Block Chain understanding
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Blockchain technology involves distributing a central ledger of transactions among various nodes and miners. Transactions are first stored and then made visible through the ledger. This central ledger is then distributed among miners, who vote to validate new transactions and add them to the distributed ledger. Winners who validate transactions are rewarded. This process makes the ledger visible to all nodes, maintains security through digital signatures, and ensures invalid transactions are cancelled.
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Blockchain mechanics
Blockchain Mechanics discusses the technical underpinnings of blockchains and cryptocurrencies like Bitcoin. It explains how blockchains replace centralized databases and authorities with distributed consensus algorithms based on cryptography. Through a series of "games" that simulate the mining process and transactions, it illustrates how blockchains incentivize computational work to validate transactions and how digital signatures prevent cheating by ensuring participants cannot alter transactions or spend coins multiple times. This allows distrustful parties to securely transact without a central authority through the rules enforced by the consensus of the blockchain network.
Blockchain mechanics
Blockchain Mechanics discusses the technical underpinnings of blockchains and cryptocurrencies like Bitcoin. It explains how blockchains replace centralized databases and authorities with distributed consensus algorithms based on cryptography. Through a series of "games" that simulate the mining process and transactions, it illustrates how blockchains incentivize computational work to validate transactions and how digital signatures prevent cheating by authenticating transaction origins. This allows blockchains to function as a global transaction system without a central authority where consensus on the rules is determined by the software run by the majority of network participants.
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More from Nimai Chand Das Adhikari
HPPS: Heart Problem Prediction System using Machine Learning
Heart is the most important organ of a human body. It circulates oxygen and other vital nutrients through blood to different parts of the body and helps in the metabolic activities. Apart from this it also helps in removal of the metabolic wastes. Thus, even minor problems in heart can affect the whole organism. Researchers are diverting a lot of data analysis work for assisting the doctors to predict the heart problem. So, an analysis of the data related to different health problems and its functioning can help in predicting with a certain probability for the wellness of this organ. In this paper we have analysed the different prescribed data of 1094 patients from different parts of India. Using this data, we have built a model which gets trained using this data and tries to predict whether a new out-of-sample data has a probability of having any heart attack or not. This model can help in decision making along with the doctor to treat the patient well and creating a transparency between the doctor and the patient. In the validation set of the data, it’s not only the accuracy that the model has to take care, rather the True Positive Rate and False-Negative Rate along with the AUC-ROC helps in building/fixing the algorithm inside the model.
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...Nimai Chand Das Adhikari
Generating forecast is undoubtedly one of the most
important sector in any industry. It not only helps the demand
planner of the company which is focusing primarily on the future
forecasting of the sales but also the inventory management and
its handling cost. Down the line of the companys supply chain,
a wrong forecast can impact in an alternate way of incurring
heavy cost. Apart from this a correct forecasting leading to a
good accuracy says how the profit and loss of the company
depends upon. This heavily depends upon the predicting machine
that the company is using for the forecasting. Demand planners
look for a consistently accurate results over a period of time.
Where the accuracy of the forecast is not only good but also
the algorithm is stable in a long period of time. This we have
illustrated through the analysis and the model takes care of the
stability of a particular algorithm selected for a SKU. In this
paper we want to highlight a new ensemble technique using the
averaging method which not only gives priority to the algorithm
which consistently maintains a good accuracy but also decreases
the deviation from the actual sales. Based upon the history it
tries to give the importance to the one which predicts better and
penalizes other algorithms which deviate from the actual sales.Face detection Using Computer Vision
A survey on different Facial recognition algorithms as well implemented on various standard dataset. Tested different feature extraction and dimensionality reduction methods to arrive at a better accuracy.
HPPS : Heart Problem Prediction System using Machine Learning
The learning speed of the feed forward neural
network takes a lot of time to be trained which is a major
drawback in their applications since the past decades. The
key reasons behind may be due to the slow gradient-based
learning algorithms which are extensively used to train the
neural networks or due to the parameters in the networks
which are tuned iteratively using some learning algorithms.
Thus, in order to eradicate the above pitfalls, a new learning
algorithm was proposed known as Extreme Learning Machines
(ELM). This algorithm tries to compute Hidden-layer-output
matrix that is made of randomly assigned input layer and
hidden layer weights and randomly assigned biases. Unlike the
other feedforward networks, ELM has the access of the whole
training dataset before going into the computation part. Here,
we have devised a new two-layer-feedforward network (TFFN)
for ELM in a new manner with randomly assigning the weights
and biases in both the hidden layers, which then calculates the
output-hidden layer weights using the Moore-Penrose generalized
inverse. TFFN doesn’t restricts the algorithm to fix the number
of hidden neurons that the algorithm should have. Rather it
searches the space which gives an optimized result in the neurons
combination in both the hidden layers. This algorithm provides a
good generalization capability than the parent Extreme Learning
Machines at an extremely fast learning speed. Here, we have
experimented the algorithm on various types of datasets and
various popular algorithm to find the performances and report
a comparison.
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...Nimai Chand Das Adhikari
The learning speed of the feed forward neural
network takes a lot of time to be trained which is a major
drawback in their applications since the past decades. The
key reasons behind may be due to the slow gradient-based
learning algorithms which are extensively used to train the
neural networks or due to the parameters in the networks
which are tuned iteratively using some learning algorithms.
Thus, in order to eradicate the above pitfalls, a new learning
algorithm was proposed known as Extreme Learning Machines
(ELM). This algorithm tries to compute Hidden-layer-output
matrix that is made of randomly assigned input layer and
hidden layer weights and randomly assigned biases. Unlike the
other feedforward networks, ELM has the access of the whole
training dataset before going into the computation part. Here,
we have devised a new two-layer-feedforward network (TFFN)
for ELM in a new manner with randomly assigning the weights
and biases in both the hidden layers, which then calculates the
output-hidden layer weights using the Moore-Penrose generalized
inverse. TFFN doesn’t restricts the algorithm to fix the number
of hidden neurons that the algorithm should have. Rather it
searches the space which gives an optimized result in the neurons
combination in both the hidden layers. This algorithm provides a
good generalization capability than the parent Extreme Learning
Machines at an extremely fast learning speed. Here, we have
experimented the algorithm on various types of datasets and
various popular algorithm to find the performances and report
a comparison.An Intelligent Approach to Demand Forecasting
This document discusses an intelligent approach to demand forecasting using an ensemble model. It first pre-processes input data to clean errors and impute missing values. It then runs two parallel forecasting engines using machine learning and time series algorithms. The final forecast combines the outputs of these models and applies further seasonality and trend corrections. This ensemble approach generates more stable and accurate forecasts compared to conventional techniques.
Credit defaulter analysis
The document discusses the development of a credit default prediction model called Def_Catch using machine learning algorithms. Def_Catch was trained on a dataset of 100,000 examples with 11 attributes related to borrowers' credit histories and demographics. Random forest achieved the highest accuracy of 93.14% at predicting which borrowers would default in the next 2 years, outperforming logistic regression, naive bayes, decision trees, and multi-layer perceptron models. The top predictors of default included credit utilization, age, number of late payments, debt ratio, and income. Def_Catch provides insights into borrower risk that are difficult to discern from raw data alone.
Image Stitching for Panorama View
The Image Panorama is a technique of stitching more images to create a more broader view which our normal eye does in a wider angle rather than that of the view which is restricted by the camera
Thesis Presentation_Extreme Learning Machine_Nimai_SC14M045
The leaning speed of the feed forward neural networks is very much slower than
as expected and this is the major drawback/pitfall in their applications since the
past decades. The key reasons behind may
1. Due to the slow gradient-based learning algorithms which are extensively used
to train the neural networks.
2. All the parameters in the networks are tuned iteratively using some learning
algorithms.
Thus, in order to eradicate the above pitfalls, a new learning algorithm was pro
posed known as Extreme Learning Machine (ELMs). This algorithm is for the
single hiddenlayer feed forward networks(SLFNs) which randomly initializes the
input hidden node weights and biases of the hidden nodes after that calculates
the output weights. This algorithm provide the good generalization performance
at an extremely fast learning speed. In this thesis we have experiemented the
algorithm on various types of datasets and various popular algorithm to find the
performances and report a comparison.
We have devised a two-layer-feedforward network for ELM in a new manner with
randomly assigning the weights and biases in both the hidden layer. We have
also studied the ELM autoencoders and thoroughly experimented it with various
datasets and deep networks.
We have implemented ELM with recommender systems to build a new music app
to recommend the user some songs based on the history of the use.
An intelligent approach to demand forecasting
Demand Forecasting, undeniably, is the single most important component of any organizations Supply Chain. It determines the estimated demand for the future and sets the level of preparedness that is required on the supply side to match the demand. It goes without saying that if an organization doesnt get its forecasting accurate to a reasonable level, the whole supply chain gets affected. Understandably, Over/Under forecasting has deteriorating impact on any organizations Supply Chain and thereby on P and L. Having ascertained the importance of Demand Forecasting, it is only fair to discuss about the forecasting techniqueswhichareusedtopredictthefuturevaluesofdemand. The input that goes in and the modeling engine which it goes through are equally important in generating the correct forecasts and determining the Forecast Accuracy. Here, we present a very unique model that not only pre-processes the input data, but also ensembles the output of two parallel advanced forecasting engines which uses state-of-the-art Machine Learning algorithms and Time-Series algorithms to generate future forecasts. Our technique uses data-driven statistical techniques to clean the data of any potential errors or outliers and impute missing values if any. Once the forecast is generated, it is post processed with Seasonality and Trend corrections, if required.Since the final forecast is the result of statistically pre-validated ensemble of multiple models, the forecasts are stable and accuracy variation is very minimal across periods and forecast horizons. Hence it is better at estimating the future demand than the conventional techniques.
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HPPS: Heart Problem Prediction System using Machine Learning
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HPPS : Heart Problem Prediction System using Machine Learning
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TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...
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Block Chain understanding
- 1. BLOCK CHAIN Understanding Nimai Chand Das Adhikari
- 2. Node 3 Node 6 Node 5 Node 4Node 1 Node 2 Steps: 1. Transaction Details Stored 2. Visibility of Transaction Details 3. Making this central “Ledger” to distributed “Ledger” Advantages: 1. Visibility 2. If Node 1 sends an invalid transaction than it will get cancelled 3. Security as each transaction is digitally signed Distributed Ledger: 1. Distributing the “Central Ledger” to “Miners” 2. “Miners” are the nodes which handle the transaction 3. Voting by the “Miners” to take the new transaction 4. Winners are rewarded Central Ledger Block Chain Technology
- 3. Node 3 Node 6 Node 5 Node 4Node 1 Node 2 Steps: 1. Transaction Details Stored 2. Visibility of Transaction Details 3. Making this central “Ledger” to distributed “Ledger” Distributed Ledger: 1. Distributing the “Central Ledger” to “Miners” 2. “Miners” are the nodes which handle the transaction 3. Voting by the “Miners” to take the new transaction 4. Winners are rewarded Central Ledger Block Chain Technology
- 4. Node 3 Node 6 Node 5 Node 4Node 1 Node 2 Steps: 1. Transaction Details Stored 2. Visibility of Transaction Details 3. Making this central “Ledger” to distributed “Ledger” Distributed Ledger: 1. Distributing the “Central Ledger” to “Miners” 2. “Miners” are the nodes which handle the transaction 3. Voting by the “Miners” to take the new transaction 4. Winners are rewarded Central Ledger Miner 2 Miner 4 Block Chain Technology
- 5. Node 3 Node 6 Node 5 Node 4Node 1 Node 2 Miner 2 Miner 4 Transaction Details: 1. Node 1 Node 3 2. Node 3 Node 4 3. ….. Transaction Details: 1. Node 2 Node 4 2. Node 4 Node 1 3. ….. New Transaction: Node 6 Node 3 Block Chain Technology
- 6. Node 3 Node 6 Node 5 Node 4Node 1 Node 2 Miner 2 Miner 4 Transaction Details: 1. Node 1 Node 3 2. Node 3 Node 4 3. ….. Transaction Details: 1. Node 2 Node 4 2. Node 4 Node 1 3. ….. New Transaction: Node 6 Node 3 Vote… Vote… Block Chain Technology
- 7. Node 3 Node 6 Node 5 Node 4Node 1 Node 2 Miner 2 Miner 4 Transaction Details: 1. Node 1 Node 3 2. Node 3 Node 4 3. ….. Transaction Details: 1. Node 2 Node 4 2. Node 4 Node 1 3. Node 6 Node 3 New Transaction: Node 6 Node 3 Winner.. Block Chain Technology
- 8. Thank You