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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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5th EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2023

ISBN: (纸本)9783031505706

the proceedings contain 121 papers. the special focus in this conference is on Multimedia Technology and Enhanced Learning. the topics include: Security Awareness Method of Opportunistic Network Routing Protocol Based on Deep Learning and Knowledge Graph;research on Pedestrian Intrusion Detection Method in Coal Mine Based on Deep Learning;personalized Recommendation Method of College Art Education Resources Based on Deep Learning;global Planning Method of Village Public Space Based on Deep Neural Network;a Multi Stage Data Attack Traceability Method Based on Convolutional Neural Network for Industrial Internet;machine Learning Based Method for Mining Anomaly Features of Network Multi Source Data;data Anti-jamming Method for Ad Hoc networks Based on Machine Learning Algorithm;a Data Fusion Method of Information Teaching Feedback Based on Heuristic Firefly Algorithm;research on Pain Information Management System Based on Deep Learning;research on Database Language Query Method Based on Cloud Computing Platform;reliability Evaluation Method of Intelligent Transportation System Based on Deep Learning;forecasting Method of Power Consumption Information for Power Users Based on Cloud Computing;power Consumption Behavior Analysis Method Based on Improved Clustering Algorithm of Big Data Technology;anomaly Detection of Big Data Based on Improved Fast Density Peak Clustering Algorithm;data Privacy Access Control Method Based on Ciphertext Policy Attribute-Based Encryption Algorithm;Evaluation Method of Enterprise Circular Economy Development Level Based on AHP Fuzzy Inference;research on Performance Evaluation of Industrial Economic Management Based on Improved Machine Learning;application of Big Data processing Technology in Power Consumption Information Acquisition.

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Attention based Image Caption Generation (ABICG) using Encoder-Decoder Architecture 5

Attention based Image Caption Generation (ABICG) using Encod...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Kulkarni, Uday Tomar, Kushagra Kalmat, Mayuri Bandi, Rakshita Jadhav, Pranav Meena, S.M. Kle Technological University Dept. of Cse Hubballi India

ISBN: (纸本)9781665474672

the image captioning is utilized to develop the explanations of the sentences describing the series of scenes captured in the image or picture forms. the practice of using image captioning is vast although it is a tedious task for the machine to learn what a human is capable of. the model must be built in a way such that when it reads the scene, it recognizes and reproduce to-the-point captions or descriptions. the generated descriptions must be semantically and syntactically accurate. Hence, availability of Artificial Intelligence (AI) and Machine Learning algorithms viz. Natural Language processing (NLP), Deep Learning (DL) etc. makes the task easier. Although majority of the existing machine-generated captions are valid, they do not focus on the crucial parts of the images, which results in lesser clarity of the captions. In the proposed paper, anew introduction to attention mechanism called Bahdanau's along with EncoderDecoder architecture is being used so as to reflect the image captions that are more accurate and detailed. It uses a pretrained Convolutional Neural Network (CNN) called InceptionV3 architecture to gather the features of images and then a Recurrent Neural Network (RNN) called Gated Recurrent Unit (GRU) architecture in order to develop captions. this model is trained on Flickr8k dataset and the captions generated are 10% more accurate than the present state of art. © 2023 IEEE.

关键词： Recurrent neural networks

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Reconfigurable Compute in Memory Using SRAM and Bit Serial Algorithm 5

Reconfigurable Compute in Memory Using SRAM and Bit Serial A...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Deepa, A. Nirmalkumar, P. Jose, Deepa Tejendra Prasad, M.V. Anna University Ceg Campus Department of Electronics and Communication Engineering Chennai India Kcg College of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350321425

In the past conventional von Neumann computer architecture, the data that are stored in the memory unit and the computing core are separated which leads to the increase in power consumption and latency as data size being increased In-memory computing, or compute in memory, aims to perform computer computations entirely within computer memory to eliminate these bottlenecks. One area of exploration for in-memory computing is deep neural network (DNN) acceleration through processing-in-memory platforms (PIM). Using bitwise digital multiplication, the digital CIM macro is designed with reconfigurable 1-8-bit input/weight and single sign bit. A column of bit cells makes up a column MAC, which performs a multiplication and accumulation operation, while dot product is computed by using a row of MACs functions as a single neuron. the bit cell includes three primary components: a user-defined 6T SRAM cell to store weight, for computing bitwise multiplication an XNOR gate is being used, and a carry look adder to accumulate partial sums. A digital CIM memory is designed using 64 × 64 SRAM bit cells for digital bit-serial computing was designed in 45-nm technology with 1-8-bit reconfigurable weight/input bits. © 2023 IEEE.

关键词： Deep neural networks

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Development of a Decision Support System for Helpdesk-Service Based on the Processor for Natural Language Text Classification 1

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5th international Russian Automation conference, RusAutoCon 2023

作者： Biryukova, E.D. Mironov, A.S. Pacific National University 136 Pacific Street Khabarovsk680035 Russia

ISBN: (数字)9783031511271

ISBN: (纸本)9783031511264

Text is one of the most common sources of information. Due to this, there is an urgent need for automated processing of text data. In vast majority of cases, classifiers are applied in text data processing. the object of this work is to develop a text classification processor for a decision support system in the Helpdesk-service-like applications. the modern approach to the text classification based on machine learning method with the engagement of artificial neural network is widely known. the article describes the most common methods for text processing and neural networks architecture. Considered are such text processing methods as: stemming, lemmatization, bag-of-words, N-gram count, TF-IDF. the following neural network architectures: multilayer perceptron, convolutional neural network and long short-term memory network. To find the most optimal algorithm for building up a processor there were considered 36 processors with the use of various combinations of the above methods and neural networks architectures. the article has an analysis and a comparison of a work quality of various classification processors conducted on such characteristic as accuracy. It was established that the processors learned on the data processed though stemming and N-gram count show the highest accuracy. the processor, at the construction of which were used stemming, N-gram count and a convolutional neural network, showed a maximum accuracy of multiclass classification. the accomplished work found an optimum choice of a classifier to be used in form of a module in decision support systems in the Helpdesk-service-like applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Text processing

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5th EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2023

ISBN: (纸本)9783031505799

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5th EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2023

ISBN: (纸本)9783031505768

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Neural Network Based Aircraft Automation Early Warning Research 5

Neural Network Based Aircraft Automation Early Warning Resea...

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5th IEEE international conference on Power, Intelligent Computing and Systems, ICPICS 2023

作者： Rong, Shanyu Yu, Xibo Liu, Liangyan Jiang, Kaiwen Zhong, Xiaowen Shandong University Jinan China Shandong University Weihai China

ISBN: (纸本)9798350333442

With the accelerated urbanization of China and the rapid growth of urban economy, the scale of China's civil aviation industry continues to expand and the civil aviation industry continues to flourish. However, the resulting safety problems and exposed safety hazards are also gradually increasing. In this paper, an automated aircraft warning model based on random forest and bp neural network is studied, and an effective scheme is given based on different flight crews, flight routes, airports, and flight records under specific flight conditions. Firstly, data pre-processing is performed to get 156 features. the important features are filtered using random forest model and the top 10 most important features are taken. A neural network model was built and after normalizing the data, the remaining 10 most relevant flight parameters were used to evaluate the pilot's flight technique, and 100% training accuracy and 79% validation accuracy were obtained. the results were then validated using support vector machines, decision trees, and plain Bayesian models, respectively, with the final neural network predicting the best. In addition, we also designed an automated aircraft warning model with a combination of random forest and neural network. the loss of which is about 0.095, and the test performance is better than the general neural network model and random forest model in the test set. © 2023 IEEE.

关键词： Neural networks

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Simple and Effective Visual Place Recognition via Spiking Neural networks and Deep Information 5

Simple and Effective Visual Place Recognition via Spiking Ne...

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5th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Che, Jinming Cao, Jian Feng, Shuo Cao, Xinyan Wang, Bingsen Wang, Yuan Peking University School of Software & Microelectronics Beijing China

ISBN: (纸本)9798350310603

Visual place recognition (VPR) is a hot topic in current research on unmanned intelligent systems. However, the false recognition caused by changes in environmental conditions (e.g. weather, lighting) and resource limitations on the mobile robot are the main problems affecting the accuracy of visual place recognition, and how to use small robots to perform visual place recognition tasks in changing environments in an efficient and energy-efficient manner has important research and practical value. In this paper, we propose a simple and effective visual place recognition system called D-VPR, which simulates a bionic approach to visual place recognition. D-VPR makes robot look around and incorporates environmental depth information, the essence of this system is to optimize the weight response of neurons by processing the environmental information, and guide the neural network to carry out correct place recognition. We design and deploy the scheme on a mobile robot, and test it with our dataset, which is called PKU-Paradise. the results shows that the system has better robustness which is maximum at 61% to the interference of light changes, while having higher accuracy and lower power consumption than advanced visual place recognition methods. the implementation of this system makes a strong contribution to build a full-spiking neural network SLAM system in the future. © 2023 IEEE.

关键词： Mobile robots

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GNSS Signal Obstruction Removal Tool for Evaluating and Improving Position Accuracy in Satellite networks 5

GNSS Signal Obstruction Removal Tool for Evaluating and Impr...

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5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Hirota, Kazuki Goswami, Bhargavi Kulkarni, Manasa Queensland University of Technology Brisbane Australia Bangalore India

ISBN: (纸本)9781665493604

the positioning accuracy of Global Navigation Satellite System (GNSS) is largely affected by the site's surroundings. However, the methods to simulate GNSS signal obstruction and the nature of signal obstruction have not yet been explored fully. In this research, we investigated a way to remove the signals received from a specific region by specifying azimuth and elevation from GNSS observation files and evaluating how the removal of signals affects GNSS positioning accuracy. In addition, we also investigated the signal blockage for buildings of certain dimensions and a mountain. Python was used as a programming language to develop a program for the signal removal. RTKPOST was used for the GNSS data processing, and RTKPLOT was used for the visualisation of processed data and analysis of positioning accuracy. We successfully developed a Python shell script to remove the signals in GNSS data file from specific region by specifying azimuth and elevation. It was also found that removing signals from azimuth 0 to 100 degree and elevation 0 to 30 degree increased the positioning accuracy within a low multipath dataset. However, when the maximum elevation angle was increased to 45 degrees, positioning accuracy degraded, indicating that the signal from certain elevations have a positive or negative impact on positioning accuracy. Further research avenues are explored as an extension of work done here. © 2023 IEEE.

关键词： Python

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Emotion Detection from Text: Classification and Prediction of Moods in Real-Time Streaming Text 5

Emotion Detection from Text: Classification and Prediction o...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Juyal, Prachi Kundalya, Amit UK Dehradun India Data Sciences Competency to the New UP Noida India

ISBN: (纸本)9798350321425

Emotion detection is a method which can be used to determine publics' attitudes, feelings, and feelings towards a specific target, such as persons, groups, organizations, various services, and products. Sentiment analysis is a superset of emotion detection since it infers the specific emotion rather than just declaring if something is good, bad, or neutral. Recent studies have concentrated on the use of verbal and facial clues to identify emotional states. Since written language lacks non-verbal indicators like voice tone, face expression, vocal pitch, etc., it can be challenging to discern emotions. the extraction of emotions from text has been proposed using a variety of natural language processing (NLP) techniques, such as the keyword-based approach, the lexicon-based approach, as well as deep learning approach. the disadvantages of keyword and lexicon-based approaches are numerous despite their focus on semantic links. In this research we suggest a BERT-based deep learning system (Fake BERT) by combining the BERT with numerous parallel blocks of a single-layer deep Convolutional Neural Network (CNN). GRU and Bi-GRU comparisons from past works were used as deep learning approaches. the proposed BERT-1D CNN generated the best results with an F1 score of 83.5, followed by Bi-GRU and GRU. © 2023 IEEE.

关键词： Convolutional neural networks

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