For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Part of the ongoing effort with Skater for enabling better Model Interpretation for Deep Neural Network models presented at the AI Conference.
https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!Sri Ambati
This meetup took place in Mountain View on January 24th, 2019.
Description:
With the effort and contributions from researchers and practitioners from academia and industry, Machine Learning Interpretation has become a young sub-field of ML. However, the norms around its definition and understanding is still in its infancy and there are numerous different approaches emerging rapidly. However, there seems to be a lack of a consistent explanation framework to evaluate and consistently benchmark different algorithms - evaluating against interpretation, completeness and consistency of the algorithms.
The idea with the gym is to provide a controlled interactive environment for all forms of Machine Learning algorithms, - initially focusing on supervised predictive modeling problems, to allow analysts and data-scientists to explore, debug and generate insightful understanding of the models by
1.Model Validation: Ways to explore and validate black box ML systems enabling model comparison both globally and locally - identifying biases in the training data through interpretation.
2.What-if Analysis: An interactive environment where communication can happen i.e. enable learning through interactions. User having the ability to conduct "What-If" analysis - effect of single or multiple features and their interactions
3.Model Debugging: Ways to analyze the misbehavior of the model by exploring counterfactual examples(adversarial examples and training)
4. Interpretable Models: Ability to build natively interpretable models - with the goal to simplify complex models to enable better understanding.
The central concept with MLI gym is to have an interactive environment where one could explore and simulate variations in the world(a world post a model is operationalized) beyond the defined model metrics point estimates - e.g. ROC-AUC, confusion matrix, RMSE, R2 score and others.
Speaker's Bio:
Pramit is a Lead Data Scientist/ at H2O.ai. His area of interests is building Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) to help the business realize their data-driven goals.
Currently, he is exploring "Model Interpretation" as means to efficiently understand the true nature of predictive models to enable model robustness and security. He believes effective Model Inference coupled with Adversarial training could lead to building trustworthy models with known blind spots. He has started an open source project Skater: https://github.com/datascienceinc/Skater to solve the need for Model Inference(The project is still in its early stages of development but check it out, always eager for feedback)
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Part of the ongoing effort with Skater for enabling better Model Interpretation for Deep Neural Network models presented at the AI Conference.
https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!Sri Ambati
This meetup took place in Mountain View on January 24th, 2019.
Description:
With the effort and contributions from researchers and practitioners from academia and industry, Machine Learning Interpretation has become a young sub-field of ML. However, the norms around its definition and understanding is still in its infancy and there are numerous different approaches emerging rapidly. However, there seems to be a lack of a consistent explanation framework to evaluate and consistently benchmark different algorithms - evaluating against interpretation, completeness and consistency of the algorithms.
The idea with the gym is to provide a controlled interactive environment for all forms of Machine Learning algorithms, - initially focusing on supervised predictive modeling problems, to allow analysts and data-scientists to explore, debug and generate insightful understanding of the models by
1.Model Validation: Ways to explore and validate black box ML systems enabling model comparison both globally and locally - identifying biases in the training data through interpretation.
2.What-if Analysis: An interactive environment where communication can happen i.e. enable learning through interactions. User having the ability to conduct "What-If" analysis - effect of single or multiple features and their interactions
3.Model Debugging: Ways to analyze the misbehavior of the model by exploring counterfactual examples(adversarial examples and training)
4. Interpretable Models: Ability to build natively interpretable models - with the goal to simplify complex models to enable better understanding.
The central concept with MLI gym is to have an interactive environment where one could explore and simulate variations in the world(a world post a model is operationalized) beyond the defined model metrics point estimates - e.g. ROC-AUC, confusion matrix, RMSE, R2 score and others.
Speaker's Bio:
Pramit is a Lead Data Scientist/ at H2O.ai. His area of interests is building Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) to help the business realize their data-driven goals.
Currently, he is exploring "Model Interpretation" as means to efficiently understand the true nature of predictive models to enable model robustness and security. He believes effective Model Inference coupled with Adversarial training could lead to building trustworthy models with known blind spots. He has started an open source project Skater: https://github.com/datascienceinc/Skater to solve the need for Model Inference(The project is still in its early stages of development but check it out, always eager for feedback)
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
REPRESENTATION OF UNCERTAIN DATA USING POSSIBILISTIC NETWORK MODELScscpconf
Uncertainty is a pervasive in real world environment due to vagueness, is associated with the
difficulty of making sharp distinctions and ambiguity, is associated with situations in which the
choices among several precise alternatives cannot be perfectly resolved. Analysis of large
collections of uncertain data is a primary task in the real world applications, because data is
incomplete, inaccurate and inefficient. Representation of uncertain data in various forms such
as Data Stream models, Linkage models, Graphical models and so on, which is the most simple,
natural way to process and produce the optimized results through Query processing. In this
paper, we propose the Uncertain Data model can be represented as Possibilistic data model
and vice versa for the process of uncertain data using various data models such as possibilistic
linkage model, Data streams, Possibilistic Graphs. This paper presents representation and
process of Possiblistic Linkage model through Possible Worlds with the use of product-based
operator.
Prediction of Answer Keywords using Char-RNNIJECEIAES
Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.
Opinion mining on newspaper headlines using SVM and NLPIJECEIAES
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.
Tutorial delivered at ECML-PKDD 2021.
TL;DR: This tutorial reviews recent developments on drug discovery using machine learning methods.
Powered by neural networks, modern machine learning has enjoyed great successes in data-intensive domains such as computer vision and languages where human can naturally perform well. Machine learning equipped with reasoning is now accelerating fields that traditionally require deep expertise such as physics, chemistry and biomedicine. This tutorial provides an overview of how machine learning and reasoning are speeding up and lowering the cost of drug discovery. This includes how machine learning can help in wide range of areas such as novel molecule identification, protein representation, drug-target binding, drug re-purposing, generative drug design, chemical reaction, retrosynthesis planning, drug-drug interaction, and safety assessment. We will also discuss relevant machine learning models for graph classification, molecular graph transformation, drug generation using deep generative models and reinforcement learning, and chemical reasoning.
Unboxing the black boxes (Deprecated version)BLECKWEN
A new version of these slides are avaible: https://www.slideshare.net/BLECKWEN-AI/unboxing-the-black-boxes-updated-version-november-18
As machine learning has become more widely adopted across many industries and involved in many aspects of decision making, machine learning interpretability is therefore becoming an integral part of the data scientist workflow and can no longer just be an afterthought. Ultimately, it’s reasonable to wonder whether we can understand and trust decisions made by a predictive model.
However, in an increasingly competitive environment, data scientists are using ever-complex machine learning algorithms like XGBoost or Deep Learning to deliver more accurate models to businesses. Unfortunately, there is a fundamental tension between accuracy and interpretability: the most accurate models are often the hardest to understand. Opaque and complicated nonlinear models limit trust and transparency, slowing adoption of machine learning models in high regulated industries like banking, healthcare and insurance. But things needn't be that way!
In this talk, Leonardo Noleto, senior data scientist at Bleckwen, will explore the vibrant area of machine learning interpretability and explain how to understand the inner-workings of black-box models, thanks to interpretability techniques. Along the way, Leonardo offers an overview of interpretability and the trade-offs among various approaches of making machine learning models interpretable. Leonardo concludes with a demonstration of open source tools like LIME and SHAP.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
REPRESENTATION OF UNCERTAIN DATA USING POSSIBILISTIC NETWORK MODELScscpconf
Uncertainty is a pervasive in real world environment due to vagueness, is associated with the
difficulty of making sharp distinctions and ambiguity, is associated with situations in which the
choices among several precise alternatives cannot be perfectly resolved. Analysis of large
collections of uncertain data is a primary task in the real world applications, because data is
incomplete, inaccurate and inefficient. Representation of uncertain data in various forms such
as Data Stream models, Linkage models, Graphical models and so on, which is the most simple,
natural way to process and produce the optimized results through Query processing. In this
paper, we propose the Uncertain Data model can be represented as Possibilistic data model
and vice versa for the process of uncertain data using various data models such as possibilistic
linkage model, Data streams, Possibilistic Graphs. This paper presents representation and
process of Possiblistic Linkage model through Possible Worlds with the use of product-based
operator.
Prediction of Answer Keywords using Char-RNNIJECEIAES
Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth.
Opinion mining on newspaper headlines using SVM and NLPIJECEIAES
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.
Tutorial delivered at ECML-PKDD 2021.
TL;DR: This tutorial reviews recent developments on drug discovery using machine learning methods.
Powered by neural networks, modern machine learning has enjoyed great successes in data-intensive domains such as computer vision and languages where human can naturally perform well. Machine learning equipped with reasoning is now accelerating fields that traditionally require deep expertise such as physics, chemistry and biomedicine. This tutorial provides an overview of how machine learning and reasoning are speeding up and lowering the cost of drug discovery. This includes how machine learning can help in wide range of areas such as novel molecule identification, protein representation, drug-target binding, drug re-purposing, generative drug design, chemical reaction, retrosynthesis planning, drug-drug interaction, and safety assessment. We will also discuss relevant machine learning models for graph classification, molecular graph transformation, drug generation using deep generative models and reinforcement learning, and chemical reasoning.
Unboxing the black boxes (Deprecated version)BLECKWEN
A new version of these slides are avaible: https://www.slideshare.net/BLECKWEN-AI/unboxing-the-black-boxes-updated-version-november-18
As machine learning has become more widely adopted across many industries and involved in many aspects of decision making, machine learning interpretability is therefore becoming an integral part of the data scientist workflow and can no longer just be an afterthought. Ultimately, it’s reasonable to wonder whether we can understand and trust decisions made by a predictive model.
However, in an increasingly competitive environment, data scientists are using ever-complex machine learning algorithms like XGBoost or Deep Learning to deliver more accurate models to businesses. Unfortunately, there is a fundamental tension between accuracy and interpretability: the most accurate models are often the hardest to understand. Opaque and complicated nonlinear models limit trust and transparency, slowing adoption of machine learning models in high regulated industries like banking, healthcare and insurance. But things needn't be that way!
In this talk, Leonardo Noleto, senior data scientist at Bleckwen, will explore the vibrant area of machine learning interpretability and explain how to understand the inner-workings of black-box models, thanks to interpretability techniques. Along the way, Leonardo offers an overview of interpretability and the trade-offs among various approaches of making machine learning models interpretable. Leonardo concludes with a demonstration of open source tools like LIME and SHAP.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Sentimental analysis is a context based mining of text, which extracts and identify subjective information from a text or sentence provided. Here the main concept is extracting the sentiment of the text using machine learning techniques such as LSTM Long short term memory . This text classification method analyses the incoming text and determines whether the underlined emotion is positive or negative along with probability associated with that positive or negative statements. Probability depicts the strength of a positive or negative statement, if the probability is close to zero, it implies that the sentiment is strongly negative and if probability is close to1, it means that the statement is strongly positive. Here a web application is created to deploy this model using a Python based micro framework called flask. Many other methods, such as RNN and CNN, are inefficient when compared to LSTM. Dirash A R | Dr. S K Manju Bargavi "LSTM Based Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42345.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42345/lstm-based-sentiment-analysis/dirash-a-r
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.
Sentiment classification aims to detect information such as opinions, explicit , implicit feelings expressed
in text. The most existing approaches are able to detect either explicit expressions or implicit expressions of
sentiments in the text separately. In this proposed framework it will detect both Implicit and Explicit
expressions available in the meeting transcripts. It will classify the Positive, Negative, Neutral words and
also identify the topic of the particular meeting transcripts by using fuzzy logic. This paper aims to add
some additional features for improving the classification method. The quality of the sentiment classification
is improved using proposed fuzzy logic framework .In this fuzzy logic it includes the features like Fuzzy
rules and Fuzzy C-means algorithm.The quality of the output is evaluated using the parameters such as
precision, recall, f-measure. Here Fuzzy C-means Clustering technique measured in terms of Purity and
Entropy. The data set was validated using 10-fold cross validation method and observed 95% confidence
interval between the accuracy values .Finally, the proposed fuzzy logic method produced more than 85 %
accurate results and error rate is very less compared to existing sentiment classification techniques.
EXPERT OPINION AND COHERENCE BASED TOPIC MODELINGijnlc
In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic
modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much
the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the
probability that a pair of highly correlated words appear together. In order to make sure that the internal
structure does not get changed too much from the rearrangement, coherence, which is a well known metric
for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure.
We developed two ways to systematically obtain the expert opinion from data, depending on whether the
data has relevant expert writing or not. The final algorithm which takes into account both coherence and
expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic
modeling method, NMF and LDA.
A SYSTEM OF SERIAL COMPUTATION FOR CLASSIFIED RULES PREDICTION IN NONREGULAR ...ijaia
Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models,
our previous research work has developed a system of a regular ontology that models learning structures
in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has
led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed
for inductive learning processes and decision making in a multiagent system. But not all processes or
models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict
the required number of rules of a non-regular ontology model given some defined parameters.
IMPROVED SENTIMENT ANALYSIS USING A CUSTOMIZED DISTILBERT NLP CONFIGURATIONadeij1
Social media and other communication platforms have become extremely popular in last few years. User comments made on such portals contain valuable information about users’ attitudes. With the advent of Natural Language Processing (NLP) algorithms, it is now possible to analyze the polarity in such comments at scale. Sentiment analysis models use machine learning which can be divided into two kinds, supervised and unsupervised learning. Supervised models train models on pre-labeled datasets and predict the labels from a subset of the data. Unsupervised sentiment analysis tools utilize pre-trained models, using them to cluster unlabeled data. Recent research indicates Bidirectional Encoder Representations from Transformers (BERT) is useful in sentiment analysis tasks. Using a restaurant review dataset specific to highlighting different sentiments of positivity and negativity, this paper presents results from a comparative study of NLP techniques for sentiment analysis. The superiority of a fine-tuned distilBERT model is proven through a systematic experimental approach. Sentiment polarity coupled with adjustable neural network configurations makes distilBERT based models more sensitive to sentiment. Compared to conventional LSTM (Long Short-Term Memory) NLP approaches with accuracies of 78%, distilBERT achieves an accuracy of 92.4%, much better than 72.3%, the result of VADER (Valence Aware Dictionary and sEntiment Reasoner)
The Smart Way to Invest in Artificial Intelligence and Machine Learning: Lisha Li, Amplify Partners
AI and ML are seeping into every startup, at least into every pitch deck. But what does it mean to build an AI/ML company? Some startups do require a closet filled with five PhD’s in data science, but that doesn’t necessarily mean yours does. Building intelligently with AI and ML.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Thesis Statement for students diagnonsed withADHD.ppt
9
1. Impulse Technologies
Beacons U to World of technology
044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in
Weakly Supervised Joint Sentiment Topic Detection from Text
Abstract
Sentiment analysis or opinion mining aims to use automated tools to detect
subjective information such as opinions, attitudes, and feelings expressed in text. This
paper proposes a novel probabilistic modeling framework called joint sentiment-topic
(JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and
topic simultaneously from text. A reparameterized version of the JST model called
Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the
modeling process, is also studied. Although JST is equivalent to Reverse-JST without a
hierarchical prior, extensive experiments show that when sentiment priors are added, JST
performs consistently better than Reverse-JST. Besides, unlike supervised approaches to
sentiment classification which often fail to produce satisfactory performance when
shifting to other domains, the weakly supervised nature of JST makes it highly portable
to other domains. This is verified by the experimental results on data sets from five
different domains where the JST model even outperforms existing semi-supervised
approaches in some of the data sets despite using no labeled documents. Moreover, the
topics and topic sentiment detected by JST are indeed coherent and informative. We
hypothesize that the JST model can readily meet the demand of large-scale sentiment
analysis from the web in an open-ended fashion.
Your Own Ideas or Any project from any company can be Implemented
at Better price (All Projects can be done in Java or DotNet whichever the student wants)
1
