scholarly journals A Spam Transformer Model for SMS Spam Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xiaoxu Liu ◽  
Haoye Lu ◽  
Amiya Nayak
Keyword(s):  
Spam Detection ◽  
Transformer Model

Power Losses in RF Inductor of Ferrite-Free Closed-Loop Inductively-Coupled Low Pressure Mercury Lamps

2020 ◽  
pp. 89-94 ◽  
Author(s):  
Ekaterina V. Lovlya ◽  
Oleg A. Popov
Keyword(s):  
Closed Loop ◽  
Low Pressure ◽  
Mercury Vapour ◽  
Plasma Power ◽  
Power Losses ◽  
Inductive Discharge ◽  
Inductively Coupled ◽  
Radial Inhomogeneity ◽  
Transformer Model ◽  
Rf Inductor

RF inductor power losses of ferrite-free electrode-less low pressure mercury inductively-coupled discharges excited in closed-loop dielectric tube were studied. The modelling was made within the framework of low pressure inductive discharge transformer model for discharge lamps with tubes of 16, 25 and 38 mm inner diam. filled with the mixture of mercury vapour (7.5×10–3 mm Hg) and argon (0.1, 0.3 and 1.0 mm Hg) at RF frequencies of 1, 7; 3.4 and 5.1 MHz and plasma power of (25–500) W. Discharges were excited with the help of the induction coil of 3, 4 and 6 turns placed along the inner perimeter of the closed-loop tube. It was found that the dependence of coil power losses, Pcoil, on the discharge plasma power, Ppl, had the minimum while Pcoil decreased with RF frequency, tube diameter and coil number of turns. The modelling results were found in good qualitative agreement with the experimental data; quantitative discrepancies are believed to be due skin-effect and RF electric field radial inhomogeneity that were not included in discharge modelling.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

scholarly journals Input Parallel Output Series Structure of Planar Medium Frequency Transformers for 200 kW Power Converter: Model and Parameters Evaluation

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1450
Author(s):  
Alessandro La Ganga ◽  
Roberto Re ◽  
Paolo Guglielmi
Keyword(s):  
Electric Vehicles ◽  
High Performance ◽  
Power Converter ◽  
Electrical Model ◽  
Solid State Transformer ◽  
Medium Frequency ◽  
Analytical Treatment ◽  
Transformer Model ◽  
Planar Medium ◽  
Parallel Output

Nowadays, the demand for high power converters for DC applications, such as renewable sources or ultra-fast chargers for electric vehicles, is constantly growing. Galvanic isolation is mandatory in most of these applications. In this context, the Solid State Transformer (SST) converter plays a fundamental role. The adoption of the Medium Frequency Transformers (MFT) guarantees galvanic isolation in addition to high performance in reduced size. In the present paper, a multi MFT structure is proposed as a solution to improve the power density and the modularity of the system. Starting from 20kW planar transformer model, experimentally validated, a multi-transformer structure is analyzed. After an analytical treatment of the Input Parallel Output Series (IPOS) structure, an equivalent electrical model of a 200kW IPOS (made by 10 MFTs) is introduced. The model is validated by experimental measurements and tests.

Enhancement of email spam detection using improved deep learning algorithms for cyber security

2021 ◽  
pp. 1-34
Author(s):  
Kadam Vikas Samarthrao ◽  
Vandana M. Rohokale
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Visual Features ◽  
Spam Detection ◽  
Learning Approaches ◽  
Learning Technique ◽  
Text Features ◽  
Optimal Feature Selection ◽  
Optimal Feature ◽  
Email Spam

Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model involves several phases like dataset acquisition, feature extraction, optimal feature selection, and detection. Initially, the benchmark dataset of email is collected that involves both text and image datasets. Next, the feature extraction is performed using two sets of features like text features and visual features. In the text features, Term Frequency-Inverse Document Frequency (TF-IDF) is extracted. For the visual features, color correlogram and Gray-Level Co-occurrence Matrix (GLCM) are determined. Since the length of the extracted feature vector seems to the long, the optimal feature selection process is done. The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA). Once the optimal features are selected, the detection is performed by the hybrid learning technique that is composed of two deep learning approaches named Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For improving the performance of existing deep learning approaches, the number of hidden neurons of RNN and CNN is optimized by the same FLI-DA. Finally, the optimized hybrid learning technique having CNN and RNN classifies the data into spam and ham. The experimental outcomes show the ability of the proposed method to perform the spam email classification based on improved deep learning.

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