In the wide area of Machine Learning (ML), Deep Learning is a branch of it. This technique is popular because of its multiple layer filtering. By going through this research you can get a better understanding of the topic in detail. Continue reading this research paper to gain more knowledge about Deep learning.
- Define Deep Learning
Deep learning is a branch of ML; this technology is built on top of the “Artificial neural networks” along with the representation learning. The word deep from deep learning symbolizes the meaning that this network uses multiple layers for protection. The techniques used here can either be supervised, unsupervised or semi-supervised.
- What is Deep Learning?
This technique is a sub-section of ML which is used to train the artificial neural network by using multiple layers in order to take decisions and do predictions by itself. This technology is built based on the function and structure of a human brain which has similar function of processing through multiple layers of neuron. This algorithm follows a hierarchical way of representing data which goes extracting feature from higher to lower level. In the growing technology Deep Learning has a separate place for it with its performance and increasing advancements in the field of Artificial Intelligence (AI). With the help of this technique an application can solve complex tasks such as language processing naturally, speech and image recognition etc.
- Where Deep Learning is used?
In this section we are going to discuss about the uses of Deep Learning. The different uses of Deep Learning in several fields include:
Computer vision – Deep learning is used in here for object detection, video and image recognition, facial recognition and image segmentation.
Natural Language Processing (NLP) – Making use of NLP in Deep learning is for sentiment analysis, speech recognition, Chatbot, machine translation and text summarization.
Autonomous vehicles – For self-driving cars, deep learning is used in object detection, decision making, tracking and localization.
Healthcare – Image analysis in medical field, drug discovery, disease diagnosis, predicting outcomes and personalized medicine.
Finance – For algorithmic trading, fraud detection, risk assessment and portfolio management industry of finance uses deep learning.
Robotics – Deep learning is used here in order to make robots grasp objects, perceive environment also to do difficult tasks.
Gaming – Deep learning can improve the performance of gaming like DeepMind’s AlphaGo.
Recommendation systems – Recommendations for user is given by this system, by analyzing the behavior and preferences of used with the help of Deep learning.
Manufacturing and Quality control – Anomaly detection, fault diagnosis and predictive maintenance are the reason why Deep learning is used in quality control.
Energy and Environment – Deep learning helps here for controlling environment system, efficient use of energy and production of renewable energy.
- Why Deep Learning is proposed? Previous Technology Issues
Moving on to the next section, here we are going to discuss about why this technique is proposed and about the challenges faced by it. This was proposed in order to overcome the issues face by earlier techniques of machine learning.
Some of the issues faced by it are mentioned here:
Generalization – The previous techniques faced problems while handling incomplete or noisy datasets. With the help of models in Deep Learning which has the capability of learning complex representations and patterns, it was simple to handle and generalize the problematic data.
Flexibility – The models in Deep Learning are very much capable and flexible to learn data representation in hierarchical order, which enhances their performance in several areas.
- Algorithms / Protocols
After knowing about the technology, uses of it and the issues faced by them in the earlier stage, now we are going to learn about the algorithms used for this technology. The algorithms provided for Deep Learning to overcome the previous issues faced by it are: “Long Short Term Memory Networks” (LSTMs), “Radial Basis Function Networks” (RBFNs), “Deep Belief Networks” (DBNs), “Autoencoders, Multilayer Perceptron’s” (MLPs), “Restricted Boltzmann Machines” (RBMs), “Convolutional Neural Networks” (CNNs), “Recurrent Neural Networks” (RNNs), “Self Organizing Maps” (SOMs) and “Generative Adversarial Networks” (GANs).
- Comparative study / Analysis
There is no any such perfect algorithm which is perfectly suitable for Deep Learning, because every algorithm has its own significance to perform some task.
Faster region-based convolutional neural networks (RCNN) is a one kind of algorithm used in Deep Learning for detecting skin cancer, but this also have some limitations with it. The drawbacks are:
High computational Cost – RCNN algorithm has more complex steps like region proposal classification and generation. This takes long time for detection when working on large datasets.
Limited training data – Usually the RCNN model of Deep learning requires huge quantity of training data in order to produce better performance. Collecting large scale dataset for skin cancer is very difficult because it needs feedback from a dermatologist expert and privacy of patient is also very crucial.
- Simulation results / Parameters
The approaches which were proposed to overcome the issues faced by SDWSN in the above section are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Accuracy, Specificity, Recall, F1-Score, AUC-ROC and Sensitivity.
- Dataset LINKS / Important URL
Here are some of the datasets and link provided for you below to gain more knowledge about Deep Learning which can be useful for you:
- https://www.mdpi.com/2075-4418/13/11/1911
- HAM10000 dataset
- PH2 dataset
- ISIC-2016 dataset
- ISIC 2020 dataset
- BCN20000
- Deep Learning Applications
In this next section we are going to discuss about the applications of Deep Learning technology. This technology has been employed in many industries, from which some of them are listed here: Detecting financial fraud and fake news, Language processing, Smart Agriculture, Facial Recognition, Autonomous Vehicles, Healthcare, Recommendation Systems, Space Travel and Personalized Marketing.
- Topology
Here you are going to learn about the different choices of topologies which can be used in Deep Learning technology. They are: “Convolutional Neural Network” (CNN), “Residual Network” (ResNet), Autoencoders, Transformer, “Feedforward Neural Network” (FNN), “Recurrent Neural Network” (RNN), “Generative Adversarial Network” (GAN) and “Variational Autoencoder” (VAE).
- Environment
Moving on to the next section, here we are going to discuss about the suitable environment for this system. The environment in which the operation of Deep Learning technology is functioning is: Visualization Libraries, Model Interpretability, Evaluation Metrics, Optimization and Training.
- Simulation Tools
Here we provide some simulation software for Deep Learning system, which is established with the usage of Python tool version 3.11.4 and along with MATLAB R2020b.
- Results
After going through this research based on Deep Learning Technology, you can understand in detail about this technology, applications of this technology, different topologies of it, algorithms followed by it also about the limitations and how it can be overcome.
Deep Learning Research Ideas
- Classification of Grapevine Leaf Images with Deep Learning Ensemble Models
- A Deep Learning-Based Model for Secondary Prediction on Deep-Sea Collector Plumes
- Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis
- DWA-Watermarking Embedding Mechanism for Deep Learning Models
- End-to-End Trainable Gaussian Filtering for Electrocardiogram Signal Classification Using Deep Learning
- Deep learning-based medical image analysis with explainable transfer learning
- Detection of AI Empathy Using Deep Learning
- A Multimodal Deep Learning Approach for Typhoon Track Forecast by Fusing CNN and Transformer Structures
- Deep learning architectures for Brain Tumor detection: A Survey
- Applications of Deep Learning and Deep Reinforcement Learning in 6G Networks
- Automatic Detection for Road Voids from GPR Images using Deep Learning Method
- Micro-expression Recognition Based on Apex Frame Using Deep Learning
- Towards Fraudulent URL Classification with Large Language Model based on Deep Learning
- Design and Research of Blended Collaborative Learning Model for Deep Learning
- Knowledge Transferring in Deep Learning of Wearable Dynamics
- Advanced Deep Learning and NLP for Enhanced Food Delivery: Future Insights on Demand Prediction, Route Optimization, Personalization, and Customer Support
- Research on Vehicle Object Detection Based on Deep Learning
- Modified EfficientNetB3 Deep Learning Model to Classify Color Fundus Images of Eye Diseases
- 2941.2-2023 – IEEE Standard for Application Programming Interfaces (APIs) for Deep Learning (DL) Inference Engines
- High performance deep learning libraries for biomedical applications
- Deep Learning for Comparative Study of Ovarian Cancer Detection on Histopathological Images
- Intelligent Deep Learning Framework for Breast Cancer Prediction using Feature Ensemble Learning
- Human Age Estimation from Images in Real-Time Application Using Machine Learning and Deep Learning Models
- A Comprehensive Survey of Trending Tools and Techniques in Deep Learning
- Fine Grained Sentiment Analysis using Machine Learning and Deep Learning
- Exploring Classification of Rice Leaf Diseases using Machine Learning and Deep Learning
- Cyber Security Intrusion Detection Using Deep Learning Approaches, Datasets, Bot-IOT Dataset
- Compressed Deep Learning and Transfer Learning Model for Detecting Brain Tumor
- EnsDeepDP: An Ensemble Deep Learning Approach for Disease Prediction through Metagenomics
- A Fusion Framework for Student Performance Prediction Using Deep Learning and Blockchain Technologies
- Building A Deep Learning Model for Multi-Label Classification of Natural Disasters
- Predicting Z Boson Decay Modes: Evaluating the Performance of Machine Learning and Deep Learning Techniques in Particle Physics
- Detecting Sentiment Polarities with Comparative Analysis of Machine Learning and Deep Learning Algorithms
- Tender Coconut Classification using Decision Tree and Deep Learning Technique
- Multiclass Classification of Remote Sensing Images Using Deep Learning Techniques
- A Study on Different Hybrid Deep Learning Approaches to Forecast air Pollution Concentration of Particulate Matter
- A Model-Based Deep Learning Approach for Self-Learning in Smart Production Systems
- LSTM deep learning model for Alzheimer’s disease prediction based on cost-effective time series cognitive scores
- towards Accurate Stress Classification: Combining Advanced Feature Selection and Deep Learning
- A Comparative Study of Detection of Tuberculosis using Machine Learning & Deep Learning
- Deep Learning Deployment on Big Data Infrastructure Using Apache Spark (Case Study: COVID-19 Detection Using X-Ray Images)
- A Comparison Study to Detect Malware using Deep Learning and Machine learning Techniques
- Detection & Classification of Tuberculosis HIV-Positive Patients using Deep Learning
- Malicious URLs and QR Code Classification Using Machine Learning and Deep Learning Techniques
- Detection of Leaf Diseases in Modern Agriculture Using Deep Learning Techniques
- Speech emotion recognition for psychotherapy: an analysis of traditional machine learning and deep learning techniques
- Rainfall Prediction Using Deep Learning and Machine Learning Techniques
- Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease – A Systematic Review
- Deep learning-based segmentation techniques for coastal monitoring and seagrass banquette detection
- A Comparative Study of Signal Recognition Based on Ensemble Learning and Deep Learning
