Automated identification of aquatic insects: A case study using deep learning and computer vision techniques.

Deep learning techniques have recently found application in biodiversity research. Mayflies (Ephemeroptera), stoneflies (Plecoptera) and caddisflies (Trichoptera), often abbreviated as EPT, are frequently used for freshwater biomonitoring due to their large numbers and sensitivity to environmental changes. However, the morphological identification of EPT species is a challenging but fundamental task.

Enhanced multimodal biometric recognition approach for smart cities based on an optimized fuzzy genetic algorithm.

Biometric security is a major emerging concern in the field of data security. In recent years, research initiatives in the field of biometrics have grown at an exponential rate. The multimodal biometric technique with enhanced accuracy and recognition rate for smart cities is still a challenging issue.

Application of deep learning in aquatic bioassessment: Towards automated identification of non-biting midges.

Morphological species identification is often a difficult, expensive, and time-consuming process which hinders the ability for reliable biomonitoring of aquatic ecosystems. An alternative approach is to automate the whole process, accelerating the identification process. Here, we demonstrate an automatic machine-based identification approach for non-biting midges (Diptera: Chironomidae) using Convolutional Neural Networks (CNNs) as a means of increasing taxonomic resolution of biomonitoring data at a minimal cost.