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Fusion of Handcrafted Features and Deep Features to Detect COVID-19 1

8th International Conference on Computer Vision and image processing (CVIP)

作者： Gunda, Koushik Chakraborty, Soumendu Culibrk, Dubravko Indian Inst Informat Technol Lucknow Lucknow Uttar Pradesh India Univ Novi Sad Inst Artificial Intelligence Novi Sad Serbia

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

The COVID-19 pandemic's rapid growth has made it crucial to develop reliable and efficient diagnostic methods. In this study, we incorporate deep features and handcrafted features to provide a unique method for COVID-19 identification using chest X-rays. In order to extract high-level features from the chest X-ray pictures, we first use a convolutional neural network (CNN) that has already been trained to take advantage of deep learning. The discriminative information regarding COVID-19 infection is captured by the obtained deep features. In addition to the deep features, we also use manually created features that are meant to capture the unique features of COVID-19 in chest X-rays. Based on earlier study findings and domain understanding, these characteristics were manually constructed. They consist of statistical measures, shape-based characteristics, and texture descriptors. Comparing the performance of the classification with the standalone applications of convolutional and handcrafted features, we find that combining the features in our innovative framework enhances performance.

关键词： COVID-19 LGHP Convolutional neural networks LGB classifier Feature level fusion

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Lightweight refined networks for single image super-resolution

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MULTIMEDIA TOOLS AND applications 2022年第3期81卷 3439-3458页

作者： Tong, Jiahui Dou, Qingyu Yang, Haoran Jeon, Gwanggil Yang, Xiaomin Sichuan Univ Coll Elect & Informat Engn Chengdu 610064 Sichuan Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Sichuan Peoples R China Sichuan Univ West China Hosp Ctr Gerontol & Geriatr Chengdu Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Incheon Natl Univ Dept Embedded Syst Engn Incheon 22012 South Korea

Recently, software architectures applied to physical agents have become a boost from the emerging artificial Intelligence(AI). In these smart physical agents, simultaneously image information processing appears vital in particular. Single image Super Resolution(SISR) serves as the foundation of the image process, presenting its prospects driven by deep learning(DL) methods. In these DL methods, convolutional layers are stacked to implement a mapping between the original low resolution(LR) image and the high resolution(HR) image. Despite improved performances, convolution neural networks(CNN) based methods with large parameters are so complex and time-consuming, which is difficult to apply in mobile devices. To tackle the above issue, we propose Distillation Information Block(DIB) and Feature Information Refined Block(FIRB). In our proposed Lightweight Refined networks for single image Super-Resolution(LRSR), to lengthen our network with fewer parameters increased, the proposed DIB grasps more information by using a large receptive field. To enhance the information utilization, we build FIRB to refine advanced features and recover more details. Furthermore, with the compact structure, the execution time can be comparatively reduced with higher performance. We conduct extensive experiments on different datasets, which demonstrate that the proposed method performs comparatively better compared with the state-of-the-art lightweight methods.

关键词： artificial intelligence Single image super-resolution (SISR) Deep learning Receptive field Contextual information

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Generating Caption From images Using Flickr image Dataset 15

Generating Caption From Images Using Flickr Image Dataset

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Piyush Kaur, Kiranpreet Gandotra, Aaryan Patial, Pranay Radhika Gaba, Sehaj Chandigarh University Department of Cse Mohali India

ISBN: (纸本)9798350370249

In an age where the Internet is dominated by visual content, the generation of animated captions has become a must. It has always been an interesting study for researchers in the Department of artificial Intelligence. Enabling the machine to describe images with the same skillful accuracy as the human has important applications in various fields such as robotic vision, manufacturing, and beyond This project integrates recurrent neural networks with is a topic of contextual parallelism dedicated to extracting features from images Natural Language processing Computer Vision and integrating them seamlessly, this research provides insights a it goes further on this interdisciplinary topic. Additionally, annotations for the sample images are created and performed a comparative analysis of different feature extraction and encoder patterns to determine which model provided the highest accuracy and delivered the desired results. © 2024 IEEE.

关键词： Caption from an image Caption of an image Encoder- Decoder outputs text from an image

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Heterogeneous face detection based on multi-task cascaded convolutional neural network

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IET image processing 2022年第1期16卷 207-215页

作者： Yang, XianBen Zhang, Wei Beihua Univ Coll Comp Sci & Technol Jilin 13200 Jilin Peoples R China

Facial target detection is an important task in computer vision. Because heterogeneous face detection shows broad prospects, it has attracted extensive attention from the academic community. In recent years, with the rise of deep learning and its applications in computer vision, face detection technology has made great strides. This paper uses multi-task cascaded convolutional neural network (MTCNN) for heterogeneous face feature detection. This algorithm makes full use of the advantages of image pyramid, boundary regression, fully convolutional attention networks and non-maximum suppression. The main idea of this paper is to use candidate frame plus classifier for fast and efficient face detection. Specifically, the candidate window is generated by the proposal network (P-Net), and the high-precision candidate window is filtered and selected by the reduced network (R-Net), and the final bounding box and facial key points are generated by the output network (O-Net). In order to prove the effectiveness of this method in visible light, near-infrared and sketch face recognition scenes, it was verified in the datasets of CUFS, CUFSF and CASIA NIR-VIS 2.0. Experiments show that this method is effective for face images in heterogeneous face and is better than the latest algorithms.

关键词： O-Net regression analysis fully convolutional attention networks P-Net reduced network CASIA NIR-VIS 2.0 heterogeneous face feature detection candidate window frame plus classifier convolutional neural nets deep learning R-Net image representation output network face images feature extraction heterogeneous face detection deep learning (artificial intelligence) face recognition MTCNN multi-task cascaded convolutional neural network CUFSF computer vision boundary regression CUFS near-infrared sketch face recognition scenes object detection face detection technology nonmaximum suppression image classification image pyramid facial target detection

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FPGA Implementation of Pattern Detection using Convolutional neural networks 2

FPGA Implementation of Pattern Detection using Convolutional...

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2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Lahari, P.L. Poola, Rahul Gowtham Yellampalli, Siva Sankar Srm University Department of Electronics and Communication Engineering Andhra Pradesh India

ISBN: (纸本)9798350347579

The main objective of this research work is the detection of number of patterns from the input image. artificial neural Network (ANN) is used for solving may complex problems. Pattern recognition is generalized according to the type of learning procedure used to generate the output value. In image processing, pattern recognition has may real world applications such as identification and authentication. Eg: license plate recognition, finger print analysis, face detection/verification and voice-based authentication. For detecting number of patterns, at first input image and input pattern need to be converted into the text file through MATLAB and that text files will be given as input in the Verilog coding. So, based on the pattern, detection of number of times pattern repeated is calculated is shown and implementation of the same algorithm in Artix 7 FPGA board is shown. © 2023 IEEE.

关键词： Convolution

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An Application of AI for Online Estimation of the Impact of Imperfections in Additive Manufactured Components 1st

An Application of AI for Online Estimation of the Impact of ...

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1st Working Conference on artificial Intelligence Development for a Resilient and Sustainable Tomorrow (AIDRST)

作者： Holfeld, Denise Theurich, Franziska Rauschert, Andre Neumann, Gregor Hahnel, Falk Markmiller, Johannes Fraunhofer Inst Transportat & Infrastruct Syst Zeunerstr 38 D-01069 Dresden Germany Tech Univ Dresden Inst Aerosp Engn D-01062 Dresden Germany

ISBN: (纸本)9783658437046;9783658437053

artificial intelligence (AI) is popular for applications in image or natural-language processing, but AI can also be used to learn complex relations in production processes. For example, an AI can predict product quality based on process data during the production. In this paper, we present an application of AI to estimate structural properties of additive manufactured components in real-time. Occurring imperfections, such as air inclusions in the component, are considered and evaluated, since these have a significant influence on the quality of the component. This approach combines finite element (FE) simulation and machine learning: based on FE simulations, a neural network is trained to represent the relation between imperfections and the robustness of the component. To predict the impact of imperfection in real-time, monitoring systems are used to detect anomalies during the printing process, which are indications for imperfections in the additive manufactured component. Afterwards, the trained model is used to evaluate the impact of the detected anomalies to the component quality. This application of AI has a great potential to improve the additive manufacturing process itself and simplifying the approval of additively manufactured components.

关键词： Machine Learning neural networks Finite Element Method Additive Manufacturing Quality Assurance

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Comparative Analysis of Machine Learning and Deep Learning Algorithms for Plant Disease Classification and Detection 2

Comparative Analysis of Machine Learning and Deep Learning A...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Kaur, Harminder Raheja, Neeraj Bindal, Amit Kumar Mullana Department of Computer Science and Engineering Haryana Ambala India

ISBN: (纸本)9798331519056

Agriculture, a crucial foundation for human existence, exists at the intersection of conventional methods and modern innovations. Any country's primary issue in agriculture science is effective plant disease control, driven by the growing need for food due to population growth. Moreover, developments in modern technology have greatly improved the precision and effectiveness of plant detection. A critical challenge in the agricultural sector is leaf diseases, significantly impacting crop production and economic profit. Integrating digitalization and technology in agriculture is imperative for augmenting productivity and ensuring food security. This research emphasizes the principal concerns and obstacles associated with classifying leaf diseases. By experimenting with different approaches, researchers have already achieved great strides in the identification and categorization of illnesses. But due to assessments, new information, and discussions, changes are required. The resilience of deep learning (DL) and machine learning (ML) techniques, such as feed-forward neural networks (FFNN), k-means clustering (KMC), fuzzy logic (FL), artificial neural networks (ANN), and genetic algorithms (GA),so forth, has been demonstrated by earlier research. Due to their inherent ability to automatically obtain relevant visual information and understand spatial hierarchies, CNNs are usually the preferred option for image identification and classification among the ML and DL methods addressed in this study. The selection between machine learning and deep learning is dependent upon numerous issues, the data that is available, and the amount of processing power that is available. Because of this, DL - primarily using CNNs are advised for many complicated image detection and classification applications. They also show outstanding results for classification and detection on their datasets but not on others. Lastly, by using a variety of methods for processing images in the field of artif

关键词： Fuzzy neural networks

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artificial Intelligence Patrol Technology for Unmanned Aerial Vehicles (UAVs) on the Whole Tower and Line Based on neural Network 4

Artificial Intelligence Patrol Technology for Unmanned Aeria...

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4th International Conference on Advances in Electrical Engineering and Computer applications, AEECA 2023

作者： Liu, Chun Jin, Peng Wang, Si Yin, Weiwei Nie, Chen Ultra-High Voltage Company of State Grid Jilin Electric Power Co. Ltd Jilin China State Grid Jilin Electric Power Co. Ltd Jilin China

ISBN: (纸本)9798350308082

The tower line inspection task in the power industry is crucial for ensuring the safe operation of transmission lines and towers. However, traditional inspection methods have problems such as low efficiency, high cost, and unstable reliability. This article proposes a neural network-based artificial intelligence patrol technology for UAVs on the entire tower line, aiming to solve the challenges of traditional patrol methods. This technology utilizes advanced neural network algorithms and unmanned aerial vehicle (UAV) platforms to achieve efficient, accurate, and automated tower line patrol task execution. By collecting and analyzing a large amount of image feature data, neural networks can identify and locate potential problems on transmission lines and towers in real-time, such as damage, corrosion, and weed coverage. Through on-site testing and verification, it has been proven that this technology can effectively improve the efficiency and quality of patrol task execution. In addition, strategies for further optimizing the model and strengthening data processing were explored to enhance the performance and practicality of the technology. The artificial intelligence patrol technology based on neural networks for UAVs on the entire tower line provides a new solution for patrol tasks in the power industry, with broad promotion and application prospects. © 2023 IEEE.

关键词： Inspection

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Ultralow Power Always-On Intelligent and Connected SNN-Based System for Multimedia IoT-Enabled applications

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IEEE INTERNET OF THINGS JOURNAL 2022年第17期9卷 15570-15577页

作者： Liu, Qi Zhang, Zhixuan South China Univ Technol Sch Future Technol Guangzhou Int Campus Guangzhou 511442 Guangdong Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China

The recent advances in artificial neural networks (ANNs) have created immense opportunities to achieve excellent results on the Internet of Things (IoT), which serve the uses of various real-time smart applications, such as in computer vision and speech recognition. However, the efficiency of ANNs comes at the expense of a huge number of computational resources. This tends to necessitate larger and wider ANNs unapplicable for embedded systems with limited hardware resources, e.g., mobile and wearable devices. To that end, biologically realistic spiking neural networks (SNNs) are first employed to build an always-on intelligent and connected integrated IoT-enabled system with ultralow power consumption, where we encode the temporal dynamic stimuli into effective, efficient, and reconstructable spike patterns to facilitate the subsequent processing. Herein, neural encoding plays a key role in faithfully describing the temporally rich patterns for downstream cognitive tasks. Therefore, a novel nonlinear piecewise latency coding approach for a fully event-driven SNN system is developed. Moreover, a surrogate postsynaptic potential kernel function is utilized to address the nondifferential nature of the spike generation scheme when using the error backpropagation learning method. The effectiveness of the proposal tandem with SNNs has been corroborated by indicative empirical results on different data sets serving cognitive tasks.

关键词： Encoding Internet of Things Sensors Deep learning Cloud computing Wireless sensor networks Intelligent sensors Deep learning efficient learning image classification Internet of Things (IoT) latency coding speech recognition spiking neural network

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image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2024年第0期

作者： Sharma, Amit Sharma, Ashutosh Tselykh, Alexey Bozhenyuk, Alexander Kim, Byung-Gyu Southern Fed Univ Inst Comp Technol & Informat Secur Taganrog Russia Chitkara Univ Inst Engn & Technol Rajpura Punjab India Henan Univ Sci & Technol Business Sch Luoyang Henan Peoples R China Southern Fed Univ Dept Informat & Analyt Secur Syst Rostov Na Donu Russia Sookmyung Womens Univ Div Artificial Intelligence Engn Seoul South Korea

Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph-structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification. The authors explore in depth the applications of GNNs in computer vision, including their design considerations, architectural challenges, applications, and implementation concerns. While conventional convolutional neural networks (CNNs) excel at object recognition in images and videos, GNN architectures offer a novel method for addressing various image and video comprehension challenges. A novel deep neural network-based model for image and video analysis is proposed, which combines a neural network with fully connected layers on a graph. The proposed architecture extracts highly discriminative information from images and videos by leveraging the graph structure. Also, the investigation focuses on the enhancement of underlying connection network estimation using cutting-edge graph learning algorithms. Experimental results on real-world datasets demonstrate that the proposed GNN model is preferable to existing state-of-the-art methods. It obtains a remarkable 96.63% accuracy on the imageNet dataset, outperforming heuristic approaches, artificial neural networks, and conventional CNN techniques. From the results, we can see that GNNs are a potent instrument for graph data analysis and pave the way for machines to achieve human-level visual intuition.

关键词： computer vision deep learning graph theory image analysis Internet of things video signal processing

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