Design of a Novel Convolutional Deep Network Model for Car Accident Prediction

Real-time collision risk estimation is thought to be essential to a sophisticated traffic management system. To swiftly determine accident probability is the goal of real-time crash risk prediction.  However, due to the complex traffic situation on urban arterials, urban arterials were rarely included in previous studies, which mostly focused on highways. This paper suggests using Convolutional Deep Network model (CDNM) to forecast the probability of vascular accidents in real time.  This model has the benefit of being able to use both LSTM and CNN.  CNN retrieves the time-invariant characteristics, while LSTM captures the data's long-term dependability. To estimate the likelihood of an accident, many sorts of data are used, including weather, traffic, and signal timing data. There are also many other data preparation methods employed. The problem of data imbalance is also addressed by normalization which oversamples the crash cases. Using a variety of measures, the CDNM is enhanced on the training data and assessed on the test data.  Five more benchmark models are constructed for model comparison. K-NN, ISVM, ANN, CNN, CNN-EVT and GAN are some of the models in this group. Experimental findings show that the proposed CDNM beats the competition in terms of sensitivity, specificity, accuracy, AUC and G-mean value. The findings of this paper demonstrate that CDNM can real-time prediction of crash risk at arterials

Can We `Feel' the Temperature of Knowledge? Modelling Scientific Popularity Dynamics via Thermodynamics

Just like everything in the nature, scientific topics flourish and perish. While existing literature well captures article's life-cycle via citation patterns, little is known about how scientific popularity and impact evolves for a specific topic. It would be most intuitive if we could `feel' topic's activity just as we perceive the weather by temperature. Here, we conceive knowledge temperature to quantify topic overall popularity and impact through citation network dynamics. Knowledge temperature includes 2 parts. One part depicts lasting impact by assessing knowledge accumulation with an analogy between topic evolution and isobaric expansion. The other part gauges temporal changes in knowledge structure, an embodiment of short-term popularity, through the rate of entropy change with internal energy, 2 thermodynamic variables approximated via node degree and edge number. Our analysis of representative topics with size ranging from 1000 to over 30000 articles reveals that the key to flourishing is topics' ability in accumulating useful information for future knowledge generation. Topics particularly experience temperature surges when their knowledge structure is altered by influential articles. The spike is especially obvious when there appears a single non-trivial novel research focus or merging in topic structure. Overall, knowledge temperature manifests topics' distinct evolutionary cycles

Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines