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A Robust Image Registration Using Multi-Scale Feature Extraction Techniques Using Deep learning 4

A Robust Image Registration Using Multi-Scale Feature Extrac...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Madasamy, Sathiyakeerthi Nelson Kennedy Babu, C. Sumathi, V. Chokkalingam, Arun Pilvi Systems Inc Solution Architect LewisvilleTX75057 United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sai Ram Engineering College Department of Mathematics Tamil Nadu Chennai44 India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

Image registration is a significant assignment in computer visualization and medical imaging, which involves aligning two or more descriptions of the same division or object taken from different viewpoints or at dissimilar epochs. Deep learning-based approaches have recently demonstrated promising outcomes in several image-processing applications, including image registration. Using multi-scale feature extraction methods and deep learning, the study provides a reliable image registration method in this study. three primary steps make up the suggested method: feature extraction, feature matching, and transformation estimation. A deep convolutional neural network (CNN) is utilized in the feature extraction process to extract multi-scale information from the input images. the CNN architecture is built to manage large-scale deformations by extracting features at various scales. Following feature extraction, a strong matching technique based on the nearest neighbor distance ratio test is utilized to match the extracted features. the proposed method uses the Random Sample Consensus (RANSAC) algorithm to estimate the transformation parameters robustly, which helps to handle outliers and noise in the feature-matching process. the proposed method is assessed on several benchmark datasets, including the Eth-80 dataset and the TUM RGB-D dataset, and compared with state-of-the-art approaches. the investigational consequences demonstrate that the proposed method outstrips prevailing approaches in terms of accuracy and robustness, especially for images with large-scale deformations. the proposed technique also demonstrates good performance in the presence of noise and outliers, which further demonstrates its robustness. © 2023 IEEE.

关键词： Feature extraction

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learning Algorithms for Breast Cancer Classification and Diagnosis 1

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4th international conference on Optimization learning Algorithms and applications

作者： Miranda Valentin, Ana Beatriz Bressan, Glaucia Maria Lizzi, Elisangela Ap da Silva Lopes, Fabricio Martins Univ Tecnol Fed Parana UTFPR Grad Program Bioinformat Ave Alberto Carazzai 1640 BR-86300000 Cornelio Procopio PR Brazil

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

the comprehension about the key features of breast tumors that lead to their classification as benign or malignant is fundamental to improving the detection and diagnosis of breast cancer, contributing significantly to survival rates and treatment effectiveness. this study proposes a multidisciplinary approach that combines analytical methods and graphical visualizations to classify breast tumors as benign or malignant, using supervised and unsupervised learning algorithms. the adopted dataset in this study is from the repository of the University of Wisconsin (USA). It comprises 569 breast tumor biopsy samples, with 32 features measured from digitized images of biopsy slides. Initially, for unsupervised learning, Pearson correlation was used as a similarity metric for hierarchical grouping, resulting in the formation of six clusters through a dendrogram. In supervised learning, the Principal Component Analysis (PCA) technique was performed to reduce the number of features, m achieving the 10 most relevant. the Support Vector machine (SVM) model was applied with and without the PCA results. the comparison between hierarchical grouping and SVM methods demonstrated a notable advantage of SVM in terms of accuracy in classifying breast tumors. the use of cross-validation showed the superiority of SVM over clustering for this specific purpose. the analysis of breast tumor features and the classification approaches offer important perspectives on improving breast cancer diagnosis and treatment practices. the use of classifiers such as SVM, together with dimensionality reduction techniques such as PCA, can result in significant improvements in diagnostic accuracy and effectiveness, directly benefiting patient care in this critical area of medicine.

关键词： Breast cancer Tumor classification Feature selection efficient diagnosis

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Defect Detection in Printed Circuit Boards Based on EdgeML and computer Vision 4

Defect Detection in Printed Circuit Boards Based on EdgeML a...

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4th international conference on Intelligent Cybernetics Technology and applications, ICICyTA 2024

作者： Rocha, Felipe G. F. Silva, Hyago V. L. B. Vimieiro, Rodrigo B. De Figueiredo, Felipe A. P. Inatel Wai Lab Santa Rita do Sapucaí Brazil São Carlos School of Engineering University of São Paulo São Carlos Brazil

ISBN: (纸本)9798331506490

the production of electronic boards is a common activity in the industrial environment, and ensuring their quality is essential for obtaining reliable products. this work presents a comparative study of the performance of three machine learning architectures: YoloV8, FOMO, and MobileNet. the dataset was created based on four classes for assembly fault detection. the model that achieved the best results was FOMO, with precision, recall, and Fl-Score above 95 %, as well as processing 10.5 frames per second and having a total size of 152 kB. © 2024 IEEE.

关键词： Adversarial machine learning

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Explainable Process Deviance Discovery with Data-Efficient Deep learning 4th

Explainable Process Deviance Discovery with Data-Efficient ...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Folino, Francesco Folino, Gianluigi Guarascio, Massimo Pontieri, Luigi Rende87036 Italy

ISBN: (纸本)9783031812408

Detecting deviant execution instances of a business process is an important issue in modern enterprises and organizations. Recently, learning deep Deviance Detection Models (DDMs) out of process traces via (semi-)supervised methods was shown to outperform traditional approaches based on standard machine learning solutions. However, deep models need to be trained with large enough amounts of data, which may not be available in several real-life settings, especially in Green-AI applications where restrictions to data access and data processing operations must be obeyed. To better suit such settings, a novel approach for discovering a deep DDM is proposed that compensates for the scarcity of training data by leveraging a complementary self-supervised learning task. Besides minimizing the deviance classification loss, the reconstruction loss of an auxiliary auto-encoder sub-net is exploited as an additional (self-)supervision signal. For the sake of explanation, the neural network is provided with a flat pattern-based encoding, on top of which two parallel stacks of feature-representation layers are learnt efficiently and robustly by leveraging residual-like skip connections. the approach effectively deals with the challenging combination of data-efficiency and explainability requirements on a case study concerning the execution traces of a real-life process. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Self-Renewal machine learning Approach for Fast Wireless Network Optimization 20

Self-Renewal Machine Learning Approach for Fast Wireless Net...

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20th IEEE international conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ajayi, Oluwaseun T. Cao, Xianghui Shan, Hangguan Cheng, Yu Illinois Institute of Technology Department of Electrical and Computer Engineering Chicago60616 United States Southeast University School of Automation Nanjing210096 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9798350324334

the throughput maximization in multi-hop wireless networks is largely limited by interference due to the reuse of the channel resources. Although machine learning (ML) can accelerate the optimization of wireless network capacity, the existing system can become limited because of insufficient knowledge from available data. We propose a self-renewal ML (SRML) method that incrementally improves the throughput maximization of future optimization instances through the design of a data selection algorithm for scheduling structure classification and application identification model retraining. With one round of implementation, the SRML method outperforms the fixed ML (FML) method, random (RAND) method and Greedy Heuristic method in the multi-commodity flow deployment setting with an average achievable throughput of 100% for small flows and at least 79% for large flows, relative to the delayed column generation (DCG) benchmark algorithm, while reducing the computational complexity and achieving a high solution efficiency. By leveraging the transfer learning of parameters during self-renewal, the computational cost of model training is reduced by at least 71.09%. © 2023 IEEE.

关键词： Heuristic methods

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Effective Prediction Analysis for Cardiovascular Using Various machine learning Algorithms 4th

Effective Prediction Analysis for Cardiovascular Using Vario...

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4th international conference on computer and Communication Technologies, IC3T 2022

作者： Shanmuga Sundari, M. Dyva Sugnana Rao, M. Kumar, Ch Anil BVRIT HYDERABAD College of Engineering for Women Bachupally Hyderabad India

ISBN: (纸本)9789811985621

Healthy life leads to healthy growth in a human’s life. Health is more important than all other things in our life. the heart is the backbone of our life. Many types of research are going on in the medical field that finds new treatments in the healthcare industry. machine learning is an important technology that helps predict the accuracy of disease. machine learning takes the attributes from the health industry and analyzes the data using many algorithms. Based on the trained data, the experiment will explore the analysis prediction in the given dataset. It helps in many ways for humans in their life. In this research, we predicted cardiovascular disease before the early stage. We collected healthcare data and applied various machine learning algorithms. We analyzed the accuracy of the support vector machine (SVM), logistic regression (LR), and stochastic gradient descent (SGD) algorithms. Finally, research proves support vector machines as the best algorithm for predicting cardiovascular disease in the early stage of our lifespan. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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GAN-Based Data Augmentation for learning Behavior Analysis in MOOCs 4th

GAN-Based Data Augmentation for Learning Behavior Analysis ...

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4th international conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Zouleikha, Betaitia Aida, Chefrour Samia, Drissi Computer Science Department Science and Technology Faculty Mohamed Cherif Messaadia University Souk Ahras Algeria LISCO Laboratory Badji Mokhtar University Annaba Algeria LiM Laboratory Faculty of Science and Technology Souk Ahras University Souk Ahras Algeria

ISBN: (纸本)9783031733437

the analysis of learning behavior in Massive Open Online Courses (MOOCs) holds immense potential for personalized learning and improving educational outcomes. While several MOOC datasets exist, such as KDD 2015 and Coursera, the learning Behavior Analytics Dataset presents a unique and rich source of information, encompassing diverse aspects of learner interaction and engagement. However, the limited size of this dataset poses a significant challenge for applying deep learning techniques, which typically require large amounts of data for effective training. To address this data scarcity issue, we propose the application of Generative Adversarial Networks (GANs) for data augmentation. By learning the underlying distribution of the original dataset, GANs can generate synthetic data points that retain the characteristics and patterns of the real data. Augmenting the learning Behavior Analytics Dataset with these synthetic samples allows for the training of more robust and generalizable deep learning models, enabling in-depth analysis of learning behaviors and unlocking new possibilities for personalized interventions and adaptive learning systems within MOOC environments. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Forecasting Click Fraud via machine learning Algorithms 4th

Forecasting Click Fraud via Machine Learning Algorithms

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4th international conference on Codes, Cryptology and Information Security, C2SI 2023

作者： Sahllal, Nadir Souidi, El Mamoun Faculty of Sciences Laboratory of Mathematics Computer ScienceApplications and Information Security Mohammed V University in Rabat BP 1014 RP Rabat10000 Morocco

ISBN: (纸本)9783031330162

Click fraud, the manipulation of online advertisement traffic figures, is becoming a major concern for businesses that advertise online. this can lead to financial losses and inaccurate click statistics. To address this problem, it is essential to have a reliable method for identifying click fraud. this includes distinguishing between legitimate clicks made by users and fraudulent clicks generated by bots or other software, which enables companies to advertise their products safely. the XGBoost model was trained on the TalkingData AdTracking Fraud Detection dataset from Kaggle, using binary classification to predict the likelihood of a click being fraudulent. the importance of data treatment was taken into consideration in the model training process, by carefully preprocessing and cleaning the data before feeding it into the model. this helped to improve the accuracy and performance of the model by reaching an AUC of 0.96 and LogLoss of 0.15. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation 4

qgym: A Gym for Training and Benchmarking RL-Based Quantum C...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Van Der Linde, Stan De Kok, Willem Bontekoe, Tariq Feld, Sebastian Applied Cryptography & Quantum Algorithms The Hague Netherlands Bernoulli Institute University of Groningen Faculty of Science and Engineering Groningen Netherlands QuTech Delft University of Technology Quantum & Computer Engineering Delft Netherlands

ISBN: (纸本)9798350343236

Compiling a quantum circuit for specific quantum hardware is a challenging task. Moreover, current quantum computers have severe hardware limitations. To make the most use of the limited resources, the compilation process should be optimized. To improve currents methods, Reinforcement learning (RL), a technique in which an agent interacts with an environment to learn complex policies to attain a specific goal, can be used. In this work, we present qgym, a software framework derived from the OpenAI gym, together with environments that are specifically tailored towards quantum compilation. the goal of qgym is to connect the research fields of Artificial Intelligence (AI) with quantum compilation by abstracting parts of the process that are irrelevant to either domain. It can be used to train and benchmark RL agents and algorithms in highly customizable environments. © 2023 IEEE.

关键词： Reinforcement learning

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An Effective Diagnosis of Alzheimer's Disease with the Use of Deep learning based CNN Model 7

An Effective Diagnosis of Alzheimer's Disease with the Use o...

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7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sharmili, K.C. Suja, G.P. Pandian, E. Walid, Md. Abul Ala Arunachalam, Sripriya Babu, G.Charles Computer Science Stella Maris College 17 Cathedral Road Chennai India Computer Science Muslim Arts College Thiruvithancode629174 India Sri Muthukumaran Institute of Technology Mangadu Master of Computer Application Chennai69 India Department of Computer Science and Engineering Khulna9203 Bangladesh Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai600 117 India GRIET Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

In recent years, Alzheimer's disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer's disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer's disease. those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. this research presents the findings and analysis of many machine learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. the proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin. © 2023 IEEE.

关键词： Convolutional neural networks

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