Fine-tuning Road Classification Models: Optimization Strategies for Deep Belief Networks in Transportation Big Data

Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly decrease personal victim by means of road traffic accidents. Data Mining techniques are used in the process of knowledge discovery for many domains’ problems. Feature Selection plays a vital role for a large number of datasets. In this paper, the classification of road accident in transportation domain was analyzed with the assistance of the proposed Intelligent classification technique. In this proposed technique, the DBN hidden layers weights are optimized by using evolutionary Genetic algorithm. This GA is utilized to enhance the classification accuracy by applying the hidden layers of Restricted Boltzmann Machine (RBM). The comparative results show that the proposed intelligent classifier gives the improved accuracy, specificity, precision, Sensitivity, F-Measure, and reduced false positive rate

AI-Based Collaborative Teaching: Strategies and Analysis in Visual Communication Design

With the rapid development of technology, AI has been widely applied in multiple fields, especially the field of education. As a discipline involving art, technology and creativity, visual communication design is facing the challenge of keeping up with the times and combining new technologies for innovation. Collaborative teaching model emphasizes multi-party participation and collaborative learning, and its proposal has injected new vitality into traditional educational patterns. However, existing studies, which combine collaborative teaching model with artificial intelligence, still have limitations in application and practice, and most of them remain in the theoretical discussion stage and lack empirical support. This study aimed to make up for this deficiency. After in-depth analysis of educational data, a forecasting model of collaborative teaching demand based on AI was proposed. Course content suitable for the collaborative teaching model was further planned for the education in visual communication design

Techniques for Stock Market Prediction: A Review

Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Shallow and deep networks intrusion detection system : a taxonomy and survey

Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table

Internet cross-media retrieval based on deep learning

With the development of Internet, multimedia information such as image and video is widely used. Therefore, how to find the required multimedia data quickly and accurately in a large number of resources , has become a research focus in the field of information process. In this paper, we propose a real time internet cross-media retrieval method based on deep learning. As an innovation, we have made full improvement in feature extracting and distance detection. After getting a large amount of image feature vectors, we sort the elements in the vector according to their contribution and then eliminate unnecessary features. Experiments show that our method can achieve high precision in image-text cross media retrieval, using less retrieval time. This method has a great application space in the field of cross media retrieval

Learning probabilistic interaction models

We live in a multi-modal world; therefore it comes as no surprise that the human brain is tailored for the integration of multi-sensory input. Inspired by the human brain, the multi-sensory data is used in Artificial Intelligence (AI) for teaching different concepts to computers. Autonomous Agents (AAs) are AI systems that sense and act autonomously in complex dynamic environments. Such agents can build up Self-Awareness (SA) by describing their experiences through multi-sensorial information with appropriate models and correlating them incrementally with the currently perceived situation to continuously expand their knowledge. This thesis proposes methods to learn such awareness models for AAs. These models include SA and situational awareness models in order to perceive and understand itself (self variables) and its surrounding environment (external variables) at the same time. An agent is considered self-aware when it can dynamically observe and understand itself and its surrounding through different proprioceptive and exteroceptive sensors which facilitate learning and maintaining a contextual representation by processing the observed multi-sensorial data. We proposed a probabilistic framework for generative and descriptive dynamic models that can lead to a computationally efficient SA system. In general, generative models facilitate the prediction of future states while descriptive models enable to select the representation that best fits the current observation. The proposed framework employs a Probabilistic Graphical Models (PGMs) such as Dynamic Bayesian Networks (DBNs) that represent a set of variables and their conditional dependencies. Once we obtain this probabilistic representation, the latter allows the agent to model interactions between itself, as observed through proprioceptive sensors, and the environment, as observed through exteroceptive sensors. In order to develop an awareness system, not only an agent needs to recognize the normal states and perform predictions accordingly, but also it is necessary to detect the abnormal states with respect to its previously learned knowledge. Therefore, there is a need to measure anomalies or irregularities in an observed situation. In this case, the agent should be aware that an abnormality (i.e., a non-stationary condition) never experienced before, is currently present. Due to our specific way of representation, which makes it possible to model multi-sensorial data into a uniform interaction model, the proposed work not only improves predictions of future events but also can be potentially used to effectuate a transfer learning process where information related to the learned model can be moved and interpreted by another body

Toward enhancement of deep learning techniques using fuzzy logic: a survey

Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin