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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6946 条记录，以下是761-770 订阅

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Design of Power Grid Full-service data Monitoring Platform Based on Cloud computing 2

Design of Power Grid Full-service Data Monitoring Platform B...

引用

2nd international conference on algorithms, Microchips, and Network Applications, AMNA 2023

作者： Li, Wei Zhang, Li Duan, Junxiang Cao, Lu Guo, Tao Customer Service Center of State Grid Tianjin300306 China Beijing China-Power Information Technology Co. Ltd. Beijing100031 China

ISBN: (纸本)9781510664791

The use of power monitoring and management system in power enterprises has significantly improved the convenience, fault tolerance, and related economic income of power grid in power supply. At present, there are still many problems in the management system, such as the lack of unified planning. Many functions related to power monitoring and management often need a lot of software to be repeatedly installed in the system to complete the functions required by customers. Meanwhile, this software often has duplicate functions. Power data can not be shared, and the upgrade of the system will encounter many obstacles. To meet the data communication requirements of component-based power monitoring and management system, this paper applies the characteristics of multi-point distribution and real-time and efficient transmission of data based on model-based information transmission. It also applies component-based data monitoring technology to complete the design. For the power data content of monitoring and calculation, a reusable framework is applied to facilitate user expansion. Users can complete the interface layout by dragging controls on the control interface of the power monitoring and management system, and then realize data customization and saving by binding the database. Finally, the monitoring of the power grid data platforms under cloud service is realized, which is conducive to the effective and timely processing of blowout power grid data. It is of great significance to the safe and stable operation of the power system. © 2023 SPIE.

关键词： Distributed database systems

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Emotion Detection in Speech: A CNN Neural Network Approach

Emotion Detection in Speech: A CNN Neural Network Approach

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2023 IEEE international conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Shashidhar, R. Shruthi, B.M. Manjunath, A.S. Roopa, M. Sharma, Neha Singh, Pardeep Jss Science and Technology University Dept. of Electronics and Communication Engineering Mysuru India Jss Science and Technology University Dept. of Computer Science and Engineering Mysuru India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350300826

Speech emotion recognition is an area of research dedicated to identifying and categorizing emotions expressed through speech. Its purpose is to comprehend and interpret the emotional content conveyed in spoken words, leveraging signal processing techniques, feature extraction algorithms, and machine learning models. The ultimate aim is to apply this knowledge in diverse applications such as human-computer relation, affective computing, and mental health diagnosis. Although the complexity and variability of emotional speech present significant challenges, recent years have witnessed notable progress through the development of advanced algorithms and the utilization of extensive training datasets. Here we proposed a CNN network pattern for speech emotion recognition for the bench mark datasets and got the accuracy of 88% for the convolutional neural network model. © 2023 IEEE.

关键词： data visualization

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Spark-KNN Parallel Pattern Recognition Technique for Substation Secondary Cable data 3

Spark-KNN Parallel Pattern Recognition Technique for Substat...

引用

3rd international conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Lu, Weizhou Huang, Haiying Luo, Huan Guangdong Power Grid Maoming Power Supply Bureau China

ISBN: (纸本)9798350382426

With the rapid development of smart grid, the substation secondary cable condition monitoring data is growing exponentially and gradually constitutes the secondary circuit condition monitoring big data. The traditional computing architecture can no longer meet the computing performance demand. Combining Spark big data processing technology and AliCloud E-MapReduce cloud computing platform, we propose a parallel pattern recognition method for substation secondary cable condition monitoring big data, aiming to improve the ability of the secondary cable online monitoring system to quickly batch analyze the alarm monitoring data that suddenly increase in a short period of time. Spark-KNN, a parallelized K-nearest neighbor classification algorithm based on Spark, is designed to realize parallel pattern recognition of massive secondary cable monitoring data. The experimental results show that the average performance of Spark-KNN is 3.17 times higher than that of Hadoop MapReduce implementation, which is more suitable for performing real-time processing tasks of secondary cable monitoring big data. © 2023 IEEE.

关键词： Online monitoring Secondary cable data Spark-KNN

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

International Conference on Artificial Intelligence, Blockch...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

ISBN: (纸本)9781032669663

The proceedings contain 271 papers. The special focus in this conference is on Artificial Intelligence, Blockchain, computing and Security. The topics include: Brain tumour detection using deep neural network via MRI images;a review on wildlife identification and classification;image caption for object identification using deep convolution neural network;Brain tumor detection using texture based LBP feature on MRI images using feature selection technique;towards computationally efficient and real-time distracted driver detection using convolutional neutral networks;a systematic study of networking design for co-working space environment;application of neural network algorithms in early detection of breast cancer;Stock market prediction using DQN with DQNReg loss function;towards improving the efficiency of image classification using data augmentation and transfer learning techniques;predicting stock market price over the years by utilizing machine learning algorithms;a survey and classification of lung, breast, thyroid, and prostate cancer detection;Modified attention based cryptocurrency price presage with convolutional Bi-LSTM;classification of vegetation, soil and water bodies of Telangana region using spectral indices;ovarian cancer identification using transfer learning;a study on automatic mathematical word problem solvers;predicting learning styles in personalized E-learning platforms;Exploratory data Analysis (EDA) based on demographical features for students’ performance prediction;vehicle detection using Artificial Intelligence for traffic surveillance;Predicting Encopresis & Enuresis treatment: Utilizing AI;object detection from images by convolutional neural networks for embedded systems using Cifar-10 images;recognition of indian sign language using hand gestures;potato plant leaf diseases detection and identification using convolutional neural networks;PG Radar.

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

International Conference on Artificial Intelligence, Blockch...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

ISBN: (纸本)9781032669663

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Forecasting Stock Prices using Gated Recurrent Unit with the Help of Feature Engineering

Forecasting Stock Prices using Gated Recurrent Unit with the...

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2023 international conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Sarma, Challa Vijay Seshachala Krishna, Bezawada Sri Bharath Baradwaz, Buyyanapragada Phani Siva Madhav, Yedupati Sai Pachala, Sunitha Lalitha, Vuyyuru Lakshmi Kl University Artificial Intelligence and Data Science Vijayawada India Koneru Lakshmaiah Education Foundation Ai&ds Department AP Vaddeswaram India

ISBN: (纸本)9798350333602

Forecasting the stock prices is one of the hardest tasks because of its volatility and uncertainty, where additional features such as foreign exchange impacts the stock markets. Introducing AI in forecasting has helped in prediction tasks, especially the time series algorithms. This study attempts to implement various regression models such as the ARIMA model, which gives us improved results for data of linear nature but it suffers from conditions such as non-linearity, unable to cope up with dynamic changes in prices, and overdependence on historical data. This study attempts to implement ML and DL mechanisms to predict stock prices. As most stock markets deal with probabilistic functions and mathematical computations, usage of technologies such include algorithms for machine learning and deep learning. The proposed objective is to forecast the stock prices to enable the users to make decisions and undertake profitable trades within certain intervals by using GRU (Gated Recurrent Unit). © 2023 IEEE.

关键词： Forecasting

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IoT integration with Big data and Cloud computing: A Multidisciplinary Approach 2

IoT integration with Big Data and Cloud Computing: A Multidi...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Sharma, Atul Aggarwal, Kapil Kumar Malik, Meena University Institute of Computing Chandigarh University Punjab Mohali India University School of Business Chandigarh University Punjab Mohali India Chandigarh University Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350369960

IoT (Internet of Things) has become the most popular technology nowadays which opens chances for home machines, wearable gadgets, and software to share and convey data on the Internet. Internet of things globally connected devices and gained popularity in the telecommunication industry. It has many applications in the area of healthcare, marketing, production, social application and smart home applications etc. But this technology also has some issues related to its implementation in various areas such as data processing and storage, security etc. In this paper, we start with general information of IoT, architecture, applications, and limitations and then the importance of the convergence of IoT, Cloud computing and Big data. Finally, this paper summarizes the researchers about how to leverage the benefits by combining these three technologies. © 2023 IEEE.

关键词： Big data Cloud computing Integration Internet of Things Sensors

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Joint Tensor Gradient and Sparsity-driven for Fast Robust Video SAR Imaging 11

Joint Tensor Gradient and Sparsity-driven for Fast Robust Vi...

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11th IEEE Asia-Pacific conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Wu, Yunqiang Gui, Shuliang Tian, Zengshan Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China

ISBN: (纸本)9798350326277

Due to the ability of providing continuous observation, the video synthetic aperture radar(ViSAR) has recently received increasing attention. Particularly focusing on imaging of fields that admit a sparse representation, we propose a tensor sparsity-driven method for video SAR imaging. Joint with high-dimension data processing, the proposed method significantly reduce computing burden. Compared with conventional algorithms, numerical simulations validate the effectiveness, accuracy and robustness of our algorithm. © 2023 IEEE.

关键词： high-dimension data processing sparsity-driven Video SAR

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2nd international conference on Women Researchers in Electronics and computing, WREC 2023

2nd International Conference on Women Researchers in Electro...

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2nd international conference on Women Researchers in Electronics and computing, WREC 2023

ISBN: (纸本)9789819970766

The proceedings contain 56 papers. The special focus in this conference is on Women Researchers in Electronics and computing. The topics include: Analysis of Heart Disease Prediction Using Various Machine Learning algorithms;Analysis of Agricultural Commodities Prices Using BART: A Machine Learning Technique;deep Learning Model-Based Approach for Agricultural Crop Price Prediction in Indian Market;enhanced Intracranial Tumor Strain Prediction and Detection Using Transfer and Multilevel Ensemble Learning;detection and Classification of Blood Cancer Using Deep Learning Framework;deep Learning-Based Multi-label Image Classification for Chest X-Rays;fine-Tuning the Deep Learning Models Using Transfer Learning for the Classification of Lung Diseases from Chest Radiographs;a Systematic Approach for Effective Apgar Score Assessment in 1 and 5 min Using Manifold Machine Learning algorithms;SOT-MRAM Memories for Energy Efficient Embedded and AI Applications;data Pre-processing Techniques for Brain Tumor Classification;a Novel Approach for Detection of Lumpy Virus;internet of Healthcare Things-Enabled Open-Source Non-invasive Wearable Sensor Architecture for Incessant Real-Time Pneumonia Patient Monitoring;VANET Security Optimization with Blowfish Algorithm and Adversarial Transfer Learning;LoRa-IoT-Based Smart Weather data Acquisition and Prediction for PV Plant Using Machine Learning;entity Perception Using Remotely Piloted Aerial Vehicle;RF-MEMS SPDT Capacitive Switch: Accelerating the Performance in B5G Applications;Investigation of Bulk, Electronic and Transport Properties of Armchair Silicene Nanoribbon as Liquefied Petroleum Gas Combustion Indicator: A DFT Study;Recent Advancement of Artificial Intelligence in COVID-19: Prediction, Diagnosis, Monitoring, and Drug Development;analysis of Corner Truncated Rectangular Microstrip Patch Antenna for IoT Applications;dual-Polarized Textile Antenna for Full-Duplex Wearable Applications.

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Encoder-Decoder Approach toward Vehicle Detection 14

Encoder-Decoder Approach toward Vehicle Detection

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14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Deb, Pushan Kumar, Sunny Preethi, N. Department of Data Science Christ University Lavasa Pune India

Vehicle Detection algorithms run on deep neural networks. But one problem arises, when the vehicle scale keeps on changing then we may get false detection or even sometimes no detection at all, especially when the object size is tiny. Then algorithms like CNN, fast-RCNN, and faster-RCNN have a high probability of missed detection. To tackle this situation YOLOv3 algorithm is being used. In the codec module, a multi-level feature pyramid is added to resolve multi-scale vehicle detection problems. The experiment was carried out with the KITTI dataset and it showed high accuracy in several environments including tiny vehicle objects. YOLOv3 was able to meet the application demand, especially in traffic surveillance Systems. © Grenze Scientific Society, 2023.

关键词： Vehicles

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