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A Rethinking of Real-Time Computer Vision-Based Lane Detection

A Rethinking of Real-Time Computer Vision-Based Lane Detecti...

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IEEE international conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Denis Vajak Mario Vranješ Ratko Grbić Nikola Teslić Faculty of Electrical Engineering Computer Science and Information Technology Osijek Kneza Trpimira 2b Osijek Croatia Narodnog fronta 23A Novi Sad Serbia

ISBN: (纸本)9781665428347

Lane detection (LD) is one of the most important Advanced Driver Assistance Systems (ADASs) in modern vehicles. In this paper, a new solution for LD based solely on classical computer vision, dubbed HistWind, is presented. A great advantage of the proposed solution is real-time application, and no need for powerful hardware. HistWind is based on a combination of image preprocessing, histogram peak detection, and sliding window line detection and fitting. the parameters are adjusted using particle swarm optimization against an existing training data set and then compared with an existing and freely available deep learning-based solution on a testing set. the results show that HistWind achieves comparable results in terms of precision, recall, and F1 measures, but significantly lower execution time when operating on a CPU. this makes HistWind suitable for real-time implementation on limited hardware resources, which is often installed in modern vehicles. HistWind can process 25 frames per second when processing 1640x590 pixel frames and running on AMD Ryzen 5 1600 CPU with 24 GB of RAM.

关键词： Computer vision Lane detection Training data Random access memory Particle measurements Real-time systems Hardware

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COVID-19 Time Series Forecasting of Daily Cases, Deaths Caused and Recovered Cases using Long Short Term Memory Networks 5

COVID-19 Time Series Forecasting of Daily Cases, Deaths Caus...

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5th IEEE international conference on Computing Communication and Automation, ICCCA 2020

作者： Bodapati, Suraj Bandarupally, Harika Trupthi, M. Jp Morgan Chase Co. Hyderabad India Chaitanya Bharathi Institue of Technology Hyderabad Computer Science and Engineering India Chaitanya Bharathi Institue of Technology Information Technology Hyderabad India

ISBN: (纸本)9781728163246

Novel Coronavirus (COVID-19) outbreak that emerged originally in Wuhan, the Hubei province of China has put the entire human race at risk. this virus was declared as Pandemic on 11th March 2020. Considering the massive growth rate in the number of cases and highly contagious nature of the virus, machine learning prediction models and algorithms are essential to predict the number of cases in the coming days. this could help in reducing the stress on health care systems and administrations by helping them plan better. In this paper the datasets used are obtained from the John Hopkins University's publicly available datasets to develop a state-of-the-art forecasting model of COVID-19 outbreak. We have incorporated data-driven estimations and time series analysis to predict the trends in coming days such as the number of cases confirmed positive, number of deaths caused by the virus and number of people recovered from the novel coronavirus. To achieve the estimations, we have used the Deep learning model long-shortterm memory network (LSTM). © 2020 IEEE.

关键词： COVID-19

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Sentiment Analysis Using Language Models: A Study

Sentiment Analysis Using Language Models: A Study

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Spraha Kumawat Inna Yadav Nisha Pahal Deepti Goel Amity University Noida India RIL Mumbai India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Sentiment analysis is concerned with extracting sentiment to ascertain the attitudes, and emotions associated with the text. It is broadly applied to voice of the customer as they convey their experience and feelings more blatantly which is why comprehending customer's emotions is a must. To identify and classify these unstructured emotions natural language processing (NLP) and machine learning approaches have been adopted in recent times. the main issue with the existing techniques is the inability to deal with the correct interpretation of context owing to lack of labeled data. In this paper, we studied deep neural network based language models to interpret and classify textual sequences into positive, negative or neutral emotions which remove the bottleneck of explicit human labeling. these models were analyzed and evaluations were performed on the Twitter US Airline Sentiment dataset. We have observed a considerable amount of improvements with respect to prior state-of-the-art approaches which closes the gap with supervised feature learning.

关键词： Deep learning Sentiment analysis Analytical models Social networking (online) Atmospheric modeling Transformer cores data models

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20th international TRIZ Future conference on automated Invention for Smart Industries, TFC 2020

20th International TRIZ Future Conference on Automated Inven...

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20th international TRIZ Future conference on automated Invention for Smart Industries, TFC 2020

ISBN: (纸本)9783030612948

the proceedings contain 34 papers. the special focus in this conference is on automated Invention for Smart Industries. the topics include: Innovative Method of Patent Design Around Guided by Technological Evolution;dynamization and Real Options – Discussing the Economic Success of Design Inventions;TRIZ-Based Approach for Improving the Adoption of Open Innovation 2.0;algorithm for Idea Landscaping and Prioritization;Conceptual Semantic Analysis of Patents and Scientific Publications Based on TRIZ Tools;build Links Between Problems and Solutions in the Patent;summarization as a Denoising Extraction Tool;problem-Solving Tools as Methods for Managing the Information Content of Systems;easy-to-Use Ideation Technique Based on Five Cross-Industry Analogies Enhances engineering Creativity of Students and Specialists;preface;Managing AI Technologies in Earthwork Construction: A TRIZ-Based Innovation Approach;MOOC vs Face to Face (F2F) Model: What Relevance for Creativity Development?;Effective Improvement Solutions in Organizations Using data Envelopment Analysis (DEA) and TRIZ: A Case Study in Higher Education;Understanding and Overcoming the Low Utilization Rate of ARIZ in TRIZ Practices;excesses in engineering Systems: A Helpful Resource;learning Eco-Innovation from Nature: Towards Identification of Solution Principles Without Secondary Eco-Problems;A Future Eco-Design Framework Based on TRIZs Contradictions and Bio-Inspired Design Process;TRIZ to Solve Challenges for Designing Sustainable, intelligent and Inclusive Buildings;Application of TRIZ Substance-Field Analysis and Situational Analysis for Risk Analysis and Development of Common Language Among Stakeholders;How to Organize a Knowledge Basis Using TRIZ Evolution Tree: A Case About Sustainable Food Packaging;an Approach of Product-Type Process Trimming.

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Evaluating the Impact of Traveling on COVID-19 Prevalence and Predicting the New Confirmed Cases According to the Travel Rate Using Machine learning: A Case Study in Iran

Evaluating the Impact of Traveling on COVID-19 Prevalence an...

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international econference on Computer and Knowledge engineering (ICCKE)

作者： Anita Ghandehari Soheil Shirvani Hadi Moradi School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665402095

COVID-19 which has spread in Iran from February 19, 2020, infected 1,550,142 people and killed 59,264 people until February 18, 2021. the immediate suggested solution to prevent the spread of this virus was to avoid traveling around. Unfortunately, on many occasions, this restriction was not enforced or respected by the citizens. thus, the goal of this study was to evaluate the impact of traveling on the COVID-19 prevalence by measuring the correlation between traveling data and new confirmed cases of COVID-19 in Iran. the data consists of the daily traffic between Iran’s provinces, air traffic, and daily COVID-19 new confirmed cases. In the first step, the importance analysis was used to determine the impact of different kinds of traveling on the COVID-19 spread. In the second step, KNN, Random Forest (RF), and Support Vector Regression (SVR) were used to predict the effect of traveling on the number of new COVID-19 cases. Although the available data was very coarse and there were no details of inner-cities commute, an R-squared of 0.89 and 0.86 on the train and test datasets was achieved respectively, showing a positive correlation between the number of travels between states and the new confirmed cases of COVID-19. It was also shown that there was an 8-day incubation period. Consequently, by considering this period the voting regressor model reached 0.92 and 0.98 R 2 scores for test and train datasets respectively. the result confirms that one of the best ways to avoid the spread of the virus is limiting or eliminating traveling around.

关键词： COVID-19 Support vector machines Radio frequency Analytical models Correlation Urban areas Coronaviruses

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A Review of Sentiment Analysis Techniques using Soft Computing Approaches

A Review of Sentiment Analysis Techniques using Soft Computi...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Upasana Jha Lakshya Tyagi Divya Kansal Subhecha Chakraborty Abhishek Singhal ASET Amity University Uttar Pradesh Noida India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Sentiment analysis and opinion mining have taken a leading position ever since the boom of the internet which led to an enormous amount of opinionated data being posted online. Hence, it has become essential to structure an efficient algorithm for analysing sentiments from text. Even though traditional machine learning algorithms were a breakthrough in the domain, there remained a demand for a better solution. this paper presents a systematic & qualitative literature review for various approaches to sentiment analysis using hybridized soft-computing techniques to cater to noise and improve the flexibility of a fuzzy inference-based sentiment analysis system. this qualitative literature review adds to the body of evidence that soft-computing and fuzzy logic techniques can be effectively applied to a range of applications where noise is inherent, especially in the case of sentiment analysis and opinion mining.

关键词： Fuzzy logic Support vector machines Sentiment analysis Uncertainty Machine learning algorithms Systematics Bibliographies

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Feature Extraction from EEG Signals: A deep learning perspective

Feature Extraction from EEG Signals: A deep learning perspec...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Awwab Mohammad Farheen Siddiqui M. Afshar Alam Jamia Hamdard New Delhi India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

the implementation of the learning model is primarily dependent on the features extracted from the EEG signals for any mental task classification model. A feature depicts an identifiable measurement, a unique property and a functional factor achieved from a section of a pattern. the main features which are extracted are mainly intended to limit the damage of significant data embedded in the signal and are also used to reduce resources required to illustrate a vast set of data precisely. this is important to reduce the expense of data handling, to limit the intricacy of usage and to remove the necessity of compressing the data. Different methods are applied to extract the features from EEG signals; among these methods are Empirical Wavelet Transform (EWT), Fuzzy C-means algorithm, Fuzzy Region of Interest Activity (FURIA). Studies have been made on EEG signals because of its capability to produce a method to calculate brain stimulation which is broadly used in brain-computer interface researches with uses in rehabilitation engineering and medical studies. the main objectives of this paper are to discuss some established methods of EEG feature extraction methods, comparing their performances for a particular task and suggesting the most appropriate method for feature extraction based on performance.

关键词： Deep learning Feature extraction Brain modeling Electroencephalography Wavelet packets Discrete wavelet transforms Task analysis

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Deep learning-based computer-aided diagnosis tool for brain tumor classification

Deep learning-based computer-aided diagnosis tool for brain ...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Sakshi Ahuja B.K. Panigrahi Tapan Gandhi Utkarsh Gautam IITD New Delhi India Research & Development NTT Data Noida India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

In the medical field, manual analysis of diseases using numerous medical imaging modalities is a challenging task. the proposed work presents a deep learning-based Computer-Aided Diagnosis (CAD) tool for the classification and localization of brain tumors. the CAD tool is implemented to classify and localize three types of brain tumors from the CA-MRI brain dataset (in all three views, i.e., axial, sagittal, coronal). the input brain MRI dataset is pre-processed and divided into a 70% training set, 15% testing set, 15% of the validation set. the deep learning-based model ineeption- ResNet-v2 is trained on an augmented training dataset. the performance of the pre-trained deep learning model is evaluated using numerous statistical parameters. the pretrained deep learning model obtained an accuracy of 98.72% on the training set, recall rate of 99.56%, and an Area Under Curve (AUC) of 1. Finally, the CAD tool is designed with the pre-trained deep learning model, and feature maps to localize the tumor area in multi-orientation testing images.

关键词： Training Deep learning Solid modeling Tools Brain modeling Tumors Testing

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Causal learning to Discover Supply Chain Vulnerability 11

Causal Learning to Discover Supply Chain Vulnerability

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11th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management (IC3K) / 11th international conference on Knowledge Discovery and Information Retrieval (KDIR)

作者： Zhao, Ying Jones, Jacob MacKinnon, Douglas Naval Postgrad Sch Monterey CA 93943 USA

ISBN: (纸本)9789897583827

this paper illustrates a methodology of causal learning using pair-wise associations discovered from data. Taking advantage of a U.S. Department of Defense supply chain use case, this causal learning approach was substantiated and demonstrated in the application of discovering supply chain vulnerabilities. By integrating lexical link analysis, a data mining tool used to discover relationships in specific vocabularies or lexical terms with pair-wise causal learning, supply chain vulnerabilities were recognized. Evaluation of results from this methodology reveals supply chain opportunities, while exposing weaknesses to develop a more responsive and efficient supply chain system.

关键词： Causal learning Counterfactual Analysis Cause and Effect Supply Chain Vulnerability Associations Correlations Lexical Link Analysis data Mining

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Explainable Chance-Constrained Programming for VR Video Resource Allocation 18th

Explainable Chance-Constrained Programming for VR Video Res...

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18th international conference on intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pan, Jeng-Shyang Song, Pei-Cheng Chu, Shu-Chuan Watada, Junzo Li, Jun-Bao College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Graduate School of Information Production and Systems Waseda University Kitakyushu Japan School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819906048

In view of the impact of uncertain factors faced by Virtual Reality (VR) video services in the Internet of Vehicles, if the impact of different uncertain factors can be identified, targeted optimization can be made to achieve a better and more personalized user experience. A stochastic resource allocation model for VR video services is developed and turned into a chance-constrained programming problem, which is solved using an optimization solver. this work discusses the concept of shaply value in cooperative games for the first time in order to analyze the marginal contributions of different random variables in chance-constrained programming, and proposes an Explainable Chance-Constrained Programming (ECCP). the marginal contributions of different random variables under different confidence levels and probability distributions are exhibited and evaluated in the experimental test, allowing the degree of influence of uncertainty factors to be explained and analyzed. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Random variables

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