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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Lavanya K A. Packialatha Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The country's economy relies heavily on agriculture, and the health of the crops is essential to its success. Crop diseases that go undetected can cost the agriculture industry. Thus early detection and identification are crucial. It is possible to avoid crop diseases from destroying the harvest if they are accurately diagnosed and detected. Because healthy and diseased plants appear identical in their early stages, farmers cannot distinguish between the two by watching the crop leaf. India exports vast mangoes, making it an economically and environmentally significant fruit. About 1500 mango species are grown in India, with over 1000 commercial types. There are a lot of diseases that harm mangoes, affecting their look, taste, and economy. The "Prediction of Disease in Mango Fruit Crops" A complex warning system that uses machine learning and IoT. One of the main objectives is to develop a system that can forecast disease outbreaks on mango fruit harvests using historical weather information and crop yield Mango trees in India are plagued by a fungus called Anthracnose, which is the most frequent disease of its kind. Anthracnose, a highly contagious fungus, requires a quick and accurate method of diagnosis. As a result, an in-depth examination of the plants is essential before initiating any control measures. This study examines machine learning(ML) and deep learning (DL) strategies for detecting and classifying mango plant diseases. The performance of ML and DL-based classification models for mango crops and their datasets and feature extraction approachesare examined in this work. Finally, a variety of issues involved in plant disease identification are explored.

关键词： Support vector machines Deep learning Plant diseases Accuracy Costs Biological system modeling Crops Data models Agriculture Internet of Things

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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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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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An Efficient Framework Classification for Diabetic Retinopathy Using MobileNetV2

An Efficient Framework Classification for Diabetic Retinopat...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 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)9798331543624

ISBN: (纸本)9798331543631

Diabetes is a disorder marked by unregulated glucose levels in the body, potentially causing retinal damage and leading to irreversible visual impairment or loss. Diabetes affects the eyes, resulting in diabetic retinopathy, a prevalent medical disorder, especially among the elderly. Consequently, timely and precise diabetic retinopathy screening is essential for disease prediction. Nonetheless, conventional manual detection is labor-intensive and often results in misdiagnosis for several individuals. In this study, we devised a deep learning (DL) model using transfer learning from the MobileNetV2 architecture, accompanied with an innovative color version preprocessing approach. It decreased the training duration and achieved an average accuracy of 0.93 when applied to the new Kaggle dataset “APTOS 2019 Blindness Detection.” Furthermore, to mitigate the issue of overfitting in the long term, we used Stratified K-fold cross-validation.

关键词： Training Deep learning Diabetic retinopathy Accuracy Visual impairment Transfer learning computer architecture Color Blindness Overfitting

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On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective 41

On Which Nodes Does GCN Fail? Enhancing GCN From the Node Pe...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Jincheng Shen, Jialie Shi, Xiaoshuang Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science City University of London London United Kingdom Sichuan Artificial Intelligence Research Institute Yibin China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

The label smoothness assumption is at the core of Graph Convolutional Networks (GCNs): nodes in a local region have similar labels. Thus, GCN performs local feature smoothing operation to adhere to this assumption. However, there exist some nodes whose labels obtained by feature smoothing conflict with the label smoothness assumption. We find that the label smoothness assumption and the process of feature smoothing are both problematic on these nodes, and call these nodes out of GCN's control (OOC nodes). In this paper, first, we design the corresponding algorithm to locate the OOC nodes, then we summarize the characteristics of OOC nodes that affect their representation learning, and based on their characteristics, we present DaGCN, an efficient framework that can facilitate the OOC nodes. Extensive experiments verify the superiority of the proposed method and demonstrate that current advanced GCNs are improvements specifically on OOC nodes;the remaining nodes under GCN's control (UC nodes) are already optimally represented by vanilla GCN on most datasets. Copyright 2024 by the author(s)

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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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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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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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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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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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