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1st international conference on Complex Computational Ecosystems, CCE 2023

1st International Conference on Complex Computational Ecosys...

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1st international conference on Complex Computational Ecosystems, CCE 2023

ISBN: (纸本)9783031443541

The proceedings contain 16 papers. The special focus in this conference is on Complex Computational Ecosystems. The topics include: Modeling Ecosystem Management Based on the Integration of image Analysis and Human Subjective Evaluation - Case studies with Synecological Farming;on the Provision of Network-Wide Cyber Situational Awareness via Graph-Based Analytics;metrics for Evaluating Interface Explainability Models for Cyberattack Detection in IoT Data;machine learning Model Applied to Higher Education;modeling El Niño and La Niña Events Using Evolutionary Algorithms;estimation of Seismic Phase Delays Using Evolutionary Algorithms;new Siamese Neural Networks for Text Classification and Ontologies Alignment;theorizing, Modeling and Visualizing Business Ecosystems. What Should Be Done?;A Comparative study of YOLO V4 and V5 Architectures on Pavement Cracks Using Region-Based Detection;evolutionary Reduction of the Laser Noise Impact on Quantum Gates;short Time Series Forecasting Method Based on Genetic Programming and Kalman Filter;performance Upgrade of Sequence Detector Evolution Using Grammatical Evolution and Lexicase Parent Selection Method;fuzzy pattern Trees with Pre-classification;models for the Computational Design of Microfarms.

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Fingerprint Classification using Deep learning 1

Fingerprint Classification using Deep learning

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

作者： Ahmad, Sumaiya Jabin, Suraiya Jamia Millia Islamia Department of Computer Science New Delhi 110025 India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Biometrics are being extensively used in person authentication;while spoofing is used by the imposters to crack into the biometric systems. The paper deals with the fingerprint image classification into two classes viz. fake or genuine using Convolutional Neural Network (CNN) on ATVS-FFP dataset of fingerprint images of 17 users. The dataset is divided into two parts named as DS_WithCooperation and DS_WithoutCooperation, both the parts contain fake and original fingerprint images. These differ with respect to the acquisition of fake fingerprint which was done with and without the consent of the users. Thus, the fake fingerprint of latter part of the dataset were of low quality and represent the real-world scenario. The images were segmented to get the ridge region from the background noise using morphological image processing methods. The segmented images were then randomly rotated at different angles and were resized to 170X170X1. In this way the DS_WithCooperation resulted into 3264 images from a total of 816 fake and real images, similarly DS_WithoutCooperation resulted into 3072 images from a total of 768 fake and real images. This data set was then split in 3 to 1 ratio to form train and test datasets. For preparing the proposed model to classify fake and genuine images, CNN was trained using the Train data set. The model gave ACE (Average Classification Error) ranging from 0 to 2.45 on test datasets of both types with and without cooperation which is comparable to the state-of-the-art. © 2022 IEEE.

关键词： Biometrics

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Automatic Licence Plate Detection and recognition System using image processing Techniques

Automatic Licence Plate Detection and Recognition System usi...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), international conference on

作者： Peddi Shyam Ganesh B. Srilekha Vukanti Sravanthi Department of ECE Siddhartha Institute of Technology & Sciences Telangana India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

The recent exponential surge in the number of vehicles on our roadways has made congestion and violations important problems. By automating traffic management using an ALPR system, we can improve access control systems, streamline traffic flow, and save time. Here, we look at how well algorithms for edge processing and morphological processing perform. The parameters of the neural network, including the regularisation parameter, the count of hidden layer units, and the count of iterations, are optimised with great care. With a focus on real-time implementation and control via a graphical user interface, this case presents a parking security system suitable for usage in office buildings, institutions, malls, etc. Operating on Raspberry Pi 2B and Matlab, the system accomplishes a 97% efficiency rate through the use of image processing techniques and machine learning algorithms.

关键词： Location awareness machine learning algorithms image edge detection Neural networks Ubiquitous computing Real-time systems Character recognition

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A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 imagery

A Parallel Workflow for Polar Sea-Ice Classification using A...

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Iqrah, Jurdana Masuma Wang, Wei Xie, Hongjie Prasad, Sushil K. Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA Univ Texas San Antonio Dept Earth & Planetary Sci San Antonio TX USA

ISBN: (纸本)9798350364613;9798350364606

The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the 52 satellite is actively capturing high-resolution imagery over the earth's surface, there are lots of images that need to be classified. One major obstacle is the absence of labeled 52 training data (images) to act as the ground truth. We demonstrate a scalable and accurate method for segmenting and automatically labeling S2 images using carefully determined color thresholds. We employ a parallel workflow using PySpark to scale and achieve 9-fold data loading and 16-fold map-reduce speedup on auto-labeling S2 images based on thin cloud and shadow filtered color-based segmentation to generate label data. The auto-labeled data generated from this process are then employed to train a U-Net machine learning model, resulting in good classification accuracy. As training the U-Net classification model is computationally heavy and time-consuming, we distribute the U-Net model training to scale it over 8 GPLJs using the Horovod framework over a DGX cluster with a 7.2 lx speedup without affecting the accuracy of the model. Using the Antarctic's Ross Sea region as an example, the U-Net model trained on autolabeled data achieves a classification accuracy of 98.97% for auto-labeled training datasets when the thin clouds and shadows from the S2 images are filtered out.

关键词： Polar Sea Ice Sentinel-2 Sea Ice Classification Auto-labeling Parallel processing Distributed Deep learning Synchronous Data Parallel

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Engagement Detection with Multi-Task Training in E-learning Environments 21st

Engagement Detection with Multi-Task Training in E-Learning ...

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

作者： Copur, Onur Nakip, Mert Scardapane, Simone Slowack, Juergen Sapienza Univ Rome I-00185 Rome Italy Polish Acad Sci Inst Theoret & Appl Informat PL-44100 Gliwice Poland Barco NV B-8500 Kortrijk Belgium

ISBN: (纸本)9783031064333;9783031064326

recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly improve the user experience and efficiency by providing valuable feedback. In this paper, we propose a novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes mean squared error and triplet loss together to determine the engagement level of students in an e-learning environment. The performance of this system is evaluated and compared against the state-of-the-art on a publicly available dataset as well as videos collected from real-life scenarios. The results show that ED-MTT achieves 6% lower MSE than the best state-of-the-art performance with highly acceptable training time and lightweight feature extraction.

关键词： Engagement detection Activity recognition E-learning Triplet loss Multi-task training

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Towards Enhanced Deep CNN for Early and Precise Skin Cancer Diagnosis 1

Towards Enhanced Deep CNN for Early and Precise Skin Cancer ...

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1st IEEE international conference on Networking and Communications, ICNWC 2023

作者： Malaiarasan, S. Ravi, R. Maheswari, D.R. Yesubai Rubavathi, C. Ramnath, M. Hemamalini, V. Francis Xavier Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India Thamirabharani Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India School of Computing SRMIST KTR Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798350336009

Most people's first experience with cancer will be with skin cancer, which is also the most prevalent and potentially fatal kind. Determining a skin cancer diagnosis also requires the use of information technologies. This highlights the need of developing and deploying highly effective deep-learning methods for the early and accurate diagnosis and detection of skin cancer. Deep Convolution Neural Network (DCNN) is proposed for automated skin cancer detection in this study. This study's unique contribution is the use of a deep convolution neural network containing 12 nested processing layers to improve the accuracy of skin cancer diagnosis and detection. As a consequence of this study's findings, researchers have determined that deep learning techniques are superior to machine learning for spotting skin cancer. As a consequence, pathologists' precision and competence may be improved by using automated evidence-based detection of skin cancer. To accurately distinguish between benign and malignant skin lesions, we present a deep convolution neural network (DCNN) model in this research that uses a deep learning technique. First, we normalize the input photos and identify characteristics that aid in correct classification, then we apply a filter or Gaussian to eliminate noise and artifacts, and lastly, we supplement the data to increase the number of images, which enhances the accuracy of the classification rate. © 2023 IEEE.

关键词： image enhancement

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE international conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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LOCALIZATION AND CLASSIFICATION OF PARASITIC EGGS IN MICROSCPIC imageS USING AN EFFICIENTDET DETECTOR 29

LOCALIZATION AND CLASSIFICATION OF PARASITIC EGGS IN MICROSC...

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IEEE international conference on image processing (ICIP)

作者： AlDahoul, Nouar Karim, Hezerul Abdul Kee, Shaira Limson Tan, Myles Joshua Toledo Multimedia Univ Fac Engn Cyberjaya Malaysia Univ St La Salle Dept Nat Sci Bacolod City 6100 Philippines Univ St La Salle Dept Chem Engn Bacolod City 6100 Philippines

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

IPIs caused by protozoan and helminth parasites are among the most common infections in humans in LMICs. They are regarded as a severe public health concern, as they cause a wide array of potentially detrimental health conditions. Researchers have been developing pattern recognition techniques for the automatic identification of parasite eggs in microscopic images. Existing solutions still need improvements to reduce diagnostic errors and generate fast, efficient, and accurate results. Our paper addresses this and proposes a multi-modal learning detector to localize parasitic eggs and categorize them into 11 categories. The experiments were conducted on the novel Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000 microscopic training images from 11 categories. Our results show robust performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the IOU distribution illustrates the high localization capability of the detector.

关键词： EfficientDet microscopic image multi-modal learning object detection parasitic egg

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Underwater image Restoration and Enhancement Based on machine and Deep learning Algorithms 1st

Underwater Image Restoration and Enhancement Based on Machin...

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1st international conference on Artificial Intelligence and Data Science, ICAIDS 2021

作者： Gupta, Yogesh Kumar Saxena, Khushboo Department of Computer Science Banasthali Vidyapith Banasthali India

ISBN: (纸本)9783031213847

Underwater image processing is the key research of the last decade. Due to attenuation and scattering of light into beneath of ocean, it is impossible to find capture image. Therefore image processing keeps into picture. To automate process and find relevant information, there is a need of good quality image. There are many of the applications, where need of processed image due to unclear of original images. In the artificial intelligence the image processing is also one of the important research domains. The machine learning (ML), deep learning (DL) approaches based image processing like image enhancement, denoising, encryption, fusion etc. are emerging trend of the research. Under-water imagery is an important carrier and presentation of under-water information, which plays a vital role in the exploration, exploitation and utilization of marine resources. This paper studied about under-water image restoration and enhancement based on machine and deep learning algorithms, it also suggest an approach to restore and enhance image. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Restoration

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GLCM Based Feature Extraction and Medical X-ray image Classification Using machine learning Techniques 1st

GLCM Based Feature Extraction and Medical X-ray Image Classi...

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

作者： Rout, Jyotiranjan Das, Swagat Kumar Mohalik, Priyabrata Mohanty, Subhashree Mohanty, Chandan Kumar Behera, Susil Kumar Department of Computer Science and Engineering Balasore College of Engineering and Technology Balasore India

ISBN: (纸本)9783031350771

The automated system is now created with excellent accuracy to detect abnormalities in X-ray images. To enhance the appearance of medical photographs, image pre-processing methods are applied, so that high accuracy can be achieved with constrained means. images are often classified based on their textural properties, which are measured using the Gray Level Co-occurrence Matrix (GLCM). The grey level correlation matrix provides statistical information of the second order on the grey levels of neighboring pixels in a picture (GLCM). In this proposed paper, medical X-ray images are classified and their features are extracted using an ensemble learning model. By extracting image features using the GLCM feature extraction method, this proposed model is able to distinguish between healthy and sick images (Gray level co-occurrence matrix).to improve the efficiency of the Ensemble learning classification method, it is compared against various algorithms using performance indicators, including Logistic regression, Gaussian Naive Bayes, as well as Random Forest. When this approach is compared to existing methods, the proposed ensemble model has an accuracy rate of 97% in classifying normal and diseased images. © 2023, ICst Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image enhancement

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