Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency.Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities alongside normal signals.Material and Methods: This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on diverse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score.Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting CVD. Conclusion: The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analysis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection.

Emotions are human mental states at a particular instance in time concerning one’s circumstances, mood, and relationships with others. Identifying emotions from the whispered speech is complicated as the conversation might be confidential. The representation of the speech relies on the magnitude of its information. Whispered speech is intelligible, a low-intensity signal, and varies from normal speech. Emotion identification is quite tricky from whispered speech. Both prosodic and spectral speech features help to identify emotions. The emotion identification in a whispered speech happens using prosodic speech features such as zero-crossing rate (ZCR), pitch, and spectral features that include spectral centroid, chroma STFT, Mel scale spectrogram, Mel-frequency cepstral coefficient (MFCC), Shifted Delta Cepstrum (SDC), and Spectral Flux. There are two parts to the proposed implementation. Bidirectional Long Short-Term Memory (BiLSTM) helps to identify the gender from the speech sample in the first step with SDC and pitch. The Deep Convolutional Neural Network (DCNN) model helps to identify the emotions in the second step. This implementation is evaluated with the help of wTIMIT data corpus and gives 98. 54% accuracy. Emotions have a dynamic effect on genders, so this implementation performs better than traditional approaches. This approach helps to design online learning management systems, different applications for mobile devices, checking cyber-criminal activities, emotion detection for older people, automatic speaker identification and authentication, forensics, and surveillance

One of the most important and influential ways to diagnose breast cancer, especially in the early stages of the disease, is mammography. Mammography images are usually of low quality due to the complexity of breast tissues, the similarity between cancerous masses and normal tissues, the different sizes and shapes of the masses, and X-ray radiation. Therefore, it is very difficult to detect lesions, especially in the early stages; Because some mass lesions are embedded in natural tissues and have weak margins or vague margins. The proposed method in this study is to present an architecture based on a deep convolutional neural network to detect cancerous masses in mammography images, which ultimately leads to classifying the masses into normal and abnormal classes. The training of the proposed network begins with the modification of the images in the pre-processing stage in order to perform more accurate drawings with high resolution on the images and finally to improve the accuracy and sensitivity of separating the mass from the breast tissue for correct diagnosis. Python programming language and TensorFlow library have been used in the Windows environment to implement the proposed method. To ensure the performance of the proposed method, the cross-validation method was used and the obtained results were evaluated by the criteria of precision, accuracy, and sensitivity. The results obtained with an accuracy of 97.67% indicate the improvement of the diagnosis accuracy and the cost reduction in the diagnosis process

Recently, deep learning methods, mostly algorithms based on Deep Convolutional Neural Networks (DCNNs) have yielded great results on pedestrian detection. Algorithms based on DCNNs spontaneously learn features in a supervised manner and are able to learn qualified high level feature representations to detect pedestrian. In this paper, we first review a number of popular DCNN-based training approaches along with their recent extensions. We then briefly describe recent algorithms based on these approaches. Also, we accentuate recent contributions and main challenges of DCNNs in detecting pedestrian. We analyze deep pedestrian detection algorithms from training approach, categorization, and DCNN model points of view, and ultimately propose a new deep architecture and training approach for deep pedestrian detection. The experimental results show that the proposed DCNN and training approach, achieve more accurate rate detection than the previously reported architectures and training approaches.

The purpuse of this essay is that scrutiny semblance of ancient myths of iran and comparison of those. The author mentions his theory about to how start of myths then he conclude the relation between myth and philosophy. In continue he scrutiny of most important similes between persian myths and adventures of prophets and unmerciful kings which mention in holy books. For example similes between siavoosh and abraham.The most important conclude of these essay is reletions between myths and historical adventures or between two diffrent myths and how efficacy of those.

Handwriting recognition has always been a challenge; therefore, it has attracted the attention of many researchers. The present study presents an offline system for the automatic detection of human handwriting under different experimental conditions. This system includes input data, image processing unit, and output unit. In this study, a right-to-left dataset is designed based on the standards of the American Society for Experiments and Materials (ASTM). An improved deep convolution neural network (DCNN) model based on a pre-trained network is designed to extract features hierarchically from raw handwritten data. A significant advantage in this study is the use of heterogeneous data. Another significant aspect of the present study is that the proposed DCNN model is independent of any particular language and can be used for different languages. The results show that the proposed DCNN model has a very good performance for identifying the author based on heterogeneous data.

Facial expression recognition (FER), which is one of the basic ways of interacting with machines, has attracted much attention in the recent years. In this paper, a novel FER system based on a deep convolutional neural network (DCNN) is presented. Motivated by the powerful ability of DCNN in order to learn the features and image classification, the goal of this research work is to design a compatible and discriminative input for pre-trained AlexNet-DCNN. The proposed method consists of 4 steps. First, extracting three channels of the image including the original gray-level image in addition to the horizontal and vertical gradients of the image similar to the red, green, and blue color channels of an RGB image as the DCNN input. Secondly, data augmentation including scale, rotation, width shift, height shift, zoom, horizontal flip, and vertical flip of the images are prepared in addition to the original images for training DCNN. Then the AlexNet-DCNN model is applied in order to learn the high-level features corresponding to different emotion classes. Finally, transfer learning is implemented on the proposed model, and the presented model is fine-tuned on the target datasets. The average recognition accuracies of 92. 41% and 93. 66% are achieved for the JAFFE and CK+ datasets, respectively. The experimental results on two benchmark emotional datasets show a promising performance of the proposed model that can improve the performance of the current FER systems.

The plot of some narratives is structured in a way that it can encapsulate paradoxical and opposite states. Conceptualization of these kinds of narratives based on classic cosmology to which most of us are accustomed is not possible; another cosmology such as quantum cosmology, however, might be helpful. The text can be interpreted through “multiverse theory, ” because in this cosmological framework of quantum mechanics the conjoining of the opposite states is possible. This article is an interdisciplinary attempt to analyze different kinds of the so-called “multiverse narratives.” The major question of this article is to explore the relationship between the notion of Parallel universes in physics and narrative semantics. My purpose is to find out the function of the notion of Parallel worlds in narrative semantics. Thus, I argue that various sorts of multiverse narratives invite the reader to reflect on the nature of space, time, identity, and memory by challenging the boundary between actual and virtual world.