scholarly journals Doppler Spread Estimation Based on Machine Learning for an OFDM System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Eunchul Yoon ◽  
Soonbum Kwon ◽  
Unil Yun ◽  
Sun-Yong Kim
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
Training Data ◽  
Doppler Spread ◽  
Learning Approach ◽  
Ofdm System ◽  
Angle Of Arrival ◽  
Estimation Errors ◽  
Machine Learning Approach ◽  
Spread Estimation

In this paper, we propose a Doppler spread estimation approach based on machine learning for an OFDM system. We present a carefully designed neural network architecture to achieve good performance in a mixed-channel scenario in which channel characteristic variables such as Rician K factor, azimuth angle of arrival (AOA) width, mean direction of azimuth AOA, and channel estimation errors are randomly generated. When preprocessing the channel state information (CSI) collected under the mixed-channel scenario, we propose averaged power spectral density (PSD) sequence as high-quality training data in machine learning for Doppler spread estimation. We detail intermediate mathematical derivatives of the machine learning process, making it easy to graft the derived results into other wireless communication technologies. Through simulation, we show that the machine learning approach using the averaged PSD sequence as training data outperforms the other machine learning approach using the channel frequency response (CFR) sequence as training data and two other existing Doppler estimation approaches.

A Survey on Intrusion Detection System for Software Defined Networks (SDN)

2020 ◽  
Vol 16 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Yogita Hande ◽  
Akkalashmi Muddana
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Network Architecture ◽  
Detection System ◽  
Learning Approach ◽  
Distinctive Features ◽  
Detection Systems ◽  
Security Challenges ◽  
Machine Learning Approach

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

scholarly journals A Comprehensive Analysis of Deep Neural-Based Cerebral Microbleeds Detection System

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2208
Author(s):  
Maria Anna Ferlin ◽  
Michał Grochowski ◽  
Arkadiusz Kwasigroch ◽  
Agnieszka Mikołajczyk ◽  
Edyta Szurowska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Cerebral Microbleeds ◽  
Training Data ◽  
Learning Approach ◽  
Dimensional Problem ◽  
Reliable System ◽  
Machine Learning Approach

Machine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved, it would streamline the radiologists work. To deal with this complex three-dimensional problem, we propose a machine learning approach based on a 2D Faster RCNN network. We aimed to achieve a reliable system, i.e., with balanced sensitivity and precision. Therefore, we have researched and analysed, among others, impact of the way the training data are provided to the system, their pre-processing, the choice of model and its structure, and also the ways of regularisation. Furthermore, we also carefully analysed the network predictions and proposed an algorithm for its post-processing. The proposed approach enabled for obtaining high precision (89.74%), sensitivity (92.62%), and F1 score (90.84%). The paper presents the main challenges connected with automatic cerebral microbleeds detection, its deep analysis and developed system. The conducted research may significantly contribute to automatic medical diagnosis.

scholarly journals A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens

2020 ◽  
Vol 6 (39) ◽  
pp. eaba9338 ◽  
Author(s):  
George W. Ashdown ◽  
Michelle Dimon ◽  
Minjie Fan ◽  
Fernando Sánchez-Román Terán ◽  
Kathrin Witmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanism Of Action ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Cross Resistance ◽  
Learning Approach ◽  
Imaging Data ◽  
Drug Induced ◽  
Effective Prevention ◽  
Machine Learning Approach

Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. These methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labeled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.

scholarly journals HAMLET

Terminology ◽  
2021 ◽  
Author(s):  
Ayla Rigouts Terryn ◽  
Véronique Hoste ◽  
Els Lefever
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Hybrid Approach ◽  
Substantial Effect ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Term Extraction ◽  
Machine Learning Approach ◽  
Different Types

Abstract Automatic term extraction (ATE) is an important task within natural language processing, both separately, and as a preprocessing step for other tasks. In recent years, research has moved far beyond the traditional hybrid approach where candidate terms are extracted based on part-of-speech patterns and filtered and sorted with statistical termhood and unithood measures. While there has been an explosion of different types of features and algorithms, including machine learning methodologies, some of the fundamental problems remain unsolved, such as the ambiguous nature of the concept “term”. This has been a hurdle in the creation of data for ATE, meaning that datasets for both training and testing are scarce, and system evaluations are often limited and rarely cover multiple languages and domains. The ACTER Annotated Corpora for Term Extraction Research contain manual term annotations in four domains and three languages and have been used to investigate a supervised machine learning approach for ATE, using a binary random forest classifier with multiple types of features. The resulting system (HAMLET Hybrid Adaptable Machine Learning approach to Extract Terminology) provides detailed insights into its strengths and weaknesses. It highlights a certain unpredictability as an important drawback of machine learning methodologies, but also shows how the system appears to have learnt a robust definition of terms, producing results that are state-of-the-art, and contain few errors that are not (part of) terms in any way. Both the amount and the relevance of the training data have a substantial effect on results, and by varying the training data, it appears to be possible to adapt the system to various desired outputs, e.g., different types of terms. While certain issues remain difficult – such as the extraction of rare terms and multiword terms – this study shows how supervised machine learning is a promising methodology for ATE.

scholarly journals Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope

2020 ◽  
Vol 21 (10) ◽  
pp. 3585 ◽  
Author(s):  
Neann Mathai ◽  
Johannes Kirchmair
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Target Prediction ◽  
Training Data ◽  
Target Space ◽  
Learning Approach ◽  
Learning Approaches ◽  
Individual Test ◽  
Machine Learning Approach ◽  
The Individual

Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.

scholarly journals A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events

2018 ◽  
Author(s):  
Neel S. Madhukar ◽  
Kaitlyn Gayvert ◽  
Coryandar Gilvary ◽  
Olivier Elemento
Keyword(s):  
Machine Learning ◽  
Adverse Events ◽  
Drug Development ◽  
Training Data ◽  
Tissue Type ◽  
Learning Approach ◽  
Tissue Specific ◽  
Machine Learning Approach ◽  
Different Types ◽  
Development Pipeline

ABSTRACTOne of the main causes for failure in the drug development pipeline or withdrawal post approval is the unexpected occurrence of severe drug adverse events. Even though such events should be detected by in vitro, in vivo, and human trials, they continue to unexpectedly arise at different stages of drug development causing costly clinical trial failures and market withdrawal. Inspired by the “moneyball” approach used in baseball to integrate diverse features to predict player success, we hypothesized that a similar approach could leverage existing adverse event and tissue-specific toxicity data to learn how to predict adverse events. We introduce MAESTER, a data-driven machine learning approach that integrates information on a compound’s structure, targets, and phenotypic effects with tissue-wide genomic profiling and our toxic target database to predict the probability of a compound presenting with different types of tissue-specific adverse events. When tested on 6 different types of adverse events MAESTER maintains a high accuracy, sensitivity, and specificity across both the training data and new test sets. Additionally, MAESTER scores could flag a number of drugs that were approved, but later withdrawn due to unknown adverse events – highlighting its potential to identify events missed by traditional methods. MAESTER can also be used to identify toxic targets for each tissue type. Overall MAESTER provides a broadly applicable framework to identify toxic targets and predict specific adverse events and can accelerate the drug development pipeline and drive the design of new safer compounds.

scholarly journals The K-Means Algorithm for Generating Sets of Items in Educational Assessment

2021 ◽  
Vol 6 (1) ◽  
pp. 93-100
Author(s):  
Lala Septem Riza ◽  
Rendi Adistya Rosdiyana ◽  
Alejandro Rosales Pérez ◽  
Asep Wahyudin
Keyword(s):  
Machine Learning ◽  
Educational Assessment ◽  
Assessment System ◽  
Training Data ◽  
Learning Approach ◽  
National Scale ◽  
National Examination ◽  
Generating Sets ◽  
Machine Learning Approach ◽  
Level Of Difficulty

In a national-scale educational assessment system, such as the National Examination, the need for several sets of questions that have the same level of difficulty is very required to avoid cheating by students. Therefore, the objective, which is to make a set of questions with the same level of difficulty automatically, is done in this research. It used a machine learning approach, namely K-Means. To achieve this goal, several following procedures need to be implemented. Firstly, we need to create banks of questions to be assigned to students. Then, we build training data by determining the value of each question based on Bloom's Taxonomy, item characters/types, and other parameters. Then, with utilizing K-Means, several cluster centers are obtained to represent the uniformity of the questions in the cluster members. By using several heuristics criteria defined previously, several sets or packages of questions that have the same characteristics and difficulty levels are obtained. From the experiments conducted, the analysis with descriptive (i.e., mean, standard deviation, and data visualization) and inference (i.e., ANOVA) statistics of results are presented showing that questions of each sets have the same characteristics to ensure the fairness of examinations. Moreover, by using this system, the contents of the questions in the generated set do not need to be the same, the package of questions can be generated automatically quickly, and the level of the difficulties can be measured and guaranteed.

scholarly journals A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4035 ◽  
Author(s):  
Abdollah Malekjafarian ◽  
Fatemeh Golpayegani ◽  
Callum Moloney ◽  
Siobhán Clarke
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Training Data ◽  
Learning Approach ◽  
Prediction Errors ◽  
Data Set ◽  
Machine Learning Approach ◽  
Vehicle Acceleration ◽  
Bridge Damage ◽  
Artificial Neural Network Ann

This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) over a healthy bridge. The vehicle acceleration or Discrete Fourier Transform (DFT) spectrum of the acceleration is used. The vehicle response is predicted from its speed for multiple passes (monitoring data set) over the bridge. Root-mean-square error is used to calculate the prediction error, which indicates the differences between the predicted and measured responses for each passage. In the second stage of the proposed method, a damage indicator is defined using a Gaussian process that detects the changes in the distribution of the prediction errors. It is suggested that if the bridge condition is healthy, the distribution of the prediction errors will remain low. A recognizable change in the distribution might indicate a damage in the bridge. The performance of the proposed approach was evaluated using numerical case studies of vehicle–bridge interaction. It was demonstrated that the approach could successfully detect the damage in the presence of road roughness profile and measurement noise, even for low damage levels.

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