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1st FICR international conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020

1st FICR International Conference on Rising Threats in Exper...

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1st FICR international conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020

ISBN: (纸本)9789811560132

The proceedings contain 95 papers. The special focus in this conference is on Rising Threats in Expert Applications and Solutions. The topics include: A Novel User Authentication Protocol Using Biometric Data for IoT Networks;comparative study of Different machine learning Techniques in the Diagnosis of Dementia;automated Voter System by Using Fingerprint and Offline Data Set with Texture image processing;A Hybrid Cluster and PCA-Based Framework for Heart Disease Prediction Using Logistic Regression;a Deep learning Approach of Collaborative Filtering to Recommender System with Opinion Mining;Analytical Design of the DIS Architecture: The Hybrid Model;a Model for Effective Software Testing in Cloud Environment;heuristic Expert Evaluation of e-learning Application;image Query-Based Tablet Identification System by Examining Various Text recognition Classifiers;assessment of Needless Code in a Program;a Survey of Load Balanced Job Scheduling Schemes in Cloud Computing;analysis of Algorithms K-Means and Apriori for Data Mining;augmenting Cloud Service Discovery Using Ontology;Performance Analysis of AES, Blowfish and Rijndael: Cryptographic Algorithms for Audio;a Secured Cloud-Based Framework for image processing Using Ant Colony Optimization;Chili Plant Leaf Disease Detection Using SVM and KNN Classification;a Review on image Segmentation;ensemble Feature Extraction-Based Detection of Abnormal Mass Present in Medical images Using machine learning;classification of image steganography in Substitution Technique;query Caching Technique Over Cloud-Based MapReduce System: A Survey;challenges and Issues in the Existing Methodology for Dynamic Data Capturing of Ontology;contextual Information Retrieval Search Engine Challenges;a Review on American Sign Language Character recognition.

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Automated Intracranial Haemorrhage Detection and Classification using Rider Optimization with Deep learning Model 1

Automated Intracranial Haemorrhage Detection and Classificat...

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

作者： Karthik, T.S. Naziya Hussain Anushkannan, N.K. Pinnamaneni, Rajasekhar Vijayakrishna Rapaka, E. Das, Shyamali Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Andhrapradesh Surampalem India School of Computers IPS Academy Madhya Pradesh Indore India Department of ECE Kathir College of Engineering Tamilnadu Coimbatore India Department of Biotechnology Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Data Science Indian Institute of Technology Madras Tamilnadu Chennai India Karnataka Bangalore India

ISBN: (纸本)9781665460842

Intracranial haemorrhage (ICH) refers to a pathological disorder that requires quick decision-making and diagnosis. Computed tomography (CT) can be accurate and dependable diagnosis method for identifying haemorrhages. Automated recognition of ICH through CT scans with a computer-aided diagnosis (CAD) method will be useful to classify and detect the distinct grades of ICH. Due to the latest development of deep learning (DL) techniques in image processing applications, numerous medical imaging methods use it. Thus, this article develops an automated ICH detection and classification using Rider Optimization with Deep learning (ICHDC-RODL) model. The presented ICHDC-RODL technique mainly determines the presence of ICH using DL concepts. In the presented ICHDC-RODL technique, the features are generated by the use of Xtended Central Symmetric Local Binary pattern (XCS-LBP) model. Moreover, the bidirectional long short-term memory (BiLstM) method is employed for ICH diagnosis. At last, the rider optimization algorithm (ROA) is exploited for the hyperparameter tuning procedure of the BiLstM method. To demonstrate the enhanced outcomes of the ICHDC-RODL technique, a series of simulations were performed and the results are examined under various aspects. The simulation outcomes indicate the enhancements of the ICHDC-RODL technique over recent approaches. © 2022 IEEE

关键词： Computerized tomography

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Introducing AV1 Codec-Level Video steganography 21st

Introducing AV1 Codec-Level Video Steganography

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

作者： Catania, Lorenzo Allegra, Dario Giudice, Oliver stanco, Filippo Battiato, Sebastiano Univ Catania Dept Math & Comp Sci Image Proc Lab Catania Italy Bank Italy IT Dept Appl Res Team Rome Italy

ISBN: (纸本)9783031064272;9783031064265

steganography is the ancient art of concealing messages into data. High research interest has grown over the last years, however, techniques in literature are only focused on standard and in some way legacy multimedia formats (e.g., H.264). Moreover, most video steganography techniques are based on concealing data into contents of each frame employing many strategies. In this paper, a codec-level video steganography technique is presented on the novel AV1: a royalty-free video compression format proposed by the Alliance for Open Media (AOM). The proposed method is based on the alteration of intra-prediction angles and, differently from other solutions, it works along the compression process, by allowing the encoder to reduce possible distortions caused by the messages to be hidden. The effectiveness of the technique was demonstrated by hiding up to 1024 characters into an highly compressed video of 40 s maintaining an average Peak Signal-to-Noise Ratio value of 37.53 dB.

关键词： steganography Information hiding Video encoding image coding

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Predictive modeling toward identification of sex from lip prints - machine learning in cheiloscopy 4

Predictive modeling toward identification of sex from lip pr...

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Joint of the 4th international conference on Applied Informatics Workshops, ICAIW 2021, 2nd international Workshop on Applied Artificial Intelligence, WAAI 2021, 3rd international Workshop on Applied Informatics for Economy, Society, and Development, AIESD 2021, 4th international Workshop on Data Engineering and Analytics, WDEA 2021, 1st international Workshop on Knowledge Management and Information Technologies, WKMIT 2021 and 4th international Workshop on Smart and Sustainable Cities, WSSC 2021

作者： Sabelli, Agustín F. Chatterjee, Parag Pollo-Cattaneo, Ma Florencia Universidad Tecnológica Nacional Facultad Regional Buenos Aires Buenos Aires Argentina

Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip traces hold multifarious features and could be analyzed in different ways to identify the links with personal identifying features. machine learning holds strong application in this domain, especially for pattern recognition and further interpretation. This work is focused on a brief survey of existing machine learning approaches in Cheiloscopy. Also, a comparative study of predictive models has been presented based on an original dataset of lip prints where supervised models have been used to predict the biological sex of the persons using their lip traces. machine learning on one hand automatizes the identification process and poses a significant potential in analyzing huge number of lip traces with considerable accuracy. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： image processing

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Automatic Tomato Leaf Diseases Classification and recognition using Transfer learning Model with image processing Techniques 1

Automatic Tomato Leaf Diseases Classification and Recognitio...

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1st international conference on Smart Technologies Communication and Robotics, stCR 2021

作者： Hemalatha, A. Vijayakumar, J. Bharathiar University Department of Electronics and Instrumentation Tamilnadu Coimbatore India

ISBN: (纸本)9781665418065

In agriculture, manual approaches to recognize tomato plant diseases are often time-consuming. The diseases spread throughout the plant, affecting the quality and yield. In this research work, a transfer learning model (DenseNet201) was designed to conduct the symptom-wise recognition of three tomato diseases such as leaf mold, early blight, and late blight are compared with healthy leaves. The transfer learning model has been trained, tested, and validated. Our trained model can recognize the category of disease. Once the images are classified, the regions are segmented, and features are extracted using k-means clustering and ORB keypoints. The transfer learning model achieved an excellent recognition result with an average accuracy of 99.688%. © 2021 IEEE.

关键词： Fruits

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Fingerprint recognition Based on Deep learning Pre-Train with Our Best CNN Model for Person Identification 1

Fingerprint Recognition Based on Deep Learning Pre-Train wit...

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1st international conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Shinde, Krishna Kayte, C.N. Department of Computer Science & IT Dr. B. A. M. U. Aurangabad India Dept. of Digital & Cyber Forensic Science Govt. Institute of Forensic Science Dr. B.A.M.U. Aurangabad India

ISBN: (纸本)9781607685395

This study uses a deep learning method to provide a unique and quick fingerprint identification solution. In the globe, a biometric fingerprint recognition technology is the most dependable for reliable recognition. One of the most accurate biometric recognition technologies is fingerprint recognition, and in this article, we use pre-train VGG16, VGG19, and ResNet50 with imageNet wights and our best CNN model to identify human fingerprint patterns. The system including pre-processing phase where the input fingerprint images first technique apply cropping and normalize for unwanted part remove of fingerprint images and normalize its dimension, second image Enhancement for removing noise in to ridgelines and last Canny Edge Detection technique for adjustment to smooth image with gaussian to remove noise. Then apply one by one model on KVKR fingerprint dataset. Our best CNN model has automatically extracted features and RMSprop optimizer use for classification this features. This study performing experimental work of each pre-processed dataset and testing these three models with different dataset size of input train, test and validation data. The VGG16 model got a better recognition accuracy than VGG19 and ResNet50 models. © The Electrochemical Society

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Gesture based Real-Time Sign Language recognition System 1

Gesture based Real-Time Sign Language Recognition System

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1st international conference on Connected Systems and Intelligence, CSI 2022

作者： Siby, Tiya Ann Pal, Sonam Arlina, Jessica Nagaraju, Shamanth Department of Information Technology Bengaluru India Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9781665458153

Real-Time Sign Language recognition (RTSLG) can help people express clearer thoughts, speak in shorter sentences, and be more expressive to use declarative language. Hand gestures provide a wealth of information that persons with disabilities can use to communicate in a fundamental way and to complement communication for others. Since the hand gesture information is based on movement sequences, accurately detecting hand gestures in real-time is difficult. Hearing-impaired persons have difficulty interacting with others, resulting in a communication gap. The only way for them to communicate their ideas and feelings is to use hand signals, which are not understood by many people. As a result, in recent days, the hand gesture detection system has gained prominence. In this paper, the proposed design is of a deep learning model using Python, TensorFlow, OpenCV and Histogram Equalization that can be accessed from the web browser. The proposed RTSLG system uses image detection, computer vision, and neural network methodologies i.e. Convolution Neural Network to recognise the characteristics of the hand in video filmed by a web camera. To enhance the details of the images, an image processing technique called Histogram Equalization is performed. The accuracy obtained by the proposed system is 87.8%. Once the gesture is recognized and text output is displayed, the proposed RTSLG system makes use of gTTS (Google Text-to-Speech) library in order to convert the displayed text to audio for assisting the communication of speech and hearing-impaired person. © 2022 IEEE.

关键词： image enhancement

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Unsupervised Anomaly Localization Using Locally Adaptive Query-Dependent Scores 21st

Unsupervised Anomaly Localization Using Locally Adaptive Que...

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

作者： Kawamura, Naoki Toshiba Co Ltd Saiwai Ku 1 Komukai Toshiba Cho Kawasaki Kanagawa 2128582 Japan

ISBN: (纸本)9783031064302;9783031064296

We propose a framework of reducing falsely-high anomaly scores on the unsupervised anomaly localization method. In recent years, the performance of unsupervised anomaly detection has been improved significantly by using pretrained deep features. A pretrained feature extractor can make appropriate descriptions for unsupervised anomaly detection, with extractors using embedding-based methods outperforming conventional deep-learning-based extractors. However, the intra-class variance of anomaly-free patterns has not been well considered yet by the existing methods while it is important to prevent misdetection and achieve more accurate localizations. In this paper, a framework is proposed that addresses this problem by leveraging the subtraction of deep pretrained features among anomaly-free images. The anomaly scores at the locations with higher intra-class variances are reduced through a novel and simple framework to tackle with misdetection. The experimental results using the standard industrial dataset show the outperformances of the proposed method especially for the overdetection reductions.

关键词： Unsupervised anomaly detection and localization Out of distribution

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Vision-Based Autonomous Car Brake and steering Assistance Using Deep learning

Vision-Based Autonomous Car Brake and Steering Assistance Us...

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AIML-Applications for Engineering & Technology (ICAET), international conference on

作者： Anees H Pooja Agrawal School of Robotics Defence institute of advanced technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies.

关键词： YOLO Instance segmentation Deep learning Visualization Accuracy Simulation Real-time systems Road safety Automobiles Autonomous vehicles

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On Topological Analysis of Cells Organization in Biological images 1st

On Topological Analysis of Cells Organization in Biological ...

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1st IAPR international Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Jimenez, Maria-Jose Univ Seville Dept Matemat Aplicada 1 Seville Spain

ISBN: (纸本)9783030766566;9783030766573

There are many problems inside the field of biomedical image analysis that can be dealt from a topological (and geometric) point of view. One of them refers to the way in which cells are self-organised inside a tissue, mainly motivated by the changes that may occur in such an organization in case of disease. This problem can be faced from different perspectives in terms, first, of how to model the cells and their 'connections' and second, how to computationally characterise their organization. We will discuss some topological approaches to this topic as well as future lines of research.

关键词： Topological data analysis Persistent homology Epithelial organization

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