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Remote Sensing image Captioning (RSIC): A Technical Review 1st

Remote Sensing Image Captioning (RSIC): A Technical Review

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1st international conference on Data Engineering and machine Intelligence, ICDEMI 2023

作者： Dhinesh, A. Sumathy, P. Department of Computer Science Bharathidasan University Tamilnadu Tiruchirappalli620023 India

ISBN: (纸本)9789819776153

Remote Sensing image Captioning (RSIC) is crucial for many researchers since it has many applications in environmental monitoring, disaster management, urban planning, image retrieval, performance of building planes, military intelligence, and autonomous vehicles. The effective procedure to generate the captions from remote sensing images complements the above-mentioned application domains. Various baseline data sets have been created by the researchers to enhance the quality of captioning by processing the diverse features of the geospatial information. In this paper, we have technically reviewed important literature that follow different algorithms for generating the captions. For example, we have presented the technical review on Vision-Language Aligning Paradigm (VLCA) under the bi-lingual caption generation model, Joint-Training Two-stage (JTTS) technique under multimodel fusion category, Multilevel and Contextual Attention Network (MLCA-Net) under context-aware captioning, LEVIR-CC belongs to transfer learning model, BERT and GPT-3 models belong to transfer-based model, Multiscale Attention (MSA) and Multifeat Attention (MFA) of Multiscale captioning model and Summarization Driven (SD)-RSIC of fine-grained captioning model. We have also presented the performance of each of these methods on various benchmark datasets. For evaluation, different well-known performance metrics are considered. The result is critically evaluated and commented on. In the future, a more rigorous review of these methods along with other relevant methods will be presented along with implementation data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Urban planning

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Cardiac Arrhythmia Diagnosis via Multichannel Independent Component Analysis: An Approach Towards a Better Health Care System 1st

Cardiac Arrhythmia Diagnosis via Multichannel Independent Co...

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1st international conference on Emerging Technology Trends in Internet of Things and Computing (TIOTC)

作者： Sarfraz, Mohammad Khan, Mudassir Hasan Mohammed, Duraid Yahya Hussain, Mays Dheya Zidan, Khamis A. Aligarh Muslim Univ Ctr Interdisciplinary Biomed & Human Factors Engn Dept Elect Engn Aligarh 202002 Uttar Pradesh India AL Iraqia Univ Coll Engn Comp Engn Dept Baghdad Iraq

ISBN: (纸本)9783030972554;9783030972547

With the evolving lifestyle, many cardiac ailments are becoming more frequent, and it has become necessary to provide detailed surveillance of the heart's functioning to ensure healthy living. ECG signals provide details regarding the various forms of arrhythmias. However, owing to the complexities and non-linearity of ECG signals, it is impossible to examine these signals manually. Conventional approaches for specialist inspection of ECGs on paper or television are inadequate for ambulatory, long-term monitoring, and sports ECGs. Automated applications that use signal processing and pattern recognition would be extremely beneficial. Identification of arrhythmias from ECGs is an essential branch of biomedical signal processing and pattern recognition. Motion-induced artifacts are well-known to be a major source of misrecognition and misdiagnosis. On the other hand, the feature extraction method has a significant effect on the reliability and performance of ECG pattern recognition. This paper proposes new approaches and algorithms for pre-processing multi-channel ECG signals and neural networks for arrhythmia classification using independent component analysis (ICA) with two distinct goals: (1) to eliminate motion-induced or associated artifacts, and (2) to better select the features and allow more effective pattern recognition. When processing noisy ECG data with the MIT dataset, cross-validation reveals a major improvement. For noisy signals, classification sensitivity of 97.9% and positive predictivity of 98.1% was achieved in this work. A tenfold neural validation rule was used to achieve 99.3% accuracy in arrhythmia classification. The lower the signal-to-noise level, the more significant is the improvement. This proposed algorithm would be a valuable method for physicians in justifying their diagnosis. With its quick reaction time, the proposed algorithm can be easily integrated into an automated healthcare management system.

关键词： Health care Independent component analysis machine learning Arrhythmia computer-aided diagnosis Feature extraction Neural network

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Feature Descriptors based on Circular Forms of Local patterns for Texture Classification 1

Feature Descriptors based on Circular Forms of Local Pattern...

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1st international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022

作者： Jagirdar, Srinivas Reddy, K. Venkata Subba Sr University Warangal India Kallam Haranadha Reddy Institute of Technology Guntur India

ISBN: (纸本)9781665436472

Five novel Local Binary pattern (LBP) based descriptors are presented in this paper for performing texture analysis of an image. The performance of any texture classification method depends upon its dimensionality. As existing lo-cal based extractor methods generate descriptors with huge dimensions, the classifiers using them will suffer. In order to deal with this problem Uniform Weighted LBP (UWLBP), strong and Uniform Weighted -LBP (SUWLBP), Uni-form Circular and Elliptical Weighted Texture Matrix (UCEWTM), strong and Uniform CEWTM (SUCEWTM) and strong and Uniform Twofold Spatial Weighted Complex patterns (SUTSWCP) are proposed in this paper. The out-puts of these descriptors are fed into machine learning algorithms like NavieBayes (NB), Multilayer-perceptron (MLP), Ibk and J48. image datasets like Brodtaz, UIUC, Outex-TC-12, KTH-TIPS, ALOT were used to train and test the models. The five descriptors were combined with MLP and compared. The CEWstM coupled with MLP attained has attained a classification rate of 93.11 % which is the best out of the proposed five descriptors. © 2022 IEEE.

关键词： Local binary pattern

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Reso-Net: Generic image Resolution Enhancement Using Convolutional Autoencoders 1st

Reso-Net: Generic Image Resolution Enhancement Using Convolu...

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1st EAI international conference on Intelligent Systems and machine learning, ICISML 2022

作者： Dutta, Koustav Gupta, Priya SA&MA: Analytics and Cognitive Deloitte Touche Tohmatsu India LLP Bengaluru India Atal Bihari Vajpayee School of Management and Entrepreneurship Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9783031350771

images are created in a variety of ways in various industries. These images are tough to work with, and as a result, they can’t be used effectively in a variety of fields. In this paper, image Resolution is improved to carry out the process of generic image enhancement tasks. In this process, the low-resolution image is enhanced so that the high-resolution image is achieved. With the help of image enhancement, the perception or in other words the process of interpreting information present in images by the human viewers is enhanced and the quality is improved to a large extent. image resolution augmentation has traditionally been accomplished using a variety of classic image processing approaches. However, these methods are not as robust as they should be in dealing with any form of noise signal associated with the image and unable to handle the problems of Error Control Mechanism, Optimization and some other problems. Therefore, this paper presents a method of image resolution enhancement using Advanced Hybrid Neural Network architecture which brings about significant improvements in the entire process. © 2023, ICst Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image resolution

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Tomato Fruits Disease Detection Using image processing 1

Tomato Fruits Disease Detection Using Image Processing

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1st international conference on Futuristic Technologies, INCOFT 2022

作者： Sudharshan Duth, P. Jhansy, T.N. Amrita Vishwa Vidyapeetham Amrita School of Arts and Sciences Department of Computer Science Mysuru India

ISBN: (纸本)9781665450461

Utilizing photographs and machine vision to accurately determine and categorize fruits throughout growing is critical, not only for reducing employment human morphology data evaluations also for optimizing harvest tasks. Computer learning-based approaches have been introduced as the province in several picture classification tasks, and they show a great deal of promise in demanding sectors like farming, in which they can cope with Great Dividing Range in background includes than classic machine vision *** Upgraded KNN and SVM classification techniques were used to recognize tomato fruit illnesses and maturation phases in images taken in a conservatory. Even though the current work's performance measures were obtained utilizing high resolution photos, experimental data on disease diagnosis of tomato fruits employing photos held captive in greenhouses with an Authentic Perception camera have been gathered, and the construction project delivery greater accuracy of disease diagnosis recognition and segmentation of tomato ripening process employing ESVM (Enhanced SVM) with 94.036 percent accuracy. © 2022 IEEE.

关键词： Fruits

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Currency and Fake Currency Detection using machine learning and image processing - An Application for Blind People using Android studio 1

Currency and Fake Currency Detection using Machine Learning ...

引用

1st IEEE international conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Vaishak, B. Hoysala, S. Pavankumar, V.H. Mohana Electronics and Telecommunication Engineering RV College of Engineering® Bangalore India RV College of Engineeringg® Bangalore India

ISBN: (纸本)9781665460842

Any development in technology is made with the intention of solving the difficulties in that field. One such identified problem is blind people were unable to make out the type of currency. The proposed model efficiently identifies the type of currency. The converse of this leads to the printing and scanning of currencies and releasing them into the market, which not only affects the economy but also reduces the value for money. This further causes some conflicts and a great threat to the country. Thus, a need for efficient currency and fake currency detection methods is required. Finding fake currencies is done by any ways. The proposed work identifies the currency's originality by examining its images. The code simulated using MATLAB extracts the features of currencies. machine learning models are used for mapping the extracted feature to its standard value, it is able to identify the fake currencies efficiently. © 2022 IEEE

关键词： Computer vision

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Relaxing the Forget Constraints in Open World recognition 21st

Relaxing the Forget Constraints in Open World Recognition

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21st international conference on image Analysis and processing (ICIAP)

作者： Fontanel, Dario Cermelli, Fabio Geraci, Antonino Musarra, Mauro Tarantino, Matteo Caputo, Barbara Politecn Torino Turin Italy

ISBN: (纸本)9783031064272;9783031064265

In the last few years deep neural networks has significantly improved the state-of-the-art of robotic vision. However, they are mainly trained to recognize only the categories provided in the training set (closed world assumption), being ill equipped to operate in the real world, where new unknown objects may appear over time. In this work, we investigate the open world recognition (OWR) problem that presents two challenges: (i) learn new concepts over time (incremental learning) and (ii) discern between known and unknown categories (open set recognition). Current state-of-the-art OWR methods address incremental learning by employing a knowledge distillation loss. It forces the model to keep the same predictions across training steps, in order to maintain the acquired knowledge. This behaviour may induce the model in mimicking uncertain predictions, preventing it from reaching an optimal representation on the new classes. To overcome this limitation, we propose the Poly loss that penalizes less the changes in the predictions for uncertain samples, while forcing the same output on confident ones. Moreover, we introduce a forget constraint relaxation strategy that allows the model to obtain a better representation of new classes by randomly zeroing the contribution of some old classes from the distillation loss. Finally, while current methods rely on metric learning to detect unknown samples, we propose a new rejection strategy that sidesteps it and directly uses the model classifier to estimate if a sample is known or not. Experiments on three datasets demonstrate that our method outperforms the state of the art.

关键词： Open world recognition Robot vision Deep learning

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Eye Diseases Classification Using Deep learning 21st

Eye Diseases Classification Using Deep Learning

引用

21st international conference on image Analysis and processing (ICIAP)

作者： Haraburda, Patrycja Dabala, Lukasz Warsaw Univ Technol Warsaw Poland

ISBN: (纸本)9783031064272;9783031064265

Eye disease recognition is a challenging task, which usually requires years of medical experience. In this work, we conducted research that can be a start for the most versatile solution. We tried to solve the problem of the classification of different eye diseases using neural networks. The first step of this work consists of gathering all publicly available eye disease datasets and preprocessing them to make the experiments as generalized as possible. This led to the creation of a dataset composed of over 30,000 images. The aim was to teach the model the actual symptoms of the diseases instead of adjusting the results to a given part of the dataset. Several deep convolutional neural networks were used as feature extractors and they were combined with the Synergic Deep learning model. We conducted experiments on the data and were able to achieve promising results.

关键词： Classification Neural networks Medical images Diabetic retinopathy Glaucoma Cataract

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1st international conference on Applied Mathematics and Computational Intelligence, ICAMCI 2020

1st International Conference on Applied Mathematics and Comp...

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1st international conference on Applied Mathematics and Computational Intelligence, ICAMCI 2020

ISBN: (纸本)9789811981937

The proceedings contain 18 papers. The special focus in this conference is on Applied Mathematics and Computational Intelligence. The topics include: steady and Unsteady Solutions of Free Convective Micropolar Fluid Flow Near the Lower stagnation Point of a Solid Sphere;low-Light image Restoration Using Dehazing-Based Inverted Illumination Map Enhancement;mathematical Modeling of Probability and Profit of Single-Zero Roulette to Enhance Understanding of Bets;Nonlinear Computational Crack Analysis of Flexural Deficit Carbon and Glass FRP Wrapped Beams;Economic Benefit Analysis by Integration of Different Comparative Methods for FACTS Devices;optimal Pricing with Servicing Effort in Two Remanufacturing Scenarios of a Closed-Loop Supply Chain;a Review on Type-2 Fuzzy Systems in Robotics and Prospects for Type-3 Fuzzy;Optimization of Palm Oil Mill Effluent (POME) Solubilization Using Linguistic Fuzzy Logic and machine learning Techniques;analysis of Heat Transfer Coefficients and Pressure Drops in Surface Condenser with Different Baffle Spacings;integration of Big Data and Internet of Things (IoT): Opportunities, Security and Challenges;intuitionistic Fuzzy Metrics and Its Application;complex structure of Number in Language processing;digital Newspaper Using Augmented Reality;nonlocal Fuzzy Solutions for Abstract Second Order Differential Equations;performance Assessment of Routing Protocols in Cognitive Radio Vehicular Ad Hoc Networks;infinite System of Second Order Differential Equations in Banach Space c0.

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Early Detection of Diabetic Retinopathy Using Deep Convoulutional Neural Network 1st

Early Detection of Diabetic Retinopathy Using Deep Convoulut...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Vijay, K. Krithiga, P. Kavirakesh, S. Swetha, S. Vishal, B. Department of CSE Rajalakshmi Engineering Chennai India

ISBN: (纸本)9783031730641

If overlooked diabetic retinopathy (DR), a degenerative eye condition that affects people with diabetes, can cause serious vision loss. Early DR detection is essential for prompt management and action. Deep convolutional neural networks (DCNNs) have become effective tools for automated processing of retinal pictures in recent years, making it possible to diagnose DR earlier. To learn and extract distinguishing features from retinal fundus images, the suggested method makes use of a DCNN architecture. For the training of models and evaluation, a dataset containing quite a few of retinal pictures from diabetic patients—including both normal and DR cases is utilised. Combining methods of supervised learning, the DCNN model undergoes training with a focus on improving performance indicators including accuracy, sensitivity, and specificity. To intentionally enhance the quantity and diversity of the training dataset, methods like image rotation, flipping, scaling and random cropping are used. This successfully simulates changes in the circumstances of collecting images and the clinical traits of DR. The efficiency of the suggested DCNN-based technique for early DR detection is shown by experimental findings. The trained model performs exceptionally well in spotting early indications of DR, such as microaneurysms, haemorrhages, and exudates, with high accuracy. An independent test dataset is used to assess the model’s performance, highlighting its potential for use in practical situations. The suggested approach advances automated DR detection, enabling immediate action and greater control of this illness that threatens eyesight. The suggested DCNN-based approach needs to be further improved and validated, and the resulting system needs to be integrated into the current medical system to facilitate widespread early DR detection and treatment. The model achieved validation accuracy of 76.80% and training accuracy of 99.58%. A training loss of 0.0132 and a validation loss of

关键词： Automated processing DCNN Supervised learning

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