scholarly journals Overview of topical issues in basic spam filtering methods and analysis of their effectiveness

2021 ◽  
pp. 98-106
Author(s):  
Вера Аркадьевна Частикова ◽  
Константин Валерьевич Козачёк
Keyword(s):  
Neural Networks ◽  
Text Analysis ◽  
Spam Detection ◽  
Bag Of Words ◽  
Classification Methods ◽  
Spam Filtering ◽  
Security Systems ◽  
Detection Systems ◽  
Modern Methods ◽  
Text Presentation

Представлен анализ основных проблем фильтрации почтового спама, современных методов фильтрации нежелательных писем и способов обхода систем защиты. Вводится понятие « легитимного спама » - новой проблемы, с которой сталкиваются пользователи электронной почты. Рассмотрены методы представления текста: bag-of-words и Embedding-пространство, а также методы классификации: искусственные нейронные сети, метод опорных векторов, наивный байесовский классификатор. В работе определены эффективные методы, построенные на анализе текста, для решения задач обнаружения различных видов спама: типичного ( известного системе ) , составленного при помощи методов обхода систем детекции спама, и легитимного. An analysis of the main problems of filtering mail spam, modern methods of filtering unwanted letters and methods of bypassing security systems is presented. The concept of “legitimate spam” is being introduced - a new problem that email users face. Methods of text presentation are considered: bag-of-words and Embedding-space, as well as classification methods: artificial neural networks, the method of reference vectors, naive Bayesian classifier. The work identifies effective methods based on text analysis, for solving the problems of detecting various types of spam: a typical (known to system), compiled using methods of bypassing spam detection systems, and legitimate.

scholarly journals ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ecg Signals ◽  
Detection Systems ◽  
Language Interpretation ◽  
Different Types ◽  
Time Courses ◽  
Natural Language Interpretation ◽  
Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

scholarly journals Security assessment and optimization of energy supply (neural networks approach)

2015 ◽  
Vol 6 (2) ◽  
pp. 129 ◽  
Author(s):  
Tomasz Jasiński ◽  
Agnieszka Ścianowska
Keyword(s):  
Neural Networks ◽  
Network Topology ◽  
Energy Supply ◽  
Input Data ◽  
Energy Market ◽  
Security Assessment ◽  
Economic Optimization ◽  
Emergency Situations ◽  
Modern Methods ◽  
Reserve Market

The question of energy supply continuity is essential from the perspective of the functioning of society and the economy today. The study describes modern methods of forecasting emergency situations using Artificial Intelligence (AI) tools, especially neural networks. It examines the structure of a properly functioning model in the areas of input data selection, network topology and learning algorithms, analyzes the functioning of an energy market built on the basis of a reserve market, and discusses the possibilities of economic optimization of such a model, including the question of safety.

scholarly journals Neural Networks Art: Solving Problems with Multiple Solutions and New Teaching Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 15-21
Author(s):  
Dmitrienko V. D ◽  
Yu. Zakovorotnyi A ◽  
Yu. Leonov S ◽  
Khavina I. P
Keyword(s):  
Neural Networks ◽  
Multiple Solutions ◽  
Input Data ◽  
Learning Algorithms ◽  
Adaptive Resonance Theory ◽  
Classification Methods ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Noisy Input ◽  
New Algorithms

A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input.

scholarly journals Machine Learning–based Analysis of English Lateral Allophones

2019 ◽  
Vol 29 (2) ◽  
pp. 393-405 ◽  
Author(s):  
Magdalena Piotrowska ◽  
Gražina Korvel ◽  
Bożena Kostek ◽  
Tomasz Ciszewski ◽  
Andrzej Cżyzewski
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Nearest Neighbor ◽  
Native Speakers ◽  
Automatic Evaluation ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Self Organizing Maps ◽  
Automatic Methods ◽  
Audio Video

Abstract Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’ and phonology experts’ speech was selected for analyses. For the purpose of the present study, a sub-list of 103 words containing the English alveolar lateral phoneme /l/ was compiled. The list includes ‘dark’ (velarized) allophonic realizations (which occur before a consonant or at the end of the word before silence) and 52 ‘clear’ allophonic realizations (which occur before a vowel), as well as voicing variants. The recorded signals were segmented into allophones and parametrized using a set of descriptors, originating from the MPEG 7 standard, plus dedicated time-based parameters as well as modified MFCC features proposed by the authors. Classification methods such as ANNs, the kNN and the SOM were employed to automatically detect the two types of allophones. Various sets of features were tested to achieve the best performance of the automatic methods. In the final experiment, a selected set of features was used for automatic evaluation of the pronunciation of dark /l/ by non-native speakers.

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