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Deep neural networks (DNNs) have demonstrated remarkable success across a wide range of machine learning tasks. However, determining an effective network architecture, particularly the sizes of the hidden layers, remains a significant challenge and often relies on inefficient trial-and-error experimentation. In this paper, we propose an automated architecture design approach based on structurally adaptive DNNs,…

In this paper we propose an algorithmic approach for Convolutional Neural Network (CNN) for digital watermarking which outperforms the existing frequency domain techniques in all aspects including security along with the criteria in the neural networks such as conditions embedded, and types of watermarking attack. This research addresses digital watermarking in deep neural networks and…

The ever-growing technology in mobile smartphones has enabled users to store sensitive and private information; as a result, it required the need for an improved security system. Previous approaches heavily relied on shallow machine learning algorithms that require feature extraction which is time consuming, laborious and can cause, resulting in poor authentication. In this paper,…

Brain tumors are a type of tumor with a high mortality rate. Since multifocal-looking tumors in the brain can resemble multicentric gliomas or gliomatosis, accurate detection of the tumor is required during the treatment process. The similarity of neurological and radiological findings also complicates the classification of these tumors. Fast and accurate classification is important…

Contextual continuous word representation showed promising performances in different natural language processing tasks. It stems from the fact that these word representations consider the context in which a word appears. But until recently, very little attention was paid to the contextual representations in Arabic question classification task. In the present study, we employed a contextual…

In this paper, we propose a revised version of the semantic decoder for multi-label classification task in the spoken language understanding (SLU) pilot task of the Dialog State Tracking Challenge 5 (DSTC5). Our model concatenates two deep neural networks – a Convolutional Neural Network (CNN) and a Recurrent Neural Networks (RNN) – for detecting semantic…

The availability of traffic data and computational advances now make it possible to build data-driven models that capture the evolution of the state of traffic along modeled stretches of road. These models are used for short-time prediction so that transportation facilities can be operated in an efficient way that guarantees a high level of service.…

Deep Neural Networks (DNNs) prove to be susceptible to synthetically generated samples, so-called adversarial examples. Such adversarial examples aim at generating misclassifications by specifically optimizing input data for a matching perturbation. With the increasing use of deep learning on embedded devices and the resulting use of quantization techniques to compress deep neural networks, it is…

The use of a battery to power an electrical or electronic system is accompanied by battery management, i.e. a set of measures intended to preserve it for preventative maintenance, thus the cost reduction. This management is generally based on two key parameters, the (remaining useful life) RUL and the (State-of-health) SOH, which relate respectively to…

In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by…

For many years, a lot of researches have been made to develop Advanced Driver Assistance Systems (ADAS) that are based on integrated systems. The main objective is to help drivers. Hence, keeping them safe under different driving conditions. Visibility for drivers remains the biggest problem faced on the road in an atmosphere of fog. In…

Aging is considered to be a complex process in almost every species’ life, which can be studied at a variety of levels of abstraction as well as in different organs. Not surprisingly, biometric characteristics from facial images play a significant role in predicting human’s age. Specifically, automatic age estimation in real-time situation has begun to…

DNN implementation and deployment is quite a challenge within a resource constrained environment on real-time embedded platforms. To attain the goal of DNN tailor made architecture deployment on a real-time embedded platform with limited hardware resources (low computational and memory resources) in comparison to a CPU or GPU based system, High Performance SqueezeNext (HPS) architecture…

With the development of Internet of Things technology and the widespread use of smart devices, artificial intelligence is now being applied as a decision-making tool in a variety of fields. To make machine learning models, including deep neural network models, more interpretable, various techniques have been proposed. In this paper, a method for explaining the…

The Internet of Things (IoT) is at a face paced growth in the advanced Industrial Revolution (IR) 4.0 in the modern digital world. Considering the current network security challenges and sophistication of attacks in the heavily computerized and interconnected systems, such as an IoT ecosystem, the need for an innovative, robust, intelligent and adaptive malware…

Research focused on training and testing of dataset after Optimizing Software Component with the help of deep neural network mechanism. Optimized components are selected for training and testing to improve the accuracy at the time of software selection. Selected components are required to be attuned and accommodating as per requirement. Soft computing mechanism such as…

Environmental sounds detection plays an increasing role in computer science and robotics as it simulates the human faculty of hearing. It is applied in environment research, monitoring and protection, by allowing investigation of natural reserves, and showing potential risks of damage that can be deduced from the environmental acoustic. The research presented in this paper…

The problem of recognizing traffic sign boards in a correct fashion is one of the major challenges since there is an alarming rate of increase in the number of road accidents happening because of incorrect interpretation of traffic sign boards in bad weather conditions. In this paper, a comparative analysis of various noise removal techniques…

Automatic speech emotion recognition (SER) techniques based on acoustic analysis show high confusion between certain emotional categories. This study used an indirect approach to provide insights into the amplitude-frequency characteristics of different emotions in order to support the development of future, more efficiently differentiating SER methods. The analysis was carried out by transforming short 1-second…

In this paper, we propose a highly accurate indoor position and direction estimation system using a simple fully connected deep neural network (DNN) model on Bluetooth Low Energy (BLE) Received Signal Strength Indicators (RSSIs). Since the system’s ultimate goal is to function as an indoor navigation system, the system estimates the indoor position simultaneously as…