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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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machine learning-Powered Mobile App for Predicting Used Car Prices 21

Machine Learning-Powered Mobile App for Predicting Used Car ...

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Proceedings of the 2021 3rd international conference on Big-data Service and Intelligent Computation

作者： Naser Shanti Akram Assi Hamza Shakhshir Adnan Salman An-najah National University Palestine

ISBN: (纸本)9781450390552

Buying and selling used cars is a common practice in all countries. When looking to sell a car, the seller decides the price of their car after monitoring the prices of similar cars in the advertisements. When someone is looking to buy a car, they watch advertisements for similar cars to get an idea of the price of the car they want to purchase. Despite the availability of blue books that provide an estimate of automotive pricing, real market prices vary depending on demand and supply. In this paper, we applied cutting-edge machine learning techniques to automate this process. The training data set is up-to-date and it was collected from an active commercial website. We created semi-automated rule-based scripts to clean and prepare the data for machine learning. Several machine learning algorithms were explored to generate an approximate value for the car pricing, including Artificial Neural Network, Support Vector machine, K-Nearest Neighbors, Random Forest, and Gradient Boosted Decision Tree. To determine the features that most affect the price, extensive data analysis and cleaning were undertaken. Our findings indicate that the testing accuracy is 90%. Finally, a mobile application was created that provides an estimated price of a given car's properties, guiding a user in determining the price of their car. The complete code and the data used to obtain these results can be accessed on GitHub at [1] [19].

关键词： Car price Prediction machine learning Regression mobile application

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Utilizing machine learning to Develop an Improved Real-Time Data Analysis System

Utilizing Machine Learning to Develop an Improved Real-Time ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Harsimrat Khandari Girija Shankar Sahoo Raja Praveen K N Ritu Shree Umamaheswari B Yashwant V Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records

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Design and Implementation of Financial learning System Based on computer Simulation Technology 2021

Design and Implementation of Financial Learning System Based...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Yanhong Wu Zhanjiang Science and Technology College China

ISBN: (纸本)9781450385046

With the rapid development of computer network technology, its popularity and application have become stronger and stronger. Information technology has already become one of the important forces of economic development and social progress, and has been highly successful and applied in various fields. The purpose of this paper is to study the design of financial learning system based on computer simulation technology. Using computer simulation technology, JSP application Technology, JavaBean database access technology and database technology, this paper designs and implements a financial learning system that can effectively meet the actual needs of enterprise employees and school teachers and students. The system can effectively reduce the difficulty of enterprises and schools in financial education, improve the learning initiative and enthusiasm of employees and students, and improve the teaching quality of teachers. In order to make the financial learning system of computer simulation system more vivid, a system combining computer simulation technology with computer-aided teaching is proposed. By combining computer simulation technology with computer-aided teaching, the application of computer-based financial learning system based on computer simulation in education is further explored. Provide students with a clearer learning direction and apply computer simulation to computer teaching. The experimental research shows that the test fluctuation of the system is relatively stable, the average value of the maximum transaction response time is about 0.84 seconds, and the probability of successful transaction processing is 100%.

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LIED: A Lightweight and Ensemble learning approach for fake face Detection

LIED: A Lightweight and Ensemble learning approach for fake ...

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Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： Rajat Budhiraja Manish Kumar M. K. Das Anil Singh Bafila Sanjeev Singh Institute of Informatics and Communication University of Delhi South Campus New Delhi India School of Computer and Information Sciences Indira Gandhi National Open University New Delhi India

For many years, machine learning problems have primarily been driven by the availability and quality of data. Being the key, data has been equally vulnerable and got a savior in the form of generative adversarial networks (GANs) which opened the floodgates for generating almost any type of real, yet synthetic data. Human face became one of the initial victims to this superior technology, where in highly realistic and convincing fake content is generated using deep learning technologies or ‘‘DeepFakes’’. Taking a giant leap forward from manipulating facial attributes, to be now able to swap expressions seamlessly and even generate new (non-existent) synthetic faces poses a grave threat not only to chosen few, but for the entire society. This upshoot has been reciprocated with significant efforts and investments for its detection, but the techniques are often marred with either lower accuracies, or, higher computation costs. This is where convolutional reservoir networks (CoRN) come to rescue owing to their lightweight nature, able to do ensemble feature extraction and its generalization ability. This paper investigates, implements and demonstrates the application of CoRN based architectures to the task of human fake face detection. The steep performance improvements as evident from our results further ratify the effectiveness of this approach, which is also shown to perform exceedingly well against smaller datasets.

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Identifying Advanced Fee Fraud Activities on Internet Using machine learning Algorithms

Identifying Advanced Fee Fraud Activities on Internet Using ...

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2010 The 3rd international conference on Computational Intelligence and Industrial application (PACIIA2010)

作者： Modupe Abiodun Oludayo O.Olugbara Sunday O.Ojo Department of Information Technology Tshwane University of Technology Department of Information Technology Durban University of Technology Faculty of Information and Communication Technology Tshwane University of Technology

This study develops a model that attempts to mine advanced fee fraud activities through internet based emails correspondence for the purpose of forensic *** extract various email document stylometrics,header and textural body content features that are used with support vector machine and Logistics regression machine learning algorithms to classify email documents into binary *** word clustering algorithm is applied to partition a set of distinct words into clusters of semantically similar words to achieve space dimension reduction and classification *** classification accuracies of support vector machine and Logistics regression are *** results of this comparison show that the mean accuracy for support vector machine (M=95.20,SD=2.85) is not significantly different from the mean accuracy for Logistics regression (M=93.70,SD=2.51) using two sample ttest for equal ***,support vector machine and Logistics regression are useful machine learning algorithms for identifying advanced fee fraud activities on the internet.

关键词： Advanced fee fraud,Automatic word clustering,Support vector machine,Logistics regression

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Identification of Tropical Dry Forest Transformation from Soundscapes Using Supervised learning 3rd

Identification of Tropical Dry Forest Transformation from ...

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3rd international conference on Smart Technologies, Systems and applications, SmartTech-IC 2022

作者： Castro-Ospina, Andrés E. Rodríguez-Buritica, Susana Rendon, Nestor Velandia-García, Maria C. Isaza, Claudia Martínez-Vargas, Juan D. Instituto Tecnológico Metropolitano Medellín Colombia Alexander Von Humboldt Institute Bogotá Colombia SISTEMIC Engineering Faculty Universidad de Antioquia-UdeA calle 67 No. 53 - 108 Medellín Colombia Universidad EAFIT Medellín Colombia

ISBN: (纸本)9783031322129

Biodiversity loss in tropical ecosystems is advancing due to factors such as human pressure and climate change, and determining their ecological transformation level can help to monitor climate change trends. Soundscapes can indicate the health of an ecosystem;therefore, passive acoustic monitoring is interesting because of its non-invasive nature, low cost, and allows recording over long periods. However, soundscapes generate a large amount of data that must be analyzed using automatic techniques such as machine learning to reduce manual labor. This process is generally based on acoustic indices, which present limitations such as sensitivity to background noise or biased results to the study sites under evaluation. Therefore, in this document, we propose training supervised models to identify landscape transformation from studied site soundscape at three stages: low, medium, and high. These models are trained on data collected in 24 tropical sites in the Colombian Caribbean region from 2015 to 2017. To achieve this, we extracted high-level features from a VGGish architecture from each recording to assess the performance of features obtained from the raw time series with neural networks. Furthermore, we compared the features from VGGish with 60 acoustic indices as features to train Random Forest, Neural Network, and XGBoost classifiers. F1-score for test data accomplishes a performance of 90% with the XGBoost classifier on the acoustic indices set, and the neural network achieves 95% for the VGGish feature set. Through supervised models, it is possible to identify the level of transformation of an ecosystem from its soundscape. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ecosystems

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Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain

Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Swati Arya Syed Anas Ansar Shruti Aggarwal Department of Computer Application Babu Banarsi Das University Lucknow India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab India

The advent of quantum computing has ignited unprecedented anticipation within the realm of IT. This has transcended the limitations of classical computation and revolutionized various facets of technological advancement. At the forefront of this quantum revolution stands the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by a delicate interplay of immense potential and persistent imperfections. This paper delves into the intricacies of NISQ-era quantum computing, unraveling its fundamental principles, challenges, and transformative implications for the IT landscape. Beginning with quantum computing basics-qubits, superposition, entanglement, and gates-the NISQ era's essence addresses error-prone operations and decoherence. Quantum algorithms for NISQ devices, like Grover's search, Shor's factorization, and Variational Quantum Eigensolver (VQE), are scrutinized for their computational potential. Central to NISQ's viability, quantum error correction and mitigation strategies are surveyed, stabilizing quantum computations amidst noise. Examining quantum hardware architectures and performance metrics unveils NISQ devices' potential. applications of NISQ quantum computing in cryptography, optimization, machine learning, and more are explored, showcasing quantum advantage's real-world potential. This paper embarks on a multidimensional exploration of NISQ quantum computing, illuminating its intricate facets, applications, and ethical implications. As quantum computing evolves from a theoretical notion to a tangible paradigm, the insights gleaned herein contribute to a holistic understanding of its role in shaping the next wave of computing.

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Research on the application of Artificial Intelligence in Piano Exercise Using Internet and computer Software and Hardware Technologies 2021

Research on the Application of Artificial Intelligence in Pi...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Li Zhao Wenfei Zeng Shaoyang University China

ISBN: (纸本)9781450385046

With the gradual deepening of the "Internet +" trend and the rise of artificial intelligence to the national strategy, it has become an inevitable trend to use new Internet and computer software and hardware technologies to serve music communication and music education. In the entire system of intelligent piano teaching, it mainly includes hardware construction methods, software design methods, music presentation forms, teaching methods, etc. Based on the existing technology and theories, different methods have different characteristics in the entire teaching. This article elaborates on the demand analysis, design, implementation and testing of the piano assistant practice system. This system can provide more convenience and better teaching effect for traditional piano teaching, provide a piano learning auxiliary tool for students, and provide a tool for teachers to supervise and manage students' piano learning.

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AC Operation Hardware Neural Circuit and the Design of Deep learning Model

AC Operation Hardware Neural Circuit and the Design of Deep ...

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2018 3rd international conference on computer Science and Information Engineering(ICCSIE 2018)

作者： Masashi Kawaguchi Naohiro Ishii Masayoshi Umeno Department of Electrical & Electronic Engineering National Institute of TechnologySuzuka College Department of Information Science Aichi Institute of Technology Department of Electronic Engineering Chubu University

In the machine learning field,many application models such as pattern recognition or event prediction have been *** Network is typically basic methods of machine *** analog neural network models were composed of the additional circuit and solid *** circuit was realized by operational *** weights means the solid resistance of *** the reason of the resistance is fixed,changing resistance value and connecting weight is quite ***,in the case of using variable resistance,we have to adjust the resistance value by our *** this study,we used analog electronic circuits using alternative current to realize the neural network learning *** circuits are composed by a rectifier circuit,Voltage-Frequency converter,amplifier,subtract circuit,additional circuit and *** connecting weights describe the frequency,converted to direct current from alternating current by rectifier *** connection weights are able to changed easily compare with another hardware neural *** model's architecture is on the analog *** learning time and working time are very short because this system is not depending on clock ***,we suggest the realization of the deep learning model about proposed analog hardware neural circuit.

关键词： Analog electronic circuit neural network AC operation deep learning

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