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检索条件"任意字段=4th International Conference on Computing and Data Science, CONF-CDS 2022"

共 1120 条记录，以下是221-230 订阅

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Predictive Analytics of Stock Market as a Time Series 4

Predictive Analytics of Stock Market as a Time Series

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

作者： Singh, Arth Bansal, Anmol Anoop Nair, V. Kaushal, Anukriti Delhi Technological University Department of Computer Science Engineering Delhi India

ISBN: (纸本)9781665474368

this paper presents a new methodology and a comparative study using past stock market data that can help businesses take investing or divesting decisions in critical situations in the future. these may be like the COVID-19 pandemic, where market volatility is extremely high, thus creating an urgent need for better decision support systems to minimise loss and ensure better profits. the results of the study are based on the comparison of different configurations of ARIMAX, Prophet, LSTM and Bidirectional LSTM Models trained on historical NSE data. By understanding the correlation and variations in the data processing and model training parameters, we have successfully proposed a LSTM neural network model training and optimising method which could successfully help businesses take both long and short term profitable decisions before and after big financial and market crises with a respective accuracy of 98.60 percent and 96.97 percent. © 2022 IEEE.

关键词： Financial markets

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Using Machine Learning Techniques for Outlier Detection Application 4

Using Machine Learning Techniques for Outlier Detection Appl...

引用

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

作者： Chahar, Ravindra Kumar Singh, Ajay Shanker Galgotias University School of Computing Science & Engineering Greater Noida India

ISBN: (纸本)9781665474368

the aim of designing models that have the ability for adapting to their surroundings and have capability for learning from past data has gained attention from numerous spheres, comprising computing engineering, artificial intelligence, statistics, and mathematics. Numerous machine learning methods have evolved from research in the field. Supervised, unsupervised and reinforcement techniques have been applied for a variety of scientific and industrial applications. this paper introduces a description of a scheme for outlier detection application that is based on Isolation Forest machine learning technique. the scheme is an unsupervised learning method. the scheme also uses Random Forest machine learning technique for the application. this method is a supervised learning method. the learning techniques are tested on a sample system. A comparison of Isolation Forest technique with other unsupervised learning algorithms is presented, along with its comparison to supervised Random Forest Classifier algorithm. © 2022 IEEE.

关键词： Anomaly detection

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Implementation of Parallel Acceleration for Real-time Extraction of Visual Features 4

Implementation of Parallel Acceleration for Real-time Extrac...

引用

4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Yu, Jiyang Huang, Dan Wang, Luyuan Tian, Miaomiao Zhang, Tianzhu China Academy of Space Technology Institute of Spacecraft System Engineering Beijing China Academy of Machinery Equipment China Research and Development Beijing China University of Science and Technology of China School of Information Science and Technology Hefei China

ISBN: (纸本)9781665463997

Visual feature extraction is the key to target localization, reconstruction and estimation. SIFT feature extraction algorithm has good adaptability and high computational complexity. In this paper, a parallel accelerated implementation of real-time extraction of visual SIFT features is designed. A parallel pipelined computing architecture is designed for the general SIFT computing process, and the data access is accelerated by combining DDR3, Block RAM and register three-level cache. Fixed-point computing is used in the computing modules of large data streams such as Gaussian pyramid, differential pyramid and extreme value detection. Hybrid precision is used in gradient histogram, feature description and matching process. When it comes to the calculation of exponential and trigonometric functions, the series expansion and table lookup method are adopted to effectively reduce the logic and storage occupation. In this paper, the design architecture and data information flow of each SIFT calculation module are given, and the whole design is verified experimentally in XC7K325. the results show that the design for 1024X1024 pixel image can reach 100 frames of calculation speed, power consumption is not more than 12W. the SIFT descriptor error is less than 1%, which ensures the calculation error of the system while the resources are effectively utilized. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Leveraging Machine Learning to Predict Volcanic Eruptions by Function Neural Networks 4

Leveraging Machine Learning to Predict Volcanic Eruptions by...

引用

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

作者： Shyamala, K. David Solomon Raju, Y. Swarnalatha, Ch. Saidulu, D. Premala tha, M. Department of Electronics and Communication Engineering Hyderabad India Mahatma Gandhi University UCE&1 Computer Science & Engineering Nalgonda India Dept of It Hyderabad India Guru Nanak Institute of Technology Dept of Ece Hyderabad India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

When it comes to volcanic eruption surveillance, techniques that rely on seismic analysis of data tend to use automatic methods that ignore the fact that volcanic formations are varied and flexible. Sophisticated procedures for data categorization and assessment, evaluation, investigation and interpretation are needed to identify small variations in seismic sequences linked with volcanic instability. Machine Learning (ML) and Deep Learning (DL) technologies are continually being devised to better machine extraction and processing of valuable data. the proposed study has employed Radial Basis Function Neural Networks (RBFNN) to predict volcanic eruptions in Indonesia. Further, the proposed RBFNN model is compared with other Neural Network (NN) models and it was observed that RBFNN has outperformed all other models in terms of accuracy. © 2022 IEEE.

关键词： Neural network models

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RFE-ACO-RF: An approach for Cancer Microarray data Diagnosis 4

RFE-ACO-RF: An approach for Cancer Microarray Data Diagnosis

引用

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

作者： Panda, Pinakshi Priyadarshi, Ankur C. V. Raman Global University Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9781665400732

Cancer now a day is playing a vital role in increasing the number of deaths throughout the world. Early detection of cancer increases the degree of recovery. Machine Learning has given various models based on biopsy data and the microarray data for cancer classification. the microarray data is having high dimension. Hence applying machine learning algorithm is directly applied to the microarray data for classification purposes then it will face the Small Sample Size (SSS) problem. So, before classification, the dimension of the dataset has to be reduced by using any available technique. In this research work an integrated approach based on the RFE-ACO-RF method has been proposed as a cancer diagnosis model. the RFE will be used for feature selection purpose, ACO is used for optimization purpose and the RF for classification purpose. the performance of the model will be calculated based on accuracy, F1 score, precision and recall. © 2022 IEEE.

关键词： Classification (of information)

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Learning to navigate on the rough terrain: A multi-modal deep reinforcement learning approach 4

Learning to navigate on the rough terrain: A multi-modal dee...

引用

4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Zhou, Bo Yi, Jianjun Zhang, Xinke School of Mechanical and Power Engineering East China University of Science and Technology Shanghai China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

How to enable safe navigation of unmanned vehicles on complex and rough terrain is challenging and meaningful research. In this paper, we propose an end-to-end reinforcement learning local navigation method with multi-modal data fusion, which effectively combines the intrinsic perception, such as Inertial Measurement Unit (IMU) measurements, and the extrinsic perception, such as three-dimensional (3D) point clouds and images, of an unmanned vehicle. A specific feature extraction network is constructed for each modal data, and the total network is effectively trained using a modal separation learning method. the experimental results show that the proposed method can effectively address various obstacles such as rough roads, vegetation obstacles, and water pool disturbances to achieve autonomous and safe navigation of unmanned vehicles in off-road scenarios. © 2022 IEEE.

关键词： Reinforcement learning

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Application of Big data Center Integration Algorithm in Abnormal Detection of College English Smart Classroom 4

Application of Big Data Center Integration Algorithm in Abno...

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

作者： Zhang, Jianwei Foreign Studies of Central South University of Science and Technology Hunan Changsha410004 China

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

this paper proposes a 3DGRU video anomaly detection algorithm based on the big data center integration algorithm. the Chinese full name of 3DGRU algorithm is a multi-example video anomaly detection algorithm based on C3D and GRU network enhanced spatiotemporal features. College English teaching is also increasingly focusing on information technology to carry out teaching, in order to change the deficiencies in the traditional teaching process and realize the innovation and optimization of teaching concepts and teaching forms. In order to explore ways and means of organically integrating ideological and political elements with English teaching, according to Wen Qiufang's output-oriented approach (POA) theory, and referring to the BOPPPS teaching model, this paper proposes an innovative model of learning-use-integrated smart teaching based on language situations under the ideological and political concept of courses. © 2022 IEEE.

关键词： Big data

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A Prediction Model of Expressway Staff Operation Time based on SW-LSTM 4

A Prediction Model of Expressway Staff Operation Time based ...

引用

4th international conference on Advances in Computer Technology, Information science and Communications, CTISC 2022

作者： Jiao, Jian Du, Fang Yang, Xiaoming Li, Lan Beijing Information Science and Technology University School of Computer Beijing China Software Center Company of Chinese Academy of Sciences It Service Department Beijing China CorporationLtd. Beijing China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

the work of Chinese expressway managers has the characteristics of singleness and continuity. the working environment has high requirements for dealing with emergencies, especially for the prediction results of new employees' future work conditions. It has a high reference value for its guidance and career planning. this paper proposes a method based on employee operation log as the main data source. the combination of sliding window and LSTM is adopted. Establish an employee behavior prediction model based on SW-LSTM. the model effectively changes the limitations of personnel management only relying on reports in the past, and improves the behavior granularity and accuracy of management. the system runs in the real environment of a provincial toll station. the prediction algorithm is used to analyze, verify and compare the real results of employee historical data. It is proved that the system has high prediction accuracy and practicability. © 2022 IEEE.

关键词： Forecasting

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Applying DDDAS Principles for Realizing Optimized and Robust Deep Learning Models at the Edge 4th

Applying DDDAS Principles for Realizing Optimized and Robust...

引用

4th international conference on Dynamic data Driven Applications Systems, DDDAS 2022

作者： Canady, Robert Zhou, Xingyu Barve, Yogesh Balasubramanian, Daniel Gokhale, Aniruddha Department of Computer Science Vanderbilt University NashvilleTN37203 United States

ISBN: (纸本)9783031526695

Edge computing is an attractive avenue to support low-latency applications including those that leverage deep learning (DL)-based model inferencing. Due to constraints on compute, storage and power at the edge, however, these DL models must be quantized to reduce their footprint while minimizing loss of accuracy. However, DL models and their quantized equivalents are often prone to adversarial attacks requiring them to be made robust against such attacks. the resource constraints at the edge, however, preclude any quantization and robustness design operations directly at the edge. Moreover, the changing dynamics of edge-based computations and resulting concept drifts in the models require an iterative approach to meet the needs of robust DL models at the edge. To address these challenges, this paper presents initial results on an iterative procedure involving a DDDAS feedback loop. DDDAS is used to dynamically instrument the edge-deployed, quantized DL models for data on the effectiveness of their quantization and robustness abilities, which in turn is used to drive an automated, cloud-based process that uses tools, such as Apache TVM, to generate quantized, optimized and robust DL models suitable for the edge. these models subsequently are automatically deployed at the edge using orchestration tools. Preliminary studies using this approach have shown its effectiveness in image classification and object detection applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Edge computing

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Offline RSSI based Object Detection in 802.11 4

Offline RSSI based Object Detection in 802.11

引用

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

作者： Akash, H.K. Akhil, M.K. Sinha, Somnath Amrita School of Arts and Sciences Mysuru Amrita Vishwa Vidyapeetham Department of Computer Science Mysore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Monitoring is one of the wide ranges of uses in Wireless sensor networks (WSN). the data that is retrieved from the sensor can be monitored with the help of the micro-controller unit. ESP8266, which gives an accurate signal measurement for the stations around us. RSSI or Received Signal Strength Indicator which can measure the signal power of receiver node which comes under Range based localization techniques. the micro-controller comes with IEEE 802.11 b/g/n standard that can measure the accurate RSSI values of the Wi-Fi devices, which can be displayed with the help of an asynwebserver, where the micro-controller acts as a Soft-Access point and the RSSI data that is collected can be monitored on the web page. Using an access point is an offline data monitoring method where users don't have an internet connection, but with the help of ESP8266, we can connect to the internet and, at the same time, users who are connected to local-host can also monitor the RSSI measurements through the Access Point. Using the help of a web-based cloud application from Google Sheets, the whole RSSI data is saved for further study or data analytics and live data is displayed in the sheets. © 2022 IEEE.

关键词： data Analytics

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