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Empirical Analysis of image Recognition and Classification through Machine Learning and Transfer Learning Approaches using Multiple Regression Analysis 2

Empirical Analysis of Image Recognition and Classification t...

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2nd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Seekoli, Avinash Gupta, Ayan Das Thaseen, Mohseena Gopinath, N. Jaiswal, Sushma Bangare, Sunil L. St.Martins Engineering College Department of CSE India Chandernagore Government College Chandernagore Government College Affiliated to the University of Burdwan Postgraduate Department of Geography West Bengal Hooghly India Nes Science College Department of Computer Science and It Maharashtra Nanded India Sri Sairam Engineering College Department of Computer Science and Engineering India India Sinhgad Academy of Engineering Savitribai Phule Pune University Department of Information Technology Pune India

ISBN: (纸本)9781665437899

image recognition and classification have taken a shift from the conventional detection process by humans to detection by Machine Learning (ML) and Transfer Learning (TL). These two approaches are being applied in various image identification processes for human development. The research paper identified the accuracy rates of three architectures in Convolutional Neural Networks (CNNs);such as AlexNet, GoogleNet and ResNet. Different sources have been evaluated to collect the accuracy rates of these three CNN architectures. These architectures were trained with a definite number of resources having the same features (1000) and accuracy was calculated after every training day. The accuracy rate was kept on Excel and regression analysis was accomplished in SPSS to identify how one-day training improves the accuracy of these architectures. Moreover, training days and layers were identified for each architecture to compare the research data and to evaluate further. Findings suggested that AlexNet is more accurate in machine learning image recognition and classification, and it requires less time for training than GoogleNet and ResNet;whereas, due to having few layers, AlexNet is less accurate in Transfer Learning. © 2022 IEEE.

关键词： image recognition

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An Enhanced Forensic Analysis and Security Surveillance Using Deep Reinforcement Learning 2

An Enhanced Forensic Analysis and Security Surveillance Usin...

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2nd International conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Nandhini, T.J. Thinakaran, K. Chennai India

ISBN: (纸本)9798350305418

Crime prevention relies on forensic analysis and security surveillance. Manual inspection and human skill are used in crime scene object detection and identification, which is time-consuming and subjective. This study proposes deep reinforcement learning for crime scene object detection to improve forensic analysis and security monitoring. Deep reinforcement learning, specifically Deep Q-Networks (DQNs), automates crime scene object detection and identification. We enable an agent to discover and classify things of interest by teaching it to learn from its environment. We use real crime scene photos and computer vision-generated data to train the DQN-based agent. Synthetic data augmentation strengthens and generalises the training model. A incentive mechanism encourages the agent to focus on important things and ignore background elements. We use a large crime scene image dataset to test our approach. Our deep reinforcement learning-based object identification approach is compared against Faster R-CNN and YOLO. Precision, recall, and F1-score assess object detecting accuracy and efficacy. Our experimental results show that our proposed approach outperforms standard object detection algorithms. The deep reinforcement learning-based agent can effectively identify crime scene objects of interest with higher precision, recall, and F1-score. This accuracy enhancement could boost forensic analysis and security surveillance, aiding law enforcement in crime investigation and prevention. Our technique handles complex and diverse crime scene scenarios. It accurately detects firearms, suspicious shipments, and other evidence. This research advances object detection approaches in forensic analysis and security monitoring, giving promising future developments. © 2023 IEEE.

关键词： convaloutional neural network deep Q-Network forensic analysis surveillance video synthetic data augmentation

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A Method for Object Extraction from Crop image Based on Visual Saliency 2

A Method for Object Extraction from Crop Image Based on Visu...

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2nd International conference on Computer, Big data and Artificial Intelligence, ICCBDAI 2021

作者： Wang, Yuting Liao, Zhouyu School of Big Data and Computer Science Hechi University Yizhou Hechi Guangxi China

In order to solve the problem of accurate extraction of crop targets in natural background, an image target extraction method based on visual saliency is proposed. Firstly, an image is divided into multiple super pixels. Secondly, the significance detection method of ‘background first’ is used to calculate the significance of the whole image. Thirdly, the optimal data source is generated according to the characteristics of the high saliency region. Finally, the optimal data source is divided into target region and background region, and the crop target is extracted from the original image. Experimental results on a large number of crop images show that this method can extract targets accurately and completely. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Crops

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2022 IEEE 2nd conference on Information Technology and data Science, CITDS 2022 - Proceedings

2022 IEEE 2nd Conference on Information Technology and Data ...

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2nd IEEE conference on Information Technology and data Science, CITDS 2022

ISBN: (纸本)9781665496537

The proceedings contain 45 papers. The topics discussed include: smart watch activity recognition using plot image analysis;single user activity recognition with density activity abstraction graphics algorithm;a multi-round bilinear-map-based secure password hashing scheme;proximity-based anomaly detection in securing water treatment;comparative analysis of deep learning models for network intrusion detection systems;negative sampling in variational autoencoders;leveraging fog computing for geographically distributed smart cities;greedy algorithm for edge-based nested community detection;clustering-based customer representation learning from dynamic transactional data;development of machine learning based model for anomaly detection and fault cause diagnosis of assets in petrochemical industries;evaluation of machine learning methods for predicting rainfall in Bangladesh;machine learning techniques applied to Bangla crime news classification;deep learning-based anomaly detection for imaging in autonomous vehicles;and neural network-based prediction for lateral acceleration of vehicles.

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MCUNet: Multidimensional cognition UNet for multi-class maize pest image segmentation 2

MCUNet: Multidimensional cognition UNet for multi-class maiz...

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2nd International conference on Robotics, Artificial Intelligence and Intelligent Control, RAIIC 2023

作者： Kang, Chenrui Wang, Rujing Liu, Zhigui Jiao, Lin Dong, Shifeng Zhang, Li Du, Jianming Hu, Haiying Southwest University of Science and Technology School of Information Engineering Mianyang China Chinese Academy of Sciences Hefei Institutes of Physical Science Hefei China

ISBN: (纸本)9798350328004

Corn leaf pest is one of the leading causes of poor quality and low yield of corn. In order to quickly and accurately identify the types and forms of corn leaf pests, this paper proposes a multi-classification corn pest segmentation network, Multidimensional Cognition UNet (MCUNet). This network can classify different pests and accurately segment the details of pest morphology. Firstly, a Nested Multi-scale Extraction (NMSE) module is designed to extract deeper dense multi-scale spatial information, enabling the network to use detailed contour information. Secondly, Multilayer Perceived Self Attention (MLPSA) is proposed to complement the long-range dependencies of feature vectors, allowing the network to better distinguish the apparent differences between target and background regions. Finally, for the multiclassification task, Squeeze-and-Excitation Networks were added to MLPSA to enhance the network's ability to discriminate between different pests. The experimental data comes from a self-built dataset of corn pests. The experimental results show that the proposed network outperforms the current mainstream neural networks' classification and segmentation performance. © 2023 IEEE.

关键词： image segmentation

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image reconstruction attacks on distributed machine learning models 2

Image reconstruction attacks on distributed machine learning...

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2nd ACM International Workshop on Distributed Machine Learning, DistributedML 2021, co-located with the 17th International conference on emerging Networking EXperiments and Technologies, CoNEXT 2021

作者： Benkraouda, Hadjer Nahrstedt, Klara University of Illinois at Urbana-Chamapaign ChamapaignIL United States

ISBN: (纸本)9781450391344

Recent developments in Deep Neural Networks have resulted in their wide deployment for services around many aspects of human life, including security critical domains that handle sensitive data. Congruently, we have seen a proliferation of IoT devices with limited resources. Together, these two trends have led to the distribution of data analysis, processing, and decision making between edge devices and third parties such as cloud services. In this work we assess the security of the previously proposed distributed machine learning (ML) schemes by analyzing the information leaked from the output of the edge devices, i.e. the intermediate representation (IR). We particularly look at a Deep Neural Network that is used for video/image classification and tackle the problem of image/frame reconstruction from the output of the edge device. Our work focuses on assessing whether the proposed scheme of partitioned enclave execution is secure against chosen-image attacks (CIA). Given the attacker has the capability of querying the model under attack (victim model) to create image-IR pairs, can the attacker reconstruct the private input images? In this work we show that it is possible to carry out a black-box reconstruction attack by training a CNN based encoder-decoder architecture (reconstruction model) using image-IR pairs. Our tests show that the proposed reconstruction model achieves a 70% similarity between the original image and the reconstructed image. © 2021 ACM.

关键词： image reconstruction

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Can incomplete chlorite grain coats preserve porosity in deep-water sandstones? 2

Can incomplete chlorite grain coats preserve porosity in dee...

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2nd International Meeting for Applied Geoscience and Energy, image 2022

作者： Marchand, Ann Maria Elisabeth Crouch, Jonathan BP

Presence of chlorite in deep-water sandstones is well-known, but the ability of this chlorite to preserve porosity through quartz cement inhibition largely remains a topic of speculation. This case study examines samples from a shallower lower temperature (1.7 km and 90°C) and a deeper higher temperature reservoir (2.3 km and 115°C) both of Mesozoic age. Samples from both reservoirs display incomplete chlorite grain coats with coverage ranging from 30% to 90%. Average porosity decreases from 23.9% in the shallow reservoir to 18.5% in the deeper reservoir, and corresponding average quartz cement volumes increase from 4.9% to 8.3%. This difference in reservoir quality between shallower and deeper reservoir demonstrates that incomplete grain coats do not have the ability to halt quartz cementation as burial and time go on. Touchstone software simulations where chlorite grain coat coverages were reduced to less than 10% were used as a benchmark to quantify the porosity preservation potential of the incomplete grain coats in our data set. These simulations yielded an average porosity of 17.8% in the shallow reservoir and 10.1% in the deeper reservoir. This means that in the absence of incomplete grain coats the shallower reservoir would have around 25% less porosity than what we see in the samples today. The deeper reservoir would have around 40% less porosity, largely due to increased quartz cement volumes. This case study demonstrates that the presence of incomplete chlorite grain coats is beneficial to reservoir quality even if quartz cementation is not entirely inhibited. © 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.

关键词： Porosity

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Text to Photo-Realistic image Synthesis using Generative Adversarial Networks 2

Text to Photo-Realistic Image Synthesis using Generative Adv...

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2nd IEEE International conference on Futuristic Technologies, INCOFT 2023

作者： Rao, Abhishek S. Bhandarkar, P Adokshaj Devanand, Padmashali Akshay Shankar, Pratheek Shanti, Srinivas Karthik Pai, B.H. Nmam Institute of Technology Dept. of Information Science & Engineering Nitte India

ISBN: (纸本)9798350308846

Innovative design patterns have traditionally been constrained by time-consuming and expertise-intensive methods, limiting diversity and creativity in design. To overcome these challenges, our research focuses on revolutionizing the design process by leveraging Generative Adversarial Networks (GANs). Our goal is to offer an affordable and cutting-edge solution that enables the creation of unique design patterns based on user input text. By harnessing the power of GANs, we can generate high-resolution images, reducing the reliance on expensive designers and fostering creativity and idea generation in the design sector. Our methodology involves building upon a diverse dataset of sunflower images and associated text data borrowed from the Oxford-102 flowers dataset. We meticulously collect, clean, and normalize this dataset to ensure optimal training. U sing an encoder-decoder architecture, we train a GAN -based generative model to produce remarkable sunflower patterns. To enhance usability, we have developed a user-friendly graphical user interface (GUI) that allows users to input text and receive corresponding sunflower images seamlessly. Our current model has exhibited promising results, achieving an inception score of 3.45 ± 0.05 in generating sunflower patterns. These outcomes demonstrate the feasibility of our approach, which not only reduces design costs but also fosters innovation and ingenuity within the industry. By making this solution publicly accessible, we aim to support local businesses and individuals with limited finances in creating distinctive design patterns. © 2023 IEEE.

关键词： deep learning generative adversarial networks inception score text to image Synthesis text-conditioned auxiliary classifier

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Feature Correlational Attention Network for image Inpainting 2

Feature Correlational Attention Network for Image Inpainting

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2nd IEEE International conference on data Science and Computer Application, ICDSCA 2022

作者： Cao, Feihan Zhu, Qifeng Suzhou City University Suzhou China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

While existing methods based on generative model are able to restore missing contents with semantical coherency, the lack of combining the correlations between different levels results in the artifacts or blurry textures in the ultimate results. In this paper, we present a Feature Correlational Attention Network (FCAN) that leverages layers' correlations for better predictions. To be specific, we employ the U-Net as the basic framework to build our network, in which we implement each layer with the partial convolution method. Inside the correlational attention module, we choose the feature maps of the intermediate layer at both textural and structural levels, and perform a fusion block to emerge new correlational feature maps. An attention module is utilized to reconstruct generated patches with the kernels extracted from intermediate layers' features. Experiments on two benchmark datasets illustrate that our model generates promising results and outperforms the state-of-the-art methods. © 2022 IEEE.

关键词： Textures

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Research on reconstruction of Pipeline Defects Under Coatings Based on Pulsed Eddy Current and Improved DCGAN

Research on Reconstruction of Pipeline Defects Under Coating...

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Algorithm, image Processing and Machine Vision (AIPMV), International conference on

作者： Song Huang Longyi Zhang Lin Yang Dingding Fan Qin Cai Chongqing Special Equipment Inspection and Research Institute Chongqing China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

The detection of surface defects in pipelines with coating layers has long been a significant challenge in the industrial field of surface defect detection. Pulsed Eddy Current Testing (PECT) can be employed to obtain approximate defect images of suspected defect areas through matrix measurements outside the coating layer. However, due to a number of variables, including lift-off height, excitation square wave parameters, and the properties of the pipeline material, the matrix approximation images obtained differ significantly from the actual defect images. This paper proposes a method based on improved DCGAN to reconstruct defect images. First, photographs are taken of samples without coating layers, and precise defect images are obtained through manual annotation as positive samples. Then, matrix imaging data collected from pipelines with coating layers are used as input samples for generation. Through the improved DCGAN, defect images that closely resemble real defect images can be ultimately generated, thereby achieving the purpose of defect detection. The method proposed in this paper can effectively identify surface defects in pipelines under coating layers.

关键词： Surface reconstruction Surface waves Pipelines Solids Coatings Reliability image reconstruction Surface treatment Defect detection Testing

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