The document describes a proposed system for recognizing hate speech through natural language processing and deep learning techniques. It discusses how hate speech on social media platforms is a growing problem. The proposed system uses techniques like TF-IDF, entropy estimation, and a fuzzy artificial neural network for hate speech recognition. The system preprocesses text data by removing special symbols, applying stemming, and removing stop words. It then classifies text as hate speech or not hate speech using the natural language processing and deep learning models. The authors conducted experiments that showed the system achieved highly positive results in hate speech recognition performance.
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
Deep Learning approaches for Hate speech detection. In this work we used the two deep learning approaches DCNN and MLP two separate classifier on four publicly available datasets.
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
These slides are an introduction to the understanding of the domain NLP and the basic NLP pipeline that are commonly used in the field of Computational Linguistics.
Paraphrase detection is an academically challenging NLP problem of detecting whether multiple phrases have the same meaning. In this talk, we’ll go through the existing traditional and deep learning approaches for this task, and see how they apply in practice as a silver-winning solution to the popular Kaggle Quora Question Pairs competition.
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
Deep Learning approaches for Hate speech detection. In this work we used the two deep learning approaches DCNN and MLP two separate classifier on four publicly available datasets.
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
These slides are an introduction to the understanding of the domain NLP and the basic NLP pipeline that are commonly used in the field of Computational Linguistics.
Paraphrase detection is an academically challenging NLP problem of detecting whether multiple phrases have the same meaning. In this talk, we’ll go through the existing traditional and deep learning approaches for this task, and see how they apply in practice as a silver-winning solution to the popular Kaggle Quora Question Pairs competition.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
This presentation contains some interesting information about PyTorch and its capabilities.
It includes:
What’s PyTorch & It’s History
PyTorch Tensors
PyTorch Modules
PyTorch Vs TensorFlow
PyTorch Benefits
PyTorch Use-Cases
PyTorch: Governing Bodies & Board Members
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Neural Language Generation Head to Toe Hady Elsahar
This is a gentle introduction to Natural language Generation (NLG) using deep learning. If you are a computer science practitioner with basic knowledge about Machine learning. This is a gentle intuitive introduction to Language Generation using Neural Networks. It takes you in a journey from the basic intuitions behind modeling language and how to model probabilities of sequences to recurrent neural networks to large Transformers models that you have seen in the news like GPT2/GPT3. The tutorial wraps up with a summary on the ethical implications of training such large language models on uncurated text from the internet.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Joseph Spisak, Product Manager at Facebook, delivers the presentation "PyTorch Deep Learning Framework: Status and Directions" at the Embedded Vision Alliance's December 2019 Vision Industry and Technology Forum. Spisak gives an update on the Torch deep learning framework and where it’s heading.
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
Deep Learning Applications | Deep Learning Applications In Real Life | Deep l...Simplilearn
This presentation about Deep Learning Applications covers the exciting areas and sectors of business that uses Deep Learning widely every day. We will see how Deep Learning is used in healthcare to improve people's life. We will understand how Amazon, Netflix use Deep Learning to provide better customer experience. We will learn to generate music, audio, and color images using Deep Learning. This video will also give us an idea of how Deep Learning is used in advertising and in predicting earthquakes. Now, let us jump into the different applications of Dee Learning.
We will be discussing the below industries and sectors of business which uses Deep Learning-
1. Healthcare
2. Entertainment
3. Composing music
4. Image coloring
5. Robotics
6. Image captioning
7. Advertising
8. Earthquake prediction
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Hate speech has been an ongoing problem on the Internet for many years. Besides, social media, especially Facebook, and Twitter have given it a global stage where those hate speeches can spread far more rapidly. Every social media platform needs to implement an effective hate speech detection system to remove offensive content in real-time. There are various approaches to identify hate speech, such as Rule-Based, Machine Learning based, deep learning based and Hybrid approach. Since this is a review paper, we explained the valuable works of various authors who have invested their valuable time in studying to identifying hate speech using various approaches.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
This presentation contains some interesting information about PyTorch and its capabilities.
It includes:
What’s PyTorch & It’s History
PyTorch Tensors
PyTorch Modules
PyTorch Vs TensorFlow
PyTorch Benefits
PyTorch Use-Cases
PyTorch: Governing Bodies & Board Members
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Neural Language Generation Head to Toe Hady Elsahar
This is a gentle introduction to Natural language Generation (NLG) using deep learning. If you are a computer science practitioner with basic knowledge about Machine learning. This is a gentle intuitive introduction to Language Generation using Neural Networks. It takes you in a journey from the basic intuitions behind modeling language and how to model probabilities of sequences to recurrent neural networks to large Transformers models that you have seen in the news like GPT2/GPT3. The tutorial wraps up with a summary on the ethical implications of training such large language models on uncurated text from the internet.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Joseph Spisak, Product Manager at Facebook, delivers the presentation "PyTorch Deep Learning Framework: Status and Directions" at the Embedded Vision Alliance's December 2019 Vision Industry and Technology Forum. Spisak gives an update on the Torch deep learning framework and where it’s heading.
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
Deep Learning Applications | Deep Learning Applications In Real Life | Deep l...Simplilearn
This presentation about Deep Learning Applications covers the exciting areas and sectors of business that uses Deep Learning widely every day. We will see how Deep Learning is used in healthcare to improve people's life. We will understand how Amazon, Netflix use Deep Learning to provide better customer experience. We will learn to generate music, audio, and color images using Deep Learning. This video will also give us an idea of how Deep Learning is used in advertising and in predicting earthquakes. Now, let us jump into the different applications of Dee Learning.
We will be discussing the below industries and sectors of business which uses Deep Learning-
1. Healthcare
2. Entertainment
3. Composing music
4. Image coloring
5. Robotics
6. Image captioning
7. Advertising
8. Earthquake prediction
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Hate speech has been an ongoing problem on the Internet for many years. Besides, social media, especially Facebook, and Twitter have given it a global stage where those hate speeches can spread far more rapidly. Every social media platform needs to implement an effective hate speech detection system to remove offensive content in real-time. There are various approaches to identify hate speech, such as Rule-Based, Machine Learning based, deep learning based and Hybrid approach. Since this is a review paper, we explained the valuable works of various authors who have invested their valuable time in studying to identifying hate speech using various approaches.
THE SURVEY OF SENTIMENT AND OPINION MINING FOR BEHAVIOR ANALYSIS OF SOCIAL MEDIAIJCSES Journal
Nowadays, internet has changed the world into a global village. Social Media has reduced the gaps among
the individuals. Previously communication was a time consuming and expensive task between the people.
Social Media has earned fame because it is a cheaper and faster communication provider. Besides, social
media has allowed us to reduce the gaps of physical distance, it also generates and preserves huge amount
of data. The data are very valuable and it presents association degree between people and their opinions.The comprehensive analysis of the methods which are used on user behavior prediction is presented in this paper. This comparison will provide a detailed information, pros and cons in the domain of sentiment and
opinion mining.
Hate speech detection on Indonesian text using word embedding method-global v...IAESIJAI
Hate speech is defined as communication directed toward a specific individual or group that involves hatred or anger and a language with solid arguments leading to someone's opinion can cause social conflict. It has a lot of potential for individuals to communicate their thoughts on an online platform because the number of Internet users globally, including in Indonesia, is continually rising. This study aims to observe the impact of pre-trained global vector (GloVe) word embedding on accuracy in the classification of hate speech and non-hate speech. The use of pre-trained GloVe (Indonesian text) and single and multi-layer long short-term memory (LSTM) classifiers has performance that is resistant to overfitting compared to pre-trainable embedding for hate-speech detection. The accuracy value is 81.5% on a single layer and 80.9% on a double-layer LSTM. The following job is to provide pre-trained with formal and non-formal language corpus; pre-processing to overcome non-formal words is very challenging.
Indonesian multilabel classification using IndoBERT embedding and MBERT class...IJECEIAES
The rapid increase in social media activity has triggered various discussion spaces and information exchanges on social media. Social media users can easily tell stories or comment on many things without limits. However, this often triggers open debates that lead to fights on social media. This is because many social media users use toxic comments that contain elements of racism, radicalism, pornography, or slander to argue and corner individuals or groups. These comments can easily spread and trigger users vulnerable to mental disorders due to unhealthy and unfair debates on social media. Thus, a model is needed to classify comments, especially toxic ones, in Indonesian. Transformer-based model development and natural language processing approaches can be applied to create classification models. Some previous research related to the classification of toxic comments has been done, but the classification results of the model still require exploration to get optimal results. So, this research uses the proposed model by using different pre-trained models at the embedding and classification stages, in the embedding stage using Indonesia bidirectional encoder representations from transformers (IndoBERT), and classification using multilingual bidirectional encoder representations from transformers (MBERT). The proposed model provides optimal results with an F1 value of 0.9032.
A scalable, lexicon based technique for sentiment analysisijfcstjournal
Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased
interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much
social media available on the web, sentiment analysis is now considered as a big data task. Hence the
conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data
available now a days. The main focus of the research was to find such a technique that can efficiently
perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative
and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large
data set of tweets using Hadoop and the performance of the technique was measured in form of speed and
accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big
sentiment data sets.
Cyberbullying includes the repeated and intentional use of digital technology to target another person with
threats, harassment, or public humiliation. One of the techniques of Cyberbullying is sharing bullying
memes on social media, which has increased enormously in recent years. Memes are images and texts
overlapped and sometimes together they present concepts that become dubious if one of them is absent.
Here, we propose a unified deep neural model for detecting bullying memes on social media. In the
proposed unified architecture, VGG16-BiLSTM, consists of a VGG16 convolutional neural network for
predicting the visual bullying content and a BiLSTM with one-dimensional convolution for predicting the
textual bullying content. The meme is discretized by extracting the text from the image using OCR. The
perceptron-based feature-level strategy for multimodal learning is used to dynamically combine the features
of discrete modalities and output the final category as bullying or nonbullying type. We also create a
“bullying Bengali memes dataset” for experimental evaluation. Our proposed model attained an accuracy
of 87% with an F1 score of 88%. The proposed model demonstrates the capability to detect instances of
Cyberbullying involving Bengali memes on various social media platforms. Consequently, it can be utilized
to implement effective filtering mechanisms aimed at mitigating the prevalence of Cyberbullying.
Cyberbullying includes the repeated and intentional use of digital technology to target another person with
threats, harassment, or public humiliation. One of the techniques of Cyberbullying is sharing bullying
memes on social media, which has increased enormously in recent years. Memes are images and texts
overlapped and sometimes together they present concepts that become dubious if one of them is absent.
Here, we propose a unified deep neural model for detecting bullying memes on social media. In the
proposed unified architecture, VGG16-BiLSTM, consists of a VGG16 convolutional neural network for
predicting the visual bullying content and a BiLSTM with one-dimensional convolution for predicting the
textual bullying content. The meme is discretized by extracting the text from the image using OCR. The
perceptron-based feature-level strategy for multimodal learning is used to dynamically combine the features
of discrete modalities and output the final category as bullying or nonbullying type. We also create a
“bullying Bengali memes dataset” for experimental evaluation. Our proposed model attained an accuracy
of 87% with an F1 score of 88%. The proposed model demonstrates the capability to detect instances of
Cyberbullying involving Bengali memes on various social media platforms. Consequently, it can be utilized
to implement effective filtering mechanisms aimed at mitigating the prevalence of Cyberbullying.
Analyzing sentiment system to specify polarity by lexicon-basedjournalBEEI
Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.
Dialectal Arabic sentiment analysis based on tree-based pipeline optimizatio...IJECEIAES
The heavy involvement of the Arabic internet users resulted in spreading data written in the Arabic language and creating a vast research area regarding natural language processing (NLP). Sentiment analysis is a growing field of research that is of great importance to everyone considering the high added potential for decision-making and predicting upcoming actions using the texts produced in social networks. Arabic used in microblogging websites, especially Twitter, is highly informal. It is not compliant with neither standards nor spelling regulations making it quite challenging for automatic machine-learning techniques. In this paper’s scope, we propose a new approach based on AutoML methods to improve the efficiency of the sentiment classification process for dialectal Arabic. This approach was validated through benchmarks testing on three different datasets that represent three vernacular forms of Arabic. The obtained results show that the presented framework has significantly increased accuracy than similar works in the literature.
Machine Learning Approach to Classify Twitter Hate Speechijtsrd
In this modern age, social media platforms have become indispensable tools for communication and information sharing. However, this unprecedented connectivity has also given rise to a concerning proliferation of hate speech and offensive content. This research article presents a comprehensive study on the development and evaluation of machine learning ML models for the automatic detection of hate speech on Twitter. We leverage a diverse dataset collected from Twitter, encompassing a wide range of hate speech categories, including hate speech targeting race, gender, religion, and more. To address the multifaceted nature of hate speech, we employ a hybrid approach that combines traditional natural language processing NLP techniques with state of the art machine learning algorithms. Our methodology involves extensive preprocessing of the text data, including tokenization, stemming, and feature extraction. We then experiment with various machine learning algorithms, including Naïve Bayes NB , K nearest Neighbor KNN , Random Forest RF , and Support Vector Machines SVM . The models are trained and fine tuned on a labeled dataset and evaluated using robust metrics to assess their performance. Subrata Saha | Md. Motinur Rahman | Md. Mahbub Alam "Machine Learning Approach to Classify Twitter Hate Speech" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59873.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/59873/machine-learning-approach-to-classify-twitter-hate-speech/subrata-saha
Sentiment Mining of Community Development Program Evaluation Based on Social ...TELKOMNIKA JOURNAL
It is crucial to support community-oriented services for youth awareness in the social media with knowledge extraction, which would be useful for both government agencies and community group of interest for program evaluation. This work provided to formulate effective evaluation on community development program and addressing them to a correct action. By using classification based SVM, evaluation of the achievement level conducted in both quantitative and qualitative analysis, particularly to conclude which activities has high success rate. By using social media based activities, this study searched the sentiment analysis from every activities comments based on their tweet. First, we kicked off preprocessing stage, reducing feature space by using principle of component analysis and estimate parameters for classification purposes. Second, we modeled activity classification by using support vector machine. At last, set term score by calculating term frequency, which combined with term sentiment scores based on lexicon.The result shows that models provided sentiment summarization that point out the success level of positive sentiment.
Framework for opinion as a service on review data of customer using semantics...IJECEIAES
At opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as opinion-as-a-service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.
A Survey on Speech Recognition with Language Specificationijtsrd
As a cross disciplinary, speech recognition is entirely based on the speech as the survey object. Speech recognition allows the machine to convert the speech signal into text or commands via the process of identification and understanding. Speech recognition involves in various fields of physiology, psychology, linguistics, computer science and signal processing, and is even related to the person’s body language, and its goal is to achieve natural language communication between man and machine. The speech recognition technology is gradually becoming the key technology of the IT man machine interface. This paper describes the development of speech recognition technology and its basic principles, methods, reviewed the classification of speech recognition systems, speech recognition approaches and voice recognition technology, analyzed the problems faced by the speech recognition. Dr. Preeti Savant | Lakshmi Sandhya H "A Survey on Speech Recognition with Language Specification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49370.pdf Paper URL: https://www.ijtsrd.com/computer-science/speech-recognition/49370/a-survey-on-speech-recognition-with-language-specification/dr-preeti-savant
An evolutionary approach to comparative analysis of detecting Bangla abusive ...journalBEEI
The use of Bangla abusive texts has been accelerated with the progressive use of social media. Through this platform, one can spread the hatred or negativity in a viral form. Plenty of research has been done on detecting abusive text in the English language. Bangla abusive text detection has not been done to a great extent. In this experimental study, we have applied three distinct approaches to a comprehensive dataset to obtain a better outcome. In the first study, a large dataset collected from Facebook and YouTube has been utilized to detect abusive texts. After extensive pre-processing and feature extraction, a set of consciously selected supervised machine learning classifiers i.e. multinomial Naïve Bayes (MNB), multi layer perceptron (MLP), support vector machine (SVM), decision tree, random forrest, stochastic gradient descent (SGD), ridge, perceptron and k-nearest neighbors (k-NN) has been applied to determine the best result. The second experiment is conducted by constructing a balanced dataset by random under sampling the majority class and finally, a Bengali stemmer is employed on the dataset and then the final experiment is conducted. In all three experiments, SVM with the full dataset obtained the highest accuracy of 88%.
Comparative Analysis of Existing and a Novel Approach to Topic Detection on C...kevig
Topic detection in dialogue datasets has become a significant challenge for unsupervised
and unlabeled data to develop a cohesive and engaging dialogue system. In this paper, we
proposed unsupervised and semi-supervised techniques for topic detection in the conversational dialogue dataset and compared them with existing topic detection techniques. The
paper proposes a novel approach for topic detection, which takes preprocessed data as an
input and performs similarity analysis with the TF-IDF scores bag of words technique
(BOW) to identify higher frequency words from dialogue utterances. It then refines the
higher frequency words by integrating the clustering and elbow methods and using the Parallel Latent Dirichlet Allocation (PLDA) model to detect the topics. The paper comprised a
comparative analysis of the proposed approach on the Switchboard, Personachat and MultiWOZ dataset. The experimental results show that the proposed topic detection approach
performs significantly better using a semi-supervised dialogue dataset. We also performed
topic quantification to check how accurate extracted topics are to compare with manually
annotated data. For example, extracted topics from Switchboard are 92.72%, Peronachat
87.31% and MultiWOZ 93.15% accurate with manually annotated data.
Comparative Analysis of Existing and a Novel Approach to Topic Detection on C...kevig
Topic detection in dialogue datasets has become a significant challenge for unsupervised
and unlabeled data to develop a cohesive and engaging dialogue system. In this paper, we
proposed unsupervised and semi-supervised techniques for topic detection in the conversational dialogue dataset and compared them with existing topic detection techniques. The
paper proposes a novel approach for topic detection, which takes preprocessed data as an
input and performs similarity analysis with the TF-IDF scores bag of words technique
(BOW) to identify higher frequency words from dialogue utterances. It then refines the
higher frequency words by integrating the clustering and elbow methods and using the Parallel Latent Dirichlet Allocation (PLDA) model to detect the topics. The paper comprised a
comparative analysis of the proposed approach on the Switchboard, Personachat and MultiWOZ dataset. The experimental results show that the proposed topic detection approach
performs significantly better using a semi-supervised dialogue dataset. We also performed
topic quantification to check how accurate extracted topics are to compare with manually
annotated data. For example, extracted topics from Switchboard are 92.72%, Peronachat
87.31% and MultiWOZ 93.15% accurate with manually annotated data.
Similar to Hate Speech Recognition System through NLP and Deep Learning (20)
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.