scholarly journals Baby Cry Detection: Deep Learning and Classical Approaches

2019 ◽  
Author(s):  
Rami Cohen ◽  
Dima Ruinskiy ◽  
Janis Zickfeld ◽  
Hans IJzerman ◽  
Yizhar Lavner
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Social Research ◽  
Signal To Noise Ratio ◽  
Support Vector ◽  
Temporal Behavior ◽  
Learning Approaches ◽  
Signal To Noise ◽  
Vector Machines ◽  
Domestic Environments

In this chapter, we compare deep learning and classical approaches for detection of baby cry sounds in various domestic environments under challenging signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network (CNN) architectures for baby cry detection, and compare their performance to that of classical machine-learning approaches, such as logistic regression and support vector machines. In addition to feed-forward CNNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized non-symmetric kernels, better results are obtained compared to common CNN architectures.

scholarly journals Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1694
Author(s):  
Mathew Ashik ◽  
A. Jyothish ◽  
S. Anandaram ◽  
P. Vinod ◽  
Francesco Mercaldo ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Support Vector ◽  
Malware Analysis ◽  
Learning Approaches ◽  
Dynamic Features ◽  
System Calls ◽  
Prevention Methods ◽  
Structural Aspects

Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

scholarly journals Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication

2021 ◽  
Vol 9 (11) ◽  
pp. 1252
Author(s):  
Yufei Liu ◽  
Feng Zhou ◽  
Gang Qiao ◽  
Yunjiang Zhao ◽  
Guang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Spread Spectrum ◽  
Communication System ◽  
Signal To Noise Ratio ◽  
Cyclic Shift ◽  
Conventional System ◽  
Signal To Noise ◽  
Underwater Acoustic ◽  
Shift Keying

A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10−3 can be obtained at a signal-to-noise ratio of −8 dB.

scholarly journals Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 147 ◽  
Author(s):  
Muhammad Aslam ◽  
Jae-Myeong Lee ◽  
Hyung-Seung Kim ◽  
Seung-Jae Lee ◽  
Sugwon Hong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Solar Radiation ◽  
Comparative Study ◽  
Short Term Memory ◽  
State Of The Art ◽  
Support Vector ◽  
Learning Approaches ◽  
Solar Generation

Microgrid is becoming an essential part of the power grid regarding reliability, economy, and environment. Renewable energies are main sources of energy in microgrids. Long-term solar generation forecasting is an important issue in microgrid planning and design from an engineering point of view. Solar generation forecasting mainly depends on solar radiation forecasting. Long-term solar radiation forecasting can also be used for estimating the degradation-rate-influenced energy potentials of photovoltaic (PV) panel. In this paper, a comparative study of different deep learning approaches is carried out for forecasting one year ahead hourly and daily solar radiation. In the proposed method, state of the art deep learning and machine learning architectures like gated recurrent units (GRUs), long short term memory (LSTM), recurrent neural network (RNN), feed forward neural network (FFNN), and support vector regression (SVR) models are compared. The proposed method uses historical solar radiation data and clear sky global horizontal irradiance (GHI). Even though all the models performed well, GRU performed relatively better compared to the other models. The proposed models are also compared with traditional state of the art methods for long-term solar radiation forecasting, i.e., random forest regression (RFR). The proposed models outperformed the traditional method, hence proving their efficiency.

Literature review and analysis on big data stream classification techniques

2020 ◽  
Vol 24 (3) ◽  
pp. 205-215
Author(s):  
B. Srivani ◽  
N. Sandhya ◽  
B. Padmaja Rani
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Data Classification ◽  
Support Vector ◽  
Complex Data ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Data Stream Classification ◽  
Big Data Classification

Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utilized, adopted classification methods, and accuracy range obtained by various techniques. Eventually, the research gaps and issues of various big data classification schemes are presented.

ReClass: An Application of Deep Learning for Real-time Reading Detection Using an Eye Tracking Device

2018 ◽  
Vol 62 (1) ◽  
pp. 307-310 ◽  
Author(s):  
Hoa T. Nguyen ◽  
Jens-Patrick Langstrand ◽  
Michael Hildebrandt
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Support Vector ◽  
Feature Engineering ◽  
Learning Approaches ◽  
Potential Applications ◽  
Time Reading ◽  
Deep Learning Neural Network ◽  
Tracking Device

We demonstrate the ReClass system, a real time reading detection classifier. ReClass detects if a user is reading text or not solely based on the user’s eye movements, without considering the content of the screen. We examined two machine learning approaches. In the first approach, we pre-processed the data using feature engineering and trained a Linear Support Vector Machine classifier. The second approach used a Deep Learning Neural Network; instead of feature engineering, we allowed the neural network to extract features from the raw data. The second approach outperformed the first by about 25%, achieving 96% accuracy. Our tool illustrates how Deep Learning can be a new innovative method for teaching machines to understand human behaviour. We discuss the potential applications of ReClass for educational assessment, medical diagnosis and training.

A Comparative Study of Transfer Learning Approaches for Video Anomaly Detection

2020 ◽  
pp. 2152003
Author(s):  
Matheus Gutoski ◽  
Manassés Ribeiro ◽  
Leandro T. Hattori ◽  
Marcelo Romero ◽  
André E. Lazzaretti ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Transfer Learning ◽  
Visual Analysis ◽  
State Of The Art ◽  
Detection Methods ◽  
Support Vector ◽  
Learning Approaches ◽  
Vector Machines ◽  
Benchmark Datasets

Recent research has shown that features obtained from pretrained Convolutional Neural Network (CNN) models can be promptly applied to a variety of problems they were not originally designed to solve. This concept, often referred to as Transfer Learning (TL), is a common practice when labeled data is limited. In some fields, such as video anomaly detection, TL is still an underexplored subject in the sense that it is not clear whether the architecture of the pretrained CNN model impacts on the video anomaly detection performance. In order to clarify this issue, we perform an extensive benchmark using 12 different pretrained CNN models on ImageNet as feature extractors and apply the features obtained to seven video anomaly detection benchmark datasets. This work presents some interesting findings about video anomaly detection using TL. The highlights of our findings were revealed by our experiments, which have shown that a simple classification process using One-Class Support Vector Machines yields similar results to state-of-the-art models. Moreover, a statistical analysis suggests that architectural differences are negligible when choosing a pretrained model for video anomaly detection, since all models presented similar performance. At last, we present an in-depth visual analysis of the Avenue dataset, and reveal several aspects that may be limiting the performance of state-of-the-art video anomaly detection methods.

scholarly journals Similarity-Based Summarization of Music Files for Support Vector Machines

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jan Jakubik ◽  
Halina Kwaśnicka
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Support Vector Machines ◽  
Loss Function ◽  
Expert Knowledge ◽  
Feature Learning ◽  
Compact Representation ◽  
Support Vector ◽  
Learning Methods ◽  
Vector Machines

Automatic retrieval of music information is an active area of research in which problems such as automatically assigning genres or descriptors of emotional content to music emerge. Recent advancements in the area rely on the use of deep learning, which allows researchers to operate on a low-level description of the music. Deep neural network architectures can learn to build feature representations that summarize music files from data itself, rather than expert knowledge. In this paper, a novel approach to applying feature learning in combination with support vector machines to musical data is presented. A spectrogram of the music file, which is too complex to be processed by SVM, is first reduced to a compact representation by a recurrent neural network. An adjustment to loss function of the network is proposed so that the network learns to build a representation space that replicates a certain notion of similarity between annotations, rather than to explicitly make predictions. We evaluate the approach on five datasets, focusing on emotion recognition and complementing it with genre classification. In experiments, the proposed loss function adjustment is shown to improve results in classification and regression tasks, but only when the learned similarity notion corresponds to a kernel function employed within the SVM. These results suggest that adjusting deep learning methods to build data representations that target a specific classifier or regressor can open up new perspectives for the use of standard machine learning methods in music domain.

Sign in / Sign up

Export Citation Format

Share Document