The proceedings contain 78 papers. The special focus in this conference is on Advanced computing. The topics include: ANN Model to Predict Religiosity Based on Social Online Behaviors and Personality Traits;concrete C...
ISBN:
(纸本)9783031356438
The proceedings contain 78 papers. The special focus in this conference is on Advanced computing. The topics include: ANN Model to Predict Religiosity Based on Social Online Behaviors and Personality Traits;concrete Crack Detection Using Deep Convolutional Generative Adversarial Network;chlorophyll Prediction System with machinelearningalgorithms in Lake Titicaca (Peruvian Sector);the Perceived Impact of Correlative Relationship between Depression, Anxiety, and Stress among University Students;machinelearning Approach for Predicting the Maladies of Humans;Forecasting Futuristic COVID-19 Trend Using machinelearning Models;machinelearning Techniques in Cardiovascular Disease Prediction;comparative Analysis of Various machinelearningalgorithms for Detection of Malware and Benign;XACML: Explainable Arrhythmia Classification Model Using machinelearning;intelligent Analytical Randomization of Clinical Trials;underwater Image Denoising and 3D Modelling of Poompuhar Site;optimizing Beat Management System Using Soft Biometrics;towards Better Gait Predictions: Sensor-Based Detection of Flexion and Extension of Human Lower Limb Joints During Walking;a Comparative Study of Stroke Prediction algorithms Using machinelearning;intelligent Crop Recommendation System Using machinelearningalgorithms;plant Disease Detection Using Multispectral Imaging;Whale Optimization Based Approach to Compress and Fasten CNN for Crop Disease and Species Identification;Few-Shot learning for Plant Disease Classification Using ILP;deep learning-Based Multiclass Classification of Cotton Leaf Images Using ResNet and Transfer learning;identification of Potato Leaf Diseases Using Hybrid Convolution Neural Network with Support Vector machine;a Substantial Deep learning Approach for Classification of Local and Coastal Fish;formulating Mechanisms for Reduction in Greenhouse Gas Emissions at Ghatkesar;intelligent & Smart Navigation System for Visually Impaired Friends.
The proceedings contain 91 papers. The topics discussed include: assessment of blockchain based energy trading methods for electric vehicles;planning and monitoring of smart grid architecture using internet of things;...
ISBN:
(纸本)9781665473804
The proceedings contain 91 papers. The topics discussed include: assessment of blockchain based energy trading methods for electric vehicles;planning and monitoring of smart grid architecture using internet of things;transformer faults detection using inrush transients based on multi-class SVM;performance assessment of distance relay owing to uncertain source impedance disparity with large wind power integration;a data-driven diagnosis and prognosis method for machinery tools based on EMD and dual-task deep neural networks;smart meter data analytics – load identification and abnormality detection using computational algorithms: a case study;placement of EV fast charging station in distribution system based on voltage stability index strategy;study and analysis of various partial discharge signals classification using machinelearning application;fault diagnosis in power transmission line using decision tree and random forest classifier;bio inspired computing based optimization of power loss in radial distribution systems;and fuzzy risk assessment of underground power distribution network cables based on geo-analytical fault vulnerability.
Sonic wave travel-time prediction is an important task in oil and gas exploration as it provides important information on the content and lithography of the rocks. Travel-time data, however, are not always accessible ...
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ISBN:
(纸本)9781665486644
Sonic wave travel-time prediction is an important task in oil and gas exploration as it provides important information on the content and lithography of the rocks. Travel-time data, however, are not always accessible due to practical considerations. Currently, machinelearning methods have been used to infer these values. In this paper, we look at the application of machinelearning in predicting sonic wave travel-time, specifically in terms of challenges, benchmarks, and datasets. In addition, we present some preliminary results of sonic wave travel-time prediction using existing machinelearning regression methods, namely curve fitting artificial neural network and multiple linear regression. Finally, this paper is aimed to act as a ”bridge” between machinelearning practitioners and domain-specific oil and gas engineers.
Deep learning models for computer vision in remote sensing such as Convolutional Neural Network (CNN) has benefitted acceleration from the usage of multiple CPUs and GPUs. There are several ways to make the training s...
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ISBN:
(纸本)9781510666184;9781510666191
Deep learning models for computer vision in remote sensing such as Convolutional Neural Network (CNN) has benefitted acceleration from the usage of multiple CPUs and GPUs. There are several ways to make the training stage more effective in terms of utilizing multiple cores at the same time by processing different image mini-batches with a duplicated model called Distributed Data Parallelization (DDP) and computing the parameters in a lower precision floating-point number called Automatic Mixed Precision (AMP). We would like to investigate the impact of DDP and AMP training modes on the overall utilization and memory consumption of CPU and GPU, as well as the accuracy of a CNN model. The study is performed on the EuroSAT dataset, a Sentinel-2-based benchmark satellite image dataset for image classification of land covers. We compare training using 1 CPU, using DDP, and using both DDP and AMP over 100 epochs using ResNet-18 architecture. The hardware that we used are Intel Xeon Silver 4116 CPU with 24 cores and an NVIDIA v100 GPU. We find that although parallelization of CPUs or DDP takes less time to train on the images, it can take 50 MB more memory than using only a single CPU. The combination of DDP and AMP can release memory up to 160 MB and reduce computation time by 20 seconds. The test accuracy is slightly higher for both DDP and DDP-AMP at 90.61% and 90.77% respectively than without DDP and AMP at 89.84%. Hence, training using Distributed Data Parallelization (DDP) and Automatic Mixed Precision (AMP) has more benefits in terms of lower GPU memory consumption, faster training execution time, faster convergence towards solutions, and finally, higher accuracy.
machinelearning has become a popular approach for automatic detection of specific patterns. However, each learning algorithm could have its own advantages and disadvantages for dealing with special types of data, e.g...
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ISBN:
(数字)9781665488358
ISBN:
(纸本)9781665488358
machinelearning has become a popular approach for automatic detection of specific patterns. However, each learning algorithm could have its own advantages and disadvantages for dealing with special types of data, e.g. heuristic algorithms could generally lead to the production of biased classifiers, especially when learning from a small data sample that is unlikely to represent a full population. In order to address the above issue, researchers have been motivated to develop ensemble learning approaches for combining individual classifiers, towards reducing the bias of classifiers and thus advancing the classification performance. In this paper, we propose a deep fusion network based ensemble learning approach, which aims to create more complex ensembles and to achieve a strategic combination of the existing rules of fusion (e.g. majority vote, mean, median and max) in a layer-by-layer processing manner, rather than simply using a single fusion rule. The proposed ensemble learning approach is used for a special type of detection tasks, which involves one class as the target class and the other class as the default class. The performance of the proposed ensemble learning approach is evaluated using 6 UCI data sets and the results show that the proposed ensemble learning approach consistently performs very close to or better than the state-of-the art approaches of individual and ensemble learning, while the individual classifiers and the other types of ensembles varied in their performance on different data sets.
With mobile communication technology development, the mobile Internet of Things (IoT) is booming, and the IoT applications are springing up all the time. However, the wireless channels are complex, and the security of...
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With mobile communication technology development, the mobile Internet of Things (IoT) is booming, and the IoT applications are springing up all the time. However, the wireless channels are complex, and the security of mobile IoT networks is facing many challenges. Energy efficiency is critical for secure communications in mobile IoT networks. To reduce energy consumption, we propose a transmit antenna selection (TAS)-based secrecy scheme employing amplify-and-forward (AF) relaying. Firstly, we derive the exact expressions, and analyze the physical layer security performance. Then, to further improve energy efficiency, we formulate the power allocation problem, which is a non-convex complicated problem. To solve this problem, we propose a novel power allocation intelligent optimization algorithm. Based on the designed power allocation function, an improved grey wolf optimization (IGWO) algorithm is employed to obtain the allocation parameter. For convergence speed and convergence precision, the proposed IGWO algorithm obtains better optimization performance than other swarm intelligence algorithms. Compared with other algorithms, the running time of IGWO is reduced by 24%, while maintaining the same optimization accuracy. This greatly improves the energy efficiency of mobile IoT networks.
The proceedings contain 19 papers. The special focus in this conference is on Theoretical Computer Science-Frontier of Algorithmic Wisdom. The topics include: Colorful Graph Coloring;on the Transversal Number of&...
ISBN:
(纸本)9783031207952
The proceedings contain 19 papers. The special focus in this conference is on Theoretical Computer Science-Frontier of Algorithmic Wisdom. The topics include: Colorful Graph Coloring;on the Transversal Number of Rank k Hypergraphs;exact algorithms and Hardness Results for Geometric Red-Blue Hitting Set Problem;bounds for the Oriented Diameter of Planar Triangulations;string Rearrangement Inequalities and a Total Order Between Primitive Words;approximation algorithms for Prize-Collecting Capacitated Network Design Problems;possible and Necessary Winner Problems in Iterative Elections with Multiple Rules;a Mechanism Design Approach for Multi-party machinelearning;budget-Feasible Sybil-Proof Mechanisms for Crowdsensing;two-Facility Location Games with Distance Requirement;constrained Heterogeneous Two-Facility Location Games with Max-Variant Cost;optimally Integrating Ad Auction into E-Commerce Platforms;verifiable Crowd computing: Coping with Bounded Rationality;equilibrium Analysis of Block Withholding Attack: An Evolutionary Game Perspective;an Approximation Algorithm for the H-Prize-Collecting Power Cover Problem;online Early Work Maximization on Three Hierarchical machines with a Common Due Date;secure Computations Through Checking Suits of Playing Cards.
The proceedings contain 78 papers. The special focus in this conference is on Distributed computing and Optimization Techniques. The topics include: A Study on Different Types of Convolutions in Deep learning in the A...
ISBN:
(纸本)9789811922800
The proceedings contain 78 papers. The special focus in this conference is on Distributed computing and Optimization Techniques. The topics include: A Study on Different Types of Convolutions in Deep learning in the Area of Lane Detection;A Study on the Impact of DC Appliances and Direct DC Power System in India;preface;1 T-1D Single-Ended SRAM Cell Design for Low Power Applications Using CMOS Technology;a Survey on Vehicle Detection and Classification for Electronic Toll Collection Applications;a Systematic Study of Sign Language Recognition Systems Employing machinelearningalgorithms;ACS Fed Coplanar Monopole Antenna with Complementary Split Ring Resonator for WLAN and Satellite Communication Applications;advance the Energy Usage in Cloud Centers Utilizing Hybrid Approach;Advanced Architecture of Analog to Digital Converter Derived from Half Flash ADC;an Assessment of Criss-Cross Multilevel Inverter with Fault Tolerance for Electric Vehicle Applications;an Energy-Efficient Load Balancing Approach for Fog Environment Using Scientific Workflow Applications;an Ensemble Model to Extract Discriminative Features for Semantic Image Classification in Large Datasets;an Evaluation of Wireless Charging Technology for Electric Vehicle;automated Dam Data Acquisition and Analysis in Real-Time;a Local Descriptor and Histogram of Oriented Gradients for Makeup Invariant Face Recognition Under Uncontrolled Environment;Chaotic System Based Modified Hill Cipher Algorithm for Image Encryption Using HLS;Chronological-Squirrel Earth Worm Optimization for Power Minimization Using Topology Management in MANET;Classification of Neurological Disorders with Facial Emotions and EEG;comparative Analysis of machinelearning Approaches for the Early Diagnosis of Keratoconus;efficient Square Root Computation–An Analysis.
The proceedings contain 46 papers. The special focus in this conference is on Applications of Evolutionary Computation. The topics include: Improving the Convergence and Diversity in Differential Evolution Through a S...
ISBN:
(纸本)9783031024610
The proceedings contain 46 papers. The special focus in this conference is on Applications of Evolutionary Computation. The topics include: Improving the Convergence and Diversity in Differential Evolution Through a Stock Market Criterion;search-Based Third-Party Library Migration at the Method-Level;multi-objective Optimization of Extreme learningmachine for Remaining Useful Life Prediction;explainable Landscape Analysis in Automated Algorithm Performance Prediction;search Trajectories Networks of Multiobjective Evolutionary algorithms;EvoMCS: Optimising Energy and Throughput of Mission Critical Services;RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation;WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution;a New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment;a Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings;dynamic Hierarchical Structure Optimisation for Cloud computing Job Scheduling;Optimising Communication Overhead in Federated learning Using NSGA-II;evolving Data Augmentation Strategies;Inheritance vs. Expansion: Generalization Degree of Nearest Neighbor Rule in Continuous Space as Covering Operator of XCS;detecting Nested Structures Through Evolutionary Multi-objective Clustering;Integrating Safety Guarantees into the learning Classifier System XCS;ANN-EMOA: Evolving Neural Networks Efficiently;augmenting Novelty Search with a Surrogate Model to Engineer Meta-diversity in Ensembles of Classifiers;neuroevolution of Spiking Neural P Systems;self-adaptation of Neuroevolution algorithms Using Reinforcement learning;swarm Optimised Few-View Binary Tomography;automating Speedrun Routing: Overview and Vision;co-evolution of Spies and Resistance Fighters;deep Catan;Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis;negative Selection Algorithm for Alzheimer’s Diagnosis: Design and
This paper describes our two approaches for the Multi-class fake news detection of news articles in English at CLEF2022-CheckThat!. The main goal of the task is as follows: given the text of a news article, determine ...
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