Audio classification is the process to classify different audio types according to contents. It is implemented in a large variety of real world problems, all classification applications allowed the target subjects to be viewed as a specific type of audio and hence, there is a variety in the audio types and every type has to be treatedcarefully according to its significant properties.Feature extraction is an important process for audio classification. This workintroduces several sets of features according to the type, two types of audio (datasets) were studied. Two different features sets are proposed: (i) firstorder gradient feature vector, and (ii) Local roughness feature vector, the experimentsshowed that the results are competitive to

Cryptography algorithms play a critical role in information technology against various attacks witnessed in the digital era. Many studies and algorithms are done to achieve security issues for information systems. The high complexity of computational operations characterizes the traditional cryptography algorithms. On the other hand, lightweight algorithms are the way to solve most of the security issues that encounter applying traditional cryptography in constrained devices. However, a symmetric cipher is widely applied for ensuring the security of data communication in constraint devices. In this study, we proposed a hybrid algorithm based on two cryptography algorithms PRESENT and Salsa20. Also, a 2D logistic map of a chaotic system is a

The focus of this paper is the presentation of a new type of mapping called projection Jungck zn- Suzuki generalized and also defining new algorithms of various types (one-step and two-step algorithms) (projection Jungck-normal N algorithm, projection Jungck-Picard algorithm, projection Jungck-Krasnoselskii algorithm, and projection Jungck-Thianwan algorithm). The convergence of these algorithms has been studied, and it was discovered that they all converge to a fixed point. Furthermore, using the previous three conditions for the lemma, we demonstrated that the difference between any two sequences is zero. These algorithms' stability was demonstrated using projection Jungck Suzuki generalized mapping. In contrast, the rate of convergenc

  Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead

Permanent deformation in asphalt concrete pavements is pervasive distress [1], influenced by various factors such as environmental conditions, traffic loading, and mixture properties. A meticulous investigation into these factors has been conducted, yielding a robust dataset from uniaxial repeated load tests on 108 asphalt concrete samples. Each sample underwent systematic evaluation under varied test temperatures, loading conditions, and mixture properties, ensuring the data’s comprehensiveness and reliability. The materials used, sourced locally, were selected to enhance the study ʼs relevance to pavement constructions in hot climate areas, considering different asphalt cement grades and con- tents to understand material variability ef

The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura

When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat