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Deep Learning Framework for Early Detection of Diseases in Coconut Tree from Leaf images

Deep Learning Framework for Early Detection of Diseases in C...

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Advances in Science and Technology (ICAST), International conference on

作者： Dhanashree Toradmalle Nidhi Bhanushali Meet Dama Manasi Dhote Department of Computer Engineering KJSIT Mumbai Maharashtra India

Coconut tree is one of the most beneficial and popular crops in the world due to its numerous uses. In countries like India, Indonesia, Philippines, Sri Lanka, etc., people primarily depend on coconut farming for their livelihood. Pest infestation or disease can largely affect the production of crops and lead to losses. Finding the disease at its earliest stage of development is necessary for disease control in plants. image processing technologies, computer vision and deep learning models are now-a-days used to detect disease in crops. Many researchers have developed effective methods using deep learning models for disease detection, Convolutional Neural Networks (CNNs) and various architectures of CNNs are used for Disease Detection in plants. this study aims to review various research methodologies and apply them to a new dataset that includes images of leaves classified primarily into 5 diseases: yellowing, drying, flaccidity, caterpillars, and leaflets. CNN, DenseNet121, InceptionResNetV2, and ANN were major algorithms used in ***2 CNN, and DenseNet121 all performed remarkably well and provided the desired accuracy of around 99% and higher.

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Interpretive self-supervised pre-training: Boosting performance on visual medical data 21

Interpretive self-supervised pre-training: Boosting performa...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Indian Statistical Institute Kolkata West Bengal Kolkata India Indian Institute of Technology West Bengal Kharagpur India

ISBN: (纸本)9781450391276

Self-supervised learning algorithms have become one of the best tools for unsupervised representation learning. Although self- supervised algorithms have achieved state-of-the-art performance for classification tasks in the case of natural image data, their application on medical data has been limited. In this work, we have proposed a novel loss function and derive it's asymptotic lower bound. We have also shown that self-supervised pre-training with the proposed loss function helps in surpassing the supervised baseline on the downstream task. We have also shown that the self-supervised pre-training helps a model in learning better representation in general to achieve better performance compared to supervised baselines. We have mathematically derived that the contrastive loss function asymptotically treats each sample as a separate class and works by maximizing the distance between any two samples and this helps to get better performance. Finally, through exhaustive experiments, we demonstrate that self-supervised pre-training helps to surpass the performance of fully supervised models on downstream tasks. © 2021 ACM.

关键词： Learning algorithms

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Automated computer Aided Detection of Diabetic Retinopathy Using Machine Learning Hybrid Model 6

Automated Computer Aided Detection of Diabetic Retinopathy U...

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6th International conference on image Information processing, ICIIP 2021

作者： Kubde, Aishwarya Mohod, Sharad Prof. Ram Meghe Institute of Technology Research Badnera Dept. of Electronic Engineering Amravati India

ISBN: (纸本)9781665433617

Diabetic retinopathy is a potentially fatal condition that affects diabetics worldwide, resulting in blurred vision or total blindness. A technique for identifying diabetic retinopathy using the fundus image obtained from the patient's retina is proposed in this paper. the method entails processing a digital image of the fundus image, which assists the ophthalmologist in examining DR. A neural network was utilized to diagnose a micro-aneurysm, a type of diabetic retinopathy that is the first stage. A comparison was made between the proposed Support Vector Machine and the existing Naive Bayes classifier. For experimental validation, the programed MATLAB/SIMULINK is employed. the preprocess image was used as input data for pattern recognition using a neural network. there has been a significant improvement in terms of sensitivity, specificity, and accuracy when compared to the aforementioned existing techniques. © 2021 IEEE.

关键词： MATLAB

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6th International Symposium on Signal processing and Intelligent Recognition Systems, SIRS 2020

6th International Symposium on Signal Processing and Intelli...

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6th International Symposium on Advances in Signal processing and Intelligent Recognition Systems, SIRS 2020

ISBN: (纸本)9789811604249

the proceedings contain 27 papers. the special focus in this conference is on Signal processing and Intelligent Recognition Systems. the topics include: Improved Long-Short Term Memory U-Net for image Segmentation;Extraction of Parcel Boundary from UAV images Using Deep Learning Techniques;Grad-CAM-Based Classification of Chest X-Ray images of Pneumonia Patients;periocular Recognition Under Unconstrained image Capture Distances;acoustic Prediction of Elephants for Localization and Movement Tracking Using Sensors and Distance Metrics;community Detection Algorithms in Complex Networks: A Survey;a Systematic Review on the Influence of User Personality in Rumor and Misinformation Propagation through Social Networks;HRIDAI: A Tale of Two Categories of ECGs;channel-Aware Decision Fusion with Rao Test for Multisensor Fusion;detection of Breast Cancer from Mammogram images Using Deep Transfer Learning;Comprehending the Dynamics of EEG Generated Under Various Odorant Stimulation on the Brain;Robust Beamforming Against DOA Mismatch with Null Widening for Moving Interferences;6G Ultra-Low Latency Communication in Future Mobile XR Applications;applying Neural Style Transfer to Spectrograms of Environmental Audio;identification of indian English by Speakers of Multiple Native Languages;Robust and Imperceptible Digital image Watermarking Based on DWT-DCT-Schur;comparative Study of Maturation Profiles of Neural Cells in Different Species with the Help of computer vision and Deep Learning;deep Learning Algorithms to Detect and Localize Acute Intracranial Hemorrhages;Explainable NLP: A Novel Methodology to Generate Human-Interpretable Explanation for Semantic Text Similarity;a Leak Detection in Water Pipelines Using Discrete Wavelet Decomposition and Artificial Neural Network;supervised Feature Learning for Music Recommendation.

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A Novel Artificial Intelligence-Based Lung Nodule Segmentation and Classification System on CT Scans 6th

A Novel Artificial Intelligence-Based Lung Nodule Segmentati...

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6th International conference on computer vision and image processing, CVIP 2021

作者： Dodia, Shubham Annappa, B. Padukudru, Mahesh A. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Department of Respiratory Medicine JSS Medical College JSS Academy of Higher Education and Research Karnataka Mysuru India

ISBN: (纸本)9783031113482

Major innovations in deep neural networks have helped optimize the functionality of tasks such as detection, classification, segmentation, etc., in medical imaging. Although computer-Aided Diagnosis (CAD) systems created using classic deep architectures have significantly improved performance, the pipeline operation remains unclear. In this work, in comparison to the state-of-the-art deep learning architectures, we developed a novel pipeline for performing lung nodule detection and classification, resulting in fewer parameters, better analysis, and improved performance. Histogram equalization, an image enhancement technique, is used as an initial preprocessing step to improve the contrast of the lung CT scans. A novel Elagha initialization-based Fuzzy C-Means clustering (EFCM) is introduced in this work to perform nodule segmentation from the preprocessed CT scan. Following this, Convolutional Neural Network (CNN) is used for feature extraction to perform nodule classification instead of customary classification. Another set of features considered in this work is Bag-of-Visual-Words (BoVW). these features are encoded representations of the detected nodule images. this work also examines a blend of intermediate features extracted from CNN and BoVW characteristics, which resulted in higher performance than individual feature representation. A Support Vector Machine (SVM) is used to distinguish detected nodules into benign and malignant nodules. Achieved results clearly show improvement in the nodule detection and classification task performance compared to the state-of-the-art architectures. the model is evaluated on the popular publicly available LUNA16 dataset and verified by an expert pulmonologist. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computerized tomography

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6th International conference on image, vision and Computing, ICIVC 2021

作者： Zhang, Shanping Xu, Xiaowei Tao, Ye Wang, Xiaodong Wang, Qiuchen Chen, Fangfang Department of Computer Science and Technologys Ocean University of China Qingdao China School of Information and Technology Qingdao University of Science and Technology Qingdao China

ISBN: (纸本)9781665443685

Text similarity measurement is a basic task in natural language processing and widely used in information retrieval, automatic question answering, machine translation, etc. Because most traditional statistical-based methods for text similarity measurement cannot efficiently extract the semantic information of the text, we propose a BiLSTM-SECapsNet hybrid model based on BiLSTM, CapsNet, and SENet for text similarity measurement. We employed the siamese BiLSTM network as sequential inference models to extract the global features. the coattention mechanism is employed to generate attention weight between the text features, which will collect local inference over sequences. the CapsNet network is also introduced to catch the local features of the text. And then the SENet network is used to automatically calibrate the importance of each local feature to obtain the local feature matrix. After that, the feature matrix is fused again and the BiLSTM network is used to extract the context information to obtain the similarity matrix of the two texts. At last, the semantic similarity of the text is measured through the fusion, pooling, and fully connected layer. the experimental results based on the Quora Questions Pairs data set show that the accuracy of the method is 87.31, and the F-measure is 87.35. Compared with other networks, the effectiveness of the method has been improved. © 2021 IEEE.

关键词： Semantics

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Data-enabled cryptocurrency market analysis and visualization platform 15

Data-enabled cryptocurrency market analysis and visualizatio...

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International conference on computer graphics, Visualization, computer vision and image processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on computer Science and Information Systems, MCCSIS 2021

作者： Zhu, Ningbo Yang, Fei Zhu, Mingzhi Sun, Xinyao Cheng, Irene University of Alberta Computing Science Department Multimedia Program 2-32 Athabasca Hall University of Alberta EdmontonABT6G 2E8 Canada

ISBN: (纸本)9789898704320

the cryptocurrency industry has evolved rapidly in recent years, and it is increasingly popular as a convenient tool to complement the traditional stock and futures exchanges. Accurate market research enables traders to make more informed decisions and benefit from their investments. Our objective is to introduce a web platform for aggregating various types of cryptocurrency data, both on- and off-chain. Its novelty lies in offering a visual representation of market data analysis, which is driven by multi-modal data fusion and representation techniques, as well as artificial intelligence. We propose a full-stack framework that consists of a front-end web application for user interaction and visualization, and a backend server for data fetching, preprocessing, and analysis. In our implementation, we used data from the cryptocurrency market, on-chain statistics, and textual data from social media, to create a deep-learning-based market trend model. For market prediction, our data analysis module processed high-frequency vocabulary extracted from social media, sentiment analysis of social media content, historical price trend, and historical hash rates. Investors and market analysts can benefit from our platform by directly observing the dynamic of multi-modal cryptocurrency data and easily exploring market trends, generated by our market prediction model delivered by a front-end application. the complete implementation can be found in our publicly available Github link upon request. © 2021 International conference on computer graphics, Visualization, computer vision and image processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on computer Science and Information Systems, MCCSIS 2021. All rights reserved.

关键词： Sentiment analysis

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Multimodal Deep Learning Framework for Enhanced Semantic Scene Classification Using RGB-D images

Multimodal Deep Learning Framework for Enhanced Semantic Sce...

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International conference on Advancements in Computational Sciences (ICACS)

作者： Aysha Naseer Ahmad Jalal Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Typically, an abstract scene is made up of multiple less abstract elements, such themes or objects. the semantic gap between low-level visual characteristics and abstract scenes makes it very challenging to infer scenes from visual features. To get around these issues, researchers have naturally turned to inserting depth information into the typical RGB image as input. By using depth data, this technique—also referred to as RGB-D object detection or depth-aware SOD—aims to enhance saliency recognition in these kinds of scenarios. Our proposed technology has proven to be significantly better than the most advanced techniques available in this industry. the high effectiveness of our suggested approach is demonstrated by extensive kind of autonomous vision applications, such as robotic vision, sports analysis, and security systems. Our system’s primary feature is its original segmentation framework, which makes use of multi-object segmentation. this framework allows the system to identify objects and recognize a scene with resilience and precision. Subsequently, Convolutional Neural Networks (CNNs) are utilized to enhance the system’s overall recognition performance by further processing these extracted properties for scene recognition. the experimental outcomes using the NYUDv1 dataset and RGB-D scene dataset show $90.10 \%$, and $93.5 \%$ total accuracy, respectively. these results validate the applicability of the method and outperform current state-of-the-art approaches.

关键词： Deep learning Visualization image recognition Service robots Semantics Object detection Transformers Vectors Convolutional neural networks Sports

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Research on Copper Foil Winding of Transformer Surface Defect Detection Based on Machine vision 21

Research on Copper Foil Winding of Transformer Surface Defec...

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6th International conference on Intelligent Information processing, ICIIP 2021

作者： Yang, Guihua Dai, Zhicheng Guilin University of Technology China

ISBN: (纸本)9781450390637

Copper foil winding transformer has been widely used in dry transformers because of its small size, light weight and low material usage. Based on Halcon machine vision software, a defect detection study is carried out on a mass-produced transformer copper foil winding, and through the algorithm design of transformer copper foil winding image pre-processing, edge denoise, feature extraction and surface defect recognition, the machine vision detection process of the copper foil surface defect of transformer is established, the imaging system and hardware platform are set up, multiple sets of copper foil images are collected, and the copper foil detection algorithm is tested, and good results are obtained. © 2021 Association for Computing Machinery. All rights reserved.

关键词： computer vision

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Performance Evaluation of Graph Partitioning on Many-core System 6

Performance Evaluation of Graph Partitioning on Many-core Sy...

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6th International conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Tapase, Anuja A. Patil, Siddheshwar V. Kulkarni, DInesh B. Walchand College of Engineering Department of Information Technology Sangli India Department of Comp. Science and Engg. Sangli India

ISBN: (纸本)9781665435598

Max-flow or min-cut is one of the important strategies for modelling and addressing practical problems in computer vision, image processing, and optimization theory using graph algorithms. It's been extensively used in a variety of applications, including image segmentation, flow network. As graph can be large and complex in terms of the amount of nodes/edges and the connection between them, the graph partitioning technique helps to divide the graph into sub-parts by using max-flow and min-cut method. According to the max-flow min-cut, the total weight of the edges in a flow network's minimal cut equals the maximum quantity of flow travelling from the source to the sink. this paper describes both serial and parallel implementations of the push-relabel approach for max-flow and min-cut. the parallel implementation on many-core system (GPGPU) shows better speedup than serial implementation of the prooosed algorithm. © 2021 IEEE.

关键词： image segmentation

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