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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： Siva Prasad Reddy K.V S. Meera Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Fast malignant melanoma tumor identification is vital for successful therapy. Melanoma has now been universally acknowledged to be the most lethal form of malignancy over all else since, even if not caught and addressed promptly, it possesses a substantially higher propensity to migrate towards remaining regions of physique. Definitive presentation of various disorders relies largely on quasi therapeutic machine vision / procuring of disorders scanned pics. These methods offer a tool for autonomous image enhancement that allows for a quick and precise appraisal of the abscess. Deficiency of vast samples constitutes one of primary hurdles for designing a trustworthy autonomous classification scheme. This research suggests integrating Deep—learning & Image—processing to develop an algorithm for artificially recognizing melanoma. This survey's phase involves amassing a repository of dermoscopy pics, pre—Processing, separation utilizing hybridized Fuzzy-C-Means, and weighted enhancement employing the whale-optimization-algo. Following the subdivision of the dermoscopic imagery, the chapped skin cellular membranes traits are captured utilizing extraction of features. For the layering of the retrieved attributes, Long -shortterm -memory {LsTm}, bidirectional LsTm && Deep-Forest predictors are deployed. After using freely searchable sample group, the reported classifier reliability was Ninety Seven Percent && the segmentation effectiveness reached ninety three percent.

关键词： Hair Training Object segmentation Melanoma Feature extraction Solids Skin

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Preprocessing and feature extraction of MRI Liver Tumour images using A novel multi-class identification (NMCI) framework

Preprocessing and feature extraction of MRI Liver Tumour ima...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： A. Bhagya S. Perumal Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

One of the leading and most serious causes of death worldwide is liver tumours. Currently, one of the most well-known computer Vision disciplines (CV) is Medical Imaging (MI), which aids radiologists and physicians in the early detection and diagnosis of liver tumours. In order to diagnose patients, radiologists and doctors read hundreds of images using manual or semi-automated technologies, such as Magnetic resonance imaging (MRI). Consequently, a fully automated approach is required to use MRI scans, the most common and extensively used imaging modality, for early tumour detection and diagnosis. Implementing machine learning (ML) techniques in finding the tumour affected parts are the main focus of the proposed work. The multiclass liver tumour classification procedure followed in the tumour identification process follows, Random Forest (RF), Random Tree (RT), Logistic Model Tree (LMT) and J48 with multiple automated Region of Interest (ROI). Hemangioma, cyst, hepatocellular carcinoma, and metastasis are the four basic tumour classes, which are to be classified before severity increases. Transformation procedures followed in image processing are conversion of collected MRI images into grayscale, then boosting the contrast with histogram equations. The Gabor filter was used to minimise noise, and an image sharpening technique was used to enhance image quality. Furthermore, employing texture, binary, histogram, and rotational, scalability, and translational (RST) algorithms, 55 characteristics of various pixel dimensions were obtained for each ROI. Out of these 55 characteristics, 20 optimised features were chosen for classification using the correlated feature selection approach. RF and RT outperformed J48 and LMT, with respective accuracy of 97.48% and 97.08%, according to the collected data. The new framework that has been suggested will aid doctors and radiologists in the diagnosis of liver tumours.

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Diverse Image Processing Techniques for Machine Learning Methods

Diverse Image Processing Techniques for Machine Learning Met...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： A. Bhagya S. Perumal Department of Computer Science Vels Institute of science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Machine learning is a trending topic in the area of computer vision, which makes the machine able to learn about it without being expressly programmed using various algorithms. When a model is designed to predict future-based on historical data using machine learning algorithms, data is vital because high quality data is needed to run the model efficiently and to predict highly accurate results. Machine learning algorithms receive data in many forms, like audio (speech recognition), images (feature recognition), numeric data (like height and weight) etc. When it comes to computer vision, the machine learning algorithms must be able to understand the image data, and before the images are processed to the algorithms it should be pre-processed (cleaned and set to the desired format) using various image pre-processing techniques. This research paper presents various image processing techniques, literature reviews on various image processing techniques and image PSNR value after applying median filter, grayscale filter and box filters.

关键词： Machine learning algorithms Image processing Signal processing algorithms Speech recognition Machine learning Gray-scale Filtering algorithms

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Minimum Consistent Subset in Trees and Interval Graphs 44

Minimum Consistent Subset in Trees and Interval Graphs

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44th IARCS Annual Conference on Foundations of Software Technology and Theoretical computer science, FSTTCS 2024

作者： Banik, Aritra Das, Sayani Maheshwari, Anil Manna, Bubai Nandy, Subhas C. Krishna Priya, K.M. Roy, Bodhayan Roy, Sasanka Sahu, Abhishek National Institute of Science Education and Research An OCC of Homi Bhabha National Institute Bhubaneswar India Theoretical Computer Science The Institute of Mathematical Sciences Chennai India School of Computer Science Carleton University OttawaON Canada Department of Mathematics Indian Institute of Technology Kharagpur India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783959773553

In the Minimum Consistent Subset (MCS) problem, we are presented with a connected simple undirected graph G, consisting of a vertex set V (G) of size n and an edge set E(G). Each vertex in V (G) is assigned a color from the set {1, 2, ⋯, c}. The objective is to determine a subset V′ ⊆ V (G) with minimum possible cardinality, such that for every vertex v ∈ V (G), at least one of its nearest neighbors in V′ (measured in terms of the hop distance) shares the same color as v. The decision problem, indicating whether there exists a subset V′ of cardinality at most l for some positive integer l, is known to be NP-complete even for planar graphs. In this paper, we establish that the MCS problem is NP-complete on trees. We also provide a fixed-parameter tractable (FPT) algorithm for MCS on trees parameterized by the number of colors (c) running in O(26cn6) time, significantly improving the currently best-known algorithm whose running time is O(24cn2c+3). In an effort to comprehensively understand the computational complexity of the MCS problem across different graph classes, we extend our investigation to interval graphs. We show that it remains NP-complete for interval graphs, thus enriching graph classes where MCS remains intractable. © 2024 Aritra Banik, Sayani Das, Anil Maheshwari, Bubai Manna, Subhas C. Nandy, Krishna Priya K. M., Bodhayan Roy, Sasanka Roy, and Abhishek Sahu.

关键词： Undirected graphs

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Improved Gaussian Grasshopper Optimization Algorithm (IGGOA) and Deep Belief Network (DBN) For Fetal Health Classification

Improved Gaussian Grasshopper Optimization Algorithm (IGGOA)...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： B. Jansi V. Sumalatha Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The effective method for continuously monitoring the fetus’s health is called Cardiotocography (CTG). For the past 60 years, CTG has been used in clinical settings, allowing medical professionals to identify early indicators of fetal deterioration. Prior research has solely focused on the classification accuracy of CTG datasets that include the entire feature set, ignoring the classifier computing time. Improved Gaussian Grasshopper Optimization Algorithm (IGGOA) and Deep Belief Network (DBN) are introduced to categorize CTG data. Several factors contribute to the introduction of IGGOA to feature selection: 1) Due to its fragile tail, Gaussian Mutation (GM) is introduced to generate a new offspring closer to the original feature. 2) The development of Levy Flight (LF) has created a more straightforward way to solve the local optimum because grasshoppers have more vital attraction forces;3) OBL has been used to update the present population, and GOA capability is used to determine the FHR classification. In the DBN classifier, CTG samples are categorized as Normal (N), Suspect (S), and Pathological (P). DBN has been analyzed as a Restricted Boltzmann Machine (RBM). Every sub-network hidden layer in the DBN classifier acts as the layer visible to the layer after it. The Softmax function has been used to determine the hidden layer results. The accuracy is about $97.3659 \%$, which is better than other models.

关键词： Accuracy Fetal heart rate Computational modeling Sociology Tail Feature extraction Probability distribution

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Enhancing Automated Payments: Impact of Iris Technology's in UPI Transactions 4

Enhancing Automated Payments: Impact of Iris Technology's in...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Galhotra, Bhavna Bajaj, Shalini Bhaskar Jatain, Aman Jaglan, Vivek Amity University Computer Science Dept Haryana India Jagan Institute of Management Studies India K.R. Mangalam University Department of Computer Science SOET Gurgaon India Amity University ASET MP Gwalior India

ISBN: (纸本)9798331515720

In the era of cashless transactions, security has become a significant concern for users. This research explores the various models and technologies employed to enhance authentication and payment security in wallets and UPI payments. Biometric authentication, specifically iris recognition, emerges as a promising solution due to its high reliability and wide-ranging applications. The study investigates the working mechanism of iris recognition, comparing captured images with stored data to verify user identity. It highlights the significance of iris authentication in online transactions, presence verification, and other payment-related areas. This study also analyzes the existing models and identifies the most commonly used iris recognition techniques. By understanding the advancements in biometric authentication, this research aims to contribute to the development of more secure and efficient cashless payment systems. © 2024 IEEE.

关键词： Authentication Protocol

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Diabetic Retinopathy Identification From Retinal Images With The Use Of A Deep Learning-Based Transfer Training Framework

Diabetic Retinopathy Identification From Retinal Images With...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Jayaprakash Venugopal Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Diabetic retinopathy (DR) is a major contributor to vision loss worldwide, highlighting the urgent need for early identification and prompt care to prevent further deterioration of vision. Diagnosing DR is intrinsically difficult, since it requires the thorough evaluation of complex retinal images by skilled professionals. DR, a consequence of diabetes, has the potential to cause vision loss if not identified in its early stages. research presents a novel model that utilizes transfer learning to detect diabetic retinopathy. The first trials yielded poor accuracies for both training and validation. The categorization of class-4, especially those depicting severe instances, yielded unexpected outcomes, since several images were categorized as moderate. Consequently, more investigation is required to ascertain the accuracy of these classifications. Therefore, our suggested approach allows for easy classification of the retina pictures of both Diabetic and Healthy individuals, resulting in a decrease in the number of evaluations required by medical specialists.

关键词： Training Diabetic retinopathy Accuracy Transfer learning Refining Retina Data augmentation Robustness Research and development Medical diagnostic imaging

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A Methodical Approach to Healthcare Cybersecurity: Handling Current Risks and Advancements

A Methodical Approach to Healthcare Cybersecurity: Handling ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： M. Husain Bathushaw S. Nagasundaram Computer Applications VELS institute of science technology and advanced studies Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The methods were evaluated using machine learning-driven interruption detection, blockchain-enhanced access control for inconsistency localization, and structured threat insight data in a simulated healthcare environment. With an accuracy range of 0.88-0.94 and lift specified between 0.75 and 1, knowledge values ranging from.86 to.92, and F1 scores between and above, the test results demonstrated that all computations were possible. BACA strengthened access control with blockchain; ADA surpassed expectations in identifying discrepancies; TIAA succeeded in risk insights management; and ML-IDS achieved outstanding results in intrusion detection. A comparison with related research highlights the significance of these algorithms in tackling specific cybersecurity issues in the healthcare sector. Because the proposed algorithms provide a holistic approach to safeguard confidential health information, ensure the validity of assessment models, and strengthen organizations against a range of dynamic cyberthreats, they are pertinent to the expanding discussion about cybersecurity in the healthcare industry.

关键词： Access control Technological innovation Accuracy Systematics Heuristic algorithms Medical services Organizations Prediction algorithms Blockchains computer security

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Evolving Distributions Under Local Motion

arXiv

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arXiv 2024年

作者： Acharya, Aditya Mount, David M. Department of Computer Science University of Maryland College Park United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States

Geometric data sets arising in modern applications are often very large and change dynamically over time. A popular framework for dealing with such data sets is the evolving data framework, where a discrete structure continuously varies over time due to the unseen actions of an evolver, which makes small changes to the data. An algorithm probes the current state through an oracle, and the objective is to maintain a hypothesis of the data set’s current state that is close to its actual state at all times. In this paper, we apply this framework to maintaining a set of n point objects in motion in d-dimensional Euclidean space. To model the uncertainty in the object locations, both the ground truth and hypothesis are based on spatial probability distributions, and the distance between them is measured by the Kullback-Leibler divergence (relative entropy). We introduce a simple and intuitive motion model where with each time step, the distance that any object can move is a fraction of the distance to its nearest neighbor. We present an algorithm that, in steady state, guarantees a distance of O(n) between the true and hypothesized placements. We also show that for any algorithm in this model, there is an evolver that can generate a distance of Ω(n), implying that our algorithm is asymptotically optimal. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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Efficient Dengue Spread Prediction Using Machine Learning Models with Various Preprocessing Techniques

Efficient Dengue Spread Prediction Using Machine Learning Mo...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： K. Saraswathi K. Rohini Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The viral disease dengue, spread by mosquitoes, is a severe hazard to public health worldwide. Predicting dengue outbreaks is an essential field of study that aims to stop and lessen the effects of the virus spread by mosquitoes. Utilizing modern technologies like data analytics and ML(Machine Learning) will significantly improve the capacity to predict and contain dengue outbreaks. The work examines how the effectiveness of Convolutional Neural Networks (CNNs) in forecasting and evaluating Dengue outbreaks is affected by several scaling strategies, including Min-Max Scaling, Z-Score Standardization, and Robust Scaling. For disease control to be effective, outbreaks must be predicted with accuracy and timeliness. Before feeding the input data into the CNN architecture, preprocess takes place in experiments utilizing Robust Scaling, Z-Score Standardization, and Min-Max Scaling. The model is methodically assessed using necessary measures like recall, accuracy, and precision. The findings show that robust scaling consistently performs better than the other scaling methods, improving CNN’s capacity to identify characteristics and patterns important for predicting Dengue outbreaks. The result was that Robust Scaling+CNN gave the best results, with an accuracy of $93 \%$, precision, and recall of 0.92 and 0.85, respectively. The tool used for execution is Python.

关键词： Accuracy Recurrent neural networks Standardization Predictive models Data models Numerical models Convolutional neural networks

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