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Transductive Support Vector machines (TSVM) with Hybrid Recurrent Neural Networks for Medical image Filtering 1

Transductive Support Vector Machines (TSVM) with Hybrid Recu...

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1st IEEE international conference on Innovations in High Speed Communication and Signal processing, IHCSP 2023

作者： Kumar, Ravi Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab India

ISBN: (纸本)9798350345957

Filtering an image is one of the most fundamental and consequential tasks in the field of image processing. As of now, scientists still face a very real and pressing dilemma. images, whether in print or digital form, are a crucial criterion in many different industries, including geosciences, aerospace, education, agriculture, entertainment, surveillance, and many more. Background noise degrades image quality, despite extensive research aimed at finding a solution to this problem and the anticipation of a plethora of possible countermeasures. Methods for picture filtering, including those based on machine learning and those based on optimization, are detailed here. It has been possible to categorize these methods by the methods they employ. Gaussian noise, white noise, salt and pepper noise, speckle noise, and poison noise were all removed from lung CT scans using a combination of Recurrent Neural Networks (RNNs) and Transductive Support Vector machines (TSVMs) in this study. Using TSVM, RNN can function better than ever before. The approach proposed here uses LstM to normalize batches of data. By simulating a large number of individual beehives, the ABC (Artificial Bee Colony) algorithm may determine the optimal size of each batch. MATLAB was used to realize the suggested method. The experimental consequences demonstrate that the suggested system overtakes the baseline in terms of PSNR, MSE, and precision. © 2023 IEEE.

关键词： Computerized tomography

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

1st International Conference on Artificial Intelligence and ...

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

ISBN: (纸本)9783031213847

The proceedings contain 43 papers. The special focus in this conference is on Artificial Intelligence and Data Science. The topics include: Challenges in Crop Selection Using machine learning;Handcrafted Features for Human Gait recognition: CASIA-A Dataset;local Binary pattern Symmetric Centre Feature Extraction Method for Detection of image Forgery;a Copy and Move image Forged Classification by Using Hybrid Neural Networks;preface;comparative Analysis of Advanced machine learning Based Techniques to Identify the Lung Cancer: A Review;cardiac Surveillance System Using by the Modified Kalman Filter;an Extensive study on Parkinson’s Disease Using Different Approaches of Supervised learning Algorithms;adaptive Convolution Neural Networks for Facial Emotion recognition;A Gravitational Search Algorithm study on Text Summarization Using NLP;analysis of Deep learning Methods for Prediction of Plant Diseases;ACHM: An Efficient Scheme for Vehicle Routing Using ACO and Hidden Markov Model;detection of Leaf Disease Using Artificial Intelligence;Deep CNN Based Whale Optimization for Predicting the Rice Plant Disease in Real Time;DCBC_DeepL: Detection and Counting of Blood Cells Employing Deep learning and YOLOv5 Model;ML_SPS: stroke Prediction System Employing machine learning Approach;Classification and Identification of Objects in images Using CNN;a System for Network Based Intrusion Avoidance Using Dedicated machine learning and Artificial Intelligence-Based Model for Application and Data Safety;AI-Based COVID Alert System on Embedded Platform;an Efficient Detection of Brain stroke Using machine learning Robust Classification;apple Leaf Diseases Detection System: A Review of the Different Segmentation and Deep learning Methods;underwater image Restoration and Enhancement Based on machine and Deep learning Algorithms.

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PSO-Optimized CNN for Feature Extraction and Accurate Classification of Satellite images Using machine learning 1

PSO-Optimized CNN for Feature Extraction and Accurate Classi...

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1st IEEE international conference on Computing and Data Science, ICCDS 2024

作者： Suganya, D. Sugumar, R. Department of Computer Science India

ISBN: (纸本)9798350365337

Satellite imagery is indeed a powerful tool for various applications, including environmental monitoring, infrastructure planning and management, urban planning, and disaster response. This technology allows for a comprehensive overview of the Earth's surface, facilitating the remote identification and examination of transformations that may be challenging to observe from a ground-level perspective. However, traditional object classification and detection algorithms can sometimes be unreliable and inaccurate when processing satellite imagery. This can be due to a variety of reasons, such as limited resolution and noise in the imagery, variations in lighting and atmospheric situations, and the complication of the objects and its features being detected. Over past few years, techniques that rely on deep learning have demonstrated impressive achievements in enhancing the dependability and precision of object classification and detection in satellite imagery intended for land classification purposes. These techniques use PSO to optimize hyperparameters for convolutional neural networks (CNNs) to extract features from the images and identify objects based on learned patterns., and SVMs can be used to classify the extracted features. These proposed techniques can be combined to create powerful and accurate image classification models with an accuracy of 95%. © 2024 IEEE.

关键词： Support vector machines

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1st edition of the international conference on Artificial Intelligence and Green Computing, ICAIGC 2023

1st edition of the International Conference on Artificial In...

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1st edition of the international conference on Artificial Intelligence and Green Computing, ICAIGC 2023

ISBN: (纸本)9783031465833

The proceedings contain 22 papers. The special focus in this conference is on Artificial Intelligence and Green Computing. The topics include: Maximization of Lifetime in Wireless Sensor Networks Using pattern Search Algorithm;task Deadline-Based Computation Offloading Algorithm for Service Time Minimization in Mobile Edge Computing;detection of Web-Based Attacks using Tree-Based learning Models: An Evaluation study;energy Minimization in Wireless Sensor Networks Based Bio-Inspired Algorithms;Heuristic Optimization of the LEACH Routing Protocol in Wireless Sensor Networks;toward Understanding the Impact of Demographic Factors on Cybersecurity Awareness in the Moroccan Context;health Data Security in a Big Data Environment;Handling the ED Problem Using a DTSA in Smart Grids;Multivariate Time Series Data Prediction Based on Social Sentiments Community and LstM Method (S-S-LstM);longitudinal study of the Thyroid Surgery Effect Based on Computer Vision;improving Wheat Yield Estimating by Using Satellites Data and machine learning—Deep learning Algorithm-In Morocco;online Process Mining: A Systematic Literature Review;machine learning with Nighttime Lights to Predict Morocco’s Gross Domestic Product;pectoral Muscle Segmentation Using Mammogram images in Medio Lateral Oblique View;a Nearest Neighbor-Based Hamiltonian Clustering Algorithm;an Acoustic Analysis of Voice Before and After Thyroidectomy;estimation of Water Turbidity by image-Based learning Approaches;an image Compression Approach Based on Convolutional AutoEncoder;offline Writer Identification Based on Diagonal Gradient Angle of Small Fragments;Enhanced Aircraft Time Delay Prediction Using Weighted Hybrid ML and Dimensionality Reduction.

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Federated learning Based Prediction of Chronic Kidney Diseases 1

Federated Learning Based Prediction of Chronic Kidney Diseas...

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1st international conference on Computational Science and Technology, ICCst 2022

作者： Nandhini, J.M. Joshi, Soshya Anuratha, K. Sri Sai Ram Institute of Technology India

ISBN: (纸本)9781665476553

Chronic Kidney diseases are increasing at an alarming rate in present days. The prediction of kidney disease consume more time as it involves a lot of laboratory tests. Traditionally, the health care domain relies on the centralized data, which may lead the system to have significant risks and difficulties. The proposed model involves uses federated learning, a machine learning technique that involves training an algorithm on multiple decentralized data. The model uses image processing technique, where the scanned image of the kidney is given as the input. The image, upon various pre-processing techniques identifies the affected area of the kidney. The affected area is also marked in color and the values of accuracy, efficiency, specificity and sensitivity are found to be higher than the existing systems. The output of the single image is again given to training dataset to increase the accuracy precisely, which is a part of the federated learning. © 2022 IEEE.

关键词： image processing

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A Deep learning Based strategies for Scene-Text VQA System used in Industrial Applications: A Critical Analysis 1

A Deep Learning Based Strategies for Scene-Text VQA System u...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, IstEMS 2024

作者： Agrawal, Mayank Singh Jalal, Anand Sharma, Himanshu Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350384246

Scene-Text Visual Question Answering (Scene-Text VQA) is an emerging research area that combines computer vision, natural language processing, and scene understanding. The goal of Scene-Text VQA is to develop models and algorithms that can comprehend scene text and accurately answer questions based on textual and visual information present in images. Scene-Text VQA models used in different areas such as quality control and inspection, equipment maintenance, safety compliance, supply chain visibility, industrial automation, industry 4.0, assistance for the visually impaired, document analysis, autonomous vehicles, e-commerce, smart cities, cultural heritage preservation, surveillance, and security. Scene-Text VQA models face unique challenges, including accurate text detection and recognition, understanding contextual cues, handling language complexities, fusing text and image features effectively, limited training data, biases in datasets, generalization to unseen text and scenes, computational complexity, and appropriate evaluation metrics. In this paper we will explain different methods and algorithms for Scene-Text VQA models that can comprehend scene text and accurately answer questions based on textual and visual information present in images. The authors will distinguish all the techniques of Scene-Text VQA for effectively process and comprehend the textual information present in images and show how can one technique is conflicting then the other as well. © 2024 IEEE.

关键词： Inspection

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Efficient Large Scale Medical image Dataset Preparation for machine learning Applications 1

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1st MICCAI international Workshop on Data Engineering in Medical Imaging (DEMI)

作者： Denner, stefan Scherer, Jonas Kades, Klaus Bounias, Dimitrios Schader, Philipp Kausch, Lisa Bujotzek, Markus Bucher, Andreas Michael Penzkofer, Tobias Maier-Hein, Klaus German Canc Res Ctr Div Med Image Comp Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Natl Ctr Tumor Dis NCT Heidelberg Germany Goethe Univ Frankfurt Univ Hosp Inst Diagnost & Intervent Radiol Frankfurt Germany Charite Univ Med Berlin Dept Radiol Berlin Germany

ISBN: (数字)9783031449925

ISBN: (纸本)9783031449918;9783031449925

In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be facilitated in machine learning algorithms. This paper introduces an innovative data curation tool, developed as part of the Kaapana (https://***/kaapana/ kaapana) open-source toolkit, aimed at streamlining the organization, management, and processing of large-scale medical imaging datasets. The tool is specifically tailored to meet the needs of radiologists and machine learning researchers. It incorporates advanced search, auto-annotation and efficient tagging functionalities for improved data curation. Additionally, the tool facilitates quality control and review, enabling researchers to validate image and segmentation quality in large datasets. It also plays a critical role in uncovering potential biases in datasets by aggregating and visualizing metadata, which is essential for developing robust machine learning models. Furthermore, Kaapana is integrated within the Radiological Cooperative Network (RACOON), a pioneering initiative aimed at creating a comprehensive national infrastructure for the aggregation, transmission, and consolidation of radiological data across all university clinics throughout Germany. A supplementary video showcasing the tool's functionalities can be accessed at https://***/MICCAI- DEMI2023.

关键词： Medical Imaging Data Curation machine learning Kaapana Dataset Preperation Quality Control Bias Detection

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Enhanced Classification Performance through Joint Mutual Information for Feature Selection in Life Science Data 1

Enhanced Classification Performance through Joint Mutual Inf...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Saha, Soumitra Kumar, Rakesh Gupta, Meenu Zanke, Pankaj Chandigarh University Department of Cse Mohali India Spaiens International Corporation Atlanta United States

ISBN: (纸本)9798331529246

Multifarious approaches exist for data preprocessing in machine learning (ML), pattern recognition, and data mining, and feature selection (FS) is regarded as the most efficacious technique for dimensionality reduction in a structured way. The existence of irrelevant, redundant, and disproportionate features directly diminishes the model's performance and dramatically expands its complexity. Distinguishable FS algorithms can outperform others, and the most influential aspect of a well-performing FS algorithm is discovering momentous features that help optimize model performance by processing data more efficiently. Therefore, this study introduces a novel FS technique based on the joint mutual information (FSJMI) technique for life science data. This technique declines the number of features and enhances the model's performance, or at least maintains it in most cases, making it a unique and promising approach. It also enriches predictive accuracy while minimizing computational overhead by substituting the correlation between the features and the correlation substitution of the class values with the features. Comprehensive experiments utilizing ten diverse life science datasets have been conducted to validate the suggested model's performance. The experimental results demonstrate a substantial improvement in the proposed techniques compared to other well-known FS techniques and no selection (NoSel) in the primary dataset. The results conveyed using widely used ML algorithms such as Decision Tree (DT), K Nearest Neighbour (KNN), Naive Bayes (NB) Classifier, and Logistic Regression (LR) reveal that the presented algorithm demonstrated superior performance and outperformed NoSel and other FS algorithms in mean accuracy by at least 2.7%, 3.466%, 2.038%, and 1.792%, respectively. Additionally, regarding the LR model, the proposed algorithm outperforms one of the FS models by at most 11.631%. These findings of the proposed algorithm regarding outcomes highlight its capabili

关键词： Dimensionality reduction

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A Deep learning Model that Diagnosis Skin Diseases and Recommends Medication 1

A Deep Learning Model that Diagnosis Skin Diseases and Recom...

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1st international conference on the Paradigm Shifts in Communication, Embedded Systems, machine learning and Signal processing, PCEMS 2022

作者： Shaik, Rayan Bodhapati, Sai Krishna Uddandam, Abhiram Krupal, Lokesh Sengupta, Joydeep Visvesvaraya National Institute of Technology Department of Electronics and Communication Engineering Nagpur India

ISBN: (纸本)9781665459044

Skin diseases are very common and the diagnosis is tricky and challenging. Latest research in the field of medicine along with the help of advanced technology has proved to be quite useful not only in diagnosis but also for treatment. Application of deep learning methods for diagnosis of skin diseases has given remarkable results. Computer aided results are quick and provide a quick overview of the *** paper aims to diagnose the skin disease from the infected skin image captured and provide details of the disease and recommend medication. To achieve this we have used stateof-the-art convolutional neural network(CNN) architecture MobileNetV2 for model building and *** is particularly helpful for both doctors and individuals to analyze the disease. For doctors, they can validate their opinion with this prediction and individuals can have an idea of the disease at the beginning itself and can be helpful to prevent the disease further since prevention is always better than cure. © 2022 IEEE.

关键词： Convolutional neural networks

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machine learning approaches for Microscopic image analysis and Microbial Object Detection(MOD) as a decision support system 1

Machine Learning approaches for Microscopic Image analysis a...

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

作者： Chaudhuri, Rapti Deb, Suman National Institute of Technology Cse Department Agartala India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Microbes are tiny living organisms beyond the scope to be seen by the naked eye that are coexisting all around the biosphere along with other animals. Significant identification of the microbes from the elementary forms of cell structure to deadly pandemic causing elements are vital for health and hygiene support systems. Manual identification of such creatures consumes infinite amount of time resulting in frequent contamination. In this work, the context is primarily focused on automated microscopic image analysis on visual features by incorporating systematic pattern matching approaches for rapid identification of the microbes. The microbes are classified and recognized using the state of art of real time object detection strategy. Experimental result analysis is done with bacterial images to confirm the precision and accuracy of the utilized technique. Visual and graphical representation of the result obtained confers the validity and correctness of the concerned procedure. Further, the challenges and the difficulties faced during the microbe identification and the techniques to tackle have also been discussed subsequently. The proposed solution is proved to be quick and swift analyzing technique of pathogenic microbes in an organized way and has potential to be used as a source of field-level pathology decision support system. © 2022 IEEE.

关键词： Object detection

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