Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine Learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using Deep machine Learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase Learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

In traditional speech processing, feature extraction and classification were conducted as separate steps. The advent of Deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and Deep architectures across diverse speech processing applications. This paper introduces different types of parametric filters and discusses their integration into various Deep architectures. Additionally, it examines the specific applications in speech processing where these filters have proven effective.

Identification of the exact location of an exon in a DNA sequence is an important research area of bioinformatics. The main issues of the previous signal processing techniques are accuracy and robustness for the exact locating of exons. To address the mentioned issues, in this study, a method has been proposed based on Deep Learning. The proposed method includes a new preprocessing, a new mapping method, and a multi-scale modified and hybrid Deep neural network. The proposed preprocessing method enriches the network to accept and encode genes at any length in a new mapping method. The proposed multi-scale Deep neural network uses a combination of an embedding layer, a modified CNN, and an LSTM network. In this study, HMR195, BG570, and F56F11.4 datasets have been used to compare this work with previous studies. The accuracies of the proposed method have been 0.982, 0.966, and 0.965 on HMR195, BG570, and F56F11.4 databases, respectively. The results reveal the superiority and effectiveness of the proposed hybrid multi-scale CNN-LSTM network.

Introduction: Cognitive functions play a vital role in how tasks are performed; for this, temporary cognitive and mental dysfunctions could lead to grave consequences, especially when an accurate and prompt response is required. Attention and reaction time to noise are among the most effective exogenous factors on the brain processing mechanism. This study aimed to measure the sustained attention of workers in the steel industry exposed to different sound pressure levels.  Material and Methods: The study was conducted in 4 general stages, including 1- Selecting predictive orientation variables (age, work history, different sound pressure levels); 2- Conducting the Cognitive Performance Test (CPT); 3 Conducting N-BACK Cognitive Performance Test and 4- Modeling cognitive performance changes using model precision methods. Results: Continuous Performance Test (CPT) results indicated that all three groups’ omission error, commission error, and response time were affected by shift time. All three components increased significantly as the shift ended, decreasing individuals’ cognitive function. Also, the higher noise impact in modeling CPT and N-Back tests indicated reduced workers’ concentration. Conclusion: These study findings suggested that greater noise weight obtained in test modeling in three-time intervals, i.e., in the beginning, middle, and end of the shift, affected the continuous performance components of the CPT and working memory performance of the N-back test, including workers’ response time and reaction time, with workers’ rate of error increasing and their focus decreasing during the shift.

Due to the rapid advancements in computer technology, researchers are attracted to solving challenging problems in many different fields. The price of rainbow options is an interesting problem in financial fields and risk management. When there is no closed-form solution to some options, numerical methods must be used. Choosing a suitable numerical method involves the most appropriate combination of criteria for speed, accuracy, simplicity and generality. Monte Carlo simulation methods and traditional numerical methods have expensive repetitive computations and unrealistic assumptions on the model. Deep Learning provides an effective and efficient method for options pricing. In this paper, the closed-form formula or Monte-Carlo simulation are used to generate data in European and Asian rainbow option prices for the Deep Learning model. The results confirm that the Deep Learning model can price the rainbow options more accurately with less computation time than Monte-Carlo simulation.

Emotion Speech Recognition (ESR) is recognizing the formation and change of speaker’ s emotional state from his/her speech signal. The main purpose of this field is to produce a convenient system that is able to effortlessly communicate and interact with humans. The reliability of the current speech emotion recognition systems is far from being achieved. However, this is a challenging task due to the gap between acoustic features and human emotions, which relies strongly on the discriminative acoustic features extracted for a given recognition task. Deep Learning techniques have been recently proposed as an alternative to traditional techniques in ESR. In this paper, an overview of Deep Learning techniques that could be used in Emotional Speech recognition is presented. Different extracted features like MFCC as well as feature classifications methods including HMM, GMM, LTSTM and ANN have been discussed. In addition, the review covers databases used, emotions extracted, and contributions made toward ESR.