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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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Identification of Medicinal Plants for Medical Usage Using VGG16, ResNet50 and ResNet152

Identification of Medicinal Plants for Medical Usage Using V...

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Computer, Electronics & Electrical Engineering & their applications (IC2E3), International conference on

作者： Abhay Pandey Abhijeet Tripathi Anushka Aggarwal Meenakshi Yadav Department of Information Technology Galgotias College of Engineering & Technology Greater Noida UP India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

The suggested study describes how to identify therapeutic plants utilizing processing approaches. The precise identification of medicinal plants is critical. The healing nature of medicinal plants, in addition to their capacity for treat a variety of ailments, necessitate the requirement for automatic identification. Different plant portions, such as the root, fruit, bark, and stem, can help with identification; however, because leaf photographs are easily accessible and provide a wealth of information, they have been widely employed. This research studies the artificial intelligence field of deep learning and proposes an ensemble learning technique for swiftly recognizing medicinal plants from leaf photos. The suggested research only focuses on the identification and categorization of distinct leaf components in order to identify the plant species. The medicinal leaf dataset has thirty categories. The approach of transfer learning was employed in our work for initializing parameters the VGG 16, ResNet50, and ResNet152 neural networks. These component models were used with the SoftMax layer to extract attributes from the input photos. The accuracy of our model is 99 in comparison to MobileNetV2-Softmax Classification and InceptionV2-softmax classification (fivefold cross-validation) is 97.49 and 98.09 respectively.

关键词： Medicinal plants Accuracy neural networks Transfer learning Predictive models Ensemble learning Residual neural networks Biomedical imaging image classification Diseases

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Rethinking and improving robustness of convolutional neural networks: a shapley value-based approach in frequency domain 22

Rethinking and improving robustness of convolutional neural ...

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Proceedings of the 36th International conference on neural Information processing Systems

作者： Yiting Chen Qibing Ren Junchi Yan Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University

ISBN: (纸本)9781713871088

The existence of adversarial examples poses concerns for the robustness of convolutional neural networks (CNN), for which a popular hypothesis is about the frequency bias phenomenon: CNNs rely more on high-frequency components (HFC) for classification than humans, which causes the brittleness of CNNs. However, most previous works manually select and roughly divide the image frequency spectrum and conduct qualitative analysis. In this work, we introduce Shapley value, a metric of cooperative game theory, into the frequency domain and propose to quantify the positive (negative) impact of every frequency component of data on CNNs. Based on the Shapley value, we quantify the impact in a fine-grained way and show intriguing instance disparity. Statistically, we investigate adversarial training(AT) and the adversarial attack in the frequency domain. The observations motivate us to perform an in-depth analysis and lead to multiple novel hypotheses about i) the cause of adversarial robustness of the AT model; ii) the fairness problem of AT between different classes in the same dataset; iii) the attack bias on different frequency components. Finally, we propose a Shapley-value guided data augmentation technique for improving the robustness. Experimental results on image classification benchmarks show its effectiveness.

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Terrain Classification through Satellite images using Deep Learning

Terrain Classification through Satellite Images using Deep L...

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Advancement in Technology (ICONAT), International conference for

作者： Virendrasinh Khuman Shubh Arora Siddhant Rajeev Kumar Gupta Samir Patel Computer Science & Engineering Pandit Deendayal Energy University Raisan Gandhinagar Gujarat

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

With the exponential growth of remote sensing data, particularly multispectral imagery, the integration of deep learning techniques such as Convolutional neural networks (CNNs) and artificial neural networks (ANNs) has become relevant for efficient classification tasks. Satellite imagery is procured in the manner of multispectral imagery, which is a collection of a few image layers enlist of the same scene, each of them acquired at a particular wavelength band. Challenges in multispectral images arise owing to the large volume of image data and the data variety due to high dimensionality and number of channels. Land Cover and Terrain classification is the process of identifying, mapping and delineating the surface of the earth into different units. It remains vital for its applications in a variety of fields. It enables environmental monitoring allowing for adaptation to ecosystem variations. Fields like Urban Planning, Disaster Management and Infrastructure development find essential advantages in their planning phases owing to such classification. Deep CNNs, or Convolutional neural networks, are advanced machine learning models renowned for their ability to extract intricate patterns from visual data, crucial in tasks like image recognition and classification. The reason behind the application of deep CNNs arises from their unparalleled computing efficiency. Through our work we try to explore the utilization of above mentioned deep learning and machine learning techniques in the classification of land cover, focusing on both multispectral imagery and provide an overview of the existing literature, examining the methodologies employed and the datasets utilized and the processing and handling of these large datasets. Additionally, we introduce various data sources and datasets commonly utilized in these studies, shedding light on the advancements, challenges, and future directions in the field of remote sensing-based land use and land cover classification.

关键词： Deep learning Urban planning Land surface Feature extraction Data models Satellite images Convolutional neural networks Data mining Surface treatment Remote sensing

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A comparative study of face recognition using discrete orthogonal moments

A comparative study of face recognition using discrete ortho...

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International Symposium on Signal processing and Its applications (ISSPA)

作者： A. J. Nor'aini Department of System Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia

This paper presents the comparative study of face recognition using discrete orthogonal moment namely Krawtchouk moments (KMs) and Tchebichef moments (TMs). Both these moments do not require numerical approximation and coordinate space normalization. The complex computation of radial polynomial as order becomes larger is not an issue and this makes KMs and TMs superior compared to continuous orthogonal moments in terms of preserving the analytical properties needed to ensure minimal information redundancy. With these properties, KMs and TMs are well suited as pattern features in the analysis of two-dimensional images. The selection of orders and parameters of KMs determines the ROI and to obtain the reconstructed face image similar to the original face image, both parameters of KMs are set equal at 0.5. As for TMs, only the selection of order is required. The experiments were carried out on the database face images from the AT&T Laboratories Cambridge University consisting of 40 distinct subjects of 10 non-similar images each. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open/closed eyes, smiling/not smiling), facial details (with and without spectacles) and different face scale. Euclidean square distance or Nearest Neighbour (NN) is used as the classifier in the recognition stage. From the experiments, KMs showed better performance as compared to TMs in terms of classification accuracy.

关键词： Face artificial neural networks Face recognition

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Performance analysis of skin classifiers in RGB and YCbCr color space

Performance analysis of skin classifiers in RGB and YCbCr co...

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IEEE International conference on Multi Topic

作者： Anam Qureshi Murk Marvi Mukhtiar Ali Unar Fahim Aziz Umrani Institute of Information and Communication Technologies (IICT) Mehran University of Engineering and Technology Pakistan

Skin detection serves as a preliminary step for number of applications like face detection, gesture recognition, internet pornographic image filtering, and surveillance system. Number of artificial neural network (ANN) based skin detection algorithms have been presented in literature which are mostly based on back propagation (BP) ANNs. This paper attempts to analyze the performance of skin classifiers using AdaBoost learning algorithm in both RGB and YC b C r color space. Three RGB based classifiers (i.e., red, green, and blue) and one YC b C r based classifier is designed in order to analyze the performance of algorithm for each case. Set of weak heuristic rules are designed for the classifiers to reduce the false positive rate (FPR) without significantly affecting the correct detection rate (CDR). The results reveal that the best performance is achieved by RGB based classifiers with heuristic rules in terms of both accuracy and processing time. Without heuristic rules the best results have been provided by Y-classifier. The classifiers are trained and tested using SFA database. The classifiers are also tested by using images of FERET and CVL database.

关键词： Skin image color analysis Classification algorithms Databases Algorithm design and analysis Training artificial neural networks

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BaCoD: An AI-Based Coconut Rhinoceros Beetle (Oryctes Rhinoceros) Detection And Monitoring Through Geographic Information System

BaCoD: An AI-Based Coconut Rhinoceros Beetle (Oryctes Rhinoc...

引用

International conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Faye Jeremiah B. Dela Cruz Angelika A. Faral Edwin Ireneo M. Guiritan Gayle Marie J. Roque Geoffrey T. Salvador John Vincent C. Sibonga Gerhard P. Tan Electronics Engineering Department Polytechnic University of the Philippines Manila Philippines

The research focuses on the detection of the most destructive coconut pest- Rhinoceros Beetle (Oryctes rhinoceros) - using image processing, deep learning neural networks, and geotagging technologies in the Philippines. The coconut industry contributes 3.6 percent of the country’s annual gross value. This could be managed using pest control and monitoring technologies based on artificial intelligence and Geographic Information Systems. The proposed system, running on Android 10 or higher, used image processing using a deep learning neural network for pest detection and Geographic Information System for visualization. The model used 15 epochs and approximately 3000 image datasets with a 7:3 ratio to reach at least 94% validation accuracy to be considered ready for deployment. During deployment, the 48 MP resolution recorded the highest accuracy with 91% and works best during sunny weather compared to 12 MP and 25 MP resolutions. Meanwhile, the gathered Euclidean distance of the proposed system from the actual coordinate ranges from 6 meters to 272 meters and has a significant weak positive correlation with the upload delay at a 0.361 correlation coefficient. Overall, the proposed solution has a satisfactory rating for functional suitability, performance efficiency, usability, reliability, maintainability, and portability based on the design specification and addressing the objectives aligned with the key stakeholders who will be using the system.

关键词： Meters Correlation Computational modeling neural networks Data models Mobile applications Usability

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Aircraft recognition using convolutional neural network

Aircraft recognition using convolutional neural network

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International conference on Green Energy, Computing and Intelligent Technology (GEn-CITy 2023)

作者： N. Wan Zulkipli J. H. Chuah M. G. Lim G. M. T. Chai Department of Electrical Engineering Faculty of Engineering University of Malaya 52200 Kuala Lumpur Malaysia Institute for Manufacturing Department of Engineering University of Cambridge Cambridge CB3 0FS UK Faculty of Engineering and Physical Sciences University of Southampton Malaysia 79200 Iskandar Puteri Johor Malaysia

Satellite image object-recognition offers detailed location representation where ground-based sampling is limited or inaccessible, such as high sea or in the mountains. However, an accurate and effective method is required to ensure the precision of the information generated due to small, ambiguous objects against the background. Having an automatic object detection system would greatly speed up satellite image recognition and find extensive applications in defense, transportation, agriculture, and emergency relief. With the advancement of artificial neural networks, the technology has seen applications in various fields from image processing to speech synthesis. We aimed to develop a neural network-based commercial aircraft detection system on satellite images at major airports. We proposed a R-CNN-based aircraft recognition system where CNN is utilized to extract features of aircraft before passing the feature maps to the binary classifier. We trained the model with 2100 images of satellite images containing aircraft which are segmented to produce positive and negative instances and validated on 900 images. The system is equipped to detect and localize the position of the aircraft in an image while registering excellent accuracy. However, as the number of aircraft in a frame increases, slight deterioration in the model performance is observed.

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Facial expression analysis for estimating patient's emotional states in RPMS

Facial expression analysis for estimating patient's emotiona...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： H.G. Hosseini Z. Krechowec Electrotechnology Department Auckland University of Technology Auckland New Zealand

Currently, a range of remote patient monitoring systems (RPMS) are being developed to care for patients at home rather than in the costly hospital environment. These systems allow remote monitoring by health professionals with minimum medical intervention to take place. However, they are still not as effective as one-on-one human interaction. The face and its features can convey patient cognitive and emotional states faster than electrical signals and facial expression can be considered as one of the most powerful features of RPMS. We present image pre-processing and enhancement techniques for face recognition applications. In particular, the project is aimed to improve the performance of RPMS, taking into account the cognitive and emotional state of patients by developing a more human like RPMS. The techniques use the value of grey scale of the images and extract efficient facial features. The extracted information is fed into input layer of an artificial neural network for face identification. On the other hand, the colour images are used by the recognition algorithm to eliminate nonskin coloured background and reduce further processing time. A data base of real images is used for testing the algorithms.

关键词： State estimation Humans Data mining Patient monitoring Hospitals Remote monitoring Biomedical imaging Face recognition Facial features artificial neural networks

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A DEEP LEARNING BASED ALTERNATIVE TO BEAMFORMING ULTRASOUND imageS

A DEEP LEARNING BASED ALTERNATIVE TO BEAMFORMING ULTRASOUND ...

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IEEE International conference on Acoustics, Speech and Signal processing

作者： Arun Asokan Nair Trac D. Tran Austin Reiter Muyinatu A. Lediju Bell Department of Electrical and Computer Engineering Johns Hopkins University Baltimore USA Department of Computer Science Johns Hopkins University Baltimore USA

ISBN: (纸本)9781538646595

Deep learning methods are capable of performing sophisticated tasks when applied to a myriad of artificial intelligent (AI) research fields. In this paper, we introduce a novel approach to replace the inherently flawed beamforming step during ultrasound image formation by applying deep learning directly to RF channel data. Specifically, we pose the ultrasound beamforming process as a segmentation problem and apply a fully convolutional neural network architecture to segment anechoic cysts from surrounding tissue. We train our network on a dataset created using the Field ii ultrasound simulation software to simulate plane wave imaging with a single insonification angle. We demonstrate the success of our architecture in extracting tissue information directly from the raw channel data, which completely bypasses the beam-forming step that would otherwise require multiple insonification angles for plane wave imaging. Our simulated results produce mean Dice coefficient of 0.98 ± 0.02, when measuring the overlap between ground truth cyst locations and cyst locations determined by the network. The proposed approach is promising for developing dedicated deep-learning networks to improve the real-time ultrasound image formation process.

关键词： Deep Learning Beamforming Ultrasound Imaging Machine Learning image Segmentation Beamforming Ultrasonography image segmentation Ultrasonic Waves artificial Intelligence Machine Learning Insonification image formation Plane waves images

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Indian Currency Recognition:A Lightweight Transfer Learning Approach for Visually Impaired

Indian Currency Recognition:A Lightweight Transfer Learning ...

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International conference on Parallel Distributed and Grid Computing (PDGC)

作者： Poonam Saini Ankit Goyal Bishwash Pokhrel Jaspreet Singh Neetish Kumar Dept. of Computer Science and Engg. Punjab Engineering College Chandigarh India

ISBN: (纸本)9781665454025

The knowledge of artificial intelligence, especially, machine learning has been widely used to handle various real-world applications and related societal challenges. One of the crucial issues faced by visually impaired people is to correctly recognize paper currency and its denomination easily. The paper proposes a convolutional neural network-based currency recognition system applied to the Indian Currency. The basic features include data pre-processing to filter out the old currency notes and incorrect labels, followed by a pre-trained model selection. The proposed model is trained using transfer learning, and afterward, a custom layer is added to recognize currency notes. Further, a fully connected customized neural network architecture has been designed. The model undergoes fine tuning of essential hyperparameters. The proposed model classifies Indian banknotes with more reliable accuracy (99.5%), which is even flexible to different image orientations (flipped, folded, out of frame, etc.) as well as empty backgrounds for a real-time application.

关键词： Training Face recognition Transfer learning neural networks Computer architecture Real-time systems Convolutional neural networks

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