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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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Prediction of Flood in Bangladesh using k-Nearest Neighbors Algorithm

Prediction of Flood in Bangladesh using k-Nearest Neighbors ...

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

作者： Noushin Gauhar Sunanda Das Khadiza Sarwar Moury Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Bangladesh is a flood-prone country. With limited resources and a major portion of the population living below the poverty line, flood impacts are severe. Deaths, malnutrition, widespread diseases, damage to infrastructure, disruption in the economy are some of the after-effects of this cataclysm. In order to put a flood management system into effect, it is essential to predict flooding events ahead of time. In this work, we applied different correlation coefficients for feature selection and k-nearest neighbors (k-NN) algorithm for the prediction of flood. The detailed result analysis shows that we achieved a high testing accuracy of 94.91%, average precision of 92.00% and an average recall of 91.00% using the k-NN machine learning model.

关键词： machine learning algorithms Sociology signal processing algorithms Prediction algorithms Feature extraction Floods Testing

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Hepatocellular Carcinoma Patient’s Survival Prediction Using Oversampling and machine learning Techniques

Hepatocellular Carcinoma Patient’s Survival Prediction Usin...

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

作者： Ferdib-Al-Islam Laboni Akter Md. Milon Islam Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Hepatocellular Carcinoma (HCC) is the most crucial liver neoplasm and the subsequent driving reason for malignant growth demise worldwide. The survival rates for patients determined to have HCC and to distinguish prognostic components, which will help in picking ideal treatment for singular patients. Throughout the long term, and for the specific instance of HCC, some investigation contemplates have been creating procedures for helping physicians, employing machine learning methods to anticipate the survival rate after treatment. In this paper, we have applied machine learning calculations to anticipate the 1-year endurance of patients and discover the feature importance. We have employed an oversampling strategy named SMOTE, for adjusting the dataset to improve the model performance. We have shown the difference in model performance before and after using SMOTE. Our proposed system with the XGBoost classifier performed better with 87% accuracy in comparison with other existing models.

关键词： machine learning Medical services Predictive models signal processing Reliability Robots Cancer

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Real-Time Human Sitting Position Recognition using Wireless Sensors 20

Real-Time Human Sitting Position Recognition using Wireless ...

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

作者： Estrada, Jheanel E. Vea, Larry A. Technological Institute of the Philippines Manila City Philippines

ISBN: (纸本)9781450376952

Human Factors denotes the application of psychological and physiological standards and theories that lead to the design of products, processes and systems. With that Human- Computer Interaction gain attention in the field of Healthcare, Education and Occupational Areas. This paper aims to develop a model that will recognize proper and improper sitting posture in front of a laptop using Digital signal processing and User- Modeling. This study used flex sensors placed in the upper areas of the body. In a go signal of the Licensed Physical Therapist, these flex sensors will capture the resistance of each point and sent to the server. Using machine learning Techniques, the study was able to develop ergonomic rules using some key features such as the distance of the table and chair, adjustable table height suited for each body frame and upper body points such as left trapezius muscle, left and right deltoids and stylomastid process. This gives way to the development of a more advanced computational systems that will prevent musculoskeletal disorders. © 2020 ACM.

关键词： machine learning

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27th international Workshop on Frontiers of Computer Vision, IW-FCV 2021

27th International Workshop on Frontiers of Computer Vision,...

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27th international Workshop on Frontiers of Computer Vision, IW-FCV 2021

ISBN: (纸本)9783030816377

The proceedings contain 25 papers. The special focus in this conference is on Frontiers of Computer Vision. The topics include: Challenges and Applications of Face Deepfake;study on Image processing of Capillaries Using Microscope: Initial Considerations;stair-Step Feature Pyramid Networks for Object Detection;the 2nd Korean Emotion Recognition Challenge: Methods and Results;crack Detection and Location Estimation Using a Convolutional Neural Network;rice Leaf Diseases Recognition Based on Deep learning and Hyperparameters Customization;ST-GCN Based Human Action Recognition with Abstracted Three Features of Optical Flow and Image Gradient;deep Visual Anomaly Detection with Negative learning;video Analysis of Wheel Pushing Actions for Wheelchair Basketball Players;robust Tracking via Feature Enrichment and Overlap Maximization;efficient Spatial-Attention Module for Human Pose Estimation;GCN-Calculated Graph-Feature Embedding for 3D Endoscopic System Based on Active Stereo;uncalibrated Photometric Stereo Using Superquadrics with Cast Shadow;robust Training of Deep Neural Networks with Noisy Labels by Graph Label Propagation;fast Separation of Specular, Diffuse, and Global Components via Polarized Pattern Projection;saliency Prediction with Relation-Aware Global Attention Module;the Emerging Field of Graph signal processing for Moving Object Segmentation;multi-scale Global Reasoning Unit for Semantic Segmentation;multi-modality Based Affective Video Summarization for Game Players;focusing on Discrimination Between Road Conditions and Weather in Driving Video Analysis;age Estimation from the Age Period by Using Triplet Network;development of an Algae Counting Application to Support Vegetation Surveys in Fishing Grounds;ISHIGAKI Region Extraction Using Grabcut Algorithm for Support of Kumamoto Castle Reconstruction.

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machine learning Based Epileptic Seizure Detection for Responsive Neurostimulator System Optimization 2

Machine Learning Based Epileptic Seizure Detection for Respo...

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2019 2nd international conference on Computer Information Science and Artificial Intelligence, CISAI 2019

作者： Shen, Yangchicheng Westtown School West ChesterPA19382 United States

This paper proposes a novel method of identifying the time of epileptic seizure happening on patients by employing feature extraction and machine learning-based classification on Electroencephalogram (EEG) signal collected from a closed-loop interface implanted in the brain of patients. The closed-loop device was served as a neurostimulator which introduced stimuli to epilepsy patients when detecting the occurrence of seizure. A set of multiple time-and frequency-domain features are extracted from intracranial electroencephalography recordings of 7 subjects with epilepsy. Trained and tested on the extracted features, an ensemble of machine learning models with parameter tuning achieves an area under the curve (AUC) score of 0.99. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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machine learning Algorithms for Diamond Price Prediction 20

Machine Learning Algorithms for Diamond Price Prediction

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

作者： Alsuraihi, Waad Al-Hazmi, Ekram Bawazeer, Kholoud Alghamdi, Hanan King Abdulaziz University Information Systems Department Jeddah Saudi Arabia

ISBN: (纸本)9781450376952

Precious stones like diamond are in high demand in the investment market due to their monetary rewards. Thus, it is of utmost importance to the diamond dealers to predict the accurate price. However, the prediction process is difficult due to the wide variation in the diamond stones sizes and characteristics. In this paper, several machine learning algorithms were used to help in predicting diamond price, among them Liner regression, Random forest regression, polynomial regression, Gradient descent and Neural network. After training several models, testing their accuracy and analyzing their results, it turns out that the best of them is the random forest regression. © 2020 ACM.

关键词： Diamonds

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machine learning with Imbalanced EEG Datasets using Outlier-based Sampling 42

Machine Learning with Imbalanced EEG Datasets using Outlier-...

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42nd Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Islah, Nizar Koerner, Jamie Genov, Roman Valiante, Taufik A. O'Leary, Gerard Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 2E4 Canada Univ Toronto Dept Surg Neurosurg Toronto ON M5T 2S8 Canada

ISBN: (纸本)9781728119908

Epilepsy is a neurological disorder which causes seizures in over 65 million people worldwide. Recently developed implantable therapeutic devices aim to prevent symptoms by applying acute electrical stimulation to the seizure-generating brain region in response to activity detected by on-device machine learning hardware. Many training algorithms require an equal number of examples for each target class (e.g. normal activity and seizures), and performance can suffer if this condition is not satisfied. In the case of epilepsy, poor performance can cause seizures to be missed, or stimulation to be applied erroneously. As there is an abundance of normal (interictal) data in clinical EEG recordings, but seizures are rare events (less than 1% of the dataset), the data available for training is severely imbalanced. There are several conventional pre-processing methods used to address imbalanced class learning, such as down-sampling of the majority class and up-sampling of the minority class, but each have performance drawbacks. This paper presents an improved method which involves reducing the majority class down to the most effective interictal outlier samples. Outliers are determined by using Exponentially Decaying Memory signal Energy (EDMSE) features with Isolation Forests and an ANOVA-based method, which involves comparing a moving feature window to a baseline reference window. Outlier-based sampling is tested with two classifiers (KNN and Logistic Regression) and achieves higher accuracy (similar to 2% increase) and fewer false positives (similar to 38% decrease), along with a lower latency (similar to 3 seconds shorter) compared to conventional training set pre-processing methods.

关键词： machine learning

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A Non-Invasive Approach for Fetal Arrhythmia Detection and Classification from ECG signals 2

A Non-Invasive Approach for Fetal Arrhythmia Detection and C...

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2nd international conference on VLSI Device, Circuit and System (IEEE VLSI DCS)

作者： Ganguly, Biswarup Das, Anirbed Ghosal, Avishek Das, Debanjan Chatterjee, Debanjan Rakshit, Debmalya Das, Epsita Jadavpur Univ Elect Engn Dept Kolkata India MSIT Elect Engn Dept Kolkata India

ISBN: (纸本)9781728119335

This paper aims to present an intelligent system for autonomous diagnosis of fetal arrhythmia based on fetal ECG recordings. The present scheme uses one dimensional (1D) convolution with a wavelet kernel to extract time domain features from subjects possessing normal fetal ECG and fetal arrhythmia ECG. Time- domain features obtained from the convoluted signals are fed to a trained artificial neural network (ANN) with gradient descent learning to identify and classify fetal ECG signals. The experimental evaluation of the proposed scheme has been tested with a six- channel fetal ECG signal, available in the NIFEADB database. An overall accuracy of 96% is obtained by evaluating standard performance metrics. The use of 1D convolution not only reduces the computational burden but also helps to specify the feature space to develop an intelligent system for portable embedded system applications.

关键词： 1D convolution artificial neural network feature extraction intelligent system signal processing

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2nd international Cognitive Cities conference, IC3 2019

2nd International Cognitive Cities Conference, IC3 2019

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2nd international Cognitive Cities conference, IC3 2019

ISBN: (纸本)9789811561122

The proceedings contain 83 papers. The special focus in this conference is on Cognitive Cities. The topics include: Research on natural language processing in financial risk detection;correlation analysis between deep displacement and multi-source landslide monitoring data;integrating artificial intelligence into steam education;an intelligent detection and notification (idn) system for handling piglet crushing based on machine learning;improvement of adhd behaviors with ai perception technology;classification of intrusion detection system based on machine learning;the ideas of robot design and application from the first-year undergraduate students;characteristics of the ionospheric disturbances caused by typhoon using gps and ionospheric sounding;a mutual authentication lightweight rfid protocol for iot devices;decentralized e-learning marketplace: Managing authorship and tracking access to learning materials using blockchain;the vision of design-driven innovation in china’s smart home industry;a study on the measurement of the development level of urban exhibition industry in china and the driving effect towards international trade in goods;study on the key items of a maker project course design in higher vocational engineering;a case study of e-government website affinity design;the requirement analysis for developing the assisted living technology for the elderly;the business model of maker space – a case study of taiwan experience;cases of hunger marketing in digital era;motivation and characteristics of social media use behavior of new generation entrepreneurs;a book-finding application based on ibeacon-a case study of ccu library;educational big-data practice: A model for combining requirements and technologies;re-trace: Relive and share your story.

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Comparison of machine learning Methods for Effective Autism Diagnosis 2nd

Comparison of Machine Learning Methods for Effective Autism ...

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

作者： Pavithra, D. Jayanthi, A.N. Nidhya, R. Department of Electronics and Communication Engineering Sri Ramakrishna Institute of Technology CoimbatoreTamil Nadu India Department of Computer Science and Engineering Madanapalle Institute of Technology and Science MadanapalleAndhra Pradesh India

ISBN: (纸本)9789811524745

Autism spectrum disorder is a neurological problem that will have challenges in social, emotional and behaviour skills. ASD can be diagnosed only by the age of three. The children with ASD will have developmental delay at every stage of their growth. The ASD is categorized as mild, moderate and severe. Proper diagnosis with treatment can cure ASD at the earliest. There are a number of tools for autism diagnosis such as the Autism Spectrum Quotient, Modified Checklist for Autism in Toddlers. Accuracy of the method mostly relies on the knowledge of the person who reviews the child. Accuracy can be improved by using AI technology like machine learning. machine learning is a part of Artificial Intelligence where the system gets the ability to learn and improve itself. ML provides classifiers to diagnose autism at a high rate of accuracy. This paper focuses on comparing the existing machine learning classifiers (Naïve Bayes, SVM, k-NN and random forest) on ISAA data set for effective autism diagnosis. © 2020, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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