learning is one of the significant technologies in online education in long-lasting terms with contrast to customary process of head-to-head educating culture for electronic information. By e-learning, a numerous learners have benefited from various programs. This research considers Electronic information Professional English learning system as the foundation and establishes the recommendation system for learning of English vocabulary using Recurrent Neural Network-Collaborative Filtering (RNN-CF). Initially, data is gathered from 100 individuals for an electronic information professional English vocabulary e-learning system like text. The stemming and stop word removal is employed to remove unwanted words. Term-Frequency-Inverse Document Frequency (TF-IDF) and Word2vec are used for feature extraction to calculate essential words. RNN-CF is performed for Professional English learning recommendation system which includes a Gated Recurrent Unit (GRU) layer in Neural Collaborative Filtering (NCF). The proposed RNN-CF achieves a better accuracy of 96.25% compared to existing techniques like Convolutional Neural Network-CF (CNN-CF), Long Short-Term Memory-CF (LSTM-CF), and GRU-CF respectively. © 2024 ieee.

关键词： Learning systems

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English Long Sentence Machine Translation Algorithm Based on U-Net Generation Adversarial Network 2

English Long Sentence Machine Translation Algorithm Based on...

引用

2nd International conference on data science and information System, ICDSIS 2024

作者： Tang, Derong Chen, Zhanfeng College Of Humanities Chongqing Metropolitan College Of Science And Technology Chongqing China Chongqing Metropolitan College Of Science And Technology College Of Architecture And Civil Engineering Chongqing China

ISBN: (纸本)9798350381665

U-Net Generative Adversarial Network (GAN) algorithm aims to solve the challenge of translating long sentences in machine translation, and improve the accuracy and fluency of such sentence translation. This paper further discusses the application of generative adversarial network (GAN) in machine translation, and then improves the translation quality through the adversarial training of the generation and discrimination network, and seeks to deal with the problem of translation accuracy skillfully through the in-depth analysis of the translation of long sentences. It can be seen from the experimental data that the translation speed of U-Net generated adversarial network's English long sentence machine translation algorithm accounts for 92.25% of the data within 1.5 seconds, and the translation accuracy rate reaches 89 points. It can be seen that the organic combination of U-Net architecture and generative adversarial network provides a novel and effective solution to the problem of long sentence translation, and ultimately significantly improves the translation accuracy and deep meaning. © 2024 ieee.

关键词： Generative adversarial networks

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Analysis of Optimization Configuration Strategies for Smart Healthcare Space Based on Cloud Computing 2

Analysis of Optimization Configuration Strategies for Smart ...

引用

2nd ieee International conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Zhang, Zhengchun Jiang, Jingjing Li, Nan Wang, Junbo Tao, Zihan Liu, Xuanqi Dalian University of Science and Technology School of Information Science and Technology Dalian China

ISBN: (纸本)9798350372892

As information technology advances, smart healthcare has become a key avenue for enhancing the quality and efficiency of medical services. Cloud computing plays a central role by providing robust data processing capabilities and resource sharing. However, optimizing medical resource allocation to improve utilization rates and service response times remains a challenge. This study introduces a model combining Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), referred to as the GA-PSO model, aimed at optimizing smart healthcare space configurations. Compared to traditional GA, Support Vector Machine (SVM), and Random Forest (RF) models, the GA-PSO model achieved a resource utilization rate of 80.6%, reduced the service response time to 12.5 minutes, and increased patient satisfaction to 90 points. With a cost-benefit ratio of 1.6, the model demonstrates economic advantages, effectively enhancing the efficiency and quality of smart healthcare configurations. These results provide significant decision support for medical institutions when selecting smart healthcare technologies and advance the application and development of cloud computing in healthcare. © 2024 ieee.

关键词： Electronic health record

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Learning Low Rank Approximation IAA for Forward-Looking Scanning Radar Super-Resolution Imaging 2

Learning Low Rank Approximation IAA for Forward-Looking Scan...

引用

2nd ieee International conference on Signal, information and data Processing, ICSIDP 2024

作者： Yang, Mingjie Jiang, Xinrui Mao, Deqing Yan, Jine Zhang, Yin Huang, Yulin Yang, Jianyu University of Electronic Science and Technology of China School of Information and Communication Engineering China Beijing Institute of Remote Sensing Equipment Beijing China Xidian University School of Optoelectronic Engineering Shaanxi Xi'an China

ISBN: (纸本)9798331515669

In forward-looking scanning radar imaging, spectral estimation method can improve the azimuth resolution. However, when dealing with large-scale echo data, the computational complexity is significantly elevated due to the inversion of high-dimensional matrices. To solve these problems, this paper proposes a low rank approximate iterative adaptive angular (IAA) super-resolution imaging method based on deep networks. By combining the advantages of data driven and model driven, this method avoids the problems of manual parameter selection and slow iteration update in traditional algorithm. At the same time, due to the learning and fitting ability of deep networks, the proposed method can maintain good angular super-resolution performance. Simulation results demonstrate that the algorithm exhibits reduced computational complexity and improved resolution compared to conventional methods. © 2024 ieee.

关键词： Radar imaging

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Research on the Application of Object Detection Algorithm Based on Deep-Learning in Photo Management System 2

Research on the Application of Object Detection Algorithm Ba...

引用

2nd ieee International conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Haiyan, Zheng Ruili, Zhang Weijun, Chen Zuoxin, Ge Institute of Information Engineering Qingdao Hengxing College of Science and Technology Qingdao China Qingdao Campus of Naval Aeronautical University Department of Aviation Equipment Support and Command Qingdao China School of International Education Qingdao Hengxing College of Science and Technology Qingdao China

ISBN: (纸本)9798350360240

Traditional image management adopts manual comparison between the real situation after maintenance and the photos in the standard atlas, which has a large maintenance workload and inaccurate detection effect. In view of this situation, this paper puts forward a method of comparing photos with the YOLO V4 algorithm using deep-learning, which uses self-made data set to train the model. A comparative analysis is carried out with the previous algorithm. The research results show that the method with YOLO V4 algorithm can achieve better results in the average accuracy of photo comparison. © 2024 ieee.

关键词： Contrastive Learning

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Review on IOMT Security through Distributed Machine Learning 2

Review on IOMT Security through Distributed Machine Learning

引用

2nd International conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Bhaskar, Swasthika Vanitha, M. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In this constructive healthcare industry, AI-based IOMT (Internet of Medical Things) is one of the highly used Technologies. The virtual world is customary to lose responsible data in cyberspace. Without any doubt, IOMT comes under this category. IOMT applications do not intensively focus on Security and privacy and it is a Serious downside of IOMT applications. Considering this crucial issue, we use the Multiple-node machine learning Technique. It is widely used for preserving the privacy of the huge records. It does not want to share its sensitive data from one node to another node, which result in providing security and privacy to medical record by adding protection to input records, output tags, parameters, etc. This paper exclusively focuses on the Security and Privacy of Medical Records and also shows the various roles of Distributed Machine Learning. © 2024 ieee.

关键词： Machine learning

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Comparative Analysis of Yield Prediction of Primary Crops in India based on Regression Models 2

Comparative Analysis of Yield Prediction of Primary Crops in...

引用

2nd ieee International conference on Advances in information technology, ICAIT 2024

作者： Padmashree, A. Krishnamoorthi Moses Regan, I. Sujith, A. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Erode India Department of Information Technology Dr.N.G.P. Institute of Technology Coimbatore India Department of Computer Technology Bannari Amman Institute of Technology Erode India

ISBN: (纸本)9798350383867

Crop yield prediction is a crucial component of agriculture since it assists growers in making educated choices regarding planting, irrigation, and fertilizer. In this work, the use of regression analysis to forecast crop yields is investigated. A sizable dataset was gathered from numerous sources, including information on climate changes, area harvested, yield and production of primary crops in India. The data for two different year bands i.e., from 2010 to 2023 and from 1995 to 2023 are collected from Food and Agricultural Organization (FAO) of the United Nations. On the dataset, a number of regression techniques, including Decision trees, Random forests, and Gradient boosting and Lasso regression were trained and assessed. With an R2 score of each model, the results showed that the Decision tree regression model performed the best to find the crops of higher importance in India. The results of this study show that regression approaches can be useful for predicting crop yield and can give useful information to improve the agricultural operations and productions in India. © 2024 ieee.

关键词： Prediction models

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Impact of Variable Sample Size on the Efficiency of Support Vector Machines in Cardiovascular Disease Detection 2

Impact of Variable Sample Size on the Efficiency of Support ...

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2nd ieee International conference on Advances in information technology, ICAIT 2024

作者： Sarraju, Vijayalakshmi Pal, Jaya Kamilya, Supreeti Computer Science and Engineering Department Birla Institute of Technology Lalpur Ranchi Mesra India Computer Science and Engineering Department Birla Institute of Technology Ranchi Mesra India

ISBN: (纸本)9798350383867

Exploring the relationship between sample size and the performance of machine learning models in diagnosing cardiovascular diseases (CVD) aids in understanding the optimal data volume required to achieve reliable and accurate diagnostic predictions. This research focuses on examining how the size of the sample influences the efficacy of a particular machine learning algorithm, Support Vector Machine (SVM) in detecting cardiovascular diseases. The efficacy of the SVM models was assessed by employing datasets with different sample sizes. These include one original dataset and two statistically representative samples with sample sizes of 170 and 208 respectively. We evaluate multiple metrics, including F1-score, accuracy, specificity, sensitivity, precision, AUC and ROC curve. Based on the findings, it is evident that a larger sample size generally leads to improved precision and specificity. However, it is important to note that a decrease in sensitivity often accompanies this improvement. In addition, the study highlights the importance of using multiple performance metrics to comprehensively assess the efficacy of SVM models. ©2024 ieee.

关键词： Support vector machines

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