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machine learning Based Method for Quantifying the Security Situation of Wireless Data Networks 4th

Machine Learning Based Method for Quantifying the Security S...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Xu, Jie Gansu Medical College Pingliang744000 China

ISBN: (纸本)9783031200984

Wireless data networks are vulnerable to Trojans, viruses, malware and other attacks, resulting in important data being leaked and tampered with. therefore, a quantitative evaluation method of wireless data network security situation based on machine learning is proposed. Based on the principles of independence, completeness and scientificity, network security situation assessment indicators are selected, combined with CVSS scoring standards, and network security situation assessment indicators are quantitatively processed to obtain time series of wireless data networks. Based on machine learning and support vector machine algorithm, the process of security situation quantification is established and the parameters of support vector machine algorithm are optimized. Experimental results show that the minimum delay of the proposed method is 4 s, the result is consistent with the actual result, and the minimum error is 5.6%, which fully proves the effectiveness of the proposed method. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network security

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Using Deep learning Systems for Imaging Methods for Recognising Brain Tumors 4

Using Deep Learning Systems for Imaging Methods for Recognis...

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4th international conference on Big Data Analytics and Practices, IBDAP 2023

作者： Sumon, Monjurul Islam Tanvir Chowdhury, Md Hayat, Syed Salman Tagwar, Mahir Rahman, Habibur Hossain, Md. Sabbir Hasan, Mahamudul Jabid, Taskeed Islam, Maheen East West University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350300192

Uncontrolled and abnormal cell proliferation is the cause of a brain tumor, a type of cancer. the healthcare industry has benefited from recent advancements in deep learning by using diagnostic imaging to identify various disorders. Task If you want to learn about images or identify images, CNN is the machine learning algorithm you should use. In a similar vein, we have used the convolutional neural network (CNN) method, Data Augmentation, and Image Processing to determine whether or not images from brain MRI scans show cancer. Using the transfer learning method, we compared the performance of our shredded CNN model to that of the already trained Xception, which ResNet-50, and Inception-v3 models. Our model achieved a perfect score of 100% on a sizable dataset, demonstrating its high accuracy and low complexity, while Xception, ResNet-50, and Inception-V3 only managed to achieve 99.3%, 98.4%, and 99.4% accuracy, respectively. In comparison to other pre-trained models, our model only requires a little amount of processing resources and achieves substantially higher accuracy levels. © 2023 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of Hybrid Deep learning Techniques for COVID-19 Detection based on Cough Sound Analysis 4

A Comparative Study of Hybrid Deep Learning Techniques for C...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Polaki, Ramya Annamalai, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Computer Science and Engineering-AI Chennai India

ISBN: (纸本)9798350306118

the COVID-19 pandemic, produced by the SARS-CoV-2 virus, has severe global consequences, resulting in substantial loss of life and posing a serious threat globally. Cough is a common sign and consequence of COVID-19. Cough sound analysis has the ability to help determine an individual's COVID-19 status. Using deep learning models, this study aims to improve the accuracy of COVID-19 identification based on cough sounds. the work employs twelve separate deep-learning models that were extensively trained on the COUGHVID dataset such as CNN, LSTM, BiLSTM, CNN-LSTM, CNN-BiLSTM with SGD, and Adamax optimizer, Attention-based CNN-LSTM with Adamax, SGD and RMSProp optimizer, Attention-based CNN-BiLSTM Adamax, SGD and RMSProp optimizer. To overcome class imbalance, procedures such as pitch shifting and time-frequency masking are used to increase the positive class. Among these variants, the integration of an attention mechanism model with a convolutional neural network (CNN), a bidirectional long short-term memory (Bi-LSTM) with Adamax optimizer achieved the highest validation accuracy, reaching 95.34%, precision 94.40 %, recall 95.50%, Fl-score 94.44%. © 2023 IEEE.

关键词： Coronavirus

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249709

the proceedings contain 113 papers. the special focus in this conference is on Applied Technologies. the topics include: Automatic Evaluation of Physiotherapy Activities Using Deep learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using Data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital Image Processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical Image Segmentation;access Control through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science Database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image Processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised machine learning Model Using Facial Recognition;crime Data Analysis Using machine learning Models;Gene therapy as a Solution to Genetic Diseases through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives through the Production of Cyclopentenol.

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Recognizing Indian Languages Speech Sound using Transfer learning Approach 4

Recognizing Indian Languages Speech Sound using Transfer Lea...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Shah, Riya Patel, Milin Joshi, Barkha M. Shah, Jayna Roy, Ronak Sardar Vallabhbhai Patel Institute of Technology Department of Computer Engineering Gujarat Vasad India

ISBN: (纸本)9798350300093

there is a lot of variation in Indian languages' pronunciation, dialects, and accents from one place to the next, making voice detection a difficult process. Deep learning-based methods have made significant progress in voice recognition in recent years. the lack of labeled data in Indian languages, however, makes it difficult to train deep neural networks from scratch. this research study investigates the feasibility of using transfer learning, a method that involves applying the expertise gained from a previously trained model to a new task, for the identification of speech sounds in Indian languages. To extract characteristics from voice signals in Indian languages, this study has used pre-trained models trained on large-scale speech recognition datasets in English and other languages. these models are then fine-tuned using a minimal quantity of labeled data in the target language. the obtained results demonstrate that, despite the scarcity of labeled data available for Indian languages, state-of-the-art performance can be achieved using transfer learning-based techniques to speech sound identification tasks. this research shows how transfer learning may be used to overcome the problem of insufficient labeled data in voice recognition tasks involving Indian languages. © 2023 IEEE.

关键词： Speech recognition

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4th Congress on Intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on Intelligent Systems, CIS 2023

ISBN: (纸本)9789819990429

the proceedings contain 35 papers. the special focus in this conference is on Intelligent Systems. the topics include: Track learning Agent Using Multi-objective Reinforcement learning;performance Assessment of Gaussian Filter-Based Image Fusion Algorithm;a Cognitive Comparative Analysis of Geometric Shape-Based Cryptosystem;a Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer;energy Optimization of Electronic Vehicle Using Blockchain Method;pattern Recognition: An Outline of Literature Review that Taps into machine learning to Achieve Sustainable Development Goals;novel Approach for Stock Prediction Using Technical Analysis and Sentiment Analysis;visualizing and Exploring the Dynamics of Optimization via Circular Swap Mutations in Constraint-Based Problem Spaces;An Approach to Increase the Lifetime of Traditional LEACH Protocol Using CHME-LEACH and CHP-LEACH;denseMammoNet: An Approach for Breast Cancer Classification in Mammograms;aspect-Based Sentiment Classification Using Supervised Classifiers and Polarity Prediction Using Sentiment Analyzer for Mobile Phone Tweets;microbial Metabolites and Recent Advancement;design of a 3D-Printed Accessible and Affordable Robotic Arm and a User-Friendly Graphical User Interface;profit Maximization of a Wind-Integrated System by V2G Method;detection of Partially Occluded Area in Images Using Image Segmentation Technique;application of IP Network Modeling Platforms for Cyber-Attack Research;enhancing Information Integrity: machine learning Methods for Fake News Detection;Optimum Selection of Virtual machine in Cloud Using Improved ACO;data Imputation Using Artificial Neural Network for a Reservoir System;depth Multi-modal Integration of Image and Clinical Data Using Fusion of Decision Method for Enhanced Kidney Disease Prediction in Medical Cloud;an Efficient Prediction of Obstructive Sleep Apnea Using Hybrid Convolutional Neural Network.

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Deep learning-Based Heartbeat Classification of 12-Lead ECG Time Series Signal 4

Deep Learning-Based Heartbeat Classification of 12-Lead ECG ...

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4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Sakib, Mohd Mustajab, Suhel Siddiqui, Tamanna Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9798350369786

Cardiovascular disease (CVD) is the most prevalent cause of mortality on a global scale. the possibility of preventing cardiac diseases is dependent on early-stage intervention. At this point, the electrocardiogram (ECG) emerges as a critical modality for detecting and predicting future cardiac abnormalities. Numerous machine-learning techniques have been used to improve result precision, computational efficiency, and model durability. Deep learning approaches have recently emerged as the preferred method for reaching even better levels of accuracy and robustness in a diverse range of applications. In this research, we developed four different neural networks. they are the Convolutional Neural Network (CNN), the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM) network, and a unique hybrid LSTM-CNN network. these networks were created to evaluate the classification performance between normal and abnormal heartbeats. Our hybrid model performed exceptionally well on the test data, with a loss of 0.12, binary accuracy of 0.97%, and area under curve (AUC) score of 0.99%. the results demonstrate its ability to successfully extract local and contextual factors within ECG signals. the combination of LSTM and CNN networks in a hybrid architecture allowed our model to exploit both local and contextual information within the ECG signals. © 2023 IEEE.

关键词： Convolutional neural networks

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An application of the Color Spatio-Temporal Decomposition in Video Series of Sign Plate Recognition 4

An Application of the Color Spatio-Temporal Decomposition in...

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4th international conference on computer Vision and Information Technology, CVIT 2023

作者： Li, Dan Xue, Muhan Chen, Yifei Wang, Yu Xu, Shuo Lu, Feng Cui, Guohui Institute of Fisheries Engineering Chinese Academy of Fishery Sciences Beijing China College of Information and Electronics Engineering China Agricultural University Beijing China

ISBN: (纸本)9781510672864

Marker recognition is vital in machine vision and applicable within many fields, such as vehicle automatic guidance, insect pest estimation, and UAV trajectory planning. the influence of illumination and complex backgrounds make such recognition applications very challenging. this paper describes a color Spatio-temporal decomposition algorithm as-applied to video images to recognize markers. In the proposed method, the Vectorial Rudin-Osher-Fatemi model weakens the textural component of the image sequences to minimize background complexity for image segmentation. the impact of illumination is reduced by transforming the color space of the obtained image sequences into HSV and equalizing the histogram for the Value channel. three different types of markers were tested under different light intensities and environments to verify the effectiveness of the algorithm. the proposed method improved the accuracy of edge detection in image segmentation and successfully minimized the interference of illumination. the algorithm also showed favorable good robustness under various vegetation density environments, with a recognition rate of about 95%. © 2024 SPIE.

关键词： Color

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Remote Sensing Image Captioning Using Hire-MLP 4

Remote Sensing Image Captioning Using Hire-MLP

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4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Wei, Yichao Li, Lin Geng, Shengling School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Qinghai Normal University Academy of Plateau Science and Sustainability Xining China National Qinghai-Tibet Plateau Scientific Data Center Qinghai Sub-center Xining China

ISBN: (纸本)9798350326444

Remote sensing image captioning has been widely applied to traffic management, geographic research, etc. Although the neural network approach has been successfully improving the performance of the Remote sensing image captioning system, Remote sensing image captioning is still facing object identification challenges due to the small size of objects, uneven distribution, and high coupling with the surrounding image background. In this paper, we propose a novel remote sensing image captioning encoder-decoder model Hierarchical rearrangement-Multi-Layer Perceptron (HMLP) whose encoder adapts hierarchical rearrangement-multi-layer perceptron to improve the capability of objection recognition. Extensive experiments have been conducted to testify HMLP by three datasets RSCID, UCM-caption, and NWPU-caption. Results show that HMLP outperforms many image captioning systems in the evaluation metrics BLEU4, METEOR, ROUGE-L, and CIDEr. © 2023 IEEE.

关键词： computer vision

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Deep learning-Based Modulation Recognition for Imbalanced Classification 4

Deep Learning-Based Modulation Recognition for Imbalanced Cl...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Luo, Runqing Sun, Jian Guo, Yanan Beijing Information Science and Technology University 100101 China Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9798400709449

Automatic modulation recognition (AMR) is a crucial task in intelligent receiver systems, with significant applications in both civilian and military scenarios. Deep learning-based modulation recognition algorithms have achieved high recognition accuracy and good robustness. However, the performance of DL-AMR heavily relies on a large amount of training data. When the training dataset has a limited number of samples or an imbalanced distribution across different classes, the accuracy of automatic modulation recognition for minority class samples may significantly decrease. therefore, a CNN model with a focal loss as the output layer's loss function is proposed to address the issue of decreased recognition accuracy for minority classes in the deep learning model when faced with imbalanced training data. Simulation results demonstrate that this model improves the recognition accuracy of classes with a limited number of samples. © 2023 ACM.

关键词： Deep learning

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