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6th international conference on Soft Computing and signal Processing, ICSCSP 2023

6th International Conference on Soft Computing and Signal Pr...

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6th international conference on Soft Computing and signal Processing, ICSCSP 2023

ISBN: (纸本)9789819986279

The proceedings contain 54 papers. The special focus in this conference is on Soft Computing and signal Processing. The topics include: Stock Price Prediction Using LSTM, CNN and ANN;IoT-based Smart Wearable Devices Using Very Large Scale Integration (VLSI) technology;plant Disease Detection and Classification Using Artificial Intelligence Approach;camera and Voice Control-based Human–Computer Interaction Using Machine Learning;optimal Crop Recommendation by Soil Extraction and Classification Techniques Using Machine Learning;ioT-based Smart Irrigation System in Aquaponics Using Ensemble Machine Learning;Performance Evaluation of MFSK Techniques Under Various Fading Environments in Wireless Communication;prediction of Breast Cancer Using Feature Extraction-based Methods;review and Design of Integrated Dashboard Model for Performance Measurements;anomaly Detection in Classroom Using Convolutional Neural Networks;Efficient VLSI Architectures of Multimode 2D FIR Filter Bank using Distributed Arithmetic Methodology;implementation of an Efficient image Inpainting Algorithm using Optimization Techniques;A Systematic Study and Detailed Performance Assessment of SDN Controllers Across a Wide Range of Network Architectures;Classification and Localization of Objects Using Faster RCNN;an image Processing Approach for Weed Detection Using Deep Convolutional Neural Network;detection of Leaf Black Sigatoka Disease in Enset Using Convolutional Neural Network;detecting Communities Using Network Embedding and Graph Clustering Approach;Deep Learning Approaches-based Brain Tumor Detection Using MRI images—A Comprehensive Review;Predicting Crop Yield with AI—A Comparative Study of DL and ML Approaches;An Empirical Analysis of Lung Cancer Detection and Classification Using CT images;trust and Secured Routing in Mobile Ad Hoc Network Using Block Chain.

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Deep Learning and Computer Vision based Warning System for Animal Disruption in Farming Environments 3

Deep Learning and Computer Vision Based Warning System for A...

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3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Mohanavel, Dhivya Muthu Ishwarya, A. Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Chennai Campus Tamilnadu Chennai600 127 India

ISBN: (纸本)9798350372120

The quarrel between humans and animals due to disruption is cited as a human-wildlife conflict (HWC). These types of incidents frequently leads to a loss of crops, farm animals and humans. In addition to that, it also endangers wildlife species casualty. The local populace in areas where wildlife borders, parks and reserves are close to their dawn have a major dilemma. In this case, animal punishment is not a deterrent. technology-based security patches must be set into practice. The proposed study suggests installation of an animal detection warning system in the form of an alarm set up in the control room to lessen the harm that untamed animals cause to the surroundings and the humans who are employees around. Animal detection can be adapted using deep learning and computer vision (AI) techniques which will be straightforward to locate and identify any creatures that attempt to attack or enter any agricultural field, or farm. The animals are further deterred by alarm systems that provide sound and signals to the field owners and forest officials. In this analysis, with deep learning as a more reliable object identification strategy, YOLO method is used to examine figures incredibly and quickly. Further, the Artificial intelligence (AI field)'s of computer vision enables machines to gather valuable data and to behave appropriately or offer suggestions following the input. These techniques can effectively handle localization, multi-classification, and target that recurs numerously and a danger signal will notify the forest ranger if an animal is spotted or a warning bell will ring at a certain point indicating field owners that animals are close to them. © 2024 IEEE.

关键词： Animals

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Design of IoT-based Smart Wearable Device for Human Safety 4th

Design of IoT-Based Smart Wearable Device for Human Safety

引用

4th international conference on IoT based Control Networks and Intelligent systems, ICICNIS 2023

作者： Reddy, Raghavendra Srinivasan, Geethasree Dhaneshwari, K.L. Rashmitha, C. Sai Krishna Reddy, C. School of Computer Science and Engineering REVA University Bangalore560064 India

ISBN: (纸本)9789819965854

Nowadays because of various circumstances, woman and children safety has become very important. There is a need for the smart device which automatically senses and rescues the victims. Proposed new approach helps to use internet of Things (IoT) technology for woman and children safety. The proposed smart wearable device consists of various hardware components and Blynk App. This smart wearable device will communicate continuously with a smart phone through internet service. It makes use of GPS and messaging services to send the location information and alert message. Whenever it receives emergency signal, the system can alert the relevant authorities, nearest police station, relatives, and emergency response teams through automated notifications, allowing them to respond quickly and efficiently. The proposed approach aims to provide a comprehensive solution for women's and child safety and covers the development of application and hardware, including the integration of the alert system and emergency calls and ensuring that the solution is user-friendly, reliable, and effective in critical situations. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global positioning system

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6th international conference on Soft Computing and signal Processing, ICSCSP 2023

6th International Conference on Soft Computing and Signal Pr...

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6th international conference on Soft Computing and signal Processing, ICSCSP 2023

ISBN: (纸本)9789819984503

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Enhancing privacy protection in intelligent and connected vehicles: a styleGAN3-based image desensitization dataset and framework 4

Enhancing privacy protection in intelligent and connected ve...

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4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Wang, Jingyan Wang, Liyong Ji, Haojie Fang, Junzhe Guo, Teng Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University No.12 Xiaoying East Road Haidian District Beijing100192 China Zaozhuang Network Security Center No. 2621 Guangming Avenue Xuecheng District Shandong Province 277020 China

ISBN: (纸本)9781510686106

Intelligent and connected vehicles (ICVs) collect vast video footage, inadvertently capturing privacy-sensitive data. To address this, we introduce the IDD-ICV dataset, featuring diverse in-vehicle camera footage. To protect privacy, we propose a styleGAN3 based framework that integrates MHA (Multi-Head Attention) for detecting and desensitizing facial data in datasets. This approach ensures compliance with data security standards and enhances ICVs' privacy protection capabilities during the life cycle of data. Our study contributes to the development of secure and trustworthy autonomous transportation systems by demonstrating the effectiveness of our framework in safeguarding privacy-sensitive information. © 2024 SPIE.

关键词： Differential privacy

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Qualitative identification of fluorescence immunochromatography strips based on residual networks 3

Qualitative identification of fluorescence immunochromatogra...

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3rd international conference on Advanced Algorithms and signal image Processing, AASIP 2023

作者： Zhong, Lingjun Jiang, Haiyan Xu, Xianjing College of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Medical Instrument and Pharmaceutical Technology Fuzhou350108 China

ISBN: (纸本)9781510668522

The article proposes a qualitative identification scheme of fluorescent immunoassay strips based on residual networks to address problems such as poor strip positioning accuracy and inadequate strip size specifications in current photoelectric fluorescent immunoassay quantitative detection systems, which result in low accuracy and detection efficiency. The proposed method employs the Hough line detection algorithm, which is based on Canny edge detection, to extract the tilt angle of the test strip. It then combines this with contour extraction of the strip image to calculate its contour center. By utilizing the test strip tilt angle and contour center, the article accurately locates the test strip position. The residual network is utilized for extracting strip features, while the extreme learning machine is employed for discriminating the validity and positive/negative results of the fluorescent strips. Validation experiments on a novel coronavirus fluorescent immunoassay strip demonstrate that the residual network model based on extreme learning machine proposed in this article achieves 100% accuracy, precision, recall, and F1-score values for strip classification, effectively improving the recognition accuracy and detection efficiency of the fluorescent immunoassay quantitative detection system. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Coronavirus

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A Deep Fake Detection System Using Diffusion Model based on Graph based image Segmentation 5

A Deep Fake Detection System Using Diffusion Model Based on ...

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5th international conference on Machine Learning and Intelligent systems, MLIS 2023

作者： Al-Din, Basim Najim Jaber, Karimpour Mahan, Farnaz Computer Science Department University of Tabriz 29 Bahman Blvd Tabriz*** Iran

ISBN: (纸本)9781643684468

With the development of technology and the increase in the need to use the internet and the transmission of personal data and save it on the cloud and personal computers and with the increase in security risks represented by penetrations and cyber threats, and among these threats are those represented by the use of deep learning techniques to produce deep fake images that are mostly used to violate cyber security through threats such as the ransom threat or publishing fake news and other cyber risks, so there was an urgent need to establish systems to detect deep forgery in images or videos, in this paper a system was proposed to detect deep forgery based on the principle of using the diffusion model based on the graph based on image segmentation where the system transforms the image to graph and then segment it into three areas which are fake, real and background. And do purification of the adversarial content in these areas using diffusion technology, and then identify the fake areas in the image and use GCN in order to distinguish between real and fake images. In order to evaluate the proposed method, it was compared with the other methods on different data sets (CELEB-DF (V2) data set, Face Forensics++ data set, and wild deep fake). The result shows that the proposed system achieved better results than the approaches in the literature. © 2023 The authors and IOS Press.

关键词： Personal computers

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FPGA-based Implementation of the Quantized Neural Network for image Super-Resolution 8

FPGA-Based Implementation of the Quantized Neural Network fo...

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8th international conference on image, Vision and Computing, ICIVC 2023

作者： Ruan, Zhihao Xiao, Shanzhu Deng, Qiuqun Tao, Huamin Wu, Jiadong Zhang, Huan College of Electronic Science and Technology National University of Defense Technology Chang Sha China

ISBN: (纸本)9798350335231

The convolutional neural network(CNN)-based method for image super-resolution(SR) reconstruction has been becoming an important part in the fields of image processing such as security monitoring, object tracking. However, the huge computational complexity of neural networks make it shackled to satisfy the requirements for power consumption and complexity of reconstruction systems in specific application circumstances. In this paper, we propose a structure-unsophisticated and parameter-slight CNN model, and design a quantization scheme that allows inference to be performed by using integer-only arithmetic to alleviate parameter storage and communication pressure in hardware. Furthermore, convolution loop unroll and parallel acceleration analysis are performed for the model under the premise of board resources to optimize hardware inference. The results show that when the quantization precision is 8 bits, memory requirement of parameters is reduced by 75%, and the inference peak signal-to-noise ratio(PSNR) value loss is less than 1%. Quantization model was performed on the Xilinx Zynq7035 platform with inference losses not exceeding 2%, while hardware resources consumption is greatly reduced. © 2023 IEEE.

关键词： Acceleration

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Traffic Sign Classification based on Deep Learning

Traffic Sign Classification based on Deep Learning

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2023 IEEE international conference on Automatic Control and Intelligent systems, I2CACIS 2023

作者： Hongxing, Tian Caballero, Jonathan M. Hernandez, Alexander A. Technological University of the Philippines Graduate Program and External Studies Manila Philippines National University College of Computing and Information Technologies Manila Philippines

ISBN: (纸本)9798350321302

In the context of 5G communication, technologies such as the internet of Things, big data, and supercomputers have been further developed, and artificial intelligence technology has also become one of the hot topics at present. In recent years, the image recognition technology of artificial intelligence has been pursued by most academic enthusiasts and has been rapidly developed. With the development of computer vision, a series of target detection algorithms have emerged in recent years. This paper classifies traffic signs based on the target detection algorithm of YOLOv5 and make use of the advantages of the accuracy and real-time performance of the YOLO algorithm. To restore the real traffic scene, different shooting angles and different shooting locations are prepared as data sets. Finally, to realize the detection and recognition of traffic signs and optimize the algorithm to improve the recognition rate. © 2023 IEEE.

关键词： Traffic signs

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A Data Hiding Scheme for JPEG images based on EMD with Coefficient Selection Strategy 23

A Data Hiding Scheme for JPEG Images Based on EMD with Coeff...

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8th international conference on Multimedia systems and signal Processing, ICMSSP 2023

作者： Zhou, Xiaoyu Yang, Guangsong Chen, Tung-Shou Su, Guan-Zhong Hong, Wien School of Ocean Information Engineering Jimei University China Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

ISBN: (纸本)9798400700606

Joint photographic experts group (JPEG) is a widely used image compression format due to its high compression ratio and relatively good image quality. Embedding data into JPEG images is useful for many applications, such as digital watermarking, copyright protection, and privacy preservation. In response to this demand, this paper proposes a new lossy data hiding scheme that can be applied to JPEG images. In 2021, Xie et al. proposed a lossy data hiding scheme based on the exploiting modification direction (EMD) embedding technology, which embeds data into the alternative current (AC) coefficients of the image after DCT transformation. However, their scheme causes significant errors in coefficient selection, leading to a reduction in image quality. Thus, we propose a new coefficient selection strategy to improve the quality of embedded images. Experimental results show that the proposed method improves Lena image quality by 1.15 dB compared to Xie et al.'s method at a quality factor of 90. © 2023 ACM.

关键词： Embeddings

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