Impact learning is a supervised and competitive learning algorithm for inducing classification, linear or nonlinear regression knowledge from examples. The primary principle of this method is to learn from a competition which is the impact of independent features; to be more specific it fits curve by the back forces or impacts of features from the intrinsic rate of natural increase (RNI); since every real dataset follows the aptitude of RNI. The input to Impact Learning is a training set of numerical data. To be more prominently, every feature of our life follows the trend of RNI, on the other hand, there are more back forces on which the feature need to be dependent. As a result, the target is impacted by other features of the back forces which can be named for a specific force as “Back Impact on Target (BIT)”. Since the target feature relies on BITs that is why every BIT also depends on the target feature. Basically, the machine learning or statistical learning datasets derive from real sectors of target territories, consequently, they flow the trend of RNI. So it will be a procedure to generate the algorithm (Impact Learning) from the flow of RNI. Furthermore, this method learns from the effect of BITs and in real life, every business sector has good competitors; the impact learning can be used in order to depict the competition among the competitors. In addition, the trained impact learning can be also used for checking multicollinearity or redundancy for feature selection. Mathematically, it can be defined as x(t) = k/a (a- (1+Con)/t-∑_(i=1)^n▒〖c_i y_i 〗(t))
-A framework of this algorithm is being developed. Very soon, it will be made open source, if you have captivating to use in your work just email me
Our proposed idea presents Artificial Doctor based on Deep Learning and Machine Learning techniques on which different diseases could be detected, medicated and treated based on data. We researched about these innovative ideas and figured out the therapy and remedy of diseases. As testing system, we used Type-2 Diabetes data and trained our system. We got about 90% accuracy on diseases detection and its treatment.
According to maternal mortality of women it still pretty high in Bangladesh due to unnecessary C-section delivery. This unexpected mode of baby birth can be reduced by making the decision making easier and reliable and convincing. In this research we have used supervised machine learning to train our machine learning model. A dataset of 13,527 delivery patient was collected from Dhaka medical college has been used to train the model. Later one the model has been verified using leveled sample data. The mode of baby delivery suggested by analyzing any given dataset showed acceptable accuracy. In details, There are two common types of baby birth e.g. vaginal birth and C-Section or cesarean delivery. Normally, women want less recovery time and willing to reduce the risk of major surgery adopt virginal birth. In some other cases like delivering a large baby compared to mother’s pelvis, or if the baby is not in a heads-down position C section delivery is adopted. Both two procedures have pros and cons based on any specific case. The decision for the process of childbirth is taken by the gynecologist based on the following some condition . The aim of this research is to formulate a computerized recommendation based on the condition of a mother's health
Competition is the main tool and a significant dimension in economics, business or other sectors. It means ‘to seek together’ and its fundamental means ‘compete’. Nowadays, competition is defined as a rivalry for selling goods in the market where the mobility of a person who looks for achieving maximum profit as well as making sales under assumptions. In the future, the competition will arise in Machine, Robot or other intelligence systems, etc.
In this technique, we represent a new competitive regression model for competitive data that add a new concept of machine learning and competitive supervised learning. This model demonstrates the competition analyzing methods and interaction rate on each competitor. This model has consisted of the Bio-mathematical model Logistic growth model. Since a risen competition always stays in business, economics for the same resources, that’s why we have trained our proposed model from the SIM subscriber’s competitive data of all Bangladeshi SIM companies and showed the analysis system of competitions with our model. We also exhibit a comparison between the existing regression techniques with the propound model and the reason to accept the proposed model.
The basic tasks can be done with this model:
1. Can be determined the target value on a certain time
2. Can be determined the time to fill a target
3. Can be analyzed of competition
4. It can be determined the impact on the target agent to each competitor.
5. The target agent can be determined of any future time
With machine learning techniques, the prediction can be determined but can be analysed competitions
The Bangla Language Toolkit, or more commonly BLTK, is a suite of Artificial Intelligence libraries and programs for symbolic analysing for Bangla language written in the Python programming language. We have used machine learning, deep leering, mathematics, and statistical techniques in order to develop BLTK. The main purpose of this tool is to perform computational research on Bangla language so that the Bangla language can keep its value in the world of AI. This tool can be significant for the business, economics, E-commerce, office, bank, and other sectors. Till we have developed various computational linguistics techniques on Bangle Language such as Chatbot, Lemmatization, Spelling Checker, Semantic Analysing, Topics Modeling, etc. Till we have completed the system behind the BLTK are summarized as:
1. Text Classification and Categorisation
2. Named Entity Recognition
3. Chatbot or intelligence bot
4. Part-of-Speech Tagging
5. Semantic Parsing and Question Answering
6. Paraphrase Detection
7. Language Generation and Multi-document Summarization
8. Spell Checking
9. Mathematical and statistical operation : SVD, NMF, LDA, PCA on bangla language
Using mathematics and statistics, I developed a optimal chatbot. The optimisation was execution time and space complexity of algorithms
using circlet images, this is a classification technique.
We developed tow new curve fitting techniques for linear or non linear using numerical methods.
Bangla Information Retrieval Intelligence Bot is an effective chatbot that helps a user to trace relevant information by Bangla Natural Language Processing. The world is being more informative and the information on Bangla language is increasing. So it can play a vital role in trace information on Bangla language. Basically, if a uses ask a question (voice or text), BIRIB can reply from providing information. This system will be helpful for students like those who are going to sit BCS or competitive exam. Their study will be easy. They don’t need to find on book and general knowledge will be easier. It can also chat with a user like a human.