Extreme Deep Learning Research Articles

Advanced optimization-based weighted features for ensemble deep learning smart occupancy detection network for road traffic parking

In day-to-day activities, the advanced technology like Internet of Things (IoT) emerges to improve the lifestyle of people. In metropolitan cities, real-time parking is the long-lasting problem that we face in our daily life activities. Urban parking regulation gained more attention because of its capability to diminish energy consumption, congested traffic, and manifestation. The parking space detection for vehicles in real-time is a crucial role for on-street parking control models where the data is delivered to the drivers through internet with the help of Global Positioning System (GPS). Hence, the network congestion gets increased, where the existing model does not performed well in the parking space effectively. This research paper recommends a novel strategy for detecting occupancy in road traffic parking by IoT and deep networks to overcome these difficulties. At first, the required information is gathered from the standard datasets as CNRPark + EXT for further processing. After the data collection process, the preprocessing phase is executed, where the data cleaning and data transformation approaches are done with a multi-scale retinex network. The pre-processing technique is effectively performed to reduce background noise. Further, the feature extraction is progressed through the Residual Attention Network, where the relevant features are extracted from the pre-processed data. Subsequently, the optimal features and weights are selected by the novel Advanced Pelican Optimization Algorithm (APOA) to get the optimal weighted feature selection. This optimal weighted feature is further given to the Ensemble Deep Networks (EDN) by integrating the Deep Conditional Random Field (DCRF), and Extreme Deep Learning (EDL) model. In final process, the detection of occupancy in road traffic parking is determined by averaging both classifiers outcomes. Finally, the results are compared with numerous optimization algorithms and classifiers to ensure the developed system's efficacy. Throughout the validation, the developed model outperforms with an accuracy as 96.6, precision as 92.9 and Mathew's Correlation Coefficient (MCC) as 92.3. Thus, the developed model shows better performance than the existing traditional approaches.

Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement

Wave-based analog signal processing holds the promise of extremely fast, on-the-fly, power-efficient data processing, occurring as a wave propagates through an artificially engineered medium. Yet, due to the fundamentally weak non-linearities of traditional electromagnetic materials, such analog processors have been so far largely confined to simple linear projections such as image edge detection or matrix multiplications. Complex neuromorphic computing tasks, which inherently require strong non-linearities, have so far remained out-of-reach of wave-based solutions, with a few attempts that implemented non-linearities on the digital front, or used weak and inflexible non-linear sensors, restraining the learning performance. Here, we tackle this issue by demonstrating the relevance of time-Floquet physics to induce a strong non-linear entanglement between signal inputs at different frequencies, enabling a power-efficient and versatile wave platform for analog extreme deep learning involving a single, uniformly modulated dielectric layer and a scattering medium. We prove the efficiency of the method for extreme learning machines and reservoir computing to solve a range of challenging learning tasks, from forecasting chaotic time series to the simultaneous classification of distinct datasets. Our results open the way for optical wave-based machine learning with high energy efficiency, speed and scalability.

Extreme deep learning in biosecurity: the case of machine hearing for marine species identification

ABSTRACTBiosafety is defined as a set of preventive measures aimed at reducing the risk of infectious diseases’ spread to crops and animals, by providing quarantine pesticides. Prolonged and sustained overheating of the sea, creates significant habitat losses, resulting in the proliferation and spread of invasive species, which invade foreign areas typically seeking colder climate. This is one of the most important modern threats to marine biosafety. The research effort presented herein, proposes an innovative approach for Marine Species Identification, by employing an advanced intelligent Machine Hearing Framework (MHF). The final target is the identification of invasive alien species (IAS) based on the sounds they produce. This classification attempt, can provide significant aid towards the protection of biodiversity, and can achieve overall regional biosecurity. Hearing recognition is performed by using the Online Sequential Multilayer Graph Regularized Extreme Learning Machine Autoencoder (MIGRATE_ELM). The MIGRATE_ELM uses an innovative Deep Learning algorithm (DELE) that is applied for the first time for the above purpose. The assignment of the corresponding class ‘native’ or ‘invasive’ in its locality, is carried out by an equally innovative approach entitled ‘Geo Location Country Based Service’ that has been proposed by our research team.