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Satellite image Classification Using ANN 1st

Satellite Image Classification Using ANN

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1st international conference on Cyber Warfare, Security and Space Research, SpacSec 2021

作者： Mathur, Pratistha Kavita Manipal University Jaipur Jaipur India

ISBN: (纸本)9783031157837

Every human being is capable of recognizing patterns. There are various problems in domains like bioinformatics, data mining, document classification, image analysis, remote sensing, and speech recognition where automatic recognition is needed. The rapid growth in computing power enables the development of such applications. Remote Sensing is one of the areas where a huge amount of data is available. In this paper, we have explored the supervised and unsupervised ANN for classifying satellite images. The adaptive pattern recognition capability of ANN is used to simulate the spatial variations in the images of an object/area. Multi-layer feed-forward neural network using backpropagation learning method and self-organizing feature map (SOFM) has experimented. Experiments are implemented using MATLAB. It is hoped that this multidisciplinary work will be useful for researchers and image processing users in the field of computer science and remote sensing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： MATLAB

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Driver Fatigue Prediction with Real Time Driver Alert System Using Deep learning 1

Driver Fatigue Prediction with Real Time Driver Alert System...

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1st international conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Jothimani, S. Madhumitha, K. Narmadha, V. Naveena, R. M. Kumarasamy College of Engineering Department of Electronics and Communication Engineering Tamilnadu Karur India

ISBN: (纸本)9781665497107

Deep learning is a type of machine learning which imitates instruction given by human. The main theme of our project is to detect whether the person was normal or abnormal while driving the vehicle, many of the accidents are caused due to the driver may be drunken or tired or feel sleepy while driving the vehicle. The proposed model will only consider the facial features like eye, nose, and lips of the intended person and detect the drowsiness by taking the eye blink duration and head posture recognition and also eye and mouth state recognition. And then detecting he/she is good to drive the vehicle or not and finally, If that person was abnormal in the sense, three different types of alert will be provided, they are self alert, message alert, and mail alert. Here, Convolutional Neural Network [CNN] algorithm is used for implementation. Convolutional Neural Network [CNN] is a type of artificial neural network, which is used in the process of image recognition and processing. It is specifically designed to process the pixel data. The proposed algorithm can be used to detect the drowsiness of the driver while driving the vehicle. © 2022 IEEE.

关键词： learning algorithms

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A Comparison of Deep learning Methods for Inebriation recognition in Humans 21st

A Comparison of Deep Learning Methods for Inebriation Recogn...

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

作者： Bhango, Zibusiso van der Haar, Dustin Univ Johannesburg Kingsway Ave & Univ Rd ZA-2092 Johannesburg South Africa

ISBN: (纸本)9783031064272;9783031064265

Excessive alcohol consumption leads to inebriation. Driving under the influence of alcohol is a criminal offence in many countries involving operating a motor vehicle while inebriated to a level that renders safely operating a motor vehicle extremely difficult. studies show that traffic accidents will become the fifth most significant cause of death if inebriated driving is not mitigated. Inversely, 70% of the world population can be protected by mitigating inebriated driving. Short term effects of inebriation include lack of balance, inhibition and fine motor coordination, dilated pupils and slow heart rate. An ideal inebriation recognition method that operates in real-time is less intrusive, more convenient, and efficient. Deep learning has been used to solve object detection, object recognition, object tracking and image segmentation problems. In this paper, we compare deep learning inebriation recognition methods. We implemented Faster R-CNN and YOLO methods for our experiment. We created our dataset of sober and inebriated individuals made available to the public. Six thousand four hundred forty-three (6443) face images were used, and our best performing pipeline was YOLO with a 99.6% accuracy rate.

关键词： Deep learning Computer vision R-CNN YOLO Inebriation recognition Inebriation detection Drunk driving

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Artificial Intelligence & Deep learning: Pet and Spect Imaging and Classification on Brain MRI images List 1

Artificial Intelligence & Deep Learning: Pet and Spect Imagi...

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1st international conference on Electronic Circuits and Signalling Technologies, ICECst 2022

作者： Goel, Amit Kumar Yadav, Sagar Zubair Jain, Naman School of Computing Science and Engineering Galgotias University India

Cognitive abilities commonly linked with human brains include learning and problem solving. Another option is just a preprogrammed rule that instructs the computer to perform a given action under specified circumstances. Voice recognition and image recognition, for instance, are both widely used in daily life and may be found everywhere. "Deep learning"(DL) approaches, in particular,due to their ability to come up with creative solutions to complex situations throughout the past decade. Both cancerous and non-cancerous tumors can be seen in the human brain (benign). Google developed the open-source deep learning library Tensor Flow. It can be combined with traditional machine learning. It was created on top of PIL that the Python Pillow module exists (Python image Library). This collection of modules is critical to Python's ability to perform image processing. "A deep learning API developed in Python"is powered by "the TensorFlow-based machine learning platform TensorFlow."NumPy is a Python library for manipulating arrays, as the name suggests. Also included are linear algebra, the Fourier transform, and matrix functions. As far as the ResNet50 model goes, it has 48 convolutional layers, a maxpool and an averagepool layer. Up to a scale of 3.8 x 109, floating point operations can be performed. ResNet-50 is a type of neural network with 50 layers. You can import a pre-trained version of the network from the imageNet database. © Published under licence by IOP Publishing Ltd.

关键词： Python

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Diagnosis of Glaucoma from Retinal Fundus image Using Deep Transfer learning 1

Diagnosis of Glaucoma from Retinal Fundus Image Using Deep T...

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1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Mostafa, Md. Shafayat Bin Bal, Debasish Sathi, Khaleda Akhter Hossain, Md. Azad Institute of Energy Technology Chittagong University of Engineering and Technology Chittagong Bangladesh International Islamic University Chittagong Department of Electrical and Electronic Engineering Chittagong Bangladesh Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chittagong Bangladesh

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

The phenemeon of retinal condition brought about by expanding intraocular strain inside the eye is knows as Glaucoma. The existing diagnosis process of glaucoma through various careful retinal tests such as ophthalmoscopy, tonometry, perimetry, gonioscopy, and pachymetry are costly as well as time-consuming. Moreover, the diagnosis processes are fully dependent on the Ophthalmologists knowledge of test report analysis. To overcome these issues, this work aims to propose the utilization of a profound deep learning model such as ResNetl52 as well as VGG16 for the primary feature extraction qualities, specifically cup-to-circle proportions, plate obligation scale harm, and unrivaled nasal fleeting lower regions to diagnose glaucoma. Performance evaluation of the model is performed based on the accuracy matrix that shows 87% and 72% of accuracy for the ResNetl52 and VGG16 models respectively. The ResNetl52 model outperformed the VGG16 model because of the capability of extracting deep structures of the retinal image with the aid of skip connections from the previous consecutive convolutional layers. © 2022 IEEE.

关键词： Ophthalmology

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Dense Disparity Map Generation for images Captured by Lunar Rover Navigation Camera 3rd

Dense Disparity Map Generation for Images Captured by Luna...

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3rd international conference on machine learning, image processing, Network Security and Data Sciences, MIND 2021

作者： Khosla, Yukti Rachna, U. Narendra Reddy, Y. Tripathi, Shikha Maji, Amit Laha, Jayanta PES University Karnataka Bengaluru India Laboratory for Electro -Optics Systems Indian Space Research Organization 1st Cross 1st Stage Peenya Industrial Estate Bengaluru560058 India

ISBN: (纸本)9789811958670

Perception of depth is required for automatic path planning for rover navigation on the surface of a celestial body. The current method of generating disparity maps involves offline computation as the rover has low computational power. This work proposes a novel method to estimate a good quality disparity map for regions where the surface is similar to that of the moon. The surface of the moon is covered by craters distributed unevenly throughout. Regions with little to no texture pose a major challenge in stereo matching. In the proposed method the processing pipeline involves image rectification using camera calibration, disparity map generation, disparity map refinement. The proposed method generates accurate disparity maps from the Lunar Navigation Camera(Navcam) images under lunar-like illumination conditions. In this method, occlusion handling is also proposed to improve the quality of generated disparity maps. The proposed method outperforms other previously established methods with the experimental data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cameras

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Brain Tumor Classification Using Optimal Features and Ensemble learning Algorithms 1

Brain Tumor Classification Using Optimal Features and Ensemb...

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1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Kuppusamy, P. Kodavaluru, Veena Sree Manojkumar Bogar, Shruti VIT-AP University School of Computer Science & Engineering India

ISBN: (纸本)9798350342796

The use of modern technology for better diagnosis of life-threatening diseases like brain cancer is becoming increasingly vital for the medical field. The manual verification of brain images lacks accuracy due to human errors. The Magnetic Resonance Imaging (MRI) dataset contains information on the tumor's existence, and different types of brain tumors like glioma, meningioma, and pituitary. Recent developments in the fields of machine learning (ML) can substantially improve the accuracy of medical scanning image (a.k.a MRI images) processing. This paper focuses on utilizing different ML algorithms to examine brain MRI images and identify the presence or absence of tumors. First, the MRI images are enhanced using an adaptive median filter. Then, the MRI dataset is pre-processed using canny edge and Gabor filter to extract features. After that, the dimension of the feature is decreased by applying Principal Component Analysis (PCA) method which decreased the dimension drastically. Several ML algorithms are then applied, namely, logistic regression, Support Vector machine (SVM) with linear, polynomial, and RBF kernels, ensemble methods Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost). The experimental results showed that (a) the performance of the ensemble method XGBoost is better than other ML algorithms such as logistic regression, SVM with linear, polynomial, and RBF kernels, Random Forest, Gradient Boosting in terms of accuracy, precision, recall, and F1-score;(b) the XGBoost consumes more training time due to the more number of trees, depth, and iterations, and (c) the experimental results proved that feature reduction using PCA decreased the training time in ML algorithms. © 2023 IEEE.

关键词： brain tumor feature extraction feature reduction Gradient Boosting Principal component analysis XGBoost

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Research on the Construction of Intelligent Customs Clearance Information System for Cross-Border Road Cargo Between Guangdong and Hong Kong 1st

Research on the Construction of Intelligent Customs Clearanc...

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1st international conference on AI-generated Content, AIGC 2023

作者： Han, Chengguo Wang, Bin Lai, Xinyu Guangdong University of Science and Technology 99 Xihu Road Nancheng District Guangdong Province Dongguan City China Shenzhen Taizhou Technology Co. Ltd. 4028 Jintian Road Futian District Guangdong Province Shenzhen City China

ISBN: (纸本)9789819975860

In order to achieve high-efficiency "single entry, dual declaration" customs clearance between Guangdong (GD) and Hong Kong (HK), this paper conducts a comprehensive investigation and analysis of the declaration process of cross-border road cargo, as well as the correlation between key data such as Harmonized System codes and commodity elements in documents from both regions. By comparing the customs supervision processes and information systems of both regions, we propose a solution for the construction of Intelligent customs clearance information system. This system integrates the customs systems of GD and HK, sharing basic data such as vehicles and goods from the road manifest and declaration merchants in both regions. It achieves automation and intelligence of customs clearance operations. To address problems such as diverse document formats and manual recognition and entry of paper texts, we propose solutions based on text image processing, Optical Character recognition (OCR), and multi-format Excel recognition. Based on big data analysis of customs declarations and using Natural Language processing (NLP) and Deep learning methods, our proposed system achieves data sharing and automatic conversion of customs codes for road manifests, customs declarations, and other data between GD and HK. The Intelligent customs clearance information system is implemented using a SaaS architecture on both PC and WeChat Mini-Program platforms to enable users in GD and HK to complete cargo manifest and customs declaration at any time and place. The research results are widely applied in China (Shenzhen) international Trade Single Window and the Qianhai HK Modern Service Cooperation Zone. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： international trade

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Milky WAI: Unlocking the Secrets of Raw Cow Milk Through Speckle pattern and AI 3

Milky WAI: Unlocking the Secrets of Raw Cow Milk Through Spe...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Bello, Valentina Bassi, Irene Fiocchi, Matteo Nuzzi, Cristina Bellassi, Paolo Cattaneo, Luca Pasinetti, Simone University of Pavia Diii Pavia Italy University of Brescia Dimi Brescia Italy Università Cattolica del Sacro Cuore DiSTAS Cremona Italy Universita Cattolica del Sacro Cuore Diana Piacenza Italy

ISBN: (纸本)9798350378009

This paper introduces a novel method for raw cow milk classification combining speckle pattern (SP) imaging and AI-based processing of statistical parameters. Raw cow milk has been classified considering (i) 6 statistical features extracted from raw SP images, and (ii) 4 features extracted from the Gray-Level Co-occurrence Matrix (GLCM) computed on each SP image. We conducted 4 experimental campaigns, resulting in 24, 000 SP images retrieved from 20 milks produced by 20 cows. We then considered two different datasets: (i) a Complete dataset (made of all 24,000 frames), and (ii) a Reduced dataset, built by excluding data from one acquisition campaign. The two datasets were then split into training and testing sub-datasets. We trained three Wide Neural Networks (WNN) using different strategies. WNN1 and WNN2 have been trained and tested using the Complete and the Reduced datasets respectively, while WNN3 has been trained on the Reduced tested on the Complete dataset. WNN3 only attained 72% accuracy (revealing sensitivity to environmental conditions), while WNN1 and WNN2 achieved a test accuracy higher than 90%, demonstrating effectiveness without relying on computationally expensive models such as CNNs. This approach could be considered as a promising initial step for cow milk classification using SP imaging. © 2024 IEEE.

关键词： Data analysis machine learning measurement science milk analysis speckle pattern

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2D Respiratory Sound Analysis to Detect Lung Abnormalities 5th

2D Respiratory Sound Analysis to Detect Lung Abnormalities

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5th international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Alice, Rafia Sharmin Wendling, Laurent Santosh, K. C. Univ South Dakota Comp Sci 2AI Appl Artificial Intelligence Res Lab Vermillion SD 57069 USA Univ Paris SIP Syst Intelligents Percept 45 Rue St Peres F-75270 Paris 06 France

ISBN: (纸本)9783031235986;9783031235993

In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients (MFCC), spectral centroid, and spectral roll-off. In our experiments, using the publicly available respiratory sound database named ICBHI 2017 (5.5 h of recordings containing 6898 respiratory cycles from 126 subjects), we received the highest performance with the area under the curve of 0.79 from Spectrogram as opposed to 0.48 AUC from the raw data from a pre-trained deep learning model: VGG16. Our study proved that 2D data representation could help better understand/analyze lung abnormalities as compared to 1D data. In addition, our results can be compared with previous works.

关键词： Lung abnormality Respiratory sound 2D data Representation Deep visual features

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