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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Guerrisi, Giorgia Del Frate, Fabio Schiavon, Giovanni Univ Roma Tor Vergata DICII I-00133 Rome Italy

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

The growing amount of data currently collected by earth observation satellites requires new processing procedures able to manage huge quantity of information. Among these, data reduction techniques represent a viable solution. In particular, data reduction on-board is significant because allows to save on-board storage space and bandwidth for data transmission to the ground. However, the algorithm used for compression must be able to preserve the key information contained in the acquired data, so that the applicability of the collected information is still guaranteed in the different fields of work. artificial intelligence, and in particular deep learning, are well suited for this purpose because of their ability to extract valuable information from complex data. This work proposes a lossy image compression procedure based on a Convolutional Autoencoder (CAE) that can be performed on-board the satellite. The images acquired by the sensor can be compressed through the algorithm, stored, and sent to the ground where they are reconstructed, saving space and bandwidth for data transmission. The performance of the compression algorithm will be evaluated in terms of original-reconstructed image similarity and also with regard to the applicability of the reconstructed images to common applicative cases. The algorithm here proposed has been idealized and is currently in development in the context of the European Space Agency's (ESA) PhiSat-2 mission, that aims at demonstrating the advantages of the artificial Intelligence (AI) on-board for Earth observation applications.

关键词： image compression on-board processing deep learning convolutional neural networks

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Integrating image Steganography Techniques Into an Existing Efficient image Recognition artificial neural Network for Optimal Performance

Integrating Image Steganography Techniques Into an Existing ...

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IEEE Power Engineering Society Conference and Exposition in Africa, PowerAfrica

作者： Matimu Caswell Nkuna Ahmed Ali Ebenezer Esenogho Department of Electrical & Electronic Engineering Science University of Johannesburg Johannesburg South Africa Department of Electrical Engineering University of Botswana Gaborone Botswana

ISBN: (数字)9798350389388

ISBN: (纸本)9798350389395

image steganography is a data-hiding process whereby a digital image media is used as a cover to encrypt and embed data within the pixels of the image. This method can be applied in various research fields, including image processing, power line systems, mobile applications, etc. This paper investigates the application of an image steganography technique that differs from traditional least significant bit substitution methods. The discrete cosine transform least significant bit-2 method is proposed in this paper. Rather than placing the hidden data bits in a deterministic manner, the proposed method inserts the data bits in random pixel locations within the cover image to alleviate steganalysis and unauthorized access to the hidden data. An image recognition artificial neural network developed using open source TensorFlow is applied and used to test the effectiveness of the prosed encryption method. The proposed encoding scheme shows more robustness and allows for higher neural network prediction confidence. This method can be applied in power line systems to determine the physical conditions of power lines, such as when they are damaged/broken. The image recognition artificial neural network can also forecast power line maintenance cycles for proactive monitoring of power systems.

关键词： Resistance Steganography image recognition image coding artificial neural networks Robustness Encoding Maintenance Power systems Discrete cosine transforms

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Convolutional neural network is used in target contour extraction

Convolutional neural network is used in target contour extra...

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2024 International Conference on Mechatronic Engineering and artificial Intelligence, MEAI 2024

作者： Bi, Mingxue Lu, Na College of Equipment Engineerings Shenyang Ligong University Liaoning Shenyang110168 China

ISBN: (纸本)9781510689176

Target contour extraction is a key task in the field of image processing, which is of great significance for applications such as image segmentation, object detection, and scene understanding. Traditional methods are often limited by complex scenarios and changing target shapes, and their effects may be unstable. In recent years, with the vigorous development of deep learning technology, Convolutional neural Network (CNN) has shown significant advantages in target contour extraction tasks due to its powerful feature representation and learning *** this paper, the loss function is used, and the loss function mechanism adopted is Softmax, which is especially suitable for multi-classification problems and is widely used in supervised and unsupervised learning scenarios. © 2025 SPIE.

关键词： Convolutional neural networks

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Hybrid classifier model for fruit classification

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MULTIMEDIA TOOLS AND applications 2021年第18期80卷 27495-27530页

作者： Gill, Harmandeep Singh Khehra, Baljit Singh Guru Arjan Dev Khalsa Coll Comp Sci Tarn Taran Punjab India BBSBEC CSE Fatehgarh Sahib India

With an advancement in artificial intelligence (AI) applications, the use of smart imaging devices has been increased at a rapid rate. Recently, many researchers have utilized deep learning models such as convolutional neural networks (CNN) for image classification models. Compared to the traditional machine learning models, CNN does not require any kind of handcrafted features. It utilizes various filters to extract the potential features of images automatically. Inspired from this, in this paper, we have proposed a novel fruit classification model which utilizes the features of CNN, Long short Term Memory (LSTM) and Recurrent neural Network (RNN) architectures. Type-II fuzzy enhancement is also used as pre-processing tool to enhance the images. Additionally, to tune the hyper-parameters of the proposed model, TLBO-MCET is also utilized. Extensive experiments are drawn by considering the existing and the proposed fruit classification models. Comparative analysis reveals that the proposed model outperforms the competitive fruit classification models.

关键词： Hybrid-Type II Fuzzy TLBO-MCET Fruit image Classification CNN RNN LSTM

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A Survey on Quaternion Algebra and Geometric Algebra applications in Engineering and Computer Science 1995-2020

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IEEE ACCESS 2021年 9卷 104326-104355页

作者： Bayro-Corrochano, Eduardo CINVESTAV Guadalajara Dept Elect Engn & Comp Sci Guadalajara Campus Zapopan 45119 Jalisco Mexico

Geometric Algebra (GA) has proven to be an advanced language for mathematics, physics, computer science, and engineering. This review presents a comprehensive study of works on Quaternion Algebra and GA applications in computer science and engineering from 1995 to 2020. After a brief introduction of GA, the applications of GA are reviewed across many fields. We discuss the characteristics of the applications of GA to various problems of computer science and engineering. In addition, the challenges and prospects of various applications proposed by many researchers are analyzed. We analyze the developments using GA in image processing, computer vision, neurocomputing, quantum computing, robot modeling, control, and tracking, as well as improvement of computer hardware performance. We believe that up to now GA has proven to be a powerful geometric language for a variety of applications. Furthermore, there is evidence that this is the appropriate geometric language to tackle a variety of existing problems and that consequently, step-by-step GA-based algorithms should continue to be further developed. We also believe that this extensive review will guide and encourage researchers to continue the advancement of geometric computing for intelligent machines.

关键词： Algebra Quaternions Rotors Three-dimensional displays Robots Computer science Calculus Geometric algebra Clifford algebra quaternion algebra screw theory signal processing electrical engineering and power systems geometric and quantum computing image processing computer vision graphic engineering artificial intelligence machine learning neural networks control engineering robotics biomedical engineering and biotechnology

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A Recognition Method for Cassava Phytoplasma Disease (CPD) Real-Time Detection based on Transfer Learning neural networks

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2021年第12期12卷 255-265页

作者： Plata, Irma T. Panganiban, Edward B. Alado, Darios B. Taracatac, Allan C. Bartolome, Bryan B. Labuanan, Freddie Rick E. Isabela State Univ CCSICT Echague Isabela Philippines

Object detection technology aims to detect the target objects with the theories and methods of image processing and pattern recognition, determine the semantic categories of these objects, and mark the specific position of the target object in the image. This study generally aims to establish a recognition method for Cassava Phytoplasma Disease (CPD) real-time detection based on transfer learning neural networks. Several methods and procedures were conducted, such as the testing of two methods in transmitting long-distance high definition (HD) video capture;establishment of a compact setup for a long-range wireless video transmission system;the development, testing of the real-time CPD detection and quantification monitoring system, providing the comparative performance analysis of the three models used. We have successfully custom-trained three artificial neural networks using transfer learning: Faster Regions with Convolutional neural networks (R-CNN) Inception v2, Single Shot Detector (SSD) Mobilenet v2, and You Only Look Once (YOLO) v4. These deep learning models can detect and recognize CPD in actual environment settings. Overall, the developed real-time CPD detection and quantification monitoring system was successfully integrated into the wireless video receiver and seamlessly visualized all the incoming data using the three different CNN models. If the consideration is the image processing speed, YOLOv4 is better compared to other models. But, if accuracy is the priority, Faster R-CNN inception v2 performs better. However, since CPD detection is the main purpose of this study, the Faster R-CNN model is recommended for adoption to detect CPD in a real-time environment.

关键词： Cassava phytoplasma disease faster regions with convolutional neural networks (R-CNN) inception v2 you only look once (YOLO) v4 object detection precision agriculture

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License Plate Recognition Using neural networks 11th

License Plate Recognition Using Neural Networks

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11th International Conference on Frontiers of Intelligent Computing: Theory and applications, FICTA 2023

作者： Satti Babu, D. Prasad, T.V. Revanth Sai, B. Sudheshna, G.D.N.S. Venkata Kishore, N. Chandra Vamsi, P. Rajamahendravaram India

ISBN: (纸本)9789819967018

This guide uses an automatic number plate recognition method. We test the program and save the number plate text and image in a directory after studying real traffic recordings. The ANPR method is effective in several circumstances, according to the results. Performance evaluation takes robustness, complexity, utility, cost, and deployment ease into account. The ANPR can scan vehicle number plates without requiring human intervention. The process of detecting and recognizing these systems is difficult due to the presence of both artificial and natural noise as well as various lighting and climatic factors. Hence, the proposed methodology uses image pre-processing techniques initially and then perform recognition process, this approach increased the accuracy gradually. While validating the results, If one alphabet or digit is erroneous, nevertheless regarded the entire plate as being inaccurate. However, The proposed CNN model has 100% accuracy for vehicle and plate detection. The overall accuracy of model is 94.2% with a very minimal average execution time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： License plates (automobile)

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A Review in the Use of artificial Intelligence in Textile Industry 1st

A Review in the Use of Artificial Intelligence in Textile In...

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1st International Conference on Innovation in Engineering (ICIE)

作者： Pereira, Filipe Carvalho, Vitor Vasconcelos, Rosa Soares, Filomena Univ Minho Algoritmi R&D Ctr Guimaraes Portugal IPCA Sch Technol 2Ai Lab Barcelos Portugal Univ Minho 2C2T R&D Ctr Guimaraes Portugal

ISBN: (纸本)9783030791681;9783030791674

This paper presents an analysis of the state of the art of artificial intelligence applications in the textile industry. A review of the existing literature was performed. This article presents three methods of analyzing textile yarn. Some techniques, used in textile fabric inspection, are presented throughout this paper, as well as the use of artificial intelligence on improving the performance of productive systems using neural networks and artificial vision. The preliminary results demonstrate that the techniques covered are an asset in obtaining defects in textile fabrics at the industrial level. Taking into account the various methods of inspection and analysis of textile yarn, all present pros and cons in applicability in the textile area. In terms of advantages, all allow a better analysis of the textile yarn and defect detection with high quality, but with applicability in more complex systems. As a disadvantage, they present the fact that they do not have an already standardized algorithm that can be used, which makes its use more complex. Some possible future applications are also described.

关键词： artificial intelligence (AI) Textile industry Industry 4.0 Yarn evaluation Machine learning image processing

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Applying Deep neural networks and NLP Techniques for Sentiment Analysis in Social Media Data

Applying Deep Neural Networks and NLP Techniques for Sentime...

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artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： B M Brinda C. Rajan K. Geetha N. Nathiya P.J. Raguraman K. Srinivasan Computer Science and Engineering Paavai College of Engineering Namakkal India Information Technology KSRCT College of Technology Namakkal India Computer Science and Engineering Excel Engineering College Namakkal India Artificial Intelligence and Data Science Paavai College of Engineering Namakkal India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

This work offers a comprehensive investigation of sentiment analysis in social media communication through the integration of deep learning techniques with a natural language processing (NLP) methodology. The goal of the project is to create a matching model that can be used in real-world social processes. This model will allow for the precise identification of pertinent content that is dynamically changing and the realtime selection of key phrases based on available data. The paper builds a message sentiment analysis model and an image message multimodal sentiment analysis model, exploring unimodal and multimodal sentiment analysis algorithms in social networks. The study shows how well it works to extract complex emotions from social media writing by fusing advanced deep neural networks—like transformers or recurrent neural networks—with natural language processing techniques. Furthermore, the study presents noteworthy results, such as 96.1% order accuracy for brief texts in deep learning models that have been optimized, sentiment filtering for positive, neutral, and negative comments in social network data that has been successful, and an assessment of sophisticated semantic similarity models that offers a thorough comprehension of their performance in classification tasks. This work provides insightful information that may be used to improve sentiment analysis models and their usefulness in tasks involving semantic similarity and social network data interpretation.

关键词： Sentiment analysis Analytical models Social networking (online) Heuristic algorithms Semantics artificial neural networks Linguistics

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Deep neural Network Morphism

Deep Neural Network Morphism

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作者： Wei, Tao State University of New York at Buffalo

学位级别：Ph.D., Doctor of Philosophy

Deep convolutional neural networks (DCNNs) have achieved state-of-the-art results on diverse general-purpose artificial intelligence tasks including image understanding and computer vision, natural language processing and speech synthesis, robotics and robust controls. However, training such a network is very time-consuming. It usually takes weeks or even months to train an effective deep network, let alone the exploration of diverse network settings. It is very much desired for these well-trained networks to be directly adopted for other related applications with minimum retraining. Considering a life-long learning system, it is highly desired that the system is able to update itself from the original version established initially, and then evolve into a more powerful one, rather than re-learning a brand new one from scratch. In this thesis, we propose a suite of novel learning schemes to tackle the above problems for deep neural networks. The thesis can be schematized into five chapters. First, we present a systematic study on how to morph a well-trained neural network into a new one so that its network function can be completely preserved. We define this as network morphism in this research. After morphing a parent network, the child network is expected to inherit the knowledge from its parent network and also has the potential to continue growing into a more powerful one with much-shortened training time. The first requirement for this network morphism is its ability to handle diverse morphing types of networks, including changes of depth, width, kernel size, and even subnet. To meet this requirement, we first introduce the network morphism equations, and then develop novel morphing algorithms for all these morphing types for both classic and convolutional neural networks. The second requirement for this network morphism is its ability to deal with non-linearity in a network. We propose a family of parametric-activation functions to facilitate the morphing of an

关键词： Deep neural network Network morphism

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