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The application of DBSCAN Algorithm to Improve Variogram Estimation and Interpretation in Irregularly-Sampled Fields 3rd

The Application of DBSCAN Algorithm to Improve Variogram Est...

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3rd international conference on Applied Informatics, ICAI 2020

作者： Mosobalaje, O.O. Orodu, O.D. Ogbe, D. Department of Petroleum Engineering Covenant University Ota Nigeria African University of Science and Technology Abuja Nigeria

ISBN: (纸本)9783030617011

The empirical variogram is a measure of spatial data correlation in geostatistical modeling and simulations. Typically, the empirical variogram is estimated for some defined lag intervals by applying method of moments on an underlying variogram cloud. Depending on the distribution of pair-wise lag values, the variogram cloud of an irregularly-sampled field may exhibit clusteredness. Issues of noisy, uninterpretable and inconsistent empirical variogram plots are commonly encountered in cases of irregularly-sampled fields with clustered variogram clouds. An insightful diagnosis of these problems and a practical solution are the subject of this paper. This research establishes the fact that these problems are caused by the neglect of variogram cloud cluster configurations when defining lag intervals. It is here shown that such neglect hinders the optimal use of spatial correlation information present in variogram clouds. Specifically, four sub-optimal effects are articulated in this paper as the consequence of the neglect. Consequently, this research presents an efficient cluster-analysis – driven technique for variogram estimation in cases of irregularly-sampled fields with clustered variogram clouds. The cluster analysis required for this technique is implemented using an unsupervised machine learning algorithm known as Density-based Spatial Clustering of applications with Noise (DBSCAN). This technique has been applied to a real field to obtain a stable, interpretable and geologically consistent variogram plot. It has also been applied to a synthetic field and was found to give the lowest estimation error among other techniques. This technique would find usefulness in geo-modeling of natural resource deposits wherein irregular sampling is prevalent. © 2020, Springer Nature Switzerland AG.

关键词： Cluster analysis

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3rd international Symposium on Human Mental Workload: Models and applications, H-WORKLOAD 2019

3rd International Symposium on Human Mental Workload: Models...

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3rd international Symposium on Human Mental Workload: Models and applications, H-WORKLOAD 2019

ISBN: (纸本)9783030324223

The proceedings contain 15 papers. The special focus in this conference is on international Symposium on Human Mental Workload: Models and applications. The topics include: On the Use of machine learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task;do Cultural Differences Play a Role in the Relationship Between Time Pressure, Workload and Student Well-Being?;ocular Indicators of Mental Workload: A Comparison of Scanpath Entropy and Fixations Clustering;eye-Tracking Metrics as an Indicator of Workload in Commercial Single-Pilot Operations;EEG-Based Mental Workload and Perception-Reaction Time of the Drivers While Using Adaptive Cruise Control;Real-Time Mental Workload Estimation Using EEG;student Workload, Wellbeing and Academic Attainment;task Demand Transition Rates of Change Effects on Mental Workload Measures Divergence;Validation of a Physiological Approach to Measure Cognitive Workload: CAPT PICArd;COMETA: An Air Traffic Controller’s Mental Workload Model for Calculating and Predicting Demand and Capacity Balancing;EEG-Based Workload Index as a Taxonomic Tool to Evaluate the Similarity of Different Robot-Assisted Surgery Systems;Deep learning for Automatic EEG Feature Extraction: An application in Drivers’ Mental Workload Classification;hybrid Models of Performance Using Mental Workload and Usability Features via Supervised machine learning.

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An application of machine learning technique in forecasting crop disease 3

An application of machine learning technique in forecasting ...

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3rd international conference on Big Data Research, ICBDR 2019

作者： Fenu, Gianni Malloci, Francesca Maridina Department of Mathematics and Computer Science University of Cagliari V. Ospedale 72 Cagliari09124 Italy

ISBN: (纸本)9781450372015

In the recent years, Big Data Analytics and machine learning techniques are playing an increasingly key role in the agriculture sector in order to tackle the increasing challenges due to the climate changes which are causing serious damage production. The analysis of environmental, climatic and cultural factors allows to establish the irrigation and nutritional needs of crops, forecast crop disease, improve crop yield, as well as improve the quantity and the quality of agricultural output while using less input. Potato late blight is considered one of the most devasting disease world over, including Sardinia. Unexpected epidemics can result in significant economic and yield losses. In this paper, we describe the test conducted using the DSS LANDS in order to predict potato late blight disease in Sardinia. The object of the study was to investigate if regional weather variables could be used to predict potato late blight risk in southern Sardinia using a machine learning approach. The disease severity is predicted using Feed-forward Neural Network and Support Vector machine Classification based on meteorological parameters provided by ARPAS weather stations. The prediction accuracy for ANN was 96% and for SVM Classification was 98%. © 2019 Association for Computing machinery.

关键词： Data Analytics

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Autism Identification and learning Through Motor Gesture Patterns 3

Autism Identification and Learning Through Motor Gesture Pat...

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3rd international conference on Innovative Computing, ICIC 2019

作者： Rafique, Insha Fatima, Kashmala Dastagir, Anum Mahmood, Sajid Hussain, Muzammil University of Mangment and Technology Department of Computer Science Lahore Pakistan University of Mangment and Technology Department of Informatics and Systems Lahore Pakistan

ISBN: (纸本)9781728146829

Autism Spectrum Disorder (ASD) is a mental development disorder which affects the processing of information in brain causing delays in the language and reasoning development. A person's behavior can make it distinguishable if he/she has the disorder or not through three main traits i.e. social interaction, restricted/repetitive behavior or communication skills. ASD is usually diagnosed clinically on the basis of some core symptoms and tests which are expensive and can be stressful for a child. Research has shown that Autistic people can be differentiated through the difference of force in their Motor Gestures which is related to restricted/repetitive behavior of Autistic individuals. Motor disturbance is observed in autistic patients which is a key factor for us to make its identification possible through a technical early stage approach which can be very effective. A preschool level Android touch application has been developed in which the motor patterns are observed through touch sensors and inertial sensors. Ten features were obtained as kids touched the screen for the gameplay. This touch and inertial data was then analyzed by processing them through machine learning (ML) algorithms to classify between Autistic and Non-Autistic group on the basis of the ten features and the motor disruption was identified. This data supports the motion disruption of movements which is identified to be as a core feature of ASD (restricted/repetitive behavior) and this demonstrates that Autism is possible to be computationally assessed through an interactive and fun gameplay on a smart device. In this way we will be able to diagnose Autism through an easy to access, learning smart phone application instead of a typical, expensive clinical diagnosis. © 2019 IEEE.

关键词： Diagnosis

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application of DBSCAN to Anomaly Detection in Airport Terminals

Application of DBSCAN to Anomaly Detection in Airport Termin...

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international conference on Engineering Technology and their applications (IICETA)

作者： Inas Alhussein Ali H. Ali Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq University of Kufa Najaf Iraq

ISBN: (数字)9781728182315

ISBN: (纸本)9781728182322

Early identification of abnormal flight conditions is extremely critical in terms of safety precautions and preventing possible incidents. Because there is a massive amount of raw data streaming daily via the ADS-B to Flightradar24, which often approaches hundreds of millions, dealing with such unorganized details and identifying abnormal cases is extremely difficult. In this paper, the DBSCAN algorithm proposes to analyze and identify unknown events within routine flight data without previous knowledge of historical flight data. DBSCAN does not need a complex configuration or consumes a significant amount of time to implement, but rather relies on the appropriate selection of features and also on the particular threshold (ε), The algorithm analyzed three dataset types for Najaf international Airport consisting of 41 B738 flights (DXB), 39 B738 flights (IST), and 29 A310 flights (IKA). The percentage of anomalies in these airlines was as follows: Fly Dubai 1.32 percent, Istanbul Airlines 0.32 percent, while Iranian Airlines recorded the highest anomalies at 1.66 percent. The results of these analyzes must be made available to airspace experts and pilots to assess the value of safety risks.

关键词： Clustering algorithms Airports Anomaly detection Aircraft machine learning machine learning algorithms conferences

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Garbage Recognition and Classification System Based on Convolutional Neural Network VGG16

Garbage Recognition and Classification System Based on Convo...

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Advanced Electronic Materials, computers and Software Engineering (AEMCSE), international conference on

作者： Hao Wang Wuhan University of Technology Wuhan China

ISBN: (数字)9781728181431

ISBN: (纸本)9781728181448

To study the application of deep learning in the field of environmental protection, the convolutional neural network VGG16 model is used to solve the problem of identification and classification of domestic garbage. This solution first used the OpenCV computer vision library to locate and select the identified objects and preprocessed the images into 224×224 pixel RGB images accepted by the VGG16 network. Then after data enhancement, a VGG16 convolutional neural network based on the TensorFlow framework is built, by using the RELU activation function and adding BN layer to accelerate the model's convergence speed, while ensuring recognition accuracy. This project finally classifies domestic garbage into recyclable garbage, hazardous garbage, kitchen waste and other garbage. After actual tests, the correct classification rate of the garbage classification system based on VGG16 network proposed in this paper is 81.1%, the result meets the needs of daily use.

关键词： component Deep learning VGG16 Network Garbage Recognition Taxonomy Wastes Garbage Neural network images machine learning Recognition (Psychology) problem identification

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Obstacle Detection Using Faster R-CNN Oriented to an Autonomous Feeding Assistance System

Obstacle Detection Using Faster R-CNN Oriented to an Autonom...

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international conference on Information and computer Technologies (ICICT)

作者： Javier Orlando Pinzón-Arenas Robinson Jiménez-Moreno Universidad Militar Nueva Granada Bogota Colombia

ISBN: (数字)9781728172835

ISBN: (纸本)9781728172842

Obstacle detection has been a relevant issue for the implementation of autonomous robotic systems, within which increasingly robust algorithms have begun to be applied, especially Deep learning techniques. However, these have not been widely used for the detection of obstacles in static robotic agents, contrary to what happens with mobile agents. For this reason, this work explores the use of one of these techniques, which is a neural network based on the Faster R-CNN, focused on detecting a specific obstacle (hands) in an application environment for a food assistance robot. For this purpose, a database containing 6205 training images and 1350 validation images was prepared, where 31 users perform different movements with their hands. To verify the capacity of the network, 3 architectures of different depths were implemented, which were evaluated and compared, resulting in the network of greater depth obtained the highest accuracy, of 77.4%, taking into account that the hands are not only still but also in movement, generating distortion in them and greater difficulty for their detection. Also, the internal behavior of the network was visualized through activations, to verify what it had learned, showing that it managed to focus on the hands, with some activations located in parts of the user's body such as face and arm.

关键词： Training Databases Task analysis Manipulators Robustness machine learning

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Deciphering the Association of Single Amino Acid Variations with Dermatological Diseases Applying machine learning Techniques 3rd

Deciphering the Association of Single Amino Acid Variations ...

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3rd international conference on Advanced Informatics for Computing Research (ICAICR)

作者： Meena, Jaishree Chauhan, Aparna Hasija, Yasha Delhi Technol Univ Dept Biotechnol Main Bawana Rd Delhi 110042 India

ISBN: (纸本)9789811501081;9789811501074

SAVDerma predicts the association of a Single Amino Acid Variation (SAVs) with dermatological diseases. SAVs are basically non-synonymous SNPs which are sometimes associated with various diseases. A single amino acid mutation can affect various physico-chemical properties of a protein which then effect the normal functionality of a particular protein and leads to disease. Studies have shown that among various mendelian diseases, around 60% of them are due to these types of SAVs. The scope of this study limits with dermatological disorders and the process can be applied to all other types of diseases. We have curated SAV's physico-chemical and sequence-based features which are associated with dermatological disorders and kept them as positive set and vice versa as negative set. This data sets were feed to machine learning classifiers for developing a model which can classify SAV's association with dermatological disorders. Our classifier obtained an accuracy of 87.29%. SAVDerma is a web application where user can find all the curated and machine generated data about SAVs and their predicted association with skin diseases. It has a user-friendly interface where information regarding particular SAV can be retrieved using three different parameters i.e. Gene symbol, Swissprot id and rs id and are provided on the search page of the website (http://***/). SAVDerma can be used as a single point information retrieval system by clinicians and thus increasing the current knowledge on skin disorders.

关键词： SAVs (Single amino acid variations) SNPs (Single nucleotide polymorphism) machine learning (ML) SAVDerma

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Using Ontologies to Express Prior Knowledge for Genetic Programming 3rd

Using Ontologies to Express Prior Knowledge for Genetic Prog...

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3rd IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 international Cross-Domain conference for machine learning and Knowledge Extraction (CD-MAKE)

作者： Prieschl, Stefan Girardi, Dominic Kronberger, Gabriel Johannes Kepler Univ Linz RISC Software GmbH Hagenberg Austria Univ Appl Sci Upper Austria Josef Ressel Ctr Symbol Regress Hagenberg Austria

ISBN: (纸本)9783030297268;9783030297251

Ontologies are useful for modeling domains and can be used to capture expert knowledge about a system. Genetic programming can be used to identify statistical relationships or models from data. Combining expert knowledge as well as statistical rules identified solely from data is necessary in application domains where data is scarce and a large body of expert knowledge exists. We therefore study if the performance of genetic programming can be improved by incorporating prior knowledge from an ontology. In particular, we include prior knowledge as additional features for genetic programming. The approach is tested with six benchmark data sets where we compare the required computational effort that is necessary to find an acceptable model with and without additional features. The results show that additional features gathered from an ontology improve the performance of tree-based GP. The probability to find acceptable solutions with a fixed computational budget is increased. For noisy data sets we observed the same effect as for the data sets without noise.

关键词： Supervised learning Ontologies Domain knowledge Genetic programming Symbolic regression

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An application of machine learning Technique in Forecasting Crop Disease 19

An Application of Machine Learning Technique in Forecasting ...

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Proceedings of the 3rd international conference on Big Data Research

作者： Gianni Fenu Francesca Maridina Malloci Department of Mathematics and Computer Science University of Cagliari Cagliari Italy

ISBN: (纸本)9781450372015

关键词： Artificial Neural Network Big Data Analytics Support Vector Classification Decision Support System Forecasting models

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