scholarly journals Bike Sharing Prediction using Deep Neural Networks

2017 ◽  
Vol 1 (3) ◽  
pp. 83 ◽  
Author(s):  
Chandrasegar Thirumalai ◽  
Ravisankar Koppuravuri
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Neural Nets ◽  
Machine Learning Techniques ◽  
Business Decisions ◽  
Learning Techniques ◽  
Bike Sharing ◽  
The Way

In this paper, we will use deep neural networks for predicting the bike sharing usage based on previous years usage data. We will use because deep neural nets for getting higher accuracy. Deep neural nets are quite different from other machine learning techniques; here we can add many numbers of hidden layers to improve the accuracy of our prediction and the model can be trained in the way we want such that we can achieve the results we want. Nowadays many AI experts will say that deep learning is the best AI technique available now and we can achieve some unbelievable results using this technique. Now we will use that technique to predict bike sharing usage of a rental company to make sure they can take good business decisions based on previous years data.

scholarly journals Morphology and Interaction of Galaxies using Deep Learning

2016 ◽  
Vol 12 (S325) ◽  
pp. 205-208
Author(s):  
Fernando Caro ◽  
Marc Huertas-Company ◽  
Guillermo Cabrera
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Visual Inspection ◽  
Interaction Analysis ◽  
Machine Learning Techniques ◽  
Huge Amount ◽  
New Approaches ◽  
Learning Techniques ◽  
First Galaxies ◽  
The Way

AbstractIn order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.

scholarly journals DETECTION OF WHALES USING DEEP LEARNING METHODS AND NEURAL NETWORKS

2017 ◽  
Vol 10 (13) ◽  
pp. 489 ◽  
Author(s):  
Saheb Ghosh ◽  
Sathis Kumar B ◽  
Kathir Deivanai
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Data Model ◽  
Natural Habitat ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Learning Techniques ◽  
Learning Technique ◽  
Extraction Model

Deep learning methods are a great machine learning technique which is mostly used in artificial neural networks for pattern recognition. This project is to identify the Whales from under water Bioacoustics network using an efficient algorithm and data model, so that location of the whales can be send to the Ships travelling in the same region in order to avoid collision with the whale or disturbing their natural habitat as much as possible. This paper shows application of unsupervised machine learning techniques with help of deep belief network and manual feature extraction model for better results.

scholarly journals Network Anomaly Detection Using Machine Learning Techniques

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 8
Author(s):  
Julio J. Estévez-Pereira ◽  
Diego Fernández ◽  
Francisco J. Novoa
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Machine Learning Techniques ◽  
Security Threats ◽  
Learning Techniques ◽  
Distribution Phase ◽  
The One ◽  
Network Anomaly Detection ◽  
And Storage

While traditional network security methods have been proven useful until now, the flexibility of machine learning techniques makes them a solid candidate in the current scene of our networks. In this paper, we assess how well the latter are capable of detecting security threats in a corporative network. To that end, we configure and compare several models to find the one which fits better with our needs. Furthermore, we distribute the computational load and storage so we can handle extensive volumes of data. The algorithms that we use to create our models, Random Forest, Naive Bayes, and Deep Neural Networks (DNN), are both divergent and tested in other papers in order to make our comparison richer. For the distribution phase, we operate with Apache Structured Streaming, PySpark, and MLlib. As for the results, it is relevant to mention that our dataset has been found to be effectively modelable with just a reduced number of features. Finally, given the outcomes obtained, we find this line of research encouraging and, therefore, this approach worth pursuing.

scholarly journals Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

2021 ◽  
pp. 43-53
Author(s):  
admin admin ◽  
◽  
◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Deep Convolutional Neural Networks ◽  
Learning Techniques ◽  
Good Classification ◽  
Learning Architectures ◽  
Ecg Data

Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.

scholarly journals Winsorization for Robust Bayesian Neural Networks

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1546
Author(s):  
Somya Sharma ◽  
Snigdhansu Chatterjee
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Versatile Technique ◽  
Aberrant Observations ◽  
Probabilistic Machine Learning ◽  
Different Levels

With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. We provide a comparative analysis of several probabilistic artificial intelligence and machine learning techniques for supervised learning case studies. Broadly, Winsorization is a versatile technique for accounting for outliers in data. However, different probabilistic machine learning techniques have different levels of efficiency when used on outlier-prone data, with or without Winsorization. We notice that Gaussian processes are extremely vulnerable to outliers, while deep learning techniques in general are more robust.

scholarly journals Sentiment Analysis of Short Russian Texts Using BERT and Word2Vec Embeddings

2021 ◽  
Author(s):  
Ekaterina Popova ◽  
Vladimir Spitsyn
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Social Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Functional Method ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Word Embeddings ◽  
Learning Techniques

This article is devoted to modern approaches for sentiment analysis of short Russian texts from social networks using deep neural networks. Sentiment analysis is the process of detecting, extracting, and classifying opinions, sentiments, and attitudes concerning different topics expressed in texts. The importance of this topic is linked to the growth and popularity of social networks, online recommendation services, news portals, and blogs, all of which contain a significant number of people's opinions on a variety of topics. In this paper, we propose machine-learning techniques with BERT and Word2Vec embeddings for tweets sentiment analysis. Two approaches were explored: (a) a method, of word embeddings extraction and using the DNN classifier; (b) refinement of the pre-trained BERT model. As a result, the fine- tuning BERT outperformed the functional method to solving the problem.

scholarly journals Analysis and Visualization of Sleep Stages based on Deep Neural Networks

2020 ◽  
Author(s):  
Patrick Krauss ◽  
Claus Metzner ◽  
Nidhi Joshi ◽  
Holger Schulze ◽  
Maximilian Traxdorf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Sleep Stage ◽  
Machine Learning Techniques ◽  
Sleep Stages ◽  
Learning Techniques ◽  
Sleep Physiology ◽  
Sleep Stage Classification ◽  
Cross Correlations

AbstractAutomatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages, would save human resources and thus would simplify clinical routines. Due to novel open-source software libraries for Machine Learning in combination with enormous progress in hardware development in recent years a paradigm shift in the field of sleep research towards automatic diagnostics could be observed. We argue that modern Machine Learning techniques are not just a tool to perform automatic sleep stage classification but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, in a way so that we can already make first assessments on sleep health in terms of sleep-apnea and consequently daytime vigilance. In the following study, we further developed our method by the innovative approach to analyze cortical activity during sleep by computing vectorial cross-correlations of different EEG channels represented by hypnodensity graphs. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions.

scholarly journals Phishing Website Detection using Supervised Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 4844-4846
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Personal Information ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Web Page ◽  
Algorithmic Approach ◽  
Learning Techniques

The website phishing is the tremendously growing problem over the internet which will lead to the loss of personal information. This process will run like, when ever user clicks a website link it will lead them to the web page that is created by the phisher to deceive the user. After this phishing has been started in order to stop it many techniques came into existence to detect the phished web site and help the user from being deceived by the attacker. Even though many techniques have adapted to stop the attackers, it is difficult because as many phished web pages are generated by the attackers within few hours. Most of the techniques to detect these phishing websites are not able to decide the fake website with legitimate one because the accuracy of getting results are much less. There are many supervised machine learning techniques which are supervised, where a primary set of data is given to the algorithm and depending on that set the algorithm will be trained and it will predict the results for the same. One of the most important techniques that is deep Learning classifiers is applied with significant features to detect phishing websites. By using this algorithm we can classify the phishing websites from genuine websites by using effective features. In this algorithmic approach to detect genuine websites a feature set is used so by analyzing these features using deep neural networks we can detect a website is phished or not. We can also increase the accuracy of our algorithm by adding certain more features and increasing the hidden layers in neural networks.

scholarly journals From Reflex to Reflection: Two Tricks AI Could Learn from Us

Philosophies ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 27
Author(s):  
Jean-Louis Dessalles
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Processes ◽  
Real World ◽  
Machine Learning Techniques ◽  
Human Intervention ◽  
Real World Data ◽  
World Data ◽  
Learning Techniques

Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection.

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