DNN

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, 'Deep Learning for Image Processing Applications', is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques

The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%.

Because of the excellent strength of concrete reinforced with fibers pulled in the
consideration of researchers throughout the most recent decades. The proposed
technique manages the experimental investigation to determine the properties of
Ternary Blended Fiber Reinforced Concrete (TBFRC) with the assistance of soft
computing methodology performed in MATLAB software. In the present experimental
examination a mix design of M50 is tried at utilizing ternary blend of Ground
Granulated Blast Furnace Slag (GGBS), Fly Ash (FA) and Metakaolin (MK) as
partial replacement by weight of concrete at different mixing rates running between
0% – 30% with extra steel and polypropylene fibers. Here, the mechanical properties,
for example, compressive strength, split tensile strength, and flexural strength, are
anticipated by utilizing Deep Learning Neural Network (DNN) strategy with various
fiber rate. The input factors for the neural network depict the materials and different
mix extents of concrete. In this network structure, the weights are enhanced by
utilizing Adaptive Crow Search Algorithm (ACSA). Additionally by utilizing this
system of ternary blended fiber reinforced concrete is delivered at a sensible cost than
that of traditional concrete. In addition, the Optimal DNN predicted the mechanical
properties optimally for all curing days (28, 56, and 90 days) compared with
experimental and existing strategies (ANN).

It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users’ tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity t...

It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users’ tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity t...

The use and dependence on software in various fields has been the reason why
researchers for past decades have spent their efforts on finding better methods to
predict software quality and reliability. Soft computing methods have been used to
bring efficient improvement in prediction of software reliability. This study proposed a
novel method called Fuzzy Greedy Recurrent Neural Network (FGRNN) to assess
software reliability by detecting the faults in the software. A deep learning model
based on the Recurrent Neural Network (RNN) has been used to predict the number of
faults in software. The proposed model consists of four modules. The first module,
attribute selection pre-processing, selects the relevant attributes and improves
generalization that improves the prediction on unknown data. Second module called,
Fuzzy conversion using membership function, smoothly collects the linear sub-models,
joined together to provide results. Next, Greedy selection deals with the attribute
subset selection problem. Finally, RNN technique is used to predict software failure
using previously recorded failure data. To attest the performance of the software, the
popular NASA Metric Data Program datasets are used. Experimental results show
that the proposed FGRNN model has better performance in reliability prediction
compared with existing other parameter based and NN based models

In today’s world there is an increased amount of network attacks such as DoS (Denial of Service), R2L (Remote to Local) attack, U2R (user to root) and probe attack. These network attacks lead to illegal usage of user accounts, stealing software, running code to corrupt systems, perform actions that prevent authorized users from accessing these resources and using data for financial gain etc. To overcome these threats Intrusion Detection Systems were adopted. The earlier Intrusion Detection systems used machine learning algorithms such as Random Forest and Decision Trees require much computational resources and higher time complexity. To overcome these hurdles, a DNN based intrusion detection system is proposed. This improves speed, accuracy and stability of neural networks. These IDS will not only detect the anomalies but also results in higher accuracy compared to existing systems.

The use and dependence on software in various fields has been the reason why researchers for past decades have spent their efforts on finding better methods to predict software quality and reliability. Soft computing methods have been used to bring efficient improvement in prediction of software reliability. This study proposed a novel method called Fuzzy Greedy Recurrent Neural Network (FGRNN) to assess software reliability by detecting the faults in the software. A deep learning model based on the Recurrent Neural Network (RNN) has been used to predict the number of faults in software. The proposed model consists of four modules. The first module, attribute selection pre-processing, selects the relevant attributes and improves generalization that improves the prediction on unknown data. Second module called, Fuzzy conversion using membership function, smoothly collects the linear sub-models, joined together to provide results. Next, Greedy selection deals with the attribute subset selection problem. Finally, RNN technique is used to predict software failure using previously recorded failure data. To attest the performance of the software, the popular NASA Metric Data Program datasets are used. Experimental results show that the proposed FGRNN model has better performance in reliability prediction compared with existing other parameter based and NN based models.

It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users’ tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity t...