Important PYQs Artificial Intelligence Class 10

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1 mark questions – Important PYQs Artificial Intelligence Class 10

1 mark question contains fill-in-the-blanks, MCQs, True False, and one word as well as very short answer type questions.

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[1] Which of the following contributes to the efficiency of an Al project?

• High Model Complexity
• Relevant and Authentic Training Data
• Minimal Preprocessing
• Limited Hardware Resources

[2] This real life application of NLP is used to provide an overview of a news item or blog post, while avoiding redundancy from multiple sources and maximizing the diversity of content obtained. Which is this application ?

• Chatbot 
• Virtual Assistant
• Sentiment Analysis
• Automatic Summarisation

[3] Which of the following represents a machine that is smart but not considered Artificial Intelligence (Al) enabled?

• A robotic vacuum cleaner that can navigate and clean floors autonomously.
• A chatbot that engages in natural language conversations and answers questions.
• A smartphone with facial recognition for unlocking the device.
• A digital alarm clock that rings at a set time every morning.

[4] Which of the following words represents an example of a lemma resulting from lemmatization for “caring” in the context to Natural Language Processing (NLP) ?

• Care
• Cared
• Cares
• Car

[5] Intrapersonal Intelligence is a concept that :

• Measures an individual’s ability to understand others’ emotions and feelings
• Assesses one’s proficiency in mathematics and logical reasoning.
• Describes the level of self-awareness someone has, starting from realizing weaknesses, strengths, to recognizing their own feelings.
• Evaluates an individual’s spatial navigation and visualisation skills.

[6] For Data Science, usually the data is collected in the form of tables. These tabular datasets can be stored in different formats. Which of the following formats is not used for storing data in a tabular format ?

• CSV
• Website
• SQL 
• Spreadsheet

[7]  ____________ is one of the parameter for evaluating a model’s performance and is defined as the fraction of positive cases that are correctly identified.

• Precision                          
• Accuracy
• Recall
• F1 score

[8] In the Al project cycle, which of the following represents the correct order of steps?

• Data Exploration, Problem Scoping, Modelling, Evaluation, Data Acquisition.
• Problem Scoping, Data Acquisition, Data Exploration, Modelling, Evaluation.
• Modelling, Data Acquisition, Evaluation, Problem Scoping, Data Exploration.
• Data Acquisition, Data Exploration, Problem Scoping, Modelling, Evaluation.

[9] ____________  is a concept to unify statistics, data analysis, machine learning and their related methods in order to understand and analyse actual phenomena with data.

• Computer Vision                
• Natural Language Processing
• Data Science 
• Computer Science

[10] In computer vision which of the following tasks is used for multiple objects?

• Classification                   
• Classification + Localisation
• Instance Segmentation
• Localisation

[11] In spam email detection, which of the following will be considered as “False Negative”?

• When a legitimate email is accurately identified as not spam.
• When a spam email is mistakenly identified as legitimate.
• When an email is accurately recognised as spam.
• When an email is inaccurately labelled as important.

[12] Which of the following applications is not associated with Natural Language Processing (NLP) ?

• Sentiment Analysis           
• Speech Recognition
• Spam Filtering in emails
• Stock Market Analysis

[13] Statement 1 : Confusion matrix is an evaluation metric.

Statement 2 : Confusion Matrix is a record which helps in evaluation.

• Statement 2 is correct and Statement 1 is incorrect.
• Both Statement 1 and Statement 2 are correct.
• Both Statement 1 and Statement 2 are incorrect.
• Statement 1 is correct and Statement 2 is incorrect.

[14] Which form of unsupervised learning does the following diagram indicate?

• Clustering 
• Regression
• Reinforcement learning
• Classification

[15] Bag of Words is a  ____________ model which helps in extracting features out of the text which can be helpful in machine learning algorithms.

• Data Science (D S)
• Virtual Reality (VR)
• Natural Language Processing (NLP)
• Computer Vision (CV)

[16] Which of the following represents an example of a recommendation system?

• An online clothing store that offers a wide variety of clothing options.
• A search engine that retrieves relevant web pages based on user queries.
• An e-commerce website that displays customer reviews and ratings for products.
• A music streaming platform that suggests songs and playlists based on user listening history.

[17] Name any two search engines.

[18] What is the primary need for evaluating an Al model’s performance in the Al Model Development process?

• To increase the complexity of the model.
• To visualize the data.
• To assess how well the chosen model will work in future.
• To reduce the amount of data used for training.

[19] Assertion (A) : The term used to refer to the number of pixels in an image is resolution.

Reason (R): Resolution in an image denotes the total number of pixels it contains, usually represented as height x width.

• Both (A) and (R) are true and (R) is the correct explanation for (A).
• Both (A) and (R) are true and (R) is not the correct explanation for (A).
• (A) is false, but (R) is true.
• (A) is true, but (R) is false.

[20] When a machine possesses the ability to mimic human traits, i.e., make decisions, predict the future, learn, and improve on its own, it is said to have :

• Computational Skills   
• Learning Capability
• Artificial Intelligence
• Cognitive Processing

[21] Statement 1 : To evaluate a models’ performance, we need either precision or recall.

Statement 2 : When the value of both Precision and Recall is 1, the Fl score is 0.

• Both statement 1 and statement 2 are correct.
• Both statement 1 and statement 2 are incorrect.
• Statement 1 is correct, but statement 2 is incorrect.
• Statement 1 is incorrect, but statement 2 is correct.

[22] The concept of  ____________ is used to apply face filters on various social media platforms.

• NLP
• Computer Vision
• Data Science
• Block Chain Technology

[23] The 4 W’s Problem Canvas helps in identifying the key elements related to the given problem. Which of the following is NOT one of the blocks of the Problem Canvas ?

• When
• Where
• What
• Why

[24] Which domain of Al is used for interacting with virtual assistants such as Siri and Alexa?

• Machine Learning (ML)
• Computer Vision (CV)
• Natural Language Processing (NLP)
• Technical Vision (TV)

[25] What is NLP?

[26] Mention any two commonly used applications of NLP.

[27] Name any two currently popular virtual assistants.

[28] Name the process of dividing the whole corpus into sentences.

[29] With reference to evaluation process of understanding the reliability of any AI model, define the Term True Positive.

[30] What is F1 Score?

[31] Two popular examples of pocket assistants are ____________ and ____________.

[32] This is a fact that all human beings have all nine types of intelligences, but at different levels. Name any two such intelligences.

[33] Identify the incorrect statements from the following .

(i) Al models can be broadly categorized into four domains.

(ii) Data sciences is one of the domain of Al model.

(iii) Price comparison websites are examples Of data science.

(iv) The information extracted through data science can be used to make decision about it.

• Only (iv)  
• (iii) and (iv)
• Only (i)    
• (ii) and (iii)

[34] During Data Acquisition, feeding previous data into the machine is called ______________. 

• Training Data
• Predicting Data
• Testing Data 
• Evaluating Data

[35] Regression is one of the type of supervised learning model, where data is classified according to labels and data need not to be continuous. (True / False)    

[36] Which of the following is defined as the measure of balance between precision and recall? 

• Accuracy                                
• F1 Score
• Reliability                             
• Punctuality

[37] Email filters, spam filters, smart assistants are the examples of ___________________:   

• Pocket Assistants
• CV
• NLP                                 
• Evaluation

[38] Select the correct features of Smart Bot:

• Smart-bots are flexible and powerful
• Coding is required to take this up on board
• Smart bots work on bigger databases and other resources directly
• All of the above

[39] For  ‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌_______ the whole corpus is divided into sentences. Each sentence is taken as a different data so now the whole corpus gets reduced to sentences. 

• Text Regulation
• Sentence Segmentation
• Tokenisation                    
• Stemming

[40] _____________ helps to find the best model that represents our data and how well the chosen model will work in future.

(i) False positive

(ii) True positive

(iii) False negative

(iv) True Negative

Choose the correct option:

• only (i)
• (ii) and (iii)
• (iii) and (iv)
• (i) and )iv)

[41] With reference to NLP, consider the following plot of the occurrence of words versus their value :

In the given graph, X represents :

• Rare / valuable words
• Punctuation words
• Popular words                       
• Pronoun

[42] Which of the following is a feature of document classification?  

• Helps in classifying the type and genre of a document.
• Helps in creating a document.
• Helps to display important information of a corpus.
• Helps in including the necessary words in the text body.

[43] Two conditions when prediction matches with reality are true positive and  _________

[44] Which of the following is the correct feature of Neural network ?

• It can improve the efficiency of two models.
• It is useful with small dataset.
• They are modelled on human brains and nervous system.
• They need human intervention.

[45] With reference to Al domain, expand the term CV.

[46] Under ______________,  one looks at various parameters which affect the problem we wish to solve, as this would make many lives better.

[47] In this learning model, the data set which is fed to the machine is labelled. Name the model. 

[48] ____________  is a term used for any word or number or special character occurring in a sentence. (Token / Punctuator)

[49] When the prediction matches the reality, the condition is termed as _____________.

[50] Smart Assistants such as Alexa, Siri are the examples Of _______________

• Natural Language Processing
• Data Science
• Machine Learning
• Computer Vision

   [51] 4Ws Problem Canvas is a part of :                                               

• Problem Scoping               
• Data Acquisition
• Modelling                         
• Evaluation

[52] It refers to the unsupervised learning algorithm which can cluster the unknown data according to the patterns or trends identified out of it

• Regression
• Classification
• Clustering
• Dimensionality reduction

[53] Which of the following talks about how true the predictions are by any model?

• Accuracy
• Reliability
• Recall                               
• F1 score

2 marks questions – Important PYQs Artificial Intelligence Class 10

[1] Differentiate between Machine Learning (ML) and Deep Learning (DL).

Ans.:

[2] What are the primary differences between Script-bots and Smart-bots?

Ans.:

[3] What do you mean by Evaluation of an Al model ? Also explain the concept of overfitting with respect to Al model Evaluation.

Ans.:

Evaluation is the process of understanding the reliability of any AI model, based on outputs by feeding test dataset into the model and comparing with actual answers.

Overfitting refers to the predictions given accurate by the model for training data but not for new data. When the model does not make correct predictions on testing data is called overfitting.

[4] For a healthcare organisation’s objective of predicting disease outbreaks and efficiently allocating resources through the analysis of medical records, would you recommend using supervised learning or unsupervised learning as the preferred machine learning approach ? Explain your choice briefly.

Ans.:

The unsupervised learning approach is preferred to predict the disease outbreaks and efficiently allocating resources though the analysis of medical records. It can automatically identify odd patterns in patient data and predicts the untreated conditions or diseases. In predicting disease outbreak the healthcare professionals don’t have any information regarding that.

[5] What role does data play in Al based applications ? Name any two sources of online data collection for building any Al based application.

Ans.:

Data plays an important role in AI based applications. AI based applications requries data to predict the results. Data helps getting the accurate predictions.

The online data collecetion can be done using:
1. Open Source Government Portals
2. Reliable Websites such as kaggle
3. World Organizations open-source statistical website.

[6] Differentiate between grayscale and RGB images.

[7] What is the purpose of Evaluation stage of Al project cycle ? Discuss briefly.

Ans.:

[8] What is Tokenization? Count how many tokens are present in the following statement:

I find that the harder I work, the more luck I seem to have.

Ans.:

Tokenization refers to separating each word, number, and special characters from the text as token. The given sentence has following tokens:

In the given text 16 tokens are there.

[9] Kaira, a beginner in the field of NLP is trying to understand the process of Stemming. Help her in filling up the following table by suggesting appropriate affixes and stem of the words mentioned there::

[10] With reference to evaluation stage of Al project cycle, explain the term Accuracy. Also give the formula to calculate it.

Ans.:

Accuracy is defined as the percentage of correct predictions out of all the observations. A prediction can be said to be correct if it matches the reality. Here, we have two conditions in which the Prediction matches with the Reality: True Positive and True Negative. Hence, the formula for Accuracy becomes:

[11] Explain the following picture which depicts one of the processes on NLP. Also mention the purpose which will be achieved by this process.

Ans:

The above image depicts the concept of converting text into a common case. The machine does not consider the case sensitivity and does not consider the same word as different just because of different cases. It reduces the forms of the same word and treats it as the same word converted into the common case by the machine.

[12] Explain any one example of Al bias.      

Ans.:  

Some facial recognition systems do not identify black skin accurately compared to fair skin. Some software identifies wrong faces leads to misinformation. Some software is gender biased such as if a person is searching for a nurse, it always gives a female nurse. Bias is also required sometimes to keep things working. Machines cannot think on their own nor do they have intelligence but any bias can transfer from the developer to the machine at the time of developing the machine.

[13] What is Dimensionality Reduction?    

Dimensionality reduction is the process of reducing dimensions from the data sets. In other words, the transformation of data from high-dimensional to low-dimensional space where low-diemension retains some meaningful information.

[14] Define Chatbot. What are its types?

Ans.:

Chatbot is a computer program that allows one to do a conversation with humans and digital devices. There are two types of chatbots:

1. Scriptbot
2. Smartbot

[15] Define Confusion Matrix.  

Ans.:

The result of comparison between the prediction and reality can be recorded in what we call the confusion matrix. The confusion matrix allows us to understand the prediction results. Note that it is not an evaluation metric but a record which can help in evaluation.

[16] Face lock feature of a smartphone is an example of computer vision Briefly discuss this feature.

Ans.:

The computer vision works on images, cameras and video-based data. The face lock feature of a smartphone requires a face to be scanned to unlock the phone. The camera detects and captures the faces and saved its features using computer vision.

[17 ]With reference to data processing, expand the term TFIDF. Also give any two applications Of TFIDF.     (Not in Syllabus)

4 marks questions – Important PYQs Artificial Intelligence Class 10

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[1] What are Neural networks? Briefly explain all the layers of a neural network.

Ans.:

Neural networks are loosely modelled after how neurons in the human brain behave. The key advantage of neural networks is that they are able to extract data features automatically without needing the input of the programmer. A neural network is essentially a system of organizing machine learning algorithms to perform certain tasks. It is a fast and efficient way to solve problems for which the dataset is very large, such as in images.

A Neural Network is divided into multiple layers and each layer is further divided into several blocks called nodes. Each node has its own task to accomplish which is then passed to the next layer.

Input Layer:
The first layer of a Neural Network is known as the input layer. The job of an input layer is to acquire data and feed it to the Neural Network. No processing occurs at the input layer.

Hidden Layer:
The next layers after input layer are the hidden layers. Hidden layers are the layers in which the whole processing occurs. Their name essentially means that these layers are hidden and are not visible to the user. Each node of these hidden layers has its own machine learning algorithm which it executes on the data received from the input layer. The processed output is then fed to the subsequent hidden layer. of the network. There can be multiple hidden layers in a neural network system and their number depends upon the complexity of the function for which the network has been configured. Also, the number of nodes in each layer can vary accordingly.

Output Layer:
The last hidden layer passes the final processed data to the output layer which then gives it to the user as the final output. Similar to the input layer, output layer too does not process the data which it acquires. It is meant for user-interface.

Reference: Subject Specific Skills CBSE Study material Page no.: 41

[2] Give any four examples of applications of Al that we see around us.

Ans.:

Ans.: The applications of AI as follows:

1. Search Engines: Search engines using AI to respond our queries with accurate answers. Not only does it come up with results to our search in a matter of seconds, it also suggests and auto-corrects our typed sentences.
2. Virtual Assistants: We nowadays have pocket assistants that can do a lot of tasks at just one command. Alexa, Google Assistant, Cortana, Siri are some very common examples of the voice assistants which are a major part of our digital devices.
3. Navigation Apps: To help us navigate to places, apps like UBER and Google Maps come in haman. Thus, one no longer needs to stop repeatedly to ask for directions.
4. Gaming: AI has completely enhanced the gaming experience for its users. A lot of games nowadays are backed up with AI which helps in enhancing the graphics, come up with new difficulty levels, encourage gamers, etc.
5. Reccommendation websites: AI has not only made our lives easier but has also been taking care of our habits, likes, and dislikes. This is why platforms like Netflix, Amazon, Spotify, YouTube etc. show us recommendations on the basis of what we like.
6. Social Media websites: the recommendations are not just limited to our preferences, they even cater to our needs of connecting with friends on social media platforms with apps like Facebook and Instagram. They also send us customized notifications about our online shopping details, auto-create playlists according to our requests and so on. Taking selfies was never this fun as Snapchat filters make them look so cool.
7. Health Sector: AI is also being used to monitor our health. A lot of chatbots and other health apps are available, which continuously monitor the physical and mental health of its users.

Write any four.
Reference: Subject Specific Skills CBSE Study material Page no.: 15-16

[3] Consider the following two documents :

Document 1 : ML and DL are part of Al.

Document 2 : DL is a subset of ML.

Implement all four steps of the Bag of Words (BOW) model to create a document vector table. Depict the outcome of each step.

Ans.:

Step 1: Collecting data and preprocess it.

Here two documents are given. Each document has one sentence each. After text normalization, the text looks likes this:

Document 1: [ML, and, DL, are, part, of, Al]

Document 2: [DL, is, a, subset, of, ML]

Step 2: Create a Dictionary

Dictionary: [ML, and, DL, are, part, of, Al, is, a, subset]

Here repeated words are removed from the document as a stopwords removal step and written only unique words.

Reference: Subject Specific Skills CBSE Study material Page no.: 15-16

Step 3 Create document vector

In this step, the vocabulary is written in the top row. Now, for each word in the document, if it matches with the vocabulary, put a 1 under it. If the same word appears again, increment the previous value by 1. And if the word does not occur in that document, put a 0 under it.

Step 4: Repeat step 3 for all documents:

Reference: Subject Specific Skills CBSE Study material Page no.: 113-114

[5] Consider the following graphs (Figure 1 and Figure 2) that demonstrate the two types of Supervised Learning Models of Artificial Intelligence. Identify and explain each model giving suitable examples of each.

Ans.:

Figure 1 : Classification
Figure 2: Regression

Classification:
Where the data is classified according to the labels. For example, in the grading system, students are classified on the basis of the grades they obtain with respect to their marks in the examination. This model works on discrete dataset which means the data need not be continuous.

Regression:
Such models work on continuous data. For example, if you wish to predict your next salary, then you would put in the data of your previous salary, any increments, etc., and would train the model. Here, the data which has been fed to the machine is continuous.

Reference: Subject Specific Skills CBSE Study material Page no.: 38

[6] A binary classification model has been developed to classify news articles as either “Fake News” or “Real News”. The model was tested on a dataset of 500 news articles, and the resulting confusion matrix is as follows :

Ans.:

(A) : There are 420 True Negative cases in the above scenario.

(B) :

Precision: There are 45 True positive cases, We have all positive cases – True Positive: 45 and False Positive: 15. Hence the precision will be calculated as follows:

Precision = TP/TP + FP *100=45/45+15=45/60=0.75

Recall: There are 45 True Positive cases and 20 False negative cases. Hence the recall will be calculated as follows:

Recall=TP/TP+FN=45/45+20=45/65=0.692

F1 Score: We have the value of precision 0.75 and recall 0.692. Hence F1 score will be calculated as:

F1 score=2*((precision x recall)/(precision + recall)) = 2*(0.75*0.692/0.75+0692)=2*(0.519/1.442)=2*0.3599=0.720

For complete Answer key for artificial intelligence class 10 follow the given link:

[7] Consider the text of following documents

Document 1 : Sahil likes to play cricket

Document 2 : Sajal likes cricket too

Document 3 : Sajal also likes to play basketball

Apply all the four steps of Bag of words model of NLP on the above given documents and generate the output.

Refer answer 7

[8] With reference to NLP, explain the following terms in detail with the help of suitable example.

• Term frequency
• Inverse Document Frequency

Ans.:

This topic is given as an optional component for extra knowledge.

[9] Traffic Jams have become a common part of our lives nowadays. Living in an urban area means you have to face traffic each and every time you get out on the road. Mostly, school students opt for buses to go to school. Many times the bus gets late due to such jams and students are not able to reach their school on time. Thus, an Al model is created to predict explicitly if there would be a traffic jam on their way to school or not. The confusion matrix for the same is                                                        

Explain the process of calculating F1 score for the given problem.

Refer answer 6.

[10] Ms. Sooji is a beginner in the field Of Artificial Intelligence. She got confused among the core terms like Artificial Intelligence (Al), Machine Learning (ML) and Deep Learning (DL).

Many a times, these terms are used interchangeably but are they the same? Justify your answer. Help her in understanding these terms by drawing a well-labelled diagram to depict the interconnection Of these three fields.

Ans.:

Artificial Intelligence

Artificial Intelligence (AI) Refers to any technique that enables computers to mimic human intelligence. It gives the ability to machines to recognize a human’s face; move and manipulate objects; understand voice commands by humans, and also do other tasks. The AI-enabled machines think algorithmically and execute what they have been asked for intelligently.
Machine Learning (ML)
It is a subset of Artificial Intelligence which enables machines to improve at tasks with experience (data). The intention of Machine Learning is to enable machines to learn by themselves using the provided data and make accurate Predictions/ Decisions.

Deep Learning (DL)
It enables software to train itself to perform tasks with vast amounts of data. In Deep Learning, the machine is trained with huge amounts of data which helps it in training itself around the data. Such machines are intelligent enough to develop algorithms for themselves. Deep Learning is the most advanced form of Artificial Intelligence out of these three. Then comes Machine Learning which is intermediately intelligent and Artificial Intelligence covers all the concepts and algorithms which, in some way or the other mimic human intelligence.
There are a lot of applications of AI out of which few are those which come under ML out of which very few can be labeled as DL. Therefore, Machine Learning (ML) and Deep Learning (DL) are part of Artificial Intelligence (AI), but not everything that is Machine learning will be Deep learning.

[11] What is the significance of Al project cycle? Also, explain in detail about how Data Acquisition is different from data exploration.

Ans.: The AI Project Cycle provides us with an appropriate framework which can lead us towards the goal. The AI Project Cycle mainly has 5 stages:

1. Problem Scoping
2. Data Acquisition
3. Data Exploration
4. Modelling
5. Evaluation

Data Acquisition: It is a process of acquiring data from various sources. Data can be collected through surveys, interviews, webscrapping, sensors, cameras, obersvation, and API programs. Data is collected before data collection. The data which is fed into the model is training data and prediction data is testing data.

Data Exploration: In data acquisition data which are collected may be complex. So data exploration is used to make some pattern from data or visualiza the data in a proper format. Here the data will be represeneted through various graphs and visualization forms.

[12] Create a document vector table from the following documents by implementing all the four steps Of Bag Of words model. Also depict the outcome of each step.

Document 1 : Sameera and Sanya are classmates.

Document 2 : Sameera likes dancing but Sanya loves to study mathematics.

Ans.:

Step 1: Collecting data and processing it

Document 1: Sameera and Sanya are classmates

Document 2: Sameera likes dancing but Sanya loves to study mathematics

After text normalization the text becomes:

Document 1: [Sameera, and, Sanya, are , classmates]

Document 2: [Sameera, likes, dancing, but, Sanya, loves, to, study , mathematics]

Step 2: Create a Dictionary

Step 3 : Create a Document Vector

Step 4: Repeat for all documents

[13] Will it be valid to say that not all the devices which are termed as “smart” are Al-enabled ? Justify this statement. Explain any two examples from the daily life which are commonly misunderstood as Al.

Ans.:

Ans.: No, not all the devices which are termed as Smart are AI enables. any machine that has been trained with data and can make decisions/predictions on its own can be termed as AI. Here, the term ‘training’ is important.

Example 1:
A fully automatic washing machine can work on its own, but it requires human intervention to select the parameters of washing and to do the necessary preparation for it to function correctly before each wash, which makes it an example of automation, not AI.

Example 2:
An air conditioner can be turned on and off remotely with the help of internet but still needs a human touch. This is an example of Internet of Things (IoT). Also, every now and then we get to know about robots which might follow a path or maybe can avoid obstacles but need to be primed accordingly each time.

Just as humans learn how to walk and then improve this skill with the help of their experiences, an AI machine too gets trained first on the training data and then optimises itself according to its own experiences which makes AI different from any other technological device/machine.

[14] Recently the country was shaken up by a series Of earthquakes which has done a huge damage to the people as well as the infrastructure. To address this issue, an Al model has been created which can predict if there is a chance Of earthquake or not. The confusion matrix for the same.

How many total cases are True Negative in the above scenario ? (ii) Calculate precision, recall, and F1 score.

Ans.:

(i) There are 20 Negative cases in the above scenario.

(ii) To calculate precision we need True Positive cases and All predicted positives. So

True Positive – 50

False Positive – 05

precision = TP/(TP+FP)*100%=50/(50+5)*100%=50/55*100%=0.91

For recall, we need True Positive and False Negative which are 50 and 25.

recall=TP/(FP+FN)=50/(50+25)=50/75=0.67

F1 score= 2*((precision*recall)/(precision+recall))=2*((0.91*06.7)/(0.91+0.67))=2*(0.6097/1.58)=2*0.386=0.772

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