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检索条件"任意字段=2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2023"

共 1096 条记录，以下是71-80 订阅

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Comparative Study of Object Detection Models for Safety in Autonomous Vehicles, Homes, and Roads Using IoT Devices

Comparative Study of Object Detection Models for Safety in A...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Hasan, Mehedi Alavee, Kazi Ahnaf Bin Bashar, Syed Ziaul Rahman, Md. Tahmid Hossain, Muhammad Iqbal Rabiul Alam, Md. Golam Brac University Department of Computer Science and Engineering 66 Mohakhali Dhaka1212 Bangladesh

ISBN: (纸本)9798350341072

Object detection plays a pivotal role in enhancing security, surveillance, and automation. It enables timely threat identification, streamlines traffic management, and facilitates efficient resource allocation. By automating object recognition, it creates value through improved safety, productivity, and resource optimization in various domains, from smart cities to industrial settings. This paper explores modern object detection methods to develop a Real-Time Responsive CCTV Camera Model. As we transition to a 5G-connected world, smart devices are poised to manage our daily security. We focus on transforming conventional CCTV cameras into responsive, internet-connected security guards. By evaluating methods like HOG, Viola-Jones, R-CNN, SSD, and YOLO, we aim to select the most efficient algorithm. We found Yolov8 perform best among all the models based on accuracy 99.8% and FPS 40. Our research strives to create cost-effective, intelligent security systems, paving the way for automated alerts and a digitally secured future. © 2023 ieee.

关键词： Security systems

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Face Image Scanning for Differentiation of Child/Adult images using a CNN-Based Model

Face Image Scanning for Differentiation of Child/Adult image...

引用

8th ieee asia-pacific conference on computer science and data engineering (ieee csde)

作者： Ahmed, Mirza Jamal Abdullah, Nurul Aza Univ Tun Hussain Malaysia FSKT Johar Malaysia Univ Tun Hussain nN Malaysia Johar Malaysia

ISBN: (纸本)9781665495523

Differentiating child and adult images is a crucial requirement for many applications. This paper proposes an approach using a Convolutional Neural Network (CNN) model to distinguish between children's images from adults. In contrast to predetermined face landmarks, the suggested approach learns complex face features and achieves 85% accuracy regardless of variation in age, gender, race, or ethnicity. The approach could be used to leverage the performance of digital image forensic, security control and, surveillance monitoring, and robotics.

关键词： CNN Machine Learning Image Classification Digital Image Forensics CSA computer Vision

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Combining First-Order and Texture Features with Convolutional Neural Network for Classification of Residential Area Density in Aerial Images

Combining First-Order and Texture Features with Convolutiona...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Remigio, Adrian S. Hong, Jonel Aborot, Jeffrey DOST-ASTI Computer Software Division Quezon city Philippines

ISBN: (纸本)9798350341072

With the increasing availability of aerial images, computational methods have been used to efficiently extract information and apply analyses for various applications. The goal of this study is to improve residential area density classification in aerial images by combining first-order and texture features with deep features from convolutional neural network (CNN). Gray level co-occurence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), gray level dependence matrix (GLDM), and neighbouring gray tone difference matrix (NGTDM) were calculated from the images as texture features. Classical machine learning (CML) and multilayer perceptron (MLP) models based on handcrafted features alone were also evaluated in terms of residential area density classification. Our results from NPWU-RESISC45 and AID dataset experiments have showed that our proposed combined deep and handcrafted features outperformed popular and state-of-the-art CNN models that are based on deep features alone. Thus, FO and texture features provide additional and non-overlapping information that can support deep features for a more accurate residential area density prediction. © 2023 ieee.

关键词： Convolutional neural networks

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Interpretable Violence Detection Using Separable Convolution and Bidirectional LSTM

Interpretable Violence Detection Using Separable Convolution...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Chakravorty, Tirthendu Prosad Abeer, Mobashra Baroi, Shaiane Prema Roy, Sristy Karim, Dewan Ziaul Brac University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

With the increasing demand for security concerns, security measures in all kinds of locations have become more dependent on the integration of surveillance cameras. Such devices are everywhere, which has significantly helped in the fight against violent crime. Continuous human monitoring becomes a laborious effort and frequently results in delayed reactions in bigger systems. Therefore, automated detection of aggressive behavior in surveillance systems can improve remote monitoring and boost reaction precision. The joint use of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have previously been used in studies to identify potentially violent actions accurately but many have struggled to minimize the computational resources required. The purpose of this study is to gain from decreased computing costs while preserving optimality for real-world applications. Hence, in this study, a rigorous model based on combinations of CNN and RNN architectures has been developed for spatiotemporal features from videos. To incorporate explainability for the AI's decision, the spatial feature extractor utilizes the LIME model, based on Explainable AI (XAI). The performance of the model then has been thoroughly analyzed using a compilation of several benchmark datasets. The suggested spatiotemporal feature-based model, in the final analysis, achieved a test accuracy of 98.75%. © 2023 ieee.

关键词： Convolution

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Devised Deephashing Models Through Self-Supervised Learning for Image Retrieval

Devised Deephashing Models Through Self-Supervised Learning ...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Kim, Sooin Jeon, Youngjin Kim, Won-Gyum Hwang, Doosung Kim, Donghoon Dankook University Dept. of Software Science Yongin Korea Republic of AiDeep Research Center Seoul Korea Republic of Arkansas State University Dept. of Computer Science JonesboroAR United States

ISBN: (纸本)9798350341072

Many image database systems utilize content-based retrieval technology using perceptual hashing to facilitate image comparison and retrieval. However, conventional hashing functions generate different hash codes for the same image domain even with minor changes, making accurate searches in the image database impossible. To overcome these limitations, a method has been devised to enable the search for images similar to queries synthesizing visual feature information, including texture, shape, and color of images. This paper proposes deephashing models that incorporate both variational autoencoders and vision trans-formers, capable of capturing semantic patterns and preserving the semantic similarity between data points. To validate this proposal, we conduct a comparative evaluation by comparing its performance with widely-used supervised learning-based models. The experimental analysis shows that the proposed approach generates compressed and discriminative hash codes with fixed-length binary representations that maintain semantic similarity and enable the search even for similar images. When evaluating the performance of our proposed model in comparison to the other studies, noticeable improvements are observed across the selected metrics in deephashing based on self-supervised learning. © 2023 ieee.

关键词： Content based retrieval

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Deep Learning Based Intrusion Detection for Internet of Things and Edge Devices

Deep Learning Based Intrusion Detection for Internet of Thin...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Moawad, Mohamad Tze, Vong Wan Hang Hui, Patrick Then Hsien Ming, Jason Yong Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Malaysia

ISBN: (纸本)9798350341072

The rapid expansion of Internet of Things (IoT) and Edge devices has introduced a variety of security challenges, particularly within resource-constrained computing environments. This paper aims to address these challenges by proposing a resource-efficient anomaly-based intrusion detection system tailored for IoT and Edge Devices, leveraging the capabilities of Binary Neural Networks (BNNs). Anomaly-based systems have demonstrated their efficacy in identifying potential cyber-attacks, but their conventional development methods often demand substantial storage and computational resources. Therefore, this research investigates the viability of BNNs for anomaly detection, offering a compelling alternative to full-precision approaches. Three distinct models are developed for this study using TensorFlow and Keras: Full-binary, Binary-weighted, and Full-precision. The models are trained and evaluated on the NSL-KDD dataset and achieved accuracy rates of 82%,87%, and 97% for these respective models. While binary networks experience a minor performance reduction due to aggressive quantization, they maintain notable levels of accuracy and precision. This observation takes into consideration the anticipated reduction in resource requirements, affirming the feasibility of BNNs' utilization for efficient anomaly detection. © 2023 ieee.

关键词： Intrusion detection

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PRODG: Practical Overlapping Community Detection in Weighted Graphs using Deep GCNs

PRODG: Practical Overlapping Community Detection in Weighted...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Muttakin, Md Nurul Azim, Muhammad Anwarul Hossain, Md Iqbal Rahman, Md. Saidur Bangladesh University of Engineering and Technology Graph Drawing & Information Visualization Lab Department of Computer Science and Engineering Dhaka1205 Bangladesh The University of Arizona TucsonAZ United States

ISBN: (纸本)9798350341072

Graph-based neural networks, including transformers, are the most used neural networks. Recently, overlapping community detection problems have been tackled using graph convolutional networks (GCN), achieving superior performance than classical methods. However, some of these methods have limited practical use as they require ground truth information to optimize the hyper-parameters. In addition, most existing works, including GCN-based methods, either ignore the edge weight of weighted graphs or fail to leverage the edge weight information to achieve better performance. To this end, we propose PRODG as a practical overlapping community detection method in edge-weighted graphs using deep GCNs. Specifically, we design weighted dilated aggregation algorithms to incorporate the information of edge weights to achieve a deep-weighted residual GCN (WResGCN) model. Moreover, we perform a completely unsupervised modularity-based depth search for deep GCNs, making our method available for practical applications. We apply our model to real-world Facebook graphs with synthetic edge weights, having reliable ground truth information. Experimentation and evaluation show that our methods effectively leverage the edge-weight in weighted graphs. Our completely unsupervised depth search strategy in deep GCNs performs competitively or better than state-of-the-art methods, bolstering the practical use of deep GCNs in overlapping community detection. © 2023 ieee.

关键词： Graph neural networks

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Estimating creativity drawing features from hand drawing logs

Estimating creativity drawing features from hand drawing log...

引用

2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Urayama, Kohei Shimakawa, Hiromitsu Harada, Fumiko Ritsumeikan University Shiga Japan

ISBN: (纸本)9781665453059

Students should engage in creative works in schools. However, there is no sensor to measure on the spot whether they engage in creative works. This work assumes engagement in creative works shows up in their writing features with pens. It uses a tablet and an electronic pen to acquire the writing features. It applies Random forests to detect the writing features. The results have showed that the features appear in writing pressure, velocity, and pen tilt angle. © 2022 ieee.

关键词： computer science creativity support tools data engineering data science Machine Learning

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Machine Learning Solutions for Investigating Streams data using Distributed Frameworks: Literature Review

Machine Learning Solutions for Investigating Streams Data us...

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8th ieee asia-pacific conference on computer science and data engineering (ieee csde)

作者： Kumar, Kunal Sharma, Neeraj Anand Ali, A. B. M. Shawkat Univ Fiji Dept Comp Sci & Math Lautoka Fiji

ISBN: (纸本)9781665495523

Distributed Frameworks such as Apache Spark have become an integral part of data analysis in the modern world. Machine learning algorithms implemented in distributed frameworks have become popular due to their impressive processing power. Research now is shifting to the analysis of data in a streaming environment that has data coming in real-time. This research focuses on identifying and conducting a literature review that investigates machine learning methods in a distributed framework using streams data. The review identifies some interesting patterns in the focus of machine learning algorithms and the use of various distributed frameworks. A lot of research opportunities are discussed while discussing research papers.

关键词： Machine Learning Apache Spark Streaming data Hadoop Apache Kafka

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Gender Classification in Bangla Language Using Deep Learning-Based Voice Analysis

Gender Classification in Bangla Language Using Deep Learning...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Labib, Md. Zarif Monsur, Sayema Binte Shuvo, Abtahi Maskawath Azrine, Tasmia Hakim, Talukder Juhaer Ali, Syed Muaz Alam, Md. Ashraful Brac University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

Gender classification based on voice analysis is one of the essential tasks in speech and audio processing, with various applications such as speech recognition systems, voice assistants, call center analytics, Etc. For speech synthesis, human-computer interaction, and speaker identification - gender classification plays a vital role. Although extensive research on this topic has been done in various languages, studies can hardly be found regarding gender classification in the Bangla language. Our research aims to recognize gender in the Bangla language using deep learning approaches and voice analysis. The proposed strategy in this study consists of three stages: i) Pre-processing of the data;ii) Feature extraction utilizing the Short-Time Fourier Transforms (STFT) and Mel-Frequency Cepstral Coefficients (MFCC);iii) Classification using Convolutional Neural Network (CNN) models such as ResNet50, EfficientNetB0, InceptionV3, and DenseNet-121. Notably, 12 distinct feature combinations are used for model training and testing, using both the MFCC and STFT features singly or in combination. After thorough training and testing, InceptionV3 and EfficientNetB0 CNN models with MFCC features as input resulted in the highest accuracy of 92%, which demonstrates the system's excellent accuracy rate and its potential for use in practical settings. © 2023 ieee.

关键词： Neural network models

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