Machine learning is a powerful technique that learns and improves from its experience by developing algorithms. Algorithms are programs that compute, access, and process input data and predict results. The machine learning model is designed to compute complex computations, and learn patterns, and based on that it gives relevant output. The model has worked well with normal datasets like numerical, bivariant, multivariate, categorical, and correlational datasets.
However, as of today, most machine learning models face challenges to predict time-series data due to limitations in understanding the context of the input data. That’s where recurrent neural networks, of which Long Short Term Memory Networks(LSTM) – a type of artificial neural network help. LSTMs can recognize patterns in sequences of data, such as numerical time series data emanating from sensors, text, genomes, handwriting, and spoken words.
What is LSTM Networks?
LSTM – Long Short Term Memory Networks, is a special type of RNN (Recurrent Neural Network). RNN is a neural network that provides the output of the previous state as input to the next stage in such a way that it can remember data from the previous state. It will predict precisely the culminating output with the help of previous data.
However, RNN suffers from the vanishing gradient problem i.e. fading of information for a longer sequence, thus, facing difficulty in persisting data for a longer timespan. This issue can be resolved by LSTM. LSTM has been manifested to get rid of the long-term dependency problem and eliminate the downside of RNN.
Importance of LSTM
In this modern era, machines are performing tasks with smartness using machine learning. Now, each industry has tremendous historical data of devices which has to be utilized in such a way that the machine learning model can forecast data effectively.
As humans, we unconventionally always pick up some important keywords and ignore other words that will help us to understand the context of any sentence or paragraph. For example, while reading the review of the movie, the brain only tries to remember or focus on some keywords like, “Action-packed”, “breathtaking”, “boring”, and “amazing”. Each time we do not have to think, remember and make a fresh start. LSTM also behaves similarly. Additionally, LSTM is unequivocally designed for sequential datasets where traditional neural networks fail. LSTM permits data to persist.
In every industry, authorities will be making any decision based on important data from the past and it will provide better outcomes. Same for machine learning models, LSTM demonstrates accurate results compared to other models because it is remembering only relevant data throughout the model learning. It can easily keep important information to make predictions.
How LSTM Works?
LSTM uses a series of various logic gates. With the help of this, it can regulate the flow of information and solve vanishing gradient problems in RNN. The vanishing gradient problem occurs during backpropagation while training the machine learning model. Gradients are the values that can be used for updating neural network weights. At the time of backpropagation, gradients will become such a minimum that can make a negligible difference in updating weights.
This will eventually stop learning for that layer so that these layers do not learn. As a result, RNN will forget data for a longer duration because of that RNN is only having short-term memory.
LSTM and RNN are similar in terms of control flow. Both are processing the information as they pass and propagate forward. The only difference is operations within each cell. The principal concept of LSTM is to carry on relevant information all over the network. Based on input it will add that data to memory, forget or delete data if it is not required, and ignore information if it is irrelevant. This is how LSTM not only passes information to the next state but also persists data for later states.
Working of LSTM is divided into three parts, and each part performs an individual function as follows:
- The first part determines that information from the previous timestamp is relevant, then remember it, and if it is irrelevant it can be forgotten. This part is called the Forget gate.
- In the second part, the cell tries to learn new data from the current input to the cell, which is known as the input gate.
- At last, in the third part, the cell passes updated information from the current timestamp to the next timestamp. This part is called the Output gate.
This is how LSTM forgets and remembers information selectively during training.
Applications of LSTM
- Industrial IoT: Every industry’s profound essential is to enhance the quality of their product consistently. In manufacturing units, there are numerous devices, sensors, and machines and each of them has an immense amount of data. These are getting very less attention which could lead the industry to a wear and tear situation.
LSTM will play a vital role in the above case. Based on historical data of different parameters such as actuators, vibration, temperature, noise, and electricity consumption, the model can predict early maintenance as well as anomaly detection of machines before the time. This will help the manufacturing unit with timely maintenance. Eventually, it will increase efficiency and diminish the downtime of machines. It is useful to increase the utilization of devices. As a result, produce products in fewer times and in greater numbers. For the giant power industries, it can forecast the upcoming power demand for a particular state, city, or street using past electricity supply data. Electricity load forecasting will help to mitigate the loss of energy, and manage and transfer power based on prediction.
- Home automation:
In home automation there are two foremost things :
- Speed in processing and performing actions
In a smart building, HVAC-Heating, Ventilation, and Air conditioning systems will help to predict energy consumption as well as indoor air temperature and control based on outside weather. Based on human actions, devices should perform predefined tasks where human activity can be recognized by analyzing one by one video frame. Here, LSTM will predict the next action precisely based on early actions.
Any audio analytics-related applications such as predicting glass break for thief detection, speech recognition, and predicting voice commands. These have time-series-based data where each information comes with its frequency and time.
In voice command or speech recognition, it is important to know the actual context which is the strength of LSTM.
- Automotive: Each industry has a massive amount of data on its respective market and with the help of that data LSTM will analyze the upcoming trend of the market and forecast the upcoming demand for products.
This will help automotive manufacturers to manage their supply chain and inventory based on demand forecasts and strategize a plan to accelerate their demand and reduce unnecessary costs. It can help the organization discover new opportunities based on the market needs. It also suggests relevant products using past orders to the retail customer which gives a better user experience and also boosts sales. LSTM gives satisfaction in terms of predicting time series data.
- Healthcare: In the healthcare industry, heartbeat, neuro patterns, blood pressure, and oxygen level are crucial for patients who are serious and admitted to the hospital. If any of these parameters change, then patients can die. These parameters should predict accurately at an early stage which helps in treating patients accordingly.
Above all parameters have time-series data in which LSTM outperforms other models. It can also diagnose any health issue like detecting cancer or any major diseases in advance using health reports. With the help of smart wearables, it can predict heart attacks ahead of time and send a message of their health conditions to the relevant person. It will suggest consulting a doctor on account of the severity of their health and giving reminders for a regular checkup.