Suppose that a customer who has given a high rating about a mobile phone writes the following review about the product: The front camera of the phone is excellent! Truly speaking, this is the best front camera I have experienced so far. From this review, we can understand two things. First, the customer holds a positive opinion about the phone. Secondly, the front camera of the phone is the targeted feature on which the opinions have been expressed in the review. In this workshop, we will be particularly interested in discovering patterns as indicated in the second case. We will discuss a framework that enables us to first discover the targets on which the opinions have been expressed in a review and then determine the polarity of the opinions. This kind of detailed analysis helps us to discover the components or features of the products which the customers have liked or disliked and thus help us to better summarize the information.
This document discusses software testing principles and methodologies. It defines software testing as executing a program under various conditions to check for correctness, completeness, and quality. The document outlines different testing levels from unit to system testing. It also distinguishes between black box and white box testing methods. Finally, it describes different types of system testing like alpha, beta, acceptance, and performance testing.
Handling Imbalanced Data: SMOTE vs. Random UndersamplingIRJET Journal
This document compares different techniques for handling imbalanced data, including random undersampling and SMOTE (Synthetic Minority Over-sampling Technique), using two machine learning classifiers - Random Forest and XGBoost. It finds that XGBoost generally performs better than Random Forest across different sampling techniques in terms of metrics like AUC, sensitivity and specificity. Random undersampling with XGBoost provided the most balanced results on the randomly generated imbalanced dataset used in this study. SMOTE without proper validation gave the best numerical results but likely overfit the data.
The document describes multilayer neural networks and their use for classification problems. It discusses how neural networks can handle continuous-valued inputs and outputs unlike decision trees. Neural networks are inherently parallel and can be sped up through parallelization techniques. The document then provides details on the basic components of neural networks, including neurons, weights, biases, and activation functions. It also describes common network architectures like feedforward networks and discusses backpropagation for training networks.
A confusion matrix is a table that shows the performance of a classification model by listing the true positives, true negatives, false positives, and false negatives. It displays how often the model correctly or incorrectly classified observations into their actual classes. The document provides an example confusion matrix for a model classifying apples, oranges, and pears, showing the number of observations the model correctly and incorrectly classified into each class.
The document summarizes various software testing techniques, including:
- White-box testing techniques like cyclomatic complexity and control flow graph analysis to derive test cases.
- Black-box techniques like equivalence partitioning and boundary value analysis to design test cases based on valid and invalid input conditions.
- The goal of testing is to systematically uncover errors by executing all independent paths and boundary values through a program.
The document discusses various software testing methods, including static testing, white box testing, black box testing, unit testing, integration testing, and system testing. It outlines the benefits and pitfalls of each method. For example, static testing can find defects early but is time-consuming, while black box testing tests from a user perspective but may leave code paths untested. The document recommends using a black box approach combined with top-down integration testing, breaking the system into subsystems and assigning specific test responsibilities.
Brief Introduction to Deep Learning + Solving XOR using ANNsAhmed Gad
This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers.
Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning.
Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer.
أحمد فوزي جاد Ahmed Fawzy Gad
قسم تكنولوجيا المعلومات Information Technology (IT) Department
كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI)
جامعة المنوفية, مصر Menoufia University, Egypt
Teaching Assistant/Demonstrator
ahmed.fawzy@ci.menofia.edu.eg
:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://menofia.academia.edu/Gad
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
This document discusses software testing principles and methodologies. It defines software testing as executing a program under various conditions to check for correctness, completeness, and quality. The document outlines different testing levels from unit to system testing. It also distinguishes between black box and white box testing methods. Finally, it describes different types of system testing like alpha, beta, acceptance, and performance testing.
Handling Imbalanced Data: SMOTE vs. Random UndersamplingIRJET Journal
This document compares different techniques for handling imbalanced data, including random undersampling and SMOTE (Synthetic Minority Over-sampling Technique), using two machine learning classifiers - Random Forest and XGBoost. It finds that XGBoost generally performs better than Random Forest across different sampling techniques in terms of metrics like AUC, sensitivity and specificity. Random undersampling with XGBoost provided the most balanced results on the randomly generated imbalanced dataset used in this study. SMOTE without proper validation gave the best numerical results but likely overfit the data.
The document describes multilayer neural networks and their use for classification problems. It discusses how neural networks can handle continuous-valued inputs and outputs unlike decision trees. Neural networks are inherently parallel and can be sped up through parallelization techniques. The document then provides details on the basic components of neural networks, including neurons, weights, biases, and activation functions. It also describes common network architectures like feedforward networks and discusses backpropagation for training networks.
A confusion matrix is a table that shows the performance of a classification model by listing the true positives, true negatives, false positives, and false negatives. It displays how often the model correctly or incorrectly classified observations into their actual classes. The document provides an example confusion matrix for a model classifying apples, oranges, and pears, showing the number of observations the model correctly and incorrectly classified into each class.
The document summarizes various software testing techniques, including:
- White-box testing techniques like cyclomatic complexity and control flow graph analysis to derive test cases.
- Black-box techniques like equivalence partitioning and boundary value analysis to design test cases based on valid and invalid input conditions.
- The goal of testing is to systematically uncover errors by executing all independent paths and boundary values through a program.
The document discusses various software testing methods, including static testing, white box testing, black box testing, unit testing, integration testing, and system testing. It outlines the benefits and pitfalls of each method. For example, static testing can find defects early but is time-consuming, while black box testing tests from a user perspective but may leave code paths untested. The document recommends using a black box approach combined with top-down integration testing, breaking the system into subsystems and assigning specific test responsibilities.
Brief Introduction to Deep Learning + Solving XOR using ANNsAhmed Gad
This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers.
Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning.
Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer.
أحمد فوزي جاد Ahmed Fawzy Gad
قسم تكنولوجيا المعلومات Information Technology (IT) Department
كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI)
جامعة المنوفية, مصر Menoufia University, Egypt
Teaching Assistant/Demonstrator
ahmed.fawzy@ci.menofia.edu.eg
:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://menofia.academia.edu/Gad
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
This document provides an introduction to software testing. It defines software testing as a process used to identify correctness, completeness, and quality of computer software. The key points covered include: why software testing is important; who should be involved in testing; when testing should start and stop in the software development lifecycle; the differences between verification and validation; types of errors; types of testing including manual and automation; methods like black box and white box testing; levels of testing from unit to acceptance; and definitions of test plans and test cases.
The document describes improvements made to the YOLO object detection model, resulting in YOLOv2 and YOLO9000. YOLOv2 uses techniques like batch normalization, anchor boxes, and multi-scale training to improve accuracy while maintaining speed. YOLO9000 further trains the model on a combination of detection and classification datasets totaling over 9,000 classes, using a word hierarchy to relate labels. This allows training an object detector on a much larger scale than typical detection datasets alone. Evaluation shows YOLO9000 can detect new classes it wasn't directly trained on, bringing object detection closer to the scale of image classification tasks.
This document discusses techniques for visualizing and interpreting convolutional neural networks (CNNs). It begins by noting that while CNNs achieve high performance on computer vision tasks, their lack of interpretability is a limitation. The document then reviews popular visualization methods including Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM), Guided Backpropagation, and Guided Grad-CAM. It discusses the properties and procedures of each technique. An example application of Grad-CAM to a binary oral cancer classification task is also presented. In conclusion, the document proposes using visualization tools like Guided Grad-CAM to increase model transparency and investigate more advanced methods such as Grad-CAM++.
The document discusses various types of non-functional testing including performance, reliability, maintainability, availability, recovery, usability, configuration, and security testing. It provides definitions and examples of how to test each type of non-functional requirement. Performance testing aims to evaluate how well a system performs under different loads, and involves measuring response times, throughput, and resource utilization. Non-functional requirements are as important as functional requirements in building quality software.
This document discusses different types of recurrent neural networks (RNNs) including vanilla RNNs, LSTMs, and GRUs. It notes that while vanilla RNNs are theoretically capable of handling long-term dependencies, they often fail to do so in practice due to the gradient vanishing problem. LSTMs address this issue through their use of cell states and gates. The document provides a step-by-step explanation of how LSTMs work and compares their architecture to GRUs, which combine the forget and input gates of LSTMs.
Handwritten Digit Recognition and performance of various modelsation[autosaved]SubhradeepMaji
This document presents a comparison of different convolutional neural network (CNN) models for handwritten number recognition that vary by layers. The models are trained on the MNIST dataset. A basic CNN model with convolutional, pooling, and fully connected layers is described. Models with different numbers and placements of layers are tested, and their training accuracy, validation accuracy, and test loss are compared. The optimal model is found to have two dropout layers and achieves 99.64% validation accuracy and the lowest test loss. User input can be tested on the model, and future work may involve improving accuracy for different writing styles.
The document describes using a random forest algorithm to detect credit card fraud. It begins with an abstract that outlines analyzing a credit card dataset, applying random forest, and identifying fraud transactions with 98% accuracy. Existing methods are discussed that achieve 60-70% accuracy. The proposed system uses random forest classification to analyze the dataset, which can process large amounts of data quickly and achieve 98% accuracy. Literature on the topic is surveyed. Random forest and the system architecture are described in more detail, including modules for data collection, preprocessing, feature extraction, model evaluation and visualization of results. The random forest model achieves 98.6% accuracy, outperforming other methods. Conclusions discuss potential improvements like using more data and preprocessing techniques.
The document discusses the MNIST dataset and classification techniques. It contains:
- An overview of the MNIST dataset containing 70,000 handwritten digit images
- Preparation of the data for classification, including splitting into training and test sets
- Binary classification to detect the digit 5 using a linear SGD classifier
- Evaluation metrics like accuracy, precision, recall, F1 score, and confusion matrices
- Tuning the classifier by adjusting decision thresholds to balance precision and recall
The document discusses dimensionality reduction techniques. It begins by explaining the curse of dimensionality, where adding more features can hurt performance due to the exponential increase in the number of examples needed. It then introduces dimensionality reduction as a solution, where the data can be represented using fewer dimensions/features through feature selection, linear/non-linear transformations, or combinations. Principal component analysis (PCA) and singular value decomposition (SVD) are described as common linear dimensionality reduction methods. The document also discusses nonlinear techniques like kernel PCA and multi-dimensional scaling, as well as uses of dimensionality reduction like in image and natural language processing applications.
Deep Learning - Overview of my work IIMohamed Loey
Deep Learning Machine Learning MNIST CIFAR 10 Residual Network AlexNet VGGNet GoogleNet Nvidia Deep learning (DL) is a hierarchical structure network which through simulates the human brain’s structure to extract the internal and external input data’s features
This document provides an overview of software testing concepts and processes. It discusses the importance of testing in the software development lifecycle and defines key terms like errors, bugs, faults, and failures. It also describes different types of testing like unit testing, integration testing, system testing, and acceptance testing. Finally, it covers quality assurance and quality control processes and how bugs are managed throughout their lifecycle.
This document provides an overview of data mining, including definitions, processes, tasks, and algorithms. It defines data mining as a process that takes data as input and outputs knowledge. The main steps in the data mining process are data preparation, data mining (applying algorithms to identify patterns), and evaluation/interpretation. Common data mining tasks are classification, regression, association rule mining, clustering, and text/link mining. Popular algorithms described are decision trees, rule-based classifiers, artificial neural networks, and nearest neighbor methods. Each have advantages and disadvantages related to predictive power, speed, and interpretability.
The document discusses topics related to software quality assurance and testing. It covers definitions of testing, types of testing activities like static and dynamic testing, different levels of testing from unit to system level. It also discusses test criteria, coverage, and agile testing approaches. The overall document provides an overview of key concepts in software quality assurance and testing.
YOLO releases are one-stage object detection models that predict bounding boxes and class probabilities in an image using a single neural network. YOLO v1 divides the image into a grid and predicts bounding boxes and confidence scores for each grid cell. YOLO v2 improves on v1 with anchor boxes, batch normalization, and a Darknet-19 backbone network. YOLO v3 uses a Darknet-53 backbone, multi-scale feature maps, and a logistic classifier to achieve better accuracy. The YOLO models aim to perform real-time object detection with high accuracy while remaining fast and unified end-to-end models.
The document discusses various techniques for tuning the learning rate when training neural networks, including adaptive learning rate methods, learning rate annealing, cyclical learning rates, and using a learning rate finder. It provides examples of implementing learning rate schedules like step decay, linear decay, and polynomial decay in Keras. Cyclical learning rates and the one cycle policy are also covered, with the one cycle policy combining learning rate and momentum scheduling.
Combinatorial software test design beyond pairwise testingJustin Hunter
This document discusses combinatorial software test design methods for selecting a small number of tests that achieve good coverage of interactions between test inputs. It explains that structured variation between tests is important to explore different areas of the software. Combinatorial testing uses algorithms to calculate the smallest number of tests needed to cover all target combinations of test inputs, like all pairs of values. This method can find many bugs with fewer tests than an unstructured approach.
Large Scale Deep Learning with TensorFlow Jen Aman
Large-scale deep learning with TensorFlow allows storing and performing computation on large datasets to develop computer systems that can understand data. Deep learning models like neural networks are loosely based on what is known about the brain and become more powerful with more data, larger models, and more computation. At Google, deep learning is being applied across many products and areas, from speech recognition to image understanding to machine translation. TensorFlow provides an open-source software library for machine learning that has been widely adopted both internally at Google and externally.
The document discusses object detection using deep learning algorithms like deep neural networks, CNNs, R-CNN, Fast R-CNN, and YOLO. It explains that YOLO applies a single neural network to the full image to divide it into regions and predict bounding boxes and probabilities for each region in one pass, unlike prior methods that apply classifiers to multiple locations and scales. Compared to Fast R-CNN, YOLO performs detection by passing the image through a fully convolutional network once to output predictions for bounding boxes and class probabilities for each grid region.
Mining Product Opinions and Reviews on the WebFelipe Japm
This document summarizes a presentation on opinion mining and sentiment analysis from user reviews and comments on the web. It discusses the basics of opinion mining, including defining the opinion model and levels of sentiment analysis. It outlines the requirements and design for a system to perform opinion mining, including functional requirements, architecture, and modules for opinion retrieval, composition, feature identification, and sentiment analysis. The design details the implementation of algorithms for identifying opinion words and orientation, as well as aggregating sentiments for features. Evaluation of the system examines effectiveness of feature identification and sentiment classification. Areas for future work are identified to improve handling of complex language patterns and domain-specific contexts.
XOLO launched an online online brand, Black, for a new line of smart phones.
This new sub-brand is all about high tech and sophistication at very attractive prices. Honest price is what the company calls it.
We provided presentation support for the launch event and kept the slides clean and minimal to reflect the ethos of the brand.
This document provides an introduction to software testing. It defines software testing as a process used to identify correctness, completeness, and quality of computer software. The key points covered include: why software testing is important; who should be involved in testing; when testing should start and stop in the software development lifecycle; the differences between verification and validation; types of errors; types of testing including manual and automation; methods like black box and white box testing; levels of testing from unit to acceptance; and definitions of test plans and test cases.
The document describes improvements made to the YOLO object detection model, resulting in YOLOv2 and YOLO9000. YOLOv2 uses techniques like batch normalization, anchor boxes, and multi-scale training to improve accuracy while maintaining speed. YOLO9000 further trains the model on a combination of detection and classification datasets totaling over 9,000 classes, using a word hierarchy to relate labels. This allows training an object detector on a much larger scale than typical detection datasets alone. Evaluation shows YOLO9000 can detect new classes it wasn't directly trained on, bringing object detection closer to the scale of image classification tasks.
This document discusses techniques for visualizing and interpreting convolutional neural networks (CNNs). It begins by noting that while CNNs achieve high performance on computer vision tasks, their lack of interpretability is a limitation. The document then reviews popular visualization methods including Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM), Guided Backpropagation, and Guided Grad-CAM. It discusses the properties and procedures of each technique. An example application of Grad-CAM to a binary oral cancer classification task is also presented. In conclusion, the document proposes using visualization tools like Guided Grad-CAM to increase model transparency and investigate more advanced methods such as Grad-CAM++.
The document discusses various types of non-functional testing including performance, reliability, maintainability, availability, recovery, usability, configuration, and security testing. It provides definitions and examples of how to test each type of non-functional requirement. Performance testing aims to evaluate how well a system performs under different loads, and involves measuring response times, throughput, and resource utilization. Non-functional requirements are as important as functional requirements in building quality software.
This document discusses different types of recurrent neural networks (RNNs) including vanilla RNNs, LSTMs, and GRUs. It notes that while vanilla RNNs are theoretically capable of handling long-term dependencies, they often fail to do so in practice due to the gradient vanishing problem. LSTMs address this issue through their use of cell states and gates. The document provides a step-by-step explanation of how LSTMs work and compares their architecture to GRUs, which combine the forget and input gates of LSTMs.
Handwritten Digit Recognition and performance of various modelsation[autosaved]SubhradeepMaji
This document presents a comparison of different convolutional neural network (CNN) models for handwritten number recognition that vary by layers. The models are trained on the MNIST dataset. A basic CNN model with convolutional, pooling, and fully connected layers is described. Models with different numbers and placements of layers are tested, and their training accuracy, validation accuracy, and test loss are compared. The optimal model is found to have two dropout layers and achieves 99.64% validation accuracy and the lowest test loss. User input can be tested on the model, and future work may involve improving accuracy for different writing styles.
The document describes using a random forest algorithm to detect credit card fraud. It begins with an abstract that outlines analyzing a credit card dataset, applying random forest, and identifying fraud transactions with 98% accuracy. Existing methods are discussed that achieve 60-70% accuracy. The proposed system uses random forest classification to analyze the dataset, which can process large amounts of data quickly and achieve 98% accuracy. Literature on the topic is surveyed. Random forest and the system architecture are described in more detail, including modules for data collection, preprocessing, feature extraction, model evaluation and visualization of results. The random forest model achieves 98.6% accuracy, outperforming other methods. Conclusions discuss potential improvements like using more data and preprocessing techniques.
The document discusses the MNIST dataset and classification techniques. It contains:
- An overview of the MNIST dataset containing 70,000 handwritten digit images
- Preparation of the data for classification, including splitting into training and test sets
- Binary classification to detect the digit 5 using a linear SGD classifier
- Evaluation metrics like accuracy, precision, recall, F1 score, and confusion matrices
- Tuning the classifier by adjusting decision thresholds to balance precision and recall
The document discusses dimensionality reduction techniques. It begins by explaining the curse of dimensionality, where adding more features can hurt performance due to the exponential increase in the number of examples needed. It then introduces dimensionality reduction as a solution, where the data can be represented using fewer dimensions/features through feature selection, linear/non-linear transformations, or combinations. Principal component analysis (PCA) and singular value decomposition (SVD) are described as common linear dimensionality reduction methods. The document also discusses nonlinear techniques like kernel PCA and multi-dimensional scaling, as well as uses of dimensionality reduction like in image and natural language processing applications.
Deep Learning - Overview of my work IIMohamed Loey
Deep Learning Machine Learning MNIST CIFAR 10 Residual Network AlexNet VGGNet GoogleNet Nvidia Deep learning (DL) is a hierarchical structure network which through simulates the human brain’s structure to extract the internal and external input data’s features
This document provides an overview of software testing concepts and processes. It discusses the importance of testing in the software development lifecycle and defines key terms like errors, bugs, faults, and failures. It also describes different types of testing like unit testing, integration testing, system testing, and acceptance testing. Finally, it covers quality assurance and quality control processes and how bugs are managed throughout their lifecycle.
This document provides an overview of data mining, including definitions, processes, tasks, and algorithms. It defines data mining as a process that takes data as input and outputs knowledge. The main steps in the data mining process are data preparation, data mining (applying algorithms to identify patterns), and evaluation/interpretation. Common data mining tasks are classification, regression, association rule mining, clustering, and text/link mining. Popular algorithms described are decision trees, rule-based classifiers, artificial neural networks, and nearest neighbor methods. Each have advantages and disadvantages related to predictive power, speed, and interpretability.
The document discusses topics related to software quality assurance and testing. It covers definitions of testing, types of testing activities like static and dynamic testing, different levels of testing from unit to system level. It also discusses test criteria, coverage, and agile testing approaches. The overall document provides an overview of key concepts in software quality assurance and testing.
YOLO releases are one-stage object detection models that predict bounding boxes and class probabilities in an image using a single neural network. YOLO v1 divides the image into a grid and predicts bounding boxes and confidence scores for each grid cell. YOLO v2 improves on v1 with anchor boxes, batch normalization, and a Darknet-19 backbone network. YOLO v3 uses a Darknet-53 backbone, multi-scale feature maps, and a logistic classifier to achieve better accuracy. The YOLO models aim to perform real-time object detection with high accuracy while remaining fast and unified end-to-end models.
The document discusses various techniques for tuning the learning rate when training neural networks, including adaptive learning rate methods, learning rate annealing, cyclical learning rates, and using a learning rate finder. It provides examples of implementing learning rate schedules like step decay, linear decay, and polynomial decay in Keras. Cyclical learning rates and the one cycle policy are also covered, with the one cycle policy combining learning rate and momentum scheduling.
Combinatorial software test design beyond pairwise testingJustin Hunter
This document discusses combinatorial software test design methods for selecting a small number of tests that achieve good coverage of interactions between test inputs. It explains that structured variation between tests is important to explore different areas of the software. Combinatorial testing uses algorithms to calculate the smallest number of tests needed to cover all target combinations of test inputs, like all pairs of values. This method can find many bugs with fewer tests than an unstructured approach.
Large Scale Deep Learning with TensorFlow Jen Aman
Large-scale deep learning with TensorFlow allows storing and performing computation on large datasets to develop computer systems that can understand data. Deep learning models like neural networks are loosely based on what is known about the brain and become more powerful with more data, larger models, and more computation. At Google, deep learning is being applied across many products and areas, from speech recognition to image understanding to machine translation. TensorFlow provides an open-source software library for machine learning that has been widely adopted both internally at Google and externally.
The document discusses object detection using deep learning algorithms like deep neural networks, CNNs, R-CNN, Fast R-CNN, and YOLO. It explains that YOLO applies a single neural network to the full image to divide it into regions and predict bounding boxes and probabilities for each region in one pass, unlike prior methods that apply classifiers to multiple locations and scales. Compared to Fast R-CNN, YOLO performs detection by passing the image through a fully convolutional network once to output predictions for bounding boxes and class probabilities for each grid region.
Mining Product Opinions and Reviews on the WebFelipe Japm
This document summarizes a presentation on opinion mining and sentiment analysis from user reviews and comments on the web. It discusses the basics of opinion mining, including defining the opinion model and levels of sentiment analysis. It outlines the requirements and design for a system to perform opinion mining, including functional requirements, architecture, and modules for opinion retrieval, composition, feature identification, and sentiment analysis. The design details the implementation of algorithms for identifying opinion words and orientation, as well as aggregating sentiments for features. Evaluation of the system examines effectiveness of feature identification and sentiment classification. Areas for future work are identified to improve handling of complex language patterns and domain-specific contexts.
XOLO launched an online online brand, Black, for a new line of smart phones.
This new sub-brand is all about high tech and sophistication at very attractive prices. Honest price is what the company calls it.
We provided presentation support for the launch event and kept the slides clean and minimal to reflect the ethos of the brand.
Product Reviews | How to Find Keywords for Content Writing.MdAzizurRahman18
Product reviews.
product reviews
education
product
review
SEO services
analysis
How to Find Keywords for Content Writing - Where You Will Get Your Keywords List | Bangla Tutorial
The OnePlus company was founded on 16 December 2013 by former Oppo vice president Pete Lau. According to government documentation, the only institutional stockholder in OnePlus is Oppo, although Lau has denied that OnePlus is a wholly owned subsidiary of Oppo.[2] The company's main goal was to design a smartphone that would balance high-end quality with a lower price than other phones in its class, believing that users would "never settle" for the lower-quality devices produced by other companies. Lau explained that "we will never be different just for the sake of being different. Everything done has to improve the actual user experience in day-to-day use."[3][4] He also showed aspirations of being the "Muji of the tech industry", emphasizing its focus on high-quality products with simplistic, user-friendly designs.[3] Continuing Lau's association with the platform from the Oppo N1,[4] OnePlus entered into an exclusive licensing agreement with Cyanogen Inc. to base its products' Android distribution upon a variant of the popular custom ROM CyanogenMod and use its trademarks outside of China.[5][6]
The company unveiled its first device, the OnePlus One, on 23 April 2014.[3] In December 2014, alongside the release of the OnePlus One in India exclusively through Amazon, OnePlus also announced plans to establish a presence in the country, with plans to open 25 official walk-in service centres across India.[7] Sales of the device in India were halted following a temporary injunction granted to Micromax Mobile, alleging that it violated its exclusive rights to distribute Cyanogen-branded products in South Asia as part of a new joint venture and that its agreement superseded the agreement it had established with OnePlus. The company disputed the arguments, noting that its Cyanogen-based software was different than that of Micromax's, and argued that the exclusivity agreement only meant that Cyanogen could not partner with any other company based in India, and did not inhibit the ability for OnePlus to market its products in the country with its trademarks.[5][8] One Plus developed a new android custom rom named Oxygen OS. The new shipment of OnePlus One also removed the Cyanogen Logo from its back and box in order to simplify production lines.
OnePlus disembarked in South-East Asia partnering with Lazada Indonesia on 23 January 2015 and is expected to expand during this year throughout the region.
In 2014, OnePlus hired Han Han as the product ambassador in China.[9]
On 9 March 2014 the company decided to expand its operations to all European Union countries, serving now over 35 countries and regions all over the world.
Presented in PEDICON 2018, Nagpur, in the CMIC group, Computer Chapter meeting on 6th Jan, 2018.
The best phones currently available, and how to choose for yourself
Title: Top Best Samsung Phones for USA
Introduction:
If you're on the lookout for the best Samsung phone available, the Samsung Galaxy has it all. Its advanced camera system captures breathtaking photos and videos, while its exceptional battery life ensures you stay connected throughout the day. The sleek design turns heads, and the user-friendly interface ensures smooth navigation. Don't miss the opportunity to own one of the most cutting-edge phones on the market with the Samsung Galaxy. You trust us, but let us explain why. We are committed to providing you with the highest-quality Samsung Galaxy phone, backed by thorough practical research. Every piece of information on this site is shared with you after experiencing and personally testing each Samsung phone.
Main Points:
Samsung Galaxy S10+:
• Specs: Snapdragon 855, 6.4" OLED, 4100mAh, up to 1TB storage, 12GB RAM.
• Pros: Verizon compatibility, ample storage, impressive camera.
• Cons: Reliability concerns.
• Review: Strong performance and storage with some drawbacks.
Samsung Galaxy S10e:
• Specs: Unlocked, 5.8" screen, 128GB storage, GSM/LTE support.
• Pros: Carrier compatibility, excellent functionality.
• Cons: SIM card issues.
• Review: Satisfying for compact, feature-packed seekers.
Samsung Galaxy S9
Specs: 64GB storage, Super Speed Dual Pixel Camera, Infinity Display.
• Pros: Camera, display, expandable storage.
• Cons: Bixby button hassle.
• Review: Impressive features, some performance issues.
Samsung Galaxy Note 8
Specs: Unlocked, 64GB storage, 6.3" AMOLED, 6GB RAM.
• Pros: Excellent condition, battery, stylus.
• Cons: Minor fingerprint sensor
• Review: Fantastic for upgrades from older models.
Samsung Galaxy Note 10
Specs: Unlocked, 256GB storage, 6.3" AMOLED, 8GB RAM, 4500mAh.
• Pros: Professionally inspected, ample storage.
• Cons: Reported issues with S Pen, battery.
• Review: Despite problems
Audience Benefits:
• Gain insights into top Samsung phones in the USA.
• Understand key specs and features.
• Assess strengths and weaknesses for informed decisions.
• Find phones for various preferences.
• Save money with the right choice.
Visual Elements:
• Visual aids showcasing the design and key features of each Samsung phone.
• Graphs or charts summarizing specifications for easy comparison.
• High-quality images of the devices.
• Icons representing pros and cons for quick reference.
Expertise:
Meet Muhammad Nasir, a passionate E-commerce professional who pivoted during the COVID-19 lockdown. Formerly an office administrator, Nasir now helps people select the ideal smartphone to stay connected. Skilled at simplifying complex subjects, he's active on social media and welcomes smartphone news to enhance lives.
Call to Action:
Ready to get your hands on one of these top Samsung phones? Click the provided links to learn more about each device and make a purchase if you find the perfect fit for your needs.
Keyword:
The best Samsung phones
This project is to show the difference between iPhone, Android and Windows Phone - the three most popular mobile operating systems - to see which one is the best of the three.
This document provides instructions for installing and calibrating a 3D surround view monitoring system. It describes routing camera wires, installing the 4 cameras, and connecting the host device. The calibration process involves placing calibration tapes and boxes around the vehicle and using the remote to mark 8 calibration points for each camera. Proper calibration is important for accurate video merging and surround view imaging.
- The document describes an empirical study of 70 performance bugs in popular Android apps and the design of a static analysis tool called PerfChecker to detect such bugs.
- The study found that the main performance bug types were GUI lagging, energy leaks, and memory bloat. Many bugs required special user interactions to manifest and developers relied on manual testing.
- PerfChecker analyzes Android apps and detects instances of common bug patterns like long operations on the main thread and violations of the view holder pattern. When tested on 29 apps, it found 126 new issues, of which developers confirmed 68 as real bugs.
This document discusses guidelines for standardized intraoral photography in orthodontic practice:
- Intraoral photographs should be taken at the beginning and end of orthodontic treatment to completely document patient records. They are useful for presentation and documentation.
- Photographs taken during treatment allow orthodontists to double-check for errors in band placement or archwire construction.
- Standardized techniques are needed to make intraoral photography as quick and simple as standardized x-ray procedures. Guidelines for camera choice, lighting, and patient positioning are provided to achieve standardized, diagnostic-quality photographs.
The document discusses the design challenges for unmanned vehicular video streaming. It proposes using multiple low-power microprocessors in parallel to achieve high processing speeds while minimizing power consumption. Test results show that combining horizontal and vertical image scanning provides the best video quality. The document also describes the architecture of a low-power camera designed by GenieView for unmanned vehicle applications.
The document discusses the design challenges for unmanned vehicular video streaming. It proposes using multiple low-power microprocessors in parallel to achieve high processing speeds while minimizing power consumption. Test results show that combining horizontal and vertical image scanning provides the best picture quality. The document also describes the architecture of a low-power camera system designed for unmanned vehicles.
Kostiantyn Yelisavenko "Mastering Macro Benchmarking in .NET"LogeekNightUkraine
This document discusses macro vs micro benchmarking in .NET. It defines macro benchmarking as measuring the performance of whole applications or parts of applications from a user's perspective, while micro benchmarking involves repeatable measurement of specific sections of code isolated from virtual machine effects. The document also covers topics like when optimization is needed, profiling tools available for .NET like dotTrace and dotMemory, and concludes that profiling can help identify performance bottlenecks and memory leaks, and forecast potential performance gains.
Permettere al cliente di apprezzare l'approccio agileSteve Maraspin
Presentazione del 27/09/2012 a Better Software - Firenze, Italia. Raccontata la nostra esperienza e l'approccio utilizzato per garantire la soddisfazione del cliente nel lungo termine
Automatic generation of dynamic behavior data of surrounding vehicles to ensu...Yoshifumi Sakamoto
Simulation technology that guarantees reliability is widely used in the development of self-driving vehicle. The simulation often focuses on the function of Path Planning. The reason is that it is difficult to reproduce in the real world because the Path Planning depends on the state of the self-driving vehicle and the surrounding environment. What is important is the reproduction of the behavior of the vehicles around the self-driving vehicle. On the other hand, self-driving vehicles are required to operate properly in situations such as accidents. However, it is difficult to identify all situations in advance. It is not hard to imagine that a serious accident is likely to occur in cases that cannot be identified in the design. We have developed a co-simulator that automatically generates test cases that satisfy unintended test spaces and reproduces the dynamic interaction between self-driving vehicles and surrounding vehicles. In this co-simulator, the amount of calculation was reduced by adopting Active Matter, and as a result, the behavior of surrounding vehicles and the interaction with self-driving vehicle were automatically and real-time generated.
SanDiegoPhotographyClass.com and Jason Kirby present to you the different kinds of photography equipment available to beginners and enthusiasts. Each piece of equipment is recommended by Jason and also provides a back link to Amazon.com to learn more about each lens.
Dr. Haykel Kamoun presentation at the Mediphacos User Meeting 2013Mediphacos
This document discusses a ring simulator software for predicting outcomes of intracorneal ring segment (ICRS) treatments. The software aims to improve predictability by simulating topographic results. It was developed over 2 years using a refractive and topographic database of past ICRS patients. The simulator allows users to enter a pre-op case and choose similar past cases for simulation. It integrates a nomogram but can suggest alternatives. Examples show simulated post-op maps closely match real results 95% of time when a similar prior case is selected. The developer concludes it is a valuable tool but the database needs expanding through multicenter evaluation to increase reliability.
Building the "right" regression suite using Behavior Driven Testing (BDT)Anand Bagmar
This document discusses building an effective regression test suite using Behavior Driven Testing (BDT). It introduces key concepts like the test pyramid, which describes the ideal distribution of test types from unit to UI tests. BDD and BDT are also covered, explaining how they help define testable behaviors and ensure tests are business-focused. Finally, different styles of writing test specifications like imperative and declarative are compared. The overall goal is to establish a shared understanding of what is being tested through well-defined behavioral tests.
Balancing the Pendulum: Reflecting on BDD in PracticeZach Dennis
Here are the slides that I used for my talk on "Balancing the pendulum: Reflecting on BDD in Practice" at the Great Lakes Ruby Bash, April 17th, 2010 in Lansing, MI.
This talk was aimed at sharing my reflections and experiences on how BDD and outside-in development has been working over the past few years.
Similar to Feature Based Opinion Mining By Gourab Nath Core Faculty – Data Science at Praxis Business School (20)
The agenda of the talk has been broken down into two parts: 1. Query Understanding [30mins]: [Sonu Sharma] • NLP based Deep Learning Models for finding the intent of a Query in a particular taxonomy/categories: Description and Jupyter-notebook demonstration [20mins]: o Multi-Label/Multi-Class Classification Model from scratch in Keras o Feature Engineering in Spark Scala and pandas o Keras Functional APIs details in TF 2.0 o ImageNet moment of NLP - Latest invention in Word embeddings – ELMO and BeRT o Understanding Deep Neural Networks like Bi-directional Long Short-term Memory (BiLSTM) and character embeddings for language modeling • NLP based Deep Learning Models for Query Tagging with entities like Brand, Color, Nutrition, product quantity, etc. using Named Entity Recognition: [10mins]: o Building custom model in Tensorflow Estimator API o Traditional Word Embeddings like Glove, Fasttext etc. o Query (Text) Preprocessing o Sequence modeling using Convolutional Random Fields (CRF) o Saving and Restore heavy model in TF using SavedModel concept 2. Related Searches [20mins]: [Atul Agarwal] • NLP based Deep Learning Model for predicting Next Search Keyword – Model Description and Jupyter Notebook demonstration[20mins]: o Building Sequence to Sequence (Seq2Seq) model using Long Short Term Memory (LSTM) concept of Deep Neural Network in Keras o comparing different word Embeddings e.g. word2vec, fasttext, glove, etc. in popular AI framework such as gensim. o Keras Sequential APIs details in Keras o Similarity Search based on Facebook AI Research aka FAISS.
This document discusses using a single channel EEG device to recognize emotions from EEG data. It collected data from 10 individuals labeled as stressed or relaxed. It preprocessed the raw EEG data using filters to isolate brain signals. It then used deep learning models including an LSTM network with and without attention to classify emotions. The LSTM with attention achieved 85% accuracy, which was an improvement over the LSTM without attention. Potential applications discussed include using EEG for stress reduction by customizing music, and emotion or word prediction. The document also discusses opportunities for future enhancements such as using convolutional layers or multi-modal networks incorporating additional physiological sensors.
Flood & Other Disaster forecasting using Predictive Modelling and Artificial ...Analytics India Magazine
Over 2.3 Billion people are affected due to floods in last 20 years and causing countless death , More than 92,million cattle are lost every year, seven million hectares of land is affected, and damage is over trillions dollars when taken globally in last 5 years. Floods are complicated natural events. It depends on several parameters, so it is very difficult to model analytically. The floods in a catchment depends on the characteristics of the catchment, rainfall and antecedent conditions. So the estimation of the flood peak is a very complex problem. Its due to the lack of Flood Prediction System which can predict the situation accurately. To Overcome this challenge we are building a Flood Prediction System using Predictive modelling. However we have divided our idea into small fragments but enough to be used globally. We have considered most flooded state of India, but can be used widely for all the low lying geographical regions. •The plains of Bihar, adjoining Nepal, are drained by a number of rivers that have their catchments in the steep and geologically nascent Himalayas. Kosi, Gandak,Burhi Gandak, Bagmati, Kamla Balan, Mahananda and Adhwara Group of rivers originates in Nepal, carry high discharge and very high sediment load and drops it down in the plains of Bihar. · About 65% of catchments area of these rivers falls in Nepal/Tibet and only 35% of catchments area lies in Bihar. · Bihar is India’s most flood-prone State, with 76 percent of the population, in the north Bihar living under the recurring threat of flood devastation. About 68800 sq Km out of total geographical area of 94163 sq Km comprising 73.06 percent is flood affected. · According to some historical data, 16.5% of the total flood affected area in India is located in Bihar while 22.1% of the flood affected population in India lives in Bihar. · From 1979 to Present day more than 8,873 Humans & 27,573 animals have lost their life due to flood. Some of Tools & Technology which is being used & can be used for Flood Prediction: •IBM. Watson Studio democratizes machine learning and deep learning to accelerate infusion of AI in to drive innovation. •An Intelligent Hydro-informatics Integration Platform for Regional Flood Inundation Warning Systems. •Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm. · Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation
AI for Enterprises-The Value Paradigm By Venkat Subramanian VP Marketing at B...Analytics India Magazine
AI is here, call it buzz, cause it a bubble, we are smack in the middle of an AI revolution. While there is a strong view building about consumer AI applications, there still seems to be some scepticism about AI for enterprises, primarily due to the lack of clarity and focus on how AI can actually deliver value for enterprises. At BRIDGEi2i, we believe it is important to have a non-fragmented view of the AI ecosystem and a “Value Roadmap” for AI in the enterprise context. As CxOs, it is important to understand where the enterprise is in the transformation journey and define value accordingly. This talk will throw light on how to look at the enterprise AI ecosystem and build the right roadmap for value.
Keep it simple and it works - Simplicity and sticking to fundamentals in the ...Analytics India Magazine
With the buzz around AI and ML there is an increasing tendency for leaders and data scientists to move towards complex problem-solving. Its important to unlearn the tendency to gravitate towards complexity. In this talk we will see why avoiding complexity in ML solutions is a wiser and a quicker way to solve business problems. We can also visit some thumb rules to build pragmatic and useful models. Simplicity and sticking to fundamentals is the next "big" thing in the world of big data.
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?
Getting your first job in Data Science By Imaad Mohamed Khan Founder-in-Resid...Analytics India Magazine
Getting your first job in Data Science is difficult. You’ve been applying to jobs, but they keep rejecting you. You don’t know what to do and how you could differentiate yourselves amidst the pool of candidates? In this talk, we’ll be going through different tips and techniques you could use to find that elusive Data Science jobs. They’ve worked for me and probably will work for you too!
With enterprises putting digital at the core of their transformation, our annual Data Science & AI Trends Report explores the key strategic shifts enterprises will make to stay intelligent and agile going into 2019. The year was marked by a series of technological advances, including advances in AI, deep learning, machine learning, hybrid cloud architecture, edge computing (with data moving away to edge data centres), robotic process automation, a spurt of virtual assistants, advancements in autonomous tech and IoT.
Everyone is talking about Artificial Intelligence — the new normal, which has entered almost every work process across industries. Enterprises are rethinking and strengthening their AI capabilities, using it as a tool to improve products and services. With AI becoming crucial to enterprise success, upskilling has become the new mantra among Indian IT professionals, who are keen to make an impact in their careers with Machine Learning and AI.
In our annual AI Study with Great Learning, we take a look at key AI trends dominating the Indian AI market-leading companies, professionals, salaries, jobs broken down by cities and how AI’s potential for industry growth has risen over the last few years. In the second half of the study, we cover AI literacy in India through Great Learning’s comprehensive AI/ML programs that are bridging the current skill gap and consequently boosting workforce transitions.
The samples were collected by asking respondents to fill in a survey created by AIM about what tools and techniques data scientists use at work. This included various sub-topics such as data visualisation tools, preferred operating systems and programming languages, among others. We took opinions from all those who practice data science — from professionals with less than two years of experience to CXOs — to get a thorough idea of the working environment in this growing field.
Our survey was met with much enthusiasm — and we got some great insights from it. Some of them were expected, and many of them were real eye-openers.
Emerging engineering issues for building large scale AI systems By Srinivas P...Analytics India Magazine
The document discusses an online 6-month certificate program in artificial intelligence and deep learning from Manipal Prolearn. It provides awarding from MAHE, hands-on training using real-world data from different domains, and instruction from industry experts. The program teaches skills for developing end-to-end AI/ML systems and covers topics like data acquisition, modeling, evaluation, and deployment.
Predicting outcome of legal case using machine learning algorithms By Ankita ...Analytics India Magazine
This document summarizes a presentation on predicting the outcomes of legal cases using machine learning models. It discusses extracting data from judgment documents and identifying key features for analysis. Exploratory data analysis was conducted on 202 observations to understand patterns. Logistic regression, KNN, random forest, and support vector machine models were developed. The tuned support vector machine model achieved the highest accuracy of 95% based on 10-fold cross-validation. Overall, support vector machine provided the best performance. The models tended to predict non-guilty outcomes more frequently due to the skewed data. Future work involves developing a mobile application for these predictive capabilities.
Bringing AI into the Enterprise - A Practitioner's view By Piyush Chowhan CIO...Analytics India Magazine
Artificial Intelligence is slowly getting its way into organisations. AI journey can be quite complex and a proper roadmap would be needed to realize the benefits. This presentation will talk about common myth and a high level approach for bringing AI into the enterprise.
www.analyticsindiasummit.com
Explainable deep learning with applications in Healthcare By Sunil Kumar Vupp...Analytics India Magazine
We started relying on the decisions made by deep learning models, however why it works and how it works are still big questions for most of us. We shall try to open that black box of deep learning which is essential to build trust for wide spread adoption. The speaker shall address the importance of feature visualization and localization in deep learning models esp. convolutional neural networks. He shares the results of applying methods such as activation map, deconvolution and Grad-CAM in healthcare.
Getting started with text mining By Mathangi Sri Head of Data Science at Phon...Analytics India Magazine
The workshop will empower you and get started with analyzing text data, discover patterns and what are the best ways to convert unstructured to structured data. We will also build a quick classification model and understand techniques to improve model performance. Towards the end lets quickly do a sentiment analysis on data corpus and discuss the next steps to improve model accuracy. Please come prepared with a working laptop with Jupyter Notebook and Python 2.7. Participants who have a minimum working knowledge of supervised models is encouraged.
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...Analytics India Magazine
The “Sexiest job of the 21st century” is often surveyed to be poorly defined, intermittently satisfying and vaguely understood in most board rooms. As success stories are widely publicized, senior business leaders’ expectations from analytics are rising quickly. And the field itself is changing rapidly - with speciality skills becoming self-service in no time. In that context, the talk explores how the various analytics roles across the spectrum are changing. And what it takes for analytics professionals to stay relevant, contribute meaningfully to business results and play a critical role in shaping business strategy.
www.analyticsindiasummit.com
"Route risks using driving data on road segments" By Jayanta Kumar Pal Staff ...Analytics India Magazine
Going out for dinner in Mumbai during an extended stay, or planning for a long road-trip across the wild west of Rajasthan, the first thing one looks at is Maps, that informs the relative distance, estimated time and congestion areas of different routes for the drive. Zendrive built state-of-the-art technologies on its huge cache of driving data from smartphones and OBD, to add a significant dimension to the route mapping of Google, that is safety risk of the route. Essentially the technology is built on millions of drivers zipping through the route or segments thereof. Automobile Insurance expands in UBI- where it has been established that tracking a driver’s behavior behind the wheels (like Hard Brake, Speeding etc) can predict significant differences between their chances of collisions. Looking at the same event data from the road perspective, aggregating the relative event density on road stretches also predict the relative chances of collision on that segment. We have used map matching using GIS techniques, parametric density estimation and rare event modeling using quasi-Poisson GLM to analyze our data, build the models and finally implement the scoring system across the GIS route maps. Key learnings : Relation between dangerous driving events and collisions Route risk as an aggregate of all the drivers (or sample thereof) and their driving risk. The route you take for commute may determine your auto insurance. Outline : Usage Based Insurance : relation between collision rates and dangerous driving. Driving events : aggressive acceleration, hard brake, speeding, phone use, aggressive turns Poisson GLM modeling to predict collision rates using driving data Events on a road segment : map-matching using GIS techniques to split trips along road stretches, and aggregate such events along the spatio-temporal dimension across all drivers. Route risk of the road segment and any route comprising such segments. Driving risk along such routes and corresponding collision risks using transfer of the GLM model. Assignment of risks to drivers on their daily route of commute, to be used in UBI.
www.analyticsindiasummit.com
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...Analytics India Magazine
By Phani Mitra VP Analytics & Strategy at Dr. Reddy’s
The “Sexiest job of the 21st century” is often surveyed to be poorly defined, intermittently satisfying and vaguely understood in most board rooms. As success stories are widely publicized, senior business leaders’ expectations from analytics are rising quickly. And the field itself is changing rapidly - with speciality skills becoming self-service in no time. In that context, the talk explores how the various analytics roles across the spectrum are changing. And what it takes for analytics professionals to stay relevant, contribute meaningfully to business results and play a critical role in shaping business strategy.
www.analyticsindiasummit.com
The analytics education market in India is exploding with analytics institutes providing a slew of instructional courses that are in line with the industry demand. This study aims to provide an overview of the analytics education landscape in India, the type of learning models offered and how online learning has become an inherent part of the analytics ecosystem in India.
Analytics & Data Science Industry In India: Study 2018 - by AnalytixLabs & AIMAnalytics India Magazine
The data analytics market in India is growing at a fast pace, with companies and startups offering analytics services and products catering to various industries. Different sectors have seen different penetration and adoption of analytics, and so is the revenue generation from these sectors.
The Analytics and Data Science Industry Study 2018 takes into account various trends that analytics industry in India is witnessing, revenue generated through various geographies, analytics market size by sector, across cities etc. It also takes into consideration analytics professionals in India across work experience and education.
This year’s study is brought to you in association with AnalytixLabs, a pioneer and one of the first analytics training institutes in India. The study is a result of extensive primary and secondary research conducted over a duration of two months, where we got in touch with analytics companies and professionals across various industries such as banking, finance, ecommerce, retail, pharma, healthcare and others.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
5. “The front camera is extremely clear. The phone comes with 8GB RAM. It has Gorilla
glass 6 on front & Gorilla glass 5 on back with aluminium frame on side. With 48MP
f/1.7 (Sony IMX 586 sensor) rear camera, it clicks amazing outdoor pics. The battery
backup is great. The speaker is not good though.”
REVIEW
6. “The front camera is extremely clear. The phone comes with 8GB RAM. It has Gorilla
glass 6 on front & Gorilla glass 5 on back with aluminium frame on side. With 48MP
f/1.7 (Sony IMX 586 sensor) rear camera, it clicks amazing outdoor pics. The battery
backup is great. The speaker is not good though.”
NON-OPINIONATED
PASSAGES
OBJECTIVE SENTENCE
7. “The front camera is extremely clear. The phone comes with 8GB RAM. It has Gorilla
glass 6 on front & Gorilla glass 5 on back with aluminium frame on side. With 48MP
f/1.7 (Sony IMX 586 sensor) rear camera, it clicks amazing outdoor pics. The battery
backup is great. The speaker is not good though.”
OPINIONATED PASSAGES ON
FEATURES
SUBJECTIVE SENTENCE
8. “The front camera is extremely clear. The phone comes with 8GB RAM. It has Gorilla
glass 6 on front & Gorilla glass 5 on back with aluminium frame on side. With 48MP
f/1.7 (Sony IMX 586 sensor) rear camera, it clicks amazing outdoor pics. The battery
backup is great. The speaker is not good though.”
OPINIONS ON FEATURES
9. front camera - extremely clear (+3)
Rear camera - amazing (+4)
Battery backup - great (+5)
Speaker - not good (-2)
FEATURE-BASED
SENTIMENT SUMMARY
16. Bing Liu
Distinguished Professor
Department of Computer Science
University of Illinois Chicago (UIC)
Minqing Hu
Data Scientist at Signifyd
PhD – Computer Science
University of Illinois Chicago (UIC)
17. Mining Opinion Features in Customer Reviews
M. Hu and B. Liu
Proceedings of the ACM SIGKDD Conference on KDD, 2004
Mining and Summarizing Customer Reviews
M. Hu and B. Liu
Proceedings of the ACM SIGKDD Conference on KDD, 2004
Opinion Observer: Analysing and Comparing Opinions on the web
M. Hu, B. Liu and J. Cheng
Proceedings of WWW, 2005
Sentiment Analysis and Subjectivity
B. Liu
Handbook of Natural Language Processing 2 (2010), 627-666
1
2
3
4
18. Object
Components
Sub
Components /
Attributes
Cellular Phone
Camera Battery Display
Front
Camera
Back
Camera
Rear
Camera
Battery
life
Battery
Size
Battery
performance
ROOT
Size Quality Type
Features being represented by
its synonyms
OBJECT
Thus, an object can be represented as a tree, hierarchy or taxonomy.
20. Explicit Feature Example:
“The battery life of this phone is too short”
Implicit Feature Example:
“The phone doesn’t fit in an usual jeans pocket though.”
Size of the Phone
FEATURES
EXPLICIT VS IMPLICIT
FEATURES
“Don’t know why I had spent so much money for the phone”
Not value for money
21. Explicit Opinions Example:
The display clarity of this phone is amazing!
Implicit Opinions Example:
The phone doesn’t fit in an usual jeans pocket though.
A fact which expresses
dissatisfaction / disappointment
OPINIONS
EXPLICIT VS IMPLICIT
OPINIONS
23. 1. Identification of Frequent Features
2. Identification of Opinions on each features
3. Opinion Orientation Identification
4. Infrequent Feature Identification
5. Summary Generation
THE PROCESS FLOW
24. Step 1: Frequent Feature Mining
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
25. POS TAGGING
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
“The front camera is extremely clear. The phone comes with 8GB RAM. It has Gorilla
glass 6 on front & Gorilla glass 5 on back with aluminium frame on side. With 48MP
f/1.7 (Sony IMX 586 sensor) rear camera, it clicks amazing outdoor pics. The battery
backup is great. The speaker is not good though.”
N N N
N N N
N N
26. front | camera | phone | RAM | gorilla | glass | aluminium | frame |
Review
rear | camera | battery | backup | speaker
EXTRACTING NOUNS
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
27. Review 1 : front | camera | phone | gorilla | glass | aluminium | frame | rear | battery | backup | speaker
Review 2 : price | descent | sound | battery | camera | body
Review 3 : phone | battery | performance | camera
Review 4 : phone | life | sound | quality | battery | picture
Review 5 : phone | buy
Review 6 : loudspeaker | time | sound | quality
EXTRACTING NOUNS
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
30. Front
ASSOCIATION RULES
MINING
SUPPORT 0.7 0.6 0.4 0.4 0.3 0.2 0.3
EXAMPLE
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
31. Front
ASSOCIATION RULES
MINING
P(Camera, Front) 0.4
EXAMPLE
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
SUPPORT
33. Front
ASSOCIATION RULES
MINING
P(Battery, Life) 0.4
EXAMPLE
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
SUPPORT
34. Front
ASSOCIATION RULES
MINING
P(Camera, Buy) 0.2
Minimum Support
Threshold = 0.4
(say)
EXAMPLE
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
SUPPORT
35. front
ASSOCIATION RULES
MINING
EXPERIMENTAL
RESULTS
One Plus 6 Features – Extracted from the Reviews written in www.amazon.in
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
36. FEATURE PRUNING
COMPACTNESS
PRUNING
The method checks features that contains at least 2 words and remove those that are likely to be meaningless
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
37. FEATURE PRUNING
COMPACTNESS
PRUNING
The method checks features that contains at least 2 words and remove those that are likely to be meaningless
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
38. FEATURE PRUNING
COMPACTNESS
PRUNING
The method checks features that contains at least 2 words and remove those that are likely to be meaningless
EXAMPLE
“The camera quality is really good”
“I love the quality of the camera”
“awesome camera and the phone comes with a quality display”
counter example
“The phone has an awesome front camera and a quality display”
Compact
Compact
Not Compact
Compact
Compact but has no
dependency
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
39. FEATURE PRUNING
COMPACTNESS
PRUNING
The method checks features that contains at least 2 words and remove those that are likely to be meaningless
COUNTER-
EXAMPLES
“Both the camera and the battery is good”
“Although good camera but not good battery”
“lovely camera quality and nice battery”
Compact
Compact
Compact
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
40. FEATURE PRUNING
COMPACTNESS
PRUNING
The method checks features that contains at least 2 words and remove those that are likely to be meaningless
COUNTER-
EXAMPLES
“Both the camera and the battery is good”
“Although good camera but not good battery”
“lovely camera quality and nice battery”
Compact
Compact
Compact
However note:
The features here are separated by conjunctions (which is mostly the cases)
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
41. FEATURE PRUNING
COMPACTNESS
PRUNING
The method checks features that contains at least 2 words and remove those that are likely to be meaningless
MODIFICATION
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
42. FEATURE PRUNING
EXPERIMENTAL
RESULTS
One Plus 6 Features – Extracted from the Reviews written in www.amazon.in
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
front
43. FEATURE PRUNING
REDUNDANCY
PRUNING
The method checks features that contains SINGLE word and remove those that are likely to be meaningless
p-support (pure support) – p support of a feature f is the number of sentences that f appears and these
sentences must contain no feature phrase that is a superset of f
Example:
Consider the feature: camera
Consider the other features that
contains the word camera:
front camera | rear Camera |
back camera | camera quality.
P-support of camera
= number of reviews in which camera
occurred along and not with any of its
supersets
= 100 – (20 + 15 + 23 + 10)
= 32
A Feature will be considered meaningful if it satisfied the minimum threshold for p-support.
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
44. front
FEATURE PRUNING
EXPERIMENTAL
RESULTS
One Plus 6 Features – Extracted from the Reviews written in www.amazon.in
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
45. FREQUENT FEATURE
MINING
FLOWCHART
Review Database Frequent Features
POS Tagging
Frequent Feature
Identification
Feature Pruning
Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
46. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
Step 2: Opinion Word Extraction
47. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
ADJECTIVES AS OPINION
Mining and Summarizing Customer Reviews
M. Hu and B. Liu
Proceedings of the ACM SIGKDD Conference on KDD, 2004
Examples:
“The camera of the phone is good”
“The display looks dull”
“the sound quality of the speaker is fantastic”
“The phone has some really cool features”
Adjective
Adjective
Adjective
Adjective
This was based on previous research works on subjectivity
The nearest
adjective is
considered
as opinion
48. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
ADJECTIVES AS OPINION
COUNTER-
EXAMPLES
Examples:
“The camera of the phone is extremely good”
“The headphone is not working”
“The speaker of the phone is doing great”
“The phone has some nice cool features”
“The display is not bad”
Adverb + Adjective
Negation + Verb
Verb + Adjective
Adjective + Adjective
Negation + Adjective
49. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
OPINION EXTRACTIONALGORITHM
Opinion Word/s Extraction:
50. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
Step 3
Opinion Orientation
Identification
51. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
OPINION ORIENTATION
IDENTIFICATIOIN
ONLY ADJECTIVES
Adjective list:
Seed list:
52. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
OPINION ORIENTATION
IDENTIFICATIOIN
WORDNET
In WordNet , adjectives are organized into bipolar clusters
53. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
OPINION ORIENTATION
IDENTIFICATIOIN
WORDNET
Fast = + 2
Seed list:
In general, adjectives share the same orientation
as their synonyms and opposite orientation as
their antonyms.
54. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
OPINION ORIENTATION
IDENTIFICATIOIN
ALGORITHM
55. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
Examples:
“The camera of the phone is extremely good”
“The headphone is not working”
“The speaker of the phone is doing great”
“The phone has some nice cool features”
“The display is not bad”
Adverb + Adjective
Negation + Verb
Verb + Adjective
Adjective + Adjective
Negation + Adjective
OPINION ORIENTATION
IDENTIFICATIOIN
LIMITATION
56. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
FLOWCHARTTILL NOW!
Review
Database
POS Tagging
Frequent Feature
Identification
Feature Pruning
Frequent
Features
Opinion Word
Identification
Opinion
Orientation
Identification
57. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
Step 4: Infrequent Feature Mining
58. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
“The picture is absolutely amazing.”
“The software that comes with it is amazing”
Note: The above two sentences shares same opinion
‘easy’ yet describing different features.
INFREQUENT FEATURE
MINING
59. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
“The picture is absolutely amazing.”
“The software that comes with it is amazing”
Note: The above two sentences shares same opinion
‘easy’ yet describing different features.
INFREQUENT FEATURE
MINING
COUNTER-
EXAMPLE
“The delivery guy was amazingly patient”
Shares the same
opinion but is not
a relevant feature
60. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
INFREQUENT FEATURE
MINING
Algorithm
61. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
Review Database
POS Tagging
Frequent Feature
Identification
Feature Pruning
Frequent
Features
Opinion Word
Identification
Opinion Orientation
Identification
Opinion
Words
Infrequent
Features
Infrequent
Feature
Identification
FLOWCHARTTILL NOW!
62. Frequent Features
Mining
Opinion Word Extraction
Opinion Orientation
Identification
Infrequent Features
Mining
Summary Generation
Step 5: Summary Generation