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6th International Conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

作者： Joshi, Neeraj Martha, Sheshikala Chaudhary, Shivam Consul, Prakhar Singh, Sushil Kumar SCSET Bennett University Uttar Pradesh Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computer Engineering Marwadi University Gujrat Rajkot India

ISBN: (纸本)9783031530845

This paper provides you with a description of this new approach for the security of ATM transactions for its subscribers. Looking towards the banking sector, ensuring the safety and security in Automatic Teller Machine (ATM) is a critical factor for ATM transactions. This paper gives a review in brief about the existing literature survey on security arrangements, types of attacks of ATM transaction, together with IR sensors, bio metric identification like (Fingerprint sensors, Iris scanner), modified encryption standards, live monitoring and OTP (One Time Password) notifications, and systems based on IOT applications. This research paper also spotted the restrictions and obstructions of the ongoing security measures, probable threats, types of attacks in transactions and recommends some areas for future research. The insights gained from this research paper can surely be helpful for improving the security system in ATMs and in upgrading the safety measure of overall ATM transactions. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Security systems

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2nd International Conference on Emerging Trends in Expert applications and Security, ICETEAS 2023

2nd International Conference on Emerging Trends in Expert Ap...

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2nd International Conference on Emerging Trends in Expert applications and Security, ICETEAS 2023

ISBN: (纸本)9789819919086

The proceedings contain 48 papers. The special focus in this conference is on Emerging Trends in Expert applications and Security. The topics include: Securing Account Hijacking Security Threats in Cloud Environment Using artificial neural networks;ioT-Based Smart Pill Box and Voice Alert System;early Identification of Plant Diseases by image processing Using Integrated Development Environment;usability Attributes and Their Mapping in Various Phases of Software Development Life Cycle;controlling Devices from Anywhere Using IoT Including Voice Commands;ioT-Based Air Quality Monitoring System;modeling of Order Quantity Prediction using Soft Computing Technique: A Fuzzy Logic Approach;development of Classification Framework Using Machine Learning and Pattern Recognition System;Human Part Semantic Segmentation Using Custom-CDGNet Network;current Web Development Technologies: A Comparative Review;effect on Compressive Strength of Portland Pozzolana Cement on Adding Admixtures Using Machine Learning Technique;tech Track for Visually Impaired People;intelligent Compression of Data on Cloud Storage;diagnosis of Diabetic Retinopathy Using Deep neural Network;multi-parameter Sensor-Based Automation Farming;comparing Ensemble Techniques for Bilingual Multiclass Classification of Online Reviews;detection of Disease in Liver image Using Deep Learning Technique;how to Quantify Software Quality Factors for Mobile applications?: Proposed Criteria;dysgraphia Detection Using Machine Learning-Based Techniques: A Survey;Designing AI for Investment Banking Risk Management a Review, Evaluation and Strategy;R-Peak-Based Arrhythmia Detection as an Impact of COVID-19;a Neutrosophic Cognitive Maps Approach for Pestle Analysis in Food Industry;assistive Agricultural Technology—Soil Health and Suitable Crop Prediction;smart Chatbot for Guidance About Children’s Legal Rights.

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Multimodal Quanvolutional and Convolutional neural networks for Multi-Class image Classification

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BIG DATA AND COGNITIVE COMPUTING 2024年第7期8卷 75页

作者： Gordienko, Yuri Trochun, Yevhenii Stirenko, Sergii Natl Tech Univ Ukraine Fac Informat & Comp Sci Igor Sikorsky Kyiv Polytech Inst UA-03056 Kyiv Ukraine

By utilizing hybrid quantum-classical neural networks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical operations. This is particularly relevant in sustainable applications where reducing computational resources and energy consumption is crucial. This study explores the feasibility of a novel architecture by leveraging quantum devices as the first layer of the neural network, which proved to be useful for scaling HNNs' training process. Understanding the role of quanvolutional operations and how they interact with classical neural networks can lead to optimized model architectures that are more efficient and effective for image classification tasks. This research investigates the performance of HNNs across different datasets, including CIFAR100 and Satellite images of Hurricane Damage by evaluating the performance of HNNs on these datasets in comparison with the performance of reference classical models. By evaluating the scalability of HNNs on diverse datasets, the study provides insights into their applicability across various real-world scenarios, which is essential for building sustainable machine learning solutions that can adapt to different environments. Leveraging transfer learning techniques with pre-trained models such as ResNet, EfficientNet, and VGG16 demonstrates the potential for HNNs to benefit from existing knowledge in classical neural networks. This approach can significantly reduce the computational cost of training HNNs from scratch while still achieving competitive performance. The feasibility study conducted in this research assesses the practicality and viability of deploying HNNs for real-world image classification tasks. By comparing the performance of HNNs with classical reference models like ResNet, EfficientNet, and VGG-16, th

关键词： artificial intelligence neural network quanvolutional neural networks convolutional neural networks image classification multi-class image classification CIFAR100 satellite image processing

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Investigation of Deep Learning architectures and features for Adversarial Machine Learning Attacks in Modulation Classifications 14

Investigation of Deep Learning architectures and features fo...

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14th IEEE image, Video, and Multidimensional Signal processing Workshop (IVMSP)

作者： Aristodemou, Marios Lambotharan, Sangarapillai Zheng, Gan Aristodemou, Leonidas Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough Leics England Univ Cambridge IIPM Lab CTM IfMDept Engn Cambridge England Katholieke Univ Leuven Dept Management Strategy & Innovat MSI Leuven Belgium

ISBN: (纸本)9781665478229

artificial intelligence (AI), and specifically machine and deep learning, are emerging as essential enabling techniques for the design of future generations of wireless networks for the Internet of Things (IoT). With the exponential increase in IoT devices in recent years, AI methods have become even more beneficial in network management including for energy efficiency, spectrum utilisation and user admission control. However, the rise of AI applications, has also caused a rise in cyber-attacks, where attackers can exploit network vulnerabilities through its use. In this research, we develop a deep learning approach for Automatic Modulation Classification (AMC) with three different feature combinations, using batch normalisation and optimised with the focal loss. Our results have implications about the performance of the AMC against adversarial examples. Firstly, we find that using batch normalisation enhances the classifier's performance against adversarial examples. Secondly, by generating white-box fast gradient sign method attacks, we show that using the phase as an input feature improves the performance of the AMC against adversarial examples.

关键词： Deep learning Multidimensional signal processing Wireless networks neural networks Modulation Frequency response Entropy

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Attention-based Transfer Learning Approach using Spatial Pyramid Pooling for Diagnosis of Polycystic Ovary Syndrome 9

Attention-based Transfer Learning Approach using Spatial Pyr...

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9th International Conference on Signal processing and Communication, ICSC 2023

作者： Sahu, Geet Karnati, Mohan Rajput, Ayush Singh Chaudhary, Mayank Maurya, Ritesh Dutta, Malay Kishore Amity Centre for Artificial Intelligence Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350383201

A considerable percentage of pregnant women are affected by PCOS, or polycystic ovarian syndrome, a metabolic condition. It causes higher amounts of androgens, or male hormones, and irregular menstrual periods. Follicles, which are tiny sacs packed with fluid that form on the ovaries and impede the regular release of eggs, are a common side effect of PCOS. Initial detection and weight control might help prevent the detrimental effects of PCOS, even though the exact aetiology is still unknown. With the help of the Spatial Pyramid Pooling Network (ASPPNet), a novel attention-based transfer learning approach for PCOS identification from ultrasound pictures is developed in this work. Initially, ResNet-18 is used to extract fundamental features, and the attention module is applied to extract significant disease-related features. Later, the Spatial pyramid pooling technique is utilized to preserve multi-scale features by adopting a fixed-length illustration that is not affected by image size/scale. Spatial pyramid pooling is additionally resistant to image component distortions. The experimental results demonstrate that the ASPPNet exceeds other well-known algorithms with a 98.79% accuracy, demonstrating its applicability in both medical and industrial applications. © 2023 IEEE.

关键词： Deep neural networks

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Source localization in resource-constrained sensor networks based on deep learning

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neural COMPUTING & applications 2021年第9期33卷 4217-4228页

作者： Javadi, S. Hamed Guerrero, Angela Mouazen, Abdul M. Univ Ghent Fac Biosci Engn Dept Environm B-9000 Ghent Belgium

Source localization with a network of low-cost motes with limited processing, memory, and energy resources is considered in this paper. The state-of-the-art methods are mostly based on complicated signal processing approaches in which motes send their (processed) data to a fusion center (FC) wherein the source is localized. These methods are resource-demanding and mostly do not meet the limitations of motes and network. In this paper, we consider distributed detection where each mote performs a binary hypothesis test to detect locally the existence of a desired source and sends its (potentially erroneous) decision to FC during just one bit (1 indicates source existence and 0 otherwise). Hence, both processing and bandwidth constraints are met. We propose to use an artificial neural network (ANN) to correct erroneous local decisions. After error correction, the region affected by the source is specified by nodes with decision 1. Moreover, we propose to localize the source by deep learning in FC which converts the network of decisions 1 and 0 to a black and white image with white pixels in the locations of motes with decision 1. The proposed schemes of error correction by ANN (ECANN) and source localization with deep learning (SoLDeL) were evaluated in a fire detection application. We showed that SoLDeL performs appropriately and scales well into large networks. Moreover, the applicability of ECANN in delineation of farm management zones was illustrated.

关键词： artificial neural network (ANN) Decentralized detection Deep learning Error type I Error type II Internet of things (IoT) Source localization Target tracking Wireless sensor networks (WSN)

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3D image Analysis of Sports Technical Features and Sports Training Methods Based on artificial Intelligence

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JOURNAL OF TESTING AND EVALUATION 2023年第1期51卷 189-200页

作者： Mei, Zhu Shenyang Jianzhu Univ Minist Sports Shenyang 110168 Liaoning Peoples R China

This article studies the three-dimensional (3D) image analysis and sports training methods of sports technical characteristics. This research uses the current sports technology diagnostic 3D video analysis system as a platform to build a database and knowledge base based on athletes' 3D sports information and sports parameters and uses algorithms based on artificial intelligence machine-learning machines to analyze sports data, learn from it, and learn from sports technology. Actions make analytical decisions and predictions. Then, it analyzes the human-motion behavior with the concept of traditional and virtual reality technology. The effectiveness of athletes' technical movements, using mathematical statistics, artificial intelligence, and other research methods, integrates and draws on the research methods of sports biomechanics, graphical imaging, human anatomy, expert systems, and neural networks. A neural network not only inherits certain characteristics of biology but also has its own unique characteristics, such as large-scale parallel processing, strong fault tolerance, and self-learning functions. neural networks have a wide range of applications in information processing, pattern recognition, optimization, and other issues. By analyzing the application status of artificial intelligence technology in sports, the development prospects of sports training based on artificial intelligence can be inferred. Based on the acquisition of sports-related data, the evaluation of functional action modes, sports techniques, etc., is established. The multi-target feedback training method ultimately helps athletes improve their training level. Experimental data show that for the human body walking toward the camera, the rotation angle between adjacent frames is close to 0 degrees, and the translational position is basically 5 cm. The experimental results show that 3D image analysis and related sports training methods based on specific sports technical characteristics are

关键词： 3D image analysis artificial intelligence sports technical features sports training multi-target feedback training sports data

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Geophysics-steered self-supervised learning for deconvolution

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GEOPHYSICAL JOURNAL INTERNATIONAL 2023年第1期234卷 40-55页

作者： Chai, Xintao Yang, Taihui Gu, Hanming Tang, Genyang Cao, Wenjun Wang, Yufeng China Univ Geosci Sch Geophys & Geomat Hubei Subsurface Multiscale Imaging Key Lab Team Geophys Constrained Machine Learning Seism Da Wuhan 430079 Hubei Peoples R China China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102200 Peoples R China State Key Lab Shale Oil & Gas Enrichment Mech & Ef Beijing 100083 Peoples R China Sinopec Key Lab Seism Elast Wave Technol Beijing 100083 Peoples R China China Univ Petr East China Key Lab Deep Oil & Gas Qingdao 266580 Shandong Peoples R China

Deep learning (DL) has achieved remarkable progress in geophysics. The most commonly used supervised learning (SL) framework requires massive labelled representative data to train artificial neural networks (ANNs) for good generalization. However, the labels are limited or unavailable for field seismic data applications. In addition, SL generally cannot take advantage of well-known physical laws and thus fails to generate physically consistent results. The weaknesses of standard SL are non-negligible. Therefore, we provide an open-source package for geophysics-steered self-supervised learning (SSL;taking application to seismic deconvolution as an example). With the wavelet given, we incorporate the convolution model into the loss function to measure the error between the synthetic trace generated by the ANN deconvolution result and the observed data, steering the ANN's learning process toward yielding accurate and physically consistent results. We utilize an enhanced U-Net as the ANN. We determine a hard threshold operator to impose a sparse constraint on the ANN deconvolution result, which is challenging for current DL platforms because no layer is available. 2-D/3-D ANNs can naturally introduce spatial regularization to the ANN deconvolution results. Tests on synthetic data and 3-D field data with available well logs verify the effectiveness of the proposed approach. The approach outperforms the traditional trace-by-trace method in terms of accuracy and spatial continuity. Experiments on synthetic data validate that sparsity promotion matters for sparse recovery problems. Field data results of the proposed approach precisely identify the layer interfaces and mostly match well with the log. All codes and data are publicly available at (Xintao Chai).

关键词： image processing Inverse theory neural networks fuzzy logic

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RGB Based Secure Share Creation in Steganography with ECC and DNN

RGB Based Secure Share Creation in Steganography with ECC an...

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International Conference on Advances and applications of artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Ahmad, S. Abidi, M.R. Department of Electronics Engineering Z.H.C.E.T. AMU Aligarh Aligarh202002 India

ISBN: (纸本)9789811948305

image steganography is a technique for concealing sensitive data inside an image. Since traditional image steganography encrypts sensitive data in the host image, the payload capability is often disregarded, and steganographic image quality for the Human Visual System (HVS) must be improved. As a result, we proposed a new deep learning-based multiple image steganography approach in this work. The secret image is embedded into the cover image using a Deep neural Network (DNN), and the embedded cover image is then encrypted using Elliptic Curve Cryptography (ECC) is used to improve the image’s anti-detection properties. The Deep neural Network is a combination of Pre-processing, Hiding, and Revealing networks that enable steganography and extraction of full-size images to enhance steganographic performance. The results of the experiments show that the network can successfully embed up to three secret images into the cover image without causing a major difference in the Structural Similarity Index (SSIM). Furthermore, the image created using this steganography method has higher SSIM and Peak Signal to Noise Ratio (PSNR) values, with values of 0.99 and 35 dB, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Improving accuracy in Text Extraction From images using Region-Based Convolutional neural networks algorithm compared to Convolutional neural Network algorithm

Improving accuracy in Text Extraction From images using Regi...

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artificial Intelligence and Data Engineering (AIDE), International Conference on

作者： Navaprakash. N S. Vikram Reddy S. Dakshinesh Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Department of Embedded System Saveetha School of Engineering SIMATS Chennai

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Text extraction is a critical task in data processing and analysis, requiring high accuracy for effective applications. This research employed a publicly accessible Kaggle dataset to assess the performance of two neural network architectures that are Region-Based Convolutional neural networks (R-CNN) with that of standard Convolutional neural networks (CNN). A total sample of 40 test images were taken and segregated into groups, with 20 samples allocated to each method. Implementation was conducted using Python, and statistical analysis was performed using ***. The study set a statistical power of 85%, with alpha level of 0.05 and 0.2 beta level set to ensure a thorough analysis. Performance was assessed based on accuracy, with R-CNN achieving 96.461%, significantly outperforming CNN, which recorded an accuracy of 83.583%. The statistical comparison yielded a two-tailed significance value of p < 0.001, highlighting the superior efficiency of R-CNN in text extraction tasks. These findings underscore the potential of R-CNN as a preferred approach for achieving higher accuracy in text extraction applications.

关键词： image segmentation Accuracy Text recognition Statistical analysis Optical character recognition Education neural networks Symbols Convolutional neural networks Standards

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