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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Bennet, Preethi Samantha Bennet, Deepthi Tabitha Anitha, D. Thiagarajar College of Engineering Department of Applied Mathematics and Computational Science Madurai India

ISBN: (纸本)9798350336238

In mission critical systems, system failure is a major hazard and may cause huge losses including loss or threat to lives. Organisations, industries, hospitals and companies can benefit hugely if an accurate prediction of the impending failure can be made, with enough time to initiate appropriate maintenance routines. Here, we propose and demonstrate that machine failure prediction can be done using suitable machine learning models with high accuracy. We apply the principles of Logistic Regression, Bootstrap Aggregation and Multinomial Logistic Regression to a predictive maintenance dataset of 10,000 data points to predict machine failure under five independent failure modes. Applying ensemble methods like bootstrap aggregation push the accuracy to greater than 99% The machine fails even if one failure mode is true. We are able to predict the possible cause of failure too, with a high accuracy of up to 99%. © 2023 IEEE.

关键词： Forecasting

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Identity based privacy preserving dynamic broadcast encryption for multi-privileged groups

Identity based privacy preserving dynamic broadcast encrypti...

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International Conference on Security in Computer Networks and Distributed Systems, SNDS 2012

作者： Muthulakshmi, Angamuthu Anitha, Ramalingam Rohini, S. Princy, Krishnan Department of Applied Mathematics and Computational Sciences PSG College of Technology Coimbatore India

ISBN: (纸本)9783642341342

An identity-based broadcast encryption scheme (IBBE), is the one in which the broadcasting sender combines the public identities of receivers and system parameters and then encrypts a message. Attacks on user privacy may happen as the user identity is not preserved. This paper presents an IBBE scheme for multi-privileged users using Chinese Remainder Theorem and bilinear pairing that preserves the user identities. The proposed scheme is proved secure under random oracle model. The proposed scheme provides a stateless broadcast, is dynamic with ease for revocation of users and also collusion resistant. © 2012 Springer-Verlag.

关键词： Public key cryptography

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A Survey - Soil Feature Analysis Using Clustering Techniques and Predict Various Crops in Madurai District 1

A Survey - Soil Feature Analysis Using Clustering Techniques...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Sakthipriya, Dhinakaran Chandrakumar, Thangavel Johnson, B. Prem, Kumar J.B. Thiagarajar College of Engineering Department of Applied Mathematics and Computational Science Madurai India

ISBN: (纸本)9781665472753

In Tamil Nadu, seasonal soil fertility, rainfall, and temperature account for more than 65% of agricultural output. Soils are one of the most valuable natural resources on the planet. The study's goal was to analyze and survey the historical changes in soil parameter Index of Madurai District and Taluks, South India, using Clustering techniques of unsupervised learning to perform well for this proposed work. In this proposed work to involved, the survey should focus on the Soil parameters (N-Nitrogen, P-Phosphorus, K-potassium), rainfall, and temperature data retrieved from the agriculture government portal for the last five years. The study compares two machine learning clustering techniques, Hierarchical Clustering and K-Means Clustering in estimating soil features at Madurai Taluks. With the use of new agricultural technology, this proposed initiative intends to offer a better suggestion for obtaining an acceptable level of crop output to the Madurai surrounding blocks to get more benefits. © 2023 IEEE.

关键词： Soils

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Securing Smart City Networks - Intelligent Detection of DDoS Cyber Attacks 5

Securing Smart City Networks - Intelligent Detection of DDoS...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Anitha, D. Thiagarajar College of Engineering Department of Applied Mathematics and Computational Science Madurai India

ISBN: (纸本)9798350398267

A distributed denial-of-service (DDoS) is a malicious attempt by attackers to disrupt the normal traffic of a targeted server, service or network. This is done by overwhelming the target and its surrounding infrastructure with a flood of Internet traffic. The multiple compromised computer systems (bots or zombies) then act as sources of attack traffic. Exploited machines can include computers and other network resources such as IoT devices. The attack results in either degraded network performance or a total service outage of critical infrastructure. This can lead to heavy financial losses and reputational damage. These attacks maximise effectiveness by controlling the affected systems remotely and establishing a network of bots called bot networks. It is very difficult to separate the attack traffic from normal traffic. Early detection is essential for successful mitigation of the attack, which gives rise to a very important role in cybersecurity to detect the attacks and mitigate the effects. This can be done by deploying machine learning or deep learning models to monitor the traffic data. We propose using various machine learning and deep learning algorithms to analyse the traffic patterns and separate malicious traffic from normal traffic. Two suitable datasets have been identified (DDoS attack SDN dataset and CICDDoS2019 dataset). All essential preprocessing is performed on both datasets. Feature selection is also performed before detection techniques are applied. 8 different Neural Networks/ Ensemble/ Machine Learning models are chosen and the datasets are analysed. The best model is chosen based on the performance metrics (DEEP NEURAL NETWORK MODEL). An alternative is also suggested (Next best - Hypermodel). Optimisation by Hyperparameter tuning further enhances the accuracy. Based on the nature of the attack and the intended target, suitable mitigation procedures can then be deployed. © 2022 IEEE.

关键词： Network security

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Factor Analysis for Spectral Estimation

Factor Analysis for Spectral Estimation

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International Conference on Sampling Theory and Applications

作者： Joakim Anden Amit Singer Program in Applied and Computational Mathematics Princeton University Department of Mathematics and Program in Applied and Computational Mathematics Princeton University

ISBN: (纸本)9781538615669

Power spectrum estimation is an important tool in many applications, such as the whitening of noise. The popular multitaper method enjoys significant success, but fails for short signals with few samples. We propose a statistical model where a signal is given by a random linear combination of fixed, yet unknown, stochastic sources. Given multiple such signals, we estimate the subspace spanned by the power spectra of these fixed sources. Projecting individual power spectrum estimates onto this subspace increases estimation accuracy. We provide accuracy guarantees for this method and demonstrate it on simulated and experimental data from cryo-electron microscopy.

关键词： Spectrum Analysis power spectra Subspace STATIONARY SOURCES Factor analysis Cryoelectron Microscopy Linear Combination statistical models (nuclear) signals Individual Power simulating

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3rd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2015

作者： Latha, R. Nadarajan, R. Department of Applied Mathematics and Computational Sciences PSG College of Technology Coimbatore India

ISBN: (纸本)9783319268316

Recommender systems are becoming more and more attractive in both research and commercial communities due to Information overload problem and the popularity of the Internet applications. Collaborative Filtering, a popular branch of recommendation approaches, makes predictions based on historical data available in the system. In particular, user based Collaborative Filtering largely depends on how users rate various items of the database and the success of such a system largely relies on pair wise similarity between users. However popular items may give a negative effect on choosing similar users of the target user. The proposed work namely User Similarity Adjustment based on Item Diversity (USA_ID) is designed to achieve personalized recommendations by modifying user similarity scores, for the purpose of reducing the negative effects of popular items in user based Collaborative Filtering framework. A Recommender system is focusing exclusively on achieving accurate recommendations i.e., providing the most relevant items for the needs of a user. From user’s perspective, they would not be interested when they are facing monotonous recommendations even if they are accurate. Whilst much research effort is spent on improving accuracy of recommendations, less effort is taken on analyzing usefulness of recommendations. Novelty and Diversity have been identified as key dimensions of recommendation utility. It has been made clear that greater accuracy leads to lower diversity which results in accuracy-diversity trade off in personalized recommender systems. The proposed work provides an approach to increase the utility of a Recommender system by improving accuracy as well as diversity. Experiments are conducted on the bench mark data set MovieLens and the results show efficiency of the proposed approach in improving quality of predictions. © Springer International Publishing Switzerland 2015.

关键词： Collaborative filtering

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How many simulations do we need for simulation-based inference in cosmology?

arXiv

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arXiv 2025年

作者： Bairagi, Anirban Wandelt, Benjamin Villaescusa-Navarro, Francisco UMR 7095 98 bis bd Arago ParisF-75014 France Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics and Astronomy Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Department of Applied Mathematics and Statistics Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544-0010 United States

How many simulations do we need to train machine learning methods to extract information available from summary statistics of the cosmological density field? Neural methods have shown the potential to extract non-linear information available from cosmological data. Success depends critically on having sufficient simulations for training the networks and appropriate network architectures. In the first detailed convergence study of neural network training for cosmological inference, we show that currently available simulation suites, such as the Quijote Latin Hypercube(LH) with 2000 simulations, do not provide sufficient training data for a generic neural network to reach the optimal regime, even for the dark matter power spectrum, and in an idealized case. We discover an empirical neural scaling law that predicts how much information a neural network can extract from a highly informative summary statistic, the dark matter power spectrum, as a function of the number of simulations used to train the network, for a wide range of architectures and hyperparameters. We combine this result with the Cramer-Rao information bound to forecast the number of training simulations needed for near-optimal information extraction. To verify our method we created the largest publicly released simulation data set in cosmology, the Big Sobol Sequence(BSQ), consisting of 32,768 ΛCDM n-body simulations uniformly covering the ΛCDM parameter space. Our method enables efficient planning of simulation campaigns for machine learning applications in cosmology, while the BSQ dataset provides an unprecedented resource for studying the convergence behavior of neural networks in cosmological parameter inference. Our results suggest that new large simulation suites or new training approaches will be necessary to achieve information-optimal parameter inference from non-linear simulations. © 2025, CC BY-SA.

关键词： Dark Matter

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A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm

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Academic Radiology 2025年

作者： Dahou, Abdelghani Elaziz, Mohamed Abd Khattap, Mohamed G. Hassan, Hend Galal Eldeen Mohamed Ali School of Computer Science and Technology Zhejiang Normal University Jinhua 321004 China Mathematics and Computer Science department University of Ahmed DRAIA Adrar 01000 Algeria Department of Mathematics Faculty of Science Zagazig University Zagazig 44519 Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Diagnostic Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo 11591 Egypt Technology of Radiology and Medical Imaging Program Faculty of Applied Health Sciences Technology Galala University Suez 435611 Egypt

Rationale and Objectives: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data. Materials and Methods: The framework utilizes MobileNetV4 for feature extraction and MHOA for optimized feature selection across different MRI sequences (STIR, T2, T1, and DWI). A dataset of 39 patients diagnosed with BP was used. The framework classifies injuries based on Seddon's criteria, distinguishing between normal and abnormal conditions as well as injury severity. Results: The model achieved excellent performance, with 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences. For injury severity classification, accuracy was 0.9820 in STIR, outperforming the original HOA and other metaheuristic algorithms. Additionally, high classification accuracy (0.9667) was observed in DWI. The proposed framework outperformed traditional methods and demonstrated high sensitivity and specificity. Conclusion: The proposed AI-based framework significantly improves the diagnosis of BP by accurately classifying nerve injury types. By integrating DL and optimization techniques, it reduces diagnostic variability, making it a valuable tool for clinical settings with limited specialized neuroimaging expertise. This framework has the potential to enhance clinical decision-making and optimize patient outcomes through precise and timely diagnoses. © 2025 The Association of University Radiologists

关键词： Brachial Plexopathy (BP) Comprehensive Learning (CL) Deep Learning (DL) Feature Selection (FS) Hiking Optimization Algorithm (HOA) Magnetic Resonance Neurography (MRN) MobileNetV4

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P-Adic and Non-Archimedean Product Representations

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Results in mathematics 1989年第3期15卷 324-334页

作者： Knopfmacher, Arnold Knopfmacher, John Department of Computational & Applied Mathematics University of the Witwatersrand Wits 2050 Johannesburg South Africa Department of Mathematics University of the Witwatersrand Wits 2050 Johannesburg South Africa

Two algorithms are introduced and shown to lead to a unique product representation for a given p-adic integer A with leading coefficient 1, as a product of terms (Formula presented.) where the a_n are certain rational numbers. The degree of approximation by the N-th partial product of such terms is investigated, and in some explicit cases the products corresponding to particular types of p-adic integers are characterized. In addition, we consider similar representations for elements of arbitrary complete non-archimedean fields with discrete valuations. © 1989, Birkhäuser Verlag, Basel. All rights reserved.

关键词： 11J61 11K41

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Normality of Some p-Adic Product Expansions

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Journal of the Australian Mathematical Society 1990年第2期49卷 258-263页

作者： Knopfmacher, Arnold Knopfmacher, John Department of Computational and Applied Mathematics University of the Witwatersrand Johannesburg Wits 2050 South Africa Department of Mathematics University of the Witwatersrand Johannesburg Wits 2050 South Africa

We consider two unique products [formula omitted] for a given p-adic integer x with leading coefficient 1, where an, bn ϵ {0, 1, …, p − 1}. It is shown that, for almost all such x relative to Haar measure on the p-ad...

We consider two unique products [formula omitted] for a given p-adic integer x with leading coefficient 1, where a_n, b_n ϵ {0, 1, …, p − 1}. It is shown that, for almost all such x relative to Haar measure on the p-adic integers, the sequences (a_n), (b_n) are normal to base p, and have standard normal distribution functions. © 1990, Australian Mathematical Society. All rights reserved.

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