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Calibration and Absolute Pose Estimation of Trinocular Linear Camera Array for Smart City Applications 25

Calibration and Absolute Pose Estimation of Trinocular Linea...

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25th international conference on pattern recognition (ICPR)

作者： Ahrnbom, Martin Nilsson, Mikael Ardo, Hakan Astrom, Kalle Yastremska-Kravchenko, Oksana Laureshyn, Aliaksei Lund Univ Fac Engn LTH Ctr Math Sci Lund Sweden Axis Commun AB Lund Sweden Lund Univ Fac Engn LTH Dept Technol & Soc Transport & Rd Lund Sweden

ISBN: (纸本)9781728188089

A method for calibrating a Trinocular Linear Camera Array (TLCA) for traffic surveillance applications, such as towards smart cities, is presented. A TLCA-specific parametrization guarantees that the calibration finds a model where all the cameras are on a straight line. The method uses both a chequerboard close to the camera, as well as measured 3D points far from the camera: points measured in world coordinates, as well as their corresponding 2D points found manually in the i mages. Superior calibration accuracy can be obtained compared to standard methods using only a single data source, largely due to the use of chequerboards, while the line constraint in the parametrization allows for joint rectification. The improved triangulation accuracy, from 8-12 cm to around 6 cm when calibrating with 30-50 points in our experiment, allowing better road user analysis. The method is demonstrated by a proof-of-concept application where a point cloud is generated from multiple disparity maps, visualizing road user detections in 3D.

关键词： Cameras

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machine learning Approach for Discovering Successful Books Based on the Achievement of Award-Winning

Machine Learning Approach for Discovering Successful Books B...

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Advanced Research in Computing (ICARC), international conference on

作者： S. M. S. T. Wijekoon R. A. H. M. Rupasingha Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Economics and Statistics Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

Many people are very interested in reading books. Some of them like to read books for various reasons like as a hobby or to learn something new. At present, with modern technology, people have become accustomed to reading books online in addition to physically reading books. The most important factor in identifying which books are the most successful is the award-winning. The goal of this research is to forecast how the most successful books such as award/winning books are identified based on 10 distinct attributes including publishing date, average ratings, author, genre, language, publisher, number of pages, ratings, title, and reviews. A data set was gathered for this purpose through the online community platform Kaggle. The dataset provides information about books gathered from the website 'Goodread' for the period between 2000 and 2021. Firstly, the data set has been pre-processed. For that, duplicate data, unnecessary special characters, and missing values have been removed. To rank the preprocessed data, the Waikato Environment for Knowledge Analysis (WEKA) data mining tool was utilized, and results are improved by hyperparameter tuning. The predictive model is generated using six machine learning approaches at the classification level: Random Forest, Support Vector machine (SVM), Decision Trees, Multilayer Perceptron (MLP), Logistic Regression, and Naive Bayes. A high accuracy of 86.32% was gained with the lowest error rates in addition to high precision, recall, and f-measure values using a Naive Bayes algorithm. Based on the results, the Naive Bayes algorithm makes a reliable prediction of how to find the most successful books using the above features.

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Making Smart Contracts Smarter 3

Making Smart Contracts Smarter

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3rd IEEE international conference on Blockchain and Cryptocurrency (IEEE ICBC)

作者： Badruddoja, Syed Dantu, Ram He, Yanyan Upadhayay, Kritagya Thompson, Mark Univ North Texas Dept Comp Sci & Engn Denton TX 76207 USA

ISBN: (纸本)9781665435789

Blockchain technology develops static smart contracts for decentralized business transactions, lacks dynamic decision-making capabilities that limit the possibilities of ever-increasing demands of modern business applications. Artificial intelligence, a computational prediction platform provides intelligent predictions, actions, and recognition that lacks the ability to hold on to the integrity of the prediction result and requires the help of external authorities to secure the system. Blockchain-based AI prediction can cover the gaps of individual technologies and can mutually benefit from one another to develop a decentralized machine learning architecture that promises to yield better security, automation, and dynamism of the application. This paper proposes a Naive Bayes prediction algorithm to perform prediction with inside blockchain smart contracts that promises to open up more opportunities in the field of Blockchain-AI decentralized applications.

关键词： Blockchain DApp Smart Contract machine learning Artificial Intelligence Naive Bayes

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Multi-layer Integrated Extreme learning machine for Mechanical Fault Diagnosis of High-Voltage Circuit Breaker 3rd

Multi-layer Integrated Extreme Learning Machine for Mechanic...

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Li, Xiaofeng Zhang, Tao Guo, Wenyong Wang, Sheng College of Power Engineering Naval University of Engineering Wuhan430072 China

ISBN: (纸本)9789811961342

Mechanical fault caused by abnormal joint clearance jeopardizes the safety of high-voltage circuit breakers (HVCB). Accurate fault diagnosis is of great significance to ensure the reliable service. Existing prediction approaches are generally based on individual classifiers, which may fail to achieve satisfactory performance, especially in multi-class fault diagnosis. In this paper, a multi-layer integrated extreme learning machine (IELM) is proposed. At first, clearance joint most likely to induce mechanical fault is found by finite element analysis. Then experiments under various working conditions are carried out, and the collected vibration is decomposed by variational mode decomposition. In feature extraction, the energy distribution in the different frequency domains is considered as the model input. In IELM, each ELM module in different layers is trained independently, and its optimal hidden layer number is adaptively searched. Then it is employed as a sub-classifier for the final diagnosis model. The fault diagnosis process of IELM is similar to a multi-stage filter since the data diagnosed by the previous layer is dynamically removed. It is demonstrated that the proposed method not only achieves the highest fault diagnosis accuracy than its counterparts but also maintains a certain recognition accuracy of unknown mechanical fault. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Comparison between 2D Classification and 3D Classification based on Deep learning

Comparison between 2D Classification and 3D Classification b...

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Embedded Systems and Artificial Intelligence (ESAI), international conference on

作者： Zouhir El Khalfi Soulaiman El Hazzat Majid Ben Yakhlef LSI Department of Computer Science Polydisciplinary Faculty of Taza Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

The rise in processing power of computers and the availability of a considerable amount of data have allowed the overlap of several machine learning approaches. This has led to breakthroughs on many deep learning tasks as speech recognition, natural language processing (NLP), object detection and image analysis. Thus, many fields as autonomous driving, industrial quality processes and mobile robotic have released multiples researches in deep learning algorithmic topics. In this paper, we have made a comparison, in terms of accuracy and process timing, between classifications based on 3D points cloud and 2D images for the same input dataset in raw format, and generated basic outlines of both deep learning methods using algorithms based on convolution networks. We discovered that 3D points cloud based classification model was slow on training stage than 2D images model timing due to the 3D complexity data representation. Besides that, classification neural networks based on 3D points cloud data performed better on accuracy than the networks based on 2D images datasets. It is to highlight that 3D based systems are so critical in terms of data processing compared to 2D images due to: data format as points cloud, meshes and volumes, the hardware resources requirement which is higher due to the third added dimension. Finally, we hypothesize that systems which would combine both of these data geometry and networks architectures would return accuracy better and low processing time wise.

关键词： Point cloud compression Deep learning Solid modeling Three-dimensional displays Accuracy Convolution Natural language processing data models Timing Classification algorithms

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3rd international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2020

3rd International Conference on Recent Trends in Image Proce...

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3rd international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2020

ISBN: (纸本)9789811604928

The proceedings contain 78 papers. The special focus in this conference is on Recent Trends in Image Processing and pattern recognition. The topics include: Ensemble of Nested Dichotomies for Author Identification System Using Similarity-Based Textual Features;Feature Combination of Pauli and H/A/Alpha Decomposition for Improved Oil Spill Detection Using SAR;a Fast and Efficient Convolutional Neural Network for Fruit recognition and Classification;copy-Move Image Forgery Detection Using Discrete Wavelet Transform;a Comprehensive Survey of Different Phases for Involuntary System for Face Emotion recognition;classification of Vehicle Type on Indian Road Scene Based on Deep learning;indian Road Lanes Detection Based on Regression and clustering using Video Processing Techniques;detection of Emotion Intensity Using Face recognition;double Authentication System Based on Face Identification and Lipreading;fuzzy Approach to Evaluate Performance of Teaching Staff in Technical Institutions;safety Gear Check at Industries and Laboratories Using Convolutional Neural Network Based on Deep learning;analysis of Changing Trends in Textual data Representation;detection of Falsary Happening on Social Media Using Image Processing: Feature Extraction and Matching;Development of Multi Faces recognition System Using HOG Features and Neural Network Classifier in Real Time Environment;extraction of Key Frame from Random Videos Based On Discrete Cosine Transformation;Prediction of SO2 Air Pollution Quality Parameter of Kolhapur City Using Time Series Analysis;A Big data Prediction for Weather Forecast Using Hybrid ARIMA-ANN Time Series Model;automatic Detection of Riots Using Deep learning;protecting Big data Sets from Unauthorized Users on Cloud;text Categorization: A Lazy learning-Based Approach.

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Improved Prediction of Electric Vehicle User Behavior with machine learning based Analysis

Improved Prediction of Electric Vehicle User Behavior with M...

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Smart Technologies for Power, Energy and Control (STPEC), IEEE international conference on

作者： Kavita Kiran Prasad Akshay Mali Vivek Agarwal Dept. of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

With the rapid growth of Electric Vehicles (EVs), charging load demand is increasing significantly. Energy providers need to analyze the load requirements and meet them efficiently. Due to the highly nonlinear characteristics of EV user behavior, researchers are turning to machine learning (ML) models suited for nonlinear data. In the proposed study, the Adaptive Charging Network (ACN)’s public charging dataset is utilized to predict EV user behavior. New features like traffic, charging currents, and number of connection-disconnection events are integrated along with the weather data to improve prediction accuracy. Also, the features are included by observing the pattern in one-hour slots, which results in substantial improvements compared to previous work. While existing research mainly focuses on session duration and energy consumption, this work also predicts the charging duration, which is useful to get an idea of when the EV is ready to use. It is shown that ensemble model performs the best among various ML models, achieving a Mean Absolute Error (MAE) of 56 minutes for session duration and 43.2 minutes for charging duration. Also, the energy consumption is best predicted with Random Forest (RF) regression with MAE of 1.36 kWh. All the key results of this work are presented.

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3rd international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2020

3rd International Conference on Recent Trends in Image Proce...

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3rd international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2020

ISBN: (纸本)9789811605062

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3rd international conference on Innovative Computing and Communication, ICICC 2020

3rd International Conference on Innovative Computing and Com...

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3rd international conference on Innovative Computing and Communication, ICICC 2020

ISBN: (纸本)9789811551123

The proceedings contain 196 papers. The special focus in this conference is on Innovative Computing and Communication. The topics include: A Primer on Opinion mining: The Growing Research Area;a Generic Framework for Evolution of Deep Neural Networks Using Genetic Algorithms;multimodal Medical Image Fusion Based on Discrete Wavelet Transform and Genetic Algorithm;driver Fatigue and Distraction Analysis Using machine learning Algorithms;a Hybridized Auto-encoder and Convolution Neural Network-Based Model for Plant Identification;curaBand: Health Monitoring and Warning System;optimization Algorithm-Based Artificial Neural Network Control of Nonlinear Systems;mHealth for Mental Health;elman and Jordan Recurrence in Convolutional Neural Networks Using Attention Window;state of Charge Estimation Using data-Driven Techniques for Storage Devices in Electric Vehicles;Application of Stochastic Approximation for Self-tuning of PID in Unmanned Surface Vehicles;dark web Activity on Tor—Investigation Challenges and Retrieval of Memory Artifacts;A Heterogeneous Dynamic Scheduling Minimize Energy—HDSME;face recognition-Based Automated Attendance System;Human Emotion recognition from Spontaneous Thermal Image Sequence Using GPU Accelerated Emotion Landmark Localization and Parallel Deep Emotion Net;delay Based Multi-controller Placement in Software Define Tactical Network;security in Cloud Computing for Sensitive data: Challenges and Propositions;Utility of Game Theory in Defensive Wireless Sensor Networks (WSNs);robust Approach for Emotion Classification Using Gait;routing in Internet of Things Using Cellular Automata;depression Detection Among Social Media Users Using machine learning;Security-Based LEACH Protocol for Wireless Sensor Network;Design of High-Speed FPGA Based CASU Using CBNS Arithmetic: Extension to CFFT Processor;blockchain-Based Boothless E-Voting System;preface.

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Recurrent Neural Networks based Bi-LSTM with Sentiment Analysis and Transfer learning approach for Medicine Recommendation System

Recurrent Neural Networks based Bi-LSTM with Sentiment Analy...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Sandeep Kumar Hegde Rajalaxmi Hegde Thangavel Murugan Department of CSE NMAM Institute of Technology NITTE (Deemed to be University) Karnataka India College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The implementation of an efficient medication recommendation system is indispensable for personalized medicine to improve patient outcomes by providing personalized treatment recommendations. This paper presents a comprehensive framework for medication recommendation based on transfer learning using deep learning methods like Recurrent Neural Networks (RNNs) and Bidirectional Long Short Term Memory networks (Bi-LSTM) along with sentiment analysis of user reviews for opinion mining. The proposed method implements a complex-sequential modeling architecture using an RNN-BiLSTM-driven deep network to model the complex sequential patterns and timing relationships among drug-related data, thereby preparing the system for learning and eventual adaptation based on past usage and feedback. Bi-LSTM retains context-information in both forward and reversed directions to attain better context understanding. Transfer learning implementation of the framework gains the knowledge from pre trained model pertaining to medical recommendation system towards ensuring high generalizability of the developed model. The proposed method essentially binds insights from historical users' data, user sentiments, and advanced deep learning to satisfy patients and treatment outcomes towards a more precise and personalized system for drug recommendations. The dataset required for conducting the experiment was gathered from the Kaggle repository. The experimental results have been validated by various measures such as ROC rate, precision, F1 Score, confusion matrix, and accuracy. The experimental results showed that the proposed method achieved a 97.6% accuracy using the measures considered, which is significantly better than traditional machine learning and deep learning algorithms.

关键词： Deep learning Sentiment analysis Adaptation models Accuracy Reviews Precision medicine Transfer learning data models Timing Recommender systems

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