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检索条件"任意字段=2012 3rd International Conference on Computer and Communication Technology, ICCCT 2012"

共 1592 条记录，以下是1-10 订阅

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Proceedings of the 2012 3rd international conference on computer and communication technology, iccct 2012

Proceedings of the 2012 3rd International Conference on Comp...

引用

2012 3rd international conference on computer and communication technology, iccct 2012

ISBN: (纸本)9780769548722

The proceedings contain 69 papers. The topics discussed include: an ontology-based topical crawling algorithm for accessing deep web content;image mining: a new approach for data mining based on texture;decision making through multi rule algorithm: an extension to 1 rule algorithm;user centric retrieval of learning objects in LMS;web pre-fetching at proxy server using sequential data mining;privacy preserving data mining techniques: current scenario and future prospects;an approach to adaptive locality based maintenance of correlated data;voltage mode single OTRA based biquadratic filters;error estimation using correlator for OFDM based wireless communication system;raditation pattern & performance analysis of rectangular microstrip feed patch antenna using EM solution IE3D;systematic study of binocular depth finding using two web cameras;and reduced variable neighborhood search for page number minimization problem.

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Empirical Analysis of Digital Currency Herding and Contagion Effect Using Deep Learning Approaches 3

Empirical Analysis of Digital Currency Herding and Contagion...

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3rd Odisha international conference on Electrical Power Engineering, communication and Computing technology, ODICON 2024

作者： Tripathy, Nrusingha Hota, Sarbeswara Mishra, Debahuti Dash, Biswajit Nayak, Subrat Kumar Tripathy, Jogeswar Deemed to Be University Department of Computer Science and Engineering Siksha 'O' Anusandhan Odisha Bhubaneswar India Deemed to Be University Department of Computer Application Siksha 'O' Anusandhan Odisha Bhubaneswar India Raajdhani Engineering College Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

In recent years, the use of virtual or cryptocurrency-based money has grown. Blockchain, a digital accounting system, is the foundation of cryptocurrency. The markets for cryptocurrencies are eminent for their high volatility and quick price swings. People tend to be more likely to follow the lead of others at times of notable price fluctuations out of a concern of losing money or missing out on possible gains. In this work, we taking the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), the hybrid combinations of LSTM-Convolutional Neural Network (CNN), LSTM-Gated Recurrent Unit (GRU) and for Facebook Prophet (Fb-Prophet) model smoothing, triple exponential smoothing (Holt-Winters Method) is utilized, which adds seasonality into the forecasting model. In this work bitcoin exchanges providing real-time information casing the period from January 2012 to July 2020 is utilized. The price of cryptocurrencies fluctuates significantly throughout a range of time periods, from short-term intraday swings to long-term patterns that last for weeks or months. By altering the network layer design and the duration of the input sequences, LSTM-GRU models configured to adapt to various time horizons. In contrast to previous iterations, the hybrid LSTM-GRU performs well. When compared to other models, the model's Mean Absolute Error (MAE) score of 0.056 and Root Mean Square Error (RMSE) score of 0.073 are quite acceptable. Forecasting the value of cryptocurrencies will help traders and investors in making better-informed choices when purchasing, disposing of, and keeping assets. © 2024 IEEE.

关键词： Convolutional neural networks

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Target Detection Based on Taylor Expansion and Bilateral Symmetric Network 3

Target Detection Based on Taylor Expansion and Bilateral Sym...

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3rd IEEE international conference on computer communication and Artificial Intelligence, CCAI 2023

作者： Li, Zhihui Jia, Xiaoshuo Li, Shuhua Liu, Suping Guangdong University of Science and Technology College of Computer Science Guangdong Dongguan China

ISBN: (纸本)9798350335262

In the task of target detection, traditional detection algorithms are prone to weak generalization and low detection accuracy due to the small number of parameters and fixed parameter values. Conversely, CNN-based detection algorithms are more accurate, but cannot be developed on mobile due to model issues. In this paper, we refer to the gradient features of HOG and propose the Taly preprocessing method which can preprocess images using Taylor extensions and extract multi-order gradient features of the images. Then TaylorNet is designed under the bilateral symmetric network. Multi - gradient features contain rich edge feature information of images. Then, the gradient feature is fused through the bilateral symmetrical network structure to achieve the fusion of low resolution and high resolution, so as to realize the accurate positioning and detection of edge features, and finally achieve the accurate detection of the target. Through the training and testing of the dataset SBD and VOC2012, the comparison results show that compared with some SOTA algorithms, TaylorNet effectively reduces the size of the model while ensuring high accuracy, so it can also effectively implement mobile development. © 2023 IEEE.

关键词： Statistical tests

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Parallel Model to Detect Attacks Using Evolutionary Based Technique 3

Parallel Model to Detect Attacks Using Evolutionary Based Te...

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3rd international conference on Advances in Computing, communication, Embedded and Secure Systems, ACCESS 2023

作者： Guruprasad, Sunitha Rio D'Souza, G.L. St Joseph Engineering College Department of Computer Science and Engineering Mangaluru India

ISBN: (纸本)9798350346169

Evolutionary-based algorithms emerged due to their flexibility and effectiveness in solving different varieties of problems. Optimization-based techniques are used in finding solutions that involve multiple conflicting objectives. Parallel evolutionary-based algorithms are used to overcome the time-consuming job of finding solutions to these types of problems. In this paper, we present a parallel genetic programming-based model that runs parallelly and obtains solutions in a minimal amount of time. The model also allows the user to select the best set of objectives based on the requirements of the users. An island model is used which runs the operations on different islands parallelly. This not only decreases the execution time of the process but also increases the diversity of the population. The results obtained in different islands are fed to an ensemble classifier to get the required result. The model was trained and tested using the state-of-the-art ISCX-2012 and CICIDS2017 datasets. In our work, we have mainly focused on detecting the attacks in a system in a short duration of time. The model developed gave significant performance improvement compared to the results obtained using the normal CPU implementation. © 2023 IEEE.

关键词： Genetic programming

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Goodness Ratio and Throughput Improvement Using Multi-criteria LEACH Method in Group Sensing Device Network 3rd

Goodness Ratio and Throughput Improvement Using Multi-criter...

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3rd international conference on Data Science, Machine Learning and Applications, ICDSMLA 2021

作者： Rekha, V.S. Siddaraju DrAmbedkar Institute of Technology Karnataka Bangalore India Department of Computer Science and Engineering Dr Ambedkar Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9789811959356

In wireless sensor networks, the entire region is disbursed with a set of sensing devices (SDs), and then, multiple sets are formed. For controlling each set, a unique SD is selected for inter-set communication. The selection of special sensing device (SSD) in each region sensing device network (RSDN) is done by computing random value of probability for the case of LEACH method. Whenever end-to-end route is established, this SSD will keep on changing so that each SD has an opportunity to deliver data across regions. The end-to-end path formation is done using SDs, base controller (BC), and SSD. The disadvantage is that the path is longer, more hops, and more energy consumption. The E-LEACH improves the selection of SSD based on remaining energy levels of SDs. The route is formed using combination of SDs, primary, and secondary SD. This will overcome the delay and hop ratio disadvantage of LEACH but suffers from lower goodness network factor. The selection of SSD for a multi-criteria system is done by measuring remaining energy, distance with respect to reference position and mobile value. Comparison is done between LEACH, E-LEACH, and multi-criteria LEACH with respect various parameters, and it is proved that multi-criteria LEACH is the best. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wireless sensor networks

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Cooperation network analysis of strength training for physical health in older adults 3

Cooperation network analysis of strength training for physic...

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3rd international conference on Electronic Information Engineering and Data Processing, EIEDP 2024

作者： Fu, Xinjin Wang, Yao Pang, Hongshen Zhu, Yuenan Xu, Xiaoqi Taiyuan University of Technology Shanxi Taiyuan China College of physical education Taiyuan University of Technology Shanxi Taiyuan China Shenzhen University Guangdong Shenzhen China College of International Studies Shenzhen University Guangdong Shenzhen China Universiti Sains Malaysia Gelugor Penang Malaysia

ISBN: (纸本)9781510680531

To deeply grasp the academic trend and development trend of strength training in the field of strength training on the health of older adults, 4827 articles about the impact of strength training on the physical health of older adults in the SCI database and SSCI database from 2012 to 2021 were selected as the data source. With the support of VOSviewer and Citespace bibliometric software, visual analysis was conducted from annual publication volume, publication country, publication author, and publication institution, respectively. The results show that according to the trend of the recent decade and the impact of the global aging, it is predicted that in the next few years, the study of strength training on the health of older adults will continue to become a hot spot and further develop;The main research fields of the author are clinical medicine, sports science, basic medicine, public health, and preventive medicine;There is less cooperation and communication between institutions, which only forms part of the cooperation cluster, and the overall cooperation density is low;The research focuses mainly on the methods of strength training for older adults, the impact of chronic diseases in older adults and the factors affecting the health of older adults. From related studies, strength training for physical health research in older adults will develop rapidly under the guidance of policy and medical innovation. © 2024 SPIE.

关键词： Clinical research

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Role of Educational Data Mining and Learning Analytics Techniques Used for Predictive Modeling 3

Role of Educational Data Mining and Learning Analytics Techn...

引用

3rd international conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Kaur, Kanksha Dahiya, Omdev School of Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350336238

The use of data mining techniques to answer important educational questions is done with educational data mining that may be related to predicting students' performance or assessment. The purpose of these remains to address the goal of meeting and fulfilling the learning objectives. Learning analytics is a new approach for teachers to think about education and involves the visualization of data about students to improve learning. It is related to management strategies that prioritize quantitative measurements, which can sometimes be at odds with a teaching-focused approach to education. The purpose of this study is to perform a systematic literature review of the various techniques used in both educational data mining and learning analytics. The area of educational data mining utilizes data mining techniques to examine educational data. Meanwhile, learning analytics centers on utilizing data to gain knowledge about learning processes and student performance. The review will consider various techniques, including statistical methods, machine learning algorithms, and data visualization techniques, which are commonly used. The goal is to identify the most effective techniques for analyzing educational data and improving student learning outcomes. By performing a systematic literature review, this study will provide an overview of the current state of research in educational data mining and learning analytics. It will also identify gaps in the literature and suggest areas for future research. Ultimately, the findings of this study will be of great value to researchers, educators, and policymakers who are interested in using data to enhance the learning experience. For performing this, a few research questions have been designed to select relevant studies. In this study, the research articles from a decade, 2012 to 2022, are taken into consideration. From the overall search results, 41 studies have been taken into consideration that has addressed the scope and significan

关键词： Data mining

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Performance Improvement of Single-stage Target Detection by Tuning Parameters in CNN 3

Performance Improvement of Single-stage Target Detection by ...

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3rd international conference on Electronic communication and Artificial Intelligence, IWECAI 2022

作者： Zhang, Gangrui Zhou, Xuecong Jxust Information And Computing Sciences Department Ganzhou China Gxust Computer Department Liuzhou China

ISBN: (纸本)9781665479974

Single-stage Target detection can find almost all the objects in the images and determine the category and location of the objects, which includes two key points: object positioning and object classifying. Deep learning methods have been widely used to target detection. However, parameters are tuned to find relations between parameters and performance of CNN, including training time needed, detecting time, and detection precision. This paper studies the influence of the parameters in CNN on the accuracy of single-stage target recognition and improves the performance of single-stage target detection. First, the data comes from PASCAL VOC 2012. Then, a series of related parameters are analyzed in our CNN model, including image size and batch size, the Strides, the Anchor size and number, etc. Finally, by changing the single parameter and keeping other parameters the same, we study how these parameters influence the performance of our model. The results show that the image size affects the time and accuracy of the training process, and there is an antagonistic relationship between them. Besides, in the training process, we find that with the training time increasing the training loss decreased. And if reasonably increasing the Strides, the Input shape and the Non-Maximum Suppression (NMS) threshold can improve the recognition rate. However, if decreasing the Anchor size and number and the Strides greatly can increase the running time. Besides the Anchor size and number is having a certain connection related to the Mean Average Precision (mAP), and the NMS has a certain positive impact on the MAP. Our analysis demonstrates that setting reasonable NMS, strides, image size and the number of anchors which belong to Convolutional Neural Network (CNN) can make the precision rate higher. Simultaneously, it can accelerate the training process. © 2022 IEEE.

关键词： computer vision

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Vehicle Detection in Smart City Surveillance Using Emerging Technologies 3

Vehicle Detection in Smart City Surveillance Using Emerging ...

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3rd international conference on Innovations in Science and technology for Sustainable Development, ICISTSD 2022

作者： Shihabudeen, H. Rajeesh, J. College of Engineering Kidangoor Department of Electronics and Communication Kerala India

ISBN: (纸本)9781665499361

A Smart City is a technologically advanced metropolitan area that employs electrical technologies to gather data to increase operational effectiveness, communicate information to the public, as well as provide improved governmental services and public welfare. So, the problem that arises in these smart cities is the congestion in traffic for identifying the vehicle information accurately, due to the lack of decision support systems. Thereby this paper centres around exhaustive writing concentrating on the current strategies for vehicle detection in a smart city. It merges the condition of the speciality of their performance measure and quantitative evaluation of forefront strategies. The last 10 years (2012-2022) of publications by various specialists clearly describe various methods for vehicle detection. The purpose of this document is to analyze the strategic achievements encapsulated in the detecting measures' success indicators for the targeted field of research. This document acknowledges the key accomplishments expressed within the detecting measures' success indicators for the targeting field of research. Furthermore, this study explored the most important findings and reasons for identifying lessons learned for the future roadmap. © 2022 IEEE.

关键词： Smart city

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Target Detection Based on Taylor Expansion and Bilateral Symmetric Network

Target Detection Based on Taylor Expansion and Bilateral Sym...

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computer communication and Artificial Intelligence (CCAI), international conference on

作者： Zhihui Li Xiaoshuo Jia Shuhua Li Suping Liu College of Computer Science Guangdong University of Science and Technology Guangdong Dongguan

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