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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

共 694 条记录，以下是21-30 订阅

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MIFD-Net: A Hand Gesture Recognition Model Based on Feature Fusion of MLP and CNN 3

MIFD-Net: A Hand Gesture Recognition Model Based on Feature ...

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3rd international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Wang, Tong Song, Ge Ni, WeiJian Zeng, QingTian College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781510661301

Gesture recognition can play a crucial role in addressing the issue of Human-computer interaction. In this paper, we proposed a vision-based Multi-input fusion deep network (MIFD-Net), which consists of Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN). MIFD-Net first processes hand keypoint data and gesture images using Euclidean distance normalization (ED-Normalization) and image segmentation technologies, respectively. Then, two kinds of data are simultaneously used as input to MIFD-Net. The experimental results show that the MIFD-Net achieves an average accuracy of 99.65% on the self-built dataset in this paper and 99.10% on the NUS hand posture datasets II (NUS-II). The MIFD-Net significantly decreases its FLOPs and the number of parameters and reduces the complexity of the model while maintaining a high recognition rate compared with other gesture recognition models. The MIFD-Net can obtain high accuracy and strong robustness in different environments, lighting, and angles. © 2023 SPIE.

关键词： Gesture recognition

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Deep Neural Network Based Cyber Intrusion Detection System

Deep Neural Network Based Cyber Intrusion Detection System

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Disruptive Technologies (ICDT), international conference on

作者： Shweta Lavaniya Sandeep Kumar Jain Department of Computer Science Dr. Bhimrao Ambedkar University Agra India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

An Intrusion Detection System (IDS) is a critical security device used to prevent intrusions, protect networks and digital assets from malicious traffic. Considering the rapid advancement of internet technology, security of networks has become a critical concern as there is high rise in data volume due to exponential growth of internet users and thus posing the serious security challenges. soft computing techniques have proven effective in handling such large datasets, outperforming traditional approaches and demonstrating superior performance. In this study, we propose a soft computing-based IDS, training the model using complete set of features. The system was simulated with the help of a hybrid real and modern threat activities synthesized in UNSW-NB15 dataset, and the results validate its effectiveness.

关键词： Training Accuracy Computational modeling Intrusion detection Training data Artificial neural networks Reconnaissance Multilayer perceptrons Grippers Tuning

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3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

3rd International Conference on Soft Computing and its Engin...

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3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

ISBN: (纸本)9783031057663

The proceedings contain 33 papers. The special focus in this conference is on soft computing and its Engineering Applications. The topics include: Sentiment Analysis of Twitter data Using Machine Learning Approaches;mining Spatio-Temporal Sequential Patterns Using MapReduce Approach;a Split-Then-Join Lightweight Hybrid Majority Vote Classifier;identification of Barriers in Adoption of IoT: Commercial Complexes in India;a Dynamically Adapting Framework for Stock Price Prediction;evaluating Binary Classifiers with Word Embedding Techniques for Public Grievances;database Concentration Method for Efficient Image Retrieval Using Clustering and Image Tag Comparison;microstructure Image Classification of Metals Using Texture Features and Machine Learning;segregation of Areca Nuts Using Three Band Photometry and Deep Neural Network;Early Diagnosis of Alzheimer’s Disease from MRI Images Using Scattering Wavelet Transforms (SWT);constraint Pushing Multi-threshold Framework for High Utility Time Interval Sequential Pattern Mining;KTSVidRec: A Knowledge-Based Topic Centric Semantically Compliant Approach for Video Recommendation on the Web;intelligent Facial Expression Evaluation to Assess Mental Health Through Deep Learning;intelligent Mobility: A Proposal for Modeling Traffic Lights Using Fuzzy Logic and IoT for Smart Cities;crop Disease Prediction Using Multiple Linear Regression Modelling;radial Basis Function Network Based Intelligent Scheme for software Quality Prediction;generating the Base Map of Regions Using an Efficient Object Segmentation Technique in Satellite Images;KCEPS: Knowledge Centric Entity Population Scheme for Research Document Recommendation;weighted Hybrid Recommendation System Using Singular Value Decomposition and Cosine Similarity;CT/MRI 3D Fusion for Cerebral System Analysis;text Analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems;arabic Cyberbullying Detection from Imbalanced dataset Using Machine Learning;in

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soft computing Method for Settling Velocity Prediction of Fine Sediment in Retention Structure 1

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3rd international Civil Engineering and Architecture conference, CEAC 2023

作者： Chin, Ren Jie Lai, Sai Hin Loh, Wing Son Ling, Lloyd Soo, Eugene Zhen Xiang Huang, Yuk Feng Yeo, Ya Qi Department of Civil Engineering Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang43000 Malaysia Department of Civil Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur50603 Malaysia Department of Mathematical and Actuarial Sciences Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang43000 Malaysia

ISBN: (数字)9789819963683

ISBN: (纸本)9789819963676

Most of the retention structures were polluted by various pollutants, particularly fine sediment carried by rainwater due to erosion. Fine sediment is the main cause of siltation which may cause numerous health and environmental problems. To date, the study on fine sediment is limited due to the technology constraint. Therefore, there is a need to formulate a mathematical model which is able to provide an acceptable level of accuracy for the settling velocity prediction of fine sediment. In this study, Radial Basis Function Network (RBFN) and Gradient Boosted Trees (GBT) models were developed and trained by using the experimental data from Particle Image Velocimetry (PIV) tests. Flow rate, particle sizes, vertical displacement and maximum depth were considered as the input while the settling velocity of fine sediment was kept as the output. The developed models were evaluated using a series of statistical analyses. For RBFN and GBT, model VI and model V respectively, has achieved the best performance in terms of coefficient of determination, mean absolute error, and root mean squared error. The findings show that GBT is more suitable than RBFN for the settling velocity prediction of fine sediment with a R2 value of 0.9591, MAE value of 0.000177 and RMSE value of 1.03E-05. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Radial basis function networks

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Structural Measurement of Cultural Relics based on 3D Scanning and Geomagic software: Taking Ceramic Artifacts as an Example 3

Structural Measurement of Cultural Relics based on 3D Scanni...

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3rd international computing Imaging conference, CITA 2023

作者： Yang, Weizhi Deng, Dingnan Zhang, Hongfei School of Physics and Electronic Engineering Jiaying University Meizhou514015 China Meizhou Intelligent Photoelectric Detection Application Engineering Technology Research Center Meizhou514015 China Lin Fengmian Academy of Fine Arts Jiaying University Meizhou514015 China

ISBN: (纸本)9781510671409

Digitization is the inevitable trend of cultural heritage protection and research, and the structural measurement of cultural relics is an important basis for three-dimensional (3D) digitization, and it is also a necessary prerequisite for other digital derivative work. Taking ceramic artifacts as an example, this paper explores the comprehensive, accurate and efficient measurement of the size of cultural relics, and realizes the comprehensive 3D digitization. The 3D data model of cultural relics was obtained by 3D scanner, and the model was processed and measured by Geomagic series software, so as to obtain the one-dimensional size parameters, overall and local area and volume information of cultural relics. The experimental results show that this method was convenient, fast and accurate, which was of great significance for understanding the structure of cultural relics, and provides a reliable basis for the comprehensive digital display and subsequent protection research of cultural relics. © 2023 SPIE.

关键词： 3D modeling

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Interactive Agent Foundation Model: A Meta-Learning Strategy

Interactive Agent Foundation Model: A Meta-Learning Strategy

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international Symposium on Advanced computing and Communication (ISACC)

作者： Mani Dwivedi Anuj Kr. Dwivedi Department of Computer Science and Engineering KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science and Engineering Galgotias College of Engineering and Technology India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Artificial intelligence (AI) systems are evolving from static, task-specific models to dynamic, agent-based systems that perform well in a variety of scenarios. We present an Interactive Agent Foundation Model using a novel multi-task agent training methodology that includes many pretraining techniques such as language modelling, visual masked autoencoders, and next-action prediction. This framework's adaptability and versatility have been shown in the robotics, gaming AI, and healthcare sectors, yielding results that are pertinent to the situation. Effective multimodal and multitask learning is made possible by utilizing a variety of data sources, such as textual information, gaming data, robotics sequences, and large-scale video datasets. Our method offers a viable way forward for creating adaptable, proactive, multimodal systems.

关键词： Training Adaptation models Visualization Foundation models soft sensors Medical services Predictive models Multitasking data models Robots

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Network Intrusion Detection System Using Decision Tree and KNN Algorithm

Network Intrusion Detection System Using Decision Tree and K...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 international conference on

作者： Km Shivani Singh Khundrapam Johnson Singh Dept. of Computer Science and Engineering National Institute of Technology Imphal Manipur

The complexity of attacks is increasing, making it increasingly difficult to effectively discover breaches. A network intrusion detection system is needed to handle the aforementioned problem. An automated software program called a network intrusion detection system alerts administrators when someone attempts to compromise the system by participating in hazardous behaviors. A system or network is protected from harmful invasions by hardware and software firewalls. They function like filters, removing any data that could put the system or network at risk. We aimed to design the best intrusion detection system possible so that it could distinguish between “normal” and “attacked” categories of network data with high accuracy. The best accuracy can be obtained by using soft computing techniques like Decision Trees and KNN. We tested a variety of techniques, such as preprocessing the data, feature selection, principal component analysis (PCA) reduction, standardization, and normalization, in order to increase our model’s accuracy scores. We also assess the two approaches’ results according to their accuracy in order to decide which way is more effective.

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Adopting Ensemble Learning and Machine Learning Techniques for Predictive Modeling in Traffic data Analysis

Adopting Ensemble Learning and Machine Learning Techniques f...

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： B Santhosh Narayanan L Shyamala School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In this study, the trends of traffic data are analyzed using predictive analysis techniques applied to the NYC taxi dataset. Various models were tested for forecasting travel time, fare, and speed. Notably, the Decision Tree Regression model demonstrated outstanding performance with an impressive R2 score of 0.9995 for travel time prediction. Additionally, an ensemble model consisting of Linear Regression and Decision Tree Regression achieved an accuracy of 0.93 for fare prediction. Furthermore, another ensemble model combining LSTM and DBN attained an R2 score of 0.90 for speed prediction. This study highlights the effectiveness of diverse predictive modeling techniques in accurately forecasting these features.

关键词： soft sensors Urban areas Predictive models Real-time systems Ensemble learning Task analysis Predictive analytics

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database Concentration Method for Efficient Image Retrieval Using Clustering and Image Tag Comparison 3rd

Database Concentration Method for Efficient Image Retrieval...

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3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

作者： Shimgekar, Soorya Ram Pathi, Preetham Reddy Vijayarajan, V. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9783031057663

As the world moves towards a digital civilization, we generate more and more data along the way. With this increase in the number of images on the internet and with social media companies, faster and scale-able image retrieval algorithms are needed for efficiency. In this paper, we propose a tag based image retrieval algorithm which also uses clustering for better efficiency in large data sets. Although there are multiple existing algorithms that use tags most of them use tag completion but not new tag generation. So, they cannot be used for completely unlabelled data. The proposed algorithm makes use of IBM’s(international Business Machines) VisualRecognitionV3 to generate new tags for completely unlabelled data. We make use of K-Means algorithm to firstly cluster the images into different clusters based on features extracted using ImageNet weights of ResNet50 architecture. We then use IBM’s VisualRecognitionV3 to label the folders based on its contents. Similar steps are also carried out with query image and using word-vectorization and cosine similarity, top-n most relevant folders are found out. Then relevant images from these best folders are saved as output based on similarity of image features, found using ResNet-50 model on ImageNet Weights. © 2022, Springer Nature Switzerland AG.

关键词： Business machines

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On the Optimal Combination of Cross-Entropy and soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness 3rd

On the Optimal Combination of Cross-Entropy and Soft Dice...

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3rd Diabetic Foot Ulcers Grand Challenge, DFUC 2022, held in Conjunction with the 25th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2022

作者： Galdran, Adrian Carneiro, Gustavo Ballester, Miguel A. González BCN Medtech Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain University of Adelaide Adelaide Australia Barcelona Spain

ISBN: (纸本)9783031263538

We study the impact of different loss functions on lesion segmentation from medical images. Although the Cross-Entropy (CE) loss is the most popular option when dealing with natural images, for biomedical image segmentation the soft Dice loss is often preferred due to its ability to handle imbalanced scenarios. On the other hand, the combination of both functions has also been successfully applied in these types of tasks. A much less studied problem is the generalization ability of all these losses in the presence of Out-of-Distribution (OoD) data. This refers to samples appearing in test time that are drawn from a different distribution than training images. In our case, we train our models on images that always contain lesions, but in test time we also have lesion-free samples. We analyze the impact of the minimization of different loss functions on in-distribution performance, but also its ability to generalize to OoD data, via comprehensive experiments on polyp segmentation from endoscopic images and ulcer segmentation from diabetic feet images. Our findings are surprising: CE-Dice loss combinations that excel in segmenting in-distribution images have a poor performance when dealing with OoD data, which leads us to recommend the adoption of the CE loss for these types of problems, due to its robustness and ability to generalize to OoD samples. Code associated to our experiments can be found at https://***/agaldran/lesion_losses_ood. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Endoscopy

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