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Traffic Sign and Vehicle Classification based on machine learning 2

Traffic Sign and Vehicle Classification based on Machine Lea...

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2nd international conference on Video, signal and Image processing, VSIP 2020

作者： Viktor, Malichenko Honggui, Han Beijing University of Technology China

ISBN: (纸本)9781450388931

In this paper, we offer a machine learning classifier model, later considered as MLCM, for classifying objects such as road signs and vehicles. Showing the influence of vocabulary size on accuracy of SVM using SURF. Based on SURF method used bag-of-words model as feature extractor. Due to its simplifying representation, it accelerates the first stage of our MLCM. We tested and analyzed accuracy of Support Vector machines, including Linear, Quadratic and Medium Gaussian SVM as flowed step model and automatically use best result for further estimation. Furthermore, we provide a brief introduction of applied methods and experimental results analysis. MLCM introduces combination of SURF method and several SVMs as well as optimized SVM. This technique shows good performance with minimum failures. Thereafter, it will be implemented for real-time video sequences. The achieved goal can be implemented in the use of self-driving of industrial machines with a safe speed. © 2020 ACM.

关键词： Support vector machines

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Automatic bird sound detection in long range field recordings using Wavelets & Mel filter bank features 2

Automatic bird sound detection in long range field recording...

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2nd IEEE international conference on Cognitive machine Intelligence (IEEE CogMI)

作者： Suhas, B. N. Penn State Univ EE Dept University Pk PA 16802 USA

ISBN: (纸本)9781728141442

The topic of bio-monitoring of fauna, especially that of birds, is an ongoing research topic. Although huge datasets of bird sound recordings are available, the classification of such sounds into bird & non bird sounds has been painstaking work, sometimes requiring manual processing. The goal of the IEEE Research Challenge in 2016 has been to address this concern and to help develop automatic algorithms for the detection of bird sounds [1]. An attempt has been made to compare and understand how wavelet based features perform against the current state of the art methods in audio processing in more detail. Two statistical/deep learning approaches namely Support Vector machines and Convolutional Neural Networks have been used to compare the results. Since most of the works in audio signal processing have employed Fourier based methods, there has been a stagnation in the development and usage of newer/other features for experiments. This is one of the points that has been addressed in this work. The Receiver Operating Characteristic curve (ROC) has been employed to test the diagnostic ability of the audio features. Wavelets have performed consistently and at a level similar to the best performing Fourier based methods highlighting the possibility of using such features as a viable alternate for future audio processing experiments.

关键词： wavelets audio processing filterbanks bird sound detection

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processing and analyzing health data in a big data context: Aspects and implementations 2nd

Processing and analyzing health data in a big data context: ...

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2nd international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

作者： Slime, Khaoula Maizate, Abderrahim Hassouni, Larbi ENSEM/UH2 Casablanca Morocco

ISBN: (纸本)9783030366636

Nowadays, the development of machine learning algorithms and big data frameworks make the processing of data a major asset in different sectors. Indeed, this technology allows us to carry out in-depth analyzes of large masses of data and thus generate very useful results for decision-making. Among the sectors where this technology is used is that of health in which analysis and prevention are essential to improve the quality of health care. Our research focuses on improving the application of this technology in the health field. In this paper we will present big data aspects, give some implementations and discuss different big data architectures in healthcare sector. © Springer Nature Switzerland AG 2020.

关键词： machine learning

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Improved machine learning based Classification Model for Early Autism Detection

Improved Machine Learning based Classification Model for Ear...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Tania Akter Md. Imran Khan Mohammad Hanif Ali Md. Shahriare Satu Md. Jamal Uddin Mohammad Ali Moni Jahangirnagar University Dhaka Bangladesh Gono Bishwabidyalay Dhaka Bangladesh NSTU Noakhali Bangladesh BSMRSTU Gopalgonj Bangladesh UNSW Digital Health Uiversity of New South Wales Sydney Australia

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Autism spectrum disorder is a complex, lifelong developmental disability where the affected people show repetitive behavior and faces abnormal communication challenges. The goal of this work is to propose an enhanced machine learning model that detects autism more accurately. Hence, we collected ASD datasets of toddler, child, adolescent, and adult from kaggle and UCI machine learning repository. The correlation among individual features was scrutinized and eliminated highly co-linear features in these datasets. Then, feature transformation methods including standardization and normalization were applied in these datasets. Different classifiers like artificial neural network, recurrent neural network, decision tree, extreme learning machine, gradient boost, k nearest neighbor, logistic regression, multilayer perceptron, naïve bayes, random forest, support vector machine, and xgboost were employed in these altered ASD datasets and determined their performances. Logistic regression shows the best result that outperforms other classifiers. This model is useful to extract significant traits and detect autism more precisely.

关键词： Support vector machines Autism Standardization signal processing Robots Regression tree analysis Logistics

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Twin Security Model—A machine learning-Based Approach for DDoS Attack Detection in SDN 1

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2nd international conference on Soft Computing and signal processing, ICSCSP 2019

作者： Singh, Sukhvinder Jayakumar, S.K.V. Department of Computer Science School of Engineering and Technology Pondicherry University Pondicherry India

ISBN: (数字)9789811524752

ISBN: (纸本)9789811524745

In the world of emerging network technologies, the software-defined network (SDN) architecture provides the global view of the entire network. Its agility and directly programming ability helped the administrators to dynamically adjust the network, so that whenever required the flow of the traffic can be directed to meet the changing needs of the network and also enhances the security perspective. SDN architecture actively monitors the incoming and outgoing traffic and identifies the threats to further enhance modification or addition of security policies by the administrator. Decoupling of control plane and data plane in SDN increased the attack perspective for the attackers (i.e., DDoS attack, replay attack, man-in-the-middle attack, overloading, etc.). This paper proposes a "twin security model" based on machine learning which amalgamates the two techniques so that the security performance with respect to detection rate will be improved. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network security

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A machine learning Approach for Graduate Admission Prediction 20

A Machine Learning Approach for Graduate Admission Predictio...

引用

2nd international conference on Image, Video and signal processing, IVSP 2020

作者： Alghamdi, Amal Barsheed, Amal Almshjary, Hanadi Alghamdi, Hanan Abdulaziz University Information Systems Dept. Saudi Arabia

ISBN: (纸本)9781450376952

With the increase in the number of graduates who wish to pursue their education, it becomes more challenging to get admission to the students' dream university. Newly graduate students usually are not knowledgeable of the requirements and the procedures of the postgraduate admission and might spent a considerable amount of money to get advice from consultancy organizations to help them identify their admission chances. However, giving the limited number of universities that can be considered by a human consultant, this approach might be bias and inaccurate. Thus, in this paper, a machine learning approach is developed to automatically predict the possibility of postgraduate admission to help graduates recognizing and targeting the universities which are best suitable for their profile. This paper evaluates three learning strategies of regression to predict the university rate given the students' profile;namely, linear regression, decision tree, and logistic regression model. This paper evaluates, these models to select the best model in terms of the highest accuracy rate and the least error. Logistic Regression model shows the most accurate prediction in our experiments, and hence, we suggest employing this model to predict the future applicant's university chance of admission. © 2020 ACM.

关键词： machine learning

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Doodle Recognition using machine learning for hearing and speech-impaired people 2

Doodle Recognition using machine learning for hearing and sp...

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2nd international conference on signal processing and Communication, ICSPC 2019

作者： Monica, Evangelyn D. Davu, Praharsha Caroline, Cynthia P. Jagannath, D.J. Karunya Institute of Technology and Sciences Dept of Electronics and Communication Engg Coimbatore India

ISBN: (纸本)9781728118499

In this paper, we test the different classifiers used in machine learning and compare the different accuracies for the doodles which are obtained from Google's Quick Draw Dataset. The classifier with the best accuracy can be fed into an application for the hearing and speech impaired people through which the can be trained to draw their immediate needs, and the machine learning model could classify it into the correct category, so it would be easy for the people nearby to help them. © 2019 IEEE.

关键词： Image classification

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machine learning Model To Predict Autism Investigating Eye-Tracking Dataset

Machine Learning Model To Predict Autism Investigating Eye-T...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Tania Akter Mohammad Hanif Ali Md. Imran Khan Md. Shahriare Satu Mohammad Ali Moni Jahangirnagar University Dhaka Bangladesh Gono Bishwabidyalay Dhaka Bangladesh NSTU Noakhali Bangladesh UNSW Digital Health University of New South Wales Sydney Australia

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Autism spectrum disorder is a neurodevelopmental disorder that characterizes by reducing concentration on social activities and improving interest in non-social tasks. The aim of this work is to investigate eye gazing images and identify autism applying various machine learning techniques. Therefore, we collected eye-tracking data from the Figshare data repository. But, these scanpath images were almost similar for normal and autistic children. To obtain similar groups, k-means clustering method was used and generated four clusters. Further, several classifiers were applied into primary data and these clusters and evaluated the performance of them using various metrics. After the assessment of overall results, MLP shows the highest 87% accuracy in cluster 1. In addition, it shows the best area under curve, f-measure, g-mean, sensitivity, specificity, fall out and miss rate respectively. This predictive model could notably useful to forecast ASD status at early stages.

关键词： Autism Sensitivity machine learning Predictive models signal processing Robot sensing systems Task analysis

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A Review on Scalable learning Approches on Intrusion Detection Dataset 2nd

A Review on Scalable Learning Approches on Intrusion Detecti...

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2nd international conference on Recent Innovations in Computing (ICRIC)

作者： Sahu, Santosh Kumar Mohapatra, Durga Prasad NIT Rourkela Dept CSE Rourkela Odisha India

ISBN: (纸本)9783030294076;9783030294069

There has been much excitement recently about Big Data and the dire need for data scientists who possess the ability to extract meaning from it. Data scientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But, now those large, complex datasets should process smartly. As a result, it improves productivity by reducing the computational process. As a result, Big Data analytics takes a vital role in intrusion detection. It provides tools to support structured, unstructured, and semi-structured data for analytics. Also, it offers scalable machine learning algorithms for fast processing of data using machine learning approach. It also provides tools to visualize a large amount of data in a practical way that motivates us to implement our model using scalable machine learning approach. In this work, we describe a scalable machine learning algorithm for threat classification. The algorithm has been designed to work even with a relatively small training set and support to classify a large volume of testing data. Different machine learning approaches implemented and evaluated using intrusion dataset. The data is normalized using the min-max normalization technique, and for SVM classification, data transforms into sparse representation for reducing computational time. Then using Apache Hive, we store the processed data into HDFS format. All the methods except the neural network are implemented using Apache Spark. Out of all the approaches, the fine KNN approach outperforms in terms of accuracy in a reasonable computational time, whereas the Bagged Tree approach achieves slightly less accuracy but takes less computational time for classifying the data.

关键词： Intrusion detection Apache Spark Big Data machine learning SVM KNN Neural network Ensemble approach

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2nd international conference on Intelligent Computing and Optimization, ICO 2019

2nd International Conference on Intelligent Computing and Op...

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2nd international conference on Intelligent Computing and Optimization, ICO 2019

ISBN: (纸本)9783030335847

The proceedings contain 66 papers. The special focus in this conference is on international conference on Intelligent Computing and Optimization. The topics include: Intelligent Electrophysiological Control of Cows Milk Reflex by Registration Electrical Skin Activity;use of Computing Techniques for Flood Management in a Coastal Region of South Gujarat–A Case Study of Navsari District;Structural Design of an LMU Using Approximate Model and Satisficing Trade-Off Method;recognition of Cow Teats Using the 3D-ToF Camera When Milking in the "Herringbone" Milking Parlor;changes in Heart Rate Dynamics with Menstrual Cycles;Verifying the Gaming Strategy of Self-learning Game by Using PRISM-Games;optimization of Parquetting of the Concentrator of Photovoltaic Thermal Module;optimization of the Process of Anaerobic Bioconversion of Liquid Organic Wastes;efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding;optimal Power Flow Considering Cost of Wind and Solar Power Uncertainty Using Particle Swarm Optimization;estimation of Electricity Prices in the Mexican Market;improvement of the Numerical Simulation of the machine-Tractor Unit Functioning with an Elastic-Damping Mechanism in the Tractor Transmission of a Small Class of Traction (14 kN);decomposition Algorithm for Irregular Placement Problems;smart Homes: Methodology of IoT Integration in the Architectural and Interior Design Process – A Case Study in the Historical Center of Athens;the Mechanism of Intensification of Heat and Moisture Transfer During Microwave-Convective processing Grain;Application of the Topological Optimization Method of a Connecting Rod Forming by the BESO Technique in ANSYS APDL;the Concept of Information Modeling in Interactive Intelligence Systems.

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