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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
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https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
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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.
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- The presentation will cover challenges with model opacity, defining interpretability, and how Bayesian rule lists can be used to build naturally interpretable models through rule extraction.
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2. Soft computing uses techniques like neural networks, fuzzy logic, and genetic algorithms to handle real-world problems involving uncertainty, ambiguity, and imprecision. It aims to build intelligent systems that can learn from experience.
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Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
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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
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linkage model, Data streams, Possibilistic Graphs. This paper presents representation and
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operator.
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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.
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Fuzzy expert system
A description of fuzzy expert system.
Its basic,applicabilty,operations and an easy example to understand it.
7
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
LSTM Based Sentiment Analysis
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O01741103108
This document presents a method called Joint Sentiment and Topic Detection (JST) that can simultaneously detect sentiment and topic from text without requiring labeled training data. JST extends the Latent Dirichlet Allocation (LDA) topic model by adding an additional sentiment layer. It assumes words are generated from a joint distribution conditioned on both a sentiment label and topic. The document evaluates JST on movie reviews and product reviews using domain independent sentiment lexicons as prior information. Experimental results show JST can accurately classify sentiment at the document level and detect topics for different domains.
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The document discusses different types of machine learning, including rote learning, learning by taking advice, and learning through problem solving. Rote learning involves simply storing examples to improve future performance, as demonstrated in Samuel's checkers program. Learning by taking advice requires translating human advice into operational rules a program can understand and apply, as shown through FOO's hearts-playing program. Learning through problem solving involves adjusting parameter weights based on success, as Samuel's program did by modifying evaluation function coefficients over time based on game outcomes.
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Artificial intelligence is a field of study that uses computational techniques to simulate human intelligence processes like learning, reasoning, and problem solving. It includes approaches like expert systems, neural networks, genetic algorithms, fuzzy logic systems, and swarm intelligence methods. The goal is to develop tools that can perform tasks requiring human-level intelligence.
Soft computing
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.
AI: Learning in AI
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2. The RN operates on sets of objects and can infer relations between objects. It outperformed non-relational baselines on CLEVR (96.4% accuracy) and achieved over 90% accuracy on other tasks.
3. An implementation of RNs in PyTorch is provided on GitHub along with example applications to the Sort-of-CLEVR dataset involving relational and non-relational questions about 3D scenes.
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This document discusses providing insights into defining a problem by generating ideas and hypotheses that can be later explored through quantitative research. It suggests qualitatively exploring a problem space to define the issue and propose potential avenues for investigation and testing with numerical data analysis.
Drug Target Interaction (DTI) prediction (MSc. thesis)
- The document proposes a multi-view stacking ensemble method for drug-target interaction (DTI) prediction that combines predictions from multiple machine learning models trained on different drug and target feature view combinations.
- It generates 126 view combination datasets from 14 drug views and 9 target views, then trains extra trees, random forest, and XGBoost classifiers on each view combination. Predictions from these base models are then combined using a stacking ensemble with an extra trees meta-learner.
- The method is shown to outperform single models and voting ensembles, and calibration of the meta-learner and use of local imbalance measures provide further improvements to predictive performance on DTI prediction tasks.
AI: Learning in AI 2
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1. It defines machine learning and discusses different types of machine learning tasks like supervised, unsupervised, and reinforcement learning.
2. It explains the key differences between traditional machine learning approaches and learning agents, noting that learning is one of many goals for agents and must be integrated with other agent functions.
3. It discusses different challenges of integrating machine learning into intelligent agents, such as balancing learning with recall of existing knowledge and addressing time constraints on learning from the environment.
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Soft computing is an emerging approach to computing that aims to model human-like decision making through techniques like fuzzy logic, neural networks, and genetic algorithms. It allows for imprecision, uncertainty, and approximation to achieve practical and robust solutions. Soft computing deals with problems that are too complex or undefined to model mathematically. It is well-suited for real-world problems where ideal solutions do not exist.
REPRESENTATION OF UNCERTAIN DATA USING POSSIBILISTIC NETWORK MODELS
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.
Knowledge acquistion
The document discusses knowledge acquisition for artificial intelligence. It defines knowledge acquisition as the process used to define rules and syntax for knowledge-based systems. It also discusses different types of knowledge, including declarative knowledge (knowing facts), procedural knowledge (knowing how to do something), meta knowledge (knowing about other types of knowledge), heuristic knowledge (rules of thumb from experience), and structural knowledge (relationships between concepts). The document provides examples and definitions for each type of knowledge.
Prediction of Answer Keywords using Char-RNN
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 NLP
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.
Machine Learning and Reasoning for Drug Discovery
This document provides an overview of a tutorial on machine learning and reasoning for drug discovery.
The tutorial covers several topics: molecular representation and property prediction, including fingerprints, string representations, graph representations, and self-supervised learning; protein representation and protein-drug binding; molecular optimization and generation; and knowledge graph reasoning and drug synthesis.
The introduction discusses the drug discovery pipeline and how machine learning can help with various tasks such as molecular property prediction, target identification, and reaction planning. Neural networks are well-suited for drug discovery due to their expressiveness, learnability, generalizability, and ability to handle large amounts of data.
Unboxing the black boxes (Deprecated version)
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.
Fuzzy expert system
A description of fuzzy expert system.
Its basic,applicabilty,operations and an easy example to understand it.
7
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
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[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.PadmapriyaIJET - International Journal of Engineering and Techniques
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The document summarizes an aspect-based sentiment analysis project that identifies aspects of entities and the sentiment expressed for each aspect in reviews. It discusses the main sub-problems of aspect extraction, category detection, polarity analysis, and category polarity. It then provides details on the algorithms and libraries used to implement solutions for each sub-problem, including using conditional random fields for aspect extraction, an SVM model for category detection, and dependency parsing with a graph approach for polarity analysis of multiple aspects.
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Inspecting the sentiment behind customer ijcset feb_2017
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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
17
This document discusses efficient rendezvous algorithms for wireless sensor networks with mobile base stations. It proposes an approach where select sensor nodes act as rendezvous points, buffering and aggregating data from other sensors. These rendezvous points then transfer the collected data to the base station when it arrives, combining the advantages of controlled mobility and in-network caching. Algorithms are presented for rendezvous design with mobile base stations having variable or fixed tracks. Both theoretical analysis and simulations validate that this approach can achieve a good balance between energy savings and reduced data collection latency in the network.
16
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
15
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
25
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
24
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
23
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
22
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
21
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
20
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
19
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
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2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
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18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
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17
This document discusses preventing private information inference attacks on social networks. It explores how released social networking data could be used to predict undisclosed private information about individuals, such as their political affiliation or sexual orientation. It then describes three sanitization techniques that could be used to decrease the effectiveness of such attacks. An experiment is conducted applying these techniques to a Facebook dataset to attempt to discover sensitive attributes through collective inference and show that the sanitization methods decrease the effectiveness of local and relational classification algorithms.
16
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N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
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Chennai-26.
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Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
15
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
14
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
13
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
12
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
11
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
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Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
10
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
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www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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- 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