Biblio

Artificial intelligence (AI) cooperates with multiple industries to improve the overall industry framework. Especially, human emotion recognition plays an indispensable role in supporting medical care, psychological counseling, crime prevention and detection, and crime investigation. The research on emotion recognition includes emotion-specific intonation patterns, literal expressions of emotions, and facial expressions. Recently, the deep learning model of facial emotion recognition aims to capture tiny changes in facial muscles to provide greater recognition accuracy. Hybrid models in facial expression recognition have been constantly proposed to improve the performance of deep learning models in these years. In this study, we proposed an ensemble learning algorithm for the accuracy of the facial emotion recognition model with three deep learning models: VGG16, InceptionResNetV2, and EfficientNetB0. To enhance the performance of these benchmark models, we applied transfer learning, fine-tuning, and data augmentation to implement the training and validation of the Facial Expression Recognition 2013 (FER-2013) Dataset. The developed algorithm finds the best-predicted value by prioritizing the InceptionResNetV2. The experimental results show that the proposed ensemble learning algorithm of priorities edges up 2.81% accuracy of the model identification. The future extension of this study ventures into the Internet of Things (IoT), medical care, and crime detection and prevention.

In this paper, the multi-user video-based facial emotion recognition is examined in the presence of a small data set with the emotions of end users. By using the idea of speaker-dependent speech recognition, we propose a novel approach to solve this task if labeled video data from end users is available. During the training stage, a deep convolutional neural network is trained for user-independent emotion classification. Next, this classifier is adapted (fine-tuned) on the emotional video of a concrete person. During the recognition stage, the user is identified based on face recognition techniques, and an emotional model of the recognized user is applied. It is experimentally shown that this approach improves the accuracy of emotion recognition by more than 20% for the RAVDESS dataset.

Child facial expression recognition is a relatively less investigated area within affective computing. Children’s facial expressions differ significantly from adults; thus, it is necessary to develop emotion recognition frameworks that are more objective, descriptive and specific to this target user group. In this paper we propose the first approach that (i) constructs video-level heterogeneous graph representation for facial expression recognition in children, and (ii) predicts children’s facial expressions using the automatically detected Action Units (AUs). To this aim, we construct three separate length-independent representations, namely, statistical, spectral and graph at video-level for detailed multi-level facial behaviour decoding (AU activation status, AU temporal dynamics and spatio-temporal AU activation patterns, respectively). Our experimental results on the LIRIS Children Spontaneous Facial Expression Video Database demonstrate that combining these three feature representations provides the highest accuracy for expression recognition in children.

Cyberattack on power system brings new challenges on the development of modern power system. Hackers may implement false data injection attack (FDIA) to cause unstable operating conditions of the power system. However, data from different power internet of things usually contains a lot of redundancy, making it difficult for current efficient discriminant model to precisely identify FDIA. To address this problem, we propose a deep learning network-based data fusion model to handle features from measurement data in power system. Proposed model includes a data enrichment module and a data fusion module. We firstly employ feature engineering technique to enrich features from power system operation in time dimension. Subsequently, a long short-term memory based autoencoder (LSTM-AE) is designed to efficiently avoid feature space explosion problem during data enriching process. Extensive experiments are performed on several classical attack detection models over the load data set from IEEE 14-bus system and simulation results demonstrate that fused data from proposed model shows higher detection accuracy with respect to the raw data.

New malware increasingly adopts novel fileless techniques to evade detection from antivirus programs. Process injection is one of the most popular fileless attack techniques. This technique makes malware more stealthy by writing malicious code into memory space and reusing the name and port of the host process. It is difficult for traditional security software to detect and intercept process injections due to the stealthiness of its behavior. We propose a novel framework called ProcGuard for detecting process injection behaviors. This framework collects sensitive function call information of typical process injection. Then we perform a fine-grained analysis of process injection behavior based on the function call chain characteristics of the program, and we also use the improved RCNN network to enhance API analysis on the tampered memory segments. We combine API analysis with deep learning to determine whether a process injection attack has been executed. We collect a large number of malicious samples with process injection behavior and construct a dataset for evaluating the effectiveness of ProcGuard. The experimental results demonstrate that it achieves an accuracy of 81.58% with a lower false-positive rate compared to other systems. In addition, we also evaluate the detection time and runtime performance loss metrics of ProcGuard, both of which are improved compared to previous detection tools.

Fog computing is defined as a decentralized infrastructure that locations storage and processing aspects at the side of the cloud, the place records sources such as software customers and sensors exist. The Fog Computing is the time period coined via Cisco that refers to extending cloud computing to an area of the enterprise’s network. Thus, it is additionally recognized as Edge Computing or Fogging. It allows the operation of computing, storage, and networking offerings between give up units and computing facts centers. Fog computing is defined as a decentralized infrastructure that locations storage and processing aspects at the side of the cloud, the place records sources such as software customers and sensors exist. The fog computing Intelligence as Artificial Intelligence (AI) is furnished by way of Fog Nodes in cooperation with Clouds. In Fog Nodes several sorts of AI studying can be realized - such as e.g., Machine Learning (ML), Deep Learning (DL). Thanks to the Genius of Fog Nodes, for example, we communicate of Intelligent IoT.

Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods do not require any labelled data, whereas semi-supervised methods leverage some known anomalies. Inspired by mixture-of-experts models and the analysis of the hidden activations of neural networks, we introduce a novel data-driven anomaly detection method called ARGUE. Our method is not only applicable to unsupervised and semi-supervised environments, but also profits from prior knowledge of self-supervised settings. We designed ARGUE as a combination of dedicated expert networks, which specialise on parts of the input data. For its final decision, ARGUE fuses the distributed knowledge across the expert systems using a gated mixture-of-experts architecture. Our evaluation motivates that prior knowledge about the normal data distribution may be as valuable as known anomalies.

Artificial intelligence (AI) has been successfully employed in industries for decades, beginning with the invention of expert systems in the 1960s and continuing through the present ubiquity of deep learning. Data-driven AI solutions have grown increasingly common as a means of supporting ever-more complicated industrial processes owing to the accessibility of affordable computer and storage infrastructure. Despite recent optimism, implementing AI to smart industrial applications still offers major difficulties. The present paper gives an executive summary of AI methodologies with an emphasis on deep learning before detailing unresolved issues in AI safety, data privacy, and data quality — all of which are necessary for completely automated commercial AI systems.

Train operation is highly influenced by the rail track state and the surrounding environment. An abnormal obstacle on the rail track will pose a severe threat to the safe operation of urban rail transit. The existing general obstacle detection approaches do not consider the specific urban rail environment and requirements. In this paper, we propose an edge intelligence (EI)-based obstacle intrusion detection system to detect accurate obstacle intrusion in real-time. A two-stage lightweight deep learning model is designed to detect obstacle intrusion and obtain the distance from the train to the obstacle. Edge computing (EC) and 5G are used to conduct the detection model and improve the real-time detection performance. A multi-agent reinforcement learning-based offloading and service migration model is formulated to optimize the edge computing resource. Experimental results show that the two-stage intrusion detection model with the reinforcement learning (RL)-based edge resource optimization model can achieve higher detection accuracy and real-time performance compared to traditional methods.

At present, pavement crack detection mainly depends on manual survey and semi-automatic detection. In the process of damage detection, it will inevitably be subject to the subjective influence of inspectors and require a lot of identification time. Therefore, this paper proposes the research and implementation of artificial intelligence real-time recognition method of crack edge based on zynq, which combines edge calculation technology with deep learning, The improved ipd-yolo target detection network is deployed on the zynq zu2cg edge computing development platform. The mobilenetv3 feature extraction network is used to replace the cspdarknet53 feature extraction network in yolov4, and the deep separable convolution is used to replace the conventional convolution. Combined with the advantages of the deep neural network in the cloud and edge computing, the rock fracture detection oriented to the edge computing scene is realized. The experimental results show that the accuracy of the network on the PID data set The recall rate and F1 score have been improved to better meet the requirements of real-time identification of rock fractures.

Edge detection is the key and difficult point of machine vision and image processing technology. The traditional edge detection algorithm is sensitive to noise and it is difficult to accurately extract the edge of the image, so the effect of image processing is not ideal. To solve this problem, people in the industry use the structural element features of morphological edge detection operator to extract the edge features of the image by carefully designing and combining the structural elements of different sizes and directions, so as to effectively ensure the integrity of edge information in all directions and eliminate large noise at the same time. This paper first introduces the traditional edge detection algorithms, then summarizes the edge detection algorithms based on mathematical morphology in recent years, finds that the selection of multi-scale and multi-directional structural elements is an important research direction, and finally discusses the development trend of mathematical morphology edge detection technology.

Nowadays, although it is much more convenient to obtain news with social media and various news platforms, the emergence of all kinds of fake news has become a headache and urgent problem that needs to be solved. Currently, the fake news recognition algorithm for fake news mainly uses GCN, including some other niche algorithms such as GRU, CNN, etc. Although all fake news verification algorithms can reach quite a high accuracy with sufficient datasets, there is still room for improvement for unsupervised learning and semi-supervised. This article finds that the accuracy of the GCN method for fake news detection is basically about 85% through comparison with other neural network models, which is satisfactory, and proposes that the current field lacks a unified training dataset, and that in the future fake news detection models should focus more on semi-supervised learning and unsupervised learning.

Social media has beneficial and detrimental impacts on social life. The vast distribution of false information on social media has become a worldwide threat. As a result, the Fake News Detection System in Social Networks has risen in popularity and is now considered an emerging research area. A centralized training technique makes it difficult to build a generalized model by adapting numerous data sources. In this study, we develop a decentralized Deep Learning model using Federated Learning (FL) for fake news detection. We utilize an ISOT fake news dataset gathered from "Reuters.com" (N = 44,898) to train the deep learning model. The performance of decentralized and centralized models is then assessed using accuracy, precision, recall, and F1-score measures. In addition, performance was measured by varying the number of FL clients. We identify the high accuracy of our proposed decentralized FL technique (accuracy, 99.6%) utilizing fewer communication rounds than in previous studies, even without employing pre-trained word embedding. The highest effects are obtained when we compare our model to three earlier research. Instead of a centralized method for false news detection, the FL technique may be used more efficiently. The use of Blockchain-like technologies can improve the integrity and validity of news sources.

This paper deals with the problem of image forgery detection because of the problems it causes. Where The Fake im-ages can lead to social problems, for example, misleading the public opinion on political or religious personages, de-faming celebrities and people, and Presenting them in a law court as evidence, may Doing mislead the court. This work proposes a deep learning approach based on Deep CNN (Convolutional Neural Network) Architecture, to detect fake images. The network is based on a modified structure of Xception net, CNN based on depthwise separable convolution layers. After extracting the feature maps, pooling layers are used with dense connection with Xception output, to in-crease feature maps. Inspired by the idea of a densenet network. On the other hand, the work uses the YCbCr color system for images, which gave better Accuracy of %99.93, more than RGB, HSV, and Lab or other color systems.

Fake news is a new phenomenon that promotes misleading information and fraud via internet social media or traditional news sources. Fake news is readily manufactured and transmitted across numerous social media platforms nowadays, and it has a significant influence on the real world. It is vital to create effective algorithms and tools for detecting misleading information on social media platforms. Most modern research approaches for identifying fraudulent information are based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, and newly built datasets and online services. There is a pressing need to develop a viable approach for readily detecting misleading information. The deep learning LSTM and Bi-LSTM model was proposed as a method for detecting fake news, In this work. First, the NLTK toolkit was used to remove stop words, punctuation, and special characters from the text. The same toolset is used to tokenize and preprocess the text. Since then, GLOVE word embeddings have incorporated higher-level characteristics of the input text extracted from long-term relationships between word sequences captured by the RNN-LSTM, Bi-LSTM model to the preprocessed text. The proposed model additionally employs dropout technology with Dense layers to improve the model's efficiency. The proposed RNN Bi-LSTM-based technique obtains the best accuracy of 94%, and 93% using the Adam optimizer and the Binary cross-entropy loss function with Dropout (0.1,0.2), Once the Dropout range increases it decreases the accuracy of the model as it goes 92% once Dropout (0.3).

False news has become widespread in the last decade in political, economic, and social dimensions. This has been aided by the deep entrenchment of social media networking in these dimensions. Facebook and Twitter have been known to influence the behavior of people significantly. People rely on news/information posted on their favorite social media sites to make purchase decisions. Also, news posted on mainstream and social media platforms has a significant impact on a particular country’s economic stability and social tranquility. Therefore, there is a need to develop a deceptive system that evaluates the news to avoid the repercussions resulting from the rapid dispersion of fake news on social media platforms and other online platforms. To achieve this, the proposed system uses the preprocessing stage results to assign specific vectors to words. Each vector assigned to a word represents an intrinsic characteristic of the word. The resulting word vectors are then applied to RNN models before proceeding to the LSTM model. The output of the LSTM is used to determine whether the news article/piece is fake or otherwise.

Deep learning have a variety of applications in different fields such as computer vision, automated self-driving cars, natural language processing tasks and many more. One of such deep learning adversarial architecture changed the fundamentals of the data manipulation. The inception of Generative Adversarial Network (GAN) in the computer vision domain drastically changed the way how we saw and manipulated the data. But this manipulation of data using GAN has found its application in various type of malicious activities like creating fake images, swapped videos, forged documents etc. But now, these generative models have become so efficient at manipulating the data, especially image data, such that it is creating real life problems for the people. The manipulation of images and videos done by the GAN architectures is done in such a way that humans cannot differentiate between real and fake images/videos. Numerous researches have been conducted in the field of deep fake detection. In this paper, we present a structured survey paper explaining the advantages, gaps of the existing work in the domain of deep fake detection.

Recently, social networks have become more popular owing to the capability of connecting people globally and sharing videos, images and various types of data. A major security issue in social media is the existence of fake accounts. It is a phenomenon that has fake accounts that can be frequently utilized by mischievous users and entities, which falsify, distribute, and duplicate fake news and publicity. As the fake news resulted in serious consequences, numerous research works have focused on the design of automated fake accounts and fake news detection models. In this aspect, this study designs a hyperparameter tuned deep learning based automated fake news detection (HDL-FND) technique. The presented HDL-FND technique accomplishes the effective detection and classification of fake news. Besides, the HDLFND process encompasses a three stage process namely preprocessing, feature extraction, and Bi-Directional Long Short Term Memory (BiLSTM) based classification. The correct way of demonstrating the promising performance of the HDL-FND technique, a sequence of replications were performed on the available Kaggle dataset. The investigational outcomes produce improved performance of the HDL-FND technique in excess of the recent approaches in terms of diverse measures.

People connect with a plethora of information from many online portals due to the availability and ease of access to the internet and electronic communication devices. However, news portals sometimes abuse press freedom by manipulating facts. Most of the time, people are unable to discriminate between true and false news. It is difficult to avoid the detrimental impact of Bangla fake news from spreading quickly through online channels and influencing people’s judgment. In this work, we investigated many real and false news pieces in Bangla to discover a common pattern for determining if an article is disseminating incorrect information or not. We developed a deep learning model that was trained and validated on our selected dataset. For learning, the dataset contains 48,678 legitimate news and 1,299 fraudulent news. To deal with the imbalanced data, we used random undersampling and then ensemble to achieve the combined output. In terms of Bangla text processing, our proposed model achieved an accuracy of 98.29% and a recall of 99%.

The rise of social media has brought the rise of fake news and this fake news comes with negative consequences. With fake news being such a huge issue, efforts should be made to identify any forms of fake news however it is not so simple. Manually identifying fake news can be extremely subjective as determining the accuracy of the information in a story is complex and difficult to perform, even for experts. On the other hand, an automated solution would require a good understanding of NLP which is also complex and may have difficulties producing an accurate output. Therefore, the main problem focused on this project is the viability of developing a system that can effectively and accurately detect and identify fake news. Finding a solution would be a significant benefit to the media industry, particularly the social media industry as this is where a large proportion of fake news is published and spread. In order to find a solution to this problem, this project proposed the development of a fake news identification system using deep learning and natural language processing. The system was developed using a Word2vec model combined with a Long Short-Term Memory model in order to showcase the compatibility of the two models in a whole system. This system was trained and tested using two different dataset collections that each consisted of one real news dataset and one fake news dataset. Furthermore, three independent variables were chosen which were the number of training cycles, data diversity and vector size to analyze the relationship between these variables and the accuracy levels of the system. It was found that these three variables did have a significant effect on the accuracy of the system. From this, the system was then trained and tested with the optimal variables and was able to achieve the minimum expected accuracy level of 90%. The achieving of this accuracy levels confirms the compatibility of the LSTM and Word2vec model and their capability to be synergized into a single system that is able to identify fake news with a high level of accuracy.

Though several deep learning (DL) detectors have been proposed for the network attack detection and achieved high accuracy, they are computationally expensive and struggle to satisfy the real-time detection for high-speed networks. Recently, programmable switches exhibit a remarkable throughput efficiency on production networks, indicating a possible deployment of the timely detector. Therefore, we present Soter, a DL enhanced in-network framework for the accurate real-time detection. Soter consists of two phases. One is filtering packets by a rule-based decision tree running on the Tofino ASIC. The other is executing a well-designed lightweight neural network for the thorough inspection of the suspicious packets on the CPU. Experiments on the commodity switch demonstrate that Soter behaves stably in ten network scenarios of different traffic rates and fulfills per-flow detection in 0.03s. Moreover, Soter naturally adapts to the distributed deployment among multiple switches, guaranteeing a higher total throughput for large data centers and cloud networks.

In this paper, we quantify elements representing video features and we propose the bitrate prediction of compressed encoding video using deep learning. Particularly, to overcome disadvantage that we cannot predict bitrate of compression video by using Constant Rate Factor (CRF), we use deep learning. We can find element of video feature with relationship of bitrate when we compress the video, and we can confirm its possibility to find relationship through various deep learning techniques.

In recent years, in order to continuously promote the construction of safe cities, security monitoring equipment has been widely used all over the country. How to use computer vision technology to realize effective intelligent analysis of violence in video surveillance is very important to maintain social stability and ensure people's life and property safety. Video surveillance system has been widely used because of its intuitive and convenient advantages. However, the existing video monitoring system has relatively single function, and generally only has the functions of monitoring video viewing, query and playback. In addition, relevant researchers pay less attention to the complex abnormal behavior of violence, and relevant research often ignores the differences between violent behaviors in different scenes. At present, there are two main problems in video abnormal behavior event detection: the video data of abnormal behavior is less and the definition of abnormal behavior in different scenes cannot be clearly distinguished. The main existing methods are to model normal behavior events first, and then define videos that do not conform to the normal model as abnormal, among which the learning method of video space-time feature representation based on deep learning shows a good prospect. In the face of massive surveillance videos, it is necessary to use deep learning to identify violent behaviors, so that the machine can learn to identify human actions, instead of manually monitoring camera images to complete the alarm of violent behaviors. Network training mainly uses video data set to identify network training.

One of the biggest studies on public safety and tracking that has sparked a lot of interest in recent years is deep learning approach. Current public safety methods are existent for counting and detecting persons. But many issues such as aberrant occurring in public spaces are seldom detected and reported to raise an automated alarm. Our proposed method detects anomalies (deviation from normal events) from the video surveillance footages using deep learning and raises an alarm, if anomaly is found. The proposed model is trained to detect anomalies and then it is applied to the video recording of the surveillance that is used to monitor public safety. Then the video is assessed frame by frame to detect anomaly and then if there is match, an alarm is raised.

With the rapid development of artificial intelligence, video target tracking is widely used in the fields of intelligent video surveillance, intelligent transportation, intelligent human-computer interaction and intelligent medical diagnosis. Deep learning has achieved remarkable results in the field of computer vision. The development of deep learning not only breaks through many problems that are difficult to be solved by traditional algorithms, improves the computer's cognitive level of images and videos, but also promotes the progress of related technologies in the field of computer vision. This paper combines the deep learning algorithm and target tracking algorithm to carry out relevant experiments on basketball motion detection video, hoping that the experimental results can be helpful to basketball motion detection video target tracking.