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Sentiment Analysis from TWITTER Using NLTK 22nd

Sentiment Analysis from TWITTER Using NLTK

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Challa, Nagendra Panini Madhavi, K. Reddy Naseeba, B. Bhanu, B. Balaji Naresh, Chandragiri VIT-AP University Amaravati India Department of CSE Sree Vidyanikethan Engineering College AP Tirupati India Department of Electronics Andhra Loyola College Vijayawada India

ISBN: (纸本)9783031274084

In the present generation, Social networking sites like Twitter, Face book play a important role for communication. Twitter is one of the Blogging platform with numerous data quantity used as an insight in various functional operations of Sentiment Analysis, elections, reviewing, market, etc. The main intension of the tweet based sentiment analysis is the ability to assess good, pessimistic, or nonpartisan opinion part in the tweet information. Tweet sentiment analysis can assist any association with tracking down individuals’ assessments of their organization and items. In this paper, we have applied feeling investigation on twitter informational collection. Our model takes input tweet, feeling, and result chosen text beginning and finishing off with input twee and concluded with accuracy of the model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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A LiDAR-Camera Fusion Network for Small Object Detection in Open-pit Mining Areas 22

A LiDAR-Camera Fusion Network for Small Object Detection in ...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Liu, Muzhuo Wang, Jie Zhou, Bin Wang, Zhangyu Liu, Wentao Wang, Mingyuan School of Transportation Science and Engineering Ministry of Industr and Information Technology Beihang University Beijing China State Key Lab of Intellgent Transportation System Ministry of Industr and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industr and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

As intelligent and autonomous driving technologies advance, the secure and productive functioning of mining trucks during autonomous operations is critically dependent on precise object detection within open-pit mining environments. Due to the sparse nature of point cloud data for small objects such as stones, cones, and equipment parts, they are susceptible to generating false negatives and false positives. Although camera images are rich in semantic content, the absence of precise depth cues renders them vulnerable to interference from background noise. Currently, the technology of fusing LiDAR point cloud and camera image data for 3D object detection is gaining popularity in this field. In this paper, we introduce a query feature generation strategy based on multi-scale image features in the cross-attention-based feature fusion module to generate high-quality fusion features for small objects. Additionally, within the traditional non-maximum suppression strategy, we introduce size factors and shape factors to alleviate the sensitivity of small objects to bounding box offsets. Notably, our network model has achieved an mAP of 88.0 and an ndS of 77.9 on the mining dataset, significantly enhancing the precision of detecting small objects over previous approaches. © 2024 IEEE.

关键词： Open pit mining

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Application of Object Detection Models for the Detection of Kitchen Furniture - A Comparison 22nd

Application of Object Detection Models for the Detection of ...

引用

22nd international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Stecker, Benjamin Brandt-Pook, Hans Bielefeld Univ Appl Sci Interakt 1 D-33619 Bielefeld Germany

ISBN: (纸本)9783031425073;9783031425080

Object detection is being applied in an increasing number of areas. In this paper, the authors investigate the application of object detection in a use case for the kitchen industry. The main goal of the use case is to extract information from kitchen scenes that can be used for kitchen planning. The use case is located in a medium-sized company that has little experience with the application of deep learning models. Therefore, this paper proposes a methodology that ensures fast and reliable testing of different object detection models to identify a suitable model for the given use case. In the first step, a dataset with kitchen images is built. Further, augmentation methods are applied to the dataset, to increase the amount and variety of the data. For object detection, there is a variety of models that are freely available, and the question of which model is best for the use case cannot be answered easily. A selection of models (Faster R-CNN, SSD, and EfficentDet) from the TensorFlow Object Detection API will therefore be tested on the image dataset created. The achieved mean average precision (mAP) of the trained models will be used as a metric to determine the best model for the use case. The purpose of this work is to provide an approximate solution, that proves that object detection and the methodology work for the use case.

关键词： Object detection Deep learning Faster R-CNN SSD EfficientDet Computer vision

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Evaluation of the Training Effect of the Driving Simulator + VR on Driving School Trainees 22

Evaluation of the Training Effect of the Driving Simulator +...

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22nd COTA international conference of Transportation Professionals, CICTP 2022

作者： He, Chenxi Wu, Yiping Zhao, Xiaohua Ding, Yang Liu, Shuo Beijing Engineering Research Center of Urban Transportation Operation Guarantee College of Metropolitan Transportation Beijing Univ. of Technology Chaoyang District Beijing China Jing'an Driver Safety and Attainment Research Institute of Beijing Beijing China

ISBN: (数字)9780784484265

ISBN: (纸本)9780784484265

As an emerging approach, driving simulator plus VR has been gradually applied in the field of driving training. There is still a lack of scientific and comprehensive methods to evaluate the effectiveness of this. Through integrating the training data of 119 driving school students, the research adopts a hierarchical evaluation method to assess the training effect of driving simulator plus VR at levels of subjective reaction, knowledge mastery, driving behavior, and the exam passing result. In terms of subjective and objective scores, the research results demonstrated that the fusion technology performs the driver training task admirably. This research provides a solution for quantitatively evaluating the training effect of driving simulator plus VR, and manifests the effectiveness of driving simulator plus VR, promoting the application of this newly emerging technology. At the same time, the discussion on training effects provides a direction for the development of differentiated and personalized training programs. © ASCE.

关键词： Driver training

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Privacy issues involved in data mining and countermeasures 2

Privacy issues involved in data mining and countermeasures

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2nd international conference on Mechanical engineering, intelligent Manufacturing and Automation Technology, MEMAT 2022

作者： Liu, Jing Law School Xi'an Jiaotong University 28 Xianning West Road Shaanxi Province Xi'an China

ISBN: (纸本)9783800757619

data mining can extract valuable information from large amounts of data and has matured in recent years as an important resource for productive life. This technology is a decision support process. It is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, database, visualization technology, etc. It is highly automated to analyse a company's data, make inductive inferences and extract potential patterns from it to help decision makers adjust their market strategies, reduce risk and make the right decisions. Technology is neutral and not entirely good or bad. However, with the rapid development of the Internet, the increasing popularity of embedded and location-based technologies and the widespread use of location-based social networks, the amount of personal privacy information has increased dramatically and it is very easy to violate personal privacy if data mining tools are applied directly to the data for comprehensive analysis. Thus, in order to be able to carry out data mining with privacy protection, this article suggests some solutions, including: 1) Establish a perfect data privacy protection assessment mechanism and legal means.;2) Develop a new theoretical framework for privacy protection;and 3) Efficient encryption algorithm. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Decision support systems

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Knowledge of Language and Natural Language Processing 22

Knowledge of Language and Natural Language Processing

引用

22nd international conference on New Trends in intelligent Software Methodologies, Tools and Techniques, SoMeT 2023

作者： Di Sciullo, Anna Maria University of Naples Federico II Italy

ISBN: (纸本)9781643684307

Using knowledge rather than data is key in knowledge science and enables artificial systems to solve novel problems. We distinguish the knowledge of language internal to the mind from the externalized language. We differentiate the Generative Model of Language from Large Language Models. We take Structure Dependency to be a First Principle of the internal language. We address the question whether Large Language Models provide reliable natural language processing. We identify limits of ChatGPT for answering queries including sentence embeddings, covert constituents, and pronominal anaphora, which rely on Structure Dependency. We draw consequences for reliable natural language processing systems. © 2023 IOS Press. All rights reserved.

关键词： Machine learning

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Evaluating Unidimensional Convolutional Neural Networks to Forecast the Influent pH of Wastewater Treatment Plants 22nd

Evaluating Unidimensional Convolutional Neural Networks to F...

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22nd international conference on intelligent data engineering and automated learning

作者： Oliveira, Pedro Fernandes, Bruno Aguiar, Francisco Pereira, Maria Alcina Novais, Paulo Univ Minho ALGORITMI Ctr Braga Portugal Univ Minho Ctr Biol Engn Braga Portugal Aguas Do Norte Guimaraes Portugal

ISBN: (纸本)9783030916077;9783030916084

One of our society's challenges today is water resources management due to its importance for human life. The monitoring of various substances present in wastewater is a crucial part of the process of Wastewater Treatment Plants (WWTPs). One of these substances is the influent's pH, which plays a fundamental role in the nitrification and nitration processes. Hence, this paper presents a study to forecast the influent pH in a WWTP for the next two days. For this purpose, several candidate models were conceived, tunned and evaluated, taking into account the one-dimensional Convolutional Neural Networks (CNNs) considering two distinct approaches in the Pooling layer: the channels' last and the channels' first. The best candidate model obtained a Mean Absolute Error (MAE) of 0.257, following the channel's last approach, compared to the channels' first that obtained a MAE of 0.272.

关键词： Convolutional Neural Networks Deep learning Influent pH Time series Wastewater Treatment Plants

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intelligent Bearing Fault Diagnosis Using Adaptive Time-Frequency Convolutional Features with Multi-task learning 22

Intelligent Bearing Fault Diagnosis Using Adaptive Time-Freq...

引用

22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Li, Simin Liu, Youjiang Qiu, Yongtao Wang, Shengli Institute of Electronic Engineering China Academy of Engineering Physics Mianyang621900 China Tsinghua University Department of Engineering Physics Beijing100084 China

ISBN: (纸本)9781665470674

Deep learning based fault diagnosis methods can extract representative and distinguish features automatically from raw signals and have achieved promising results in recent years. However, deep models rely heavily on massive labeled data and do not generalize well under varying load conditions. Besides, both temporal features and frequency features of raw vibration signals provide useful information in fault diagnosis and should be fully exploit to acquire better performance. To address these issue, a dual-input model based on a convolutional neural network with attention mechanism and multi-task learning strategy is proposed in this paper. The model uses both temporal and frequency adaptive features to achieve end-to-end fault diagnosis. First, a two-branch one-dimensional CNN is designed to learn rich and complementary features from both temporal and frequency data in parallel. Moreover, attention mechanism is adopted in the two-branch CNN to further increase the adaptability of the proposed model under different working loads. Finally, to obtain robust and discriminative features as well as enhance recognition accuracy, a multi-task learning strategy is also employed to better optimize the whole network. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset demonstrate the superiority of our proposed model and can achieve a high fault recognition rate under variable load conditions with fewer training samples. © 2022 IEEE.

关键词： Convolutional neural networks

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The Deployment of Microservice at Edge based on MQTT for Low Latency 22

The Deployment of Microservice at Edge based on MQTT for Low...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Han, Mengqi Yang, Bo Liu, Yuxiang Liu, Sicheng Liu, Qi Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798331527471

With the rapid development of smart manufacturing, existing microservice deployment strategies based on the TCP protocol tend to encounter problems such as network latency, excessive header length, and port exhaustion in high concurrency scenarios. MQTT Broker can relay data between microservices, effectively alleviating these issues. However, the communication overhead of the Broker is significantly related to its deployment and the dependency of microservices. An appropriate deployment solution can significantly improve service quality. To address this issue, this paper models the deployment of MQTT Broker and microservices as a graph with cycles. By introducing auxiliary variables, the NP-hard problem is transformed into a mixed-integer quadratic programming problem, improving problem-solving efficiency and optimizing the microservices deployment strategy to reduce communication overhead. Meanwhile, a real-world heterogeneous cluster was constructed to experiment on fault detection applications, elaborating on the superiority of the microservice deployment strategy designed in this paper. Experimental results demonstrate that our approach reduces communication overhead by 20% in comparison to both the greedy strategy and the K8s strategy. This suggests that achieving an optimal balance between the number of Brokers and microservices is vital for ensuring efficient resource utilization and maintaining low communication latency, particularly as the scale of services expands. © 2024 IEEE.

关键词： Smart manufacturing

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Advanced convolutional neural network for optimizing AC power flow

Advanced convolutional neural network for optimizing AC powe...

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international conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Quan Tran Joydeep Mitra Nga Nguyen Electrical Engineering and Computer Science University of Wyoming Laramie WY USA Electrical and Computer Engineering Michigan State University East Lansing MI USA

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

Current methods for solving optimal power flow have difficulties obtaining real-time solutions due to the complexity and nonlinearity of large power systems. Taking advantage of machine learning (ML) algorithms, trained machine-learning models are viable alternatives to tackle the challenges of online OPF-solving. This paper proposes the application of neural network models with self-attention to reduce an inductive bias and overcome the drawbacks of traditional ML-based approaches in solving OPF. By incorporating all power system parameters into a multiple-channel matrix akin to an image, the proposed weighing-convolutional neural network (WCNN) can extract typical features from training data and learn the underlying patterns. The high computational burden is alleviated through the training phase, and an OPF solution can be produced instantly by inference of the trained model. The proposed method is implemented on three IEEE standard systems to prove its efficacy. The experiment results show that the proposed WCNN algorithm obtains a high level of accuracy, which facilitates its potential capability for deployment in the practical operation of complex power systems.

关键词： Training Electric potential Machine learning algorithms Accuracy Neural networks Training data Machine learning Feature extraction Power systems Convolutional neural networks

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