A Loss Function Base on Softmax for Expression Recognition

With the rapid development of artificial intelligence, deep learning is developing rapidly. As the main framework of in-depth learning, convolution neural network achieved much in semantic segmentation and image classification. Based on the low speed engineering vehicle tracking system project, this paper studies the activation function, which is an important component of convolution neural network. Firstly, it summarizes the advantages and disadvantages of four traditional activation functions such as Sigmoid, and then introduces three new activation functions: Swish, FTS, and Relu-Softplus. Finally, using the TensorFlow framework, a single new activation function and a hybrid new activation function are applied to the convolution layer of the four neural networks constructed, and the image recognition comparison is performed in the data set CIFAR-10. It is concluded that the recognition precision of mixing the new activation functions is higher than that of using a new activation function alone, and the average time is shorter and the convergence speed is faster.

Convolutional neural networks (CNNs) have become a useful tool for a wide range of applications such as text classification. However, CNNs are not always sufficiently accurate to be useful in certain applications. The selection of activation functions within CNN architecture can affect the efficacy of the CNN. However, there is limited research regarding which activation functions are best for CNN text classification. This study tested sixteen activation functions across three text classification datasets and six CNN structures, to determine the effects of activation function on accuracy, iterations to convergence, and Positive Confidence Difference (PCD). PCD is a novel metric introduced to compare how activation functions affected a network’s classification confidence. Tables were presented to compare the performance of the activation functions across the different CNN architectures and datasets. Top performing activation functions across the different tests included the symmetrical multi-state activation function, sigmoid, penalised hyperbolic tangent, and generalised swish. An activation function’s PCD was the most consistent evaluation metric during activation function assessment, implying a close relationship between activation functions and network confidence that has yet to be explored.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Deep learning has been successfully applied in classification of white blood cells (WBCs), however, accuracy and processing time are found to be less than optimal hindering it from getting its full potential. This is due to imbalanced dataset, intra-class compactness, inter-class separability and overfitting problems. The main research idea is to enhance the classification and prediction accuracy of blood images while lowering processing time through the use of deep convolutional neural network (DCNN) architecture by using the modified loss function. The proposed system consists of a deep neural convolution network (DCNN) that will improve the classification accuracy by using modified loss function along with regularization. Firstly, images are pre-processed and fed through DCNN that contains different layers with different activation function for the feature extraction and classification. Along with modified loss function with regularization, weight function aids in the classification of WBCs by considering weights of samples belonging to each class for compensating the error arising due to imbalanced dataset. The processing time will be counted by each image to check the time enhancement. The classification accuracy and processing time are achieved using the dataset-master. Our proposed solution obtains better classification performance in the given dataset comparing with other previous methods. The proposed system enhanced the classification accuracy of 98.92% from 96.1% and a decrease in processing time from 0.354 to 0.216 s. Less time will be required by our proposed solution for achieving the model convergence with 9 epochs against the current convergence time of 13.5 epochs on average, epoch is the formation white blood cells (WBCs) and the development of granular cells. The proposed solution modified loss function to solve the adverse effect caused due to imbalance dataset by considering weight and use regularization technique for overfitting problem.

The convolutional neural network (CNN) has been widely used in image recognition field due to its good performance. This paper proposes a facial expression recognition method based on the CNN model. Regarding the complexity of the hierarchic structure of the CNN model, the activation function is its core, because the nonlinear ability of the activation function really makes the deep neural network have authentic artificial intelligence. Among common activation functions, the ReLu function is one of the best of them, but it also has some shortcomings. Since the derivative of the ReLu function is always zero when the input value is negative, it is likely to appear as the phenomenon of neuronal necrosis. In order to solve the above problem, the influence of the activation function in the CNN model is studied in this paper. According to the design principle of the activation function in CNN model, a new piecewise activation function is proposed. Five common activation functions (i.e., sigmoid, tanh, ReLu, leaky ReLus and softplus–ReLu, plus the new activation function) have been analysed and compared in facial expression recognition tasks based on the Keras framework. The Experimental results on two public facial expression databases (i.e., JAFFE and FER2013) show that the convolutional neural network based on the improved activation function has a better performance than most-of-the-art activation functions.

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.

Deep Learning and Beyond: AI’s New Horizons” is an expansive monograph that delves into the dynamic and rapidly evolving field of deep learning, a key area of artificial intelligence. This comprehensive work provides an insightful exploration of the revolutionary developments, foundational concepts, and future directions of deep learning, positioning itself as a crucial resource for understanding this transformative technology. The monograph commences with an introductory chapter that lays the groundwork by elucidating the background of machine learning and chronicling the ascent of deep learning. This foundational perspective is vital for comprehending the underlying principles and methodologies that have propelled deep learning to the forefront of technological innovation. Subsequent chapters delve into the “Historical Evolution” of AI, tracing the origins of neural networks from their early inception to the Renaissance period of deep learning. This historical analysis is pivotal in understanding the field’s trajectory and significant milestones. The “Foundations of Deep Learning” chapter presents an in-depth analysis of neural networks, covering their basic architecture, activation functions, layers, and the critical process of backpropagation and gradient descent. This is followed by examining “Advanced Neural Network Architectures,” where the focus shifts to state-of-the-art models such as convolutional neural networks, recurrent neural networks, and groundbreaking transformers and attention mechanisms. In addressing the “Training Deep Models,” the monograph explores regularization techniques, optimization strategies, and the increasingly significant role of transfer learning and pre-trained models. The application-oriented chapter, “Applications of Deep Learning,” showcases real-world implementations in domains like image recognition, natural language viii processing, and autonomous systems, illustrating deep learning technologies’ vast potential and versatility. The monograph also tackles the “Challenges in Deep Learning,” including overfitting, interpretability, and the ethical considerations surrounding AI deployment. It then ventures into the “Future of Deep Learning,” contemplating emerging trends such as integrating quantum computing, neural architecture search, and the convergence of neuroscience and deep learning. “Case Studies” provide practical insights and empirical evidence of the impact of deep learning. At the same time, the concluding chapter synthesizes the current state of deep learning, offers predictions for the future, and provides reflective thoughts on this transformative field. “Deep Learning and Beyond: AI’s New Horizons” educates and informs readers about the vast potential and future possibilities of deep learning and artificial intelligence. Keywords: Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Quantum Computing, Ethical AI, Neural Architecture Search, Natural Language Processing, Autonomous Systems, AI in Healthcare, Future of AI, AI Applications, AI Challenges.

Sentiment Analysis was a process for identifying whether a source of text contains certain opinions, emotions, and polarity. Twitter Sentiment Analysis was a process for identifying sentiment and polarity on tweet. Twitter Sentiment Analysis provided a way for did survey about public sentiment to product, or particular even through collections of tweet. Main problem in sentiment identifying was how to determine classification model that gave high accuracy to classifying sentiment of tweet. One of the method for classifying sentiment of tweet was Deep Learning. Convolutional Neural Network (CNN) was special type of architecture from Deep Learning that its architecture had convolution layer. Convolution layer was important for extract relevant feature from text for classifying sentiment. The objective of this research was for found out the best CNN model for classifying sentiment of tweet. By using a dataset of tweets about public opinion on the Smartfren 4G network service, we searched the best CNN model using 6 combination parameters, that is the computational eficiency method, window size, and dimension of word embedding for parameters in Word2Vec Skip-gram model, then activation function in convolution layer, dropout rate, and pool size in pooling layer for parameters in CNN. The test is done using 10-fold cross validation for each search for the best parameter value and produced the best CNN model with an accuracy value of 88,21%.

To improve the robustness and to reduce the energy consumption of facial expression recognition, this study proposed a facial expression recognition method based on improved deep residual networks (ResNets). Residual learning has solved the degradation problem of deep Convolutional Neural Networks (CNNs); therefore, in theory, a ResNet can consist of infinite number of neural layers. On the one hand, ResNets benefit from better performance on artificial intelligence (AI) tasks, thanks to its deeper network structure; meanwhile, on the other hand, it faces a severe problem of energy consumption, especially on mobile devices. Hence, this study employs a novel activation function, the Noisy Softplus (NSP), to replace rectified linear units (ReLU) to get improved ResNets. NSP is a biologically plausible activation function, which was first proposed in training Spiking Neural Networks (SNNs); thus, NSP-trained models can be directly implemented on ultra-low-power neuromorphic hardware. We built an 18-layered ResNet using NSP to perform facial expression recognition across datasets Cohn-Kanade (CK+), Karolinska Directed Emotional Faces (KDEF) and GENKI-4K. The results achieved better anti-noise ability than ResNet using the activation function ReLU and showed low energy consumption running on neuromorphic hardware. This study not only contributes a solution for robust facial expression recognition, but also consolidates the low energy cost of their implementation on neuromorphic devices, which could pave the way for high-performance, noise-robust and energy-efficient vision applications on mobile hardware.

With the work and study pressure on people increasing and the importance of mental health problems on the rise, facial expression analysis plays an important role in mental health auxiliary diagnosis and treatment (MHADT). This study proposes a facial expression localization and recognition model that integrates Main Directional Mean Optical Flow (MDMO) with the Transformer architecture. It addresses the problems of insufficient generalization ability and limited temporal modeling ability of traditional methods in facial expression recognition. The study is based on the authoritative Audio/Visual Emotion Challenge 2019 Depression Detection Sub-challenge (AVEC2019 DDS) dataset in the mental health field, which contains 163 training samples, 56 validation samples, and 56 test samples. With Explicit Shape Regression, Local Binary Patterns, Mnemonic Descent Method, Convolutional Neural Network, and benchmark models as comparison objects, it systematically evaluates the model's performance in error, accuracy, and processing speed. The results show that the model achieves the best performance in both facial expression localization and recognition tasks. The validation set errors are 7.26 and 6.85, the localization accuracy reaches 89.6%, and the recognition accuracy reaches 88.9%, which is significantly better than other methods. At the same time, its image processing time is 55 milliseconds (ms) and 44ms, balancing high precision and real-time performance. The study indicates that the fusion of MDMO and Transformer can effectively capture the spatial and temporal features of facial expressions. Thus, this fusion method provides an efficient, stable, and scalable technical solution for emotion recognition in MHADT. This improves both the FER and localization accuracy and effect. Besides, it provides a novel reference in both approaches and application level for the intelligent development of mental health evaluation.

Handwritten Digit Recognition is still an essential issue in artificial intelligence and pattern recognition. Convolutional Neural Networks (CNNs) have shown outstanding accuracy on standardized datasets such as MNIST. Still, overfitting and incorrect hyperparameter tuning can cause CNNs to perform worse when applied to noisy real-world data. For example, handwritten digits taken from realistic Sudoku boards provide variation and noise that are not present in benchmark datasets, making it difficult to generalize CNN models to these datasets. The present study gives a systematic approach to improve handwritten digit recognition on custom datasets by improving CNN hyperparameters. The primary dataset derived from Kaggle’s “Sudoku Digit Classification” comprises 70,000 grayscale images of the digits 1 through 9, including zero representing empty cells. we use 10,000 images for this study, 7000 for training, 1500 for validation, and 1500 for testing. A personal handwritten digit dataset is a custom dataset that has only 2 images per class, and 9 classes represent the real world with small data. It is artificially extended using various kinds of data augmentation techniques, including rotation, scaling, flipping, shear transformation, brightness and contrast correction, and noise addition. Data augmentation increases each class to 100 images. These techniques enhance the model’s performance on unknown data and help it to become more generalizable. Training occurs using an Adam optimizer, a batch size of 32, and an initial learning rate of 0.001. To optimize the model’s performance, these hyperparameters are properly tuned. The Kaggle dataset is used to train, validate, and test the model on unseen data, and the custom unseen dataset is used for testing. The proposed model indicated great potential for accurate handwritten digit recognition with a training accuracy of approximately 95% and a validation accuracy of up to 99% after 30 epochs. Strong generalization over unseen handwritten digits is determined by testing accuracy of 97% on the Sudoku dataset. Testing accuracy on the personal dataset is 94.44%, and testing accuracy on the augmented personal dataset is 77.85%.

Deep learning algorithms can be used to design systems that report data on patients and deliver warnings to medical applications or electronic health information if there are changes in the patient's health. These systems could be created using deep Learning. This helps verify that patients get the proper effective care at the proper time for each specific patient. A healthcare decision support system was presented using the Internet of Things and deep learning methods. In the proposed system, we examined the capability of integrating deep learning technology into automatic diagnosis and IoT capabilities for faster message exchange over the Internet. We have selected the suitable Neural Network structure (number of best-hidden layers and activation function classes) to construct the e-health system. In addition, the e-health system relied on data from doctors to understand the Neural Network. In the validation method, the total evaluation of the proposed healthcare system for diagnostics provides dependability under various patient conditions. Based on evaluation and simulation findings, a dual hidden layer of feed-forward NN and its neurons store the tanh function more effectively than other NN. To overcome challenges, this study will integrate artificial intelligence with IoT. This study aims to determine the NN's optimal layer counts and activation function variations.

SummaryThe present existing weather prediction systems deployed by weather forecasting agencies predict the anomalous events using numerical simulation models developed based on geophysics and oceanographic theories. The major disadvantage with existing models is that are explicit simulation models. So, we try to build a real time prediction system that will automatically predict the intensity of any marine natural disaster events and alert the authorities to take necessary actions using a satellite and observational real time data set including moored met ocean data. We propose a novel hybrid model which is a combination of Convolutional Neural Network (CNN) and Long Short‐Term Memory (LSTM). The paper analyzes various other models such as Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi‐LSTM) Recurrent Neural Network (RNN), and Extreme Learning Machine. In addition the other standard CNN architectures are also analyzed. Since the residual CNN network is the most effective architecture, the paper proposes a modification of the residual CNN network by combining the residual CNN network with LSTM/GRU RNN network along with a proposed new activation function which was experimentally proved to be more effective than the deep learning community standard activation function ReLU. The proposed new activation function overcomes the dead neuron propagation problem in ReLU activation function and hence the model's accuracy is increased by more than 1%. The proposed CNN + LSTM/GRU model is benchmarked with UCR time series benchmark dataset and it is experimentally seen that the model performs better than all existing algorithms.

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

This paper presents the ‘hyper-sinh’, a variation of the m-arcsinh activation function suit-able for Deep Learning (DL)-based algorithms for supervised learning, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), such as the Long Short-Term Memory (LSTM). hyper-sinh, developed in the open-source Python libraries TensorFlow and Keras, is thus described and validated as an accurate and reliable activation function for shallow and deep neural networks. Improvements in accuracy and reliability in image and text classification tasks on six (N=6) medium-to-large open-source benchmark datasets are discussed. Experimental results demonstrate that the overall competitive classification performance of the novel hyper-sinh function on shallow and deep neural networks yielded the highest performance. Furthermore, this activation is evaluated against other gold standard activation functions, demonstrating its overall competitive accuracy and reliability for both image and text classification tasks.