Finger-Knuckle-Print (FKP) is an accurate and reliable biometric in compare to other hand-based biometrics like fingerprint because of the finger's dorsal region is not exposed to surfaces. In this paper, a simple end-to-end method based on convolutional neural network (CNN) is proposed for FKP recognition. The proposed model is composed only of three convolutional layers and two fully connected layers. The number of trainable parameters hereby has significantly reduced. Additionally, a straightforward method is utilized for data augmentation in this paper. The performance of the proposed network is evaluated on Poly-U FKP dataset based on 10-fold cross-validation. The best recognition accuracy, mean accuracy and standard deviation are 99.83%, 99.18%, and 0.76, respectively. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of recognition accuracy and the number of trainable parameters. Also, in compare to four fine-tuned CNN models including AlexNet, VGG16, ResNet34, and GoogleNet, the proposed simple method achieved higher performance in terms of recognition accuracy and the numbers of trainable parameters and training time.

Deepfake refers to a category of fake and artificial data in which fake content is produced based on existing content. This content can include image, video and audio signals. Deepfake production is based on Deep generative networks that manipulate data or produce fake images and videos. In recent years, many studies have been conducted to understand how Deepfakes work, and many methods based on Deep learning have been introduced to identify videos or images produced by Deepfakes and distinguish them from real images. In order to improve the accuracy of Deep-fake detection and simultaneously use the capabilities of different types of convolutional neural networks, in this article, a hybrid model is presented using four convolutional neural networks: DenseNet201, EfficientNetB2, Inception-ResNet-V2, and ResNet152. turns Relying on the high capabilities of these networks in extracting effective features from the input image, the proposed model is able to simultaneously recognize whether the input image is Deep or not by these four models. The results presented on the three databases of 140k real and fake faces, DFDC faces and Deepfake and real images indicate the improvement of the results compared to the existing models.

Facial expressions are part of human language and are often used to convey emotions. Since humans are very different in their emotional representation through various media, the recognition of facial expression becomes a challenging problem in machine learning methods. Emotion and sentiment analysis also have become new trends in social media. Deep convolutional neural network (DCNN) is one of the newest learning methods in recent years that model a human's brain. DCNN achieves better accuracy with big data such as images. In this paper an automatic facial expression recognition (AFER) method using the DCNN. In this work, a way is provided to overcome the overfitting problem in training the DCNN for AFER, and also an effective pre-processing phase is proposed that improved the accuracy of facial expression recognition (FER). Here the results for recognition of seven emotional states (neutral, happiness, sadness, surprise, anger, fear, disgust) have been presented by applying the proposed method on the two largely used public datasets JAFFE and CK+. The results show that in the proposed method, the accuracy of AFER is better than traditional FER methods and is about 98. 59% and 96. 89% for JAFFE and CK+ datasets, respectively.

Introduction: Given that agriculture has the most important role in ensuring food security (Johnston & Kilby, 1989), it is necessary to prepare a map that shows the spatial distribution, land area, and type of crops cultivated with high accuracy (Cai et al., 2018). Agricultural land cover is relatively dynamic and variable at relatively short intervals. This makes it difficult to classify crops on satellite imagery (Bargiel, 2017). The lack or absence of ground truth data is another cause. Therefore, methods that are less dependent on ground samples and use phenological features derived from time series of bands and vegetation indices to classify crops will be more appropriate (Ashourloo et al., 2020). The purpose of this study is to use a Deep learning method based on convolutional networks to classify the crop types and improve the performance of this network by using feature channels as an input image to the network and increasing the classification accuracy. Materials and methods: In this study, the visible and near-infrared bands of Sentinel-2 satellite on 10 different dates from 2019 for an area in Idaho, USA, as an important agricultural area, and the cropland data layer for extracting the crop types ground labels was used (Han et al., 2012). Then, in MATLAB software, the time series of spectral bands were constructed and using them, temporal profiles of NDVI for any crop were extracted to identify the unique phenological features of crops. Then, the functions developed based on the phenological characteristics of crops were applied to the time series of the bands and a feature channel was obtained for each crop that in two separate processes, once bands and once again feature channels were used as input to the CNN and the network was trained and the results of network performance on crop classification in the test site, were compared. Results and discussion: In the first stage, the time series of bands formed the input of the Deep convectional neural network and the network was trained in the training area, using the tempo-spectral information of bands as the input channels and crops ground samples as the related labels. Due to the spectral overlap of the crops in some time periods, network training was associated with a relatively high loss and therefore, for the test area, the overall classification accuracy was 69% (percent) and the kappa coefficient was 0. 55. In the next step, the functions that were developed as phenological features for crops were applied on the time series of the bands, and for each crop, a feature channel was obtained as the special feature of that crop. Then the algorithm was implemented using these feature channels in the test area and the overall accuracy was upgraded to 86% and the kappa coefficient to 0. 82 compared to which indicated a significant improvement in the results compared to the previous case. Conclusion: The Deep convolutional neural network is very sensitive to the type of input channels for detecting agricultural crops and selecting the channels with suitable tempo-spectral characteristics for different types of crops, has a great impact on the accuracy of network training and can reduce the loss of training network and increase its efficiency in the classification of various crops.

Condition monitoring and fault diagnosis of large industrial equipment has become very important role nowadays. Powerful artificial intelligent methods can be appropriately used on big data without any further statistical assumption. In this research, two compromising methods including Deep neural network and convolutional neural network have been used to classify faults of a laboratory gearbox. Both networks have been used to classify nine faulty classes and one healthy class of the gearbox using vibration signal. The data have been collected at six different load and speed combinations. The measured time domain vibration signal was used as neural network input. The classification accuracy of both methods have been obtained. The effect of challenging parameters such as window size, learning rate and number of extracted features on the classification accuracy have been studied. Finally after the comparison of the results, it was concluded that the accuracy of the convolutional neural network was superior.

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.

In this paper, brain tumor detection is addressed through the application of advanced Deep-learning techniques. The approach involves the development and training of a comprehensive convolutional neural network (CNN) architecture. Leveraging an extensive dataset of brain magnetic resonance imaging (MRI), the proposed model expresses its proficiency in the classification of normal brain tissue and tumor-affected regions. The architecture encompasses multiple layers, including convolutional, batch normalization, and pooling layers, culminating in a robust classification layer. Through rigorous training and optimization, the introduced CNN achieves a high level of accuracy in brain tumor classification. The effectiveness of the proposed model is showcased through comprehensive experimentation, demonstrating its potential to significantly contribute to the medical field’s efforts in precise brain tumor diagnosis.