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International Conference on IoT, Intelligent Computing and Security, IICS 2021

作者： Fatima, Nishat Yadav, Vrinda Department of Computer Science and Engineering Centre for Advanced Studies Uttar Pradesh Lucknow226031 India

ISBN: (纸本)9789811981357

Human curiosity has recently stretched beyond the ground to include the sky and the water. Automated fish categorization is becoming more popular as a way to accurately identify fish species and traits in a standardized, non-invasive, and cost-effective manner. Deep learning, which uses a variety of neural network techniques to model human brains, can be facilitated by an advanced system with efficient computation. The findings of a convolutional neural network (CNN) used to identify fish species across a large-scale fish dataset are presented in this paper. A cross-layer pooling approach is proposed, which avoids the requirement for a significant quantity of training data by leveraging a convolutional neural network as a generalized feature detector (MobileNetV2) and was successful in achieving an accuracy of 99.89% © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fish

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Deep Learning based Target detection and Recognition using YOLO V5 algorithms from UAVs surveillance feeds 2

Deep Learning based Target detection and Recognition using Y...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Kumar, Sushil Kumar, C.R.S. Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

This study aims to show how the most recent object identification algorithms (YOLO V5 and YOLO V7) may be used to recognise targets in a real-world setting using surveillance feed collected by UAVs/*** Russia-Ukraine conflict provided UAV imagery that was gathered and processed to better understand how object identification might result in deducing vital inputs for rapid and timely decision making in a warlike *** on the video was performed using the best and most accurate models from YOLOV5 which was obtained from open-source UAVs *** of its superior OD capabilities, unmanned aerial vehicles (UAVs) are essential to the realisation of any robot's full *** the YOLO V5 model was trained for a greater total number of epochs to substantially improve the object class detection *** analysing the Confusion matrix and the performance hyperparameters of the algorithm, this study compiles the performance metrics to make conclusions and optimise outcomes. © 2023 IEEE.

关键词： Antennas

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Early Detection of Pathological Myopia in Fundus Images Using Deep Learning 2nd

Early Detection of Pathological Myopia in Fundus Images Usin...

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2nd International Conference on Signal and Data Processing, ICSDP 2022

作者： Fatima, Nishat Yadav, Vrinda Department of Computer Science and Engineering Centre for Advanced Studies Uttar Pradesh Lucknow226031 India

ISBN: (纸本)9789819914098

High myopia or Pathological Myopia (PM) is a common cause of blindness worldwide, with a high prevalence in Asia. Pathological Myopia (PM) is a severe form of myopia that is accompanied by retinal degeneration and can result in irreversible visual loss. Patients will be able to delay or even prevent suffering from irreversible visual loss, if early discovery is followed by rapid treatment and active follow-up. Experts in Pathologic Myopia, on the other hand, are still only a few around the world. Myopic maculopathy, or Pathologic detection, is a difficult diagnosis for ophthalmologists and optometrists. As a result, automated detection utilizing Deep Learning can aid clinicians in identifying Pathologic Myopia in early stages using fundus images which are commonly used in basic eye care settings around the world, they are non-invasive and easy to obtain. Our dataset contains 473 myopic fundus images and 600 normal images. We proposed a new model based on the VGG-19 convolutional neural network with an adjusted densely connected classifier that is used in this study to achieve an accuracy of 99.069%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Machine Learning Based Approach to Disinformation Detection Using Twitter Data 2

Machine Learning Based Approach to Disinformation Detection ...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Yadav, Sanjeev Kumar, C.R.S. Defence Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

Fake news detection on social media has assumed enhanced importance and is a trending research topics for data analysts around the world. While most of fake news may be aimed at commercial or personal gains, some of it may cause deliberate harm to a nation's progress. Twitter, as part of social media platforms (SMPs) is a leading portal being used by everyone for gaining access and giving out information. Leading the spread of disinformation propagation via fake news, twitter has emerged as one of the leading SMPs. The sheer quantum of tweets makes it very challenging to sort the news and remove anomalies. Various machine learning techniques have been devised to identify fake tweets. This report aims at studying prominent machine learning techniques for detection of disinformation propagation on Twitter. Generally, researchers have identified detection of fake tweets as a classification problem but some unsupervised and unconventional learning models are also available. Major effort for any supervised learning technique is creation of data corpus. The paper highlights some techniques for the same. Consequently, inclusion of context as part of feature extraction has been achieved to align the intent behind the tweet with the text. As a first step towards the solution, a sentiment analysis of tweets from a set of users has been recommended details have been illustrated in subsequent sections. Various context based technique have been studied and implemented on data corpus and results produced have been compared. The report also studies various NLP techniques for data pre-processing and feature extraction before using classifiers to draw the outcome. © 2023 IEEE.

关键词： Feature extraction

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Object Detection Algorithm based on Structural Modification in the Convolutional Neural Network for Remote Sensing Satellite İmages 8

Object Detection Algorithm based on Structural Modification ...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Singh, Abhimanyu Nene, Manisha J Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9798350334012

for machine vision to make sense of an image, object recognition techniques must first locate all of the items of interest within the frame of reference. Several strategies have been proposed to address this issue, many of which draw inspiration from computer vision and deep learning techniques. Existing methods, however, consistently underperform when trying to recognise small, dense objects and often fail altogether when trying to detect objects that have undergone random geometric alterations. In this research, we examine and compare state-of-the-art object detection techniques, then suggest a tweaked convolutional object detection network to address the problems with the status *** investigation shows superior performance than current best practices. For this purpose, we employ deep convolutional networks to collect multi-scaled features, and we tweak the convolutional structures to account for geo-metric transformations.. © 2023 IEEE.

关键词： Object detection

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Pose-Critical Keypoint Attention model for Dynamic Yoga Pose Classification 2

Pose-Critical Keypoint Attention model for Dynamic Yoga Pose...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Meghana, J.H. Chethan, H.K. Prakash, S.P. Shiva Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science and Engineering Maharaja Institute of Technology Karnataka Mysore India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Karnataka Mysuru India

ISBN: (纸本)9781837242672

Yoga pose detection and classification have garnered considerable attention in recent years due to their significant applications in various domains, including fitness, health monitoring, and rehabilitation programs. Accurate detection and classification of yoga poses not only enhance the effectiveness of fitness regimes but also facilitate the assessment of practitioners' health and recovery processes. However, existing methods often struggle with the challenges posed by high-dimensional data processing, particularly in real-time applications. This paper introduces the Pose-Critical Keypoint Attention (PCKA) model, a novel approach designed to overcome the limitations of traditional methods in yoga pose analysis. The PCKA model employs an advanced attention mechanism to dynamically prioritize the most relevant keypoints from a comprehensive set of 33 extracted keypoints. This dynamic focus allows for efficient classification without the drawbacks associated with static keypoint reduction, ensuring that the model remains adaptable and robust across various poses. Extensive experiments evaluate the performance of the PCKA model against conventional classification frameworks. The results indicate that this model significantly outperforms traditional approaches, achieving an impressive accuracy of 91.08%, precision of 89.70%, recall of 91.93%, and an F1-score of 90.78%. These metrics demonstrate the model's capability to deliver accurate pose classification while providing realtime feedback for corrective measures, which is crucial for users striving to improve their yoga practices. © The Institution of Engineering & Technology 2024.

关键词： Data handling

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Rock / Mine Classification Using Supervised Machine Learning Algorithms

Rock / Mine Classification Using Supervised Machine Learning...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Sivachandra, K. Kumudham, R. Kumar, P Sathish Rajendran, V. Lakshmi, G.R.Jothi Vels Institute of Science Technology and Advanced Studies Department of ECE Chennai India Rajalakshmi Institute of Technology Department of Computer and Communication Engineering Chennai India

ISBN: (纸本)9781665462631

Nowadays, Artificial Intelligence appears in the domain of goetechnics, underwater acoustics, tunneling, geomorphology engineering and also in several fields too. This paper focused on the prospectivefor machine learning approaches which are sub field of artificial intelligence especially in underwater acoustics domain. In this proposal, machine learning approaches such as light gradient boosting, logistic regression, and random forest classifier algorithms are used for categorizing rocks or mines from collected sonar dataset. Based on performance metrics such as precision, F-score, recall, execution time, accuracy and confusion matrix, evaluate overall performance of machine learning models. Here, the experimental results shows that among all classifier algorithms, light gradient boosting achieves greater validation accuracy as 95% also training accuracy as 100∗ moreover, random forest classifier achieves 100% accuracy during training phase. © 2023 IEEE.

关键词： Underwater acoustics

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Unpaired remote sensing image super-resolution with content-preserving weak supervision neural network

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science China(Information sciences) 2023年第1期66卷 324-325页

作者： Jie WU Runmin CONG Leyuan FANG Chunle GUO Bob ZHANG Pedram GHAMISI College of Electrical and Information Engineering Hunan University Institute of Information Science Beijing Jiaotong University College of Computer Science Nankai University Department of Computer and Information Science University of Macau Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Institute of Advanced Research in Artificial Intelligence (IARAI)

Dear editor,Image super-resolution(SR) uses image processing methods to increase the resolution of the image without increasing any hardware cost. Remote sensing images with high spatial resolution contain rich imagery information, which can be utilized for object detection, building segmentation, building extraction, change detection, and so on.

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Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images 9

Analysis of U-Net and Modified VGG16 Technique for Mitosis I...

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Lijo, Jithy Saleema, J.S. Karnataka Bengaluru India Christ Academy Institute for Advanced Studies Karnataka Bengaluru India Christ Deemed to Be University Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350350845

One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%. © 2024 IEEE.

关键词： Diseases

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Smooth Distance Approximation 31

Smooth Distance Approximation

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31st Annual European Symposium on Algorithms, ESA 2023

作者： Abdelkader, Ahmed Mount, David M. Google LLC Mountain ViewCA United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States

ISBN: (纸本)9783959772952

Traditional problems in computational geometry involve aspects that are both discrete and continuous. One such example is nearest-neighbor searching, where the input is discrete, but the result depends on distances, which vary continuously. In many real-world applications of geometric data structures, it is assumed that query results are continuous, free of jump discontinuities. This is at odds with many modern data structures in computational geometry, which employ approximations to achieve efficiency, but these approximations often suffer from discontinuities. In this paper, we present a general method for transforming an approximate but discontinuous data structure into one that produces a smooth approximation, while matching the asymptotic space efficiencies of the original. We achieve this by adapting an approach called the partition-of-unity method, which smoothly blends multiple local approximations into a single smooth global approximation. We illustrate the use of this technique in a specific application of approximating the distance to the boundary of a convex polytope in Rd from any point in its interior. We begin by developing a novel data structure that efficiently computes an absolute Ε-approximation to this query in time O(log(1/Ε)) using O(1/Εd/2) storage space. Then, we proceed to apply the proposed partition-of-unity blending to guarantee the smoothness of the approximate distance field, establishing optimal asymptotic bounds on the norms of its gradient and Hessian. © Ahmed Abdelkader and David M. Mount.

关键词： Approximation algorithms

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