Deep Learning-Based Weed Detection in Turf: A Review
Autonomous precision herbicide spraying in turf requires accurate and reliable detection of weeds.
Papers
1,138 of 1,138 verified cases.
Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques
Engineering & Materials
Machine Learning in Materials Science
ai assisted
importance 4/10
scale stated
The factors influencing VGF-GaAs crystal growth, such as achieving a favorable interface shape, are hard to derive and optimize.
Pill Detection Model for Medicine Inspection Based on Deep Learning
Medicine & Health
Industrial Vision Systems and Defect Detection
ai assisted
importance 4/10
scale stated
Detecting individual pills in images containing multiple pills is difficult when training data is limited and pill combinations grow exponentially with pill types.
Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu
Predicting paddy yield in the Cauvery Delta Zone, Tamil Nadu, is difficult because yields depend on interacting genotype, environmental and management factors; statistical methods were inaccurate.
Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition
Automatically recognizing emotions in human speech at the utterance level, needed for applications from mental disease diagnosis to human-robot interaction.
The Framing of machine learning risk prediction models illustrated by evaluation of sepsis in general wards
How problem framing choices (prediction windows, observation windows, event triggers) affect the performance and clinical usability of machine learning risk prediction models, illustrated with sepsis in general wards.
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
Deep learning ECG predictions lack human-understandable explanations, limiting clinical trust and knowledge discovery.
Deep learning-based real-time detection of neurons in brain slices for in vitro physiology
Finding a healthy neuron in acute brain slices for patch clamp recording is done manually, is slow, and challenges novices.
Imaging in focus: An introduction to denoising bioimages in the era of deep learning
Live-sample fluorescence microscopy must use low light to avoid phototoxicity, producing very noisy images that severely complicate interpretation.
Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning
Reliably recognizing and mapping windthrow areas in forests from very-high-resolution satellite imagery.
Dysarthric Speech Recognition using Convolutional Recurrent Neural Networks
Automatic speech recognition performs poorly on dysarthric (impaired) speech, which is unintelligible, irregular, and data-scarce.
Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears
There was no objective visual method to identify individual animals of species lacking unique body markings, such as brown bears.
Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence
Camera traps now collect enormous quantities of footage that researchers struggle to classify, and the paper reviews how citizen science and AI are being integrated to handle this.
Optimization of Silicone 3D Printing with Hierarchical Machine Learning
Engineering & Materials
Additive Manufacturing and 3D Printing Technologies
partially solved
importance 4/10
scale stated
3D printing silicone via freeform reversible embedding requires optimizing many correlated material, formulation, and processing variables simultaneously.
q2-sample-classifier: machine-learning tools for microbiome classification and regression
Microbiome researchers need to predict outcomes or differentiate samples from microbial composition, but supervised learning methods were not easily accessible or systematically benchmarked.
A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information
Identifying which proteins bind specific non-coding RNAs with wet-lab high-throughput techniques is expensive and time-consuming, so computational prediction is needed.
Clustering-Based Selection for the Exploration of Compiler Optimization Sequences
Exhaustively exploring all viable compiler optimization sequences is infeasible, yet pass ordering strongly impacts performance and depends on platform and application.
Forecasting Demand in Supply Chain Using Machine Learning Algorithms
Business & Industry
Forecasting Techniques and Applications
ai assisted
importance 4/10
scale stated
Big retail stores struggle to predict customer demand for most items in multi-level supply chains.
A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
Robotics & Autonomy
Infrastructure Maintenance and Monitoring
ai assisted
importance 4/10
scale stated
Autonomous vehicles need reliable road damage detection and road surface classification for safe driving, and the field lacks a clear comparison of deep learning methods.
Being confident in confidence scores: calibration in deep learning models for camera trap image sequences
Biology & Life Sciences
Advanced Neural Network Applications
ai assisted
importance 4/10
scale stated
Automated deep learning classifiers for camera trap images produce confidence scores that may not be interpretable as true probabilities, limiting their use in ecological tasks like occupancy estimation.
Multi-Branch Deep Learning Framework for Land Scene Classification in Satellite Imagery
Automatically classifying land scenes in high-resolution satellite imagery is difficult due to complex textures, cluttered backgrounds, small objects and large scale variations.
Green Building Energy Cost Optimization With Deep Belief Network and Firefly Algorithm
Engineering & Materials
Building Energy and Comfort Optimization
ai assisted
importance 4/10
scale stated
Commercial building HVAC systems consume high energy costs while needing to maintain acceptable indoor air quality.
Protein Secondary Structure Prediction With a Reductive Deep Learning Method
Determining protein secondary structures experimentally via X-ray, cryo-EM or NMR is time-consuming and expensive.
Learning and interpreting the gene regulatory grammar in a deep learning framework
Limited knowledge of how combinatorial transcription factor binding (regulatory grammar) drives enhancer activity, and difficulty interpreting what deep neural networks learn about regulatory sequences.