Deep learning (DL) is a machine learning (ML) subdomain that involves algorithms taken from the brain function named artificial neural networks (ANNs). Recently, DL approaches have gained major accomplishments across various Arabic natural language processing (ANLP) tasks, especially in the domain of Arabic sentiment analysis (ASA). For working on Arabic SA, researchers can use various DL libraries in their projects, but without justifying their choice or they choose a group of libraries relying on their particular programming language familiarity. We are basing in this work on Java and Python programming languages because they have a large set of deep learning libraries that are very useful in the ASA domain. This paper focuses on a comparative analysis of different valuable Python and Java libraries to conclude the most relevant and robust DL libraries for ASA. Throw this comparative analysis, and we find that: TensorFlow, Theano, and Keras Python frameworks are very popular and very used in this research domain.
Julia: A modern language for software 2.0Viral Shah
This talk introduces the Julia language, the size of the community, the package ecosystem, differentiable programming, compiler design, and applications of scientific machine learning.
Rajat Monga, Engineering Director, TensorFlow, Google at MLconf 2016MLconf
Machine Learning with TensorFlow: TensorFlow has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk will go over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
Julia: A modern language for software 2.0Viral Shah
This talk introduces the Julia language, the size of the community, the package ecosystem, differentiable programming, compiler design, and applications of scientific machine learning.
Rajat Monga, Engineering Director, TensorFlow, Google at MLconf 2016MLconf
Machine Learning with TensorFlow: TensorFlow has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk will go over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
Talk given at PYCON Stockholm 2015
Intro to Deep Learning + taking pretrained imagenet network, extracting features, and RBM on top = 97 Accuracy after 1 hour (!) of training (in top 10% of kaggle cat vs dog competition)
이 발표에서는 TensorFlow의 지난 1년을 간단하게 돌아보고, TensorFlow의 차기 로드맵에 따라 개발 및 도입될 예정인 여러 기능들을 소개합니다. 또한 2017년 및 2018년의 머신러닝 프레임워크 개발 트렌드와 방향에 대한 이야기도 함께 합니다.
In this talk, I look back the TensorFlow development over the past year. Then discusses the overall development direction of machine learning frameworks, with an introduction to features that will be added to TensorFlow later on.
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
On-device machine learning: TensorFlow on AndroidYufeng Guo
Machine learning has traditionally been the solely performed on servers and high performance machines. But there is great value is having on-device machine learning for mobile devices. Doing ML inference on mobile devices has huge potential and is still in its early stages. However, it's already more powerful than most realize.
In this demo-oriented talk, you will see some examples of deep learning models used for local prediction on mobile devices. Learn how to use TensorFlow to implement a machine learning model that is tailored to a custom dataset, and start making delightful experiences today!
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...Edureka!
** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Introducing TensorFlow: The game changer in building "intelligent" applicationsRokesh Jankie
This is the slidedeck used for the presentation of the Amsterdam Pipeline of Data Science, held in December 2016. TensorFlow in the open source library from Google to implement deep learning, neural networks. This is an introduction to Tensorflow.
Note: Videos are not included (which were shown during the presentation)
Upload photos Copy this Meetup
Things we will discuss are
1.Introduction of Machine learning and deep learning.
2.Applications of ML and DL.
3.Various learning algorithms of ML and DL.
4.Quick introduction of open source solutions for all programming languages.
5.Finally A broad picture of what you can do with Deep learning to this tech world.
Language translation with Deep Learning (RNN) with TensorFlowS N
The author is going to take you into the realm of Recurrent Neural Network (RNN). He will be training a sequence to sequence model on a dataset of English and French sentences that can translate new (unseen) sentences from English to French.
This will be a walkthrough of an end to end technique to train a Deep RNN model. You will learn to build various components necessary to build a Sequence-to-Sequence model.
You will learn about the fundamentals of Deep Learning, mainly RNN, concepts that will be required in this solution. A familiarity of Deep Learning concepts would be handy, but most of the concepts used in this example will be covered during the demo.
Technologies to be used:
Python, Jupyter, TensorFlow, FloydHub
Source code: https://github.com/syednasar/deeplearning/blob/master/language-translation/dlnd_language_translation.ipynb
...
Hadoop Summit 2014 - San Jose - Introduction to Deep Learning on HadoopJosh Patterson
As the data world undergoes its cambrian explosion phase our data tools need to become more advanced to keep pace. Deep Learning has emerged as a key tool in the non-linear arms race of machine learning. In this session we will take a look at how we parallelize Deep Belief Networks in Deep Learning on Hadoop’s next generation YARN framework with Iterative Reduce. We’ll also look at some real world examples of processing data with Deep Learning such as image classification and natural language processing.
BigDL webinar - Deep Learning Library for SparkDESMOND YUEN
BigDL is a distributed deep learning library for Apache Spark*
and a unified Big Data Platform Driving Analytics and Data Science.
If you like what you read be sure you ♥ it below. Thank you!
hadoop training in mumbai at Asterix Solution is designed to scale up from single servers to thousands of machines, each offering local computation and storage. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
Slides from the TensorFlow meetup at eBay NYC 06/07/2016 based on my blog https://medium.com/@st553/using-transfer-learning-to-classify-images-with-tensorflow-b0f3142b9366
Mastering Computer Vision Problems with State-of-the-art Deep LearningMiguel González-Fierro
Deep learning has been especially successful in computer vision tasks such as image classification because convolutional neural nets (CNNs) can create hierarchical
representations in an image. One of the most remarkable advances is ResNet, the CNN that surpassed human-level accuracy for the first time in history.
ImageNet competition has become the de facto benchmark for image classification in the research community. The “small” ImageNet data contains more than 1.2 million images distributed in 1,000 classes.
Miguel González-Fierro explains how to train a state of the art deep neural network, ResNet, using Microsoft RSever and MXNet with the ImageNet dataset. (While most of the deep learning libraries are programmed in C++ and Python, only MXNet offers an API for R programmers.) Miguel then demonstrates how to operationalize this training for real-world business problems related to image classification.
This talk was presented at Strata London 2017: https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/57428
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017StampedeCon
This technical session provides a hands-on introduction to TensorFlow using Keras in the Python programming language. TensorFlow is Google’s scalable, distributed, GPU-powered compute graph engine that machine learning practitioners used for deep learning. Keras provides a Python-based API that makes it easy to create well-known types of neural networks in TensorFlow. Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to train neural networks of much greater complexity. Deep learning allows a model to learn hierarchies of information in a way that is similar to the function of the human brain.
Different valuable tools for Arabic sentiment analysis: a comparative evaluat...IJECEIAES
Arabic Natural language processing (ANLP) is a subfield of artificial intelligence (AI) that tries to build various applications in the Arabic language like Arabic sentiment analysis (ASA) that is the operation of classifying the feelings and emotions expressed for defining the attitude of the writer (neutral, negative or positive). In order to work on ASA, researchers can use various tools in their research projects without explaining the cause behind this use, or they choose a set of libraries according to their knowledge about a specific programming language. Because of their libraries' abundance in the ANLP field, especially in ASA, we are relying on JAVA and Python programming languages in our research work. This paper relies on making an in-depth comparative evaluation of different valuable Python and Java libraries to deduce the most useful ones in Arabic sentiment analysis (ASA). According to a large variety of great and influential works in the domain of ASA, we deduce that the NLTK, Gensim and TextBlob libraries are the most useful for Python ASA task. In connection with Java ASA libraries, we conclude that Weka and CoreNLP tools are the most used, and they have great results in this research domain.
Deep learning has exceeded massive powers of human mind and most popularity for using scientific computing, and its algorithmic procedures to purposeful industries that solve complete difficulties.
Talk given at PYCON Stockholm 2015
Intro to Deep Learning + taking pretrained imagenet network, extracting features, and RBM on top = 97 Accuracy after 1 hour (!) of training (in top 10% of kaggle cat vs dog competition)
이 발표에서는 TensorFlow의 지난 1년을 간단하게 돌아보고, TensorFlow의 차기 로드맵에 따라 개발 및 도입될 예정인 여러 기능들을 소개합니다. 또한 2017년 및 2018년의 머신러닝 프레임워크 개발 트렌드와 방향에 대한 이야기도 함께 합니다.
In this talk, I look back the TensorFlow development over the past year. Then discusses the overall development direction of machine learning frameworks, with an introduction to features that will be added to TensorFlow later on.
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
On-device machine learning: TensorFlow on AndroidYufeng Guo
Machine learning has traditionally been the solely performed on servers and high performance machines. But there is great value is having on-device machine learning for mobile devices. Doing ML inference on mobile devices has huge potential and is still in its early stages. However, it's already more powerful than most realize.
In this demo-oriented talk, you will see some examples of deep learning models used for local prediction on mobile devices. Learn how to use TensorFlow to implement a machine learning model that is tailored to a custom dataset, and start making delightful experiences today!
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...Edureka!
** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Introducing TensorFlow: The game changer in building "intelligent" applicationsRokesh Jankie
This is the slidedeck used for the presentation of the Amsterdam Pipeline of Data Science, held in December 2016. TensorFlow in the open source library from Google to implement deep learning, neural networks. This is an introduction to Tensorflow.
Note: Videos are not included (which were shown during the presentation)
Upload photos Copy this Meetup
Things we will discuss are
1.Introduction of Machine learning and deep learning.
2.Applications of ML and DL.
3.Various learning algorithms of ML and DL.
4.Quick introduction of open source solutions for all programming languages.
5.Finally A broad picture of what you can do with Deep learning to this tech world.
Language translation with Deep Learning (RNN) with TensorFlowS N
The author is going to take you into the realm of Recurrent Neural Network (RNN). He will be training a sequence to sequence model on a dataset of English and French sentences that can translate new (unseen) sentences from English to French.
This will be a walkthrough of an end to end technique to train a Deep RNN model. You will learn to build various components necessary to build a Sequence-to-Sequence model.
You will learn about the fundamentals of Deep Learning, mainly RNN, concepts that will be required in this solution. A familiarity of Deep Learning concepts would be handy, but most of the concepts used in this example will be covered during the demo.
Technologies to be used:
Python, Jupyter, TensorFlow, FloydHub
Source code: https://github.com/syednasar/deeplearning/blob/master/language-translation/dlnd_language_translation.ipynb
...
Hadoop Summit 2014 - San Jose - Introduction to Deep Learning on HadoopJosh Patterson
As the data world undergoes its cambrian explosion phase our data tools need to become more advanced to keep pace. Deep Learning has emerged as a key tool in the non-linear arms race of machine learning. In this session we will take a look at how we parallelize Deep Belief Networks in Deep Learning on Hadoop’s next generation YARN framework with Iterative Reduce. We’ll also look at some real world examples of processing data with Deep Learning such as image classification and natural language processing.
BigDL webinar - Deep Learning Library for SparkDESMOND YUEN
BigDL is a distributed deep learning library for Apache Spark*
and a unified Big Data Platform Driving Analytics and Data Science.
If you like what you read be sure you ♥ it below. Thank you!
hadoop training in mumbai at Asterix Solution is designed to scale up from single servers to thousands of machines, each offering local computation and storage. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
Slides from the TensorFlow meetup at eBay NYC 06/07/2016 based on my blog https://medium.com/@st553/using-transfer-learning-to-classify-images-with-tensorflow-b0f3142b9366
Mastering Computer Vision Problems with State-of-the-art Deep LearningMiguel González-Fierro
Deep learning has been especially successful in computer vision tasks such as image classification because convolutional neural nets (CNNs) can create hierarchical
representations in an image. One of the most remarkable advances is ResNet, the CNN that surpassed human-level accuracy for the first time in history.
ImageNet competition has become the de facto benchmark for image classification in the research community. The “small” ImageNet data contains more than 1.2 million images distributed in 1,000 classes.
Miguel González-Fierro explains how to train a state of the art deep neural network, ResNet, using Microsoft RSever and MXNet with the ImageNet dataset. (While most of the deep learning libraries are programmed in C++ and Python, only MXNet offers an API for R programmers.) Miguel then demonstrates how to operationalize this training for real-world business problems related to image classification.
This talk was presented at Strata London 2017: https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/57428
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017StampedeCon
This technical session provides a hands-on introduction to TensorFlow using Keras in the Python programming language. TensorFlow is Google’s scalable, distributed, GPU-powered compute graph engine that machine learning practitioners used for deep learning. Keras provides a Python-based API that makes it easy to create well-known types of neural networks in TensorFlow. Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to train neural networks of much greater complexity. Deep learning allows a model to learn hierarchies of information in a way that is similar to the function of the human brain.
Different valuable tools for Arabic sentiment analysis: a comparative evaluat...IJECEIAES
Arabic Natural language processing (ANLP) is a subfield of artificial intelligence (AI) that tries to build various applications in the Arabic language like Arabic sentiment analysis (ASA) that is the operation of classifying the feelings and emotions expressed for defining the attitude of the writer (neutral, negative or positive). In order to work on ASA, researchers can use various tools in their research projects without explaining the cause behind this use, or they choose a set of libraries according to their knowledge about a specific programming language. Because of their libraries' abundance in the ANLP field, especially in ASA, we are relying on JAVA and Python programming languages in our research work. This paper relies on making an in-depth comparative evaluation of different valuable Python and Java libraries to deduce the most useful ones in Arabic sentiment analysis (ASA). According to a large variety of great and influential works in the domain of ASA, we deduce that the NLTK, Gensim and TextBlob libraries are the most useful for Python ASA task. In connection with Java ASA libraries, we conclude that Weka and CoreNLP tools are the most used, and they have great results in this research domain.
Deep learning has exceeded massive powers of human mind and most popularity for using scientific computing, and its algorithmic procedures to purposeful industries that solve complete difficulties.
Deep Learning libraries and first experiments with TheanoVincenzo Lomonaco
In recent years, neural networks and deep learning techniques have shown to perform well on many
problems in image recognition, speech recognition, natural language processing and many other tasks.
As a result, a large number of libraries, toolkits and frameworks came out in different languages and
with different purposes. In this report, firstly we take a look at these projects and secondly we choose the
framework that best suits our needs: Theano. Eventually, we implement a simple convolutional neural net
using this framework to test both its ease-of-use and efficiency.
Python libraries presentation Contains all top 10 labraries information like numpy,tenslorflow,scikit-learn,Numpy,keras,PyToruch,LightGBM,Eli5,scipy,theano,pandas
Benchmarking open source deep learning frameworksIJECEIAES
Deep Learning (DL) is one of the hottest fields. To foster the growth of DL, several open source frameworks appeared providing implementations of the most common DL algorithms. These frameworks vary in the algorithms they support and in the quality of their implementations. The purpose of this work is to provide a qualitative and quantitative comparison among three such frameworks: TensorFlow, Theano and CNTK. To ensure that our study is as comprehensive as possible, we consider multiple benchmark datasets from different fields (image processing, NLP, etc.) and measure the performance of the frameworks’ implementations of different DL algorithms. For most of our experiments, we find out that CNTK’s implementations are superior to the other ones under consideration.
Guide to programming using object oriented languages such as java, python, c, c++ among other programming languages that focus on tying data closely to the function that operates on it rather than procedural oriented programming.
Discover the best programming languages for AI. From Python to R and TensorFlow, explore leading languages for cutting-edge AI development. Stay ahead in artificial intelligence with the best programming languages for AI.
The Best Programming Langauge for Data Science.pptxAvinash Sharma
Data Science is an empowering field. To become the best in this field, you need to learn the best programming languages for data science that will help you grow your skills and build the best career. Check out this page to learn more about the best data scientist course in Delhi.
by Dave Nielsen, Technical Program Manager, Big Data Technologies, STO, Intel
In this workshop we explain how to use Deep Learning to recognize objects in photos using the BigDL framework for Apache Spark. The workshop starts by explaining how Convolution Neural Network models like SSD, VGG and Inception identify edges and then higher level characteristics in multiple layers in order to identify the object. Then we will describe how to build an end-to-end pipeline for image classification including data prep, model building, training, scoring. The workshop includes a Zeppelin Notebook with python code you can modify to train the model and test for accuracy. The code provided in this workshop can be used to run examples on your laptop, on-premises or on Amazon Web Services managed by Qubole. Level 100
Python is a widely-used, high-level programming language known for its simplicity, readability, and extensive library support. It is favored by developers for its ease of use and ability to handle diverse tasks, making it suitable for various applications ranging from web development to data analysis and artificial intelligence.
Top 5 AI Programming Languages to Use in 2024.pdfLaura Miller
AI is a revolutionary technology that transforms the way we live and work. Read the blog to know what AI programming languages are used in AI development.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
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.
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.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
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.
A powerful comparison of deep learning frameworks for Arabic sentiment analysis
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 1, February 2021, pp. 745~752
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i1.pp745-752 745
Journal homepage: http://ijece.iaescore.com
A powerful comparison of deep learning frameworks for Arabic
sentiment analysis
Youssra Zahidi1
, Yacine El Younoussi2
, Yassine Al-Amrani3
1,2
Information System and Software Engineering Laboratory, Abdelmalek Essaadi University, Morocco
3
Technologies de l’Information et Modélisation des Systèmes, Abdelmalek Essaadi University, Morocco
Article Info ABSTRACT
Article history:
Received Oct 9, 2019
Revised Aug 11, 2020
Accepted Aug 23, 2020
Deep learning (DL) is a machine learning (ML) subdomain that involves
algorithms taken from the brain function named artificial neural networks
(ANNs). Recently, DL approaches have gained major accomplishments
across various Arabic natural language processing (ANLP) tasks, especially
in the domain of Arabic sentiment analysis (ASA). For working on Arabic
SA, researchers can use various DL libraries in their projects, but without
justifying their choice or they choose a group of libraries relying on their
particular programming language familiarity. We are basing in this work on
Java and Python programming languages because they have a large set of
deep learning libraries that are very useful in the ASA domain. This paper
focuses on a comparative analysis of different valuable Python and Java
libraries to conclude the most relevant and robust DL libraries for ASA.
Throw this comparative analysis, and we find that: TensorFlow, Theano,
and Keras Python frameworks are very popular and very used in this
research domain.
Keywords:
ANLP
ASA
DL
Java libraries
Python libraries
This is an open access article under the CC BY-SA license.
Corresponding Author:
Youssra Zahidi,
Information System and Software Engineering Laboratory,
Abdelmalek Essaadi University,
Tetuan, Morocco.
Email: youssra1994zahidi@gmail.com
1. INTRODUCTION
Arabic sentiment analysis or opinion mining aims to determine the sentiment polarity (positivity,
negativity, or neutrality) of a writer. A large variety of opinions are borne in posts on different social media
platforms like Twitter, YouTube, Instagram, Facebook. This field of research has recently attracted
increasing attention [1, 2], especially in English. Although the Arabic language is deemed as the most useful
language on social media platforms, only some works have relied on ASA so far.
There is a set of machine learning models powering natural language processing (NLP) applications.
Recently, DL approaches have gained high performance across various NLP tasks [3]. Specifically, it has
held good results in the sentiment analysis domain [4-7], and it is the state-of-the-art model in different
languages [8-10] while the state-of-the-art accuracy for ASA still requires ameliorations.
DL is a part of the ML field concerned with a collection of algorithms based on the brain function
named artificial neural networks (ANNs). It is a ML method that teaches computers to do things that are
natural to humans by creating architecture made up of an input and output layer with various hidden layers
(encoders) between them. Numerous techniques are applied with DL like recurrent neural networks (RNN),
deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), etc.
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 1, February 2021 : 745 - 752
746
As part of our Arabic sentiment analysis research project, we attempt to perform an in-depth
comparative evaluation to achieve a summarization of the most valuable programming languages, which are
abundant in terms of ASA libraries. We try to compare these tools to specify the most useful ones.
Recently, the NLP community has attended numerous penetrations due to the application of DL.
This later has offered salient ameliorations in the domain of sentiment analysis in English. However, less
research has been done on employing DL in ASA. Due to its complication, morphological, and syntactic
abundance, Arabic has deemed the most difficult language, and it has a limited number of DL libraries
compared to other famous languages like English.
Choosing the most useful libraries is very complicated and needs an in-depth analysis. For this
reason, we rely on many comparative levels. For the purpose of this work, we compared the most powerful
DL libraries for ANLP in Python and Java to select the most convenient group of libraries that addresses our
purposes in the ASA domain.
The rest of this article is divided as follows: Section 2 shows the critical Java and Python libraries in
the Arabic language for DL. Section 3 lays emphasis on a detailed comparative study, and it also provides
a conclusion about the most useful deep learning libraries in the field of ASA. The results are debated in
detail in Section 4, and the work is finished with the final ideas in Section 5.
2. ARABIC SENTIMENT ANALYSIS LIBRARIES FOR DL
This section presents four open-source libraries for Deep Learning, namely Theano, TensorFlow,
Keras, and Deeplearning4j.
2.1. Arabic supporting Python libraries for DL
Nowadays, DL is the hottest trend in ML and AI. We selected some of the best Python libraries for
the Arabic language, which is deemed the most powerful DL libraries in ASA.
TensorFlow: it is an open-source software library for dataflow and differentiable programming on
a range of tasks. It is a symbolic math tool and is also applied for ML applications like neural networks.
It comes with robust support for ML and DL, and the flexible numerical computation core is employed across
various other scientific fields. TensorFlow library can run on multiple GPUs and CPUs (with optional SYCL
and CUDA extensions for general-aim computing on graphics processing units). It is obtainable on 64-bit
Windows, Linux, and mobile computing platforms, containing iOS and android. For making it simple for
users to understand, debug, and optimize TensorFlow programs, there is an excellent group of visualization
tools named TensorBoard [11]. Figure 1 presents the TensorFlow python library architecture.
Theano: [12, 13], is a cross-platform open-source tool that permits the researcher to evaluate
mathematical expressions, including multi-dimensional arrays worthily. It is a mathematical library, but it
was initially created to facilitate research in the DL field. Based on the advantages of Theano, divers
packages have been developed, such as Keras, Pylearn2, Blocks, and Lasagne [14]. Theano has many
features like it provides most of NumPy’s functionality, but adds automatic symbolic differentiation, offers
transparent employment of a GPU also it does the derivatives for functions with one or numerous inputs.
Figure 2 presents the Theano architecture.
Figure 1. Tensorflow python library architecture Figure 2. Theano Python library architecture
Keras: It is a top-level neural networks API, able to work on top of Theano, TensorFlow, and other
tools. It was created to permit rapid experimentation with deep neural networks. Keras runs seamlessly on
CPU or GPU. It provides simple and rapid prototyping and sustains both recurrent networks and
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convolutional networks, as well as conjunctions of the two. Figure 3 shows the Keras architecture.
Depending on the literature, we found that these libraries are very used in the ASA field, and many authors
recommend them in their works. Table 1 highlights a comparison of many valuable deep learning libraries in
Arabic sentiment analysis.
Figure 3. Keras Python library architecture
Table 1. Comparison of the most used DL libraries in ASA
Library Creator Has Pre-trained
Models
Platform Interface Software
Licence
Works in ASA
Tensorflow Google Brain team Yes Linux, macOS,
Windows,
Android
Python (Keras),
C/C++, Java,
Go, R
Apache 2.0 [15][16][17][18]
[19][20][21][22]
Theano Université de
Montréal
Through
Lasagne's model
zoo
Cross-platform Python (Keras) BSD license [23][24][25][26]
[27][28][29][30]
Keras François Chollet Yes Linux, macOS,
Windows
Python, R MIT license [20][25][26][29]
[31][28][32][33]
2.2. Arabic supporting Java toolkits for DL
We chose the Deeplearning4j library because it is considered as the most useful Java deep learning
library in Arabic sentiment analysis:
Deeplearning4j: It is released under apache license 2.0, created mainly by a ML set headquartered
in Tokyo and San Francisco and led by Adam Gibson. It is a free and cross-platform tool, and it was designed
to integrate with Spark, Hadoop, and other Java-based distributed software. It is designed for Java virtual
Machine and Java as well as computer frameworks that broadly support deep learning algorithms. It has
pre-trained models and sustains CUDA, but it can be more quickened with cuDNN. This library also offers
GPU support for the distributed framework, and we can select native CPUs or GPUs for our backend linear
algebra processes. Figure 4 shows the Deeplearning4j architecture.
Figure 4. Deeplearning4J Java library architecture
3. COMPARATIVE EVALUATION OF DL TOOLS
In this part, we will present our in-depth comparative study on various levels: first, we show
the many essential standards and the most critical application areas. Then, we rely on the GitHub results and
the history of Google Trends to conclude the most powerful DL libraries that satisfy our specific
requirements very well.
3.1. Comparison of the most famous open-source DL libraries
The comparison of these DL tools can rely on different criteria. Table 2 shows a variety of
parameters adopted in this comparative study:
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Table 2. Evaluation of different deep learning libraries
Keras Theano TensorFlow Deeplearning4j
Written in Python Python C++, Python C++, Java
RBM/DBNs Yes Yes Yes Yes
Parallel Execution Yes (only possible with the
TensorFlow backend)
Yes Yes Yes
Documentation The Keras project has rich
documentation, and a group
of examples aiming at a
large number of difficulties
is provided [34].
Documentation for Theano and
TensorFlow is very abundant; it offers
a large awareness of the Tensorflow
and Theano basics, from installation to
creating huge networks on the
distributed environment [35].
The website of the
Deeplearning4j
library offers
helpful examples
and documentation
[35].
Parallelizing
technique
support
CUDA Support Yes Yes Yes Yes
OpenMP Support Only if using Theano as
backend
Yes No Yes
OpenCL Support On the roadmap for the
TensorFlow backend and
under creation for the
Theano backend
The backend was
built to support
OpenCL, but current
support is deficient
(under creation)
On roadmap
but already
with SYCL
support
On roadmap
3.2. Comparison by supported treatments in DL and NLP fields
In Tables 3 and 4, we highlight the essential supported treatments of DL tools TensorFlow, Keras,
Theano, and Deeplearning4j. We also aim to compare them for concluding the library that sustains a very
high amount of covered tasks. According to this benchmarking study, Python libraries TensorFlow, Keras,
and Theano support almost the same tasks in ANLP or deep learning. Compared to the Deeplearning4j Java
library, these three Python libraries support a large number of applications.
Table 3. Comparison of DL libraries in terms of different network types
Variable Network types
Recurrent
Neural
Networks
(RNN)
Long-Short
Term Memory
Networks
(LSTM)
Recursive
Neural
Networks
(RNN)
Sequence to
Sequence
(seq2seq)
models
Convolutional
neural networks
(CNN)
Bi-directional
LSTMs
Keras Yes Yes No Yes Yes Yes (No Arabic)
Theano Yes No No Yes (No Arabic) Yes No
Tensorflow Yes Yes No Yes Yes Yes (No Arabic)
Deeplearning4j Yes Yes Yes Yes (No Arabic) Yes No
Table 4. Evaluation of DL tools in terms of NLP tasks
Keras Theano Tensorflow Deeplearning4j
Natural Language Processing
Tasks
Arabic Sentiment Analysis Yes Yes Yes Yes
Machine translation Yes Yes Yes No
Conversations / Question Answering (QA) Yes Yes Yes No
Language modeling Yes No Yes No
Speech recognition Yes Yes Yes No
Text classification Yes Yes Yes Yes
Text summarization Yes Yes Yes No
Text generation Yes Yes Yes No
Named Entity Recognition (NER) Yes Yes Yes No
Part-Of-Speech tagging (POS) Yes Yes Yes Yes
Dependency parsing Yes Yes No No
Word embeddings Yes Yes Yes Yes
Semantic Role Labelling (SRL) No Yes No No
Sequence tagging No No No No
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3.3. Comparative evaluation of the open software tools focused on the forks, stars, commits, and
contributors received by the GitHub community
The GitHub site numbers are constantly variable. That is why we will select the consultation date of
these pieces of information (17/06/2020). Table 5 shows the GitHub results. Relied on the following results,
we deduce that Tensorflow is the most used, pursued by Keras and finally Theano and Deeplearning4j.
Table 5. GitHub results
Library TensorFlow Keras Theano Deeplearning4j
Language Python Python Python Java
Stars 146000 48700 9200 11700
Forks 81700 18400 2500 4800
Contributors 2529 818 332 37
Commits 88278 5343 28120 969
3.4. Comparison of libraries according to Google Trends and GitHub pull request history
In Figures 5 and 6, we highlight the results of Google Trends and GitHub pull request history.
According to Google Trends history results, we can conclude that Keras and TensorFlow libraries are very
famous within the users' community. Focusing on GitHub pull request history results, TensorFlow is
considered to be the most commonly used compared to the three other libraries in recent years.
Figure 5. Google Trends history
Figure 6. GitHub pull request history
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4. RESULTS AND DISCUSSION
In this comparative evaluation, we selected Java and Python programming languages because they
are very popular and have many useful DL libraries used in ASA. It is very difficult to deduce that one tool is
greatest than other tools, as these tools are very valuable and have a large popularity in these fields.
The following part presents our conclusions:
Theano: is great for creating networks from available components and reusing pre‐trained networks,
but more challenging as far as the building of perfect solutions is concerned. The main disadvantage of this
library is frequently long compile times when creating huge models.
TensorFlow: it built to substitute Theano. These two tools are, in fact, quite similar. A neural
network is declared as a computational graph like in Theano, which is optimized during compilation.
TensorFlow, however, has a faster compile time than Theano, but it is slower than other libraries.
A significant new feature is the implementation of data parallelism, which is identical to the Iterative
MapReduce from Deeplearning4j.
Keras: is easy to use and offers actionable feedback upon user error, and it permits fast prototyping.
Keras library sits atop Theano and TensorFlow. Deeplearning4j focuses on Keras as its Python API and
imports models from Keras and through Keras from TensorFlow and Theano. Its programs are generally
smaller than the equivalent Theano and TensorFlow programs.
Deeplearning4j: provides great solutions for beginners interested in exploring deep neural networks,
appropriate for educational and training objectives. On the other hand, TensorFlow and Theano tools are
more suitable for experienced users who need to have much more control over network architectures.
As a summarization of this section, each DL tool is characterized by its benefits in ANLP tasks.
Although they share almost the same characteristics and advantages, Tensorflow outperforms other libraries
according to Google Trends and GitHub pull request history. However, when we talk about Arabic Sentiment
analysis, and according to the literature and various great works in the field of ASA, we find that:
TensorFlow, Theano, and Keras are very popular and very used in this research domain.
5. CONCLUSION
In this work, we described a variety of Python and Java DL tools that are deemed most helpful in
ANLP. Besides, we attempted also to conclude the most powerful and useful DL libraries for the ASA field.
Moreover, we have compared each library using several aspects. In conclusion, every DL library is
characterized by its advantages and benefits in ANLP tasks. For this reason, we chose this set of libraries
because they made great results in the ASA task.
REFERENCES
[1] Y. Al Amrani, et al., “Recovery of the opinions through the specificities of documents text,” in 2019 International
Conference on Wireless Technologies, Embedded and Intelligent Systems, WITS 2019, 2019.
[2] Y. Al Amrani, M. Lazaar, and K. E. El Kadirp, “Random forest and support vector machine based hybrid approach
to sentiment analysis,” in Procedia Computer Science, 2018, vol. 127, pp. 511–520.
[3] Y. Zahidi, Y. El Younoussi, and C. Azroumahli, “Comparative Study of the Most Useful Arabic-supporting Natural
Language Processing and Deep Learning Libraries,” in 2019 International Conference on Optimization and
Applications, ICOA 2019, 2019.
[4] S. Aich, et al., “Convolutional neural network-based model for web-based text classification,” International
Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 6, pp. 5185-5191, 2019.
[5] A. Wahdan, S. AL Hantoobi, S. A. Salloum, and K. Shaalan, “A Systematic Review of Text classification Research
based on Deep Learning Models in Arabic Language,” International Journal of Electrical and Computer
Engineering (IJECE), vol. 10, no. 6, pp. 6629-6643, Dec. 2020.
[6] Y. Al-Amrani, M. Lazaar, and K. E. Elkadiri, “Sentiment analysis using supervised classification algorithms,” in
ACM International Conference Proceeding Series, vol. Part F1294, 2017.
[7] Y. Al-Amrani, M. Lazaar, K. Eddine, and E. L. Kadiri, “Sentiment Analysis Using Hybrid Method of Support
Vector Machine and Decision Tree,” J. Theor. Appl. Inf. Technol., vol. 96, no. 7, pp. 1886-1895, 2018.
[8] G. Al-Bdour, R. Al-Qurran, M. Al-Ayyoub, A. Shatnawi, “Benchmarking open source deep learning frameworks,”
International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 5, pp. 5479-5486, Oct. 2020.
[9] N. P. Shetty, B. Muniyal, A. Anand, S. Kumar, and S. Prabhu, “Predicting depression using deep learning and
ensemble algorithms on raw twitter data,” International Journal of Electrical and Computer Engineering (IJECE),
vol. 10, no. 4, pp. 3751–3756, Aug. 2020.
[10] Y. Al Amrani, M. Lazaar, and K. E. El Kadiri, “A novel hybrid classification approach for sentiment analysis
of text document,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 6,
pp. 4554–4567, 2018.
7. Int J Elec & Comp Eng ISSN: 2088-8708
A powerful comparison of deep learning frameworks for arabic sentiment analysis (Youssra Zahidi)
751
[11] M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in Proceedings of the 12th USENIX
Symposium on Operating Systems Design and Implementation, OSDI 2016, pp. 265–283, 2016.
[12] The Theano Development Team et al., “Theano: A Python framework for fast computation of mathematical
expressions,” CoRR, May 2016.
[13] F. Bastien et al., “Theano: new features and speed improvements,” CoRR, vol. abs/1211.5, Nov. 2012.
[14] J. Bergstra et al., “Theano: Deep Learning on GPUs with Python,” J. Mach. Learn. Res., vol. 1, pp. 1–48, 2011.
[15] M. M. Fouad, A. Mahany, N. Aljohani, R. A. Abbasi, and S. U. Hassan, “ArWordVec: efficient word embedding
models for Arabic tweets,” Soft Comput., vol. 24, no. 11, pp. 8061–8068, 2020.
[16] R. Baly et al., “Comparative Evaluation of Sentiment Analysis Methods Across Arabic Dialects,” in Procedia
Computer Science, 2017, vol. 117, pp. 266–273.
[17] E. Omara, M. Mosa, and N. Ismail, “Deep Convolutional Network for Arabic Sentiment Analysis,” in 2018
Proceedings of the Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2018,
pp. 155–159, 2019.
[18] M. Alfonse and E.-S. M. El-Horbaty, “Opinion Mining for Arabic Dialects on Twitter,” Egypt. Comput. Sci. J.,
vol. 42, no. 4, pp. 52–61, 2018.
[19] R. M. Alahmary, H. Z. Al-Dossari, and A. Z. Emam, “Sentiment analysis of saudi dialect using deep learning
techniques,” in ICEIC 2019 - International Conference on Electronics, Information, and Communication,
Auckland, New Zealand, pp. 1-6, 2019.
[20] H. Abdellaoui and M. Zrigui, “Using tweets and emojis to build TEAD: An Arabic dataset for sentiment analysis,”
Comput. y Sist., vol. 22, no. 3, pp. 777–786, 2018.
[21] R. Baly, et al., “Comparative Evaluation of Sentiment Analysis Methods Across Arabic Dialects,” in Procedia
Computer Science, vol. 117, pp. 266–273, 2017,.
[22] A. Elnagar, L. Lulu, and O. Einea, “An Annotated Huge Dataset for Standard and Colloquial Arabic Reviews for
Subjective Sentiment Analysis,” in Procedia Computer Science, vol. 142, pp. 182–189, 2018.
[23] S. Rosenthal, N. Farra, and P. Nakov, “SemEval-2017 Task 4: Sentiment Analysis in Twitter,” in Proceedings of
the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 502–518, 2017.
[24] A. Barhoumi, Y. Estève, C. Aloulou, and L. H. Belguith, “Document embeddings for Arabic Sentiment Analysis,”
in Proceedings of the First Conference on Language Processing and Knowledge Management, vol. 1988, 2017.
[25] S. Al-Azani and E. S. El-Alfy, “Emojis-based sentiment classification of Arabic microblogs using deep recurrent
neural networks,” in 2018 International Conference on Computing Sciences and Engineering, ICCSE 2018 -
Proceedings, pp. 1–6, 2018.
[26] S. Al-Azani and E. S. M. El-Alfy, “Hybrid Deep Learning for Sentiment Polarity Determination of Arabic
Microblogs,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics), vol. 10635 LNCS, pp. 491–500, 2017.
[27] M. Al-Smadi, B. Talafha, M. Al-Ayyoub, and Y. Jararweh, “Using long short-term memory deep neural networks
for aspect-based sentiment analysis of Arabic reviews,” Int. J. Mach. Learn. Cybern., vol. 10, no. 8, pp. 2163–2175,
Aug. 2019.
[28] A. Dahou, S. Xiong, J. Zhou, M. H. Haddoud, and P. Duan, “Word embeddings and convolutional neural
network for Arabic sentiment classification,” in COLING 2016-26th International Conference on Computational
Linguistics, Proceedings of COLING 2016: Technical Papers, pp. 2418–2427, 2016.
[29] S. Al-Azani and E. S. El-Alfy, “Emojis-based sentiment classification of Arabic microblogs using deep recurrent
neural networks,” in 2018 International Conference on Computing Sciences and Engineering, ICCSE 2018 -
Proceedings, pp. 1–6, 2018.
[30] M. Shahin, J. Epps, and B. Ahmed, “Automatic classification of lexical stress in English and Arabic languages
using deep learning,” in Proceedings of the Annual Conference of the International Speech Communication
Association, INTERSPEECH, pp. 175–179, 2016.
[31] P. Nakov, et al., “SemEval-2016 Task 4: Sentiment Analysis in Twitter,” in Proceedings of the 10th International
Workshop on Semantic Evaluation (SemEval-2016), pp. 1–18, 2016.
[32] A. M. Alayba, V. Palade, M. England, and R. Iqbal, “A combined CNN and LSTM model for Arabic sentiment
analysis,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics), vol. 11015 LNCS, pp. 179–191, 2018.
[33] S. R. El-Beltagy, M. El kalamawy, and A. B. Soliman, “NileTMRG at SemEval-2017 Task 4: Arabic
Sentiment Analysis,” in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017),
pp. 790–795, 2017.
[34] B. J. Erickson, P. Korfiatis, Z. Akkus, T. Kline, and K. Philbrick, “Toolkits and Libraries for Deep Learning,”
Journal of Digital Imaging, Springer New York LLC, vol. 30, no. 4, pp. 400–405, Aug. 2017.
[35] A. Parvat, J. Chavan, S. Kadam, S. Dev, and V. Pathak, “A survey of deep-learning frameworks,” in Proceedings of
the International Conference on Inventive Systems and Control, ICISC 2017, 2017.
8. ISSN: 2088-8708
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BIOGRAPHIES OF AUTHORS
Youssra Zahidi is a Ph.D. student in Computer Science, Information System, and Software
Engineering Laboratory, Abdelmalek Essaadi University, Tetuan, Morocco. She is a Computer
Sciences engineer, graduated in 2017 from the National School of Applied Sciences,
Abdelmalek Essaadi University.
Yacine El Younoussi is a Ph.D. doctor and professor of computer sciences at the National
School of Applied Sciences of Tetuan, Information System and Software Engineering
Laboratory, Abdelmalek Essaadi University, Tetuan, Morocco. He is a supervisor of many
Thesis, and he is part of many boards of international journals and international conferences.
Yassine Al-Amrani is a Ph.D. doctor and professor of Computer Sciences at
the Multidisciplinary Faculty of Larache, Abdemalek Essaadi University, Morocco. He is
a Computer Sciences Engineer, and he is part of many boards of International Journals and
International Conferences.