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A heterogeneous graph neural network model with multi-head attention integrating review information for recommendation 4

A heterogeneous graph neural network model with multi-head a...

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4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Li, Chenyun Zhang, Guanhong Odbal, H. School of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Computing Anhui Vocational and Technical College Hefei230601 China

ISBN: (纸本)9781510686106

Recommendation systems (RS) can significantly boost the profitability of e-commerce platforms. Traditional RS methods rely on users' historical behaviors and product attributes but struggle with data sparsity, cold start problems, and complex user-item interactions. Graph neural networks (GNNs) have become popular in RS due to their ability to capture complex relationships and features within graph-structured data, improving accuracy and robustness. this paper proposes a model that combines product review information with GNNs to enhance recommendations. We design a heterogeneous graph neural network with multi-head attention integrating review information (HG-MHAR). this model constructs a user-item heterogeneous graph with ratings as edges and includes review information to build user and item feature embeddings. the model has two main components: the review feature aggregation graph learning module and the multi-head attention graph learning module. these components capture multiple relational patterns in the graph in parallel, improving the stability and robustness of feature representations. Experiments on five Amazon datasets show that HG-MHAR outperforms benchmark models, improving nearly 4% over traditional methods. this demonstrates the potential of GNNs in recommendation systems. © 2024 SPIE.

关键词： data modeling Performance modeling Neural networks Systems modeling Modeling Convolution Equipment Deep convolutional neural networks Mathematical optimization Matrices

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Review of Artificial Intelligent based Algorithm for Prediction of Polycystic Ovary Syndrome(PCOS) from Blood Samples 4

Review of Artificial Intelligent based Algorithm for Predict...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Chitra, P. Sumathi, M. Srilatha, K. Jayasudha, F.V. Amudha, S. Sathyabama Institute of Science and Technology India Srm Institute of Science and Technology Chennai India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Polyfolicular ovarian syndrome or (PCOS) is one of a medical disorder that affects female pregnancy. this paper proposes a method of prediction of PCOS using an Artificial Neural Network (ANN). these are disorders that occur in female ovaries which will lead to infertility. A lot of factors that will be considered to predict whether a woman comes under PCOS or not are obesity, hair distribution, menstrual abnormalities (BMI), Follicle-stimulating hormone, Luteinizing hormone), e.t.c. the data set considered for analysis consists of 44 features for detecting PCOS but the problem here with the data is its imbalance. A more significant number of PCOS fewer patient samples are contributed to the dataset to overcome a proposed method is used in imbalanced learning techniques such as oversampling minority data, under-sampling majority data, SMOTE, and ensemble. the features are extracted from the data set and classified with an ANN-based algorithm. the scaling technique is done on the data to have the same range and fed into the network for training and testing. data visualization is done to find the correlation between the data. © 2022 IEEE.

关键词： Neural networks

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A Review of An Idea For Secure Resource Usage And Load Balancing Using Integrated Cloud computing Strategies 4

A Review of An Idea For Secure Resource Usage And Load Balan...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Ruchika Khurana, Dhiraj Uiet Mdu Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9781665474368

In this study, numerous cloud computing load balancing algorithms are examined, and important difficulties that must be considered while building new load balancing algorithms are also explored. Various measurement criteria, such as performance, scalability, throughput, resource usage, fault tolerance, reaction time, and others, are studied in the literature to compare current static and dynamic load balancing methods. Cloud computation offloading and work schedules have been the subject of some research. Degrading cloud computing performance is a major concern for cloud enterprises of the huge amount of data and varied sources inside this cloud that must be managed. To maximize system performance and reduce carbon emissions, load balancing aims to use resources in the most efficient way possible while minimizing the number of resources used. this paper focuses on cloud computing load balancing strategies. Several qualitative factors, including throughput, reliability, energy-saving features, performance, scalability, and associated overhead, are considered to examine, and compare the present techniques of load balancing. © 2022 IEEE.

关键词： Fault tolerance

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Campus Placements Prediction Analysis using Machine Learning 4

Campus Placements Prediction Analysis using Machine Learning

引用

4th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2022

作者： Shahane, Priyanka Aissms Institute of Information Technology Department of Artificial Intelligence and Data Science Maharashtra Pune India

ISBN: (纸本)9781665400732

Campus placement is an activity of participating, identifying and hiring young talent for internships and entry level positions. Reputation and yearly admissions of the institute invariably depend upon the placements provided by the institute to the students. therefore, most of the institutions, assiduously, try to boost their placement department in order to improve their organization on a full scale. Any assistance during this specific space can have a good impact on the institute's capability to position it's students. In this study, the target is to analyze student's placement data of last year and use it to determine the probability of campus placement of the present students. For this we have experimented with four different machine learning algorithms i.e. Logistic Regression, Decision Tree, K Nearest Neighbours and Random Forest. © 2022 IEEE.

关键词： Students

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4th international conference on Pattern Analysis and Intelligent Systems, PAIS 2022 - Proceedings

4th International Conference on Pattern Analysis and Intelli...

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4th international conference on Pattern Analysis and Intelligent Systems, PAIS 2022

ISBN: (纸本)9781665461610

the proceedings contain 57 papers. the topics discussed include: a deep learning-based system for detection at-risk students;an explainable self-labeling grey-box model;development of a biometric authentication platform using voice recognition;binary Archimedes optimization algorithm based feature selection for regression problem;semantic segmentation of breast cancer histopathology images using deep learning;a biased random-key genetic algorithm for extractive single-document summarization;a virtual clustering for data dissemination in vehicular fog computing;a novel method of requirements analysis for big data projects;combination of closed-set and open-set biometric identification;a formal integrated approach for cyber physical systems;and testing cyber-physical production system: test methods categorization and dataset.

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Remote sensing identification of seasonal pasture based on Sentinel-2 23

Remote sensing identification of seasonal pasture based on S...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Su, Shiqi Fang, Panfei Qian, Rui Zheng, Pengfei Qiang, Zhenping College of Big Data and Intelligent Engineering Southwest Forestry University Swfu Yunnan Kunming China Southwest Forestry University Swfu Faculty of Forestry Yunnan Kunming China

ISBN: (纸本)9781665494571

In order to research the distribution of seasonal pastures, we used the Sentinel-2 remote sensing data of Golog Tibetan Autonomous Prefecture in 2017-2020 warm season (270-300d) to construct 22 spectral features and topographic features based on GEE platform. Random forest classifier was used to identify seasonal pastures. the results showed that: (1) the overall accuracy and Kappa coefficient of seasonal pasture identification were 82.41% and 0.74, respectively;the producer's accuracy and user's accuracy were (cold season pasture: 84.08%, 80.57%;Warm season pasture: 71.92%, 75.26%);(2) the total area was about 7320972.81 hm2, including 3080067.88 hm2of cold-season pasture and 4240904.93 hm2of warm season pasture. the cold season pasture was mainly distributed in Banma, Jiuzhi and Maqin counties, while the cold-season pasture was mainly distributed among Maduo, Dari and Gande counties. (3) the imagery of the end of the warm season (270-300d) during 2017-2020 were synthesized to ensure the integrity of the image of the study area as much as possible and eliminate the influence of clouds;(4) the water loss and surface exposure to herbage after grazing and treading by livestock are similar to the carbonized surface after fire. therefore, the spectral characteristics of NDVI and NDMI which sensitive to water and CSI, NBR and NBR2 which sensitive to bare surface have good effects on the identification of seasonal pasture. (5) Most of the areas with high altitude belong to other types, indicating that elevation factors perform well in distinguishing seasonal pastures from other types of land. the results of this study can provide a reference to using remote sensing images to quickly identify seasonal pasture distribution and formulate scientific grazing management policies. © 2021 IEEE.

关键词： Water Smart cities Time series analysis Vegetation mapping Land surface Fires Agriculture

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A Novel CNN-Based Approach for Intelligent Garbage Detection and Localization 4

A Novel CNN-Based Approach for Intelligent Garbage Detection...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Sanjana Patel, G.M. Suraj, K.C. The National Institute of Engineering Department of Computer Science and Engineering Mysore India

ISBN: (纸本)9781665474368

In India, the problem of waste management is one of the most critical concern as tonnes of waste is generated every year of which only a very minute percentage is treated. the rest of it is either are not treated properly or dumped in landfills. Governments around the world have extensively tried to develop intelligent waste or garbage management techniques. the potential threat to sanitation, public health and safety caused by improper waste management is immense. In light of rapid increase in computational efficiency as neural networks became more capable of processing deeper data, deep learning was inevitable for garbage management systems. this paper attempts to provide an end-to-end solution to automating the process of garbage detection and alerts, beginning right at the source. the solution proposed comprises employing a highly accurate training strategy using the acclaimed VGG-16 network using a subset of the COCO dataset and also includes an accompanying Android app that makes the process of garbage localization and alerts seamless and effective. Finally, we guide you through our mobile application that is designed to assist municipalities in remotely monitoring garbage accumulation areas which help to take appropriate measures to keep surrounding areas clean and hygienic. the experimental accuracy of this system was over 95.92% which is much greater than that required for real-world scenarios. © 2022 IEEE.

关键词： Health risks

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Neural network based breast cancer detection using hybrid artificial bee colony and black hole algorithm 4

Neural network based breast cancer detection using hybrid ar...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Singh, Indu Dhalwal, Mohit Himanshu Maurya, Mridul Delhi Technological University Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665474368

When there is development of malignant tumor in breast notably in female is known as Breast cancer. Detection in the early stage of this disease is the only feasible solution. In this paper a new approach has been proposed to detect this disease in the early stage by employing neural networks and optimizing them with the help of a hybrid of artificial bee colony algorithm and black hole algorithm. this combination of neural networks with nature inspired algorithm is able to understand the deep patterns inside the dataset and produce great results. Using the proposed approach an accuracy of 98.94% is achieved on the Breast Cancer Wisconsin (Diagnostic) data Set and as a result, is a trustworthy alternative to human experts when it comes to the classify the breast cancer tumors. © 2022 IEEE.

关键词： Diseases

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Novel Model for the Efficient Vehicle Movement in Vehicular Ad hoc Network Based IOT 4

Novel Model for the Efficient Vehicle Movement in Vehicular ...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Gupta, Alok Kapoor, Er. Nitika Chandigarh University Department of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9781665474368

Networks that have multiple vehicle nodes that have complete freedom to enter or exit the network are called vehicle ad-hoc networks (VANETs). Establishing a protecting and competent pathway between end-to-end terminals is a complicated job by virtue of to the excessive movement of automobiles in the vehicular network. the selection of a pathway between source and destination relies on two evaluation criteria, namely sequence number and lowest hop count. In this project, this pathway has been formed by adopting a multicasting strategy. this strategy routes the data to select the source nodes from the network. A source node plays an important role in choosing a path between the end-to-end terminals. the methodology designed is implemented in NS2 tool and analytical results are achieved on the basis of some universal performance indices. © 2022 IEEE.

关键词： Multicasting

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Vision Based Vehicle-Pedestrian Detection and Warning System 4

Vision Based Vehicle-Pedestrian Detection and Warning System

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Loungani, Bhumika Agrawal, Janvi Jacob, Lija Christ University Department of Data Science Pune India

ISBN: (纸本)9781665474368

Road Sense must be respected and obeyed by both the pedestrian and the driver. Moreover, urbanization has led to a steadfast rise in the fleet of vehicles, their speed, as well as non-compliance with road safety measures, and other such factors have provoked an inescapable increase of accidents in road traffic involving pedestrians. Pedestrian collisions can be predicted and prevented. At the very basic, there has to be vehicle and pedestrian detection along with speed estimation, which can be further applied to Vehicle-Pedestrian Collisions and various emerging fields like Industrial Automation, Transportation, Automotive, Security/Surveillance, or in Dangerous environments. this paper reviews the literature on vehicle and pedestrian detection based on two significant categories: pre-processing phase and detection phase, with a detailed comparative analysis. the papers reviewed cover video-based surveillance systems. © 2022 IEEE.

关键词： Computer vision

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