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IProceedings of the 4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

IProceedings of the 4th International Conference on Artifici...

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4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

ISBN: (纸本)9798400708831

The proceedings contain 200 papers. The topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on the extraction of law enforcement relationships in administrative law enforcement instrument data;online algorithm for exploring a grid polygon with two robots;research on gesture recognition method by improving dung beetle algorithm to optimize BP neural network;an improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan;machine learning-based research on reserve prediction of natural-gas-hydrates;enhancing coal mine safety monitoring algorithm using graph computing techniques;reverse distillation support vector data description for unsupervised anomaly detection;and few-shot object counting model based on self-support matching and attention mechanism.

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Transfering RGB-D Models to RGB by Multi-View Embedding Space Learning for Object Detection 4

Transfering RGB-D Models to RGB by Multi-View Embedding Spac...

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4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Liu, Jinde Zhang, Zhang University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665463997

In this paper, we propose a new framework for detecting objects in RGB images captured by conventional cameras by leveraging a set of labeled RGB-D data. We formulate this problem into a new multi-view learning problem, where we have the depth information in the training set that is not available in the testing set. To solve this problem, we use a three-view CCA learning method which combines visual features, depth features, and label information together to get the projection matrices. Sufficient experiments are done to demonstrate the effectiveness of our method, and improvement measures in the future are also proposed. © 2022 IEEE.

关键词： Object detection

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Attention-guided Unsupervised Semantic Fusion for Infrared and Visible Images 15

Attention-guided Unsupervised Semantic Fusion for Infrared a...

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15th international conference on Graphics and Image Processing, ICGIP 2023

作者： Qi, Qian Kong, Quan Liao, Qing Hu, Ziming Li, Jinglei Zhang, Jialian Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology University China School of Art & Design Wuhan Institute of Technology University China

ISBN: (纸本)9781510675018

Infrared and visible image fusion aims to synthesize a new image with complementary information of the source images such as the thermal radiation information and detailed texture information. However, the existing methods applied in the image fusion tasks tend to pay more attention to the improvements of visual quality and multiple evaluation indicators, while ignoring the importance of semantic information in high-level missions of image processing. In this paper, we present a novel approach to image fusion, where the synthesis of fused images is achieved through the utilization of semantic layouts generated via unsupervised learning. With the introduction of Attention Mechanism, the relationships between each pair of pixel points are gained to construct soft semantic layouts and further capture the global context information to make the same kind of semantics show the same fusion effect. By leveraging the semantic information, our method enables the automatic learning of fusion weights for two source images at the same spatial position. In comparison with other state-of-the-art image fusion methods, our experimental results obtained excellent performance both on qualitative results and quantitative indicators. Moreover, our method retains the high-level semantic information to the greatest extent, which is one of our outstanding characteristics. © 2024 SPIE. All rights reserved.

关键词： Image fusion

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soft computing-Based Approach for Face recognition on Plastic Surgery and Skin Colour-Based Scenarios Using CNN 6th

Soft Computing-Based Approach for Face Recognition on Plasti...

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Sinha, Divyanshu Pandey, J.P. Chauhan, Bhavesh Uttarakhand Technical University Dehradun India Department of Electrical Engg MMMUT Gorakhpur India Department of ECE SRMCEM Lucknow India

ISBN: (纸本)9789811935749

In recent years, face recognition has become one of the most common technologies in the identification of humans which is widely used as *** many of the biometric systems that are already out there, facial recognition has become one of the most important technologies for quickly identifying people without having to ask *** will not cause any unnecessary ***, there are several face-recognition systems that rely on traditional machine learning as *** they do not work when it comes to things like facial expressions, posture, occlusion, and *** face-recognition method described in this article employs a convolutional neural network (CNN) to locate faces within a ***–Jones face detection is used to locate faces in a picture, and a pre-trained CNN extracts facial traits from the faces ***, a large database of facial images is made in order to have more images for each subject and to include different noise levels for the best training of *** accuracy of the system was increased to 98.81% which will depict the effectiveness of deep face *** attempt to design an algorithm that overcomes the difficulties associated with facial identification owing to skin by feeding faces into a CNN for feature extraction and then passing them to a softmax classifier for *** to develop an algorithm to overcome the challenges that occur in the face recognition due to external and artificial features like plastic surgery. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Meta soft Label Generation for Noisy Labels 25

Meta Soft Label Generation for Noisy Labels

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25th international conference on pattern recognition (ICPR)

作者： Algan, Gorkem Ulusoy, Ilkay Middle East Tech Univ Elect Elect Engn Ankara Turkey ASELSAN Yenimahalle Ankara Turkey

ISBN: (纸本)9781728188089

The existence of noisy labels in the dataset causes significant performance degradation for deep neural networks (DNNs). To address this problem, we propose a Meta soft Label Generation algorithm called MSLG, which can jointly generate soft labels using meta-learning techniques and learn DNN parameters in an end-to-end fashion. Our approach adapts the meta-learning paradigm to estimate optimal label distribution by checking gradient directions on both noisy training data and noise-free meta-data. In order to iteratively update soft labels, meta-gradient descent step is performed on estimated labels, which would minimize the loss of noise-free meta samples. In each iteration, the base classifier is trained on estimated meta labels. MSLG is model-agnostic and can be added on top of any existing model at hand with ease. We performed extensive experiments on CIFAR10, Clothing1M and Food101N datasets. Results show that our approach outperforms other state-of-the-art methods by a large margin. Our code is available at https://***/gorkemalgan/MSLG_noisy_label.

关键词： deep learning label noise noise robust noise cleansing meta-learning

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STOCK MARKET PREDICTION USING LONG SHORT-TERM MEMORY (LSTM) 2

STOCK MARKET PREDICTION USING LONG SHORT-TERM MEMORY (LSTM)

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2nd IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Abu Nadif, Mohammad Samin, Towhidur Rahman Islam, Tohedul Amer Int Univ Bangladesh Dept Comp Sci Dhaka Bangladesh

ISBN: (纸本)9781665411202

The stock market is one of the most unpredictable and highly concerned places in the world. There is no fundamental way to forecast stock market share prices. So people think stock market prediction is a gamble. Nevertheless, it is possible to generate a constructive pattern by using different types of algorithms and predict the share price. But when the characteristics are complex, and the largest portion of these classification methods are linear, resulting bad performance in class label prediction. In this paper we suggest a non-linear technique based on the Long Short-Term Memory (LSTM) architecture. According to studies LSTM-based models predict time and sequential models better than other models and RNN is the first algorithm with an internal memory that remembers its input, making it perfect for sequential data machine learning issues. For our experiment we collected the share market data from a particular company named Beximco for the last 11 years. To reassert the effectiveness of the system different test data are used. This work introduces a robust method that can predict stock price accurately based on LSTM.

关键词： Time series analysis Stock Exchange Machine learning pattern recognition Stock price prediction

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Human Activity recognition with Video Classification 4

Human Activity Recognition with Video Classification

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Nath, Rajiv Kumar Rasool, Atif Gangwar, Nitin Jha, Harshit Galgotias College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida201310 India

ISBN: (纸本)9781665474368

Human Activity recognition (HAR), is a broader place of research area concerned with recognition and motion of someone primarily based totally on information obtained from one-of-a-kind sensors. Movements are the one-of-a-kind activities executed inside or outdoors, which include running, jumping, waving, and reading. There also are different activities which includes cleaning, eating, and riding a car. The goal is to document sensor information and the corresponding actions performed by a particular subject, to form a model primarily based on the provided information, and what's more, to sum up the model to order the activities of a completely new, inconspicuous informational collection in view of the sensor data. © 2022 IEEE.

关键词： pattern recognition

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The New Answer to Drug Discovery: Quantum Machine Learning in Preclinical Drug Development 4

The New Answer to Drug Discovery: Quantum Machine Learning i...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Wong, Yew Kee Zhou, Yifan Liang, Yan Shing Qiu, Haichuan Wu, Yu Xi He, Bin Basis International School Guangzhou Guangzhou China

ISBN: (纸本)9798350324303

The Research & Development (R&D) phase of drug development Drug discovery and development (D&D) is a complex and costly endeavor, typically requiring six to nine years [1] and four hundred to fourteen hundred million USD [2] for the research and development (R&D) phase alone. To transform this process, we propose a novel concept that combines Quantum-based Machine Learning network (QML) and Quantum computing Simulation (QS) to expedite the R&D phase to three to six months and reduce the cost to merely fifty to eighty thousand USD. Our approach takes as inputs the target protein/gene structure and the primary assay [3]. For Hit Generation [3], the QML network generates potential hits [4] based on the molecular structure of the target protein, while the QS filters molecules from the primary assay according to their reaction and binding efficacy with the target protein. Then, for Lead Optimization [3], the resulting molecules from QML and QS are compared, and the ones that are common to both processes are subjected to dozens of molecular variations, while others are only modified slightly. Finally, all optimized molecules undergo multiple rounds of QS filtering with high standards for reaction efficacy and safety, producing a few dozen pre-clinical-trial-ready drugs. Our concept of QML and QS integration can also be applied to other fields, such as agricultural research, genetic editing, and aerospace engineering. Keywords: Quantum Computer, Hit to Lead, Lead Optimization Simulation, Machine Learning. © 2023 IEEE.

关键词： Molecules

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3rd international conference on Innovations in computing Research, ICR 2024

3rd International Conference on Innovations in Computing Res...

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3rd international conference on Innovations in computing Research, ICR 2024

ISBN: (纸本)9783031655210

The proceedings contain 67 papers. The special focus in this conference is on Innovations in computing Research. The topics include: Exploratory Analysis of Gamblers’ Financial Transactions to Mine Behavioral pattern Data;The Detection of Misstated Financial Reports Using XBRL Mining and Intelligible MLP;university Student Enrollment Prediction: A Machine Learning Framework;early Prediction of Sepsis Utilizing Multi-branches Multi-tasks Hybrid Deep Learning Model;comprehensive Analysis of Iris Dataset Using K-Mean and Fuzzy K-Mean Clustering Algorithm;An Efficient and Reliable scRNA-seq Data Imputation Method Using Variational Autoencoders;Prediction of Automotive Vehicles Engine Health Using MLP and LR;medical Image Character recognition Using Attention-Based Siamese Networks for Visually Similar Characters with Low Resolution;toward Smart Bicycle Safety: Leveraging Machine Learning Models and Optimal Lighting Solutions;vThrot: Fine-Grained, Virtual I/O Resource Redistribution Scheme;Bayesian Optimization-Based CNN Model for Blood Glucose Estimation Using Photoplethysmography Signals;comparing Convolutional Neural Networks and Transformers in a Points-of-Interest Experiment;Gender and Age Extraction from Audio Signal Using Convolutional Neural Network, MFCC and Spectrogram;The Hybrid Model Combination of Deep Learning Techniques, CNN-LSTM, BERT, Feature Selection, and Stop Words to Prevent Fake News;comparative Analysis of Decision Tree Algorithms Using Gini and Entropy Criteria on the Forest Covertypes Dataset;A Comparative Analysis of Random Forest and Support Vector Machine Techniques on the UNSW-NB15 Dataset;a Comparative Study of Speed Measurement Using Radar Guns and Pneumatic Counter;Comparative Analysis of Preprocessing Techniques for KNN Classification on the Diabetes Dataset;code Smells for Assessing and Improving Students’ Coding Skills and Practices;Analysis of eSIM/iSIM for Critical Communications.

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Design and Implementation of Anti-Peeping Screen Algorithm Based on soft-NMS YOLOv7

Design and Implementation of Anti-Peeping Screen Algorithm B...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Baining Zhang Hao Chen Junlong Chen Huawei Dai School of Integrated Circuit Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Huizhou University Huizhou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

The occurrence of privacy breaches through screen peeping has highlighted the increasing importance of anti-peeping screen algorithms. In the traditional YOLOv7 algorithm, it may not accurately detect faces when individuals are mutually obscured. In order to improve the success rate of face detection, we propose a new anti-peeping screen algorithm, soft-NMS-YOLOv7. Compared to the traditional NMS-YOLOv7, soft-NMS-YOLOv7 prevents face leakage by specially handling the scores of adjacent detection boxes. Therefore, soft-NMS-YOLOv7 exhibits better detection performance and robustness, increasing the accuracy of face detection in anti-peeping screen algorithms from 90.05% to 94.71%. Additionally, we have developed a physical model of soft-NMS-YOLOv7 based on Raspberry Pi 4B.

关键词： Measurement Visualization Accuracy Face recognition Privacy breach Robustness Hardware Face detection Security Faces

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