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Efficient spam filtering through intelligent text modification detection using machine learning
No full textSpam emails have long been a source of concern in the field of computer security. They are both monetarily and echnologically costly, as well as extremely harmful to computers and networks. Despite the rise of social networks and other Internet-based information exchange venues, email communication has become increasingly important over time, necessitating the urgent improvement of spam filters. Although various spam filters have been developed to help prevent spam emails from reaching a user's mailbox, there has been little research into text modifications. Because of its simplicity and efficiency, Naive Bayes is currently one of the most used methods of spam classification. However, when emails contain leetspeak or diacritics, Naive Bayes is unable to correctly categorize them. As a result, we created a novel method to improve the accuracy of the Naive Bayes Spam Filter to detect text alterations and correctly classify emails as Spam or ham in this proposal. When compared to Spamassassin, our Python approach uses a combination of semantic, keyword, and machine learning algorithms to improve Naive Bayes accuracy. Furthermore, we identified a link between email length and spam score, indicating that Bayesian Poisoning, a contentious concept, is an actual occurrence used by spammers
Use of Machine Learning in Healthcare: Empowering Physicians with IoT‐Enabled Technologies
No full textInter country poetry classification using Topic modeling
Get PDFAbstract—Poetry is an art of arranging the carefully picked
words in a specific order to express the authors experience and
emotions. Poetry in India has its strong roots with world famous
and excellent poets, amongst few renowned poets are “Universal Poet” Rabindranath Tagore, “Nightingale of India” Sarojini Naidu and “Swami” Vivekananda. Poetry style varies from country to country depending on the author. Author’s poetry topics, words and style depends on the circumstances they raised in, situations they faced, and their mind set. Many authors who belongs to India but settled in western countries and written their poems, in this context automatically identifying a poem’s author is a challenging task for the literary scholars who analyze the poetry. In this work authors proposed a method based on Latent Dirichlet Allocation(LDA) topic modeling to classify the poetry written by Indian or western(American)poet based on the distribution of topics per document. The experiment is performed on 3 data sets 128, 1600 and author wise poems respectively. This experiment is performed based on semantic features. Best result 91% precision and 88% accuracy is achieved on author wise poems data set using random forest algorith
Analysis of image security by triple DES
Get PDFIn this research, we provide a regression-based technique for counting and classifying freeway traffic. This technique does not need the normal division and monitoring of personal vehicles. Numerous pre-existing routines. It is thus important that this solution be used. Significant obstructions or low-resolution vehicles may benefit from this tool low, where the derived characteristics are notoriously unstable. For example, our method has two important contributions. Firstly, the backdrop segments are detected using a stretching approach that has been devised. Unclassified cars are found in these areas.
Mathematical models vehicles may be tracked using Kaplan filtering (e.g. it’s not necessary to minimize the deformation of the vehicle induced by the mesh grid with a quel motion parallax effect throughout the warping process, transformations are calculated and applied. N process. A subset of these low-level traits is then extracted. Construct a tumbled linear regression for the foreground section to directly num-
ber and categorize automobiles, without the aid of any third party pertaining to connected vehicles. There are three distinct ways to approach this. The creation and assessment of regression regressions. Our find- ings are supported by experiments. With low-quality datasets, linear extrapolation algorithms are accu- rate and resilient many present techniques fail effectively extract useful information from qualities you
can rely on high index terms
Fixed point results of -weak contractions in ordered -metric spaces
Get PDFThe purpose of this paper is to prove some results on fixed point, coincidence point, coupled coincidence point and coupled common fixed point for the mappings satisfying generalized -contraction conditions in complete partially ordered -metric spaces. Our results generalize, extend and unify most of the fundamental metrical fixed point theorems in the existing literature. A few examples are illustrated to support our findings
Biocompatible and UV triggered energy transfer based color tunable emission from Ce3+/Eu3+ co-doped lithium zinc borate glasses for white light applications
No full textA series of Ce3+/Eu3+ co-doped biocompatible lithium zinc borate glasses were synthesized via melt quenching. X-ray diffraction, Raman and scanning electron microscopy studies were carried out for synthesized glasses. The photoluminescence spectrum of Ce3+ glass exhibited a broad blue band at 447 nm (5d→4f) with λexc = 350 nm. The optimized content of Eu3+ (1.0 mol%) doped glass exhibited an intense red emission at 612 nm (5D0→7F2) under λexc = 393 nm. The energy transfer is supported by spectral overlap, fluorescence, decay dynamics, and Commission Internationale de l'Elcairage color coordinates. Inokuti–Hirayama model revealed energy transfer from Ce3+ to Eu3+ is dominated by dipole-dipole interaction. The chromaticity coordinates of Eu3+ have been shifted from red to white region by inclusion of Ce3+ ions due to efficient energy transfer from Ce3+ to Eu3+. In vitro Prestoblue assay result confirms non-cytotoxicity with appreciable biocompatibility. Based on these spectral features, Ce3+/Eu3+ co-doped biocompatible lithium zinc borate glasses can be suggested as a promising candidate for white light applications
An enhanced bacterial foraging optimization algorithm for secure data storage and privacy-preserving in cloud
Get PDFCloud fle access is the most widely used peer-to-peer (P2P) application, in which users share their data and other users can
access it via P2P networks. The need for security in the cloud system grows day by day, as organizations collect a massive
amount of users' confdential information. Both the outsourced data and the unprotected user's sensitive data need to be
protected under the cloud security claims since the advanced P2P networks are prone to damage. The recurring security
breach in the cloud necessitates the establishment of an advanced legal data protection strategy. Various researchers have
attempted to develop privacy-preserving cloud computing systems employing Artifcial Intelligence (AI) techniques, however, they have not been successful in achieving optimal privacy. AI approaches implemented in the cloud assist applications in efcient data management by analyzing, updating, classifying, and providing users with real-time decision-making support. AI approaches can also detect fraudulent activity by analyzing deviations in normal data patterns entering the system. To handle the security concerns in the cloud, this paper presents a novel cybersecurity architecture using the Chaotic chemotaxis and Gaussian mutation-based Bacterial Foraging Optimization with a genetic crossover operation (CGBFO-GC) algorithm. The CGBF0-GC algorithm cleanses and restores the data using a multiobjective optimal key generation mechanism based on the following constraints: data preservation, modifcation, and hiding ratio. The simulation results show that the proposed methodology outperforms existing methods in terms of convergence, key sensitivity analysis, and resistance to known and chosen-plaintext attacks
Super Resolution for Magnetic Resonance Images Using Self-Super Resolution Technique
Get PDFHigh resolution magnetic resonance~(MR) imaging~(MRI) is desirable in many clinical applications, however, there is a trade-off between resolution, speed of acquisition, and noise. It is common for MR images to have worse through-plane resolution~(slice thickness) than in-plane resolution. In these MRI images, high frequency information in the through-plane direction is not acquired, and cannot be resolved through interpolation. To address this issue, super-resolution methods have been developed to enhance spatial resolution. As an ill-posed problem, state-of-the-art super-resolution methods rely on the presence of external/training atlases to learn the transform from low resolution~(LR) images to high resolution~(HR) images. For several reasons, such HR atlas images are often not available for MRI sequences. This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image. We use a blurred version of the input image to create training data for a state-of-the-art super-resolution deep network. The trained network is applied to the original input image to estimate the HR image. Our SSR result shows a significant improvement on through-plane resolution compared to competing SSR methods
Multi-Class Classification and Prediction of Heart Sounds Using Stacked LSTM to Detect Heart Sound Abnormalities
Get PDFThe changes in lifestyle, food habits, and working conditions cause various diseases in human lives, cardiovascular diseases are one of those. Not only aged people, middle-aged and young people are also suffering due to this and lead to death in the early ages. So there is a significant need in detecting cardiovascular diseases in beginning itself. Through early detection and persistent treatment, the death rate in the early ages due to cardiovascular diseases can be reduced. However, it is necessary to have an efficient model to detect heart disease at an early stage even without the presence of a trained clinical expert. This paper studies the implementation of deep learning models to classify heartbeat sounds into various classes. We proposed a stacked LSTM model to classify the heartbeat sound into multiple classes based on the features obtained from the audio signals. The implementation can even predict the class of an unlabelled heartbeat sound. The model classifies the heartbeat sounds into 4 classes with accuracies 85% and 87% on training and validation sets respectively. In further the proposed model parameters can be improved to increase the classification and prediction accuracy
Video Usefulness Detection in Big Surveillance Systems
Get PDFFeature selection methods have been issued in the context of data classification due to redundant and irrelevant features. The above features slow the overall system performance,
and wrong decisions are more likely to be made with extensive data sets. Several methods have been used to solve the feature selection problem for classification, but most are specific to be
used only for a particular data set. Thus, this paper proposes wide-ranging approaches to solve maximum feature selection problems for data sets. The proposed algorithm analytically
chooses the optimal feature for classification by utilizing mutual information (MI) and linear correlation coefficients (LCC). It considers linearly and nonlinearly dependent data features for
the same. The proposed feature selection algorithm suggests various features used to build a substantial feature subset for classification, effectively reducing irrelevant features. Three
different datasets are used to evaluate the performance of the proposed algorithm with classifiers which requires a higher degree of features to have better accuracy and a lower
computational cost. We considered probability value (p value <0.05) for feature selection in experiments on different data sets, then the number of features is selected (such as 7, 5, and 6
features from mobile, heart, and diabetes data set, respectively). Various accuracy is considere