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international conference on Computing and Networking Technology (ICCNT)

作者： K. Senthamil Selvan Samaksh Goyal Aravindan M K Omkaresh S. Kulkarni K. Yuvaraj Nitish Vashisht Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai india Quantum University Research Center Quantum University School of Engineering and Technology Mechaical Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune INDIA Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Generative opposed Networks (GANs) are a generative model broadly utilized in device mastering, PC vision, and herbal language processing (NLP). GANs hire neural networks, a generator, and a discriminator that are trained collectively to be able to generate realistic-looking statistics, along with photographs, audio clips, or 3-D scenes. In this painting, we are aware of $3-\mathrm{D}$ scene reconstruction using GAN-based total methods. We aim to generate 3-D scenes from given 2nd pics, allowing us to benefit from insight into the structure and layout of complicated 3-D environments. To this end, we endorse leveraging a method known as inverse rendering to enhance the accuracy of the reconstructed 3-D scenes. We compare our method to the usage of artificial and real-international images, and the effects display the efficacy of our approach. Finally, we talk about capacity destiny guidelines for three-D scene reconstruction and the usage of GAN-based total strategies.

关键词： Three-dimensional displays Shape Neural networks Layout Generative adversarial networks Rendering (computer graphics) Generators image reconstruction Painting Guidelines

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An Edge Detection Method for Suspicious Local Regions in CT images with Jaxtapleural Nodules

An Edge Detection Method for Suspicious Local Regions in CT ...

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2018 2nd international conference on Electronic Information Technology and computer Engineering (EITCE 2018)

作者： Changli Feng Haiyan Wei Min Li Xin Li Min Ding Department of Information Science and Technology Taishan University Department of Teachers and Education Taishan University Imaging Department Tai'an No.1 People's Hospital

Juxtapleural lung nodules are often excluded from the lung region in many CT image processing algorithms which are based on intensity information. For solving this problem, a suspicious edge line detection algorithm is proposed to obtain the edge line of the suspicious local lung region in this manuscript. Firstly, the lung region in the CT image is extracted by a fixed threshold. Then a SIFT algorithm is used to detect the feature point in the lung region. To filter out the useless feature points, a closest point matching method is used. Then a K-mean method is introduced to divide those feature points into several parts in which the edges of juxtapleural Lung nodules are contained. Experiments over CT slices show that the proposed method has a great performance in detecting the edge line of suspicious regions.

关键词： image An Edge Detection Method for Suspicious Local Regions in CT images with Jaxtapleural Nodules

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Detection of Corn Leaf Infection Using CNN with Various Optimizers

Detection of Corn Leaf Infection Using CNN with Various Opti...

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Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： G. R. Sreekanth R. S. Latha R. Roja S. Sankarnanth A. Gayathri Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Computer Technology Kongu Engineering College Erode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Corn, a grain categorized within the grass family, stands as a fundamental staple crop globally. It plays a crucial role in supplying sustenance for both humans and livestock, in addition to serving as a raw material for various industries. In 2017, Africa produced 7.4% of the 1135 million tons produced worldwide in 40 million hectares. Throughout its growing process, corn plant is impacted by numerous illnesses. Lack of early detection of its diseases may result in a loss in productivity and even crop failure. It is proposed to analyze the leaves of the corn plant for identifying the diseases by exploiting deep learning methods. A wide range of deep learning algorithms are being utilized to detect the corn leaf with infection with good range of accuracy. To categorize the leaf, image processing models are mostly used for these kind of disease detections. Since it has in-built convolutional layer, CNN can reduce the image with high dimensionality without losing any kind of information. In this proposed work CNN with Adam optimizer has been implemented for better results and achieved 91.68 % of accuracy.

关键词： Deep learning Productivity Crops Medical services Raw materials Convolutional neural networks Internet of Things

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Transfer Learning Approach for Plant Leaf Disease Detection Using CNN with Pre-Trained Feature Extraction Method Mobilnetv2

Transfer Learning Approach for Plant Leaf Disease Detection ...

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international conference on Robotics,Electrical and Signal processing Techniques (ICREST)

作者： Raida Moyazzoma Md. Al Amin Hossain Md. Hasanuzzaman Anuz Abdus Sattar Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

Economy of Bangladesh mostly depended on agriculture. As a small country, our population is over the limit. We are also a developing country also. For national GDP growth must be to increase the production. Every year a huge amount of agricultural production losses for the crop disease. As we know most of the farmers in our country are illiterate, have no proper knowledge about the disease, that's why they cannot manually detect the disease. I think if you properly detect the disease at the early stage we can solve the issue. We develop a model that can classify leaf disease. We focus on 5 major production crops in Bangladesh. Using computer vision technique our farmer will get the benefit. We use convolutional neural networks for classifying images. An algorithm cannot properly capture the features of existing data that's why we use a pre-trained feature extraction method that is MobileNetv2. MobileNetv2 is very useful for mobile devices. The research contains the proportions of validation accuracy of 90.38%. This approach resulted in the agriculture sector that will help a farmer to classify disease from harvest. The main goal of our model is to minimize the damage of suffering plants that can help to the growth of production. By solving this issue themselves farmers can also reduce cost. Our goal is that they can cure their crop at the right time. To achieve this goal we tend to think that we should develop a way to detect the leaf disease. We collect several kinds of cucumber leaves. And then any leave can be tasted by our model. By using our model we try to reduce the leaf disease.

关键词： Convolution Computational modeling Production Feature extraction Agriculture Diseases image classification

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Research on Rapid Reconstruction of Three-dimensional Model in Medical image processing Based on VTK Library

Research on Rapid Reconstruction of Three-dimensional Model ...

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2019 2nd international conference on computer Science and Advanced Materials(CSAM 2019)

作者： Li Qiong Gan Yongjin Ning Weilian Yulin Normal University

Medical image three-dimensional model reconstruction technology, which uses two-dimensional medical image sequence to reconstruct three-dimensional model, provides doctors with intuitive, comprehensive and accurate information of lesions and normal tissues, is one of the hotspots in the field of medical imaging. According to different reconstruction models, virtual arbitrary cutting is realized, and surface cutting and volume cutting, and the interactive method of virtual cutting is discussed. The minimum error in merging is calculated to determine the location of the new vertex. Using the toolkit and library to develop a medical image 3D model reconstruction and visualization system, can read medical image data in a specific format, perform 3D model reconstruction, and finally real-time interaction. The experimental results show that the proposed method can effectively preserve the graphical features of the original model and provide convenient use for real-time rendering. The VTK toolkit and library developed a medical image 3D model reconstruction and visualization system that can read medical image data in a specific format, perform 3D model reconstruction, and ultimately real-time interaction.

关键词： VTK Medical image Three-dimensional model

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Identification Rice Varieties Based on K-means Clustering Algorithm and BP Neural Network

Identification Rice Varieties Based on K-means Clustering Al...

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2017 2nd international conference on Advanced Materials Science and Environment Engineering(AMSEE2017)

作者： Shu-fang QIN Chang-hua LIU Shen-ao HUANG School of Mathematics & Computer Science Wuhan Polytechnic University

BP neural network is characterized by adaptability and real-time learning, so it is used widely in classification. While the more complex samples classify, the lower the accuracy of BP neural network is. So a method was purposed to identify rice varieties that combined K-means clustering algorithm with BP neural network. The gray-scale mean value, aspect ratio and circularity,which were the three parameters that expressed rice, were extracted by image processing. The K-means clustering algorithm was used to classify the data based on the aforementioned three parameters and the classification result was entered into the BP neural network and trained to get the classifier. The overall results indicate that the method mentioned is more effective than using K-means clustering algorithm or BP neural network singly and the accuracy is up to 80%.Experimental results show that combination of K-means clustering algorithm and BP neural network is feasible for identifying rice varieties.

关键词： Identification Rice varieties K-means BP neural network

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Efficient Detection and Categorization of Thyroid Nodules from Medical Ultrasound images

Efficient Detection and Categorization of Thyroid Nodules fr...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER international conference on

作者： Prarthana A. Deshkar Punit Fulzele Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur Datta Meghe Institute of Higher Education and Research Sawangi Wardha

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The use of ML and DL in medical imaging for early illness symptom prediction is substantial. Since 2013, DL has been one of the rising trends in general data analysis. With its multiple hidden layers that allow for a high degree of data abstraction, it is an enhancement of artificial neural networks (ANN). One promising approach for computer vision applications is the use of convolutional neural networks (CNNs). Automatic learning of raw data, particularly photographs, is a capability of deep CNNs. Both the capture and interpretation of images are crucial for the correct diagnosis or evaluation of illness. Devices are able to gather data at a rapid pace with improved resolution because to advancements in image acquisition over the last decade. Nevertheless, computer technology has only just started to improve picture interpretation. Since they are subjective and need highly trained doctors, they are often created by radiologist, physicians, and senior doctors. In medical imaging, computerised tools play a crucial role in facilitating results and improving diagnosis. Early detection and categorization of thyroid nodules by visual inspection and manual study of thyroid ultrasonography (USG) images has traditionally been a tedious process. Identifying benign from malignant thyroid nodules requires manual evaluation of thyroid USG images, which may be a time-consuming and laborious process. The medical industry has seen the emergence of many deep learning models, particularly for the categorization of thyroid nodules, thanks to the growth in processing resources and the fast improvement in technology. Clinical interventions and therapies may be more successful when these nodules are detected early. Thus, a growing number of academics are pushing for the use of computer diagnostic systems (CDS) as a means to statistically and scientifically evaluate USG pictures of thyroid nodules. Creating effective models for identifying and classifying thyroid nodules using ML and DL

关键词： Industries Deep learning Computational modeling Optimization methods Ultrasonography Manuals Convolutional neural networks Medical diagnostic imaging Thyroid Cancer

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Crop-Guided Neural Network Segmentation of High-Resolution Skin Lesion images 2nd

Crop-Guided Neural Network Segmentation of High-Resolution ...

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2nd international conference on Digital Transformation in Education and Artificial Intelligence Applications, MoStart 2024

作者： Benčević, Marin Habijan, Marija Galić, Irena Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia Department of Telecommunications and Information Processing TELIN-GAIM Ghent University Ghent Belgium

ISBN: (纸本)9783031620577

Medical images are often exceedingly large in width and height, limiting the maximum batch size when training convolutional neural networks and requiring models with a large number of parameters. Typically, images are uniformly downsampled, leading to losing fine-detailed information. Instead of uniformly downsampling images, we introduce a two-stage end-to-end segmentation network utilizing image crops to reduce network input size. Initially, a uniformly downscaled image is first segmented with a rough segmentation module, and the rough segmentation is used as a saliency map to crop the original high-resolution image to a region of interest. This crop is then re-segmented with a fine segmentation module. Our method’s effectiveness is demonstrated in segmenting lesion boundaries in clinical images across two datasets. We establish that this technique maintains comparable segmentation quality to a baseline model while reducing the network input size. Furthermore, our approach enhances the robustness of segmentation outcomes with smaller input sizes, outperforming uniformly downscaled images and baseline models. This improvement is consistent in both in-sample and out-of-sample evaluations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crops

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A Review on Fusion Strategy based on Underwater image Enhancement

A Review on Fusion Strategy based on Underwater Image Enhanc...

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international conference on Electronics, Communication and Aerospace Technology

作者： Pooja Honnutagi V. D. Mytri Lalitha, Y. S. Department of CSE VTU Belagavi India Department of ECE AIET Kalaburagi India Department of ECE NMIT Bangalore India

ISBN: (纸本)9781538609668

image enhancement is one of the pre-processing steps in various computer vision applications. It is a procedure of improving the quality of the image without any information loss. Usually, the images are affected by various facts. Particularly in the underwater image, haze and hue variations significantly affected. Practically, our fusion-based method plans to yield a final image that overcomes the deficiencies existing in the degraded input images by employing several weight maps that discriminate the regions characterized by poor visibility. In this paper, a literature survey is done by study various techniques of an image enhancement and fusion for underwater images.

关键词： Underwater images image Enhancement Fusion strategy

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2^nd international conference on Computing and Applied Informatics 2017

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Journal of Physics: conference Series 2018年第1期978卷 011001-011001页

Introduction from the Chair The 2nd international conference on Computing and Applied Informatics organized by Faculty of computer Science and Information Technology, Universitas Sumatera Utara (USU) Medan, Indonesia is published by IOPPin the UK and indexed by Scopus. This international forum provides an opportunity for the community of computer science, information technology and engineering to share and discuss their current area of research. Area of research in this conference includes: (1) Artificial Intelligence, Machine Learning, Big Data, Data Mining, (2) Smart City, E-government, E-commerce, E-health (3) Bio Informatics, Socio Informatics, GIS, Information Security, (4) Human computer Interaction, Augmented Reality, image vision, Natural Language processing, Natural Language Generation, and (5) Software Engineering, computer Graphics and Networking. There are 248 papers submitted to this conference and have reviewed by reviewers according to their respective field of interest. The acceptance rate of the paper is 123 (49.5%). Accepted papers come from Indonesia, Malaysia, Japan, Germany, Saudi Arabia, Iran and Egypt. We would like to express our sincere thanks to our keynote speakers: Prof Dr. Graham Kendall and Prof. Dr. Masafumi Nishimura. Moreover, our sincere gratitude Prof. Dr. Runtung Sitepu, Rector of Universitas Sumatera Utara, Mahyudin Nasution, ***, Phd, Vice Rector for Research, Community Service and Cooperation, Universitas Sumatera Utara, Prof. Dr. Opim Salim Sitompul, MSc, Dean of Faculty of computer Science and Information Technology Universitas Sumatera Utara, all presenters, all participants, all reviewers, all the committee members, faculty staff and students for their effort and supports in this conference. Lastly, on behalf of the organizing committee, I would like to welcome you to our second international conference on computing and applied informatics, ICCAI 2017. Our hope is that you have an enjoyable and inspiring as well as a succ

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