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检索条件"丛书名=Lecture Notes in Networks and Systems"

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A Convolution Neural Network Based VANET Traffic Control System in a Smart City

A Convolution Neural Network Based VANET Traffic Control Sys...

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Intelligent systems Conference (IntelliSys)

作者： Mathiane, Malose Tu, Chunling Adewale, Pius Nawej, Mukatshung Tshwane Univ Technol Comp Syst Engn Pretoria South Africa

ISBN: (纸本)9783031477171;9783031477188

Among many methods presented for traffic congestion detection and control, the traffic light control system is effective, however, produces a slew of issues, including excessive delays and high energy consumption. To increase the efficiency, the traffic light length must be adapted dynamically corresponding to real-time traffic. Computer vision techniques have been commonly used for traffic detection. In this article, to regulate the signal for traffic, a Two-Dimensional Convolutional Neural Network (2D-CNN) is constructed for traffic detection and the TensorFlow is used to implement the 2D-CNN. The live traffic data is then used to regulate traffic lights. To validate the proposed method, it is compared with Vehicular Adhoc Network (VANET) adaptive traffic light control, using Simulation of Urban Mobility (SUMO) platform. The simulation's outcome shows that the proposed method is more effective at controlling traffic signals and reduces traffic congestion. The results showed 96% accuracy on the testing dataset.

关键词： Traffic Light Control Vehicular Adhoc Network Convolutional Neural Network

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IoT-Based Standalone Device for Beehives Monitoring Toward Honey Yield Optimization 9th

IoT-Based Standalone Device for Beehives Monitoring Toward H...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Umutoni, Marie Ritha Ntawuzumunsi, Elias Hanyurwimfura, Damien Kumaran, Santhi Habiyaremye, Joseph Mukanyiligira, Didacienne Sibomana, Louis Univ Rwanda ACEIoT Coll Sci & Technol Kigali Rwanda Copperbelt Univ Sch ICT Kitwe 21692 Zambia Narada Elect Kigali Rwanda Natl Council Sci & Technol Kigali Rwanda

ISBN: (纸本)9789819732982;9789819732999

Beekeeping has been a long-standing tradition in Rwanda for many decades, passed down through the generations and carried out by established norms and practices. As Rwanda's export and domestic markets expanded, so did the importance of beekeeping. Several researchers have devoted their efforts to overcoming the difficulties connected with monitoring and controlling beehives, all in the quest to optimize honey yield. However, there is still a persistent gap, resulting in unsatisfactory production levels. When it comes to honey production, security is a major issue that concerns beekeeping in numerous parts of the world. People not only steal valuable hives, but they also steal honey during harvesting season, leaving empty hives behind. Some animals destroy the beehives, for instance large invasion of ants. To address these concerns, a cutting-edge smart device has evolved that effectively monitors in real time and controls beehives by using the power of the Internet of Things (IoT). This smart device, which is equipped with an array of modern sensors including temperature, humidity, light, gas, sound, weight, and motion sensors, revolutionizes beekeeping methods where hives can be monitored using phones or computers. It ensures the monitoring of beehive conditions and reliable detection of moving objects such as humans or animals. In the case of an intruder, the system uses a GSM communication module, to promptly notify the hive owner in real time while also activating a siren to alert the local neighborhood. Notably, the smart device uses solar energy to power itself via an inbuilt solar panel, making it an eco-friendly and sustainable energy source. This breakthrough assures uninterrupted operation and removes the need for battery replacements regularly. The device has been successfully developed and taken to beekeepers for its real-time application and it will be improved by utilizing machine learning techniques to create an autonomous analytical model by combini

关键词： Beehive Internet of Things GSM

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A Multi-label Classification Approach to Detect Types of Traumas on Social Media 1

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Joshi, Sharvari Parikh, Aayush Walavalkar, Praniket Vekaria, Varun Sonawane, Pankaj Dwarkadas J Sanghvi Coll Engn Mumbai India

ISBN: (数字)9789819720538

ISBN: (纸本)9789819720521;9789819720538

In an age of blooming virtual communities, social media is the apex of mass divulgence and idea expression. Access to a plethora of social media platforms allows people to publicly share their joys and sorrows. However, deciphering a message or comment's genuine sentiment or trauma is challenging. This study aims to categorize text information mined from Reddit posts or comments into one or more of the 22 distinct categories of trauma. The proposed workflow starts with the collection of textual data followed by generation of text embeddings for each text. After being processed, the data is fed to Light Gradient Boosting Machine (LGBM), Support Vector Classifier (SVC), and Random Forest (RF) classifiers for single-label classification and LGBM with Binary Relevance and Random k-Label sets with Overlap (RAKELO) for multi-label classification. The maximum accuracy for single-label classification was yielded by LGBM (94%), whereas the highest accuracy for multi-label data was achieved by RAKELO (72%).

关键词： Multi-label classification Random forest Support vector machine Random k-label sets Light gradient boosting machine Binary relevance

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Interpretable Machine Learning Based Prediction of Bearing Capacity of Rectangular RC Columns 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Yu, Dehu Zhang, Shicheng Yang, Shujuan Wang, Chunhui Shandong Jianzhu Univ Jinan 250101 Peoples R China Qingdao Univ Technol Qingdao 266033 Shandong Peoples R China

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Reinforced concrete (RC) columns are the main stress components in reinforced concrete structures;accurately obtaining the yield load and ultimate load of RC columns under low cyclic load can guarantee the safety of reinforced concrete structures and improve structural stability and reliability. Firstly, 160 sets of test data of RC columns were collected. Five machine learning algorithms, namely, Random Forest, AdaBoost, GBRT, XGBoost, and CatBoost, were used to establish the yield load and ultimate load prediction models, and the Bayesian optimisation algorithm was used to optimise the hyper-parameter combinations of the prediction models, to obtain the five prediction models under the optimal hyper-parameter combinations. Then, different evaluation indexes are used to compare the prediction results of the optimised prediction models horizontally, and the empirical formula is used to calculate the ultimate bearing capacity of the test set sample data;the calculated results are compared with the prediction results of the optimal model. Finally, the SHAP method gives the global and local interpretations of the optimal prediction models. The results show that among the five machine learning prediction models, the CatBoost algorithm-based prediction model outperforms the other prediction models and empirical formulas in four evaluation indexes, R2, MAE, MAPE, and MSE, in yield and ultimate load prediction, and R2 reaches 0.9347 for the yield load prediction model and 0.9382 for the ultimate load prediction model, the two prediction models have higher overall prediction accuracy. Among all the essential characteristics, the shear-to-span ratio, section height, axial compression ratio, and section width have a more significant influence on the predictions of yield and ultimate loads;the results of the study can provide a reference for the calculation of yield and ultimate loads of rectangular RC columns under low-cycle reversed loading.

关键词： Machine Learning RC Column Hyperparameter Optimization SHAP CatBoost

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Is Artificial Intelligence a "Learning Material" in Higher Education? Students Perception on Using Artificial Intelligence on Language Learning 4th

Is Artificial Intelligence a "Learning Material" in Higher E...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Bantoto, Franchesca Marie Flores, Berhana Jindal, Mayank Western Mindanao State Univ Zamboanga Philippines

ISBN: (纸本)9783031686528;9783031686535

In the expansion of artificial intelligence technology, a few is shed to light of its use for learning of a language in the Philippine setting. Push-Pull Mooring Habit (PPMH) Theoretical Framework was adapted to analyze the perception of students in using chatbots in higher education. This study used a descriptive-quantitative research design with a 12-item survey questionnaire consisting of three different aspects to measure the perception of students in terms of Performance Expectancy, Effort Expectancy, and Social Influence. Artificial intelligence is being used in the educational field as well as by organizations and governments. However, there is not much known research on artificial intelligence usage in higher education for language learning. The result suggest that the students are ready to adapt artificial intelligence in their English language learning.

关键词： Pre-service teachers artificial intelligence

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Interpolation and Prediction of Piezometric Multivariate Time Series Based on Data Augmentation and Transformers

Interpolation and Prediction of Piezometric Multivariate Tim...

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Intelligent systems Conference (IntelliSys)

作者： Rabah, Mohamed Louay Mellouli, Nedra Farah, Imed Riadh Univ Manouba RIADI Lab Manouba 2010 Tunisia Univ Paris 08 LIASD Lab F-93200 Paris France

ISBN: (纸本)9783031477232;9783031477249

The aim of this study is to predict groundwater levels for several stations in France using a dataset composed of piezometric data, precipitation, evaporation, and stream flow as input to train the models. One of the primary challenges addressed in this study is the data gaps, which are interpolated to fill the missing values. The study is divided into three main parts. In the first part, we analysed the data by running various statistical tests to identify trends and patterns in the data. The second part of the study focused on interpolation, which involved filling in the missing values in the dataset. We compared four approaches: XGBoost, linear regression, KNN, and our proposed approach called SMOTEINT. The third and final part of the study involved predicting groundwater levels using several approaches. We compared three existing methods: VAR, LSTM, and GRU. Our main contribution to this part of the study was the application of transformers in ground-water prediction. We evaluated the performance of each method and identified the most effective approach for predicting groundwater levels using the given dataset. Overall, the study provides valuable insights into the use of various approaches for analyzing and predicting groundwater levels, which can have important implications for resource management and environmental protection efforts.

关键词： Groundwater Transformers Recurrent Neural networks ARIMA Interpolation Data Augmentation Multivariate Time Series

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Estimation of Kidney's Blood Vessels Deformations for Robot-Assisted Surgery 18th

Estimation of Kidney's Blood Vessels Deformations for Robot-...

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Lastrico, Riccardo Maccio, Simone Carfi, Alessandro Traverso, Paolo Mastrogiovanni, Fulvio Univ Genoa Dept Informat Bioengn Robot & Syst Engn Via Opera Pia 13 I-16145 Genoa Italy Univ Genoa Dipartimento Sci Chirurg & Diag Integrate Viale Benedetto XV 6 I-16132 Genoa Italy

ISBN: (纸本)9783031449802;9783031449819

Surgical robotics may use augmented reality (AR) to provide surgeons with contextual information during operations. Relevant information to show to the surgeon are soft-tissue deformations. However, real-time computation of soft-tissue deformations is challenging with current techniques. A novel approach is proposed using Machine Learning (ML) to estimate, based on organ pose, deformations in the vein and artery. Simulated experiments were conducted using a kidney model from a patient's CT scan in a Simulation Open Framework Architecture (SOFA) platform, and data on blood vessel deformations were collected. Two ML models were trained, a feed-forward neural network and an LSTM, and their performances were compared. Although the results are preliminary, they demonstrate the potential of the proposed approach, particularly given the limited information required to perform the inference.

关键词： Soft tissue deformation Augmented surgery Machine learning

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A Bearing Fault Diagnosis Framework Based on Few-Shot Learning with Distribution Consistency and Structural Reparameterization 7th

A Bearing Fault Diagnosis Framework Based on Few-Shot Learni...

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7th International Conference on Green Technology and Sustainable Development

作者： Gia-Bao Truong Nhu-Linh Than Manh-Hung Vu Van-Truong Pham Thi-Hue Nguyen Thi-Thao Tran Hanoi Univ Sci & Technol Sch Elect & Elect Engn Dept Automat Engn Hanoi Vietnam

ISBN: (纸本)9783031761966;9783031761973

Nowadays, end-to-end deep learning networks inherit the development of convolutional neural networks to have good performance, but they can only do well when they are learned on a large number of samples. However, building a large dataset is difficult for specific data, such as bearing faults. In this paper, a novel end-to-end few-shot model is proposed for bearing fault diagnosis even in the case of limited data with artificial and real faults. The architecture is designed to optimally exploit the feature extraction process as well as the covariance metric based on the distribution consistency for the few-shot classification tasks. In particular, we propose a module for the feature extraction stage with structural re-parameterization combined with a self-attention mechanism. Specifically, the input data goes through the STEM block and is then put into three stages, which consist of two stages called RepMixer block using re-parameterizing skip connections and the other stage called Attention block. We also use a covariance metric to help the feature extractor learn more precise features and enhance its discriminative performance during training. Experimental results based on the public imbalanced dataset CWRU, with different degrees of training samples, show that our model can significantly improve the accuracy of identifying faults-bearing to guarantee safety in industrial systems. Our code is released at https://***/giabao804/few-shot-structural-rep.

关键词： Bearing faults diagnosis Few-shot learning Self attention Mechanism Structural reparameterization Distribution consistency

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A Text Mining Procedure for Recognizing the Awareness of Industry 4.0 and Digitalization Perceptions in Construction Industry 1

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Sahin, Ceren Yildirim, Nihan Kaya, Tolga Istanbul Tech Univ Management Fac Istanbul Turkiye

ISBN: (数字)9783031671951

ISBN: (纸本)9783031671944;9783031671951

Construction industry is one of the most labor and productivity intense industries, requires a continuous improvement at every time. The basic ones are through technological destruction. In the era of Industry 4.0 and Society 5.0, focus is on machine learning, artificial intelligence and deep learning algorithms by limitless attempts. Nevertheless, in the sense of urgency, industry practitioners do not consider these approaches as much. With that ground, this paper analyzes if the terms artificial intelligence, machine learning, deep learning, internet of things (considered as digitalization), are known by construction industry practitioners. Text mining methodology through Naive Bayes Algorithm in Turkish construction practitioners is used with lists of non-academical media in Turkish construction industry journals (n = 59). Text mining approach is based on "wordcloud" and "sentiment analysis" according to the Bayesian Theorem without classifying and pointing the emotions using R programming and corresponding packages. Model supports the hypothesis with 79% performance. Results showed that practitioners are substantially behind digitalization attempts in understanding and field needs to be consolidated by practice at construction sites.

关键词： Industry 4.0 construction industry text mining machine learning digitalization

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Deep Convolutional Neural Network (DCNN) for the Identification of Striping in Images of Blood Cells 2nd

Deep Convolutional Neural Network (DCNN) for the Identificat...

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Ahmed, Saadaldeen Rashid Khaleel, Mahdi Fadil Abubaker, Brwa Abdulrahman Sulaiman, Sazan Kamal Hussain, Abadal-Salam T. Taha, Taha A. Fadhil, Mohammed Alayan Univ Coll Engn Artificial Intelligence Engn Dept Nasiriyah Iraq Northern Tech Univ Tech Inst Kirkuk Kirkuk Iraq Bayan Univ Comp Sci Erbil Iraq Knowledge Univ Coll Engn Dept Comp Engn Erbil Iraq Al Kitab Univ Tech Engn Coll Dept Med Instrumentat Tech Engn Kirkuk Iraq Northern Tech Univ Unit Renewable Energy Kirkuk Iraq

ISBN: (纸本)9783031628801;9783031628818

To discover and map mineral zones, hyperspectral remote sensing collects reflectance or emittance data in many contiguous and narrow spectral bands. Due to biology and biological data, it is easier to comprehend the physical characteristics of surface-captured images and filters. We use a Deep Convolutional Neural Network to DE stripe hyperspectral remote sensing images using the Hyperion dataset (DCNN). By comparing the widely used layers of the DCNN model for de-striping hyperspectral images, it is easily obvious how crucial it is to undertake proper pre-processing of Hyperion data due to its low signal-to-noise ratio. Using the techniques, the results reveal a significant reduction in the black hue and all higher stripes in an image, which is directly linked to the change of Hyperion data. Hyperion imagery, on the other hand, can de-stripe hyperspectral images using a DCNN model with a 91.89 percent success rate. The suggested DCNN can attain high accuracy 150 s after the start of the evaluation phase and maintain it throughout. This would be an acceptable choice given that the high inference time of the pre-trained DCNN model technique is less than that of currently known strategies, which are less effective for de-striping.

关键词： Hyperspectral imagery biological data de-striping DCNN Hyperion data

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