scholarly journals Comprehensive Analysis of Machine Learning Algorithms for Face Detection

2019 ◽  
Vol 8 (10) ◽  
pp. 2479-2482
Keyword(s):  
Machine Learning ◽  
Face Detection ◽  
Processing Speed ◽  
Deep Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cascade Classifier ◽  
Histogram Of Oriented Gradient ◽  
Haar Cascade ◽  
Common Application

Face detection is the most common application used in security system, cameras, fun face filter apps, etc. many techniques and algorithms are introduced by developers for face detection in real time but all techniques or algorithms does not give best results while applying on all ranges of processors. In this, three machine learning algorithms i.e. Histogram of Oriented Gradient, Haar cascade classifier and deep neural networks implemented on different processors for verifying processing speed of each algorithm on the different processor.

scholarly journals Visitor/Intruder Monitoring System using Haar-cascade Classifier Algorithm

2020 ◽  
pp. 229-231
Author(s):  
Jenifa G ◽  
Yuvaraj N ◽  
SriPreethaa K R
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Monitoring System ◽  
Face Detection ◽  
Security System ◽  
Machine Learning Algorithms ◽  
Advanced Technology ◽  
Cascade Classifier ◽  
Home Security ◽  
Haar Cascade

Home security system plays a predominant role in the modern era. The purpose of the security systems is to protect the members of the family from intruders. The main idea behind this system is to provide security for residential areas. In today’s world securing our home takes a major role in the society. Surveillance from home to huge industries, plays a significant role in the fulfilment of our security. There are many machine learning algorithms for home security system but Haar-cascade classifier algorithm gives a better result when compared with other machine learning algorithm This system implements a face recognition and face detection using Haar-cascade classifier algorithm, OpenCV libraries are used for training and testing of the face detection process. In future, face recognition will be everywhere in the world. Face recognition is creating a magic in every field with its advanced technology. Visitor/Intruder monitoring system using Machine Learning is used to monitor the person and find whether the person is a known or unknown person from the captured picture. Here LBPH (Local Binary Pattern Histogram) Face Recognizer is used. After capturing the image, it is compared with the available dataset then their respective name and picture is sent to the specified email to alert the owner.

EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS

2021 ◽  
pp. 44-51
Author(s):  
E. Yu. Shchetinin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Deep Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Audio Recordings ◽  
Computer Studies

The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotions: 0 – neutral, 1 – calm, 2 – happiness, 3 – sadness, 4 – anger, 5 – fear, 6 – disgust, 7 – surprise. In total, the database contains 16 classes (8 emotions divided into male and female) for a total of 1440 samples (speech only). To train machine learning algorithms and deep neural networks to recognize emotions, existing audio recordings must be pre-processed in such a way as to extract the main characteristic features of certain emotions. This was done using Mel-frequency cepstral coefficients, chroma coefficients, as well as the characteristics of the frequency spectrum of audio recordings. In this paper, computer studies of various models of neural networks for emotion recognition are carried out on the example of the data described above. In addition, machine learning algorithms were used for comparative analysis. Thus, the following models were trained during the experiments: logistic regression (LR), classifier based on the support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting over trees – XGBoost, convolutional neural network CNN, recurrent neural network RNN (ResNet18), as well as an ensemble of convolutional and recurrent networks Stacked CNN-RNN. The results show that neural networks showed much higher accuracy in recognizing and classifying emotions than the machine learning algorithms used. Of the three neural network models presented, the CNN + BLSTM ensemble showed higher accuracy.

scholarly journals Detecting Emotions in English and Arabic Tweets

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 98 ◽  
Author(s):  
Tariq Ahmad ◽  
Allan Ramsay ◽  
Hanady Ahmed
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Current State ◽  
Optimal Thresholds ◽  
Alternative Approach

Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.

scholarly journals Machine Learning Algorithms in Cardiology Domain: A Systematic Review

2020 ◽  
Vol 13 (1) ◽  
pp. 25-40
Author(s):  
Aleksei Dudchenko ◽  
Matthias Ganzinger ◽  
Georgy Kopanitsa
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Risk Of Mortality ◽  
Prisma Statement ◽  
Conference Papers ◽  
Huge Variety ◽  
Learning Data

Background: It could be seen in the previous decades that Machine Learning (ML) has a huge variety of possible implementations in medicine and can be of great use. Nevertheless, cardiovascular diseases cause about a third of the total global deaths. Does ML work in the cardiology domain and what is the current progress in this regard? To answer this question, we present a systematic review aiming at 1) identifying studies where machine learning algorithms were applied in the domain of cardiology; 2) providing an overview based on the existing literature about the state-of-the-art ML algorithms applied in cardiology. Methods: For organizing this review, we adopted the PRISMA statement. We used PubMed as the search engine and identified the search keywords as “Machine Learning”, “Data Mining”, “Cardiology”, and “Cardiovascular” in combinations. Scientific articles and conference papers published between 2013-2017 reporting about implementations of ML algorithms in the domain of cardiology have been included in this review. Results: In total, 27 relevant papers were included. We examined four aspects: the aims of ML systems, the methods, datasets, and evaluation metrics. The major part of the paper was aimed at predicting the risk of mortality. A promising branch of Machine Learning, the ‘Reinforcement Learning’, was also never proposed in the observed papers. Tree-based ensembles are common and show good results, whereas deep neural networks are poorly represented. Most papers (20 of 27) have used datasets that are hardly available for other researchers, e.g. unpublished local registries. We also identified 28 different metrics for model evaluation. This variety of metrics makes it difficult to compare the results of different researches. Conclusion: We suppose that this systematic review will be helpful for researchers developing medical machine learning systems and for cardiology in particular.

scholarly journals Identification of Network Traffic over IOT Platforms

2021 ◽  
Vol 35 (4) ◽  
pp. 349-357
Author(s):  
Shilpa P. Khedkar ◽  
Aroul Canessane Ramalingam
Keyword(s):  
Machine Learning ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
Heavy Traffic ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Deep Learning Algorithm ◽  
Iot Platforms ◽  
The Internet Of Things

The Internet of Things (IoT) is a rising infrastructure of 21st century. The classification of traffic over IoT networks is attained significance importance due to rapid growth of users and devices. It is need of the hour to isolate the normal traffic from the malicious traffic and to assign the normal traffic to the proper destination to suffice the QoS requirements of the IoT users. Detection of malicious traffic can be done by continuously monitoring traffic for suspicious links, files, connection created and received, unrecognised protocol/port numbers, and suspicious Destination/Source IP combinations. A proficient classification mechanism in IoT environment should be capable enough to classify the heavy traffic in a fast manner, to deflect the malevolent traffic on time and to transmit the benign traffic to the designated nodes for serving the needs of the users. In this work, adaboost and Xgboost machine learning algorithms and Deep Neural Networks approach are proposed to separate the IoT traffic which eventually enhances the throughput of IoT networks and reduces the congestion over IoT channels. The result of experiment indicates a deep learning algorithm achieves higher accuracy compared to machine learning algorithms.

A Very Large-Scale Bioactivity Comparison of Deep Learning and Multiple Machine Learning Algorithms for Drug Discovery

2020 ◽  
Author(s):  
Thomas R. Lane ◽  
Daniel H. Foil ◽  
Eni Minerali ◽  
Fabio Urbina ◽  
Kimberley M. Zorn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Drug Discovery ◽  
Deep Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

<p>Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies we have applied multiple machine learning algorithms, modeling metrics and in some cases compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and comparison of our proprietary software Assay Central<sup>TM</sup> with random forest, k-Nearest Neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (3 levels). Model performance <a>was</a> assessed using an array of five-fold cross-validation metrics including area-under-the-curve, F1 score, Cohen’s kappa and Matthews correlation coefficient. <a>Based on ranked normalized scores for the metrics or datasets all methods appeared comparable while the distance from the top indicated Assay Central<sup>TM</sup> and support vector classification were comparable. </a>Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case where minimal tuning was performed of any of the methods. If anything, Assay Central<sup>TM</sup> may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay Central<sup>TM</sup>performance, but support vector classification seems to be a strong competitor. We also apply Assay Central<sup>TM</sup> to prospective predictions for PXR and hERG to further validate these models. This work currently appears to be the largest comparison of machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors and algorithms, as well as further refining methods for evaluating and comparing models. </p><p><b> </b></p>

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